essays on segment reporting and valuation · this thesis consists of three papers in the area of...
TRANSCRIPT
Peter Aleksziev
ESSAYS ON
SEGM
ENT REPO
RTING
AN
D VA
LUA
TION
ISBN 978-91-7731-155-3
DOCTORAL DISSERTATION IN BUSINESS ADMINISTRATION STOCKHOLM SCHOOL OF ECONOMICS, SWEDEN 2019
Peter Aleksziev
ESSAYS ON SEGMENT REPORTING AND VALUATION
ESSAYS ON SEGMENT REPORTING AND VALUATION
This thesis consists of three papers in the area of segment reporting and valuation.
Paper I: The role of segment reporting in corporate valuation
Paper II: On the usefulness of segment information for earnings forecasting
Paper III: Heterogeneous investor beliefs and value creation through equity carve-outs
PÉTER ALEXANDER ALEKSZIEV holds a BSc in Finance and Accounting from the University of Szeged, Hungary, and an MSc in Accounting and Financial Management from the Stockholm School of Economics. Between 2014 and 2019 he was a PhD student at the Department of Accounting at SSE. He is currently teaching account-ing- and finance-related courses at the Bachelor, Master, and Executive MBA levels at SSE and the University of Chicago Booth School of Business.
Peter Aleksziev
ESSAYS ON
SEGM
ENT REPO
RTING
AN
D VA
LUA
TION
ISBN 978-91-7731-155-3
DOCTORAL DISSERTATION IN BUSINESS ADMINISTRATION STOCKHOLM SCHOOL OF ECONOMICS, SWEDEN 2019
Peter Aleksziev
ESSAYS ON SEGMENT REPORTING AND VALUATION
ESSAYS ON SEGMENT REPORTING AND VALUATION
This thesis consists of three papers in the area of segment reporting and valuation.
Paper I: The role of segment reporting in corporate valuation
Paper II: On the usefulness of segment information for earnings forecasting
Paper III: Heterogeneous investor beliefs and value creation through equity carve-outs
PÉTER ALEXANDER ALEKSZIEV holds a BSc in Finance and Accounting from the University of Szeged, Hungary, and an MSc in Accounting and Financial Management from the Stockholm School of Economics. Between 2014 and 2019 he was a PhD student at the Department of Accounting at SSE. He is currently teaching account-ing- and finance-related courses at the Bachelor, Master, and Executive MBA levels at SSE and the University of Chicago Booth School of Business.
Essays on Segment Reporting and Valuation
Péter Alexander Aleksziev
Akademisk avhandling
som för avläggande av ekonomie doktorsexamen vid Handelshögskolan i Stockholm framläggs för offentlig granskning
fredagen den 29 november 2019, kl 13.15, sal Ragnar, Handelshögskolan, Sveavägen 65, Stockholm
Essays on segment reporting and valuation
Essays on segment reportingand valuation
Peter Aleksziev
Dissertation for the Degree of Doctor of Philosophy, Ph.D.,in Business AdministrationStockholm School of Economics, 2019
Essays on segment reporting and valuationc© SSE and Peter Alexander Aleksziev, 2019
ISBN 978-91-7731-155-3 (printed)ISBN 978-91-7731-156-0 (pdf)
This book was typeset by the author using LATEX.
Front cover photo: c© Peter Alexander Aleksziev
Printed by:BrandFactory, Gothenburg, 2019
Keywords:segment reporting, disclosure, valuation, earnings forecasting, heterogeneousbeliefs, diversification discount, equity carve-out.
To my family
Foreword
This volume is the result of a research project carried out at the Departmentof Accounting at the Stockholm School of Economics (SSE).
The volume is submitted as a doctoral thesis at SSE. In keeping with thepolicies of SSE, the author has been entirely free to conduct and present hisresearch in the manner of his choosing as an expression of his own ideas.
SSE is grateful to Bankforskningsinstitutet, the Swedish Bank ResearchFoundation for financing this project for three years. SSE is also grateful forthe financial support provided by Louis Fraenckels Stipendiefond, Friedlän-der Stipendiefond and C. F. Liljevalch J:rs Donationsfond which has made itpossible to carry out the project.
We are also grateful for the support provided by Jan Wallanders och TomHedelius Stiftelse and Torsten Söderbergs Stiftelse to FIRE, the Research Schoolin Accounting.
Göran Lindqvist Kalle KrausDirector of Research Professor and Head of the
Stockholm School of Economics Department of AccountingStockholm School of Economics
Acknowledgements
I would like to thank everyone who supported and encouraged me during theseyears. Without you, this dissertation would not exist. However, first I wouldlike to thank Bankforskningsinstitutet, the Swedish Bank Research Founda-tion, for financing this project for three years.
I would like to thank my main supervisor, Kenth Skogsvik, for his tremen-dous help during these five years. You have taught me how to conduct researchwith integrity. You urged me to look at the big picture and understand it be-fore starting to ask research questions. You were also kind to me during thoseyears and went out of your way to help me, even corrected the grammaticaland stylistic mistakes in the countless versions of this document. Thank you!
I would also like to thank my advisory committee members Niclas Hell-man, Rickard Sandberg and Vasiliki Athanasakou. Niclas, I could not haveimagined a better advisor on financial reporting issues. You often answeredquestions I did not even know existed. I would like to thank Vasiliki for guid-ing me in the development of publishable research questions and helping mein improving the papers. Rickard, thank you for helping me with the econo-metrics issues in these papers and ensuring the appropriateness of my method-ological approaches.
Many other people contributed to the development of this thesis. I wouldlike to thank Martin Walker for his guidance during the early years of thisproject. I also greatly benefited from the detailed discussions and on-the-spotsuggestions by Henrik Nilsson and Sebastian Tideman. Earlier versions of thepapers in this dissertation also benefited from the comments by Mattias Ham-berg, Erik Johannesson, Joachim Landström, Jan Marton and Emma Myl-lymäki.
Special thanks go to the people that challenged and encouraged me duringthe years. I am grateful for the always skeptical comments from Milda Tylaite;your no-nonsense approach helped me make my arguments more convincing.
viii ESSAYS ON SEGMENT REPORTING AND VALUATION
Ting Dong was always ready to help me, be it to offering comments on mypapers or to sharing bits of wisdom about life. I really appreciate the supportof Hanna Setterberg during my whole time at SSE. Tack!
Thank you Florian Eugster for always trying to find some time for myquestions despite your always busy schedule. I had great evening discussionsabout financial analysis with my fellow night owl, Ingolf Kloppenburg. I havealso appreciated the interesting discussions with the ‘quant’ Brown Bag seminarseries members: Noor Alshamma, Liwei Zhu, Mariya Ivanova, and AntonioVazquez.
I would also like to thank the Data Center at the Swedish House of Financefor providing access to the SDC Platinum database.
An important part of my PhD years was my development as a teacher.Thanks go to Erik Alenius, who supported me in the beginning of this journey,and to Johnny Lind, who challenged me to start teaching in Swedish early on.Tomas Hjelström, you had the most skin in this game. I really appreciate youtaking me on as a co-teacher. I have learnt so much from you about how to be abetter teacher. Per Strömberg was the first to trust me with executive teaching.Thank you! It was a pleasure to teach with Gustav Johed, Catharina Pramhälland Katarina Warg at different points in time.
Discussions with FIRE Workshop participants broadened my perspectivein accounting. Thank you for all the insights, Ebba, Emilia, Kai, Kalle, Lukas,Martin, Patrik and Torkel. It was great to have the company of my fellowquant PhD students from other Swedish universities: Carl, Jason, Magnus andSavvas.
I had truly remarkable colleagues at the department. Per was always readyto give me career advice. I enjoyed discussing the PhD process and studentrepresentation with Anastasiya. Johan made sure we did not only work andarranged social activities for us. Thanks for all the fika, lunch and corridordiscussions Anja, Christoph, Henning, Henrik A, Katerina, Lars L, Lars Ö,Malin, Marek, Peter, Stina, Tina, Walter and Zeping. Thank you, Anne andChristina for helping me with all kinds of problems at SSE and beyond!
When I doubted whether I could finish this project, Maja was always readyfor some pep talk on Skype. Thank you! I am glad that Bence, Emese, Márk,Nóri and Patrik provided me with some non-academic free time activities dur-ing these years in Stockholm; Frici, Karina, Gréta, Bence and Robi in Hungary;and Laci, my fellow traveler, all over the world.
ix
I would like to thank my family for supporting me with this decision to doa PhD in Stockholm. I am particularly grateful to my partner, Judit, for beingby my side during this journey.
Stockholm, September, 2019
Péter Aleksziev
Contents
1 Introduction and overview of the dissertation 11.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Prior research on segment reporting . . . . . . . . . . . . . . . 6
1.2.1 Segment reporting regulation . . . . . . . . . . . . . . 61.2.2 The economics of segment disclosure . . . . . . . . . . 121.2.3 Segment based information for financial forecasting . 181.2.4 Segment reporting and valuation . . . . . . . . . . . . 20
1.3 Summary of thesis papers . . . . . . . . . . . . . . . . . . . . . 231.3.1 Summary of Paper I: The role of segment reporting in
corporate valuation . . . . . . . . . . . . . . . . . . . . 241.3.2 Summary of Paper II: On the usefulness of segment in-
formation for earnings forecasting . . . . . . . . . . . 251.3.3 Summary of Paper III: Heterogeneous investor beliefs
and equity carve-outs . . . . . . . . . . . . . . . . . . . 271.4 Synthesis and conclusions - The valuation relevance of segment
reporting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 281.4.1 Limitations and future research . . . . . . . . . . . . . 32
2 Paper I: The role of segment reporting in corporate valuation 352.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 352.2 Regulation, disclosure practices and the information needs of
valuation models . . . . . . . . . . . . . . . . . . . . . . . . . 392.2.1 Accounting-based firm valuation models . . . . . . . . 392.2.2 Segment reporting requirements . . . . . . . . . . . . 422.2.3 Evaluation of firms’ segment reporting disclosure prac-
tices . . . . . . . . . . . . . . . . . . . . . . . . . . . . 452.2.4 Valuation of the firm segment by segment . . . . . . . 50
xi
xii ESSAYS ON SEGMENT REPORTING AND VALUATION
2.3 Research question . . . . . . . . . . . . . . . . . . . . . . . . . 562.4 The valuation relevance of segment disclosure - theoretical anal-
ysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 572.4.1 Segment interdependence . . . . . . . . . . . . . . . . 572.4.2 Partial disclosure - partial solution . . . . . . . . . . . 612.4.3 The valuation effect of full disclosure . . . . . . . . . . 642.4.4 Summary of the findings . . . . . . . . . . . . . . . . . 68
2.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 702.5.1 Special issues with segment-based corporate valuation 702.5.2 Segment-by-segment valuation model for multi-segment
corporates . . . . . . . . . . . . . . . . . . . . . . . . . 762.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
Appendix 802.A Abbreviations used in the study . . . . . . . . . . . . . . . . . 802.B The equivalence of valuation models . . . . . . . . . . . . . . 812.C Segment disclosure requirements . . . . . . . . . . . . . . . . 862.D Analytical example . . . . . . . . . . . . . . . . . . . . . . . . 892.E An illustrative example . . . . . . . . . . . . . . . . . . . . . . 972.F Quantifying the difference in value estimates . . . . . . . . . 1042.G Expressing the difference in AOIG forecasts . . . . . . . . . . 106
3 Paper II: On the usefulness of segment disclosure for earnings fore-casting 1073.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1083.2 Literature review . . . . . . . . . . . . . . . . . . . . . . . . . 111
3.2.1 The core of valuation models: earnings forecasts . . . 1113.2.2 Segment reporting regulation . . . . . . . . . . . . . . 1143.2.3 The advantages and disadvantages of disclosing more
segment information . . . . . . . . . . . . . . . . . . . 1153.2.4 The interplay between label and quantitative segment
information . . . . . . . . . . . . . . . . . . . . . . . . 1173.2.5 Forecasting using segment information . . . . . . . . . 118
3.3 Research questions . . . . . . . . . . . . . . . . . . . . . . . . 1203.4 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
3.4.1 Firm-specific, time series method . . . . . . . . . . . . 1213.4.2 Pooled cross-sectional regressions . . . . . . . . . . . . 124
CONTENTS xiii
3.4.3 The importance of segment reporting characteristics . 1283.5 Empirical data and sampling . . . . . . . . . . . . . . . . . . . 1313.6 Empirical results . . . . . . . . . . . . . . . . . . . . . . . . . . 140
3.6.1 Firm-based time-series model . . . . . . . . . . . . . . 1403.6.2 Economy-wide, cross-sectional model . . . . . . . . . 1443.6.3 Segment reporting characteristics . . . . . . . . . . . . 155
3.7 Summary and conclusions . . . . . . . . . . . . . . . . . . . . 157
Appendix 1603.A Variable definitions . . . . . . . . . . . . . . . . . . . . . . . 160
4 Paper III: Heterogeneous investor beliefs and value creation throughequity carve-outs 1614.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1624.2 Literature review . . . . . . . . . . . . . . . . . . . . . . . . . 169
4.2.1 Conglomerates and the diversification discount . . . . 1694.2.2 Corporate restructuring . . . . . . . . . . . . . . . . . 1704.2.3 Value creation through equity carve-outs . . . . . . . . 1724.2.4 The heterogeneous beliefs theory . . . . . . . . . . . . 175
4.3 Research question development . . . . . . . . . . . . . . . . . 1784.3.1 The heterogeneous beliefs hypothesis . . . . . . . . . . 1784.3.2 Testable empirical hypotheses . . . . . . . . . . . . . . 181
4.4 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . 1844.5 Data and sample . . . . . . . . . . . . . . . . . . . . . . . . . . 1884.6 Descriptive statistics . . . . . . . . . . . . . . . . . . . . . . . . 1924.7 Empirical results . . . . . . . . . . . . . . . . . . . . . . . . . . 1954.8 Summary and conclusions . . . . . . . . . . . . . . . . . . . . 200
Appendix 2024.A Variable definitions . . . . . . . . . . . . . . . . . . . . . . . 2024.B List of equity carve-outs . . . . . . . . . . . . . . . . . . . . . 2034.C Illustrative example . . . . . . . . . . . . . . . . . . . . . . . 205
Bibliography 208
Chapter 1
Introduction and overview of thedissertation
1.1 Introduction
This dissertation is about segment reporting and valuation. It consists of fourparts, this introduction and summary and three separate research papers. Allthree papers address the valuation relevance of segment reporting, albeit fromdifferent angles and using different methodologies.
Segment reporting provides financial information, such as sales, earningsor assets, about different parts of a firm, incremental to the consolidated (firm-level) financial statements. It is essentially providing a breakdown of the con-solidated numbers, attributing these numbers to different parts of the firm.Previous research reports that users of financial statements use segment report-ing for the purposes of valuing the whole entity or its shares (Previts et al.,1994; You, 2014). Given that there is consolidated information available tovalue the firm, it is an intriguing area to investigate how the segmental break-down of firm-level information can be incrementally valuation relevant. Thisis the overarching topic of this dissertation.
The three research papers in this dissertation are the following:
• Paper I: The role of segment reporting in corporate valuation
• Paper II: On the usefulness of segment information for earnings forecast-ing
2 ESSAYS ON SEGMENT REPORTING AND VALUATION
• Paper III: Heterogeneous investor beliefs and value creation through eq-uity carve-outs
Paper I is a theoretical piece focusing on how segment reporting can beused in valuation. Previous research documented that financial statement usersdeem segmental information as valuation relevant, however, there is little an-alytical evidence in the literature as to how such information can be incre-mentally valuation relevant. The paper investigates the valuation relevanceof segment disclosure from a theoretical and analytical angle. I benchmark thespecific disclosure requirements of the different segment reporting standards,as well as the disclosure provided by the firms on the information needs ofaccounting information based valuation models, such as the discounted cashflow (DCF) model, the value added valuation (VAV) model and the abnormaloperating income growth (AOIG) model.
I find that the standards do not require enough information on the seg-ment level to enable the use of such models to value parts of the firm separately.Furthermore, the disclosure of the reporting entities seem to fall short of theinformation needs of the valuation models. I investigate the valuation benefitsof 1) providing sales and earnings on the segment level (partial disclosure) com-pared to no segment disclosure and 2) providing all inputs required by the val-uation models (full disclosure) compared to partial disclosure. The valuationrelevance of partial segment disclosure increases with the cross-segment differ-ence in growth rates and margins and decreases with segment concentration.The analytical investigation comparing full disclosure and partial disclosurereveal that using earnings multiples for valuing segments separately can resultin a value that is close to the value achieved through more sophisticated mod-els. However, firms with high priced-in growth (i.e. for which a larger part ofthe market value cannot be explained by current earnings1) can achieve highervaluation through signalling their superior quality, if the segment disclosurereveals that 1) they reinvest more of the operating income in the lower-risksegment, or 2) if their lower-risk segment is generating a larger share of the ab-
1In other words, where a larger part of the market value cannot be explained by the current
earning power of the firm, as defined in Graham and Dodd (1951); the concept of earn-
ing power "combines a statement of actual earnings, shown over a period of years, with
reasonable expectation that these will be approximated in the future, unless extraordinary
conditions supervene." (p.418-419)
INTRODUCTION AND OVERVIEW OF THE DISSERTATION 3
normal operating income growth (AOIG)2 and has more durable competitiveadvantage (higher economic goodwill).
Paper II is an empirical paper focusing on the earnings forecasting bene-fits of partial disclosure (disclosing sales and earnings on the segment level).Previous research has documented that segment information can be useful forforecasting earnings (Collins, 1976). However, more recent evidence suggeststhat this result hinges on within-industry, cross-company comparability of seg-ment performance (Fairfield et al., 2009; Schröder and Yim, 2018). This com-parability has decreased after the introduction of management approach intosegment reporting. Previously, under SFAS 14, companies were required topresent their segments according to the business lines the firms operate in (in-dustry approach). SFAS 131, effective after 1998, requires companies to presentsegments in line with the internal organization of the firm. This new standardresulted in decreased comparability of segment performance between firms.Schröder and Yim (2018) documented that while industry information couldimprove earnings forecasts under the industry approach, this information doesnot improve earnings predictions under SFAS 131. Building on this research,my paper investigates whether segment information reported following thedifferent standards can help to predict earnings, absent within-industry com-parability. Segment information following the management approach revealswithin-segment trends which might be useful for predicting earning. More-over, economy-wide comparability of the information presented might alsoimprove earnings forecasts. I also investigate how segment reporting charac-teristics are associated with more accurate earnings forecasts.
The main findings of the paper is that segment information can improveearnings forecasts even without industry information, both under SFAS 14and SFAS 131. However, my results suggest that the usefulness of segmentinformation for forecasting earnings has not improved with the introductionof SFAS 131. I also find that disclosing more segments is associated with moreaccurate earnings forecasts but that proprietary information in the segmentfiling does not help forecasting.
Paper III is an empirical paper focusing on the value creation through eq-uity carve-outs. In an equity carve-out, the firm sells shares in one of its sub-
2Abnormal operating income growth is an increase of operating income beyond the expected
increase due to additional capital employed.
4 ESSAYS ON SEGMENT REPORTING AND VALUATION
sidiaries (segments). Previous literature has documented that equity carve-outannouncements are associated with positive abnormal returns. Two possibleexplanations have emerged in the literature to explain the value creation ofequity carve-outs. The information asymmetry hypothesis in Nanda (1991)argued that when the firm decides to raise capital through selling shares in asubsidiary (instead of selling their own shares), they reveal private informationthat the firm is undervalued. The market receives this signal and corrects themisvaluation, leading to a higher share price. The divestitures gains hypoth-esis in Schipper and Smith (1986) on the other hand, suggests that there arebenefits for listing the subsidiary separately on the stock market, such as bet-ter contracting on the group or subsidiary level or better monitoring from themarket. The expectations of such benefits lead to the positive announcementreturns.
Empirical studies have provided inconclusive evidence with respect to thesehypotheses (Hulburt et al., 2002; Powers, 2003; Dasilas and Leventis, 2018;Dereeper and Mashwani, 2018).
In Paper III, a third hypothesis built on the heterogeneous beliefs theory ofMiller (1977) is presented. This theory argues that it is difficult to value firms,which requires considerable judgment and when investors are presented withthe same information, they arrive at different value estimates. The investorswith the highest value estimates ("optimistic" investors) invest in the share andthe others forgo the investment opportunity. The hypothesis being put for-ward in the paper suggests that the abnormal return arises from the expecta-tion that the firm will sell shares in the subsidiary to investors who have highvalue estimates of the subsidiary. They will pay a price higher than the valueestimates of current investors, and the increase in the share price correspondsto this expected profit from selling subsidiary shares.
I document a significant decrease in the value of the consolidated firmfrom before the announcement to the completion of the carve-out (observedthrough a higher diversification discount). This is not in line with the asym-metric information and the divestitures gains hypotheses, the first predictinghigher valuation levels as a result of revealing undervaluation and the seconddue to expected better performance in the future. The result is however inline with the heterogeneous beliefs hypothesis, as when investors are given thepossibility to own shares in a segment separately, investors who are more op-timistic about the segment will sell their shares in the group (decreasing its
INTRODUCTION AND OVERVIEW OF THE DISSERTATION 5
valuation) and buy shares in the subsidiary separately. This is the first paperto provide empirical evidence investigating the heterogeneous beliefs theoryfor corporate restructuring. It also contributes to the equity carve-out litera-ture through offering an additional hypothesis for the value creation in equitycarve-outs. Moreover, it provides additional evidence regarding sum-of-partsvaluation and the diversification discount.
I continue this introduction by presenting segment reporting regulationover time under US GAAP and IFRS. Then, I review the relevant literature,starting with investigating the effects of regulation changes under the two regimes,mainly relating to evaluating the effect of the change from an industry ap-proach to a management approach. This literature is also closely related toPaper I and II.
Disclosing segment information can be valuation relevant in two mainways. First, it might alter the expectation of future cash flows generated bythe firm (the numerator effect). I present papers investigating the usefulness ofsegment information for forecast sales and earnings. This literature is closelylinked to Paper II and the input improvement discussions in Paper I. Second,given the additional information, segment reporting can decrease informationasymmetry between market participants which in turn can decrease the costof capital and increase the value of the firm. Previous research has investigatedthe cost of capital effect of segment information extensively. In the dissertationI focus mainly on the numerator effect.
Providing segment information voluntarily is a choice and the manage-ment needs to evaluate the costs and benefits of providing this information.Apart from the potential benefits, segment disclosures can bring about differ-ent costs, such as proprietary costs, agency costs and costs of producing theinformation. The third part of the literature review is about these costs andbenefits of segment disclosures.
Finally, I review the literature on the diversification discount. An underly-ing assumption of the investigations in Paper I is that the value of the group canbe calculated as the sum of the value of the parts. However, previous literaturehas documented the diversification discount, meaning that firms are valued lessas a group compared to the value of the parts. Applying sum-of-parts valuationfor investment decisions in practice requires an understanding of the diversi-fication discount phenomenon. The investigation in Paper III is built on theclientele effect explanation of the diversification discount.
6 ESSAYS ON SEGMENT REPORTING AND VALUATION
This discussion of regulation and prior literature sets the stage for the dis-sertation. In the next section I present an extended summary of the three re-search papers. The introduction and summary section of this dissertation fin-ishes with a discussion about how the papers fit into the literature on segmentreporting and valuation.
1.2 Prior research on segment reporting
Segment reporting should supposedly provide useful information to marketinvestors. Probably the first firm to provide segment information was LeverBrothers in the 1920s. While there was no explicit segment disclosure in itsaccounting reports, the chairman regularly addressed the performance of thedifferent parts of the firm. Lever Brothers merged with two other oil- and fat-based product companies into Unilever in 1937, and Unilever kept providingsegment information in the years following the merger (Camfferman and Zeff,2003).
1.2.1 Segment reporting regulation
The importance of segment reporting became more evident in the 1960s. Con-glomerates grew by acquiring firms in different industries and conglomerate-level information appeared to be less useful for assessing the performance of theparts of the firm. SEC releases and APB opinions required disclosures aboutdifferent parts of the business, such as different product lines and foreign op-erations. The first segment reporting standard by FASB was SFAS No. 14,effective after 1976.
SFAS 14 required companies to provide information about different busi-ness lines and geographic areas (FASB, 1976). Line-of-business (LOB) segmentswere defined following the industry approach: "Industry segment: A componentof an enterprise engaged in providing a product or service or a group of relatedproducts and services primarily to unaffiliated customers (i.e., customers outsidethe enterprise) for a profit." (paragraph 10. a).
Effective after 1998, FASB introduced a new segment reporting standard,SFAS No. 131. This standard defined segments following the management ap-proach, meaning that segments no longer were required to be engaged in pro-viding similar products or services. Instead the management approach required
INTRODUCTION AND OVERVIEW OF THE DISSERTATION 7
reporting entities to define operating segments in line with the internal orga-nization of the firm. In particular, SFAS 131 (FASB, 1997) defined operatingsegments as: "a component of an enterprise: a. That engages in business activi-ties from which it may earn revenues and incur expenses (including revenues andexpenses relating to transactions with other components of the same enterprise),b. Whose operating results are regularly reviewed by the enterprise’s chief oper-ating decision maker to make decisions about resources to be allocated to the seg-ment and assess its performance, and c. For which discrete financial information isavailable." (paragraph 10.). FASB argued that comparability was impossible toachieve and that the management approach provides more relevant and reliableinformation about the different parts of the firm.
Segment reporting standards under IFRS developed similarly. IAS 14, ef-fective from 1983 required disclosure about the different parts of the firm fol-lowing the industry approach. This standard was later revised. IAS 14R waseffective for fiscal years beginning after 1 July 1998. It still followed the in-dustry approach of defining a segment, but extended the segment reportingrequirements of IAS 14 after reflecting on previous criticism (Prather-Kinseyand Meek, 2004).
IFRS 8, effective for fiscal years beginning on or after 1 January 2009, wasa result of harmonizing US GAAP and IFRS. IFRS 8 is largely similar to SFAS131 and also requires segments to be defined using the management approach.Table (1.1) provides an overview of studies investigating the effects of changesin segment reporting standards. Nichols et al. (2013) provided an extensivereview of the literature on the change from the industry approach to the man-agement approach of segment reporting.
Ettredge et al. (2002a) investigated firms’ views on SFAS 131 by analyzingcomment letters for the exposure draft (ED). They found that firms believedthat the new standard would require more disclosures. Firms’ lobbying posi-tions could be explained by expected competitive harm as a result of the dis-closures required by the new standard.
Tab
le1.
1:Pr
iore
mp
iric
alr
ese
arc
hin
vest
iga
ting
seg
me
nt
rep
ort
ing
reg
ula
tion
Art
icle
Reg
ulat
ion
Pur
pose
ofst
udy
Sam
ple
Sam
ple
tim
e-fr
ame
Num
ber
ofob
serv
a-ti
ons
Var
iabl
eof
inte
rest
Mai
nfin
ding
s
Her
rman
nan
dT
hom
as(2
000)
SFA
S13
1C
ompa
reth
ese
gmen
tre
port
ing
prac
tices
un-
der
SFA
S14
and
SFA
S13
1.
100
ofth
e25
0la
rges
tUS
com
pani
es
1997
-199
8(a
dopt
ion
year
and
the
year
befo
re)
100
firm
sD
escr
iptiv
est
atis
tics
Mor
efir
ms
prov
ide
segm
enti
nfor
mat
ion,
com
pani
esdi
sclo
seso
mew
hat
mor
eite
ms
per
segm
ent,
mor
eco
mpa
nies
repo
rtco
untr
y-ba
sed
GE
Odi
sclo
sure
in-
stea
dof
wid
erge
ogra
phic
alar
eas.
Stre
etet
al.
(200
0)SF
AS
131
Exp
lori
ngth
ese
gmen
tre
port
ing
prac
tices
offir
msu
nder
SFA
S13
1as
com
pare
dto
SFA
S14
US
com
pani
esin
Busi
ness
Wee
kG
loba
l100
0,ex
cl.
ener
gy&
fin.
firm
s,no
M&
Aac
tiviti
es
1997
-199
816
0fir
ms
Des
crip
tive
stat
istic
sT
henu
mbe
rof
segm
ents
repo
rted
and
thei
rco
nsis
-te
ncy
incr
ease
din
1998
,sev
eral
com
pani
esst
arte
dre
-po
rtin
gm
ore
segm
ents
.Few
com
pani
esin
crea
sed
the
item
srep
orte
dan
dpr
ofitfi
gure
srep
orte
dbe
cam
ele
ssco
mpa
rabl
e.
Stre
etan
dN
icho
ls(2
002)
IAS
14R
Exp
lori
ngth
ese
gmen
tre
port
ing
prac
tices
offir
ms
unde
rIA
S14
Ras
com
pare
dto
IAS
14.
Firm
srep
ortin
gun
der
IFR
Sw
ithE
nglis
han
nual
repo
rts
1998
-199
921
0fir
ms
Des
crip
tive
stat
istic
sSi
gnifi
cant
incr
ease
inth
enu
mbe
rof
item
sdi
sclo
sed
unde
rIA
S14
R.I
mpr
oved
cons
iste
ncy
betw
een
othe
rpa
rts
ofth
eA
R.
Ave
rage
num
ber
ofpr
imar
yse
g-m
ents
did
noti
ncre
ase
sign
ifica
ntly
,how
ever
,13
firm
sch
ange
dto
disc
losi
ngm
ore
segm
ents
unde
rIA
S14R
.E
ttre
dge
etal
.(20
02b)
SFA
S13
1E
xplo
ring
the
mot
ives
behi
ndse
gmen
tagg
rega
-tio
nun
der
SFA
S14
.
US
mul
tiseg
men
tfir
msr
epor
ting
segm
entd
ata
in19
96-1
998
1996
-199
834
35fir
ms
Hig
hest
and
low
est
deci
leof
the
diff
eren
cebe
twee
nth
enu
mbe
rof
4-di
gitS
ICin
dust
ries
and
num
ber
ofse
gmen
tsre
port
edun
der
SFA
S14
;CA
Rar
ound
even
tdat
es.
Larg
efir
ms
inhi
ghly
conc
entr
ated
indu
stri
esw
ere
mor
elik
ely
toag
greg
ate
anu
mbe
rof
busi
ness
area
sin
toea
chof
thei
rse
gmen
tun
der
SFA
S14
.SF
AS
no.
131
impo
sed
unan
ticip
ated
cost
son
firm
spr
evio
usly
repo
rtin
ghi
ghly
aggr
egat
edin
form
atio
n.
Ett
redg
eet
al.(
2002
a)SF
AS
131
Rel
atin
gfir
mch
arac
ter-
istic
san
dco
mpe
titiv
eha
rmto
firm
s’po
sitio
non
SFA
S13
1.
Res
pond
ents
for
the
ED
prec
edin
gSF
AS
131
1996
202
resp
onse
lett
ers
from
com
pani
es
Bina
ryor
3-ca
tego
rysc
ale
onth
epo
sitio
nof
the
resp
onde
nt
Firm
sbel
ieve
dth
atth
enew
stan
dard
will
requ
iret
hem
todi
sclo
sem
ore
info
rmat
ion.
Lobb
ying
posi
tions
can
beex
plai
ned
byex
pect
edco
mpe
titiv
eha
rmgi
ven
chan
gesi
ndi
sclo
sure
.SFA
S13
1w
asex
pect
edto
have
subs
tant
iale
cono
mic
cons
eque
nces
.
(con
t.)
Tab
le1.
1:(c
on
tinue
d)
Art
icle
Reg
ulat
ion
Pur
pose
ofst
udy
Sam
ple
Sam
ple
tim
e-fr
ame
Num
ber
ofob
serv
atio
nsV
aria
ble
ofin
tere
stM
ain
findi
ngs
Berg
eran
dH
ann
(200
3)SF
AS
131
Inve
stig
atin
gif
firm
spr
o-vi
dem
ore
info
rmat
ion
un-
der
SFA
S13
1an
dif
itaf
-fe
cts
inve
stor
s’an
dan
alys
ts’
abili
tyto
pred
ictfi
rmpe
rfor
-m
ance
.
US
firm
s,m
in.2
0M
USD
sale
s19
97-1
999
2999
obse
rvat
ions
Num
ber
ofse
gmen
ts,d
isag
-gr
egat
ion,
segm
ent
conc
en-
trat
ion,
cros
s-sub
sidi
zatio
n,lo
ssse
gmen
ts;
mec
hani
cal
earn
ings
and
sale
sfo
reca
sts
and
thei
rer
rors
;ex
cess
valu
e.
SFA
S13
1re
sulte
din
mor
ese
gmen
tsbe
ing
repo
rted
(and
high
erdi
sagg
rega
tion)
.Si
g-ni
fican
tim
prov
emen
tin
anal
yst
fore
cast
accu
racy
.Im
prov
edin
form
atio
nen
viro
n-m
enta
ndm
onito
ring
.
Bar-Y
osef
and
Ven
ezia
(200
4)
SFA
S13
1In
vest
igat
ing
whe
ther
SFA
S13
1ac
tual
lypr
ovid
esca
pi-
talm
arke
tpa
rtic
ipan
tsw
ithm
ore
pred
ictiv
eab
ility
than
the
prev
ious
regu
latio
n.
Two
firm
s,th
ree
diff
eren
texp
erim
ent
grou
ps(a
ccou
ntin
gst
uden
tsan
dpr
ofes
sion
alan
alys
ts)
1998
-199
932
pair
sof
acco
untin
gst
uden
ts,1
0pr
ofes
sion
alan
alys
ts
Fore
cast
accu
racy
;te
stin
gth
eco
nfide
nce
ofpa
rtic
i-pa
nts
with
aski
ngth
emfo
r95
%co
nf.in
terv
als
The
fore
cast
sobt
aine
dba
sed
onSF
AS
131
wer
em
argi
nally
mor
eac
cura
tein
two
ofth
eex
peri
men
ts,
but
less
accu
rate
inth
eth
ird
one.
Mea
sure
sof
accu
racy
show
edhi
gher
disp
ersi
onfo
rSFA
S13
1su
bjec
tsin
allt
heex
peri
men
ts.
Ett
redg
eet
al.
(200
5)SF
AS
131
Eff
ect
ofSF
AS
131
onth
est
ock
mar
ket’s
effe
ctst
opr
e-di
ctfu
ture
earn
ings
.
Com
pust
atsa
mpl
e,av
aila
ble
obse
rvat
ions
inbo
thpe
riod
s,no
M&
Aac
tiviti
es
1995
-200
168
12fir
ms,
2169
8fir
m-y
ear
obse
rvat
ions
Mar
ketr
etur
nFE
RC
incr
ease
dfo
rpr
e-SF
AS
131
mul
-tis
egm
ent
firm
sir
resp
ectiv
eof
them
in-
crea
sing
the
num
ber
ofse
gmen
tsor
not.
FER
Cal
soin
crea
sed
forl
arge
firm
scha
ng-
ing
from
repo
rtin
gon
ese
gmen
tto
bein
gm
ultis
egm
ent.
Ett
redg
eet
al.
(200
6)SF
AS
131
How
did
SFA
S13
1af
fect
seg-
men
tdi
sclo
sure
qual
ityan
dw
hat
driv
esfir
ms’
segm
ent
disc
losu
rede
cisi
ons.
US
mul
tiseg
men
tfirm
sun
der
SFA
S13
119
94-2
000
3492
0fir
m-y
ears
,of
whi
ch80
44ar
efir
msw
hodi
sclo
sed
mor
eth
anon
ese
gmen
tund
erbo
thst
anda
rds
Segm
ent
repo
rtin
gch
arac
-te
rist
ics,
cros
s-seg
men
tdi
f-fe
renc
ein
profi
tabi
lity
topr
oxy
for
qual
ity
SFA
SN
o.13
1re
sulte
din
asl
ight
incr
ease
inth
enu
mbe
rof
repo
rted
segm
ents
dis-
clos
edan
da
high
ercr
oss-s
egm
entv
aria
bil-
ityof
repo
rted
profi
ts.
Itin
crea
sed
the
tran
spar
ency
ofre
port
edse
gmen
tpr
of-
itabi
lity,
butc
ontin
ued
toal
low
man
ager
sof
high
prop
riet
ary
cost
firm
sso
me
abil-
ityto
conc
eal
com
petit
ivel
yha
rmfu
lin
-fo
rmat
ion.
Hop
eet
al.
(200
8)SF
AS
131
The
effe
ctof
SFA
S13
1on
fore
ign
earn
ings
mul
tiple
san
din
vest
ors’
mis
pric
ing
offo
reig
nea
rnin
gs.
US
firm
swith
both
dom
estic
and
fore
ign
curr
enta
ndla
gged
obse
rvat
ions
ofpr
etax
inco
me
and
inco
me
taxe
s
1985
-200
422
12fir
ms
Ann
uala
bnor
mal
retu
rnFo
reig
nea
rnin
gsre
spon
seco
effic
ient
in-
crea
ses
sign
ifica
ntly
afte
rSF
AS
131.
For-
eign
earn
ings
mis
pric
ing
disa
ppea
rsfo
l-lo
win
gth
ein
trod
uctio
nof
SFA
S13
1.
Park
(201
1)SF
AS
131
The
effe
ctof
SFA
S13
1on
the
stoc
km
arke
t’sab
ility
topr
edic
tind
ustr
y-w
ide
and
firm
-spec
ific
com
pone
nts
offu
ture
earn
ings
.
US
firm
s19
95-2
001
1962
firm
s,85
58fir
m-y
ear-
obse
rvat
ions
CA
RT
hem
arke
t’sen
hanc
edab
ility
topr
edic
tfu
ture
earn
ings
ism
ostly
driv
enby
the
impr
oved
abili
tyto
pred
icti
ndus
try-
wid
e,cr
oss-i
ndus
try
perf
orm
ance
s,ra
ther
then
futu
refir
m-sp
ecifi
cea
rnin
gsco
mpo
nent
s.
(con
t.)
Tab
le1.
1:(c
on
tinue
d)
Art
icle
Reg
ulat
ion
Pur
pose
ofst
udy
Sam
ple
Sam
ple
tim
e-fr
ame
Num
ber
ofob
-se
rvat
ions
Var
iabl
eof
inte
rest
Mai
nfin
ding
s
Nic
hols
etal
.(2
012)
IFR
S8
Segm
ent
disc
losu
rech
arac
-te
rist
ics
and
qual
ityun
der
IFR
S8
and
com
pare
dto
IAS
14R
.
Com
pani
esin
the
top-
tier
inde
xof
14E
urop
ean
stoc
kex
chan
ges(
exc.
UK
)
2008
-200
9(o
rot
her
ifad
opte
dea
rlie
ror
late
r)
361
firm
sD
escr
iptiv
est
atis
tics
Und
erIF
RS
8co
mpa
nies
repo
rtsi
gnifi
-ca
ntly
mor
eop
erat
ing
segm
ents
onav
er-
age,
but
mos
tfir
ms
dono
tre
port
mor
ese
gmen
ts.
Sign
ifica
ntde
crea
sein
the
amou
ntof
item
sdi
sclo
sed.
Sign
ifica
ntim
prov
emen
tsin
the
disc
losu
refin
enes
sof
segm
entd
ata.
Nic
hols
etal
.(2
013)
IFR
S/U
SG
AA
PR
esea
rch
revi
ewon
the
ef-
fect
soft
hem
anag
emen
tap-
proa
ch.
12IF
RS
8st
udie
sand
25SF
AS
131
stud
ies
1997
-201
3*37
stud
ies
Lite
ratu
rere
view
Farí
asan
dR
odrí
guez
(201
5)
IFR
S8
Ifse
gmen
trep
ortin
gha
sim
-pr
oved
due
toch
ange
from
IAS
14R
toIF
RS
8.
Span
ish
liste
dfir
ms
exce
ptfo
rea
rly
adop
ters
and
segm
ent
com
posi
tion
chan
gers
2008
-201
010
4fir
ms
Des
crip
tive
stat
istic
sO
nly
18.3
%of
firm
sin
crea
sed
thei
rre
-po
rtin
g.M
any
firm
sco
ntin
ueto
repo
rtse
gmen
tsth
atar
ein
cons
iste
ntw
ithth
eir
oper
atio
ns.
Buge
jaet
al.
(201
5)IA
S14
R,
IFR
S8
Inve
stig
atin
gth
enu
mbe
rof
segm
ents
repo
rted
afte
rIA
S14
Ran
dIF
RS
8,re
latin
git
tofir
min
dust
rial
dive
rsity
,le
velo
find
ustr
yco
ncen
tra-
tion
and
num
ber
oflo
ss-
mak
ing
segm
ents
.M
otiv
esfo
rde
crea
sing
disc
losu
re.
Aus
tral
ian
liste
dco
mpa
nies
2001
-200
2an
d20
08-2
009
1241
firm
sfo
rIA
S14
Rad
op-
tion
and
1617
firm
obse
rva-
tions
for
IFR
S8
adop
tion
Cha
ngeu
pin
dica
tor
vari
able
,log
itre
gres
sion
onex
plan
ator
yva
riab
les.
Fore
cast
erro
ran
ddi
sper
sion
Both
IAS1
4Ran
dIF
RS8
resu
ltin
high
ernu
mbe
rof
segm
ents
repo
rted
.The
find-
ings
for
IAS
14R
isco
nsis
tent
with
the
agen
cypr
inci
ple
but
for
neith
erst
an-
dard
sw
ithth
epr
opri
etar
yco
stpr
inci
-pl
e.A
naly
stfo
reca
stpr
oper
ties
did
not
impr
ove,
eith
er.
Cho
(201
5)SF
AS
131
Cap
itala
lloca
tion
effic
ienc
yin
inte
rnal
capi
tal
mar
kets
follo
win
gSF
AS
131.
US
mul
tiseg
men
tfirm
s(n
on-fi
nanc
ial,
non-
utili
ty)
1996
-199
9de
cye
ar-e
nd;
1997
-200
0no
n-de
cye
ar-e
nd
1391
firm
-yea
rsC
apita
lallo
catio
nef
ficie
ncy
(sig
ned
cape
xde
viat
ion
vari
able
,pro
xyin
gfo
rcr
oss-s
ubsi
diza
tion)
Cha
nge
firm
sex
peri
ence
dgr
eate
rim
-pr
ovem
ent
inca
pita
lal
loca
tion
effi-
cien
cyth
anth
eno
n-ch
ange
cont
rols
am-
ple.
Mor
epr
onou
nced
for
com
pani
esw
ithle
ssin
depe
nden
tbo
ards
,m
ore
di-
vers
ified
inte
rnal
capi
tal
mar
kets
and
grea
ter
take
over
thre
at.
Not
es:F
ER
Cis
forw
ard
earn
ings
resp
onse
coef
ficie
nt.
GE
Ore
fers
toge
ogra
phic
segm
entd
iscl
osur
e.M
USD
ism
illio
nsof
US
dolla
rs.
‘*’m
arks
that
the
stud
iesr
evie
wed
wer
epu
blis
hed
inth
epe
riod
.
INTRODUCTION AND OVERVIEW OF THE DISSERTATION 11
Studies investigating the change from SFAS 14 to SFAS 131 have docu-mented that firms in fact disclosed more segments (Herrmann and Thomas,2000; Street et al., 2000; Berger and Hann, 2003; Ettredge et al., 2006) and moreitems per segment (Herrmann and Thomas, 2000; Street et al., 2000) under thenew standard. Also, fewer companies disclosed earnings on the geographicalsegment level (Herrmann and Thomas, 2000) as this no longer was required un-der SFAS 131 (FASB, 1997). Firms also changed their reported segment struc-ture (Herrmann and Thomas, 2000; Street et al., 2000), i.e. it became morein line with the organizational structure presented elsewhere in the annual re-port. However, many firms still presented a different structure as compared tothe MD&A section of the 10-K filings (Street et al., 2000).
Ettredge et al. (2002b) found that firms in highly concentrated industrieswere more likely to aggregate business areas into one segment under SFAS 14.They found that SFAS 131 had a negative impact, suggesting that the new stan-dard imposed additional proprietary costs on reporting entities. Ettredge et al.(2006) also documented that the transparency of reported segment profitabil-ity increased, however, they argued that the standard still allowed managers ofhigh proprietary cost firms to conceal information.
The higher disaggregation under SFAS 131 was also documented in Bergerand Hann (2003). They found that the additional information helped analyststo improve earnings forecasts and resulted in a better information environmentand potentially better monitoring. Botosan and Stanford (2005) documentedthat information asymmetry and analyst forecast errors increased marginallyfollowing the introduction of SFAS 131. Moreover, Ettredge et al. (2005) foundincreased future earnings response coefficients (FERC) after the introductionof SFAS 131 for firms disclosing more than one segment under SFAS 14, ir-respective of whether these firms increased the number of segments reported,suggesting that SFAS 131 brought about earnings numbers that were more in-formative compared to SFAS 14. Furthermore, Hope et al. (2008) documentedincreased foreign earnings response coefficients under SFAS 131, documentingthat geographical segment disclosure under SFAS 131 helped to eliminate earn-ings mispricing. Park (2011) found that the market’s enhanced ability to pre-dict earnings arised from the improved ability to predict industry-wide, cross-industry performance, and not firm-specific earnings components.
Bar-Yosef and Venezia (2004) observed in an experimental study that pro-fessional users of financial information (analysts and accounting students close
12 ESSAYS ON SEGMENT REPORTING AND VALUATION
to graduation) were only slightly helped by segment information reported un-der SFAS 131 to predict earnings. Berger and Hann (2003) documented thatfirms revealed previously unreported information about cross-segment resourcetransfers. Analyzing cross-segment resource transfers, Cho (2015) found thatfirms that changed their reporting structure following SFAS 131 experiencedbetter capital allocation efficiency. This effect was found to be more pronouncedfor firms with less independent boards, more diversified internal capital mar-kets and that experienced greater takeover threat.
Next, I turn to studies investigating segment reporting under IFRS. Streetand Nichols (2002) found that firms did not disclose significantly more pri-mary segments under IAS 14R as compared to IAS 14. However, Bugeja et al.(2015) documented a significant increase in the number of segments reportedfor an Australian sample. Street and Nichols (2002) studied a global sample of117 firms and found that there were 13 firms that started reporting multiplesegments from 1999, when IAS 14R became effective. The average number ofitems disclosed per segment has increased. Also, they found that the segmentstructure presented was not consistent with the structure presented elsewherein their annual reports.
Nichols et al. (2012); Farías and Rodríguez (2015); Bugeja et al. (2015) in-vestigated the change from IAS14R to IFRS 8 using European, Spanish andAustralian samples, respectively. They documented an increase in the numberof operating segments reported. Moreover, there was a decrease in the num-ber of items reported per segment (Nichols et al., 2012; Bugeja et al., 2015).Firms were found to disclose more than one earnings measure and the fine-ness of geographical segment definition was also found to improve (Nicholset al., 2012). The consistency of the segment structure and the organization ofthe firm presented elsewhere in the annual report was not improved followingIFRS 8 (Nichols et al., 2012; Farías and Rodríguez, 2015).
1.2.2 The economics of segment disclosure
The decision to provide segment information is affected by the expected costsand benefits of disclosure. Table (1.2) presents an overview of studies discussingthe economics of segment reporting, i.e. the possible costs and benefits associ-ated with disclosure.
In order to investigate potential costs of disclosure, Edwards and Smith(1996) conducted a survey study. 139 large UK firms returned the question-
INTRODUCTION AND OVERVIEW OF THE DISSERTATION 13
naires (at least partially completed), and the results showed that almost twothirds of the respondents viewed competitive disadvantage as an important con-cern. 30% of the respondents reported information production costs as an im-portant cost of providing segment disclosures, revealing that they changed theinformation system in order to produce the required data. The respondentsalso indicated that proprietary cost concerns were due more to geographicalsegment disclosures than disclosures for operating segments.
Harris (1998); Botosan and Stanford (2005) documented evidence in linewith the proprietary cost of segment disclosures. Both papers found that op-erations in less competitive industries are less likely to be reported as separatesegments. Furthermore, Bens et al. (2011) analyzed plant-level data for USfirms and found that firms aggregate high-profit operations with other partsof the business for reporting purposes, presumably in order to protect theircompetitive advantage.
Givoly et al. (1999) suggested that segment data are subject to managementintervention and firms shift costs across segments due to stock market andstakeholder cost considerations. However, they also found that investors ap-pear to ignore noisy segment data. Mcgowan and Vendrzyk (2002) argued thatdefense contractor firms with mixed segments (segments engaging in both pri-vate and governmental contracts) have the opportunity to shift costs, however,they could not find supporting evidence for cost-shifting on government con-tracts in low-competition environment. André et al. (2016) found that whenmanagers face proprietary costs of disclosing segment information, they de-crease the quantity of information provided below standard guidance, or de-crease the quality of the information provided.
Berger and Hann (2007); Bens et al. (2011) also documented that segmentdisclosures are associated with agency costs. In line with these findings, Bensand Monahan (2004) found that high quality segment disclosures provide share-holders with means to monitor management, mitigating cross-subsidizationof underperforming segments. The results of Wang and Ettredge (2015) indi-cated that firms disclose lower cross-segment earnings growth differences whenfacing higher proprietary or agency costs. However, firms in need of exter-nal financing were associated with larger differences in cross-segment earningsgrowth.
Tab
le1.
2:Pr
iore
mp
iric
alr
ese
arc
ha
dd
ress
ing
co
sts
an
db
en
efit
so
fse
gm
en
td
isclo
sure
sA
rtic
leP
urpo
seof
stud
ySa
mpl
eSa
mpl
eti
me-
fram
eN
umbe
rof
obse
rvat
ions
Var
iabl
eof
inte
rest
Mai
nfin
ding
s
Koc
hane
k(1
974)
Exa
min
ing
the
secu
rity
mar
ket
re-
actio
nsof
segm
ent-l
evel
acco
untin
gin
form
atio
nre
cipi
ents
,if
segm
ent
info
rmat
ion
help
spre
dict
ing
futu
reea
rnin
gsch
ange
sand
lead
sto
low
erse
curi
typr
ice
fluct
uatio
ns.
US
firm
swith
atle
ast3
mer
gers
betw
een
1960
-196
8re
sulti
ngin
20%
orm
ore
incr
ease
inas
sets
,with
oper
atio
nsin
seve
rali
ndus
trie
s
1966
-196
937
firm
sSe
gmen
tdis
clos
ure
scor
esp
litat
the
mea
nsc
ore
toG
ood
and
Poor
repo
rter
s
Pred
ictio
nsof
futu
reea
rnin
gsw
ere
faci
li-ta
ted
byth
eav
aila
bilit
yof
segm
ent
data
.M
oreo
ver,
low
erw
eekl
yst
ock
pric
eva
ri-
abili
tyov
ertim
eth
anfir
msn
otpr
ovid
ing
sub-
entit
yin
form
atio
n.
Swam
inat
han
(199
1)T
heef
fect
ofse
gmen
tdat
aav
aila
bil-
ityan
don
pric
eva
riab
ility
arou
ndth
eda
teof
rele
ase
of10
-Ks
and
di-
verg
ence
ofbe
liefs
amon
gm
arke
tpa
rtic
ipan
ts.
Als
o,if
the
com
plex
-ity
ofth
efir
mm
oder
ates
thes
ere
la-
tions
hips
.
US
firm
s,at
leas
t3an
alys
tsfo
llow
ing,
Dec
.31
year
-end
1969
-197
016
0tr
eate
d(y
ear
1970
)and
101
cont
rol
(yea
r19
69)
firm
-yea
rob
serv
atio
ns
Perc
enta
gech
ange
inpr
ice
vari
abili
tyre
lativ
eto
the
pre-
peri
od,E
PSfo
reca
stdi
verg
ence
diff
eren
ce
SEC
man
date
dse
gmen
tre
port
ing
regu
la-
tion
was
foun
dto
bepo
sitiv
ely
rela
ted
topr
ice
vari
abili
tyar
ound
10K
filin
gda
tes
and
nega
tivel
yre
late
dto
dive
rgen
cein
an-
alys
tfor
ecas
ts.
Gre
enst
ein
and
Sam
i(1
994)
Ifse
gmen
tdi
sclo
sure
isas
soci
ated
with
low
erbi
d-as
ksp
read
sand
how
itis
mod
erat
edby
firm
char
acte
ris-
tics.
US
firm
slis
ted
onN
YSE
1969
-197
022
2fir
ms
Log
ofw
eekl
ybi
d-as
ksp
read
Firm
srep
ortin
gse
gmen
tdat
afo
rth
efir
sttim
ein
thei
r19
7010
-Kre
port
sha
vea
grea
ter
dow
nwar
dsh
ift
inth
eir
rela
-tiv
ebi
d-as
ksp
read
com
pare
dto
cont
rol
grou
ps,t
hisi
ncre
ases
inth
enum
bero
fseg
-m
ents
repo
rted
.C
onov
eran
dW
alla
ce(1
995)
Ifm
ore
deta
iled
segm
entd
iscl
osur
ele
adst
obe
tter
stoc
kpe
rfor
man
ce.
Non
-fina
ncia
lfirm
s,ad
opte
dSF
AS
52(fo
reig
ncu
rren
cytr
ansl
atio
n)an
dus
ecu
rren
trat
em
etho
dfo
rat
leas
tone
affil
iate
,lis
ted
onN
YSE
orA
ME
X,p
rovi
deG
EO
segm
enti
nfo
unde
rSF
AS
14
202
trad
ing
days
befo
rean
d38
1tr
adin
gda
ysaf
ter
5th
Apr
il,19
83
230
firm
sC
AR
Geo
grap
hic
segm
ent
info
rmat
ion
has
apo
sitiv
ere
latio
nto
CA
R.
Edw
ards
and
Smith
(199
6)In
vest
igat
ing
com
petit
ive
disa
dvan
-ta
gean
dot
herc
osts
ofdi
sclo
sure
inco
nnec
tion
with
the
intr
oduc
tion
ofSS
AP
25se
gmen
trep
ortin
gst
an-
dard
inth
eU
K.
Larg
efir
msl
iste
din
the
UK
,sal
esbe
twee
n18
1M
GBP
-100
0M
GBP
1990
103+
36(p
artia
lre
spon
se)
ques
tionn
aire
sre
turn
ed
36%
ofth
ere
spon
dent
san
swer
edth
atco
mpe
titiv
ead
vant
age
isno
tan
impo
rtan
tco
ncer
n.30
%di
dso
me
amen
dmen
tsin
the
info
rmat
ion
syst
emin
orde
rto
pro-
duce
the
requ
ired
data
,10
%m
ade
huge
amen
dmen
ts.
Com
petit
ive
disa
dvan
tage
conc
erns
are
mor
edu
eto
geog
raph
ical
than
tose
gmen
tald
iscl
osur
e.
(con
t.)
Tab
le1.
2:(c
on
tinue
d)
Art
icle
Pur
pose
ofst
udy
Sam
ple
Sam
ple
tim
e-fr
ame
Num
ber
ofob
serv
atio
nsV
aria
ble
ofin
tere
stM
ain
findi
ngs
Her
rman
nan
dT
hom
as(1
996)
Eff
ects
ofco
untr
y,si
ze,i
ndus
try
and
exch
ange
listin
gon
segm
ent
repo
rtin
gqu
ality
unde
rth
eE
Ufo
urth
dire
ctiv
e.
65la
rges
tfir
ms
per
coun
try
-30
rand
omly
sele
cted
;Sa
les
betw
een
50-6
0M
USD
1992
-199
322
3fir
ms
Num
ber
ofG
EO
and
LOB
segm
ents
repo
rted
Qua
lity
ofse
gmen
tdi
sclo
sure
s(n
umbe
rof
segm
ents
disc
lose
d)va
ries
byco
untr
yan
dsi
ze,
the
exch
ange
listin
gis
only
impo
rtan
tfo
rcr
oss-l
iste
dfir
ms.
Har
ris(
1998
)T
here
latio
nbe
twee
nle
vels
ofin
dust
ryco
mpe
titio
nan
dm
an-
ager
s’ch
oice
sof
whi
chop
era-
tions
tore
port
asbu
sine
ssse
g-m
ents
.
US
mul
tiseg
men
tfirm
s19
87-1
991
929
firm
sIn
dica
tor
vari
able
whe
ther
ath
ree-
digi
tSIC
inw
hich
the
firm
has
oper
atio
nsis
repo
rted
aspr
imar
yor
seco
ndar
ySI
Cfo
ra
busi
ness
segm
ent
Ope
ratio
nsin
less
com
petit
ive
indu
stri
esar
ele
sslik
ely
tobe
repo
rted
asin
dust
ryse
g-m
ents
.T
heco
mpe
titiv
eha
rmas
adi
sinc
en-
tive
tode
taile
dse
gmen
trep
ortin
gar
ises
from
ade
sire
topr
otec
tabn
orm
alpr
ofits
and
mar
-ke
tsha
rein
less
com
petit
ive
indu
stri
es.
Lobo
etal
.(1
998)
How
SFA
SN
o.14
affe
cts
anal
ysts
’ea
rnin
gsfo
reca
sts
and
pric
eva
rian
ce.
US
mul
tiseg
men
tfirm
s19
75-1
977
76fir
msf
orco
mpa
riso
nbe
twee
n19
75an
d19
77
Stan
dard
ized
daily
and
cum
ulat
ive
pric
eva
riab
ility
mea
sure
sar
ound
annu
alre
port
date
;ana
lyst
fore
cast
erro
rs
Incr
ease
dpr
ice
vari
abili
tyat
the
time
offir
stdi
sclo
sure
and
incr
ease
dan
alys
tfor
ecas
tacc
u-ra
cyin
the
post
peri
od.
Giv
oly
etal
.(1
999)
Mea
sure
men
ter
rors
inse
gmen
tre
port
ing.
US
firm
sw
ithda
taav
aila
ble
from
Com
pu-
stat
Indu
stry
Segm
ent,
Com
pust
atG
eogr
aphi
-ca
lSam
ple,
CR
SP
1978
-199
637
10fir
ms
Stoc
km
arke
ttes
tsSe
gmen
tda
taar
esu
bjec
tto
man
agem
ent
in-
terv
entio
n.Se
gmen
tsa
les
prov
ides
mor
ein
-cr
emen
tal
info
rmat
ion
than
segm
ent
earn
-in
gs.
Inve
stor
sig
nore
segm
ent
data
with
mor
em
easu
rem
ent
erro
r.Se
gmen
tda
tais
mor
e‘n
oisy
’tha
nsi
ngle
-firm
data
.Bo
tosa
nan
dH
arri
s(20
00)
Wha
tdr
ives
firm
s’de
cisi
onto
prov
ide
quar
terl
yse
gmen
tdi
s-cl
osur
es,a
ndw
hata
reth
eeff
ects
ofdi
sclo
sure
?
US
mul
tiseg
men
tfirm
s19
87-1
994
107
firm
s,65
chan
gefir
ms
prov
idin
gvo
lunt
ary
quar
terl
yse
gmen
tdi
sclo
sure
and
42 non-
disc
losi
ngfir
ms.
Dis
clos
ure
dum
my
whe
ther
the
com
pany
prov
ides
quar
terl
yse
gmen
tdis
clos
ure
Firm
sin
itiat
ing
quar
terl
yse
gmen
tdi
sclo
-su
rese
xper
ienc
edad
eclin
ein
trad
ing
volu
me,
incr
ease
inin
form
atio
nas
ymm
etry
(mea
-su
red
byan
alys
tdi
sagr
eem
ent)
duri
ngth
e2
year
spr
eced
ing
the
chan
gein
disc
losu
refr
e-qu
ency
.C
hang
efir
ms
wer
em
ore
likel
yto
mak
ean
acqu
isiti
onan
dha
veop
erat
ion
inin
-du
stri
esw
here
othe
rspr
ovid
ese
gmen
tinf
or-
mat
ion
quar
terl
yas
wel
l.
(con
t.)
Tab
le1.
2:(c
on
tinue
d)
Art
icle
Pur
pose
ofst
udy
Sam
ple
Sam
ple
tim
e-fr
ame
Num
ber
ofob
serv
atio
nsV
aria
ble
ofin
tere
stM
ain
findi
ngs
Mcg
owan
and
Ven
drzy
k(2
002)
The
effe
ctof
com
petit
ion
for
defe
nse
cont
ract
son
repo
rted
segm
entp
rofit
abili
tyfo
rra
nked
and
unra
nked
busi
ness
cont
rac-
tors
.
Mul
tiseg
men
tde
fens
eco
n-tr
actin
gU
Sfir
ms
(firm
sw
ithde
fens
eco
ntra
cts
inea
chof
the
first
6ye
ars)
with
atle
ast
one
segm
ent
with
pure
lyco
mm
er-
cial
(non
-gov
ernm
enta
l)re
venu
es
1984
-198
9;19
94-1
998
35fir
msi
nth
efir
stpe
riod
,23
firm
sin
the
seco
nd
Segm
entR
OA
No
evid
ence
that
cost
shif
ting
isre
spon
-si
ble
for
unus
ually
high
profi
tson
gov-
ernm
entc
ontr
acts
inlo
w-c
ompe
titio
nen
-vi
ronm
ents
,des
pite
man
ager
s’op
port
uni-
ties
tosh
ift
cost
sw
ithin
mix
edse
gmen
ts.
Low
com
petit
ion
for
defe
nse
cont
ract
sre-
sults
inex
cess
profi
tson
gove
rnm
entc
on-
trac
ts,e
ven
with
outc
osts
hift
ing.
Bens
and
Mon
ahan
(200
4)
Val
uatio
nim
plic
atio
nsof
diff
er-
ence
sin
firm
s’di
sclo
sure
prac
-tic
esfo
rase
toffi
rmst
hata
redi
-ve
rsifi
edby
line
ofbu
sine
ss.
US
firm
swith
AIM
Rra
nk-
ings
prov
idin
gse
gmen
tdis
-cl
osur
efo
rat
leas
ton
ese
g-m
ent
inC
ompu
stat
,no
fi-na
ncia
lse
gmen
ts,
min
20M
USD
insa
les
1980
-199
612
28m
ultis
egm
ent
firm
-yea
rsan
d12
91si
ngle
-segm
ent
firm
year
s
Exc
ess
valu
e,cr
oss-
subs
idiz
atio
nva
riab
le,
ab-
norm
alre
turn
(ABR
ET
)
Posi
tive
rela
tion
betw
een
disc
losu
requ
al-
ityan
dth
eex
cess
valu
eat
trib
utab
leto
di-
vers
ifica
tion.
Com
mitm
ents
from
man
-ag
ers
todi
sclo
sepr
ovid
esh
areh
olde
rsw
itha
mea
nsof
mon
itori
ngm
anag
e-m
ent’s
beha
vior
,m
itiga
ting
the
cros
s-su
bsid
izat
ion
ofun
derp
erfo
rmin
gse
g-m
ents
.Bo
tosa
nan
dSt
anfo
rd(2
005)
Wha
tmot
ives
dom
anag
ersh
ave
tow
ithho
ldse
gmen
tdis
clos
ures
and
how
did
the
intr
oduc
tion
ofSF
AS
no.1
31af
fect
anal
ysts
’in-
form
atio
nen
viro
nmen
t?
Firm
ssi
ngle
-segm
ent
un-
der
SFA
S14
but
mul
ti-se
gmen
tund
erSF
AS
131
1996
-199
861
5fir
ms
Unc
erta
inty
and
cons
en-
susm
easu
resb
ased
onH
ar-
ris(
1998
)
Com
pani
esw
ithhe
ldse
gmen
tin
form
a-tio
nun
der
SFA
S14
inor
der
topr
o-te
ctpr
ofits
inle
ssco
mpe
titiv
ein
dust
ries
.O
vera
llun
cert
aint
yan
dth
em
agni
tude
ofm
ean
fore
cast
erro
rin
crea
sed
mar
gina
llyun
der
SFA
S13
1.Be
rger
and
Han
n(2
007)
Ifm
anag
ers
tend
tom
erge
seg-
men
tsdu
eto
prop
riet
ary
orag
ency
cost
mot
ives
.
SFA
S13
1m
ulti-
segm
ent
firm
sw
ithsa
les
over
20M
USD
,C
RSP
mon
thly
retu
rnan
dI/
B/E/S
deta
ilda
taba
seco
vera
ge
1997
-199
8(a
dopt
ion
ofSF
AS
131)
796
firm
sM
anag
ers
with
hold
segm
ent
info
due
toag
ency
cost
mot
ive,
mix
edre
sults
for
pro-
prie
tary
cost
mot
ive.
Kou
and
Hus
sain
(200
7)
Exp
lori
ngth
em
ulti-
dim
ensi
onal
char
acte
rist
ics
ofse
gmen
tdis
clos
ures
and
thei
rim
pact
onim
prov
ing
anal
yst
insi
ght(
fore
cast
erro
rs).
FTSE
-100
larg
efir
ms
2001
-200
245
firm
sA
anal
ystf
orec
aste
rror
Impr
oved
anal
ysti
nsig
htis
asso
ciat
edw
ithm
atri
xfo
rmat
disc
losu
re,
disc
losi
ngge
-og
raph
icse
gmen
tin
form
atio
nbo
thfo
rm
arke
tsan
dor
igin
s,th
ege
ogra
phic
fine-
ness
ofdi
sclo
sure
and
indu
stry
com
para
-bi
lity.
Not
es:(
cont
.)
Tab
le1.
2:(c
on
tinue
d)
Art
icle
Pur
pose
ofst
udy
Sam
ple
Sam
ple
tim
e-fr
ame
Num
ber
ofob
serv
atio
nsV
aria
ble
ofin
tere
stM
ain
findi
ngs
Bens
etal
.(2
011)
Rea
sons
behi
ndag
greg
atin
gse
g-m
ent
info
rmat
ion;
how
dopr
i-va
teco
mpe
titor
saf
fect
the
dis-
clos
ure
deci
sion
s?
US
Com
pust
atm
anuf
actu
ring
firm
s19
87,
1992
and
1997
1625
firm
-yea
rsM
AT
CH
(indi
cato
rva
ri-
able
whe
ther
the
plan
t-ba
sed
pseu
do-se
gmen
t’s4-
digi
tSI
Cco
deis
mat
ched
byan
yof
the
repo
rted
seg-
men
tsco
de
Prop
riet
ary
cost
sre
late
dto
priv
ate
com
pe-
titio
nap
pear
tobe
the
key
mot
ive
for
non-
disc
losu
refo
rsi
ngle
-segm
ent
firm
s.Bo
thpr
opri
etar
yan
dag
ency
cost
mot
ives
are
im-
port
antd
eter
min
ants
ofm
ulti-
segm
entfi
rms’
segm
entd
iscl
osur
ede
cisi
ons.
Wan
get
al.
(201
1)W
hat
firm
char
acte
rist
ics
are
asso
ciat
edw
ithfir
ms
disc
losi
nghi
gher
diff
eren
ces
inre
port
edse
gmen
tea
rnin
gsgr
owth
?T
hest
udy
inve
stig
ates
agen
cyan
dpr
opri
etar
yco
sts,
finan
cing
need
san
dad
optio
nof
SFA
S13
1.
Mul
tiseg
men
tco
mpa
nies
with
avai
labl
ese
gmen
tdat
ain
Com
pust
at
1991
-200
454
0fir
ms;
7556
firm
-yea
rob
serv
atio
nsbe
fore
and
afte
rth
ead
optio
nof
SFA
S13
1
Cro
ss-se
gmen
tdi
ffer
ence
sin
earn
ings
grow
thva
ri-
abili
ty(r
ange
ofse
gmen
tea
rnin
gsgr
owth
)
Prop
riet
ary
and
agen
cyco
sts
are
asso
ciat
edw
ithlo
wer
cros
s-seg
men
tea
rnin
gsgr
owth
diff
eren
ces.
Com
pani
esw
ithex
tern
alfin
anc-
ing
need
sre
veal
larg
ergr
owth
diff
eren
ces
acro
ssse
gmen
tsan
dco
mpa
nies
reve
alm
ore
diff
eren
cesi
nea
rnin
gsgr
owth
afte
rthe
adop
-tio
nof
SFA
S131
.
Blan
coet
al.
(201
5)In
vest
igat
ing
ifse
gmen
tdi
sclo
-su
rein
fluen
cesc
osto
fcap
ital.
US,
non-
regu
late
d,no
n-fin
anci
alfir
ms
2001
-200
616
67fir
ms
Dec
ilera
nks
ofun
ex-
pect
edse
gmen
tdi
sclo
sure
(res
idua
lte
rmfr
omre
-gr
essi
ngdi
sclo
sure
onfir
mch
arac
teri
stic
s);
fore
cast
erro
r
Bett
erse
gmen
tdi
sclo
sure
decr
ease
sfo
reca
ster
ror;
high
qual
ityre
port
ing
redu
ces
the
firm
’sbe
ta;
segm
ent
repo
rtin
gre
duce
sim
-pl
ied
cost
ofca
pita
l;qu
ality
ofse
gmen
tre
-po
rtin
gis
anad
ditio
nalr
isk
fact
or.
And
réet
al.
(201
6)Se
gmen
tdi
sclo
sure
qual
ityan
dqu
antit
y,ho
wit
isaf
fect
edby
prop
riet
ary
and
agen
cyco
sts
and
how
itaf
fect
sana
lyst
s’ea
rn-
ings
fore
cast
accu
racy
.
Stox
xE
urop
é60
0in
dex
mul
tiseg
men
t,no
n-fin
anci
alfir
mst
hat
repo
rtse
gmen
tass
ets,
less
thos
efo
llow
ing
US
GA
AP
2009
270
com
pani
esSe
gmen
trep
ortin
gqu
ality
(cro
ss-se
gmen
tpr
ofita
bil-
itydi
ffer
ence
),qu
antit
yan
dfo
reca
ster
ror
ofan
alys
ts
Man
ager
sso
lve
prop
riet
ary
cost
sby
decr
eas-
ing
the
quan
tity
ofin
form
atio
nbe
low
stan
-da
rdgu
idan
ceor
,by
decr
easi
ngth
equ
ality
ofre
port
ing
oper
atin
gse
gmen
ts.
Too
muc
hin
-fo
rmat
ion
impa
irfin
anci
alan
alys
ts’a
bilit
yto
accu
rate
lyfo
reca
stea
rnin
gs.
Sain
iand
Her
rman
n(2
013)
Segm
enti
nfor
mat
ion’
seff
ecto
nco
stof
capi
tal,
and
how
info
r-m
atio
nas
ymm
etry
affe
cts
the
rela
tion.
US
firm
swith
Dec
.ye
ar-e
nd20
05-2
006
101
firm
sLW
OR
DS
(lnof
nrof
wor
dsin
the
segm
ent
re-
port
ing
sect
ion
offir
m10
Ks)
,ln
(PIN
)(p
roba
bil-
ityof
info
rmed
trad
ing
mea
sure
),R
(impl
ied
cost
ofca
pita
lus
ing
the
four
diff
eren
tmea
sure
s)
Bett
erse
gmen
tdi
sclo
sure
isas
soci
ated
with
low
erco
stof
equi
tyca
pita
l.T
heef
fect
ofbe
t-te
rse
gmen
tdis
clos
ure
ishi
gher
ifth
epr
oba-
bilit
yof
info
rmat
ion-
base
dtr
adin
gis
high
er.
Not
es:C
AR
iscu
mul
ativ
eab
norm
alre
turn
.MU
SDis
mill
ions
ofU
Sdo
llars
.
18 ESSAYS ON SEGMENT REPORTING AND VALUATION
Segment information can help analysts with earnings forecasting (Kochanek,1974; Blanco et al., 2015). Previous studies have documented mixed evidencewith respect to price variability. While Kochanek (1974) documented lowerprice variability in the presence of segment data, Swaminathan (1991); Loboet al. (1998) documented increased price variability after initiating segment dis-closure. Greenstein and Sami (1994) found lower bid-ask spread for firms pro-viding segment information, suggesting that it is related to lower informationasymmetries. Geographical segment information was also found to be relatedto improved analyst insight (Kou and Hussain, 2007) and the information wasfound to have a positive relation to CAR (Conover and Wallace, 1995).
Better segment disclosure is also associated with lower cost of equity capital(Blanco et al., 2015; Saini and Herrmann, 2013), and higher valuation levels(Bens and Monahan, 2004).
In the next section, I present prior literature documenting the more di-rect valuation usefulness of segment disclosure, together with literature on thediversification discount.
1.2.3 Segment based information for financial forecasting
Segment information provides details about the performance of different partsof the firm. This information can help investors in predicting firm-level finan-cial figures better. Table (1.3) presents examples of studies that investigate theusefulness of segment disclosures for forecasting earnings.
Kinney (1971) used voluntary segment disclosures in connection with ex-ternal industry forecasts to improve earnings predictions. He documented thatcombining segment level information with industry forecasts improved earn-ings predictions.
Silhan (1983) investigated sales and margin forecasts using segment data.He could not improve one-year ahead sales or margins forecasts using segmentdata, but documented improvements in forecasts one quarter ahead. Baldwin(1984) was the first to investigate analysts’ earnings forecasts and how segmentreporting is associated with better earnings forecasts. He documented that se-curity analysts were able to make more accurate forecasts for multi-segmentfirms after segment reporting disclosure was required in the 1970s.
Tab
le1.
3:Pr
iore
mp
iric
alr
ese
arc
ho
nse
gm
en
tre
po
rtin
ga
nd
fore
ca
stin
gA
rtic
leIF
RS/
US
GA
AP
Pur
pose
ofst
udy
Sam
ple
cons
truc
tion
Sam
ple
tim
e-fr
ame
Num
ber
ofob
-se
rvat
ions
Var
iabl
eof
inte
rest
Mai
nfin
ding
s
Kin
ney
(197
1)U
SG
AA
PU
sing
disa
ggre
gate
dea
rnin
gsin
-fo
rmat
ion
topr
edic
tnex
tyea
r’s
earn
ings
.
US
firm
svol
unta
riy
repo
rtin
gse
gmen
tsin
1967
1967
-196
924
firm
sE
arni
ngsf
orec
ast
diff
eren
ces(
t-tes
ts)
Segm
ent
leve
lsa
les
and
earn
ings
to-
geth
erw
ithin
dust
ry-le
vel
pred
ic-
tions
impr
ove
fore
cast
ing.
Silh
an(1
983)
US
GA
AP
Usi
ngqu
arte
rly
segm
ent
earn
-in
gsan
dm
argi
nin
form
atio
nto
fore
cast
earn
ings
.
Non
regu
late
d,U
Sco
rpor
atio
nsw
ith48
cont
inuo
usqu
arte
rsof
sale
san
dea
rnin
gsda
ta
12ye
ars
60fir
ms
Mea
nA
bsol
ute
Rel
ativ
eE
rror
(MA
RE
)and
Mea
nra
nks
No
impr
ovem
entf
oron
e-ye
arah
ead
sale
sor
mar
gin
fore
cast
s.Bo
thse
g-m
ents
ales
and
segm
entm
argi
nsco
n-tr
ibut
edto
fore
cast
impr
ovem
ents
one
quar
ter
ahea
d.Ba
ldw
in(1
984)
US
GA
AP
The
effe
ctof
the
intr
oduc
tion
ofse
gmen
tea
rnin
gsdi
sclo
sure
re-
quir
emen
ton
anal
ysts
’ear
ning
sfo
reca
stac
cura
cy.
US
Firm
swith
fore
cast
sfro
mth
esa
me
anal
yst
1969
,70,
72,7
3
1969
-197
318
8fir
ms
Ear
ning
sfor
ecas
terr
orFo
rm
ultis
egm
entfi
rms,
secu
rity
an-
alys
tsw
ere
able
tom
ake
mor
eac
-cu
rate
earn
ings
proj
ectio
nsaf
ter
seg-
men
trep
ortin
gw
asad
opte
din
1970
.Fa
irfie
ldet
al.
(200
9)U
SG
AA
PT
hein
crem
enta
lin
form
atio
nin
indu
stry
-leve
lan
alys
es,
com
pare
dto
anec
onom
y-w
ide
anal
ysis
,fo
rpr
edic
ting
firm
grow
than
dpr
ofita
bilit
y.
Non
-fina
ncia
lC
ompu
stat
firm
s19
69-2
003
3527
2ob
serv
a-tio
nsus
edfo
rfo
reca
stin
g
Abs
olut
efo
reca
ster
rors
for
seve
ralm
easu
res
Indu
stry
-leve
lan
alys
esar
ein
cre-
men
tally
info
rmat
ive
info
reca
stin
ggr
owth
met
rics
,bu
tth
eydo
not
impr
ove
fore
cast
sof
profi
tabi
lity
met
rics
.Sc
hröd
eran
dY
im(2
018)
US
GA
AP
Whe
nan
dw
hyin
dust
ry-sp
ecifi
cpr
ofita
bilit
yfo
reca
stin
gm
odel
sca
nbe
usef
ul.
US
Com
pust
atsa
mpl
e19
66-2
011
5870
8fir
m-
year
obse
rva-
tions
;80
127
segm
ent-y
ear
obse
rvat
ions
Fore
cast
impr
ovem
ent,
the
diff
.bet
wee
nin
dust
ry-sp
ecifi
can
dec
onom
ic-w
ide
abs.
fore
cast
erro
rdi
ffer
ence
s
Con
side
rabl
ein
dust
ry-e
ffec
tsfo
rsi
ngle
-segm
ent
firm
profi
tabi
lity
fore
cast
ing
and
toso
me
exte
ntto
busi
ness
segm
ent
profi
tabi
lity
fore
cast
ing
(out
-of-s
ampl
e).
No
indu
stry
-eff
ects
for
mul
tiseg
men
tfir
ms.
Not
es:M
USD
ism
illio
nsof
US
dolla
rs.
20 ESSAYS ON SEGMENT REPORTING AND VALUATION
More recently, studies have investigated the usefulness of the segment’s in-dustry information to predict earnings. Fairfield et al. (2009) compared model-based earnings forecasts using industry-level mean reversion and economy-widemean reversion of margins. They documented significant improvements inforecasting growth but not margins.
In a follow-up study, Schröder and Yim (2018) argued that Fairfield et al.(2009) did not find industry information to be useful because they used theprimary industry of the firm. When forecasting earnings using industry infor-mation at the segment level, Schröder and Yim (2018) documented a significantimprovement in forecasting margins under the industry approach (SFAS 14).These improvements disappeared under the management approach (SFAS 131).
1.2.4 Segment reporting and valuation
In this section, I present prior literature that discusses sum-of-parts valuationusing segment reporting and the diversification discount. Table (1.4) presentsan overview of the relevant reports in the area.
Ortman (1975) was one of the first studies that investigated the usefulnessof segment information for valuation. 72 security analysts were asked to valuea hypothetical company. The results suggested that analysts’ valuation werein line with industry average P/E ratios using segment data. Analysts alsoprovided less dispersed price estimates when they had access to segment infor-mation, suggesting that segment information is associated with greater pricestability. Tse (1989) documented evidence suggesting that the earning-price re-lationship for the firm depends on which industry the earnings is coming from.Earnings from high-growth industries is valued higher as compared to earningsfrom low-growth industries.
Lang and Stulz (1994) investigated the correlation between diversificationand firm valuation. They calculated diversification using market values and im-plied values, where implied values were calculated as the multiple of segmentassets and the median Tobin’s q value of the industry. The paper documentedthat highly diversified firms had lower average and median q values as com-pared to single-segment firms. Furthermore, looking at a sample of diversify-ing firms, they found that firms performed poorly before the diversification,indicating that they had likely exhausted their organic growth opportunities.
Tab
le1.
4:Pr
iore
mp
iric
alr
ese
arc
hin
vest
iga
ting
the
valu
atio
nus
efu
lne
sso
fse
gm
en
tre
po
rtin
ga
nd
the
div
ers
ific
atio
nd
isco
un
tSt
udy
Pur
pose
ofst
udy
Sam
ple
Sam
ple
tim
e-fr
ame
Num
ber
ofob
serv
atio
nsV
aria
ble
ofin
tere
stM
ain
findi
ngs
Ort
man
(197
5)In
vest
igat
esw
heth
erse
gmen
tda
tais
help
ful
for
anal
ysts
inva
luat
ion.
Hyp
othe
tical
com
pany
tobe
valu
edby
41+
31(e
xper
imen
tgr
oup+
cont
rolg
roup
)an
alys
ts
72an
alys
tre
plie
sA
naly
sts’
valu
ees
timat
esT
heva
lue
ofea
chfir
m’s
stoc
kw
asin
line
with
the
pres
ent
valu
eof
itsex
pect
edre
turn
sas
re-
flect
edby
indu
stry
aver
age
P/E
ratio
sw
hen
an-
alys
tsre
ceiv
edse
gmen
tdat
a.Se
gmen
tdis
clos
ure
coul
dre
sult
inm
ore
stab
ility
inst
ock
pric
es.
Tse
(198
9)T
hero
leof
indu
stry
segm
ent
data
inex
plai
ning
secu
rity
pric
es.
Ifin
dust
ryse
gmen
tm
embe
rshi
ppr
ovid
esin
-cr
emen
tal
info
rmat
ion
over
cons
olid
ated
data
for
asse
ss-
ing
indu
stry
-rel
ated
grow
thpr
ospe
cts.
Com
pust
atm
ultis
egm
entfi
rms
1975
-197
918
8-36
7fir
ms
per
year
,al
toge
ther
arou
nd15
00fir
m-y
ear
obse
rvat
ions
Equ
itym
arke
t-to-
book
valu
eT
heea
rnin
gs-p
rice
rela
tions
hip
depe
nds
onw
heth
erth
eea
rnin
gsis
from
ahi
gh-g
row
thor
low
-gro
wth
indu
stry
.Se
gmen
tda
taap
pear
toim
prov
eth
ein
form
ativ
enes
sof
such
clas
sific
a-tio
nsfo
rse
curi
typr
ices
.
Lang
and
Stul
z(1
994)
Inve
stig
atin
gth
eco
rrel
atio
nbe
-tw
een
dive
rsifi
catio
nan
dfir
mva
luat
ion.
Com
pust
atm
ultis
egm
entfi
rms,
asse
tsov
er10
0M
USD
1978
-199
018
255
firm
-yea
rob
serv
atio
nsTo
bin’
sqH
ighl
ydi
vers
ified
firm
sha
velo
wer
aver
age
and
med
ian
qra
tios
com
pare
dto
sing
le-se
gmen
tfir
ms.
Firm
spe
rfor
mpo
orly
befo
redi
vers
ifica
-tio
n,in
dica
ting
that
they
have
exha
uste
dth
eir
grow
thop
port
uniti
esin
tern
ally
and
seek
grow
thth
roug
hdi
vers
ifica
tion.
Berg
eran
dO
fek
(199
5)T
heef
fect
ofdi
vers
ifica
tion
onfir
mva
lue
and
the
pote
ntia
lso
urce
sofv
alue
gain
sor
loss
es.
US
firm
s,m
in.2
0M
USD
sale
s,no
segm
ents
inth
efin
anci
alin
dust
ry
1986
-199
136
59fir
ms,
1618
1ob
serv
atio
ns,o
fw
hich
5233
are
mul
ti-se
gmen
t
Exc
essv
alue
,im
pute
dva
lue
usin
gas
sets
,sal
esan
dE
BIT
mul
tiple
s
Com
pari
ngth
esu
mof
segm
ents
tand
-alo
neva
l-ue
sto
the
firm
’sac
tual
valu
eim
plie
sa
13%
to15
%av
erag
eva
lue
loss
from
dive
rsifi
catio
ndu
ring
1986
-199
1.O
ver-i
nves
tmen
tan
dcr
oss-
subs
idiz
atio
nco
ntri
bute
toth
eva
lue
loss
.
(con
t.)
Tab
le1.
4:(c
on
tinue
d)
Stud
yP
urpo
seof
stud
ySa
mpl
eSa
mpl
eti
me-
fram
eN
umbe
rof
ob-
serv
atio
nsV
aria
ble
ofin
tere
stM
ain
findi
ngs
Che
nan
dZ
hang
(200
3)
Incr
emen
tal
valu
ere
leva
nce
ofse
gmen
tac
coun
ting
data
.T
heef
fect
ofdi
verg
ence
ofse
gmen
tpr
ofita
bilit
y(D
OP)
oneq
uity
valu
egi
ven
over
all
grow
than
dpr
ofita
bilit
y.
US
mul
tiseg
men
tfirm
s19
86-1
997
1346
3fir
m-y
ear
obse
rvat
ions
Div
erge
nce
ofpr
ofita
bilit
y(d
iffer
ence
betw
een
the
profi
tabi
lity
oftw
ose
gmen
ts)
The
usef
ulne
ssof
segm
entd
ata
rela
test
oth
ehe
t-er
ogen
eity
ofin
vest
men
top
port
uniti
esac
ross
segm
ents
caus
edby
diff
eren
ces
inse
gmen
tpro
f-ita
bilit
yan
dgr
owth
.T
hein
crem
enta
lexp
lana
-to
rypo
wer
ofse
gmen
tinf
orm
atio
nis
low
,pro
b-ab
lydu
eto
mea
sure
men
terr
ors.
Vill
alon
ga(2
004)
Whe
ther
dive
rsifi
catio
nca
uses
the
‘div
ersi
ficat
ion
disc
ount
’.U
Sm
ulti-
segm
ent
firm
s,sa
leso
ver
20M
USD
1978
-199
760
930
firm
-yea
rob
serv
atio
nsE
xces
sval
uesb
ased
onin
dust
ryq-
sD
iver
sific
atio
ndo
esno
tde
stro
yva
lue.
Usi
ngpr
open
sity
scor
em
atch
ing
and
Hec
kman
-type
sele
ctio
nm
odel
s,th
ees
timat
eddi
vers
ifica
tion
disc
ount
disa
ppea
rs.
Che
nan
dZ
hang
(200
7)
Div
erge
nce
ofse
gmen
tpr
of-
itabi
litie
sin
prev
ious
year
sex
-pl
aini
ngdi
vest
men
ts.
Com
pust
atfir
msw
ithdi
scon
tinue
dop
erat
ions
1990
-200
155
4di
vest
men
tsC
AR
;pro
fitab
ility
dive
rgen
ceva
riab
leFi
rms
have
the
mot
ivat
ion
for
earn
ings
shif
t-in
gto
incr
ease
firm
valu
e.D
isto
rted
repo
rtin
gca
uses
mar
ket
valu
esto
devi
ate
from
intr
insi
cva
lues
.Div
estm
enta
rise
sasa
volu
ntar
yco
mm
it-m
entt
oim
prov
erep
ortin
gqu
ality
and
toco
rrec
tm
isva
luat
ion.
You
(201
4)T
heab
norm
alpr
ofita
bilit
yof
ase
gmen
tan
dits
rela
tive
valu
a-tio
nw
ithin
the
firm
,an
dho
wth
ehe
tero
gene
ityof
segm
ents
with
inth
efir
maf
fect
sth
isre
la-
tions
hip.
US
firm
swith
mor
eth
an20
MU
SDas
sets
,no
n-fin
anci
al,
non-
utili
ty
1998
-200
790
783
firm
-yea
rob
serv
atio
nsE
xces
sval
ue,a
bnor
mal
RO
Aof
the
segm
ent,
rela
tive
segm
ent
valu
atio
nse
gmen
tva
luat
ion
disp
ersi
on
Man
ager
sha
vein
cent
ives
totr
ansf
erea
rnin
gsfr
omse
gmen
tsop
erat
ing
inin
dust
ries
with
low
valu
atio
nsto
thos
ew
ithhi
gher
valu
atio
ns.
The
posi
tive
asso
ciat
ion
betw
een
segm
ent
abno
rmal
profi
tabi
lity
and
segm
ent
rela
tive
valu
atio
nis
stro
nger
for
firm
sw
ithm
ore
disp
erse
dse
gmen
tva
luat
ions
,whe
reth
eben
efito
fsuc
hpr
ofitt
rans
-fe
rsar
ela
rger
.
Not
es:P/E
ispr
ice-
to-e
arni
ngsr
atio
.MU
SDis
mill
ions
ofU
Sdo
llars
.
INTRODUCTION AND OVERVIEW OF THE DISSERTATION 23
Berger and Ofek (1995) studied the diversification discount, but used indus-try median sales, EBIT and asset multiples to calculate implied values. Theyfound that the sum of stand-alone values were 13%-15% higher as comparedto the firm’s market value. The value loss was lower when firms operatedin similar segments. Their findings suggested that over-investment and cross-subsidization could explain the value loss and that the value loss was reducedby the tax benefits of diversification. In a related study, however, Villalonga(2004) found that the diversification discount disappears after propensity scorematching and Heckman-type selection models, suggesting that it is not the di-versification per se that causes the diversification discount but that firms thatdiversify are different from firms that do not diversify.
Chen and Zhang (2003) presented a model showing that the incrementalvalue relevance of segment data arises because of the heterogeneity in invest-ment opportunities across segments caused by growth and profitability differ-ences. Their empirical results showed, however, that the incremental explana-tory power of segment information is low, probably due to noise in segmentdata. You (2014) documented a positive association between segment abnor-mal profitability and segment relative valuations which was stronger for firmswith more dispersed segment valuation. This provides suggestive evidence forcross-segment earnings transfers to high-multiple segments.
Chen and Zhang (2007) also started out with the potential incentive formanagers to transfer profits across segments. In an analytical model, theyshowed that if the market anticipates this earnings transfer, it leads to under-valuation. In order to correct the undervaluation, firms might choose a costlysignal - to divest the low-margin segment. Their empirical results supportedthis conjecture.
Next, I present short summaries of the three papers included in the disser-tation.
1.3 Summary of thesis papers
Short summaries of the three papers in the dissertation are presented in thissection.
24 ESSAYS ON SEGMENT REPORTING AND VALUATION
1.3.1 Summary of Paper I: The role of segment reporting in corpo-rate valuation
Segment reporting provides information about the performance, risk and sizeof different parts of the firm’s business. Previous literature has documentedthat segment reporting was considered an important source of informationfor analysts and investors (AIMR, 1993; Previts et al., 1994; Chen and Zhang,2003). This paper presents a theoretical framework to explore how segmentinformation can be incrementally useful for valuation, in addition to consol-idated information. It evaluates segment reporting standards and the disclo-sure practices of firms using accounting information-based valuation modelsadapted for sum-of-parts valuation.
Under the management approach, firms are required to present their oper-ating segments in line with the internal organization of the firm. My analysissuggests that as segments might derive their revenues or incur their costs intransactions that are not on an arm’s length basis, valuing such segments sep-arately might not provide reliable value estimates. I suggest users of segmentinformation to aggregate segments into relevant objects of valuations, parts ofthe firm that derives most of its revenues and incurs its costs from transactionswith arm’s length counterparts.
The paper takes segment reporting standards (SFAS 14, IAS 14, IAS 14R,SFAS 131, IFRS 8) as a starting point and benchmarks the disclosure require-ments of these standards, as well as the disclosure provided by the firms on theinformation needs of accounting information-based valuation models. It in-vestigates how the information provided by reporting entities can be used forsum-of-parts valuation as well as what additional information would be usefulto value parts of firms separately. The findings suggest that the requirementsof segment reporting standards cannot provide sufficient information to valuefirms segment by segment. Moreover, while some firms provide segment dis-closures beyond what is specifically required by a standard, most firms do notprovide enough information on the segment level to enable outside investors tovalue firms segment by segment using more sophisticated accounting models.
This paper is similar to Skogsvik (1998), which focused on the informa-tion needs of the residual income valuation model. The paper investigatedwhat information is needed on the firm level to apply the valuation modeland concluded that managers would provide the information voluntarily if the
INTRODUCTION AND OVERVIEW OF THE DISSERTATION 25
disclosure resulted in higher market expectations.Paper I continues by investigating the disclosure question from the oppo-
site angle compared to Skogsvik (1998); given the disclosures provided by thefirms, how can an investor use the segment information for valuation purposes.A partial solution for the valuation problem is provided using an adaptationof the abnormal operating income growth model for sum-of-parts valuation.The results imply that investors can come reasonably close to firm values givenpartial segment disclosure. The difference between the valuation obtained us-ing the partial solution and using full disclosures is analyzed to shed light onwhich firms would provide segment information beyond what is specifically re-quired by the standard to enable segment-by-segment valuation. Full segmentdisclosure - compared to partial disclosure - is more relevant for firms with highpriced-in growth expectations. Such firms can signal their superior expectedabnormal operating income growth (AOIG) if they show that proportionallymore of their operating income is reinvested in less risky segments.
Furthermore, firms can achieve higher valuations if they reveal that moreof their AOIG comes from low risk segments and if low risk segments havemore durable competitive advantages. Given no difference in segment risks,firms can achieve higher valuations through signalling that segments with moreeconomic goodwill generate larger shares of AOIG.
In addition, the paper pinpoints difficulties with applying sum-of-partsfirm valuation and how to handle these problems in practice. It identifies twomain problems. First, segment earnings might not add up to consolidated earn-ings. Second, the operating asset relation might not hold, i.e. the change in netoperating assets might not be explained by after-tax operating income, acqui-sition and divestment of assets.
1.3.2 Summary of Paper II: On the usefulness of segment informa-tion for earnings forecasting
Previous literature has documented the decreased usefulness of informationabout industry in segment reporting under the management approach, as com-pared to the industry approach, presumably due to lower comparability withinindustries (Fairfield et al., 2009; Schröder and Yim, 2018). This paper con-tributes to the literature by investigating the usefulness of segment informationfor earnings forecasting, absent cross-industry and cross-geography compara-bility.
26 ESSAYS ON SEGMENT REPORTING AND VALUATION
Companies reporting under US GAAP have been required to provide seg-ment information since 1976 when the first segment reporting standard, SFASNo. 14, was introduced. This standard required companies to present finan-cial information about the different geographic segments and lines of business(LOB) that they operated in (FASB, 1976). The basis for presentation for thesegments was derived from the different product lines the company generatedits revenues from. This approach was superseded by SFAS No. 131 from 1999which required companies to define segments according to the internal organi-zation of the firm. While FASB acknowledged the loss of comparability withSFAS 131, they argued that information provided that followed the manage-ment approach should be more relevant and reliable (FASB, 1997).
Schröder and Yim (2018) documented mixed evidence for improved earn-ings forecasts using industry information. The authors found that industrydefinitions were helpful for earnings forecasting under the industry approachof segment reporting (SFAS 14), but that they did not improve earnings fore-casting under the management approach (SFAS 131). In the paper, I investi-gate whether segment information can be relevant for earnings forecasting inthe absence of within-industry comparability. The earnings forecasting mod-els are designed to pick up segment growth trends and margin mean reversion(firm-specific approach), as well as economy-wide comparability of segmentoperating performances (cross-sectional, economy-wide model). I investigatethe usefulness of this information both under SFAS 14 and SFAS 131 to shedlight on whether the management approach can provide more relevant infor-mation.
The findings of the paper show that quantitative segment disclosures donot improve earnings or sales forecasts for either the business or the geographicsegment sample. The results from the cross-sectional model show that the fore-casts are better for several subsamples. Notably, this holds in particular forsegment information presented under the industry approach. The findings aredistinctively weaker for the management approach subsample. I also investi-gated whether the reporting characteristics were associated with lower earningsforecast errors. My results show that if more segments are disclosed, more ac-curate segment-based forecasts are obtained for most subsamples. Moreover,disclosing proprietary information appears to be associated with larger earn-ings forecast errors, suggesting that the disclosure of such information does notimprove earnings forecasting.
INTRODUCTION AND OVERVIEW OF THE DISSERTATION 27
1.3.3 Summary of Paper III: Heterogeneous investor beliefs andequity carve-outs
In an equity carve-out (ECO), the parent company separates ("carves out") theoperations of a segment of a firm into a subsidiary. The parent company liststhe shares of the carved-out subsidiary on a stock exchange and sells some ofthe shares on the market. The parent company usually retains control over thecarved-out entity.
Previous research on sum-of-parts valuation and the diversification dis-count used implied valuation for valuing segments of the firm (Berger andOfek, 1995; Villalonga, 2004; Jiao et al., 2013), as there were no market pricesavailable for the firms’ segments. In an equity carve-out, the market providesa valuation for a segment of a firm. In this paper, I use this additional informa-tion to form inferences about sum-of-parts valuation.
Previous papers investigating equity carve-outs have documented positiveannouncement returns for the parent company. These studies presented twomain hypotheses to explain the positive announcement returns. In this paper,I provide a novel, third hypothesis and use a US sample of 102 equity carve-outs between 1980 and 2015 to investigate the predictions of the hypothesisempirically.
The first explanation in previous literature is referred to as the asymmet-ric information hypothesis (Nanda, 1991). This hypothesis implies that whenfirms raise financing through selling equity in a subsidiary (segment of a firm)instead of issuing equity in the parent firm, they signal that the stock of the par-ent is undervalued. The market understands this signal and reacts positively,which results in an increase of the parent’s share price.
The second explanation is referred to as the divestiture gains hypothesis(Schipper and Smith, 1986). This hypothesis suggests that a carve-out changesthe structure of the firm, resulting in better visibility and monitoring for thecarved-out entity, which leads to better contracting, better access to capitalmarkets, or other operational benefits for the subsidiary. Follow-up studieshave provided mixed empirical evidence with respect to the two explanations.
I present a third hypothesis built on the heterogeneous beliefs theory andinvestigate whether companies create value for current shareholders throughselling part of the company to investors who value its attributes the most. Theheterogeneous beliefs theory, first described in Miller (1977), argues that when
28 ESSAYS ON SEGMENT REPORTING AND VALUATION
investors are presented with the same information they arrive at different valueestimates. The investors with the highest value estimates will invest in a projector a firm. This theory also provides an explanation for the diversification dis-count. Investors are usually optimistic about one or more, but not all segmentsof a firm. When they have to hold all segments, they require a discount com-pared to what the parts would sell for as separate entities on the stock market.
In an equity carve-out, the parent firm sells equity in its subsidiary on themarket. Given that new owners can buy shares in the subsidiary only, themarket price of the subsidiary will be higher than the value estimates of theinvestors of the parent. Expecting the gain on the equity sales, these investorsthen increase their price estimates, which explains the positive announcementreturn. This theory also predicts that the valuation level of the parent will de-crease following the restructuring compared to the pre-announcement period.When these investors sell their holdings in the group, the share price decreases.This prediction of decreased valuation level after the completion of the restruc-turing stands in contrast to the previous two hypotheses in the literature.
The paper investigates the valuation of parent company shares before theannouncement and after the completion of the carve-out. The comparisonof diversification discounts (excess values) suggests that the valuation levels ofparent firms have decreased after the ECO compared to the pre-announcementperiod. This result provides empirical support for the heterogeneous beliefshypothesis. The decrease in valuation levels of the parent entities is almost10%.
1.4 Synthesis and conclusions - The valuation rel-evance of segment reporting
The papers in this dissertation extend our knowledge in terms of the valuationrelevance of segment reporting and provide additional evidence for all fourstreams of literature discussed above. Paper I starts with discussing the impor-tance of segment definition for valuation. Some ways of defining segments aremore suitable for generating segment-based forecasts and valuing segments ontheir own. Firms might present their segments in a way that makes the val-uation of standalone segments less reliable. For example, if segments derivetheir revenues or incur their costs from transactions with other parts of the
INTRODUCTION AND OVERVIEW OF THE DISSERTATION 29
firm, the accounting numbers of the segments might not be representative ofsimilar numbers of a standalone entity. Valuing such segments separately, andthen adding up such values to get a consolidated valuation can be misleading.Instead, users of segment information could identify reasonable objects of val-uation and value them separately. A reasonable object of valuation can be asegment or a combination of segments which mostly derives its revenues orincurs its costs from arm’s length transactions.
Paper I identifies two main ways in which investors can improve their val-uation models with segment information. First, they can use segment levelaccounting numbers to improve their consolidated-level forecasts, i.e. by im-proving the inputs of their valuation models. Second, investors might use theinformation provided on the segment level to value the parts of the businessseparately. Then, after adding together the value of the parts, they arrive at aprobably more accurate firm value.
Appendix 2.D in Paper I presents an analytical investigation about the use-fulness of segment reporting for earnings forecasting. I compare sales and earn-ings forecasts achieved with and without segment information and find thatsegment reporting is more useful when growth rates and margins differ acrosssegments. The comparison of input improvements suggest that firms havinghigh-growth segments that are more profitable would be more likely to providesegment information.
The analytical model in Paper I and the empirical investigation in Paper IIfocus on earnings forecasts. However, the two papers approach this questionfrom two directions. The model in Paper I takes a signalling theory perspectiveand investigates which firms would provide segment information to signal theirsuperior quality to the market and achieve higher forecasts. Paper II presentsan empirical investigation of the usefulness of segment information for fore-casting earnings. It provides further evidence with respect to the change fromthe industry approach to the management approach in segment reporting reg-ulation. Previous research has documented that the industry information ofsegments seemed to be less helpful for earnings forecasting after the introduc-tion of the management approach in SFAS 131. FASB had argued that the man-agement approach is more relevant and reliable due to the approach revealingthe internal organization of the firm. Paper II contributes by providing evi-dence for the usefulness of within-segment sales growth and margin trends forforecasting earnings, both under the industry approach and the management
30 ESSAYS ON SEGMENT REPORTING AND VALUATION
approach.Paper I investigates the second use of segment information (valuing the
segment separately) from a modelling perspective, and Paper III documentsempirical evidence relating to sum-of-parts valuation. Paper I benchmarksthe different regulatory requirements on the information needs of sophisti-cated accounting information based valuation models. I extend accountinginformation-based valuation models to enable the incorporation of segment-level accounting information. I find that both the disclosure requirements andthe reporting practices of firms are insufficient for providing outsiders withenough information to value segments separately. I also investigate the moti-vations for reporting entities to provide valuation-relevant segment disclosurebeyond what is specifically required by the standard.
From a signalling theory point of view, a firm provides information volun-tarily if the disclosure leads to a higher value estimate in the market. I comparethe valuation output of models with and without the incorporation of segmentinformation to investigate what type of firms would be more inclined to pro-vide segment information beyond what is specifically required by the standard.
I start by presenting a partial solution given limited segment disclosure.When firms do not provide the information needed for applying valuationmodels on the segment level, users can apply valuation multiples to segment-level financial numbers. Using the abnormal operating income growth valua-tion model, I show that the difference between this solution and the solutionone could arrive at with full segment disclosure might not be large. This pro-vides an understanding why investors think segment information is value rele-vant, even though most firms provide only limited information on the segmentlevel, and it provides a possible explanation for the popularity of valuation mul-tiples for sum-of-parts valuation in practice and research [e.g. Berger and Ofek(1995); You (2014)].
Next, I analyze the difference in the value estimates between partial disclo-sure and full disclosure. This extends the analysis between no disclosure andpartial disclosure (see Appendix 2.D). These analyses offer insights as to whycertain firms provide no segment disclosure, partial disclosure or full disclo-sure.
My results suggest that full segment disclosure is more relevant from a val-uation perspective for firms with high priced-in growth expectations. Firmswith high priced-in growth can signal superior quality, as well as achieve higher
INTRODUCTION AND OVERVIEW OF THE DISSERTATION 31
expected abnormal operating income growth (AOIG) and higher valuations ifthey show that more of their operating income is reinvested in the less riskysegments.
Furthermore, firms can achieve higher valuations if more of their AOIGcomes from low risk segments and if low-risk segments have more durable com-petitive advantages. Given no difference in segment risks, the firm can achievehigher valuation through signalling that the segments with more durable com-petitive advantage produce larger shares of the AOIG.
Previous research has documented the importance of segment growth andrisk. However, there is an important difference between growth and AOIG.While growth is the increase in earnings, AOIG is the value created of growth.If the growth in (after-tax, operating) earnings is not higher than the requiredreturn on the additional capital employed, the growth does not create eco-nomic value. Paper I also contributes to the literature by showing that givenfull disclosure, AOIG is a more relevant value driver as compared to earningsgrowth.
I also discuss difficulties with sum-of-parts valuation in Paper I. Even ifcompanies provide detailed segment level information, they might not pro-vide all items needed for valuation, or they might define items in a way thatis not useful for valuation. Commonly, the sum of operating segment earn-ings and sales are larger than the consolidate number. Companies often report‘other’, ‘corporate’ or ‘eliminations’ segments. Section 2.5 in Paper I providesa discussion on how to assess the valuation effect of such segments when doingsum-of-parts valuation.
Previous literature on the diversification discount presents evidence sug-gesting that value of the firm might not equal the sum of its parts’ value in prac-tice. Paper III documents an empirical phenomenon in line with this streamof literature, providing evidence that supports the clientele explanation of thediversification discount.
The focus of Paper III is equity carve-outs, when a firm sells shares in oneof its segments on the market. This setting allows me to observe market val-uation for part of the company. Previous literature has documented positiveabnormal returns to equity carve-out announcements, offering two differentexplanations for this. Paper III uses the heterogeneous beliefs theory by Miller(1977) to formulate a third hypothesis to explain the positive announcementreturns upon equity carve-outs. This hypothesis suggests that carve-outs are
32 ESSAYS ON SEGMENT REPORTING AND VALUATION
associated with positive abnormal returns because firms can sell equity in theirsubsidiaries to investors who value their attributes more than investors of thegroup.
In Paper I, I assumed homogeneous expectations, i.e. given the reportedfinancial numbers, investors arrive at a certain value estimate. In contrast tothis, Paper III assumes heterogeneous expectations, that given a certain set of re-ported financial information, different investors have different value estimates.Investors with high value estimates pay a higher price for the shares, producinga gain from the perspective of group owners.
I investigate pre-announcement and post-completion valuation levels (di-versification discounts) around the restructurings. I find evidence consistentwith the heterogeneous beliefs hypothesis. After the completion of a carve-out, investors of the group who like the the carve-out part more can sell sharesin the group and buy the carved-out entity. This change in investor clienteleleads to a decrease in the valuation level of the group. The result is also in linewith the investor clientele explanation of the diversification discount for con-glomerates and the closed-end investment fund puzzle. Investors are willingto pay more for separate shares compared to what they are willing to pay ifthey have to own a basket of shares. Hence, the use of segment informationfor valuation seems to be more complicated in practice than what my valua-tion modelling in Paper I suggests; the sum of the parts might be more thanthe value of the whole.
1.4.1 Limitations and future research
The dissertation has a number of limitations. First, the analytical model inPaper I takes on a signalling theory perspectives, disregarding other costs orbenefits of segment disclosures. Moreover, the model applies several assump-tions that are necessary for conducting the analysis, but where relaxation ofthe assumptions potentially could influence my results. One of the importantassumptions is the linear decrease of AOIG to zero in n years (together with theassumption of no accounting conservatism and inflation). While these assump-tions help me arrive at a neat solution for the modelling problem, the investiga-tion of AOIG over time is an interesting empirical question that I have not seenso far addressed in the literature. Ohlson and Juettner-Nauroth (2005) assumesAOIG to be constant and Penman (2007) presents increasing values over time,but exploring the properties of abnormal operating income growth figures of
INTRODUCTION AND OVERVIEW OF THE DISSERTATION 33
companies could provide valuable insights. As the partial solution in Paper Isuggests, the difference between fundamental value and a valuation obtainedusing an EV/EBIT multiple can partially be explained by the expected AOIGtrend in the future.
Another important assumption of the model is that I disregard proprietarycosts arising from segment disclosure. This can be particularly important forthe interpretation of the findings. From a signalling perspective, firms coulddisclose information about high growth, high margin, or low risk, high AOIGsegments, exactly where proprietary costs are considerable. Therefore, thefindings from the model might not be observable empirically. It would be in-teresting to explore the firms that reveal such high growth, high profitability,low risk segments. A theoretical expectation would be that firms with suchsegments, but with low proprietary costs, would be willing to provide suchdisclosures. Firms with segments that enjoy monopoly or patent protectioncould probably benefit from providing full segment disclosure as they do notface competitive threats arising from disclosure.
Paper II applies a parsimonious earnings forecasting model that only picksup segment growth trends and margin mean reversion characteristics. It gen-erates mechanical earnings forecasts from reported financial figures. Investorshave vast amounts of information which presumably enables them to arrive atmore informed forecasts. A more sophisticated forecasting model might haveprovided different results. In the paper, I also assume that the forecast errorsobtained by these models proxy for forecasting difficulty. However, investorsmight be provided with other information that helps with forecasting earnings(for example, management guidance). Presumably, such guidance alters the useof segment information by investors, which is an intriguing question for futureresearch. Given the mixed results of Paper II, it is still an open question if andhow segment information under the management approach can be relevant forforecasting earnings. Furthermore, the study could be extended to a compar-ison of the usefulness of information provided under IAS 14 and IFRS 8 forforecasting earnings.
The sample selection criteria used in Paper II and III provide a limitationto the research results. The firm-based model in Paper II requires firms to pro-vide segment information following the same structure for three years in a row.Firms not providing segment reporting this way are excluded from the sample.The economy-wide approach is less restrictive when it comes to sampling, but
34 ESSAYS ON SEGMENT REPORTING AND VALUATION
if the excluded firms are different in terms of forecasting, it can adversely affectthe generalizability of the results. A further limitation of Paper III is the rel-atively small sample size. A larger sample could have provided more nuancedinsights about the value creation following equity carve-outs. Moreover, a lim-itation for both Paper II and III is that the sample firms are all listed in theUS. Even though the US is a large economy with an important and well de-veloped stock market, the findings are probably not generalizable to differenteconomies and less developed stock markets. For example, analyzing equitycarve-outs in other markets could shed light on whether the findings regard-ing the heterogeneous beliefs theory hold under different settings with respectto investor culture and regulation. Moreover, by extending the sample timeperiod, investigations could be done to contribute to the heterogeneous beliefsliterature; for example whether the effects documented for mergers in Jiao et al.(2013) can be found (in the opposite direction) when investigating demergers.
Chapter 2
Paper I: The role of segment reportingin corporate valuation
Abstract. Segment reporting is important for valuation purposes as it en-ables outsiders to become informed about the prospects and performance ofthe different parts of a firm. This paper provides a framework for the useful-ness of segment disclosure for valuation purposes and benchmarks regulatorysegment reporting requirements on the needs of accounting information-basedfirm valuation models. The analysis of the standard requirements and the seg-ment reporting practices of firms reveal that both the information requiredby the standards and the disclosure provided by the firms are insufficient forthe valuation of the firm on a segment-by-segment basis. A partial solution isdiscussed for the valuation problem using only earnings on the segment level.The comparison of the value estimates given partial and full disclosure revealspossible, valuation-based motivations for voluntary segment disclosure. Theframework presented in the paper helps to understand the valuation effects ofsegment disclosure. It also informs firms that are willing to be forthcoming intheir disclosure with what accounting information to provide on the segmentlevel and helps regulators to create standards that are more useful for investors.
2.1 Introduction
Segment reporting provides information to the financial market about the per-formance, risks and size of the different parts of a business. This informa-
36 ESSAYS ON SEGMENT REPORTING AND VALUATION
tion is especially helpful when companies consist of heterogeneous parts, i.e.without segment reporting, financial forecasts for such firms could only bebased on judgmental weightings of the profitability, growth and risk of thedifferent parts of the business. The Association for Investment Managementand Research (AIMR) argued that "[Segment information] is vital, essential,indispensable, and integral to the investment analysis process [...]. Differentsegments will generate dissimilar streams of cash flows to which are attacheddisparate risks and which bring about unique values. Thus, without disaggre-gation, there is no sensible way to predict the overall amounts, timing, or risksof a complete enterprise’s future cash flows. There is little dispute over the ana-lytic usefulness of disaggregated financial data." (AIMR, 1993, p.59-60; in Chenand Zhang (2003), p.397). Investigating the valuation relevance of segment in-formation, Chen and Zhang (2003) provided an analytical model suggestingthat segment reporting is relevant if segments have different profitability andgrowth perspectives.
Analysts often use segment information in their valuation process. Usingcontent analysis, Previts et al. (1994) found that segment-related phrases ap-peared 47.6 times per equity report on average, the second largest frequencyamong their groupings just after income-statement related phrases.
Previous studies identified two main ways for outsiders, both researchersand practitioners, to use segment information for valuation purposes. First,there is a abundant literature discussing the use of segment information to fore-cast sales or earnings on the company level (Kinney, 1971; Silhan, 1983; Bald-win, 1984; Lobo et al., 1998; Berger and Hann, 2003; Bar-Yosef and Venezia,2004; Ettredge et al., 2005; Kou and Hussain, 2007; Fairfield et al., 2009; Schröderand Yim, 2018; André et al., 2016). By using the additional information pro-vided in the segment filings, outsiders can arrive at better forecasts, which theycan then use as inputs in their valuation models. For example, Previts et al.(1994) documented that sell-side analysts often estimate future earnings pershare through disaggregating the company into its segments, forecasting seg-ment performance separately and re-aggregating the forecasts to form a com-pany EPS estimate.
Second, segment information can also be used to value parts of a firm sep-arately, and then add the value of the separate parts to arrive at the value of thewhole operations. There is considerably less research on the use of segmentdisclosure for valuing parts of a business separately. You (2014) provided evi-
THE ROLE OF SEGMENT REPORTING IN VALUATION 37
dence that analysts use segment-level earnings for conducting sum-of-parts val-uation. They multiplied the earnings for each segment with relevant valuationmultiples based on peers’ earnings figures and market value to approximate thevalue of the segments separately. Then, they aggregated these values to arriveat a value for the whole firm. Berger and Ofek (1995); Lang and Stulz (1994)also used sales, earnings and asset figures on the segment level together with in-dustry multiples to value parts of firms separately in order to arrive at a firm’svalue through adding up the calculated value of the parts.
Previous literature has documented several benefits of (voluntary) segmentdisclosures. First, segment reporting can provide the financial market withbetter sales and earnings forecasts [e.g. Kinney (1971); Silhan (1983); Bald-win (1984); Lobo et al. (1998); Berger and Hann (2003); Bar-Yosef and Venezia(2004); Ettredge et al. (2005); Kou and Hussain (2007); Fairfield et al. (2009);Schröder and Yim (2018); André et al. (2016)]. Second, by providing incre-mental information about the different operations of the firm, segment disclo-sure can decrease the information asymmetry between shareholders and insid-ers [e.g. Swaminathan (1991); Greenstein and Sami (1994); André et al. (2016);Saini and Herrmann (2013)]. Lower information asymmetry is associated withinvestors requiring lower returns for financing the firm, decreasing the costof capital [e.g. Saini and Herrmann (2013); Blanco et al. (2015)], resulting inhigher valuations.
While there are benefits from providing segment information, firms mayalso face costs in connection with such disclosures. Disclosing informationabout profitable and quickly growing segments can result in proprietary costsrelated to increased competition from other actors that in turn can decrease fu-ture cash flows of the firm (Ettredge et al., 2002a; Bens et al., 2011). Disclosinginformation about poorly performing segments will affect agency costs (Bergerand Hann, 2007; Bens et al., 2011), as shareholders might put pressure on man-agement to use the funds provided efficiently. Furthermore, providing detailedinformation about the performance of different parts of the firm might lead tohigher costs of information production (Prencipe, 2004).
There is a broad consensus among researchers and practitioners that seg-ment reporting is useful for valuation purposes. Also, studies investigating thevaluation methods employed by analysts suggest that they rely on segment in-formation. However, the mechanics of this usefulness is less understood. Inthis paper, I investigate the incremental benefits from segment disclosures for
38 ESSAYS ON SEGMENT REPORTING AND VALUATION
valuing multi-segment firms. Given the possible costs of segment disclosure,it is beneficial for firms to understand what information is valuation-relevanton the segment level and how one can use this information for valuation pur-poses. Such knowledge would enable them to disclose relevant information onthe segment level.
Prior research has discussed the use of segment information for earningsand sales forecasting extensively. In this paper, I address such use of segmentreporting as well, but the focus is more on the usefulness of segment infor-mation for valuing parts of a firm separately. I use sophisticated, accountinginformation-based valuation models to investigate the possible use of segmentinformation for valuation purposes. Furthermore, in comparison with the ex-tensive literature discussing the information asymmetry and cost of capital ef-fect of segment disclosure, my paper focuses on the role of segment reportingfor altering expectations about future cash flows. From a discounted cash flowvaluation perspective, the information asymmetry literature discusses the de-nominator effect of segment disclosure for valuation, i.e. how segment disclo-sure affects the cost of financing (risk perception effect of segment disclosure).In comparison to that, my paper focuses on the numerator effect of valuation,i.e. how segment information affects the future expected cash flows (expecta-tion alteration effect).
This paper is similar to Skogsvik (1998), which focused on the informationneeds of the residual income valuation (RIV) model. He specified a linkage be-tween compulsory financial information and the valuation model. Then, thepaper investigated what additional information would be valuation relevant,and when would managers decide to report these pieces of information volun-tarily.
This paper follows a somewhat similar structure. I start with introducingfirm valuation models and the requirements of the segment disclosure stan-dards. Given that recent standards following the management approach areprinciples based, the actual disclosures of the firms differ considerably fromthe list of items the standards specifically require to be disclosed. I benchmarkthe standard requirements and firms’ disclosures on the information needs ofaccounting valuation models adapted for segment-by-segment valuation. Therequirements of the standards and the disclosures of firms do not provide out-siders with enough information to apply these models. I present a partial so-lution for the valuation problem - similar to the "naive" valuation in Skogsvik
THE ROLE OF SEGMENT REPORTING IN VALUATION 39
(1998). Then, I investigate the difference between the valuation outcome fromthe partial solution with that given full segment disclosure. The analysis ofthe difference sheds light on the valuation relevant segment disclosure itemsas well as under what conditions management would be voluntarily disclosingsuch items.
2.2 Regulation, disclosure practices and the infor-mation needs of valuation models
2.2.1 Accounting-based firm valuation models
In order to investigate the usefulness of segment reporting for valuation pur-poses, I use accounting information-based valuation models. Accounting in-formation can be used to calculate the fundamental value of a firm. The mostcommon fundamental valuation models used in practice can be grouped intotwo categories based on what is the focus of valuation. First, the direct equityvaluation models such as Present Value of Expected Dividends (PVED), Abnor-mal Earnings Growth (AEG) or Residual Income Valuation (RIV) models seta value on shareholders’ equity directly (Feltham and Ohlson, 1995; Ohlson,1995; Penman, 2007). These models discount flows to equity holders with costof equity. However, firms rarely provide information about the financing ofthe different segments and therefore the lack of this information results in thatequity valuation models are not suitable for segment valuation. Moreover, thecost of equity is sensitive to leverage decisions by the firm and so equity valu-ation models are not so suitable for complex valuation cases.
The other group of models assess the value of invested capital or operatingnet assets - so-called firm valuation models. Such models are the DiscountedCash Flow (DCF), the Value Added Valuation (VAV) model - Penman (2007)refers to this model as the Residual Operating Income (ReOI) model - or theAbnormal Operating Income Growth (AOIG) model (Skogsvik, 2002; Pen-man, 2007; Easton, 2007; Koller et al., 2010; Jennergren and Skogsvik, 2011).These models deduct net debt from the value of operating net assets (V (ONA))to arrive at the equity value. These models are built on discounting flows withthe weighted average cost of capital that is less sensitive to leverage decisions. Itis also possible to separate the valuation effects of the financing decisions fromvaluing the operating business. Myers (1974) presents the adjusted present
40 ESSAYS ON SEGMENT REPORTING AND VALUATION
value (APV) valuation model, where one values a firm as if it was all-equityfinanced, and in the second step adjusts the valuation for the value effects offinancing decisions. Previous research shows that when applied properly (andassuming the clean surplus relationship and the operating assets relation), val-uation models yield the same value estimates (such discussions can be found inPenman and Sougiannis (1998); Shrieves and Wachowicz Jr. (2001); Skogsvik(2002); Penman (2007); Easton (2007)).
Firm valuation models are the most popular sophisticated valuation mod-els used by analysts. In a study investigating the valuation models used by UKsell-side equity analysts, Demirakos et al. (2004) found that the most popularways to value a firm were simple, multiples-based valuation models. Most ana-lyst reports used earnings and sales valuation multiples (88.5% and 50% of thereports, respectively) in their study. The third most popular way for firm val-uation was the discounted cash flow (DCF) model used in 38.5% of the reportsinvestigated. Valuation using multiples involve simplifications and restrictiveassumptions (Koller et al., 2010). Therefore, in order to investigate the valua-tion usefulness of segment information, I use sophisticated valuation modelsin this paper.
In the following subsections I present the DCF model. The other twomodels and the equivalence of them are presented in Appendix 2.B to conservespace.
Discounted Cash Flow (DCF) model
The DCF valuation model can be written in the following way:
V ONA0 =
∞∑t=1
E(0)(FCFt)
(1 + E(0)(rWACC))t. (2.1)
• V ONA0 = The value of the Operating Net Assets (ONA) at period t = 0,
• t = periods of time, t = 1, 2...∞• E(0)(.) = the expectation operator based on information available at pe-
riod t = 0,• FCFt = free cash flow for year t,• rWACC = the weighted average cost of capital (WACC).
In the above equation E(0)(.) refers to the expectation operator based oninformation available at period t = 0, FCFt refers to the free cash flow for yeart and rWACC is the weighted average cost of capital.
THE ROLE OF SEGMENT REPORTING IN VALUATION 41
Investors need to forecast the future free cash flows at the current point intime (in year t = 0):
E(0)(FCFt) (2.2)
The free cash flow of the company can be decomposed in the following way:
FCFt = OIt −∆ONAt (2.3)
• OIt = (after-tax) Operating Income at time period t• ∆ONAt = change in Operating Net Assets, see the definition later.
HereOIt refers to the after-tax operating income of the firm in year t and and itcan be calculated as OIt = (Salest) ∗ (OMt), where OM refers to the operatingmargin of the firm, calculated as OI divided by sales. ∆ONAt refers to thechange in operating net assets, in particular
∆ONAt = ONAt −ONAt−1. (2.4)
A decomposed DCF model can be written as:
V ONA0 =
∞∑t=1
E(0)(Salest) ∗ E(0)(OMt)− E(0)(ONAt −ONAt−1)
(1 + E(0)(rWACC))t. (2.5)
Here,
• Salest refers to sales in time period t• OMt refers to (after-tax) operating margin of the firm in time period t,OM = OI
Sales
• rWACC refers to the weighted average cost of capital (WACC) of the firm.
The DCF valuation model requires information about sales, after-tax oper-ating profit margins, change in operating net assets and WACC. Next, I discussthe segment reporting requirements under US GAAP and under IFRS overtime.
42 ESSAYS ON SEGMENT REPORTING AND VALUATION
2.2.2 Segment reporting requirements
In this section, I review the segment reporting regulations under US GAAPand IFRS with respect to the disclosure requirements on the segment level.The regulations distinguish between two main types of segmentation of thebusiness. Business segments comprise businesses with similar operations. Ge-ographical segments include the parts of the business that are performed in dif-ferent geographical areas. SFAS 14, IAS 14 and IAS 14R required business seg-ments to be defined in line with the industry approach. A segment was definedin SFAS 14 as a "component of an enterprise engaged in providing a productor service or a group of related products and services primarily to unaffiliatedcustomers (i.e., customers outside the enterprise) for a profit" (para 10. SFAS14). IAS 14 and IAS 14R also required line-of-business segments to be definedas parts of the business earning revenues from similar products or services.
In contrast to the industry approach used in SFAS 14, IAS 14 and IAS 14R,SFAS 131 and IFRS 8 required segments to be defined using the managementapproach, i.e. in line with the internal organization of the firm. SFAS 131 de-fines an operating segment as "a component of an enterprise a) that engages inbusiness activities from which it may earn revenues and incur expenses (includ-ing revenues and expenses relating to transactions with other components ofthe same enterprise), b) whose operating results are regularly reviewed by theenterprise’s chief operating decision maker to make decisions about resourcesto be allocated to the segment and assess its performance, and c) for which dis-crete financial information is available." (SFAS 131, paragraph 10).
The standards based on the industry approach are rules-based ones. As forthe line item disclosures on the segment level, the standards require specificitems to be disclosed. Firms comply with the standards if they disclose theseitems and they might provide disclosures about additional items voluntarily. Incontrast, IFRS 8 and SFAS 131 are principles-based standards where reportingentities are required to follow the principle of the disclosure as well as reportthe items specifically required. For example, the high-level disclosure principleof IFRS 8 requires entities to disclose information that enables users to evaluatethe nature and financial effects of the different business activities of the firm,as well as the economic environments in which it operates. The standard pro-vides a list of items that the reporting entity should disclose given that theseitems are regularly provided to the chief operating decision maker (CODM).The only item the standard specifically requires companies to disclose on the
THE ROLE OF SEGMENT REPORTING IN VALUATION 43
segment level is profit or loss. The standard allows different interpretations,as it is difficult to observe what information the CODM receives. This makesenforcement difficult due to variabilities in firm characteristics and disclosure,as it requires second-guessing about whether the management has chosen itsaccounting policy in ‘good faith’ Wüstemann and Wüstemann (2010). Thisflexibility possibly leads to non-compliance from firms which have incentivesto conceal information (Hellman et al., 2018).
The high-level principle in IFRS 8 raises another issue with respect to vol-untary disclosure, that is, disclosure of relevant information by the firm that isnot required by the standard. A disclosure is relevant if it has the capability ofchanging decisions. The standard requires entities to disclose the same set ofinformation that the CODM receives for evaluating segment performance. Itcan be argued that the information is relevant if the CODM uses it to evaluatesegment performance. In this case, the standard requires all relevant informa-tion to be disclosed and in order to comply with the standard, the entity mightbe required to provide several line items on the segment level. Even though theentity would disclose more than what is specifically required by the standard(profit or loss), no part of the disclosure would be voluntary. Any other dis-closure, however, would be irrelevant information. Alternatively, one mightargue that what can change the decision of an outside user of segment infor-mation is not the same as what is used within a firm. Following this approach,if an entity discloses information that is not used by the CODM but might berelevant for an outside user, it would be disclosed voluntarily. However, con-trary to under a rules-based standard such as IAS 14R, it is impossible underIFRS 8 to separate voluntary and mandatory disclosure just from the amountof disclosure observed on the segment level.
All in all, the management approach is arguably more flexible and providesthe reporting entity with considerable leeway for defining business segments.This approach can also result in less cross-company comparability of businesssegments. The standard setters argue that comparability is impossible to reachand that viewing the entities through the eyes of management is preferable forthe users of financial statements. The findings of Bens et al. (2011) suggestthat companies applied considerable discretion when allocating parts of theirbusiness (plants) to the reported industry segments under SFAS 14 - mainly
44 ESSAYS ON SEGMENT REPORTING AND VALUATION
driven by proprietary and agency cost motives.1
Previous research suggests that industry information was more helpful inpredicting earnings under SFAS 14 compared to SFAS 131 (Schröder and Yim,2018). The management approach arguably made the cross-sectional compar-ison of business segments more difficult and forced outsiders to rely on time-series forecasts.
Appendix 2.C presents an overview of the disclosure requirements underthe different segment reporting standards. Most standards require sales, earn-ings and asset information on the segment level, only some requiring additionalitems. Cash flow forecasts are core inputs for valuation, however, SFAS 131 isthe only segment reporting standard that explicitly states that its objective isto help users better assess the prospects of the enterprise for future net cashflow. However, the standard does not require specifically that an enterprisereport segment cash flows (paragraph 6). FASB argues that as companies of-ten use other operating cash flow measures than that according to SFAS No.95 Statement of Cash Flows, such a requirement would require companies togather and process information only for external purposes (BC paragraph 94).Instead, the standard requires the reporting of certain items that "may providean indication of the cash-generating ability or cash requirements of an enter-prise’s operating segments" (paragraph 6).
The background information and basis for conclusions (BC) of SFAS 131is directly adopted by the IASB and it is included in the basis for conclusionsof IFRS 8. Paragraph 43 discusses the needs of the financial statement users forinformation for the evaluation of future cash flow prospects and paragraph 60argues that the ability to see an enterprise "through the eyes of the manage-ment" helps users with predicting management actions that can affect futurecash flows. Furthermore, the standard argues that the requirement to disclosesignificant non-cash items also helps users to assess the cash-generating poten-
1This classification manipulation - and the emergence of a ‘shadow standard’ is in line with the
model of Dye (2002). This model predicts that the standard setter will move towards increas-
ing the standard, that is, where classification manipulation is more costly. One could argue
that SFAS 131 is such a standard, as it requires alteration of internal practices for classifica-
tion manipulation. Thus, as the number of segments and the number of items per segment
reported increased with the introduction of SFAS 131, the introduction of the management
approach could be considered as an ‘increase’ in the standard according to the Dye model.
THE ROLE OF SEGMENT REPORTING IN VALUATION 45
tial and cash requirements of the segments.Appendix 2.C provides an overview about the disclosure requirements of
the different standards and Table (2.1) summarizes some of the disclosure re-quirements relevant for calculating operating cash flows. Underlining indicatesthat the standard requires the disclosure of the specific item specifically. Italicsindicate that disclosure is required if the business segment is identified by thecompany as the primary way of segmentation (IAS 14R) and normal font in-dicates that the disclosure is required if the items are regularly provided to theCODM (SFAS 131 and IFRS 8).
In the next section I give an overview of previous literature to see whataccounting items companies provide on the segment level.
2.2.3 Evaluation of firms’ segment reporting disclosure practices
In the previous section, I presented the disclosure requirements by the differentsegment reporting standards under US GAAP and IFRS. There is considerableleeway in the standards as to what items companies present on the segmentlevel. Companies might present additional items apart from what is specifi-cally required by the standards, either voluntarily or because the CODM isregularly provided with accounting information, the segment disclosure mightcontain additional information to enable valuation using more sophisticated,accounting information-based models.
The actual segment disclosures thus might contain more information thanwhat is specifically required by the standards. In this section, I provide anoverview of segment-disclosure practices under the different standards basedon previous studies. Herrmann and Thomas (2000); Street et al. (2000) investi-gate the segment reporting practices of US firms under SFAS 14 and after theadoption of SFAS 131. Street and Nichols (2002) and Nichols et al. (2012) pro-vide a similar overview of segment disclosure practices under IAS 14 and IAS14R and IAS 14R and IFRS 8 respectively. In this study, I summarize the find-ings of these studies in terms of disclosure practices for the valuation-relevantdata items under the different segment reporting standards.
Herrmann and Thomas (2000) investigate 100 of the 250 largest US firmsfor the adoption year (which is 1999, or 1998 for early adopters) and Streetet al. (2000) investigate 160 US firms from the Business Week Global 1000 listfor the adoption year. Street and Nichols (2002) investigate a global sample of210 companies referring to the use of IAS to investigate their segment reporting
Tab
le2.
1:Th
ed
isclo
sure
req
uire
me
nts
oft
he
sta
nd
ard
s
SFA
S14
IAS
14IA
S14
RSF
AS
131
IFR
S8
Rev
enue
sSa
les
Sale
sR
even
ues
Rev
enue
sO
pera
ting
profi
tor
loss
Res
ult
Res
ult
Profi
tor
loss
Profi
tor
loss
Iden
tifiab
leas
sets
Ass
ets
Ass
etsa
ndlia
bilit
ies
Ass
ets
Ass
etsa
ndLi
abili
ties
Cap
ex,D
DA
-C
apex
,D
Aan
dot
her
non-
cash
expe
nses
DD
A,C
apex
(exc
l.in
tan-
gibl
es)
and
othe
rno
n-ca
shite
ms
DD
A,C
apex
(incl
.in
tan-
gibl
es)
and
othe
rno
n-ca
shite
ms
--
-In
com
eta
xex
pens
eIn
com
eta
xex
pens
e
Not
es:T
heta
ble
show
sthe
disc
losu
rere
quir
emen
tsun
der
the
diff
eren
tsta
ndar
ds(s
elec
ted
item
sfor
the
curr
ent
stan
dard
s).
Und
erlin
ing
indi
cate
sdi
sclo
sure
ssp
ecifi
cally
requ
ired
,ita
lics
indi
cate
disc
losu
rere
quir
edif
the
busi
-ne
ssse
gmen
tisi
dent
ified
byth
ecom
pany
asth
epri
mar
yw
ayof
segm
enta
tion
(IA
S14
R)a
ndno
rmal
font
indi
cate
sdi
sclo
sure
sth
atar
ere
quir
edif
the
item
sar
ere
gula
rly
prov
ided
toth
eC
OD
M(S
FAS
131
and
IFR
S8)
.C
apex
isca
pita
lexp
endi
ture
s,D
DA
isde
prec
iatio
n,de
plet
ion
and
amor
tizat
ion,
DA
isde
prec
iatio
nan
dam
ortiz
atio
n.
THE ROLE OF SEGMENT REPORTING IN VALUATION 47
practices under IAS14 and IAS 14R (1998 and 1999). Nichols et al. (2012) in-vestigate reporting practices of European blue-chip companies comprising thetop-tier indexes of 14 European stock exchanges (excluding the UK). In total,their sample size is 361 companies and they look at the adoption year (whichis normally 2009, however, there are early and late adopters).
The reporting practices of firms under US GAAP and IFRS are not directlycomparable using the findings of these studies as the sample characteristic anddata period are not comparable. However, looking at these studies may give ussome general insights about what segment-level disclosure companies provide.In Table (2.2), I provide an overview of the findings of the studies with respectto the share of companies that provide segment information about the specificline items.
Almost all firms in the sample provide information about segment rev-enues and some profitability measures. Most companies also provide informa-tion about segment assets, however, only 69% provides this information un-der IAS 14. With respect to segment liabilities, only 3% of the firms providethis information under SFAS 1312 and none under SFAS 14. Only 19% of thefirms provide information about liabilities under IAS 14, but under IAS 14Rand IFRS 8 this number increases to 71-87%. Under IAS 14, only 44% and36% of sample firms provided information on capital expenditures and depre-ciation respectively; under all other regimes more than 75% of the firms didso. Income tax expenses were rarely allocated to the different segments withNichols et al. (2012) documenting the highest figure of 20% under IFRS 8.
The profitability measure to be disclosed on the segment level was regu-lated in detail by IAS 14, IAS 14R and SFAS 14. However, the introduction ofthe management approach in SFAS 131 and later in IFRS 8, the requirementwas to discuss the profitability measure for the segment level which is inter-nally used by the CODM. Therefore, under these two standards, companiesreport different profitability measures on the segment level. Table (2.3) sum-marizes the findings of Street et al. (2000) for SFAS 131 and Nichols et al. (2012)for IFRS 8 with respect to their findings on to which GAAP earnings figure docompanies report.3
2An additional 6% of the firms providing net asset information on the segment level according
to Herrmann and Thomas (2000).3Herrmann and Thomas (2000) does not report any details on what earnings level the com-panies disclose on the segment level.
Tab
le2.
2:Th
ep
erc
en
tag
eo
fsa
mp
lec
om
pa
nie
sre
po
rtin
gse
lec
ted
ac
co
untin
gite
ms
on
the
seg
-m
en
tle
vel
Line
item
sSN
SNN
SCN
SCH
TH
TSN
GSN
GIA
S14
IAS
14R
IAS
14R
IFR
S8
SFA
S14
SFA
S13
1.SF
AS
14SF
AS
131
Rev
enue
s91
100
100
100
100
100
100
100
Profi
tor
loss
7499
100
100
9799
100
99A
sset
s69
9396
9397
9087
89(C
urre
nt)L
iabi
litie
s19
7687
71-
3-
-C
apex
4481
8173
9688
8086
Dep
reci
atio
n36
7785
8696
9489
93In
com
eta
xex
pens
e-
416
209
164
9
Not
es:T
heta
ble
show
sthe
perc
enta
geso
fcom
pani
esre
port
ing
the
diff
eren
tlin
eite
mso
nth
ese
gmen
tlev
elac
-co
rdin
gto
the
diff
eren
tstu
dies
.SN=
Stre
etan
dN
icho
ls(2
002)
;NSC=
Nic
hols
etal
.(20
12);
HT=
Her
rman
nan
dT
hom
as(2
000)
;SN
G=
Stre
etet
al.(
2000
).Fi
ndin
gsfo
rIA
S14
Ris
forc
ompa
nies
whe
reth
elin
e-of
-bus
ines
sis
iden
tified
asth
epr
imar
yw
ayof
segm
enta
tion.
Cap
exre
fers
both
toca
pita
lexp
endi
ture
sand
toad
ditio
nsto
(long
-live
d)se
gmen
tass
ets.
THE ROLE OF SEGMENT REPORTING IN VALUATION 49
Most of the companies report operating income on the segment level, fol-lowed by earnings before interest and taxes (EBIT) and income before taxes(IBT, SFAS No.131 firms) and earnings before taxes (EBT, IFRS 8 firms). Un-der IFRS 8, 17% of sample firms report net income on the segment level. It isalso notable that 25% of the companies in the sample of Nichols et al. (2012) re-ported more than one profitability measure, while Street et al. (2000) providedno information on whether companies provide more than one profitabilitymeasure on the segment level.
Table 2.3: The number of sample companies reporting selected operatingprofits on the segment level
Profitability measure Street et al. (2000)) Nichols et al. (2012)n=106 n=314
Operating profit 43 180EBIT 25 73IBT / EBT 12 56EBITDA 9 50Other 9 -After-tax OperatingIncome
4 -
No disclosure of method 3 -Net Income - 53
Notes: The table shows the number of companies reporting the different lineitems on the segment level according to the different studies. EBIT: earn-ing before interest and taxes; IBT = income before taxes, reported by Streetet al. (2000); EBT = earnings before taxes, reported by Nichols et al. (2012);EBITDA = earnings before interest, taxes, depreciation and amortization
The comparison of the different standards show that most companies pro-vide information about revenues, profitability and segment assets under bothunder SFAS 14 and SFAS 131 and IAS 14R and IFRS 8. However, some firmsprovide more disclosure than specifically required by the standard. On a sam-ple of 270 European multi-segment companies included in the STOXX Europe600 index André et al. (2016) found that the interquartile range of the numberof line items disclosed per segment under IFRS 8 is 9 to 14. 25% of the com-panies in their sample disclose 2-9 items while the top 25% of the companiesdisclose 14-63 line items per segment on average. They label these three groups
50 ESSAYS ON SEGMENT REPORTING AND VALUATION
of companies as box-tickers,4 under-disclosers and over-disclosers.All in all, this overview suggests that firms provide additional information
on the segment level as compared to what is specifically required by the dif-ferent standards, either voluntarily or because the information is used by theCODM.
2.2.4 Valuation of the firm segment by segment
Now I introduce segment information into the valuation models. There aretwo main ways segment information can be used for valuation. First, financialinformation about the segments can help with forecasting these items on thesegment level. Then, adding up segment-level financial forecasts can provide uswith consolidated-level financial forecasts. Then, these probably more accurateconsolidated-level forecasts can be used as inputs for the valuation model.
Second, segments can be valued on their own. After valuing the differentparts of a business separately, one can sum up the value of the parts to arrive atthe value of the whole firm. In this section, I extend the firm valuation modelsto express the value of the company as the sum of the values of its segments.Then, I discuss what accounting information the different firm valuation mod-els need for sum-of-the-parts corporate valuation.
Consider a company with more than one segment, denoted i = 1, 2, ..., I .The value of the company can be expressed as the sum of the value of its parts:
V ONA0 =
I∑i=1
V ONA0,i (2.6)
• V ONA0,i = value of ONA in segment i, i = 1, 2..I at time t = 0
The value of these parts can be expressed in using the three different firmvaluation models discussed in the previous section. In this section, I show whatpieces of accounting information one needs on the segment level to apply thesemodels for segment valuation.
4The name box-ticker suggests that these companies disclose more or less the items listed in
the illustrative table in the implementation guidance (11 items) or the main text (14 items)
of the standard.
THE ROLE OF SEGMENT REPORTING IN VALUATION 51
The fully decomposed DCF model becomes:
V ONA0 =
I∑i=1
∞∑t=1
E(0)(Salesi,t) ∗ E(0)(OMi,t)− E(0)(ONAi,t −ONAi,t−1)
(1 + E(0)(rWACCi))t
.
(2.7)Here,
• Salesi,t refers to sales of segment i in time period t,• OMi,t refers to (after-tax) operating margin of segment i in time period t,OM = OI
Sales and• rWACCi refers to the weighted average cost of capital (WACC) of segmenti.
To apply this valuation method, one needs segment level information aboutfree cash flows, or alternatively, information about the accounting figures thatenables one to calculate (and forecast) free cash flows. Such accounting itemsare sales, (after-tax) operating margin and net operating asset. Apart from theseaccounting line items one also needs to have WACC estimates for the differentsegments.5
The Value Added Valuation model can be applied to segment-based valua-tion in the following way:
V (ONA)0 =
I∑i=1
ONAi,0 +
I∑i=1
T∑t=1
ONAi,t−1(R∗ONA,i,t − rWACC,i)
(1 + rWACC,i)t+
+
I∑i=1
ONAi,T ∗ (Qi,T − 1)
(1 + rWACC,i)T(2.8)
5The operations of the different segments might have different risks and therefore, opera-
tional cash flows should be discounted using segment-level discount rates. However, WACC
might not be the proper discount rate to use on the segment level, as firms raise debt on the
consolidated entity level. It is probably more precise to apply the sum-of-parts valuation
using the APV model, discounting the operating cash flows from the segments using rA,i,
rA referring to the return compensating for the riskiness of assets, and then, adding the
value effects of financing decision to the sum of the all-equity segment values to arrive at
the consolidated value of the enterprise. The focus of this paper is rather the numerator of
the valuation model and not the denominator (the discount rate) and therefore, I will use
segment-level WACC as the discount rate.
52 ESSAYS ON SEGMENT REPORTING AND VALUATION
• Qi,T = Steady-state Tobin’s Q value in time period T, expressing the ac-counting conservatism of the firm
This formula shows that in order to apply the Value Added Valuation modelfor segment-based valuation one needs segment-level information about after-tax return on ONA - or after-tax operating income and operating net assets -and required return on ONA (WACC). In order to apply the model, one alsoneeds to estimate the long-term Q value of the segment - referring to accountingconservatism.
Finally, the segment-by-segment AOIG valuation model can be spelled outas follows:
V NOA0 =
I∑i=1
1
rWACCi
[OI1,i+
+
∞∑t=2
[OIi,t + rWACCi ∗ FCFi,t−1]− (1 + rWACCi) ∗OIi,t−1
(1 + rWACCi)t−1
](2.9)
To apply this valuation model, one needs information about segment operatingincome (sales and margin), free cash flow generated and WACC. Alternatively,one can use the information about ONA and after-tax operating income tocalculate FCF.
To summarize this section, Panel B of Table (2.4) presents the segment-levelinformation need of the different valuation models discussed above. Despiteexplicitly not needed, information about sales helps with forecasting futureearnings of the segment through shedding light on the sales growth and mar-gin development of the segment. Given these pieces of information, a valuecan be put on every business segment and - assuming that a multi-segment con-glomerate is made up from separate business segments - summing up the valuesfor the business segments can yield us the value of the whole conglomerate.
Table (2.4) indicates that the minimum disclosure requirements of the stan-dards are not full-scale enough for valuing the segments on their own. Further-more, none of the standards require disclosure about the discount rate.6 Thus,
6However, the company might provide disclosure about the discount rate for the segment.
IAS 36 requires companies to allocate goodwill to cash generating units (CGUs) or groups of
THE ROLE OF SEGMENT REPORTING IN VALUATION 53
Table 2.4: The disclosure requirements of the standards and the informationneed of the valuation models
Panel A: The disclosure requirement of the different segment reporting standardsSFAS 14 IAS 14 IAS 14R SFAS 131 IFRS 8Revenues Sales Sales Revenues RevenuesOperating profitor loss
Result Result Profit or loss Profit or loss
Identifiable assets Assets Assets and liabil-ities
Assets Assets and Lia-bilities
Capex, DDA - Capex, DA andother non-cashexpenses
DDA, Capex(excl.intangibles)and othernon-cashitems
DDA, Capex(incl. intangi-bles) and othernon-cash items
- - - Income taxexpense
Income tax ex-pense
Panel B: The information requirement of the different valuation modelsDCF VAV AOIGSales Sales SalesOI (a.t) OI (a.t) OI (a.t)FCF FCFONA ONA ONAWACC WACC WACC
Notes: Panel A shows disclosure requirements under the different standards (selecteditems for the current standards). Underlined indicates disclosures specifically required,italics indicate disclosure required if the business segment is identified by the company asthe primary way of segmentation (IAS 14R) and normal font indicates disclosures thatare required if the items are regularly provided to the CODM (SFAS 131 and IFRS 8).Panel B shows the information needs of the different valuation models. OI (a.t) refers tothe after-tax operating income.
54 ESSAYS ON SEGMENT REPORTING AND VALUATION
when comparing the disclosure requirements and the information needs of thevaluation models, it is apparent that in terms of disclosure requirement it is IAS14R that requires most information that is useful for applying these models,when the primary way of segmentation is business segments. Moreover, IFRS8 (and SFAS 131) can require even more disclosure about valuation-relevant in-formation if the items are regularly provided to the CODM. Even though thedisclosure of these line items are not specifically required by the standards, sev-eral firms prepare segment disclosure through box-ticking (André et al., 2016),in which case they might provide extensive valuation relevant information onthe segment level.
Previous standards required disclosure about revenues both from sales toexternal customers and intersegment sales. However, the current standardsonly require such disclosure if the information is regularly provided to theCODM. Furthermore, all standards require disclosure about some segmentprofit and loss measure and its reconciliation to a GAAP measure on the con-solidated level. Using this information - and some other items (if provided),such as depreciation, depletion and amortization (DDA), interest revenue andexpense, income taxes, extraordinary items, etc. the users of the financial re-ports might have the chance to calculate after-tax segment EBIT that can beused in the valuation model. Thus, under IAS 14R and IFRS 8 firms mightdisclose segment assets and liabilities which can allow users of financial reportsto calculate segment operating net assets, depending on the asset and liabilitiesdefinition applied by the company. If these items are provided, companies alsoneed to report the basis of the measurement of these items and their reconcil-iations to total entity amounts [IFRS 8 paragraph 21(b); SFAS 131 paragraph25(c)]. This disclosure can help users with calculating Operating Net Assetfigures on the segment-level.
The discussion above shows that these valuation models can only be ap-plied on the segment level if the company provides additional information on
CGUs, that represents the lowest level within the entity at which the goodwill is monitored
for internal management purposes and that is not larger than an operating segment (IAS
36 paragraph 80). Therefore, in case the goodwill is allocated on the operating segment
level, the discount rate used for impairment testing purposes is the segment-level WACC.
The comparable standard, SFAS 142 under US GAAP does not require disclosure about the
discount rate applied for goodwill impairment assessment purposes.
THE ROLE OF SEGMENT REPORTING IN VALUATION 55
the segment level compared to what is specifically required in the standards,as none of the standards require enough information on the segment level toenable the valuation of the segments on their own.
The overview of disclosure practices of the companies under the differentstandards in Table (2.2) shows that most companies did not provide enough in-formation on the segment level most of the time to enable segment-by-segmentvaluation of the firms. The most striking difference from a segment valuationpoint of view is the lack of information about segment liabilities under theUS standards, as this information is vital to calculate operating net assets. Thecomparison above thus suggests that firms reporting under IAS 14R and IFRS8 provide the best information set for segment-based valuation of the com-pany. Furthermore, there is no requirement in the segment reporting stan-dards to provide a discount rate for segment project evaluations.7 Investorshave better chances of receiving enough information on the segment level tovalue firms segment-by-segment when a firm reports under IFRS. In cases whenbox-tickers under IFRS 8 (André et al., 2016) disclose operating assets and op-erating liabilities for the segments as well as EBIT as the profit or loss measureand consistently, the income tax on the segment operating income as the in-come tax expense, investors can use their required rate of return on segmentoperations to calculate the value of the segments. Therefore, box-ticking com-panies under IFRS 8 might disclose enough information to enable the valuationof the segments separately. However, as the regulation requires companies toreport the accounting measures internally used, it is not certain that the re-ported measures can be used for segment valuation. Furthermore, as SFAS 131does not list the disclosure of segment liabilities if the CODM regularly uses
7As discussed above, companies are required to disclose the discount rate they use for goodwill
impairment testing under IFRS. In case the goodwill is allocated to a group of cash generating
units that is the business segment (which is the highest possible level), the discount rate
disclosed is the WACC for the segment. Carlin and Finch (2009) investigate a sample of
200 large Australian firms with goodwill and find that only 52 of these firms use multiple
discount rates. 105 firms used a single discount rate for goodwill valuation (another 19 do not
provide enough disclosure, 17 use the fair value less cost to sell approach for valuation that
does not require discount rate disclosure and the rest uses mixed methods). This indicates
that in practice, even under IFRS, there is only a fraction of firms that might disclose WACC
for the different segments.
56 ESSAYS ON SEGMENT REPORTING AND VALUATION
it, box-ticking companies under SFAS 131 cannot be evaluated on a segment-by-segment basis.
All in all, the disclosure requirement of the different segment reportingstandards fall short of providing investors with enough information for con-ducting segment-by-segment valuation of firms. Thus, the disclosure practicesof the firms do not complement the regulatory requirement to an extent thatenables segment-by-segment valuation of the firms either.
2.3 Research question
As discussed above, there are costs associated with segment disclosure. Pro-viding information about profitable or highly growing segments can attractadditional competition and drive down profits. Apart from this proprietarycost, disclosing weakly performing segments can result in agency costs as thedisclosure informs owners about inefficient use of funds. Consequently, com-piling segment disclosure involves cost of information production.
Previous research provides evidence that segment information is perceiveduseful and is widely used by practitioners for valuation purposes. This suggeststhat the information provided by firms under segment reporting is valuationrelevant. The evidence presented above suggests that the standard requirementsand firms’ disclosure both fall short of the information needs of sophisticated,accounting-based valuation models. This, together with the highly perceivedusefulness of segment information by outsiders, provides an intriguing ques-tion: how can segment information be used for valuation?
Reviewing the positive theory on GAAP, Kothari et al. (2010) concludethat standards serve the objective of GAAP to facilitate the efficient allocationof capital through providing information that is useful for efficient contract-ing. However, the interpretation of this efficient capital allocation objective isnot uncontroversial. They argue that the performance measurement and stew-ardship view of efficient contracting stands as an alternative to the valuationrelevance view and therefore, standards might serve one or another view bet-ter.8 From this point of view, investigating the valuation relevance of segment
8The authors provide the example of fair value accounting where these views are opposing.
If fair values are not based on observable prices in liquid secondary markets, the perfor-
mance measurement and stewardship view would not prefer implementing FVA while the
THE ROLE OF SEGMENT REPORTING IN VALUATION 57
disclosure can provide additional evidence for our understanding of the role ofaccounting standards.
This paper focuses on one type of benefit from segment disclosure - theusefulness of the information provided for valuation purposes. While the anal-ysis presented looks at the issue from several different angles, the two researchquestions of the paper can be stated as follows:
RQ: What makes segment disclosure relevant for valuation purposes from ananalytical perspective? What type of firms would provide more valuation relevantinformation in their segment disclosure?
I use accounting-based firm valuation models to investigate this question.
2.4 The valuation relevance of segment disclosure- theoretical analysis
2.4.1 Segment interdependence
The current segment reporting standards, IFRS 8 and SFAS 131, require re-porting entities to identify segments using the management approach, in linewith the way the company is internally organized.9 The adoption of this ap-proach results in firms defining reportable segments based on their organiza-tional structure that can differ considerably across firms. Therefore, in orderto understand the differences in segmentation one needs to consider the role
valuation view would do so.9Previously, as discussed before, SFAS 14 and IAS 14 and IAS 14 revised required companies
to identify line-of-business segments using the industry approach, that is to present parts of
operations arranged around similar product lines or services. Previous studies investigating
the shift from this industry approach to the management approach find both under IFRS
and US GAAP that firms disclose more segments and more items per segment under the
management approach and that their reporting structure is more in line with the organi-
zational structure presented in other parts of the annual reports (Herrmann and Thomas,
2000; Street et al., 2000; Ettredge et al., 2002a; Berger and Hann, 2003; Botosan and Stanford,
2005; Nichols et al., 2012, 2013; Bugeja et al., 2015). Moreover, Street and Nichols (2002)
find a significant increase in the number of reported items presented on the primary level
of segmentation under IAS 14R compared to IAS 14, but they find no significant increase in
the number of reported segments.
58 ESSAYS ON SEGMENT REPORTING AND VALUATION
of the segments within a firm.Hayes (1977) uses the typology suggested by Thompson (1967) to exam-
ine interdependence between the different subunits of a firm. According tothis typology, stable, pooled interdependence occurs when the only connec-tion between two subunits is that they belong to the same organization; i.e.no exchange occurs between them. Sequential interdependence occurs whenoutputs of one subunit are inputs to another and so a serial non-symmetricalrelationship exists between the subunits. Reciprocal interdependence occurswhen there is a symmetrical flow between the two subunits: an input to one isan output to the other.
Under the management approach, the reportable business segments aresubunits or groups of subunits so the interrelatedness between the reportablesegments is similar to those of the subunits described by Thompson (1967)and Hayes (1977). From a valuation point of view there is a whole spectrumof firms from portfolio-type firms existing mainly for diversification purposes(firms with segments almost independent from each other, i.e. stable, pooledinterdependence according to the Thompson typology) to firms with segmentsthat are sequentially or reciprocally interrelated. An example of the latter typeof firm is the global fashion and design company H&M Group. The segmentreporting of H&M10 presents the company as a vertically integrated organiza-tion reporting revenue centers (geographical areas where they sell their prod-ucts) and one cost center (group functions including all the costs of purchasedgoods for resale to other subsidiaries) separately.
Interrelatedness is not only about vertical integration. Consider the caseof Ericsson, a world-leading provider of communications networks, telecomservices and support solutions, that presents segments that are somewhat lessinterrelated than the H&M segments. Ericsson11 presents four segments: Net-works, Digital Services, Managed Services and Emerging Business and Other.These segments are also interrelated, in that, for example, cash flows from theservices sold in the Digital Services and Managed services segments depend oncapital expenditures in the Networks segment.
Moving even more towards portfolio-type firms on the spectrum of in-
10H&M Annual Report 2018 available at: https://hmgroup.com/investors/reports.html, ac-cessed on 21-7-2019.
11https://www.ericsson.com/492985/assets/local/investors/documents/financial-reports-
and-filings/annual-reports/ericsson-annual-report-2018-en.pdf, 21-7-2019.
THE ROLE OF SEGMENT REPORTING IN VALUATION 59
terrelatedness, firms appear that have some segments that are interrelated andothers that are mainly independent from the rest of the company. At the end ofthe spectrum, one can find holding companies that consist of segments mainlyfor diversification purposes.
Segment information reported by these firms can be used in different ways.Diversified firms consist of parts that have different future prospects and it issound to assume that outsiders prefer to value these parts separately, which is inline with documented analyst practices by You (2014). However, the more wemove along this interrelatedness scale towards organizations with integratedsegments, the less separable the segments become and the less reasonable itbecomes to value the segments separately.
For example, interrelatedness between the segments might result in arbi-trarily allocated assets (and capital expenditures) across the segments. In thiscase, segment reporting might be used for forecasting sales or even some earn-ings measure on the segment level, however, it might not make sense to valuethe segments separately. Segment filings can be used to forecasts sales and earn-ings figures on the segment level and then after adding up these forecasts thesums can be used as inputs for a consolidated-level valuation model.
Furthermore, transfer pricing practices of firms with vertically integratedsegments can result in profits allocated arbitrarily between the different seg-ments.12 If the transfer price used in transactions involving different parts ofthe firm is not comparable to an arms length transaction, one can argue that seg-ment information might not be used reliably for forecasting segment earnings.However, even in this case, segment filings could be used to forecast segment-level sales and then the sum of segment sales forecasts could be used as an inputfor the firm-level valuation model.
Considering the possible differences in interrelatedness between the seg-ments, I suggest the following way to approach the valuation problem: First,one needs to identify the reasonable object of valuation. Following the discus-
12While SFAS 14 required an industry segment to provide products or services to unaffiliated
customers, SFAS 131 and IFRS 8 allows segments to earn revenues and incur expenses from
business activities with other components of the same enterprise. IAS 14 and IAS 14R
identifies a business segment as reportable if the majority of its revenue is earned from sales
to external customers (and if it meets any of the thresholds of significance).
60 ESSAYS ON SEGMENT REPORTING AND VALUATION
sion above, this is a segment which is (almost13) independent from the rest ofthe firm. If a segment of the firm is interrelated with another segment withinthe same firm, one might consider the group of the two segments as a reason-able object of valuation. A reasonable object of valuation is a part of a firm (onesegment or a group of segments) that incurs most of its costs in connection withindependent suppliers and derives its revenues from independent customers.
For a portfolio-type firm, each of the segments are reasonable objects ofvaluation and for a vertically integrated firm, this object might be the firm asa whole. As independent segments of a firm might have different risk charac-teristics, profitability and growth prospects, these should be valued separatelyand the value of the conglomerate should be calculated as the sum of the valuesof the independent segments.
Second, if one or more of the reasonable objects of valuation identified con-sist of more than one reportable business segment, the segment informationabout the interrelated operations can be used to forecast sales and/or earningson the segment level. After adding up these segment-level forecasts, the sumcan be used as an input in the valuation model of the valuation object. Ap-pendix 2.D provides an analysis of how segment information can be used toforecast sales and earnings inputs for valuation models. The analysis suggeststhat segment information is more useful if the segments presented have dif-ferent sales growth prospects, different profitability outlooks and when theyare presented in a less concentrated structure. Thus, my findings suggest thatsegment disclosures lead to higher earnings forecasts if the disclosures signalthat the segment with higher profitability is the one growing more quickly.This is in line with the real option-based valuation model presented by Chenand Zhang (2003), suggesting that segment reporting is more relevant if thesegments have different growth and profitability prospects and firms achievehigher valuation if the segment with higher growth prospects is the more prof-itable one.
The overview presented in this subsection suggests that the interrelated-ness of reported business segments matter for how the segment informationpresented can be used for corporate valuation purposes. The arguments sug-
13As segments are very rarely totally independent from each other, there are some special
issues that arise when conducting sum-of-parts valuation. I describe these issues in Section
2.5 and provide suggestions as to how to handle them from a valuation perspective.
THE ROLE OF SEGMENT REPORTING IN VALUATION 61
gest that it is beneficial to value largely independent parts separately and usesegment information about interrelated subunits to improve valuation modelinputs on the aggregated level. Practical issues with this method will be dis-cussed in Section 2.5.
At the one end of the interrelatedness spectrum, one can consider the wholefirm as one reasonable object of valuation and use segment reporting for fore-casting sales or earnings to improve the inputs for the consolidated-level valu-ation model. At the other end of the spectrum, the different segments of thefirm can be valued separately, and then the group can be valued as the sumof the value of the different parts. From this point, I will focus on the use ofsegment information for sum-of-parts valuation.
2.4.2 Partial disclosure - partial solution
The discussion in the section above suggests that segment-by-segment valuationof a company is only possible if the disclosures by the reporting entities provideinformation that fits the valuation models. Therefore, if some of these piecesof information are missing or the definitions of line items are not in line withthe requirements of the valuation models, I will refer to these cases as ‘partialdisclosure’ from here on; these valuation models cannot be used on the segmentlevel.14 Thus, as investors prefer comparability of the information reported bythe companies, they might not use all the inputs if they cannot compare themwith peers.
One solution for the valuation problem for firms with partial segment dis-closures is to value the company on a segment-by-segment basis using valuationmultiples. You (2014) provides evidence that this is a popular way among ana-lysts to use segment information for valuation purposes.15 This is in practice isa multi-stage process. First, one needs to identify firms with operations simi-
14The valuation models require information about Operating assets, Operating liabilities and
EBIT per segment as well as income tax expenses on operating activities. While these might
be the definitions used internally by the company, if other definitions are used for the line
items, the information provided can not be used as inputs for the valuation models.15As an alternative partial solution, investors might use reported segment sales and profit
figures to forecast inputs for the valuation model on the consolidated level, as discussed in
Section 2.2. Appendix 2.C discusses the relevance of segment disclosure for such partial
solutions.
62 ESSAYS ON SEGMENT REPORTING AND VALUATION
lar to the segments, for example, industry peers. Then, the valuation multiplesneed to be calculated based on observed market prices of equity and debt andaccounting measures, such as profit or sales, for those peers. Finally, usingthe valuation multiples calculated for the peers, a suitable valuation multiple16
needs to be applied to the accounting measure on the segment, the productof the multiple and the accounting measure yielding the value of the segmentoperations.
Inspired by this popular method, I discuss the possibilities of an investorusing segment information for valuation in the case of partial disclosure. Thisanalysis also shows how close to the segment-based fundamental valuation onecan get with multiples-based valuation (partial solution). The difference be-tween the value estimates using the two different methods sheds light on thepossible motivations of firms to voluntarily disclose segment-level informationfrom a valuation perspective.
First, consider the AOIG model for valuation17:
V NOA0 =
T∑t=1
OI0 ∗ (1 + rWACC)
rWACC+
1
rWACC
[AOIGt
(1 + rWACC)t−1+ ...
]. (2.10)
The expression OIrWACC
can be thought of as a risk multiple applied on nextyear’s normal after-tax operating income. If the valuation multiple applied forOI (a.t.) is higher (lower) than 1
rWACC, it reflects expected future positive (neg-
ative) abnormal operating income growth. This shows that the value of oper-ations can be thought of as the value arising from the current earning powerof operations V CEP =
OI0∗(1+rWACC)rWACC
) and the value arising from any expected
abnormal growth in income in the future V Growth = 1rWACC
[AOIGt
(1+rWACC)t−1 + ...],
that is:
16A suitable multiple can be the average of peer multiples; alternatively the average multiple
adjusted upwards or downwards taking into account the risks and growth prospects of the
segment.17Here the starting point of the model is the last reported after-tax operating income (OI0)
and the AOIG is calculated from year 1 instead of the standard form of the model that
begins with the forecast for the next year (OI1) and starts calculating AOIG from year 2.
THE ROLE OF SEGMENT REPORTING IN VALUATION 63
V NOA0 = V CEP
0 + V Growth0 =
=
T∑t=1
OI0 ∗ (1 + rWACC)
rWACC+
T∑t=1
1
rWACC
[AOIGt
(1 + rWACC)t−1
]. (2.11)
So far, I have split the AOIG valuation formula into two parts. The valuearising from the expected abnormal growth in operating income can be fur-ther simplified. It is possible to approximate the sum of future AOIGs usingAOIG in year one. On the one hand, competition on the market results in thedeterioration of economic goodwill18 over time suggesting that AOIG con-verges to zero. On the other hand, due to accounting conservatism, AOIG in-creases over time in steady-state competitive equilibrium (Ohlson and Juettner-Nauroth, 2005). Given assessment about the operations’ competitive advan-tage and the accounting conservatism applied by a firm, one can forecast futureAOIGs.
In this case, the value of expected future growth can be expressed as
V Growth0 =
1
rWACC
[AOIGt
(1 + rWACC)t−1+ ...
]=AOIG1 ∗ θrWACC
, (2.12)
where theta (θ) is an operations-specific constant that (1) increases with thelongevity of economic goodwill and the measure for accounting conservatismand (2) decreases with WACC. Appendix 2.F provides some quantifications forθ given WACC and the longevity of economic goodwill.
The transformation of the AOIG model shows that the value of the firm isthe sum of the value of its current earning power and the value of future growthopportunities. The value of future growth opportunities increases with con-temporary market power (asAOIG1 is the abnormal operating income growththe firm can generate in the next year), the longevity of economic goodwill andthe measure of accounting conservatism, and decreases with WACC. This anal-ysis shows that by using forecasts about next year’s earnings and the currentmarket power of a firm/segment, one can arrive at a reasonable valuation -somewhat similar to multiples valuation.
18I define economic goodwill as the third goodwill component in Johnson and Petrone (1998).
Using the terminology in Johansson et al. (2016), the entity has the ability to earn excess
return on net assets. The capitalized value of this excess return is economic goodwill.
64 ESSAYS ON SEGMENT REPORTING AND VALUATION
2.4.3 The valuation effect of full disclosure
In this section, I analytically investigate the valuation effect of full segmentdisclosure, i.e. providing enough information on the segment level to enablesegment-by-segment valuation. Segment reporting serves as a signaling devicefor firms. Previous studies have focused on the effect of dishonest signaling,that is, the effect of management discretion in allocating earnings across thesegments.19 Chen and Zhang (2007) show, using a real options-based model,that firms can transfer earnings from one segment to another to achieve highervaluation. You (2014) shows with an analytical model that firms engage in shift-ing earnings from low-multiple segment to high-multiple ones in their desireto achieve higher equity valuations and presents empirical evidence suggestingthat the market is aware of such manipulation techniques.
Signaling theory predicts that good quality firms provide voluntary seg-ment information that signals their superior quality (Morris, 1987). As far as Iknow, this is the first study investigating the signaling role of segment report-ing assuming truthful signaling.
After the discussions about the AOIG model in the section above, I analyzethe value effect of full segment disclosure compared to partial segment disclo-sure. I assume the following: in cases of full disclosure, the investors get all theinformation needed to use the AOIG model for calculating the values of thesegments separately.20 In cases of partial segment disclosure the firm providesenough information on the segment level for forecasting segment operatingincome (a.t.). This earnings forecast doesn’t improve with the additional infor-mation under full disclosure. I also assume that the firm discloses enough infor-mation for investors to assess the riskiness of the segment operations (WACCi),the longevity of the economic goodwill of the different segments and of thefirm, and the relative size of the segments (λ) - i.e. investors know the WACCof the segments and for the whole company and additional accounting infor-mation about the segments does not affect the perceived competitive advantageof the firm in the given operation. Moreover, additional information about the
19Empirical investigation by Givoly et al. (1999) suggests that reported earnings on the seg-
ment level is in fact subject to management intervention.20Here I assume that all segments are reportable business segments and disregard the potential
practical problems with conducting sum-of-parts valuation in practice discussed later in
Section 2.5.
THE ROLE OF SEGMENT REPORTING IN VALUATION 65
segments doesn’t lead to altered risk perception.21
In terms of future AOIG, I use the following assumptions: 1) no inflation2) no accounting conservatism and 3) any abnormal operating income due tocompetitive advantages fades away in n years, linearly. The no inflation andno accounting conservatism assumptions result in AOIG being 0 when steadystate competitive equilibrium is reached.22 The third assumption results inAOIG in year 1 determining AOIG in later years, given n.
The sum-of-the-parts valuation model used with full disclosure
V NOA0 =
∑i
OIi,0 ∗ (1 + rWACCi)
rWACCi+
T∑t=1
1
rWACCi
[ AOIGi,t(1 + rWACCi)
t−1
]. (2.13)
Given the assumptions, this model can be rewritten as
V NOA0 =
∑i
T∑t=1
OIi,0 ∗ (1 + rWACCi)
rWACCi+AOIGi,1 ∗ θirWACCi
. (2.14)
This model cannot be used in cases of partial disclosure, because there is noinformation about segment FCF, which is needed to forecast AOIGi,1. How-ever, FCF is available on the consolidated level, and using the segment forecastsfor after-tax operating income, AOIG can be calculated on the firm level foryear 1. Therefore, investors can modify the model to arrive at the partial solu-tion:
V NOA0 =
∑i
OIi,0 ∗ (1 + rWACCi)
rWACCi+
T∑t=1
1
rWACC
[AOIGt
(1 + rWACC)t−1
], (2.15)
that is
V NOA0 =
∑i
OIi,0 ∗ (1 + rWACCi)
rWACCi+
T∑t=1
AOIG1 ∗ θrWACC
. (2.16)
21Please note that this assumption disregards the information asymmetry (cost of capital)
effect of segment disclosure discussed extensively in the previous literature. As a result, my
comparison isolates the expectation alteration (signaling) effect of segment disclosure from
the risk perception effect.22Otherwise, AOIG would slightly increase in steady-state (Ohlson and Juettner-Nauroth,
2005).
66 ESSAYS ON SEGMENT REPORTING AND VALUATION
Here, the value arising from the current earning power of the segments iscalculated in the same way, but the value from the future abnormal operatingincome growth in future years is calculated on the consolidated level.
In cases of full disclosure, Model (2.14) can be used to calculate the valueof the company. The partial solution in cases of partial disclosure is Model(2.16). The value arising from the current earning power of the segments arecalculated identically, the difference between the two value estimates arise fromthe calculation of the value arising from future abnormal operating incomegrowth.
Next, I investigate the differences in value estimates between the partialsolution and the solution given full disclosure. The difference between thetwo value calculations can be written as follows:
V NOA0 − V NOA
0 =
=∑i
T∑t=1
1
rWACCi
[ AOIGi,t(1 + rWACCi)
t−1+...]−
T∑t=1
1
rWACC
[AOIGt
(1 + rWACC)t−1+...].
(2.17)
In order to analyze the difference between the value estimates, I simplifythis model to two segments, A and B. The notation of the θ for segment A isθA, for segment B is θB and on the consolidated level is θ. Thus, I drop the timesubscript from AOIG; AOIG refers to the first year AOIG as it determinesfuture values of AOIG. I refer to the segment WACC values as WACCA andWACCB and to the consolidated WACC as WACC. The difference betweenthe two value estimates can be written as follows:
V NOA0 −V NOA
0 = V Growth0 −V Growth
0 =AOIGA ∗ θAWACCA
+AOIGB ∗ θBWACCB
−AOIG ∗ θWACC
.
(2.18)Next, as the AOIG forecast depends on the information set,
AOIG = AOIGA + AOIGB − AOIGε, (2.19)
where AOIGε stands for the increase in the forecast abnormal operating in-come growth given one is provided with full disclosure and can be expressedas
THE ROLE OF SEGMENT REPORTING IN VALUATION 67
AOIGε = ∆ONAt−1 ∗ (φ− λ) ∗ (WACCB −WACCA), (2.20)
see Appendix 2.G. Here, λ refers to the share of capital employed in segmentA, as
WACC = WACCA ∗ λ+WACCB(1− λ), (2.21)
and φ refers to the share of ONA reinvestment in year t-1 to segment A:
φ =∆ONAA,t−1
∆ONAt−1. (2.22)
AOIGε is positive and so full disclosure results in higher AOIG forecastwhen a firm reveals that it (re)invests proportionally more into the segmentwith lower risk, i.e. when a firm increases the relative size of the lower-risksegment through reinvestment.
The difference between value estimates becomes:
V NOA0 − V NOA
0 =
=AOIGA ∗ θAWACCA
− AOIGA ∗ θWACC
+AOIGB ∗ θBWACCB
− AOIGB ∗ θWACC
+AOIGε ∗ θWACC
.
(2.23)
This difference is then
AOIGA ∗ (θA ∗WACC − θ ∗WACCA)
WACC ∗WACCA+
+AOIGB ∗ (θB ∗WACC − θ ∗WACCB)
WACC ∗WACCB+AOIGε ∗ θWACC
. (2.24)
Multiplying all three terms with AOIGAOIG , labeling the the ratio AOIGA
AOIG as sA,the ratio AOIGB
AOIG as sB and the ratio AOIGεAOIG as sε yields the difference to be
AOIG
WACC∗[sA ∗ (θA ∗
WACC
WACCA− θ) + sB ∗ (θB ∗
WACC
WACCB− θ) + sε ∗ θ
]. (2.25)
Furthermore, as V Growth0 = AOIG∗θ
WACC , the difference between the value esti-mates can be expressed as a proportion to the priced-in growth opportunitiesgiven partial disclosure:
68 ESSAYS ON SEGMENT REPORTING AND VALUATION
V Growth0 ∗
[sA ∗
θAθ∗ WACC
WACCA+ sB ∗
θBθ∗ WACC
WACCB− 1]. (2.26)
Formula (2.26) shows that the valuation difference is proportional to thepriced-in value of growth opportunities V Growth
0 given partial disclosure. Thissuggests that voluntary disclosure has larger absolute effects for growth firms(as opposed to value firms). Given positive V Growth
0 , the expression increasesin sε, (as sB = 1 − sA + sε), i.e. if the firm achieves higher expected AOIG incases of full segment disclosure, it also leads to higher valuation. As discussedbefore, full segment disclosure results in higher AOIG estimates when a firmreveals that it (re)invests proportionally more into the segment with lower risk,i.e. when a firm increases the relative size of the lower-risk segment throughreinvestment.
Thus, the valuation difference is zero if sε is zero, there is no differencein segment-level WACCs and durability of competitive advantages (economicgoodwill). Furthermore, as WACC and θ are weighted averages of segment-level WACCs and θ-s, the valuation difference is increasing when the lower-risksegment has produces a larger share of AOIG in year 1 and has a more durablecompetitive advantage (larger economic goodwill).
The analysis presented in this section suggests that full segment disclosure- compared to partial disclosure - is more relevant from a valuation perspectivefor firms with high priced-in growth expectations. Firms with high priced-in growth can signal superior quality, can achieve higher expected AOIG andhigher valuations, if they show that proportionally more of their operatingincome is reinvested in the less risky segment.
Consequently, firms can achieve higher valuations if they reveal that pro-portionally more of their AOIG comes from the low risk segment and if thelow-risk segment has larger economic goodwill (more durable competitive ad-vantage). Given no difference in segment risks, a firm can achieve higher val-uation through signalling that the segment with the more durable economicgoodwill produces a larger share of the AOIG.
2.4.4 Summary of the findings
The analysis in this paper shows that the specific disclosure requirements ofthe currently prevailing segment reporting standards do not comprise enough
THE ROLE OF SEGMENT REPORTING IN VALUATION 69
segment-level information for the valuation of the segments separately. Em-pirical evidence in previous literature suggests that while firms disclose moreitems than specifically required by the standards, the information providedstill does not enable outsiders to use sophisticated, accounting-based valuationmodels to value the firm by segments. The only line item the disclosure ofwhich is currently specifically required under both IFRS and US GAAP is seg-ment earnings. The partial solution discussed suggests that outsiders can usethis information to value the segments on their own using multiples, and theAOIG-type model solution suggests that this partial valuation solution can ar-rive fairly close to the solution that one would get if all the accounting itemsneeded were provided on the segment level.
The analysis of the difference in value estimates suggests that firms benefitfrom voluntary disclosure if the segment operations have different risks andthe segment with lower risk produces proportionally more free cash flow (rel-ative to capital employed), and if its economic goodwill is more durable. Thedifference in value estimates increases linearly with the priced-in growth givenpartial disclosure, that is, the market value of the firm less the value of currentearning power.
The analysis suggests that - given the two assumptions made, that (1) seg-ment earnings forecasts can be made using partial disclosure as well and (2)AOIG in year 1 determines future AOIG forecasts given n, the longevity ofeconomic goodwill - the valuation relevance of full disclosure is more relevantfor growth companies, and in particular for those with segments that have dif-ferent risk profiles. Given the costs associated with segment disclosure - in par-ticular the proprietary cost arising from providing sensitive, segment-level in-formation - the choice of the standard setter not to require full disclosure fromreporting entities is a reasonable one. Companies can compare the potentialbenefits of providing full disclosure with the costs associated with reportingsensitive information. Firms trading around the value of their current earningpower (without any additional expected value from future growth prospects)can barely see any benefits from providing full disclosure, however, they yetneed to bear the costs of disclosure. Firms which produce more abnormal re-turns in their low-risk segment, and which have large economic goodwill fromdurable competitive advantage in that segment could benefit the most fromproviding full segment disclosure from a signalling point of view. However,these are exactly the firms that probably bear the highest proprietary costs of
70 ESSAYS ON SEGMENT REPORTING AND VALUATION
disclosure. Therefore, I conclude from this analysis that the standard settermoved towards optimal standard setting [as defined in Dye (2002)] by not re-quiring firms to provide full segment disclosure.
2.5 Discussion
This paper has so far presented how segment information can be used to valuethe reported business segments separately. However, the application of thissum-of-the-parts approach is somewhat complicated in practice for several rea-sons. First, the sum of the accounting items for the reportable business seg-ment does not equal the figures reported on the consolidated level for differentreasons (e.g. interrelated segments or non-GAAP accounting measures andmeasurements). Furthermore, the operating asset relation might not hold. Inthe next section I discuss these issues in detail. I start with a description of theissues and then I show how they can be handled from a valuation perspective.
2.5.1 Special issues with segment-based corporate valuation
The Gap - Sum of reportable business segment figures does not equal theconsolidated figures
Accounting figures reported on the consolidated level might not equal the sumof the figures presented for each of the reportable operating segments.23
The term reportable operating (business) segment is defined in accordancewith SFAS 131 and IFRS 8 (SFAS 14 and IAS 14) and refers to revenue gener-ating parts of the business that either meet the quantitative threshold on theirown or are aggregated with other such parts of the business. There are parts ofthe business which are not included in any of the reportable business segments,either because such parts do not meet the quantitative threshold of being re-portable or because they do not meet the definition of being an operating seg-ment (such as the corporate headquarters). These parts of the business can bereported in an additional ‘All Other’ segment. Another reason for the Gap isthat there can be revenues and costs allocated to a segment but that arise from
23This has been referred to in previous research as the Gap (Wang and Ettredge, 2015), FSD
(firm-segment reconcilable difference) (Alfonso et al., 2011) or SER (segment earnings rec-
onciliations) (Hollie and Yu, 2012).
THE ROLE OF SEGMENT REPORTING IN VALUATION 71
transactions within the company. Such inter-segment revenues and costs arerecognized in several segments and the standard setters require the reportingentities to provide information about inter-segment eliminations.
Furthermore, companies are required to report profit or loss using the def-inition that the Chief Operating Decision Maker (CODM) uses internally. Ifthis accounting item is not a GAAP figure, a company needs to provide a rec-onciliation of this income item to the nearest GAAP level income reported onthe consolidate level. There are two main reasons for earnings reconciliations.First, companies might provide a non-GAAP earnings measure on the segmentlevel. This is the case when they measure the earnings after excluding amor-tization, depreciation or similar items. Second, the measurement applied bythe company might not be allowed under GAAP. An example of this can befound in Veidekke’s Annual Report 2016. Veidekke recognizes property devel-opment revenues using the percentage-of-completion method on the segmentlevel. However, this method is not allowed under IFRS. In the IFRS consoli-dated financial statements the company applies the completed contract method(Veidekke, Annual Report 201624).
For reasons mentioned above, segment filings might also include specialparts called ‘Corporate’ segment, ‘Eliminations’, ‘Reconciliations’ or similar,separate from the reportable business segments.
The operating asset relation does not hold
The second special issue related to the application of valuation models forsegment-based valuation is that the operating asset relation might not hold.The operating asset relation requires that the change in operating net assets ina year to be explained by acquisition and divestment of operating assets and li-abilities, profit or loss for the year and free cash flow. However, in practice, thereported operating income on the segment level might not include some otherprofit or loss items that are not financial in their nature. Such items can bereported ‘below the line’, for example if the company presents some ‘specialitems’ below the profit or loss line. Similarly, under IFRS companies mightexclude profit or loss items from segment operating income that are presentedin the Other Comprehensive Income (OCI). In order for the operating assetrelation to hold and in order for the valuation models discussed to work, these
24Available at http://hugin.info/172/R/2094866/792179.pdf, 31.12.2017
72 ESSAYS ON SEGMENT REPORTING AND VALUATION
operating items also need to be considered when valuating the segments. In thefollowing, I discuss how to handle such extra segments and accounting itemsfrom a valuation perspectives.
Previous findings on the effect of the Gap
Wang and Ettredge (2015) find that most companies report a negative gap, thatis, the sum of segment earnings is higher than consolidated earnings. They findthat this is mostly due to companies excluding expenses from the segment-levelprofit measure. Their findings suggest that these expense items are those thatare transitory or expenses that business managers are not held responsible forand cannot affect. The empirical tests suggest that sum-of-segment earningsare more persistent and informative (compared to consolidated earnings) fornegative gap firms. For positive gap firms they find the consolidated earningsmore informative. Hollie and Yu (2012) find that investors underestimate thepersistence of earnings reconciliations and provide a long-short portfolio strat-egy to earn positive abnormal returns. Alfonso et al. (2011) provide compara-ble descriptive statistics of positive, negative and no Gap firms and find thatthe segment reporting choice of the management with respect to the Gap isaffected by the agency cost motive.
Value effects of special issues
A segment-by-segment valuation of a firm requires not only the valuation of thereportable operating segments but also the valuation of these ‘special issues’. Inthis section, I discuss how these items affect valuation and what informationis needed in the segment filings by an outsider to be able to account for suchitems in the valuation.
As most companies exclude some expense items from their segment-leveloperating earnings (Wang and Ettredge, 2015), I start with a basic valuationmodel for assessing the value effect of these excluded costs:
V NOASpec,0 =
OI1
rWACC −Gr, (2.27)
where
• OI = (after-tax) core operating income generated by this segment (usuallya negative number)
THE ROLE OF SEGMENT REPORTING IN VALUATION 73
• Gr = long-term growth of the company.
This model assumes some core expense forecasts can be made for the futureyears and that these expenses grow in line with the company as a whole. NowI discuss the applicability of this model for the special issues.
The corporate headquarters is part of the business that usually generatesnegative income (cost) and also regularly comprise some assets. It can be con-sidered a cost center (liability) from a valuation point of view that is generat-ing negative income from year to year and this negative income is presumablygrowing in line with the whole company. With these assumptions, formula(2.27) can be used to assign a value to the corporate segment. For this, oneneeds to forecast the (negative) earnings it is generating and firm-level WACCand growth. For forecasting the negative earnings reported in this segment,one needs information about the ‘core’ earnings presented under this segment.
SFAS No. 131 allows companies to present the corporate headquarters seg-ment together with other business segments that do not meet the quantitativethreshold.25 Such an ‘All Other’ segment has limited usefulness from an out-sider point of view as it blends together a revenue and profit generating, small,but ‘normal’ business segment with a cost generating corporate headquarterssegment. These parts of the business have different risk characteristics, prof-itability and growth, and information about these differences is essential forsegment-based corporate valuation.
Inter-segment eliminations are usually presented as an additional columnafter the reportable business segments and corporate headquarters but beforeany reconciliations. This column includes revenue, cost and income items thatare generated from transactions between the different business segments of thefirm. Such transactions should be deducted from the sum of the reported seg-ment items so that the resulting figures show the performance of the companytowards external parties.
The presence of such a reported column indicates that during the valuationof the reported operating segments the revenues and costs were overestimated.The correction of this estimation would require one to put a value on any prof-its or losses additionally accounted for and deduct such value from the valueof the different reported business segments. In cases when the inter-segmentresults cannot be traced back to the individual business segments, the value of
25SFAS No. 131, paragraph 21.
74 ESSAYS ON SEGMENT REPORTING AND VALUATION
the inter-segment performance can be deducted from the sum of the calculatedsegment values in order to arrive at a more reasonable estimate of the value ofthe multi-segment company.
The valuation of such inter-segment results can be done similarly to thevaluation of the corporate headquarters. In cases when the operating practicesof the company do not change, such inter-segment eliminations can be expectedto occur every year. Therefore, a ‘core’ eliminated operating income needs tobe calculated and formula (2.27) can be applied for approximating the valueeffect of inter-segment operations. In order to apply this type of valuation,one needs information about the ‘core’ operating income eliminated over theyears.
SFAS 131 and IFRS 8 require companies to report the segment items [profitor loss, assets (specifically required only under SFAS 131), other accountingitems reviewed by the CODM] using the measure that the CODM internallyuses when evaluating the performance of the segment, even if such a measureis not in line with GAAP. The standards furthermore require companies toprovide reconciliation of the reported accounting items for the segments tothose reported on the consolidated level. Reconciliation shall be provided forthe profit or loss figures for the segments to an enterprise’s consolidated in-come before income taxes, extraordinary items, discontinued operations, andthe cumulative effect of changes in accounting principles.
Such reconciliation disclosures might include information about profit orloss items that are not allocated to the segments. For example, if the CODMis reviewing the earnings before interests, taxes, depreciation and amortization(EBITDA) measure as the ‘segment profit or loss’ measure on the segment level,such reconciliation will provide information about depreciation, amortizationand interests. In this case, the calculated value on the segment level is mostprobably overstated as one does not account for depreciation and amortizationexpenses on the segment level and so uses an overstated measure of segmentprofit or loss. Therefore, the disclosure about reconciliation also has a valueeffect that can be measured similarly to the other special issues.
The valuation problem is more complicated if the reconciliations arise frommeasurement differences (for example the case of Veidekke). Here the recon-ciliation amount shows the current difference between the earnings accordingto the percentage-of-completion method and the completed contract method.While one measure might be more persistent or informative than the other, in
THE ROLE OF SEGMENT REPORTING IN VALUATION 75
the long-run, the value of the segments should be the same, no matter whichmeasurement is considered. In this case, the two different measurements pro-vide investors with additional information for assessing the future performanceof the segment.
If the operating asset relation does not hold, there are changes in ONAthat are not explained by additions or divestments of assets or operating in-come.26 The valuation effect of such dirty operating surplus is similar to theeffect of reconciliations. Reconciliations have a value effect because some oper-ating profit and loss items are excluded from the operating profit or loss on thesegment level. Similarly, an operating dirty surplus have a value effect becausesome operating profit and loss items are not accounted for in the operatingprofit or loss on the consolidated level (such items can be found in the OCIunder IFRS).
Intuitively, the value effect of the dirty operating surplus is similar to thatof the reconciliation segment. One needs to forecast ‘core dirty operating sur-plus’ and approximate the value effect using formula (2.27). In order to seewhat items are expected to be recurring year after year, the specific items needto be analyzed separately.27
After these adjustments, the sum of segment values plus the value of ad-ditional items discussed adds up to the value of the operating net assets of thecompany.
Assume that some core yearly effect can be singled out for each of theseeffects and assume furthermore that if we increase this expected yearly effectafter year 1 with the growth rate of a firm, then we have reasonable estimates ofthese effects for the future years. In this case the effects of the above discussedspecial issues can be assigned a value using formula (2.27) and the inputs are
26The operating asset relation for ONA is similar to the clean surplus relation for the equity.
If new share issues, dividends and net income cannot explain the difference in the change
in equity, there is some dirty surplus.27For example currency translation items can be considered non-recurring if one has no ex-
pectations about future exchange rate trends (however, this assumption will then affect rev-
enue and costs forecasts as well). Similarly, if the company applies a liberal (in contrast
to conservative) accounting valuation of the pension liability arising from defined benefit
pension plans, one might need to expect recurring revaluations of such liabilities year after
year, which then in turn does affect the ‘core dirty operating surplus’.
76 ESSAYS ON SEGMENT REPORTING AND VALUATION
the sum of the core expected income effects for the next year arising from thedifferent special issues, the growth and the WACC of the firm.
Following this discussion, the sum-of-the-parts valuation models above canbe extended with the valuation model of the special issues in order to arrive atthe value of the whole conglomerate.
2.5.2 Segment-by-segment valuation model for multi-segment cor-porates
A segment-by-segment valuation model can be spelled out following the dis-cussion above. One can think about a company as consisting of several oper-ating segments and one (or more) eliminations, reconciliations or corporatesegment. Thus, the valuation of the operating net assets of the company usingthe AOIG model is as follows 28:
V NOA0 =
∑i
1
rWACCi
[OI1,i +
∞∑t=2
AOIGi,t(1 + rWACCi)
t−1
]+
EO1
rWACC −Gr. (2.28)
Here, i refers to the different operating segments of the company. The in-dex values 1 and t=2,3,.. refer to time periods in the future. EO refers to theoperating income forecast pertaining to the different special issues discussedabove. The formula expresses that the value of the operating net assets of thecompany can be expressed as the sum of the values of the operating segmentsplus the (possibly negative) value of the corporate/eliminations/reconciliationssegment and other special issues. Appendix 2.D presents a numerical examplefor the valuation effect of segment disclosures in presence of a corporate/eliminationssegment.
2.6 Conclusions
In this paper, I investigate the usefulness of segment reporting for corporatevaluation purposes. There are several aspects of this usefulness to investigate.First, companies differ with respect to their internal organizational structures.
28The segment-by-segment valuation model for the other firm valuation techniques can be
spelled out similarly.
THE ROLE OF SEGMENT REPORTING IN VALUATION 77
The current segment disclosure standards both under IFRS and US GAAP re-quire firms to define segments in line with their internal organization (man-agement approach). This requirement results in cross-company differentiationwith respect to how segment information can be used for valuation purposes.I argue that some ways of defining segments are more preferable than otherswhen it comes to the usefulness of valuing firms segment-by-segment. I alsodiscuss how segment information can be used for valuation purposes depend-ing on the degree of interrelatedness between the segments.
Second, the disclosure requirements of the standards and the disclosurepractices - both mandatory and voluntary disclosure - of the firms affect howthis information can be used. I use the Discounted Cash Flow (DCF) modeland its two modifications, the Value Added Valuation (VAV) and AbnormalOperating Income Growth (AOIG) models to establish the segment-level in-formation needs of the valuation models to conduct sum-of-parts valuation.
The paper continues with benchmarking the regulatory requirements im-posed by the different segment reporting standards under IFRS and US GAAPon the information needs of the different valuation models. Here I discuss thedisclosure requirements of SFAS 14, IAS 14 and IAS 14R and the latest seg-ment reporting standards applying the management approach, SFAS 131 andIFRS 8. I compare the specific disclosure requirements of these standards tothe information needs of the valuation models. This way I investigate whetherthe regulations require the disclosure of valuation-relevant information on thesegment level. I find that the specific requirements of the segment reportingstandards are not sufficient to conduct segment-by-segment valuation of firms.
The standards with management approach condition disclosure of account-ing items whether they are regularly used internally by the chief operatingdecision maker (CODM). This approach results in firm-specific disclosure re-quirements and can lead to firms disclosing more line items than specificallyrequired by the standards. Using previous literature on the disclosure prac-tices of companies around segment reporting regulation changes, I comparethe items disclosed by the companies on the segment level to the informationneeds of the valuation models and find that most companies do not providesufficient information for segment-by-segment valuation of the firm.
The only line item specifically required by both standards with manage-ment approach is segment profit or loss. I present a partial solution for thesegment-by-segment valuation problem given partial disclosure and analyze
78 ESSAYS ON SEGMENT REPORTING AND VALUATION
the difference between this value estimate and the value estimate when fulldisclosure is available - similarly to (Skogsvik, 1998). The difference in valueestimates sheds light on the circumstances under which segment reporting ismore relevant for valuation and suggests possible, valuation-based motivationsfor voluntary segment disclosure. I find that the benefit of voluntary disclosureis proportional to the priced-in growth, that is, the value of the company notexplained by the current earning power of the operations. Companies withhigher priced-in growth benefit from full segment disclosure when comparedto partial segment disclosure if they reveal that they reinvest proportionallymore of the operating income to the lower-risk segment and if their lower-risksegment is generating a larger share of the AOIG, and has more durable com-petitive advantage (higher economic goodwill).
Finally, I discuss some practical challenges in segment-by-segment valua-tion of firms. Companies are required to define segments in line with their in-ternal organization and provide information about the accounting figures reg-ularly provided to the Chief Operating Decision Maker (CODM). Both thesegment definition and the internally used accounting numbers can providechallenges for outsiders to use these numbers for valuation purposes. As partof the valuation framework presented in this paper, I discuss the practical valu-ation implications of these special issues separately. I start with presenting thedifference between the sum of the reportable business segments and the con-solidated figures and how to handle them from a valuation point of view. Thisdifference is referred to as SER, segment reconciliation differences in previousliterature, and mainly consists of the corporate headquarters, inter-segmenteliminations, and non-GAAP reconciliations. Second, I discuss the possibilityand the implications when the operating asset relation does not hold.
This paper makes several contributions. As to my knowledge, this is thefirst paper to evaluate the segment disclosure standards from a valuation pointof view. Given the firm-specificity of the current segment reporting standards,the framework presented in the paper provides information to firms that arewilling to provide valuation-relevant information in their segment disclosureswith how to define segments and what accounting line items to provide on thesegment level. Furthermore, the analysis provided in the paper informs stan-dard setters about the valuation implications of the current and previous stan-dards with respect to segment definition and disclosure requirements. Finally,the analytical modeling of the valuation difference between using segment-level
THE ROLE OF SEGMENT REPORTING IN VALUATION 79
earnings and full segment disclosure, sheds light on the circumstances underwhich full disclosure is more relevant for outsiders and provides the readerwith possible, valuation-based motivations for segment disclosures beyond thespecifically required line items. The discussion on the practical implicationof special issues in valuation using segment information informs the users offinancial statements to arrive at more informed value estimates.
80 ESSAYS ON SEGMENT REPORTING AND VALUATION
Appendix 2.A - Abbreviations
Abbreviations used in the study(a.t.) After-tax.AOIG Abnormal operating income growth, the growth in operating income
exceeding the required growth rate.Capex Capital expenditures, additions to (long-lived) assets.DDA Depreciation, depletion and amortization expenses.EBIT Earnings before interest and taxes.EBITDA Earnings before interest, taxes, depreciation and amortization.EBT Earnings before taxes.FCF Free cash flow.g Growth.Gr Long-term growth rate of the company.i Refers to segments within the company.IBT Income before taxes.OI Operating income (after-tax).OM Operating margin (after-tax), calculated as after-tax operating income di-
vided by revenues.∆ONA Change in operating net assets (year-on-year), calculated as ONAt −
ONAt−1.ONA, NOA Operating net assets.ONA turnover Calculated as sales divided by ONA.PPE Property, plant and equipment.Qi,T Steady-state Tobin’s Q value in time period T, expressing the accounting
conservatism of the firm.R∗ONA After-tax return on operating net assets.t Refers to time periods.Tc Corporate tax rate.V(ONA) Value of operating net assets.WACC, rWACC Weighted average cost of capital.
THE ROLE OF SEGMENT REPORTING IN VALUATION 81
Appendix 2.B - The equivalence of valuation mod-els
In this section I present the three valuation models discussed and show that incase one uses the same expectations for the future, the three valuation modelsyield the same value estimates.
Discounted Cash Flow (DCF) model
The DCF valuation model can be written in the following way:
V ONA0 =
∞∑t=1
E(0)(FCFt)
(1 + E(0)(rWACC))t. (2.29)
• V ONA0 = The value of the Operating Net Assets (ONA) at period t = 0,
• t = periods of time, t = 1, 2...∞• E(0)(.) = the expectation operator based on information available at pe-
riod t = 0,• FCFt = free cash flow for year t,• rWACC = the weighted average cost of capital (WACC).
In the above equation E(0)(.) refers to the expectation operator based oninformation available at period t = 0, FCFt refers to the free cash flow for yeart and rWACC is the weighted average cost of capital.
Investors in fact need to forecast the future free cash flows at the currentpoint in time (in year t = 0):
E(0)(FCFt) (2.30)
The free cash flow of the company can be decomposed in the following way:
FCFt = OIt −∆ONAt (2.31)
• OIt = (after-tax) Operating Income at time period t,• ∆ONAt = change in Operating Net Assets, see the definition later
Here OIt refers to the after-tax operating income of the firm in year t and∆ONAt refers to the change in operating net assets, in particular
∆ONAt = ONAt −ONAt−1. (2.32)
82 ESSAYS ON SEGMENT REPORTING AND VALUATION
Value Added Valuation (VAV) model
The next firm valuation model to discuss is the Value Added Valuation model.29
This model is an application of the Residual Income Valuation model for thevaluation of Operating Net Assets (ONA) instead of the common equity. Themodel can be expressed in the following form Skogsvik (2002):
V (ONA)0 = ONA0 +
T∑t=1
ONAt−1(R∗ONA,t − rWACC)
(1 + rWACC)t+V (ONAT )−ONAT
(1 + rWACC)T.
(2.33)
• V (ONAT ) refers to the Value of Operating Net Assets at time T,• R∗ONA,t refers to the (after-tax) return on ONA in time period t and• rWACC is the weighted average cost of capital that can be viewed as the
required rate on ONA.
In the above expression the after tax return operating net assets is denotedas R∗ONA , which can be calculated as follows:
R∗ONA,t :=EBITt ∗ (1− Tc)
ONAt−1. (2.34)
In this expression
• EBITt refers to Earnings before interests and taxes at time t,• Tc is the Corporate tax rate, and• EBITt ∗ (1− Tc) equals to OIt.
Abnormal Operating Income Growth (AOIG) model
A third valuation model to consider is the Abnormal Operating Income Growthmodel. This model is similar to the Abnormal Earnings Growth model (Ohlsonand Juettner-Nauroth, 2005) in that it is focusing on abnormal growth, that isgrowth beyond the normal (required) rate.
The AOIG model can be spelled out as follows (Penman, 2007):
V NOA0 =
OI1
rWACC+
1
rWACC
[AOIG2
1 + rWACC+
AOIG3
(1 + rWACC)2+
AOIG4
(1 + rWACC)3+ ...
].
(2.35)
29Penman (2007) refers to this model as the Residual Operating Income model.
THE ROLE OF SEGMENT REPORTING IN VALUATION 83
• AOIGt = Abnormal Operating Income Growth in time period t.
In this formula V NOA0 stands for the value of net operating assets at time 0,
WACC is the cost of capital, OI is operating income and AOIG is the abnormaloperating income growth, and can be calculated as
AOIGt =′ Cum− dividend OI ′t −Normal OIt =
= [OIt + rWACC ∗ FCFt−1]− (1 + rWACC) ∗OIt−1. (2.36)
The equivalence of the DCF and VAV models is shown in Skogsvik (2002)and Easton (2007):
Beginning again with the Free cash flow model:
V NOA0 =
T∑t=1
FCFt(1 + rWACC)t
+V (ONAT )
(1 + rWACC)T(2.37)
• V (ONAT ) = Value of Operating Net Assets at time T
one can use the same transformations as in Skogsvik (2002):
FCFt = EBITt ∗ (1− Tc)−∆ONAt, (2.38)
• EBITt = Earnings before interest and taxes at time t• Tc = Corporate tax rate
where∆ONAt = ONAt −ONAt−1. (2.39)
One can denote the after tax return operating net assets as R∗ONA , where
R∗ONA,t :=EBITt ∗ (1− Tc)
ONAt−1. (2.40)
• R∗ONA,t = (after-tax) return on ONA in time period t.
rWACC is the weighted average cost of capital that can be viewed as the re-quired rate on ONA. Therefore, in order to arrive to a model similar to theRIV model, one can use the following expression of Free Cash Flow:
FCFt = ONAt−1 ∗ [rWACC + (R∗ONA,t − rWACC)]− (ONAt −ONAt−1) (2.41)
84 ESSAYS ON SEGMENT REPORTING AND VALUATION
to rewrite the model into the following form Skogsvik (2002):
V (ONA)0 = ONA0 +
T∑t=1
ONAt−1(R∗ONA,t − rWACC)
(1 + rWACC)t+V (ONAT )−ONAT
(1 + rWACC)T.
(2.42)Next, I show that the DCF and the AOIG models are equivalent.I start with the AOIG model:
V ONA0 =
1
rWACC
[OI1 +
∞∑t=2
[OIt + rWACC ∗ FCFt−1]− (1 + rWACC) ∗OIt−1
(1 + rWACC)t−1
](2.43)
Using equation (2.31) this can be rewritten into the following formula:
V ONA0 =1
rWACC
[OI1 +
∞∑t=2
[OIt + rWACC ∗OIt−1 − rWACC ∗∆ONAt−1]− (1 + rWACC) ∗OIt−1
(1 + rWACC)t−1
](2.44)
Separating out the OI and ∆ONA parts yields the following expression:
V ONA0 =
1
rWACC
[OI1 +
∞∑t=2
OIt −OIt−1
(1 + rWACC)t−1
]−∞∑t=2
∆ONAt−1
(1 + rWACC)t−1, (2.45)
which can be transformed into
V ONA0 =
1
rWACC
[ ∞∑t=1
OIt − OIt1+rWACC
(1 + rWACC)t−1
]−∞∑t=1
∆ONAt(1 + rWACC)t
. (2.46)
This in turn can be rewritten to
V ONA0 =
1
rWACC
[ ∞∑t=1
OIt(1− 11+rWACC
)
(1 + rWACC)t−1
]−∞∑t=1
∆ONAt(1 + rWACC)t
. (2.47)
Here
1
rWACC∗ (1− 1
1 + rWACC) =
1
rWACC∗ 1 + rWACC − 1
1 + rWACC=
1
1 + rWACC, (2.48)
THE ROLE OF SEGMENT REPORTING IN VALUATION 85
and so the valuation model can be spelled out as follows:
V ONA0 =
[ ∞∑t=1
OIt(1 + rWACC)t
]−∞∑t=1
∆ONAt(1 + rWACC)t
. (2.49)
This, using equation (2.31), is equivalent to the DCF valuation model:
V ONA0 =
∞∑t=1
FCFt(1 + rWACC)t
. (2.50)
Similarly to the proof provided by Ohlson and Juettner-Nauroth (2005) forthe equivalence of the abnormal earnings growth (AEG) model and the presentvalue of expected dividends (PVED) models, I apply similar steps to show theequivalence of DCF and AOIG models:
V ONA0 =
∞∑t=1
FCFt(1 + rWACC)t
=FCF1
(1 + rWACC)1+
FCF2
(1 + rWACC)2+ .... (2.51)
Then, adding and subtracting the same term OItrWACC∗(1+rWACC)t−1
V ONA0 =
OI1
rWACC+
FCF1
(1 + rWACC)1− OI1
rWACC+
+OI2
rWACC(1 + rWACC)1+
FCF2
(1 + rWACC)2− OI2
rWACC(1 + rWACC)1+ ... (2.52)
equals
V ONA0 =
OI1
rWACC+OI2 − FCF1 ∗ rWACC −OI1(1 + rWACC)
(1 + rWACC)1 ∗ rWACC+ ... (2.53)
or
V ONA0 =
OI1
rWACC+
∞∑t=2
OIt − FCFt−1 ∗ rWACC −OIt−1(1 + rWACC)
(1 + rWACC)t−1 ∗ rWACC, (2.54)
the latter being equivalent to the general form of the AOIG model.
86 ESSAYS ON SEGMENT REPORTING AND VALUATION
Appendix 2.C - Segment disclosure requirements
This appendix provides information on the segment-level information require-ment of the different segment reporting standards. I start with overviewingSFAS 14 and IAS 14 and IAS 14 revised (IAS 14R). Then, I present the require-ments of SFAS 131 and discuss the few differences in IFRS 8 as compared toSFAS 131.
SFAS 14
SFAS 14 required the following disclosures on the segment level:
• revenues from sales to unaffiliated customers and to other parts of theentity separately,• operating profit or loss,• identifiable assets,• depreciation, depletion and amortization,• capital expenditures, and• the enterprise’s equity in the net income from and investment in the net
assets of unconsolidated subsidiaries and other equity method investeeswhose operations are vertically integrated with the operations of that seg-ment. Disclosures shall also be made of the geographical areas in whichthose vertically integrated equity method investees operate.• The effect of change in accounting principles for the operating profit of
the reportable segments in the period in which the change is made.
IAS 14 and IAS 14R
IAS 14 required four principal items of information for both industry and ge-ographical segments:
• sales or other operating revenues, distinguishing between revenue derivedfrom customers outside the entity and revenue derived from other seg-ments;• segment result30;
30IAS 14 allowed differences in the definition of segment result among entities. IAS 14R
provides more detailed guidance as to specific revenue and expense items to be included
THE ROLE OF SEGMENT REPORTING IN VALUATION 87
• segment assets employed; and• the basis of inter-segment pricing.
For an entity’s primary basis of segment reporting (business segments or geo-graphic segments), IAS 14R requires the same items plus:
• segment liabilities• cost of property, plant, equipment (PPE), and intangible assets acquired
during the period;• depreciation and amortization expense;• non-cash expenses other than depreciation and amortization; and• the entity’s share of the profit or loss of an associate, joint venture, or other
investment accounted for under the equity method, if substantially all ofthe associate’s operations are within only that segment, and the amountof the related investment.
For an entity’s secondary basis of segment reporting, IAS 14R drops the re-quirement for segment result and replaces it with the cost of PPE and intangi-bles acquired during the period.
SFAS 131 and IFRS 8
SFAS 131 requires companies to report
• profit or loss and• segment assets
for each reportable segment.IFRS 8 requires disclosure of
• profit or loss
for each of the reportable segments.IFRS 8 also requires the disclosure of segment assets and liabilities if the
information is regularly provided to the chief operating decision maker.
or excluded from the segment measures. Accordingly, IAS 14R defines the measures to be
reported in a more standardized way, however, only if the revenue and operating expense
items can be directly attributed or reasonably allocated to the segments.
88 ESSAYS ON SEGMENT REPORTING AND VALUATION
SFAS 131 paragraph 27 specifies additional information requirements incase these items are included in the measure of segment profit and loss reviewedby the chief operating decision maker (CODM):
• Revenues from external customers• Revenues from transactions with other operating segments of the same
enterprise• Interest revenue• Interest expense• Depreciation, depletion, and amortization expense• Unusual items as described in paragraph 26 of APB Opinion No. 30, Re-
porting the Results of Operations — Reporting the Effects of Disposalof a Segment of a Business, and Extraordinary, Unusual and InfrequentlyOccurring Events and Transactions• Equity in the net income of investees accounted for by the equity method• Income tax expense or benefit• Extraordinary items• Significant non-cash items other than depreciation, depletion, and amor-
tization expense.
Enterprises are also required to disclose the following items about eachreportable segment if the specified amounts are included in the determinationof segment assets reviewed by the chief operating decision maker (SFAS 131,para 28.):
• The amount of investment in equity method investees• Total expenditures for additions to long-lived assets other than financial
instruments, long-term customer relationships of a financial institution,mortgage and other servicing rights, deferred policy acquisition costs, anddeferred tax assets.
In comparison to SFAS 131, which lists the disclosure about expendituresfor additions to segment ‘long-lived’ assets as an item to be disclosed if regularlyprovided to the CODM, IFRS 8 lists the disclosure about expenditures for ad-ditions to non-current assets, the latter also including intangible assets. Whilethese are not disclosure requirements, as firms tend to provide segment disclo-sures through ticking boxes (André et al., 2016), the definition items listed inthe standard can affect the disclosure provided by the reporting entities.
THE ROLE OF SEGMENT REPORTING IN VALUATION 89
Appendix 2.D - Analytical example
How can segment reporting be used to improve model-basedforecasts?
Previous research suggests that actors on the financial market can use segmentinformation to improve their inputs (such as sales or earnings forecasts) for firmvaluation models [e.g. Kinney (1971); Silhan (1983); Berger and Hann (2003);Fairfield et al. (2009); Schröder and Yim (2018)]. The forecasting methods inthese papers range from forecasting using time-series models ((Silhan, 1983;Berger and Hann, 2003) to using external industry and GNP forecasts (Kinney,1971; Fairfield et al., 2009; Schröder and Yim, 2018). In this section of thepaper, I show analytically how these forecasting methods result in differentforecasts. I also show the difference in forecasts given the different informationsets.
Outsiders need to forecast both sales and earnings when applying the DCFvaluation model. In this part of the paper, I show an example for how segmentinformation can help outsiders with more accurate consolidated earnings andsales forecasts to improve the outcome of their valuation models. This exampleis based on strict assumptions and it is only meant to illustrate the differencesin forecasts due to additional segment information under these assumptions.
For every scenario discussed below, I have specific strict assumptions aboutthe information set of outsiders that helps me show the effect of lacking differ-ent information on the segment level.
Forecasting sales one year ahead
Assume the company has operations in two segments A and B, and the com-pany has growth opportunity gA = GA − 1 in segment A and gB = GB − 1 insegment B in the following year, which outsiders by assumption know. The to-tal sales of the company is S and the company’s sales in segment A was sA = λ∗Sand in segment B sB = (1 − λ) ∗ S in year t . Therefore, next years’ sales is asfollows:
St+1 = λSt ∗ (1 + gA) + (1− λ)St ∗ (1 + gB) (2.55)
Here
• St = Sales in time period t,
90 ESSAYS ON SEGMENT REPORTING AND VALUATION
• λ = Share of total sales generated in segment A,• gi = growth of sales in segment i, i ∈ A;B.
If the company does not provide any segment disclosures, one way to fore-cast sales is to use previous consolidated sales growth as an approximation forfuture sales growth. This can be due to the information about the share of in-dustries of total sales being too expensive for the market to acquire or there issuch a small difference in segment growth that the costs of information acquisi-tion outweigh the benefits. In this case, outsiders might base their sales forecaston historical consolidated sales growth when there is no segment informationdisclosed by the company. Moreover, I assume here that the actual growth ofthe segments in year t + 1 is the same as in year t, which outsiders know, andthey approximate consolidated sales growth in year t+1 to equal that in year t.This assumption leads to the segment-based forecast being the actual outcome,which makes the illustration easier.31 Denoting the share of sales in industryA in year t − 1 for the company λt−1 and true (constant) sales growth in seg-ment A and B as gA and gB and total sales growth in year t as Gt, one can showthe sales forecast bias in case outsiders approximate sales growth with previousconsolidated sales growth.
In year t one can observe sales in year t-1 ( St−1 ) and sales in year t (St).
St = λt−1St−1 ∗GA + (1− λt−1)St−1 ∗GB (2.56)
Using this information sales growth Gt can be calculated as:
Gt := St/St−1 = λt−1GA + (1− λt−1)GB = λt−1(GA −GB) +GB (2.57)
Using Gt, sales for year t+1 can be forecasted as:
St+1 = St ∗Gt (2.58)
This is technically equivalent to St+1 = St−1 ∗ G2t , as Gt = St
St−1. The logic be-
hind this equivalence is the assumption of constant sales growth, as the forecastfor year t+ 1 is made using the growth in year t.
Using this formula,
E0c (St+1) := St+1 = St−1 ∗G2
t = St−1 ∗ (λt−1 ∗GA + (1− λt−1) ∗GB)2, (2.59)
31However, this assumption is not a crucial one, it is enough to assume that the segment-based
forecast is more accurate than the consolidated-based one.
THE ROLE OF SEGMENT REPORTING IN VALUATION 91
Where
• Etc(St+1) = sales forecast for year t+1 at year t using consolidated infor-mation. This is denoted later on in this scenario as St+1.
On the other hand, assuming constant segment growth rates the alternativeforecast using segment information (and by assumption the outcome) wouldbe:
St+1 = λt−1St−1 ∗G2A + (1− λt−1)St−1 ∗G2
B (2.60)
And so the forecast error due to the lack of segment information is the follow-ing:
St+1 − St+1 = St−1 ∗ (λ2t−1G
2A +G2
B(1− λt−1)2)+
+ 2 ∗St−1 ∗GA ∗GB(1−λt−1)λt−1−λt−1 ∗St−1 ∗G2A− (1−λt−1)St−1 ∗G2
B =
(2.61)
= St ∗ λt−1 ∗G2A(λt−1 − 1) + St−1 ∗G2
B(1− λt−1)(1− λt−1 − 1)+
+ 2 ∗ St−1 ∗GA ∗GB(1− λt−1)λt−1 = (2.62)
= St−1 ∗ λt−1 ∗ (1− λt−1)(−G2A −G
2B + 2GAGB) = (2.63)
= −λt−1(1− λt−1) ∗ St−1 ∗ (GA −GB)2 (2.64)
As the segment concentration index (λt−1(1−λt−1)) is always non-negativeso as St−1 and (GA − GB)2, someone using consolidated growth instead ofsegment-based sales growth figures always underestimate future sales in casethere is some difference in segment growth rates. The size of the error is largerwith smaller underlying segment concentration, larger sales in year t and largerdifference between segment growth rates.32
32This is due to the strict assumption that segment growth rate in year t+1 equals the growth
rate in year t. When the total growth is applied for the sales in year t to arrive at the sales
forecast at year t+1 it underestimates the actual growth. The share of sales in the faster
growing segment is larger in year t than that was in year t-1. Therefore, when applying
the previous years’ total growth to forecast sales one does not take the increased share of
sales in the faster growing segment in year t in account and therefore the future sales will be
underestimated. This assumption is of course barely realistic on a longer term due to the
mean reversion characteristic of growth (Koller et al., 2010; Fairfield et al., 2009).
92 ESSAYS ON SEGMENT REPORTING AND VALUATION
In the following, I present an alternative scenario. In this scenario outsidersacquire information about the shares of the total sales attributable to the twosegments and based on this information use an approximate figure for the shareof industries within the company. Outsiders gather private information andreceive the following signal for the share of industry A of total sales: λ = λ+ b,where b is a constant bias (s.t. 0 ≤ λ, λ ≤ 1 ). Thus, the sales forecast is asfollows:
St+1 = (λ+ b) ∗ St ∗ (1 + gA) + (1− λ− b) ∗ St ∗ (1 + gB) (2.65)
The difference between the biased forecast and that with segment informa-tion is
St+1 − St+1 = b ∗ St(gA − gB) (2.66)
The forecast error due to no segment disclosure depends on the total sales inyear t (S), the segment size approximation error (b) and the difference betweenthe growth rates of the segments (gA − gB). This analysis suggests that report-ing sales on the segment level is more relevant when the growth rates of thesegments in the firm are more different.
Earnings forecast one year ahead
Next, I turn to investigating the relevance of providing earnings informationon the segment level. Assume the company provides sales information by busi-ness segments, but does not provide any information about segment earnings(margin). Denote consolidated operating income OI, operating margin M,which are publicly disclosed, similarly to λ and S, and outsiders have a commonexpectation about gA and gB. Assume next year’s margin for both segments tobe the same as this year’s. The margins for segment A and B are denoted mA
and mB, respectively. We know that total earnings equals the sum of segmentearnings and so mB can be derived as a function of mA:
OI := S ∗M = λ ∗ S ∗mA + (1− λ)S ∗mB (2.67)
And somB =
M − λ ∗mA
1− λ(2.68)
With information aboutmA andmB,OIt+1 forecast would be as follows (which- by assumption is the outcome for next year’s operating income):
OIt+1 = λ ∗ S ∗GA ∗mA + (1− λ)S ∗GB ∗mB (2.69)
THE ROLE OF SEGMENT REPORTING IN VALUATION 93
OIt+1 = λ ∗ S ∗GA ∗mA + S ∗GB ∗ (M − λ ∗mA) =
= λ ∗ S ∗GA ∗mA + S ∗GB ∗M − S ∗GB ∗ λ ∗mA (2.70)
However, outsiders do not have information about segment margins. Inthis case, I discuss two possible ways of forecasting OIt+1. First, outsidersmight take the corporate margin in year t as the forecast for the corporate mar-gin in year t+1. Later I discuss another scenario where outsiders approximatemA and mB.33 Here I start with discussing what happens if outsiders chooseto forecast consolidated earnings without any explicit expectations about seg-ment margins with taking the consolidated earnings margin from year t as anapproximate for next years’ margin:
E(t)c,alt.1 := OIt+1 = [λt ∗ St ∗GA + (1− λt)St ∗GB] ∗M (2.71)
Then the forecast error bias is as follows:
OIt+1−OIt+1 = λt∗St∗GA(M−mA)+St∗GB∗(M−λt∗M−M+λt∗mA) (2.72)
OIt+1 −OIt+1 = λt ∗ St ∗GA(M −mA) + λt ∗ St ∗GB ∗ (mA −M) (2.73)
OIt+1 −OIt+1 = λt ∗ St ∗ (M −mA)(GA −GB) (2.74)
As M = λt ∗mA + (1− λt) ∗mB, replacing the expression yields
OIt+1 −OIt+1 = λt ∗ St ∗ (λt ∗mA + (1− λt)mB −mA)(GA −GB) (2.75)
OIt+1 −OIt+1 = St ∗ λt(1− λt) ∗ (mB −mA)(GA −GB) (2.76)
The expression λ ∗ (1 − λ) is the segments’ relative sizes multiplied, whichcan be thought of as a concentration figure. The higher the multiple, the lessconcentrated the segments within the company.
The bias formula for operating income implies that the forecast accuracydifference between the forecasts based on consolidated reporting and segment
33The first way of forecasting can be probably considered a better naive forecast in case out-
siders know little about segment margins. However, if the two segments of the company
has very different margins and outsiders believe they have a fairly good approximation for
the margins (E(0)(|mA −mB|)� 0, |mA −mB| � E(0)(d)), the second forecasting alter-
native can also be a realistic one.
94 ESSAYS ON SEGMENT REPORTING AND VALUATION
disclosure depends on the difference between segment growth rates, the dif-ference between segment margins and the reporting concentration of the seg-ments. Outsiders this way overestimate earnings in case the segment withlarger growth has smaller margin and underestimate earnings otherwise (thisis in line with the results of Chen and Zhang (2003) on the effect of cross-segment growth and profitability differences on firm value). Companies witha profitable, quickly growing segment could benefit from providing segmentdisclosure for the market as it might result in higher valuation through higherearnings forecasts.
In this scenario, outsiders use the consolidated margin M to forecast earn-ings. This can be due to their assumption that the margin for the two segmentsare really close or just the cost of acquiring information about segment marginsmight outweigh the benefits of having a more precise segment margin figure.As shown in (2.76), using the consolidated margin M in conjunction with theassumption that mA = mB for forecasting earnings is not expected to result inany forecast bias.
As a second possibility, outsiders might estimate segment margins witherror:
E(t)c,alt.2(mAt+1
) := mA = mA + d (2.77)
HereE(t)c (mAt+1
) refers to the expectations of the margin of segment A for yeart+1 (the expected margin by assumption equals the margin in year t) based onconsolidated information in time t. This (biased) expectation I denote as mA,d is the error term d ∈ R, to keep the example realistic, |d| < |mA|. Usingthis information one can arrive to the (biased) expectation about the margin ofsegment b:
mB =M − λt ∗ (mA + d)
1− λt= mB −
λt ∗ d1− λt
(2.78)
Therefore, the earnings forecast for year t+1 is as follows:
E(t)c (OIt+1) := ˜OIt+1 = λt ∗ St ∗GA ∗ (mA + d)+
+ (1− λt)St ∗GB ∗ (mB −λt ∗ d1− λt
) (2.79)
THE ROLE OF SEGMENT REPORTING IN VALUATION 95
˜OIt+1 = λt ∗ St ∗GA ∗mA + λt ∗ St ∗GA ∗ d+
+ (1− λt)St ∗GB ∗mB −(1− λt)St ∗GB ∗ (λt ∗ d)
1− λt(2.80)
Therefore, the forecast error arising from missing segment information is asfollows:
˜OIt+1 −OIt+1 = λt ∗ St ∗GA ∗ d− (1− λt)St ∗GB ∗λt ∗ d1− λt
(2.81)
˜OIt+1 −OIt+1 = St ∗ λt ∗ d ∗ (GA −GB) (2.82)
The earnings forecast error given this approach of forecasting is higher, thehigher total sales are, the relative size of the segment for which the margin wasforecasted with error d, the size of margin forecast error d, and the differencebetween segment growth rates.
Earnings forecast one year ahead - no disclosure versus sales andmargin disclosure
In this section, I compare the earnings forecasts one can come up with givenno disclosure on the segment level compared to disclosure about both salesand margins on the segment level. This discussion also contributes to under-standing the drivers of the valuation difference in the numerical example inAppendix 2.E. Given sales and margin disclosure, outsiders have informationabout GA, GB, S, λt,mA,mB. Given no disclosure on the segment level, thefollowing information is available: Gt, St,M . Gt refers to the ratio of sales inyear t to that of year t-1. A or B indexes refer to segment-level information. Srefers to sales, λ is the share of sales generated by segment A. M refers to firm-level margin while m refers to segment-level margin. Assume that forecasts aremade using historical growth and profitability levels - both at the segment-leveland the consolidated level. In the discussion above, I will derive the forecastsfrom λt−1 and St−1, as the difference partly arises from different growth expec-tations, calculated from sales figures in year t-1.
The earnings forecast given segment information
OIt+1 = St−1λt−1 ∗G2A ∗mA + (1− λ−1) ∗G2
B ∗mB). (2.83)
The earnings forecast given no segment information is as follows:
OIt = St−1 ∗G2 ∗M. (2.84)
96 ESSAYS ON SEGMENT REPORTING AND VALUATION
Using thatG = λt−1 ∗GA + (1− λt−1) ∗GB (2.85)
andM =
λt−1 ∗GA ∗mA + (1− λt−1) ∗GB ∗mB
G, (2.86)
the difference between the earnings forecasts can be expressed as
OIt −OIt = St−1 ∗ λt−1 ∗ (1− λt−1) ∗ (GB −GA)(GA ∗mA −GB ∗mB) =
= St−1 ∗ λt−1(1− λt−1) ∗ (gB − gA)(mA −mB + gA ∗mA − gB ∗mB). (2.87)
This result suggests that the difference in earnings forecasts is proportionalto sales and decreases in segment concentration. Segment information is morerelevant if there are larger differences in segment growth rates and margins.
In the expression above, St−1 and λt−1 ∗ (1−λt−1) are always positive. Tworemarks can be made when considering the expression (mA−mB+gA∗mA−gB∗mB): 1) it is positive for most cases when mA > mB (if mA is only marginallylarger than mB and gB is larger than gA, the expression can be negative), and 2)if mA = mB(= M), it collapses to (gA − gB) ∗M .
A negative difference in equation (2.87) suggests that the forecasts madeusing segment information are higher than the uninformed forecasts. Provid-ing sales and earnings information on the segment level can result in higherearnings forecasts (and so, negative forecast bias OIt − OIt) if the firm revealsthrough segment disclosure that segment with the higher growth potential alsohas higher margins.
Similarly, that the same logic can be applied if one uses ONA or capitalemployed instead of sales, growth in ONA or capital employed instead of salesgrowth and RONA or ROIC instead of profit margin.
THE ROLE OF SEGMENT REPORTING IN VALUATION 97
Appendix 2.E - An illustrative example
This section presents a simplified example of how segment reporting can beused for corporate valuation. The aim of this section is to show that in casesegment information alters the expectations of outsiders about the future per-formance of the company, it also affects approximated value of the company.
The firm in this example has two different operating segments. SegmentA is a large and more mature part of the business with slower growth andmore considerate margin and Segment B is a smaller, but quickly growing,more profitable part of the business. The company also reports a Corpo-rate/Eliminations segment which reports positive assets and negative income.The historical financial performance of the company is presented in Table (2.5).For the sake of simplicity, the WACC of the company and that of each of itssegments is 10%.
Table 2.5: Historical financial figures and financial ratios on the consoli-dated level
t -5 -4 -3 -2 -1 0ConsolidatedSales 197,4 213,7 252,6 303,6 335,2 362,1Op.Inc (a.t.) 23,9 23,4 31,9 38,9 43,2 45,1ONA 104,2 114,5 131,3 151,5 177,4 203,5WACC 0,1 0,1 0,1 0,1 0,1 0,1
RONA 0,22 0,28 0,30 0,28 0,25Operating margin 12,1% 11,0% 12,6% 12,8% 12,9% 12,5%Growth in ONA 0,099 0,147 0,154 0,171 0,147
Notes: ONA is measured at the end of period t. Op.Inc (a.t.) refers toafter-tax operating income. RONA is calculated as after-tax operatingincome divided by ONA. Operating margin is calculated as after-taxoperating income divided by sales.
Valuation using consolidated figures only
There are several ways to form expectations about the future financial perfor-mance of the company. In this example, I use a simple three-step approach.
98 ESSAYS ON SEGMENT REPORTING AND VALUATION
First, I forecast key ratios (growth in ONA, RONA and operating margin)for the first two years in the future as the average of the key ratios presentedby the company in the last three years. Then, I form expectations about thesteady-state, which I arbitrarily set to start at year 6. Here I assume 4% growthin ONA and AOIG=0. This yields key ratio estimates for the steady-state.In the third step, I use linear interpolation between year 2 and 6 to forecastAOIG and then back out the implied forecasts for the accounting figures andthe key ratios for the rest of the years.34 There are other ways to form expec-tations as well and in case segment information alters ones expectations aboutthe performance of the firm, the logic presented in this example will still apply.
After calculating the forecasts of AOIG for the future years, I calculate thefinancial figure forecasts, key ratios and the inputs for the valuation models (seeTable (2.6).
Table 2.6: Consolidated financial ratio forecasts, forecasts of financial figuresand calculated valuation inputst -2 -1 0 1 2 3 4 5 T=6ConsolidatedSales 303,6 335,2 362,1 445,7 515,8 578,6 628,5 663,6 682,0Op.Inc (a.t.) 38,9 43,2 45,1 56,7 65,6 73,6 79,9 84,4 86,7ONA 151,5 177,4 203,5 235,5 272,6 307,4 337,8 361,2 375,6WACC 0,1 0,1 0,1 0,1 0,1 0,1 0,1 0,1 0,1Q 2,3
FCF 24,6 28,5 38,7 49,6 61,0 51,0AOIG 5,7 4,3 2,9 1,4 0,0Value added 33,0 34,7 34,8 33,6 31,4
Notes: ONA is measured at the end of period t. Op.Inc (a.t.) refers to after-tax op-erating income. RONA is calculated as after-tax operating income divided by ONA.Operating margin is calculated as after-tax operating income divided by sales. Q is cal-culated using the assumption that AOIG in and from the beginning of the steady-state(year T=6) is 0.
The value of the firm can be calculated using the DCF model the following
34The operating margin is only used to calculate sales as in this valuation example after-tax
operating income is driven by AOIG. Therefore, operating margin is set to be the same for
all years.
THE ROLE OF SEGMENT REPORTING IN VALUATION 99
way35:
V ONA0 =
24, 6
(1, 1)1+
28, 5
(1, 1)2+
38, 7
(1, 1)3+
49, 6
(1, 1)4+
61, 0
(1, 1)5+
51, 0
(0, 1− 0, 4)∗ 1
(1, 1)5= 674.
(2.88)
Segment-by-segment valuation of the company
The valuation method segment by segment is similar to the one used for fore-casting consolidated figures discussed above. The forecasts are made the sameway for segments 1 and 2. The Corporate/Eliminations segment is handleddifferently. I calculate the ratio of after-tax operating income (and ONA) pre-sented in this segment and the after-tax operating income (and ONA) presentedunder the other two segments for the historical years. Then, I use the averageof this ratio in year t= -2, -1 and 0 as a forecast ratio for years t=1,2..6 for after-tax operating income and t=1,2...5 for ONA. Then, I set steady-state growth inONA to 4%, WACC to 10% and segment AOIG to 0 and calculate a consistentONA value for year T=6. After forecasting all the key ratios for the segments,I calculate the financial numbers needed for the valuation of every segment sep-arately. Then, I calculate the values of the different segments and finally, I addup these values to arrive at the sum-of-the-parts value of the company.
Based on the forecasts presented in Table (2.8) the value of Segment 1 usingthe DCF model can be calculated as follows:
V ONA0 =
18, 04
(1, 1)1+
20, 57
(1, 1)2+
25, 85
(1, 1)3+
31, 24
(1, 1)4+
36, 59
(1, 1)5+
28, 97
(0, 1− 0, 4)∗ 1
(1, 1)5= 396, 7.
(2.89)Similarly, the value of Segment 2 is 354,1 and the value of Segment E is
-62,84. This results in a value of 688 for the whole company,36 in contrast to674 calculated using only the consolidated figures, a 2,08% difference.
The difference is driven by the more profitable segment growing faster thanthe rest of the company, which results in higher earnings forecast (see formula
35Allowing for rounding errors. The value of the company can also be calculated using the
other two valuation models discussed. The input values for the other models are also calcu-
lated/disclosed in the tables but for the sake of brevity and due to the equivalence of models
are presented in the previous appendix, I do not show the calculations explicitly.36The value of the company can also be calculated using the sum of the forecasts for the
valuation inputs shown in Table 2.9).
100 ESSAYS ON SEGMENT REPORTING AND VALUATION
(2.76) applied for ONA, growth in ONA and RONA in Appendix 2.D). Thishigher forecast earnings for the explicit forecast period results in higher valuefor the current earning power as of year 1 (V CEP
1 , see formula (2.11)). As all thesegments have the same operating risks, there is no difference in the AOIG fore-casts arising from asymmetric reinvestments into the segment with less riskyoperations (formula (2.20)), but the higher earnings forecast for year 2 togetherwith the positive abnormal operating income growth results in higher AOIGfor year 2. There is neither any difference between segment thetas (formula(2.90)) as the segments have the same WACC, same T and the simplifying as-sumption that AOIG is zero from year T. The higher AOIG for year 2 and thelack of difference in segment thetas result in higher valuation of future growthopportunities (V Growth
1 ) given full disclosure.
THE ROLE OF SEGMENT REPORTING IN VALUATION 101
Table 2.7: Historical figures and financial ratios on the segment level
t -5 -4 -3 -2 -1 0Segment 1Sales 120,8 128,6 151,4 180,9 192,3 205,0Op.Inc (a.t.) 22,3 22,4 25,9 27,2 24,0 26,0ONA 60,2 65,7 75,6 85,8 98,6 112,0WACC 0,1 0,1 0,1 0,1 0,1 0,1
RONA 0,37 0,39 0,36 0,28 0,26Operating margin 18,5% 17,4% 17,1% 15,0% 12,5% 12,7%Growth in ONA 0,09 0,15 0,13 0,15 0,14
Segment 2Sales 76,6 85,1 101,2 122,7 142,9 157,1Op.Inc (a.t.) 3,4 3,1 8,8 14,6 22,4 22,5ONA 36,0 40,0 46,0 55,0 67,0 79,0WACC 0,1 0,1 0,1 0,1 0,1 0,1
RONA 0,09 0,22 0,32 0,41 0,34Operating margin 4,5% 3,6% 8,6% 11,9% 15,7% 14,3%Growth in ONA 0,11 0,15 0,20 0,22 0,18
Segment ESales 0,0 0,0 0,0 0,0 0,0 0,0Op.Inc (a.t.) -1,8 -2,0 -2,8 -2,9 -3,2 -3,4ONA 8,0 8,8 9,7 10,7 11,8 12,5WACC 0,1 0,1 0,1 0,1 0,1 0,1
Op.Inc/Seg Op.Inc -0,080 -0,080 -0,070 -0,070 -0,070ONA/Seg ONAs 0,083 0,083 0,080 0,076 0,071 0,065
Notes: ONA is measured at the end of period t. Op.Inc (a.t.) refersto after-tax operating income. RONA is calculated as after-tax operat-ing income divided by ONA. Operating margin is calculated as after-taxoperating income divided by sales..
102 ESSAYS ON SEGMENT REPORTING AND VALUATION
Table 2.8: Segment-level financial ratio forecasts, forecasts of financial figures andcalculated valuation inputs
t -2 -1 0 1 2 3 4 5 T=6Segment 1RONA 36,0% 28,0% 26,4% 30,1% 30,1% 26,2% 25,9% 25,5% 25,1%Operating margin 15,0% 12,5% 12,7% 13,4% 13,4% 13,4% 13,4% 13,4% 13,4%Growth in ONA 0,13 0,15 0,14 0,14 0,14 0,115 0,09 0,065 0,04Q 2,5
Sales 180,9 192,3 205,0 251,6 286,9 317,9 342,1 358,9 367,5Op.Inc (a.t.) 27,2 24,0 26,0 33,7 38,4 42,6 45,8 48,1 49,2ONA 85,8 98,6 112,0 127,7 145,6 162,3 176,9 188,4 195,9WACC 0,1 0,1 0,1 0,1 0,1 0,1 0,1 0,1 0,1
FCF 18,04 20,57 25,85 31,24 36,59 28,97AOIG 3,15 2,36 1,58 0,79 0,00Value added 20,47 21,22 21,06 20,23 18,88
Segment 2RONA 31,8% 40,7% 33,6% 35,4% 35,4% 29,2% 28,7% 28,2% 27,8%Operating margin 11,9% 15,7% 14,3% 14,0% 14,0% 14,0% 14,0% 14,0% 14,0%Growth in ONA 0,20 0,22 0,18 0,198 0,198 0,158 0,119 0,079 0,040Q 2,74
Sales 122,7 142,9 157,1 200,0 239,5 274,1 301,1 319,4 327,7Op.Inc (a.t.) 14,6 22,4 22,5 27,9 33,5 38,3 42,1 44,6 45,8ONA 55,0 67,0 79,0 94,6 113,3 131,2 146,8 158,5 164,8WACC 0,1 0,1 0,1 0,1 0,1 0,1 0,1 0,1 0,1
FCF 12,32 14,76 20,37 26,48 32,96 26,75AOIG 3,96 2,97 1,98 0,99 0,00Value added 18,22 19,83 20,26 19,77 18,59
Segment EOp.Inc/Seg Op.Inc -0,070 -0,070 -0,070 -0,07 -0,07 -0,07 -0,07 -0,07 -0,07ONA/Seg ONAs 0,076 0,071 0,065 0,07 0,07 0,07 0,07 0,07 0,07
Sales 0,0 0,0 0,0 0 0 0 0 0 0Op.Inc (a.t.) -2,9 -3,2 -3,4 -4,3 -5,0 -5,6 -5,8 -5,9 -5,7ONA 10,7 11,8 12,5 15,8 18,4 20,8 23,0 24,6 25,6WACC 0,1 0,1 0,1 0,1 0,1 0,1 0,1 0,1 0,1
FCF -7,58 -7,63 -8,01 -7,97 -7,52 -3,24AOIG -1,04 -0,78 -0,52 -0,26 0,00
Notes: ONA is measured at the end of period t. Op.Inc (a.t.) refers to after-tax operating income.RONA is calculated as after-tax operating income divided by ONA.
THE ROLE OF SEGMENT REPORTING IN VALUATION 103
Table 2.9: Sum of segment forecasts of financial figures and calculated valu-ation inputs
Sum of segmentsSales 303,6 335,2 362,1 451,6 526,4 592,0 643,3 678,3 695,2Op.Inc (a.t.) 38,9 43,2 45,1 57,3 66,9 75,3 82,1 86,8 89,3ONA 151,5 177,4 203,5 238,1 277,2 314,4 346,7 371,5 386,4WACC 0,1 0,1 0,1 0,1 0,1 0,1 0,1 0,1 0,1
FCF 22,79 27,70 38,21 49,74 62,03 52,48AOIG 6,07 4,55 3,04 1,52 0,00Value added 33,63 35,59 35,77 34,59 32,39
Notes: ONA is measured at the end of period t. Op.Inc (a.t.) refers to after-tax operatingincome. The values in this table can be calculated as the sum of forecasts for the separatesegments.
104 ESSAYS ON SEGMENT REPORTING AND VALUATION
Appendix 2.F - Quantifying the difference in valueestimates
In this section I quantify θ given different WACC estimates and longevity ofeconomic goodwill. For this, I need the two assumptions discussed previouslyabout θ. First, the assumption of no accounting conservatism and second,the assumption of AOIG to linearly fade away to zero by the starting yearof steady-state T. Then, θ can be expressed as
θ =
T∑t=1
(T − t)/(T − 1)
(1 +WACC)t−1. (2.90)
Here, theta is a future expected growth valuation coefficient given WACCand T.
Table (2.10) shows that theta increases in T and decreases in WACC, thatis, the longer the durability of economic goodwill and the lower the discountrate the higher the (uncapitalized) value of the future growth opportunities.
Tab
le2.
10:
The
valu
es
ofθ
giv
en
Ta
nd
WA
CC
wac
cT
0,03
0,04
0,05
0,06
0,07
0,08
0,09
0,1
0,11
0,12
0,13
0,14
0,15
0,16
0,17
0,18
0,19
0,2
1N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
21
11
11
11
11
11
11
11
11
13
1,48
51,
481
1,47
61,
472
1,46
71,
463
1,45
91,
455
1,45
01,
446
1,44
21,
439
1,43
51,
431
1,42
71,
424
1,42
01,
417
41,
961
1,94
91,
937
1,92
61,
914
1,90
31,
892
1,88
21,
871
1,86
11,
851
1,84
11,
832
1,82
21,
813
1,80
41,
796
1,78
75
2,42
82,
406
2,38
42,
362
2,34
22,
322
2,30
22,
283
2,26
42,
246
2,22
92,
211
2,19
52,
178
2,16
22,
147
2,13
22,
117
62,
886
2,85
12,
816
2,78
32,
751
2,72
02,
690
2,66
02,
632
2,60
42,
578
2,55
22,
527
2,50
22,
479
2,45
52,
433
2,41
17
3,33
53,
284
3,23
53,
188
3,14
23,
099
3,05
63,
015
2,97
62,
938
2,90
12,
865
2,83
12,
798
2,76
52,
734
2,70
42,
674
83,
775
3,70
73,
641
3,57
83,
517
3,45
93,
403
3,35
03,
298
3,24
83,
200
3,15
43,
110
3,06
73,
026
2,98
62,
948
2,91
09
4,20
74,
119
4,03
43,
953
3,87
63,
803
3,73
23,
664
3,60
03,
538
3,47
83,
421
3,36
63,
314
3,26
33,
214
3,16
73,
122
104,
631
4,52
04,
415
4,31
54,
220
4,13
04,
043
3,96
13,
883
3,80
83,
736
3,66
83,
602
3,53
93,
479
3,42
13,
366
3,31
311
5,04
64,
912
4,78
44,
664
4,55
04,
441
4,33
94,
241
4,14
84,
060
3,97
63,
895
3,81
93,
746
3,67
63,
609
3,54
63,
485
125,
454
5,29
35,
142
5,00
04,
865
4,73
94,
619
4,50
54,
397
4,29
54,
198
4,10
64,
019
3,93
63,
856
3,78
13,
709
3,64
013
5,85
45,
666
5,48
95,
324
5,16
85,
022
4,88
44,
754
4,63
14,
515
4,40
64,
302
4,20
34,
110
4,02
13,
937
3,85
73,
780
146,
246
6,02
95,
826
5,63
65,
459
5,29
25,
136
4,98
94,
851
4,72
24,
599
4,48
34,
374
4,27
14,
173
4,08
03,
992
3,90
815
6,63
16,
383
6,15
25,
937
5,73
75,
550
5,37
55,
212
5,05
94,
915
4,77
94,
652
4,53
24,
419
4,31
24,
211
4,11
54,
024
167,
009
6,72
86,
468
6,22
86,
004
5,79
65,
603
5,42
25,
253
5,09
54,
947
4,80
94,
678
4,55
54,
439
4,33
04,
227
4,13
017
7,37
97,
065
6,77
56,
508
6,26
16,
032
5,81
95,
621
5,43
75,
265
5,10
54,
954
4,81
44,
681
4,55
74,
440
4,33
04,
226
187,
743
7,39
47,
073
6,77
96,
507
6,25
66,
024
5,81
05,
610
5,42
55,
252
5,09
04,
940
4,79
84,
666
4,54
24,
425
4,31
519
8,10
07,
714
7,36
27,
040
6,74
36,
471
6,22
05,
988
5,77
35,
574
5,38
95,
217
5,05
74,
907
4,76
64,
635
4,51
24,
396
208,
450
8,02
77,
643
7,29
26,
971
6,67
66,
406
6,15
75,
927
5,71
55,
518
5,33
65,
166
5,00
74,
859
4,72
14,
592
4,47
0
106 ESSAYS ON SEGMENT REPORTING AND VALUATION
Appendix 2.G - Expressing the difference in AOIGforecasts between full discloure and partial disclo-sure (AOIGε)
Starting from formula (2.19),
AOIG = AOIGA + AOIGB − AOIGε, (2.91)
where AOIGε stands for the increase in the forecast abnormal operating in-come growth given one is provided with full disclosure.
Applying formula 2.36 on the segment level, AOIGA can be expressed as:
AOIGA = OIA,t +WACCA ∗ FCFA,t−1 − (1 +WACCA) ∗OIA,t−1. (2.92)
As OIt = OIA,t +OIB,t,
AOIGε = AOIGA + AOIGB − AOIG =
= WACCA ∗ FCFA,t−1 +WACCB ∗ FCFB,t−1 −WACC ∗ FCFt−1+
+ (1 +WACC) ∗OIt−1− (1 +WACCA) ∗OIA,t−1− (1 +WACCB) ∗OIB,t−1,
(2.93)
which can be rearranged to
AOIGε = (OIA,t−1 − FCFA,t−1) ∗ (WACCB −WACCA)−− (OIt−1 − FCFt−1) ∗ (WACCB −WACC). (2.94)
Using formula (2.31) and (2.22),
AOIGε = ∆ONAt−1 ∗(WACC−WACCB−φ∗(WACCA−WACCB)), (2.95)
and using formula (2.21), one can write
AOIGε = ∆ONAt−1 ∗ (φ− λ) ∗ (WACCB −WACCA). (2.96)
The AOIG estimates are larger in case of full disclosure when the firm(re)invests proportionally more into the segment with lower risk, i.e. when thefirm increases the relative size of the lower-risk segment through reinvestment.
Chapter 3
Paper II: On the usefulness of segmentdisclosure for earnings forecasting
Abstract. Earnings are key financial figures that company valuations are basedon. Market participants analyze firms by comparing performance across peers.Segment reporting can be useful for comparing the performance of firms’ seg-ments to firms (or parts of other firms) that have similar operations. The man-agement approach in segment reporting in SFAS No. 131 provided lower com-parability of segments across firms, as compared to the industry approach inSFAS No. 14. However, FASB argued that the information presented usingthe management approach should be more relevant and reliable. This paperinvestigates whether 1) segment information under the different standards isrelevant for earnings forecasting purposes without cross-industry comparabil-ity and 2) what segment reporting characteristics are associated with bettersegment-based earnings forecasts. I use two parsimonious earnings forecastmodels, one firm-based model and one economy-wide model to investigatewhether segment-based earnings forecasts outperform consolidated information-based forecasts. The firm-based model captures within-segment trends and theeconomy-wide model captures cross-segment comparability of segment perfor-mance, two components that can facilitate earnings forecasting. I find, for sev-eral subsamples, that the segment-based forecasts outperform the consolidated-based ones. The findings suggest that segment-based information can help withearnings forecasting despite the lack of comparability. Even though I find seg-ment information under the management approach to be relevant for earningsforecasting, the management approach did not provide any improvements in
108 ESSAYS ON SEGMENT REPORTING AND VALUATION
this respect, as compared to the industry approach. When it comes to segmentreporting characteristics, only the number of segments disclosed shows the ex-pected relationship with better earnings forecasts. Moreover, I find that dis-closures including proprietary information are associated with higher forecasterrors.
3.1 Introduction
US companies are required to provide segment information since 1976 whenthe first segment reporting standard, SFAS No. 14, was introduced. Thisstandard required companies to present financial information about the differ-ent geographic segments and lines of business (LOB) they operated in (FASB,1976). The basis for presentation for the LOB segments was derived from thedifferent industries in which the company operated. For years after 1998, com-panies were required to provide segment information in line with SFAS No.131, which requires companies to define segments according to the internalorganization of the company. This management approach can decrease compa-rability of segments across firms, as segments no longer are required to consistof operations around a product or service line, or a geographical area. WhileFASB acknowledged the loss of comparability with SFAS 131, they argued thatinformation provided in accordance with the management approach should bemore relevant and reliable (FASB, 1997), as it provides information that is usedinternally. In this paper, I investigate the relevance of segment information forearnings forecasting.
Earnings forecasts are a key input in equity and firm valuation modelling.The forecasting and the potential valuation usefulness of segment informationcomes from two different sources. First, companies provide financial measuressuch as sales, earnings or assets for different parts of the firm that are helpfulto assess the historical performance of different segments. Moreover, by defin-ing segments, investors can better incorporate future industry and/or countryforecasts into their valuation models to forecast the future performance of thesegment. I refer to the disaggregated numerical information as quantitativesegment information and the labelling of the segment as qualitative segmentinformation.
Early research on segment reporting found that quantitative and qualita-tive segment information could improve earnings forecasts, as compared to
SEGMENT REPORTING AND EARNINGS FORECASTING 109
only using financial information on the consolidated level (Collins, 1976). How-ever, more recently Fairfield et al. (2009) could not provide evidence for theusefulness of qualitative segment information (industry membership) for earn-ings forecasting. In a follow up study, Schröder and Yim (2018) documentedmixed evidence for improved earnings forecasts using qualitative segment infor-mation. The authors found that industry definitions are helpful for earningsforecasting under the industry approach of segment reporting (SFAS 14), butthat they do not improve earnings forecasts under the management approach(SFAS 131). In this paper, I investigate whether segment information can berelevant for earnings forecasting in the absence of within-industry compara-bility. I look at the usefulness of this information, both under SFAS 14 andSFAS 131, to shed light on whether the management approach brought morerelevant information.
There is a cost-benefit trade-off involved in providing segment informa-tion (Wang et al., 2011). Previous research has discussed the usefulness of seg-ment disclosures and that such disclosures can have positive effects on companyvalue. The studies found that better segment disclosures lead to lower costsof capital (Greenstein and Sami, 1994) and lower forecast errors [e.g. Silhan(1983); Baldwin (1984); Balakrishnan et al. (1990); Lobo et al. (1998); Bergerand Hann (2003); Ettredge et al. (2005); Blanco et al. (2015)]. However, thedisadvantages of segment information are not negligible. Disclosing informa-tion about parts of a business can give rise to proprietary and agency costs thataffect the firm adversely (Berger and Hann, 2007; Wang et al., 2011). Prior re-search suggests that the benefits of disclosing segment information outweighthe costs (Blanco et al., 2015). In this paper, I provide more evidence about thepotential benefits of segment disclosures. In particular, I investigate whetherquantitative segment information provided under two different segment re-porting standards can improve earnings forecasting. I also investigate whethersegment disclosure characteristics, that are attributed to better disclosure qual-ity are associated with more accurate earnings forecasts.
My sample includes all US listed companies providing segment disclosuresover the period 1977 to 2014 for business (BUS) and geographical segments.Companies often disclose different levels of earnings and therefore the analysesare conducted for different earnings levels. I investigate the research questionsby comparing model-based earnings forecasts using two different sets of infor-mation 1) financial information on the consolidated level only and 2) financial
110 ESSAYS ON SEGMENT REPORTING AND VALUATION
information on the segment level together with the consolidated information.I use two earnings forecasting models; a firm-specific time-series model and apooled cross-sectional regression approach. The firm-specific model uses thehistorical information about the firm and its segments and captures within-unit time-series trends. It is expected to fit the management approach of seg-ment reporting better. The pooled cross-sectional regression approach usesthe economy-wide comparability between the segments in different firms. Themodels capture within-unit trends and cross-company comparability, attributesof segment disclosure that are relevant for earnings forecasting. For each of thetwo forecasting approaches, I compare segment information-based forecaststo consolidated information-based ones. The forecast errors are a proxy forthe difficulty of earnings forecasting using a given set of financial information.Therefore, when segment-based forecasts outperform consolidated-based ones,I interpret the results as segment information being more relevant for earningsforecasting.
My findings regarding the first research question, using the firm-specificapproach, show that quantitative disclosures do not improve earnings or salesforecasts for either of the two disclosure samples. The results from the cross-sectional model suggest that the forecasts using quantitative segment infor-mation are significantly better for several of the subsamples. Notably, thisholds in most cases for segment information presented under the industry ap-proach. The findings are distinctly weaker for the management approach sub-sample, suggesting that the management approach decreased cross-companycomparability of the segments. Previous literature documented the loss ofwithin-industry comparability following the introduction of the managementapproach. They documented that industry information does not facilitate segment-based earnings forecasting. In this paper, I contribute to the literature about theforecasting relevance of segment reporting by showing that segment reportingcan help with forecasts even when the information presented is not comparableto industry peers. This result reinforces the argument of FASB, that informa-tion using the management approach is relevant for users, even though SFAS131 sacrificed comparability across segments.
The second research question investigates whether the reporting character-istics are associated with lower earnings forecast errors. My results show thatif more segments are disclosed, more accurate segment-based forecasts are ob-tained for most subsamples. Moreover, disclosing proprietary information is
SEGMENT REPORTING AND EARNINGS FORECASTING 111
associated with larger earnings forecast errors suggesting that the disclosure ofsuch information does not improve earnings forecasting.
The paper is organized as follows. In the following section, I review priorliterature. In section 3.3, I formulate the research questions. The methodologyis presented in Section 3.4 and the data and sample in Section 3.5. Section 3.6describes the results from the empirical investigations and Section 3.7 summa-rizes the study.
3.2 Literature review
In this section, I present the relevant literature. The discussion starts withthe importance of earnings forecasts for valuation purposes. Then the differ-ent segment reporting regulations during the sample period are presented, fol-lowed by a short overview of previous research about the benefits and possiblecosts of segment reporting. Later, I discuss the interplay of quantitative andlabel information that can help us with better predictions and review the rele-vant literature investigating earnings forecast improvements using this type ofinformation. The last sub-section presents the research questions.
3.2.1 The core of valuation models: earnings forecasts
Valuation models are usually based on expectations about future financial flowsthat are discounted by some expected cost of financing. There are several valua-tion models that can be used to assess the value of a company. Demirakos et al.(2004) studied international analysts’ equity reports valuing UK companies.They found that analysts use both single-period comparative valuation models(multiples) and hybrid and multi-period models. According to their study, themost popular multiples-based models analysts use are earnings multiples, salesmultiples, cash flow multiples and rating to economic profit (REP) multiples.1
1Rating to economic profit is calculated as the ratio of market to book value at the enterprise
level to the expected return on invested capital scaled by the weighted average cost of capital.
The market-to-book on the enterprise level is calculated as enterprise value divided by in-
vested capital. The intuition behind this ratio is that market value of the enterprise should
be higher compared to the book value if the expected return on invested capital is higher
than the weighted average cost of capital (Demirakos et al., 2004).
112 ESSAYS ON SEGMENT REPORTING AND VALUATION
The most popular models among analysts for hybrid and multi-period mod-elling are discounted cash flows (DCF) model and accounting rates of return(ARR).2 Other valuation methods discussed frequently by academics are theResidual Income Valuation (RIV) model and the Abnormal Earnings Growth(AEG) model (Penman, 2007).
Earnings forecasts are of key importance for the different multi-periodmodels. The DCF model is based on expected future free cash flows that arecalculated starting out from NOPLAT (Koller et al., 2010), calculated as net op-erating profit less taxes on operating profit. This net operating profit is EBITAadjusted for operating lease interest. Furthermore, the ROIC-type ARR modeldiscussed in Demirakos et al. (2004) also uses NOPLAT as the financial flowto discount for the purpose of valuation. Similarly, as discussed in Skogsvik(2002), the Value Added Valuation framework is built on return on net op-erating assets that is calculated as EBIT divided by operating net assets.3 Ad-ditionally, when considering the use of forward-looking multiples, cash flowmultiples and rating to economic profit (REP) build on NOPLAT and EBITas well.
Another group of models use net income forecasts for valuation purposes.The residual income calculation for the RIV model starts out from this mea-sure (Penman, 2007; Skogsvik, 2002), similarly to the AEG model (Penman,2007). The ROE-type ARR model also uses net income forecasts for valuation(Demirakos et al., 2004).
In order for analysts to arrive at better forecasts using these models, theyneed more information about the key financial figures used in the models. Suchfinancial figures are Net Income and Earnings Before Interests and Taxes (andAmortization). Furthermore, as taxes paid are less connected to the operationsof the company but rather local regulation, in order to forecast the after tax net
2Valuation methods are categorized in Demirakos et al. (2004) as ARR when analysts use
ROE or ROIC ratios as valuation models (for example to triangulate valuations based on
ROE comparison across comparable firms) and not simply as indicators of economic prof-
itability.3Despite that it does not appear regularly in equity reports, the VAV model yields identical
results to the DCF model. Furthermore, as argued in Skogsvik (2002), due to the fact that
cash flows might fluctuate over time, the VAV model has the advantage that it builds on
RONA, which is less volatile and easier to predict.
SEGMENT REPORTING AND EARNINGS FORECASTING 113
income measure, analysts might consider to forecast Pre-tax Income and thenapply the effective tax rate. Therefore, Pre-tax Income is another earnings levelreported that might help analysts to achieve better forecasts.
Koller et al. (2010) discussed that when valuing using multiples, analystsuse earnings measures such as EBITDA, EBITA or EBIT, and sometimes NI forcalculating P/E, EV/EBIT, PEG and similar ratios. The authors furthermoreclaim that multiples not based on earnings measures such as price-to-sales are"not particularly useful for explaining company valuations" (p. 327), as theyimpose additional restrictions about operating margins.4 Additionally, non-financial multiples such as price-to-visitor or price-to-subscriber can be mean-ingless if these visitors or subscribers cannot be translated into future profits,and therefore multiples based on financial forecasts are more appropriate. Allin all, most commonly applied and reliable forms of valuation use earningsmeasures directly or indirectly, even though there is no single earnings mea-sure that seems to emerge as the best for valuation purposes.
Previts et al. (1994) studied 327 sell-side analyst reports covering US firms.In connection to the analysts’ use of segment information, they documentedthat analysts made EPS forecasts by first disaggregating the company into itsoperating units, developed forecasts for the different units separately and reag-gregated the segment forecasts to arrive at a consolidated-level EPS estimate.
In this paper, I investigate the use of segment information for earnings fore-casting. Segment reporting can be helpful for investors to forecast earnings.While the financial figures reported on the segment level might not be the sameas what the investor would prefer, improved forecasts on any earnings level canhelp forecasting other levels of earnings.5
4Demirakos et al. (2004) report that every second equity report they investigated included
valuation based on sales multiples, resulting in this valuation method being the second most
popular type after earnings-based multiples.5For example, if an investor prefers using net income in the valuation model, such forecasts
can be improved if firms disclose pre-tax income on the segment level. The investor can
make a pre-tax income forecast using the additional segment information and then use the
effective tax rate to calculate net income. This method can be superior to forecasting pre-tax
income using consolidated financial information and then applying the effective tax rate to
arrive at the net income forecast.
114 ESSAYS ON SEGMENT REPORTING AND VALUATION
3.2.2 Segment reporting regulation
Companies publish financial information about their operations in order tohandle the information asymmetry between shareholders and the management(Beyer et al., 2010), which presumably helps analysts, investors, creditors andother stakeholders, when assessing the operating performance and risks of thecompany. The need for additional information about company operations isalso addressed by the regulator. The U.S. Securities and Exchange Commis-sion (SEC) requires companies listed on stock exchanges in the United Statesto follow the US Generally Accepted Accounting Principles (US GAAP). Thisconsists of several standards that regulate the format and content of financialreports, imposing several disclosure criteria on adopters.
US companies are required to provide segment information since 1976when the first segment reporting standard, SFAS No. 14, was introduced (FASB,1976). This standard required companies to present financial information aboutthe geographic segments and lines of business (LOB) that they operate in. Thebasis for presentation for the LOB segments was derived from the differentindustries in which the company operates, commonly referred to as industryapproach.
SFAS 14 was superceded by SFAS 131, effective for fiscal years startingafter 15 December, 1997, which introduced the management approach in seg-ment reporting. The management approach requires companies to report seg-mented information "based on the way that management organizes the seg-ments within the enterprise for making operating decisions and assessing per-formance” (para 4. p. 6). When issuing SFAS 131, FASB communicated thatthe standard does not aim for companies to provide comparable informationabout their operations, as such an approach would harm relevance and reliabil-ity (FASB, 1997). In this paper, I contribute to the literature by providing addi-tional evidence on the usefulness of segment disclosure absent within-industry,cross-company comparability.
There is extensive literature exploring the effects of the implementation ofSFAS 131. Many reports provide supportive evidence on SFAS 131 improvingsegment reporting with regard to many aspects (Herrmann and Thomas, 2000;Nichols et al., 2000; Behn et al., 2002; Botosan and Stanford, 2005; Hope et al.,2008; Wang et al., 2011). However, the usefulness of disclosed segment report-
SEGMENT REPORTING AND EARNINGS FORECASTING 115
ing information still highly depends on the company’s judgment.6 The regu-lation is flexible in defining segments which leads to some companies definingsegments in a way that is inconsistent with information presented elsewherein the 10-K. Moreover, the comparability of the disclosed information is oftenlow,7 as standard setters have not clearly defined the level of segment profit orloss to be disclosed (Nichols et al., 2000; Berger and Hann, 2007).8 As firmsdisclose different earnings measures on the segment level, in this paper, I fore-cast earnings (and analyze the forecast errors) on the different earnings levelsseparately.
3.2.3 The advantages and disadvantages of disclosing more seg-ment information
Prior research claims that incentives to disclose information involve a trade-off between the benefits of reducing information asymmetry and the propri-etary costs of revealing information for competitors [e.g. Verrecchia (1983);Scott (1994); Cormier and Magnan (1999, 2003)]. This trade-off has also beenidentified for segment reporting. Disclosing additional information about dif-ferent parts of the business decreases information asymmetry (Greenstein andSami, 1994), however, the additional disclosure also comes with proprietaryand agency costs for the company (Wang et al., 2011). This section gives anoverview of previous research about the benefits and costs of disclosing moredetailed segment information.
Prior research has documented several advantages gained through provid-ing segment information. Research has provided evidence that segment report-ing is useful for investors in terms of forecasting as it reduces the dispersion of
6In an experimental study, Bar-Yosef and Venezia (2004) concluded that the new regulation
did not help users to make better predictions, neither when users were advanced level ac-
counting students nor when they were professional analysts.7The findings on IFRS samples are similar after the introduction of IFRS 8 [e.g. Street and
Nichols (2002); Nichols et al. (2012); Farías and Rodríguez (2015)], which is of little sur-
prise as both standards had the outspoken purpose to harmonize accounting regulations
(Moldovan, 2014).8Herrmann and Thomas (2000) argue that the profit and loss measures disclosed might bear
important additional information as the standard setter requires companies to disclose the
earnings measure used internally by the management for making operating decisions.
116 ESSAYS ON SEGMENT REPORTING AND VALUATION
forecasts (Swaminathan, 1991) and earnings forecast errors [e.g. Collins (1976);Silhan (1983); Baldwin (1984); Balakrishnan et al. (1990); Lobo et al. (1998);Berger and Hann (2003); Ettredge et al. (2005); Blanco et al. (2015)], and im-proves cash flow predictions (Blanco et al., 2015).
Previous studies document mixed evidence as to whether additional seg-ment reporting decreases the risk of investors and thus, the cost of financing[e.g. Easley and O’hara (2004); Gietzmann and Ireland (2005); Lambert et al.(2007, 2011); Saini and Herrmann (2013); Leung and Verriest (2015)]. Nev-ertheless, Conover and Wallace (1995) found that geographical segment dis-closures are value relevant and Blanco et al. (2015) found that the quality ofsegment reporting is a priced risk factor in both CAPM and a Fama-French 3factor model augmented with momentum and liquidity factors.
The most important cost associated with segment disclosures is related tocompetitive disadvantages (Ettredge et al., 2002a).9 Blanco et al. (2015) arguedthat disclosing the margins of profitable segments can attract additional compe-tition to the industry, which in turn can increase the risk of future cash flows(Hayes and Lundholm, 1996; Harris, 1998). Troberg et al. (2010) found thathigher profitability firms report segments with lower cross-segment diversityin risks and returns. Similarly, Park (2011) found that firms that had to in-crease their number of business segments due to the introduction of SFAS 131,decreased their proprietary costs by terminating earnings disclosures on thegeographical segment level.10
Apart from the proprietary cost motive, reports on accounting disclosurehave identified other types of motivation as well for disclosing less segment in-formation. One such motive is agency costs (Berger and Hann, 2007; Wanget al., 2011), and that segment disclosures can reveal certain operational and fi-nancial risks (such as low profitability segments) which can have adverse effects(Johnstone, 2015). Other possible motivations for non-disclosure can be diffi-culties in identifying segment assets, the high cost of producing such informa-tion (Edwards and Smith, 1996) or costs associated with different stakeholders
9In contrast, Edwards and Smith (1996) found that 36% of the firm-representatives asked
reported that competitive advantage is not an important concern for them in terms of vol-
untary segment disclosures.10Edwards and Smith (1996) found geographical segment disclosures to be more a important
source for competitive disadvantage as compared to business segment disclosures.
SEGMENT REPORTING AND EARNINGS FORECASTING 117
such as stock market participants and political entities (Givoly et al., 1999).All in all, prior research suggests that the benefits of segment informa-
tion outweigh its possible costs (Blanco et al., 2015). However, in light of thepossible disadvantages and costs of disclosing additional segment information,companies might consider hiding segment financial information. Valuationmodels build on firm-level financial forecasts and such forecasts can also bemade using financial numbers on the consolidated level. However, additional,segment-level information might help investors with forecasting financial num-bers. Therefore, it is a worthwhile question to investigate empirically whethersegment information can be useful in forecasting different financial figures and,in particular, company earnings. In this paper, I provide additional evidenceabout the benefits of segment disclosure.
In the next subsection, I review prior literature on the use of segment-levelfinancial information for forecasting sales and earnings numbers.
3.2.4 The interplay between label and quantitative segment in-formation
Segment disclosures of company performance assist outsiders to identify partsof the operations with different risk, profitability and growth opportunities.This information potentially helps them in improving their sales and earningsforecasts. The usefulness of segment reporting for improving forecasts dependson the interplay between two types of information being disclosed. One is thefinancial performance of different parts of the operations presented in a disag-gregated way, defined here as quantitative information. This provides informa-tion on the size and historical performance of different parts of the firm. Theother is the definition of the segment, qualitative or label information, that en-ables the use of external information about the potential development of theindustry or geographic area and within-industry comparisons of segments.11
The disclosure of financial figures for different segments might also be use-ful by itself (without additional information about the segment’s industry orgeographic area) in cases when there are persistent differences in the segments’characteristics that are reflected in the reported figures. Persistence in segment
11For example, Conover and Wallace (1995) argue that definitions of segments in the form
of country name(s) are more useful for the market than labelling segments with names of
large geographical areas.
118 ESSAYS ON SEGMENT REPORTING AND VALUATION
characteristics such as sales growth and earnings margin can be particularlyhelpful in earnings forecasting. If segment time-series trends are useful forearnings forecasting, investors might be able to improve their forecasts evenwithout incorporating label information.
Quantitative segment information can be supplemented by label informa-tion about the segment. By considering the segment’s name, outside investorscan combine the historical performance of the segment with external projec-tions of its future performance. For example, the expected GDP growth forthe country or area can be of particular interest for earnings forecasting basedon geographic segment disclosures. Similarly, for business segments, externalforecasts for the industry’s development can complement quantitative infor-mation on the segment’s historical performance Collins (1976); Fairfield et al.(2009); Schröder and Yim (2018). Additionally, using their own informationabout the characteristics of the industry, analysts can perhaps more accuratelyforecast the segment’s performance compared to the industry average. Also, la-bel information can enable the incorporation of information into the financialmodels in case there is a shock in the industry or geographic area. Contem-poraneous shocks are not reflected in historical figures, therefore presentingsegment information and the definition of the segment is important for fore-casting future financial performance.12
3.2.5 Forecasting using segment information
Previous research on the usefulness of segment information for forecasting ismainly focusing on earnings forecasts, with a few studies investigating potentialimprovements in sales forecasts as well. I start with reviewing the literature onsales forecasts and continue with the use of segment reporting information forforecasting earnings.
Collins (1976) used external predictions for the segments’ industry in or-der to forecast segment sales and compared it to the estimates from six differ-
12Assume a company has some operations in Country X. If there is a shock affecting the
country’s economy (civil war, embargo, natural disaster, etc.), assessing its effect on the
company operations is of importance. In cases when no segment information is provided,
investors can only guess the size of business operations in the given area. However, present-
ing the country as a separate segment gives more accurate information about the size of the
operation in the given geographical area.
SEGMENT REPORTING AND EARNINGS FORECASTING 119
ent models forecasting consolidated sales. In six of the prediction models, heonly used historical quantitative information for forecasting. In one predictionmodel he incorporated external forecasts as well (GNI growth forecasts for theUS). He found that the models using only quantitative information to forecastsales were all outperformed by the segment-based forecast model. However, af-ter incorporating external predictions, the forecasts were only marginally lessaccurate compared to segment-based forecasts. Silhan (1983) found that fore-casts using quantitative segment disclosures did not improve sales forecasts oneyear ahead compared to forecasts using consolidated financials.
Previous research documents improvements in earnings forecasts when us-ing label and quantitative segment information side-by-side. Collins (1976)found that a segment-based forecast using external predictions for industrysales development and historical information about earnings margin outper-formed several less sophisticated earnings forecasts. He used different time-series models for forecasting earnings on the consolidated level, finding thatforecasts using segment information outperformed all forecasts using only con-solidated information. He also used a more sophisticated, segment-based pre-diction model and found that the forecasts outperformed a model using market-wide index of sales and consolidated earnings margins. These findings sug-gested that the parallel use of label and quantitative segment information helpsin achieving better forecasts.
Since Collins (1976) other papers have investigated the improvement inearnings forecast due to the disaggregated presentation of financial figures (quan-titative information) and the additional information from the segments’ names(label information) separately. Silhan (1983) investigated whether earnings fore-casts using only quantitative segment information outperformed consolidatedbased ones and found significant improvement for one quarter-ahead forecasts.Fairfield et al. (2009) used a US sample to investigate whether informationabout a firm’s industry membership could help to predict earnings. They ar-gued that the definition of a company’s main industrial segment gives the possi-bility to predict profit margins more accurately, using the characteristics of theprofit margins within the industry. They found no evidence of improvementsusing this information. Schröder and Yim (2018) took the approach of Fairfieldet al. (2009) one step further and argued that the main industry of the companymight not be representative and suggested to predict every segment separatelyinstead. They compared these more sophisticated forecasts assuming mean re-
120 ESSAYS ON SEGMENT REPORTING AND VALUATION
version to the industry average with economy-wide segment-based earningsforecasts which assumed the reversion of segment margins to the economy av-erage. Their comparison showed that such additional information yields bet-ter forecasts when predicting segment earnings under the industry approach ofSFAS 14, whereas they could not document any improvements after the changein the accounting regulation.13 In this paper, I build on the findings of Fairfieldet al. (2009); Schröder and Yim (2018) and investigate whether segment infor-mation is relevant for forecasting earnings in the absence of within-industry,cross-company comparability across segments reported under the industry ap-proach and the management approach.
3.3 Research questions
The findings in Fairfield et al. (2009) and Schröder and Yim (2018) indicate thatlabel information is not helpful for forecasting earnings, particularly when us-ing segment disclosures following the management approach of SFAS 131. Abenefit of segment disclosures is that it enables outsiders to compare the per-formance of a part of the firm to parts of other firms. However, losing com-parability might result in the loss of relevance for segment disclosures when itcomes to forecasting earnings.
Nevertheless, FASB argued that segment disclosures under the manage-ment approach should be more relevant despite the loss in comparability. Inthis paper, I investigate whether quantitative segment disclosures are relevantfor forecasting earnings. This is a relevant question given that FASB and IASBboth require segment disclosures to be prepared following the managementapproach, which decreased within-industry comparability of segment perfor-
13An explanation for the findings under SFAS 131 in Schröder and Yim (2018) can be that
the more flexible definition of earnings and segments under SFAS 131 results in lower com-
parability in the information reported. Furthermore, multi-segment firms might allocate
revenues and costs sub-optimally across segments (Givoly et al., 1999). All these effects re-
sult in segments that belong to the same industry are less comparable which reduces the
usefulness of segment information (Kou and Hussain, 2007). According to Park (2011), the
market’s enhanced ability to predict future earnings is mostly driven by the improved abil-
ity to predict industry-wide, cross-industry performances (rather than future firm-specific
earnings components).
SEGMENT REPORTING AND EARNINGS FORECASTING 121
mances. Apart from within-industry comparability, earnings forecasting rele-vance of segment disclosures can also come from the presentation of consistentparts of the businesses over time. Given differences in growth and profitabil-ity across segments, segment information can generate more accurate forecasts.Such information, for example, might shed light on quickly growing and prof-itable parts of the company.
The usefulness of quantitative segment information for forecasting earn-ings is a question worthy of empirical investigation. This study investigateswhether such quantitative segment information helps in forecasting corporateearnings.
RQ1: Does quantitative segment information improve the forecastingof corporate earnings?
Previous literature suggests that the segment information gives more usefulinformation for investors when the segments have dissimilar financial perfor-mance, i.e. differences between the profitability and growth of the segments arehigher [e.g. Foster (1975); Tse (1989); Chen and Zhang (2003); Hirshleifer andTeoh (2003)]. The second research question investigates the relation betweensegment information-based forecast errors and segment disclosure characteris-tics.
RQ2: What segment reporting characteristics are important to ex-plain differences in segment-based forecast errors?
In the following section, I discuss the methodology for the firm-specifictime series and the cross-sectional model and then for the investigation con-cerning the usefulness of segment reporting characteristics.
3.4 Methodology
In this section, I explain the methodology for both the firm-specific and thecross-sectional model. In the next section, I introduce the regressions investi-gating the usefulness of different segment reporting characteristics.
3.4.1 Firm-specific, time series method
The time series approach is a parsimonious way of forecasting future financialnumbers based on firm-specific historical information. The analysis comparesthe accuracy of sales and earnings forecasts based on historical firm-wide ag-
122 ESSAYS ON SEGMENT REPORTING AND VALUATION
gregate and segment data. Due to the mean reversion characteristics of suchaccounting figures, forecasting models based on historical numbers might pro-vide a robust alternative as compared to more time-consuming and subjectiveforecasts.
I make forecasts using accounting information reported for the last threefinancial years (t, t-1, t-2). I require companies to have the same segments re-ported during this period. While forecasts could potentially become more ac-curate with using historical information from year t-3 and before, the sam-ple would considerably decrease as firms that changed their segment structurewould have to be eliminated. Moreover, as the competitive positions of thefirms change over time, the incremental added information from historicalearnings margins and growth rates is likely to be low. Therefore, in the fore-casting procedure, I use financial information from the last three years’ 10-Kfilings.
The total sales figure is forecasted for every company i year t+1 based onthe actual sales for year t and the expected sales growth for year t+1, where theexpected sales growth for year t+1 is the average of yearly sales growth figuresbetween year t-2 and t, denoted as gci,t.
14
Salesci,t+1 = Salesi,t ∗ (1 + gci,t+1), (3.1)
where
gci,t+1 = gci,t (3.2)
andgci,t =
( Salesi,tSalesi,t−1
+Salesi,t−1
Salesi,t−2
)/2. (3.3)
Here gci,t+1 denotes the expected sales growth for company i year t+1 whenconsolidated financial information (c) is used. gci,t denotes the average salesgrowth for company i from t-2 to year t when consolidated financial infor-mation (c) is used.
In a second step, I forecast the operating margin as the average of the pre-vious three years’ figure.
14Another way to forecast sales growth would be to take the geometric average of the histor-
ical growth rates (CAGR).
SEGMENT REPORTING AND EARNINGS FORECASTING 123
OM ci,t+1 = OM c
i,t (3.4)
andOM c
i,t = (OMi,t +OMi,t−1 +OMi,t−2)/3, (3.5)
whereOMi,t =
Earningsi,tSalesi,t
. (3.6)
Here OMi,t denotes the operating margin figure for firm i year t, OM ci,t refers
to the average of the yearly operating margins between year t-2 and t, andEarningsi,t denotes reported earnings for firm i year t.
Finally, I forecast earnings using sales forecast and operating margin fore-cast for year t+1.
Earningsci,t+1 = Salesci,t+1 ∗ OM ci,t+1. (3.7)
I calculate the segment information based forecasts similarly. I start fore-casting sales and operating margin for every segment separately. I forecast seg-ment sales using the previous year’s reported sales and the average sales growthof the segment over the last two years. Then, I forecast the operating marginfor every segment separately, as the last three years’ average margin for the seg-ment. Finally, I forecast the segment earnings for year t+1 as the sales forecastfor segment j multiplied by the operating margin forecast. After calculating thesales and earnings forecasts for every segment separately, I sum segment-levelforecasts for every firm-year to arrive at the segment based sales and earningsforecasts.
Earningssi,t+1 =
J∑j=1
Salessi,j,t+1 ∗ OMsi,j,t+1, (3.8)
where Earningssi,t+1 stands for the segment information based (s) earningsforecast for company i year t+1, and j=1, 2, ..., J refers to the segments.
The forecasting method used for forecasting consolidated earnings and seg-ment earnings (the sum of which then becomes the segment-based earningsforecast) are essentially the same. The difference between the consolidated
124 ESSAYS ON SEGMENT REPORTING AND VALUATION
information-based earnings forecast and the segment information-based earn-ings forecast arises from the fact that segment information reveals differencesin margins and growth rates across the segments.15
3.4.2 Pooled cross-sectional regressions
The second approach for predicting earnings is the pooled cross-sectional pre-diction model. This model does not require time-series data on companies andsegments and therefore a larger sample of firms can be analyzed.
Regression models have been used for segment-based earnings forecastingfor many years. Early research [e.g. Kinney (1971); Collins (1976); Silhan(1983)] used simple mean reversion models of firm-level sales, earnings andprofitability to forecast earnings. Fairfield et al. (2009); Schröder and Yim(2018) forecasted margins using industry-wide (and economy-wide) mean re-version models. In this paper, the cross-sectional regressions approach uses anaccounting information-based forecasting method which is somewhat similarto the cross-sectional profitability model in Hou et al. (2012).16 The model isbased on firm-year observations including accounting numbers for the startingyear, and next years’ earnings as the dependent variable in order to forecast firmearnings.17 I forecast earnings for year t as the multiple of reported financial fig-ures from year t-1 and the coefficient estimates from the pooled cross-sectionalregression using firm-year observations from year t-10 to t-1. The regression co-
15Intuitively, if a firm has a quickly growing, more profitable segment, its earnings will be
higher than if the less profitable segment is growing more quickly. I provide an analytical
model discussing the differences in earnings forecasts obtained using consolidated informa-
tion and segment information in Appendix 2.D of Paper I in this dissertation.16The regression used in this study is a modification of that used by Hou et al. (2012). Both
cross-sectional models use accounting figures to forecast earnings, however, their model is
only used to forecast total figures. As the model in this paper is also used in order to predict
segment earnings, some variables from the original model such as the indicating variable
for dividends paid and loss years are not used in this study.17As the observations used for the regression include financial information from year 0 and
the actual earnings for year 1 – as the dependent variable, it has a time-series element as
well. The setting is still called cross-sectional in this study following Hou et al. (2012) whose
settings also included earnings from the following year as dependent variable and called it
cross-sectional.
SEGMENT REPORTING AND EARNINGS FORECASTING 125
efficients are estimated for every year t on a pooled cross-sectional sample usingfinancial information from the previous 10 years. This means that for examplein order to forecast earnings for 1987 based on 1986 historical financial infor-mation, all firm-year observations are pooled together for years between 1977and 1986. Then, the following initial regression is estimated which forecastsearnings for year t based on financial statement information from year t-1 forevery year t:
Earningsi,t = α0 +β1 ∗Ati,t−1 +β2 ∗Atgri,t−1 +β3 ∗ROAi,t−1 +β4 ∗OMi,t−1+
+ β5 ∗ Salesi,t−1 + β6 ∗ gi,t−1 + β7 ∗ omchgi,t−1 + β8 ∗ Earningsi,t−1 + εi, t.
(3.9)
Here
– subscript i refers to firm i,– Earningst is reported earnings for the company for year t,18
– Att−1 is the total assets at the end of year t-1,– Atgrt−1 is the relative asset growth between year t-2 and t-1,– ROAt−1 is the return on asset for year t-1 calculated as Earningst−1/Att−2,– OMt−1 is the operating margin for year t-1 calculated asEarningst−1/Salest−1,
– Salest−1 is the sales for year t-1,– gt−1 is the relative sales growth between year t-2 and t-1 in percentages,
measured as ( Salesi,tSalesi,t−1
− 1) ∗ 100,– omchgt−1 is the difference in operating margin between year t-2 and t-1 and– Earningst−1 is the lagged value of the dependent variable and included in
the regression similarly to Hou et al. (2012).
For example, the initial regression for year 1987 is based on cross-sectionalfinancial data pooled over 1977-1986 to estimate the coefficients of forecastingyear t using financials from year t-1 and year t-2. The initial regressions areestimated every year between 1987 and 2014.
18The segment earnings disclosed – as discussed before – add up to different levels of earn-
ings for different firms. Therefore it is investigated to which type of earnings the segment
earnings add up to and the regressions are estimated separately for the different types of
earnings.
126 ESSAYS ON SEGMENT REPORTING AND VALUATION
This pooled cross-sectional approach has several advantages. For example,it does not require long reporting histories for the firms, as forecasts are notbased on firm-specific time series. The economy-wide pooled sample also pro-vides statistical power (Hou et al., 2012). Hou et al. (2012) also found that theearnings forecasts yielded by their quantitative model based on historical fi-nancials resulted in higher levels of earnings response coefficient (ERC) thananalyst’s earnings forecasts.
After estimating 27 initial regressions, I include all variables which are sig-nificant (sig=5%) in more than one initial regression in the actual regressions.19
The estimated coefficient parameters from the actual regressions are recordedfor each year t (insignificant coefficients are replaced with zero to avoid over-specification) and forecasts for year t+1 are estimated by multiplying the finan-cials from year t by the estimated significant coefficients.
Segment earnings are forecasted by the type of earnings firms disclose onthe segment level. The forecasting is performed similarly. I pool together thesegment-level cross-sectional data for the previous 10 years by earnings type -the level of earnings for which segment earnings add up to for each the givencompany-year. I estimate the same initial regressions using segment-level in-formation and then keep the variables which are significant in more than oneregression for the actual regressions. I record the estimated parameters for theactual regressions and use them to forecast segment earnings similarly to to-tal earnings. Then, I sum the segment earnings forecasts across the company
19The initial regression is estimated for the 27 rolling 10-year periods over the sample pe-
riod (1977-2014). Next, I exclude all variables which are not significant predictors of the
dependent variables in at least two regressions. After excluding these variables, I run the
regressions on the pooled sample again to obtain the predicted coefficients for the forecast-
ing model. I refer to these regressions as the actual regressions. The effect of excluding
the insignificant predictors from the actual regressions on the estimated coefficients is very
small as the estimated coefficients of the variables excluded are not significantly different
from zero. Keeping such variables might have slightly increased in-sample fit, however, not
excluding them leads to over-specification for out-of-sample forecasts. Moreover, in some
instances I include variables in the actual regressions which only load significantly in the
initial regressions in later years. However, this also has little effect on the result, as when
these variables do not appear to be significant predictors for earnings in the given year, their
estimated coefficients are replaced with zero.
SEGMENT REPORTING AND EARNINGS FORECASTING 127
to obtain the segment information-based earnings forecast for firm i, year t+1( Earningssi,t+1).
After forecasting earnings using the different approaches, the forecasts arecompared to actual next years’ earnings for the company. The absolute valueof forecast errors are scaled using sales in year t. I use the absolute values offorecast errors in order to avoid the possible mitigating effect of positive andnegative errors. I calculate the relative absolute earnings forecast errors as theabsolute errors scaled by sales from year t.20 In contrast to the Mean AbsoluteRelative Error (MARE) approach in Silhan (1983), the earnings forecast errorsare scaled by total sales instead of total earnings in order to avoid possible biasesarising from dividing by close-to-zero or negative numbers.
I hence calculate the relative absolute forecast errors as:
RAEci,t+1 =∣∣∣ Earningsci,t+1 − Earningsi,t+1
Salesi,t
∣∣∣, (3.10)
and
RAEsi,t+1 =∣∣∣ Earningssi,t+1 − Earningsi,t+1
Salesi,t
∣∣∣, (3.11)
where RAE is the relative absolute error and exponents c and s refer to whetherthe forecasts are based on consolidated or segment-level information. The fore-casts errors are calculated for firm i and year t+1.
One might argue that segment financials without label information do notprovide useful information compared to firm aggregates. This can be due tolower variations in sales growth and earnings margins on the firm level, whichcan result in more robust forecasts. In cases when companies disclose segmentswith rapid changes in margins and profitability, then segment disclosures mightbecome noisy, and perhaps redundant for investors. However, it should thenbe noted that finer information in principle can not result in "worse" infor-mation. As shown in Blackwell (1953); Blackwell and Girshick (1954), finerinformation about the parts of the total cannot be less informative than thecoarse information about the total. Adapted to this study, if segment infor-mation does not result in better forecasts, one can ignore (collapse) such finer
20For the historical averages-based approach, the same errors are recorded and analyzed for
sales forecasts as well in order to investigate whether the findings of Silhan (1983) hold over
time and for a larger sample.
128 ESSAYS ON SEGMENT REPORTING AND VALUATION
information and forecast earnings based on company totals.I compare the means (expected values) of the relative absolute company-
wide and segment-based forecast errors using paired t-tests. The hypothesisof the t-tests, stated in the alternative form, is that forecasts using segment in-formation are better than those using consolidated financials. I use directedhypothesis as the disaggregated segment information can be collapsed if it doesnot result in better earnings forecasts.
Formally, the hypothesis is as follows:
H0 : RAEci,t+1 −RAEsi,t+1 ≤ 0
H1 : RAEci,t+1 −RAEsi,t+1 > 0(3.12)
3.4.3 The importance of segment reporting characteristics
Prior research suggests that some disclosures of segment information are moreinformative than others. The second research question addresses this issue,whether some characteristics lead to lower segment-based forecast errors. Inthis section, I introduce the variables and motivate why they supposedly cap-ture the usefulness of segment disclosures. I also present the regression used toaddress the research question.
Previous papers provide evidence that reporting a larger number of seg-ments, and segments that are heterogeneous in their performance and risk,implies higher information content. Managers can underreport the numberof segments in order to protect competitive advantage (Hayes and Lundholm,1996; Arya et al., 2010).21 Others suggest that increasing the number of geo-graphical segments gives additional information to the market (Conover andWallace, 1995). Baldwin (1984) argues that disclosing operating segments thatare different in growth, profitability or risk is more useful for users of financialstatements. The variables I use to assess the information content of segmentdisclosures reflect these characteristics.
I regress the segment-based forecast errors on the different segment report-ing characteristics in order to investigate whether different reporting charac-
21In particular, they argue that presenting the profits of two dissimilar segments aggregated
leads to competitors being unable to precisely estimate the more profitable segment’s prof-
itability.
SEGMENT REPORTING AND EARNINGS FORECASTING 129
teristics are associated with more accurate forecasts.
RAEsi,t+1 = α0 + β1 ∗ Conci,t−1 + β2 ∗ Sdsalesgri,t−1 + β3 ∗Relsdsalesi,t−1+
+ β4 ∗Nrsegi,t−1 + β5 ∗RelsdROAi,t−1 + β6 ∗Maxmargindi,t−1+
+ β7 ∗Minmargindi,t−1 + εt. (3.13)
Here
– Conci,t−1 is the segment concentration index calculated as∑J
j=1 s2j,i,t−1,
where sj,i,t−1 = Salesj,i,t−1
Salesi,t−1, following the HERF variable in Berger and
Hann (2003),– Sdsalesgri,t−1 is the standard deviation of the growth rates of the segments
within the firm, calculated as σg,si,t =
√1J ∗∑J
j=1(gj,i,t − gj,i,t)2, where
gj,i,t =∑J
j=1 gj,i,t/J ,– Relsdsalesi,t−1 is the relative standard deviation of segment sales within a
company, calculated as√
1J∗∑J
j=1(Salesj,i,t−Salesj,i,t)2
Salesj,i,t,
where Salesj,i,t =∑J
j=1 Salesj,i,tJ ,
– Nrsegi,t−1 is the number of segments disclosed in year t-1, following e.g.Ettredge et al. (2006); Wang et al. (2011),
– relsdROAi,t−1 is the relative standard deviation of return on assets (ROA)measures of the segments within the firm,22
calculated as√
1J∗∑J
j=1(ROAj,i,t−ROAj,i,t)2
ROAj,i,t, where ROAj,i,t =
∑Jj=1ROAj,i,t
J ,– Maxmargindi,t−1 is the difference between the most profitable segment’s
operating margin and the operating margin for the whole company, cal-culated as maxj(OMj,i,t −OMj,i,t) and
– Maxmargindi,t−1 is the difference between the least profitable segment’soperating margin and the operating margin for the whole company, cal-culated as −maxj(OMj,i,t −OMj,i,t).23
22I include this term as Chen and Zhang (2003) found that the difference across segment ROA
measures reported are related to the valuation of the firm and that higher difference across
reported segment ROAs can signal lower information quality. This term is only included in
the cross-sectional error regressions because requiring complete disclosure about segment
assets would further decrease my sample for the firm-specific approach.23Ettredge et al. (2006) used one variable (RepdProfVar) for cross-segment margin differences,
130 ESSAYS ON SEGMENT REPORTING AND VALUATION
Reporting one large segment would not generate much information aboutthe different risks and opportunities of the company, as a very large segmenttends to have similar characteristics to the whole group. In order to accountfor firms reporting a large segment, I include the segment concentration in-dex variable, Conct−1, which is calculated similarly to the Herfindahl industryconcentration index. This shows at what level of aggregation a company re-ports its segments.24 I expect it to have a positive coefficient as proportionallylarger segments are expected to result in higher forecast errors. Sdsalesgrt−1
25
is higher if there is a larger difference between the sales growth rate of companysegments and I expect the coefficient to have a negative sign. The Conct−1 vari-able does not capture the potential upside coming from reporting several smallsegments. As such a reporting practice would arguably increase the informa-tion content on different growth opportunities, risks and profitability acrossthe segments, I use the variable Relsdsalest−1 to investigate the possible useful-ness of such disclosures. While Conct−1 depends more on the relative size of
calculated as the difference between the lowest and the highest margin across segments
in a year. André et al. (2016) used a similar measure for segment reporting quality. I de-
cided to split the RepdProfVar variable of Ettredge et al. (2006) intoMaxmargindi,t−1 and
Maxmargindi,t−1 to investigate the effects of revealing highly profitable segments and low
profitability segments.24This variable is also used in Berger and Hann (2003) to account for the information disag-
gregation in segment reporting. An alternative way of measuring disaggregation could be
conducted by following André et al. (2016). Their measure combines the segment aggre-
gation (how many segments firms report) and information aggregation (how many items
firms report per segment). They measured the number of items the companies reported in
their segment disclosure. In this paper, I only use observations where firms report sales,
earnings (and assets) for every reported segments, and make earnings forecast based on this
information, thus, the number of segments reported is more relevant than the disaggrega-
tion level of firms’ segment reporting.25Similarly to the DISSIM measure in Givoly et al. (1999); Ettredge et al. (2002a), calculated
as one minus the average pair-wise correlation between the sales of the firm’s segments
over time, I also use a variable capturing growth difference across segments. The DISSIM
variable requires four years’ data. Due to data constraints, I decided to measure sales devel-
opment differences using the standard deviation of segment growth rates across the firm,
which only requires two years’ data.
SEGMENT REPORTING AND EARNINGS FORECASTING 131
the largest segment reported which drives the variable upwards, Relsdsalest−1
can not only be driven upwards by one large segment but also by some (very)small segments, the latter barely affecting Conct−1.26 Therefore a negative signfor the coefficient of Relsdsalest−1 would indicate that disclosing small seg-ments does improve earnings forecasts, while a positive sign would mean that inthis model-based forecasting context disclosing small segments add additionalnoise and thus does not help forecasting earnings. I expect the coefficient ofNrsegt−1 to have a negative sign, since the disclosure of more segments is ex-pected to lead to lower forecast errors. relsdROAt−1 takes on a high value ifcompanies report segments with different profitability, and is thus expected tohave a negative coefficient. Maxmargindt−1 indicates proprietary information[e.g. Blanco et al. (2015)]. The variable is expected to have a negative coeffi-cient. Minmargindi,t−1 indicates agency information [e.g. Berger and Hann(2007)], and it is expected to have a positive coefficient.
In the next section, I present the empirical data and the sampling process.
3.5 Empirical data and sampling
I retrieved the segment reporting data of US listed firms from Compustat His-torical Segments - North America database for the period 1976-2014 both forgeographic and line-of business segment disclosures. I downloaded consoli-dated numbers for companies with available segment data from CompustatNorth America – Annual Fundamentals.27 I conduct the analysis on the busi-ness and the geographic segment disclosure samples separately.28 For bothsamples I begin the selection from the available multi-segment firm-year ob-servations and then exclude observations for financial years shorter than 12
26The relative size of the segment reported was found to be related to disclosure informative-
ness by Givoly et al. (1999).27Both databases are provided by Wharton Research Data Services (WRDS).28I include observations in the business segment (BUS) sample for which the segment type
variable in Compustat is BUSSEG. These are line-of-business segment disclosures under
SFAS 14 and operating segment disclosures under SFAS 131. Referring to operating seg-
ments as business segment is somewhat imprecise, as the basis of segmentation under the
management approach is not the business lines (see section 2.2), but how the firm is inter-
nally organized.
132 ESSAYS ON SEGMENT REPORTING AND VALUATION
months. Furthermore, I exclude all firm-year observations with less than 2million USD annual sales. This approach is less restrictive than that of Bergerand Ofek (1995); Fairfield et al. (2009); Schröder and Yim (2018)).
I exclude firm-year observations for which the sum of the sales and assetson the segment level differs more than 1% from firm consolidated sales or assetsand sum-of-segment earnings differ more than 1% from total earnings.29 I referto such observations as incomplete segment disclosures. I use the term com-plete segment disclosures for observations with segment financial numbers thatdo add up to the consolidated numbers. This is considerably more restrictivethan previous approaches. Schröder and Yim (2018); Berger and Ofek (1995);Berger and Hann (2007) excluded observations for which the sum-of-segmentsales deviates more than 5% from company totals and their limit for asset devi-ations was 25%. All three papers allocate the deviation proportionally to eachsegment based on total sales (assets). Berger and Hann (2007) conduct their testson more restrictive samples as well (10%, 5% and 2% asset deviation samples).Their results differ across different samples, suggesting that sample selectioncan impact data quality. I use a more restrictive approach on sampling. Whilethis approach results in a smaller sample, the findings are expected to be lessbiased due to better data quality.
The above selection criteria resulted in a sample of 70 294 firm-year obser-vations used for the forecasting of sales based on business segment disclosuresand 63 459 firm-year observations based on geographic segment disclosures - seePanel A in Table (3.1). One forecast using the firm-specific approach requiresthat the segment disclosures provided by a firm during the previous two yearshave the same structure. Firms sometimes change their segments for variousreasons. Such reasons can be that a part of the business crosses the materialitythreshold, the firm acquires or divests a business line, restructures its opera-tions or redefines its segments. One such change results in two years withoutforecasts in my time-series sample. Moreover, any missing data point from thedatabase result in similar effects. All in all, the number of forecasts made is
29I define earnings as the Operating profit or loss measure disclosed by the company on the
segment level. Firms measure segment earnings differently and for each firm-year, I in-
vestigate the earnings level to which sum of segment earnings add up to. For the given
firm-year observation, I use that earnings level for forecasting earnings both for segment
information-based and consolidated information-based forecasts.
SEGMENT REPORTING AND EARNINGS FORECASTING 133
Table 3.1: Sample descriptionPanel A: Sample development
BUS sample GEO sampleMulti-segment firm-year observations 92 128 81 350Less firm-years that could not be matched with consolidated sales -2 003 -2 664Less firm-years with total sales under 2 MUSD -2 691 -1 116Less incomplete segment reporting -17 140 -14 110Observations used for forecasting sales 70 294 63 459Less firm-years where operating profit per segment not disclosed -10 613 -34 232Less firm-years where not complete operating profit disclosure -40 361 -18 860Observations used for forecasting earnings (firm-based model) 19 320 10 367Less assets disclosure not presented or not complete -8 248 -4 070Observations used for forecasting earnings (cross-sectional model) 11 072 6 297
Number of sales forecasts 20 310 26 113(of which at least one segment forecast is based on extreme growth (7 373) (9 922)
Number of earnings forecasts (firm-based model) 1091 981(of which at least one segment forecast is based on extreme values) (293) (330)
Number of earnings forecasts (main sample, cross-sectional model) 824 980
Panel B: Observations for firm-based model forecasting by segment earnings disclosureEarnings disclosed for segment BUS sample GEO sampleOperating Income After Depreciation 14 839 6 261EBIT 14 199 6 240Pre-tax Income 3 731 2 691Sales less Operating Expenses 1 166 484EBITDA 1 086 468Operating Income Before Depreciation 1 086 468Net Income 929 1 643Income Before Extraordinary Items 929 1 590Gross Profit 425 89Sales less Cost of Goods Sold 425 89Total 19 320 10 367
Notes: Panel A shows the sample development. BUS refers to line-of-business sample under SFAS 14 andthe operating segment sample under SFAS 131. GEO refers to the geographic sample. The breakdownshows the number of observations used for forecasting sales using the firm-based model, earnings using thefirm-based model and earnings using the cross-sectional approach. This panel also shows the number offorecasts made and the number of forecasts that are made using inputs with extreme growth or margins.Panel B shows the level of earnings reported on the segment level for the observations used for the firm-based approach.
134 ESSAYS ON SEGMENT REPORTING AND VALUATION
Figure 3.1: Number of firm-year observations used for forecasting sales andsales forecasts per year, BUS sample
Figure 3.2: Number of firm-year observations used for forecasting sales andsales forecasts per year, GEO sample
much smaller than the number of observations used for making such forecasts.Figures (3.1) and (3.2) present my firm-year observations, those with com-
plete sales disclosure, the number of firm-year observations for which the modelcould produce a sales forecast, and the number of sales forecasts based on non-extreme growth segments over time for the GEO and BUS samples, respec-tively. There are 20 310 (26 113) firm-year observations with sales forecastsbased on business (geographic) segment data using the firm-specific approach.
Figures (3.1) and (3.2) provides some insights into the data. First, the changein segment reporting regulation after 1998 resulted in more firms providingsegment disclosures. Also, the number of firms disclosing business segmentsdecreased under both segment reporting regulations (from the early 1980s to
SEGMENT REPORTING AND EARNINGS FORECASTING 135
Figure 3.3: Number of observations used for forecasting earnings and earningsforecasts per year, BUS sample
the late 1990s under SFAS 14 and then from the early 2000s to 2013 under SFAS131). Geographical segment disclosure increased over time under SFAS 14,but decreased over time under SFAS 131. The dip in reported forecasts around2000 is due to the forecasting methodology. Many firms changed their segmentstructures as a result of the new regulation and my forecasting model requiresfirms to keep their segment structure for three years. Therefore, I could notmake forecasts for many firms around the regulation change. Moreover, whileboth the number of firms providing business and geographical segment disclo-sure increased following the introduction of SFAS 131, the number of forecastsmade increased for the GEO sample but not for the BUS sample. The reasonis that firms often changed their BUS segmentation under SFAS 131 and myforecasting model can only make forecasts if the firm kept the same reportingstructure during the three preceding year.
After excluding 10 613 and 40 361 (34 232 and 18 860) firm-year observa-tions from the BUS (GEO) sample with no disclosure or incomplete disclo-sures of operating profit on the segment level, I obtain a sample of 19 320 (10367) observations for forecasting earnings on the segment level with the firm-based model on the BUS (GEO) sample. The model could forecast 1 091 (981)firm-year observations for the BUS (GEO) sample, of which 293 (330) forecastswere made using observations with extreme growth or profitability. Figures(3.3), (3.4) and (3.5), (3.6) present the number of firm-year observations withearnings disclosure on the segment level, those with complete earnings disclo-sure, the number of forecasts made, and the number of forecasts made using
136 ESSAYS ON SEGMENT REPORTING AND VALUATION
Figure 3.4: Number of earnings forecasts per year, BUS sample
observations with non-extreme growth and profitability for the BUS and GEOsample.
Figure (3.3) shows that the number of firm-year observations with BUSearnings disclosures did not increase following the introduction of SFAS 131.Moreover, firms often changed the segment reporting structure and the earn-ings definition under SFAS 131, which led to decreased amount of forecastsmade using the firm-specific approach after 1998, see also figure (3.4).
Figure (3.5) shows that the number of firm-year observations with GEOearnings disclosures decreased following SFAS 131, as the standard did not re-quire firms to disclose earnings on the geographical segment level (unless itwas used by the chief operating decision maker). This resulted in consider-ably fewer firms disclosing earnings figures on the geographical segment level,which in turn resulted in very few model-based forecasts for the period follow-ing 1998.
Firms define segment earnings in different ways and I investigate the earn-ings level to which the segment operating profits disclosed add up to for everyfirm-year observation. Panel B in Table (3.1) shows the number of observationsused for forecasting earnings using the firm-based model by earnings level towhich segment-level operating profits add up to. The panel shows that morethan 60% of the firms disclose segment earnings that can be aggregated intofirm-level Operating Income After Depreciation (or EBIT) measures. The nextmost used measure on the segment level is Pre-tax Income with somewhat lessthan 20% (more than 25%) of the firms reporting it on the segment level in theBUS (GEO) sample.
Table (3.2) provides descriptive statistics about the sample of firm-year ob-
SEGMENT REPORTING AND EARNINGS FORECASTING 137
Figure 3.5: Number of observations used for forecasting earnings and earningsforecasts per year, GEO sample
Figure 3.6: Number of earnings forecasts per year, GEO sample
servations with earnings forecasts applying the firm-specific approach. Themean of firm assets in the BUS sample is 4 392 MUSD with standard deviationof 20 509 MUSD. The median of firm assets is 316,8 MUSD. The mean salesamounts to 2 860 MUSD and the mean of earnings is 295 MUSD. The mean offirm assets in the GEO sample is 3 077 MUSD with standard deviation of 14927 MUSD. The median of firm assets in the GEO sample is 170 MUSD. Themean sales amounts to 2 294 MUSD and the mean of earnings is 186 MUSD.The descriptive statistics for other subsamples used in the paper show similarmeans and medians, but with considerable differences around the tails.
After excluding 8 248 (4 070) observations without the required asset dis-
138 ESSAYS ON SEGMENT REPORTING AND VALUATION
Table 3.2: Descriptive statisticsPanel A: Business segment sample of earnings forecasts, firm-specific approachVariable Mean sd 1% 25% Median 75% 99%Assets 4 392 20 509 2,6 50,5 316,8 1
828,676 204
Sales 2 860 11 907 2,8 54,5 284,1 1 432 50 673Earnings 295 1 306 -94 1,9 25 153,8 4 669Panel B: Geographic sample of earnings forecasts, firm-specific approachVariable Mean sd 1% 25% Median 75% 99%Assets 3 077 14 927 3,1 47,4 170 964 62 712Sales 2 294 9 807 3,5 54,9 192,7 923 41 795Earnings 186 776 -115 1,5 14,4 78,3 3 517
Notes: This table provides descriptive statistics about the total assets, sales and earnings ofthe firms included in the earnings forecasts samples using the firm-specific approach. Panel Ashows statistics for the BUS sample - the line-of-business sample under SFAS 14 and the oper-ating segment sample under SFAS 131. Panel B shows statistics for the geographic disclosuresample. sd refers to standard deviation.
closures on the segment level, I obtain a sample of 11 072 (6 297) observa-tions that can be used for forecasting earnings on the segment level using theeconomy-wide cross-sectional model for the BUS (GEO) sample. Using theseinputs the model could forecast earnings for 824 (980) firm-year observationsfor the BUS (GEO) sample.
Table (3.3) shows the correlation between the different segment reportingcharacteristics variables. The variable definitions are presented in Appendix3.A.
Tab
le3.
3:C
orre
latio
nta
ble
,se
gm
en
tre
po
rtin
gc
ha
rac
teris
tics
varia
ble
s,G
EOsa
mp
leN
rseg
Con
cM
argi
ndiff
Rel
sdds
ales
Rel
sddR
OA
Max
mar
gind
Min
mar
gind
Sdsa
lesg
rN
rseg
1.00
00C
onc
-0.4
718
1.00
00M
argi
ndiff
0.02
450.
0492
1.00
00R
elsd
dsal
es-0
.369
70.
9353
0.02
961.
0000
Rel
sddR
OA
0.01
330.
0236
0.00
100.
0536
1.00
00M
axm
argi
nd0.
0901
-0.0
481
0.02
78-0
.043
00.
0045
1.00
00M
inm
argi
nd-0
.022
4-0
.050
4-0
.999
7-0
.030
6-0
.000
9-0
.004
51.
0000
Sdsa
lesg
r0.
0450
0.03
210.
0373
0.03
33-0
.001
10.
0015
-0.0
373
1.00
00
Not
es:
Thi
sta
ble
show
sth
ePe
arso
nco
rrel
atio
nbe
twee
nth
ese
gmen
trep
ortin
gch
arac
teri
stic
sva
riab
les
usin
gth
ege
ogra
phic
aldi
sclo
sure
sam
ple.
Var
iabl
esar
ede
fined
inTa
ble
(3.1
4).
140 ESSAYS ON SEGMENT REPORTING AND VALUATION
3.6 Empirical results
This section presents empirical results for forecasting sales and earnings usingthe firm-based model and the cross-sectional model. I also present results forthe association between segment-based forecast errors and segment reportingcharacteristics. I investigate the first question using paired t-tests, with the alter-native hypothesis being segment-based relative absolute forecast errors beingsmaller than consolidated-based forecast errors. I investigate the second ques-tion using ordinary least squares regressions with the dependent variable beingsegment-based relative absolute forecast errors and the independent variablesbeing different segment reporting characteristics.
3.6.1 Firm-based time-series model
Table (3.4) includes results for the firm-based approach. The results show thatsales forecasts using segment information do not outperform consolidated-basedforecasts for either the business or the geographic sample, as the differenceRAEci,t+1 − RAEsi,t+1 is not significantly larger than zero for any of the sub-samples.
Earnings forecasts using business segment data are not significantly betterthan the ones using only consolidated information, either for the total sample,or after excluding forecasts which are based on observations with extreme prof-itability or growth segments, for the restricted sample. The difference is notsignificant under either the industry approach of SFAS 14 or the managementapproach of SFAS 131.
Table (3.5) presents the results for sales forecasts using the firm-based ap-proach using the BUS sample. The tests are conducted by forecast year. I findthat there are only five years for which segment-based sales forecasts outper-form consolidated-based ones (1984, 1987, 1994, 1995 and 2011). Segment-based sales forecasts are not significantly better than consolidated-based onesfor the rest of the years. The results are similar for the GEO sample.
Turning to the results for the geographical sample, segment-based earningsforecasts are not significantly better than consolidated-based ones for any of thesubsamples either. SFAS 131 no longer requires companies to disclose earningson the geographical level which resulted in a sample of 20 observations. Themacroeconomic sample split shows that segment-based forecasts are not signif-
Tab
le3.
4:Re
sults
,firm
-ba
sed
mo
de
lBU
Ssa
mpl
eG
EO
sam
ple
Sale
sE
arni
ngs
Sale
sE
arni
ngs
Ndi
ff.
Ndi
ffN
diff
.N
diff
.To
tals
ampl
eof
fore
cast
s20
294
-0,2
209
109
1-0
,005
326
088
-0,1
283
981
-0,0
03(t
),(s
e)(-2
,903
6)(0
,076
1)(-1
,806
9)(0
,002
9)(-6
,030
7)(0
,021
3)(-3
,570
0)(0
,000
9)Fo
reca
stsb
ased
onno
rmal
grow
ing
segm
ents
1293
2-0
,004
179
80,
0002
1618
1-0
,001
765
1-0
,000
0(t
),(s
e)(-2
,044
4)(0
,002
)(0
,574
8)(0
,000
3)(-8
,095
7)(0
,000
1)(-0
,008
4)(0
,000
1)SF
AS
14pe
riod
(198
0-19
99),
norm
algr
owth
1039
2-0
,005
742
0,00
028
153
-0,0
013
624
-000
00(t
),(s
e)(-1
,992
7)(0
,002
5)(0
,964
7)(0
,000
3)(-6
,669
0)(0
,000
2)(-0
,084
4)(0
,000
1)SF
AS
131
peri
od(2
001-
2014
),no
rmal
grow
th2
381
-0,0
005
41-0
,000
07
695
-0,0
009
20N/A
(t),
(se)
(-1,1
286)
(0,0
004)
(-0,0
024)
(0,0
03)
(-4,5
550)
(0,0
002)
N/A
N/A
Cri
sisy
ears
,nor
mal
grow
th2
002
-0,0
053
62-0
,001
92
160
-0,0
047
90-0
,000
5(t
),(s
e)(-6
,068
7)(0
,000
8)(-1
,438
9)(0
,001
3)(-1
1,92
8)(0
,000
4)(-1
,272
1)(0
,000
4)Bo
omye
ars,
norm
algr
owth
418
6-0
,002
830
90,
0001
357
8-0
,000
622
30,
0001
(t),
(se)
(-1,1
137)
(0,0
025)
(0,2
745)
(0,0
004)
(-2,4
375)
(0,0
003)
(0,6
997)
(0,0
002)
Oth
erye
ars,
norm
algr
owth
674
4-0
,004
642
70,
0005
1044
3-0
,000
633
80,
0000
(t),
(se)
(-1,3
063)
(0,0
035)
(1,2
714)
(0,0
004)
(-3,2
998)
(0,0
002)
(0,2
292)
(0,0
002)
Not
es:
The
tabl
esh
ows
the
num
ber
ofob
serv
atio
ns(N
)an
dth
em
ean
diff
eren
ce(d
iff.)
betw
een
cons
olid
ated
-bas
edan
dse
gmen
t-bas
edre
lativ
eab
solu
tefo
reca
ster
rors
(RAE
c i,t+
1−RAE
s i,t+
1)u
sing
the
firm
-bas
edap
proa
ch.B
US
refe
rsto
the
line-
of-b
usin
esss
ampl
eun
derS
FAS
14an
dth
eop
erat
ing
segm
ents
ampl
eun
der
SFA
S13
1.T
-val
ues
(t)i
npa
rent
hese
sbe
low
N,s
tand
ard
erro
rs(s
e)in
pare
nthe
ses
belo
wm
ean
diff
eren
ce.
Posi
tive
diff
eren
cem
eans
that
segm
ent-b
ased
fore
cast
sar
em
ore
accu
rate
than
tota
ls-b
ased
ones
.St
ars
indi
cate
the
sign
ifica
nce
leve
loft
hese
diff
eren
ces
bein
ghi
gher
than
zero
.N/A
indi
cate
ssa
mpl
esi
zeun
der
50on
whi
chte
sts
are
notc
ondu
cted
.*s
igni
fican
tat1
0%le
vel;
**si
gnifi
cant
at5%
leve
l;**
*sig
nific
ant
at1%
leve
l.C
risi
sye
ars
are
defin
edas
year
sw
hen
the
US
GD
Pgr
owth
was
nega
tive
(198
0,19
82,1
991,
2008
,200
9),b
oom
year
sare
year
swhe
nth
eU
SG
DP
grow
thw
asov
er4%
(198
3,19
84,1
985,
1988
,199
4,19
97,1
998,
1999
,200
0)an
dno
rmal
grow
thye
arsa
real
lthe
othe
rye
arsi
nth
esa
mpl
e.
142 ESSAYS ON SEGMENT REPORTING AND VALUATION
Table 3.5: Comparision of sales forecasts per year, BUS sample, normal growthYear N diff. t-value Year N diff. t-value1980 423 -0,0057 -2,9158 1998 418 -0,0016 -0,88471981 645 -0,0018 -1,2721 1999 276 0,0030 1,06711982 704 -0,0070 -4,2287 2000 113 0,0015 0,41861983 686 -0,0023 -1,5352 2001 46 -0,0839 -2,02911984 662 0,0069** 2,2969 2002 101 -0,0024 -1,65251985 555 -0,0139 -1,2796 2003 177 0,0001 0,06631986 531 -0,0038 -1,9101 2004 185 0,0019 1,12711987 521 0,00348** 1,8667 2005 188 -0,0014 -0,63471988 456 -0,0149 -0,8409 2006 200 -0,0011 -0,72821989 428 -0,0606 -1,1006 2007 180 -0,0029 -1,47241990 431 -0,0045 -1,6063 2008 184 -0,0014 -0,75441991 502 -0,0045 -2,2492 2009 189 -0,0047 -5,06721992 544 -0,0011 -0,6519 2010 214 0,0011 1,13151993 574 -0,0017 -0,6619 2011 196 0,0056*** 5,09801994 546 0,0032** 2,2559 2012 189 -0,0017 -0,88361995 520 0,0025* 1,4863 2013 204 -0,0013 -1,10611996 496 -0,0024 -1,3234 2014 174 0,0005 0,44491997 474 -0,0048 -2,0937
Notes: This table presents the results from the t-tests comparing segment-based andconsolidated-based sales forecasts using the firm-specific approach for the business segmentsample by forecast year. Forecasts are not based on segments with extreme growth. N refersto the number of observations and diff. refers to the mean difference between consolidated-based forecast errors and segment-based forecast errors (RAEc
i,t+1 − RAEsi,t+1). A positive
difference means better segment-based forecasts. *significant at 10% level, **significant at 5%level, ***significant at 1% level.
icantly better for any of the subsamples.
Table (3.6) presents results from the firm-based earnings forecast modelsby the level of earnings the companies disclose on the segment level. Segment-based earnings forecasts are not significantly different from consolidated-basedones in any of the subsamples.
Tab
le3.
6:Re
sults
by
ea
rnin
gs
typ
e,fi
rm-b
ase
dm
od
el
BUS
sam
ple
GE
Osa
mpl
eE
arni
ngst
ype
Ndi
ff.
t-val
ueN
diff
.t-v
alue
EBI
T53
80,
0001
0,41
5627
0-0
,000
1-0
,276
5In
com
ebe
fore
extr
aord
inar
yite
ms
N/A
N/A
N/A
139
0,00
000,
1263
Net
Inco
me
N/A
N/A
N/A
146
0,00
000,
1333
Ope
ratin
gIn
com
eaf
ter
depr
ecia
tion
746
0,00
030,
8388
270
-0,0
001
-0,2
765
Pre-
tax
Inco
me
75-0
,000
8-1
,081
426
50,
0002
1,14
58A
llty
pesc
ombi
ned
798
0,00
020,
2828
651
-0,0
000
-0,0
084
Not
es:T
hist
able
pres
ents
the
resu
ltsfr
omth
et-t
ests
com
pari
ngse
gmen
t-bas
edan
dco
nsol
idat
ed-b
ased
earn
-in
gsfo
reca
stsu
sing
the
firm
-spec
ific
appr
oach
for
the
BUS
(line
-of-b
usin
essu
nder
SFA
S14
and
oper
atin
gse
g-m
ents
unde
rSF
AS
131)
and
GE
O(g
eogr
aphi
cse
gmen
t)sa
mpl
e,re
spec
tivel
y.N
refe
rsto
the
num
bero
fobs
er-
vatio
nsan
ddi
ff.re
fers
toth
em
ean
diff
eren
cebe
twee
nco
nsol
idat
ed-b
ased
fore
cast
erro
rsan
dse
gmen
t-bas
edfo
reca
ster
rors
(RAE
c i,t+
1−RAE
s i,t+
1).
Apo
sitiv
edi
ffer
ence
mea
nsbe
tter
segm
ent-b
ased
fore
cast
s.N/A
in-
dica
tes
sam
ple
size
unde
r50
onw
hich
test
sar
eno
tco
nduc
ted.
EBIT
refe
rsto
earn
ings
befo
rein
tere
sts
and
taxe
s.
144 ESSAYS ON SEGMENT REPORTING AND VALUATION
3.6.2 Economy-wide, cross-sectional model
The operating profit (loss) figure disclosed for the segments add up to differentlevels of income. Therefore, I make forecasts using regressions that are esti-mated for the specific earnings types to which the segment earnings add up.This way, all earnings forecasts are made using historical information aboutthe same earnings type. Pooling all earnings together would result in biasedestimates, for example, net income would then be forecasted based on charac-teristics that gross profit figures have shown in the past. The results presentedin this section are based on eight different analysis, three different types of earn-ings (OIADP, EBIT and Pre-tax Income) for the BUS sample and five earningstypes (OIADP, EBIT, Pre-tax Income, Net Income and IB) for the GEO sam-ple. Net Income and IB forecasts are only analyzed for the GEO sample asthere were too few observations in the BUS sample.
I first estimate regression (3.9) using pooled historical data from year t-1 tot-10 to establish which variables help predict the dependent variable. All vari-ables that are significant in more than one regression for a given earnings typeand information set (segment-based or consolidated-based) are considered use-ful predictors. Then, I re-estimate the regressions using the variables that wereuseful for predicting earnings based on the initial regressions. After having thecoefficients estimated for each variable, I use financial information from year t-1 and the estimated (significant) coefficients to forecast earnings for year t. I usethe same method for each of the earnings subsamples within both the businessand the geographic disclosure samples. I present the results for each earningslevel separately. At the end of the section, I present the summary results forthe different subsamples in Table (3.12).
Operating Income After Depreciation (OIADP) sample
The coefficients for the business segment-based OIADP forecasts are estimatedin the following regression:
Earningsj,t = β0+β1∗Atj,t−1+β2∗gj,t−1+β3∗Salesj,t−1+β4∗Earningsj,t−1+εj,t.
(3.14)I record the coefficients and use the financial numbers reported for seg-
ment j year t-1 to predict earnings for each segment using the segment-basedprediction model. I calculate segment-based earnings forecasts as the sum of
SEGMENT REPORTING AND EARNINGS FORECASTING 145
segment forecasts within each firm-year.Similarly, I estimate the consolidated-based OIADP forecasts for the busi-
ness segment sample using the following regression:
Earningsi,t = β0 + β1 ∗ Ati,t−1 + β2 ∗ROAi,t−1 + β3 ∗OMi,t−1+
+ β4 ∗ Salesi,t−1 + β5 ∗ Earningsi,t−1 + εi,t. (3.15)
The coefficients for the geographical segment-based OIADP forecasts areestimated in the following regression30 :
Earningsj,t = β0 +β1∗Atj,t−1 +β2∗Salesj,t−1 +β3∗Earningsj,t−1 +εj,t. (3.16)
I estimate the geographical consolidated-based OIADP forecasts using thefollowing regression:
Earningsi,t = β0 +β1 ∗Ati,t−1 +β2 ∗Salesi,t−1 +β3 ∗Earningsi,t−1 +εi,t. (3.17)
I record the coefficients and use the financial numbers reported for firm iyear t-1 to predict earnings for each firm using the firm-level prediction model.After calculating the earnings forecasts based on segment-level informationand consolidated-level information, I calculate the relative absolute earningsforecast errors (RAEs) for both forecasts. Then, I investigate if the differenceRAEci,t+1 −RAEsi,t+1 is higher than zero.
30As described in the methodology section, I first estimate regression (3.9) using pooled his-
torical data from year t-1 to t-10 for every year t between 1987 and 2013 (the sample period is
1977-2013 and I need ten years of historical data to estimate the regression). The goodness-
of-fit measures of the regressions vary from 0.21 to 0.96 and show a gradually increasing
trend over time. The number of cross-pooled segment-year observations used for the esti-
mation varies between 379 and 1547. The coefficient of Earningsj,t−1 is significant in all
27 regressions, while those of Salesj,t−1,Atj,t−1, gj,t−1 andROAj,t−1 are significant in 26,
15, 1 and 1 regressions, respectively, while the rest of the coefficients are not significant in
any of the regressions. Therefore, I useEarningsj,t−1, Salesj,t−1 andAtj,t−1 in the actual
regressions to estimate the coefficients for forecasting segment earnings in year t.
146 ESSAYS ON SEGMENT REPORTING AND VALUATION
Table 3.7: OIADP sample, cross-sectional approachBUS sample GEO sample
N diff. t-value N diff. t-valueTotal sample of forecasts 824 0,1704*** 4,6416 980 -0,041 -3,0840
(0,0367) (0,0133)SFAS 14 period (1987-1999) 449 0,1497*** 5,4557 803 -0,0519 -3,2128
(0,0274) (0,0161)SFAS 131 period (2001-2014) 324 0,1809** 2,1388 138 0,0114* 1,3643
(0,0846) (0,0083)Crisis years 99 0,1712** 1,7686 74 -0,1904 -3,7190
(0,0962) (0,0512)Boom years 173 0,3457*** 5,1668 276 -0,0377 -4,0922
(0,0669) (0,0092)Other years 552 0,1154*** 2,4350 630 -0,0249 -1,2913
(0,0474) (0,0193)
Notes: The table shows the number of observations (N) and the mean difference (diff.)between totals-based and segment-based relative (absolute) errors (RAEc
i,t+1−RAEsi,t+1) of
operating income after depreciation (OIADP) forecasts using the cross-sectional approach.BUS refers to the line-of-business sample under SFAS 14 and the operating segment sampleunder SFAS 131. Standard errors in brackets. A positive difference means that segment-based forecasts are more accurate than totals-based ones. Stars indicate the significance levelof these differences being higher than zero. *significant at 10% level; **significant at 5%level; ***significant at 1% level. Crisis years are defined as years when the US GDP growthwas negative (1980, 1982, 1991, 2008, 2009), boom years are years when the US GDP growthwas over 4% (1983, 1984, 1985, 1988, 1994, 1997, 1998, 1999, 2000) and normal growth yearsare all the other years in the sample.
Table (3.7) presents the results using the economy-wide cross-sectional ap-proach on the OIADP sample. The results show that segment-based OIADPforecasts are significantly better than consolidated-based ones (sig.> 1%) forthe line-of business segments. The forecasts are better both under the industryapproach of segment reporting (SFAS 14, N=449, sig. > 1%) and the man-agement approach (SFAS 131, N=329, sig.> 5%). Moreover, the results holdfor all three macro subsamples. Segment-based forecasts are significantly bet-ter than consolidated ones when it comes to forecasting crisis years (defined asyears with negative GDP growth in the US, N=99, sig > 5%), boom years (USGDP growth over 4%, N=173, sig.> 1%) and other years (N=552, sig.> 1%).The results for the geographic segment sample show that segment-based fore-casts are only better than consolidated-based ones during the SFAS 131 period(N=138, sig.> 10%).
SEGMENT REPORTING AND EARNINGS FORECASTING 147
Earnings Before Interest and Taxes (EBIT) sample
The coefficients for the business segment-based EBIT forecasts are estimated inthe following regression:
Earningsj,t = β0+β1∗Atj,t−1+β2∗gj,t−1+β3∗Salesj,t−1+β4∗Earningsj,t−1+εj,t.
(3.18)Similarly, I estimate the consolidated-based EBIT forecasts for the business
segment sample using the following regression:
Earningsi,t = β0 + β1 ∗ Ati,t−1 + β2 ∗ROAi,t−1 + β3 ∗OMi,t−1+
+ β4 ∗ Salesi,t−1 + β5 ∗ Earningsi,t−1 + εi,t. (3.19)
The coefficients for the geographical segment-based EBIT forecasts are es-timated in the following regression:
Earningsj,t = β0 +β1∗Atj,t−1 +β2∗Salesj,t−1 +β3∗Earningsj,t−1 +εj,t. (3.20)
I estimate the geographical consolidated-based EBIT forecasts using the fol-lowing regression:
Earningsi,t = β0 + β1 ∗ Ati,t−1 + β2 ∗ROAi,t−1 + β3 ∗ Salesi,t−1+
+ β4 ∗ Earningsi,t−1 + εi,t. (3.21)
Table (3.8) presents the results using the economy-wide cross-sectional ap-proach on the BUS and GEO EBIT samples. The results show that relativeabsolute forecast errors are significantly lower for segment-based forecasts com-pared for aggregated-based forecasts for the total BUS sample. Segment-basedforecasts are significantly better than aggregated-based ones for forecasting fu-ture EBIT figures. The results are significant for both the SFAS 14 period (sig-nificant at the 1% level) and the SFAS 131 period (operating segments reported,significant at the 5% level). Furthermore, segment-based forecasts outperformconsolidated-based forecasts when such forecasts are made for crisis years (sig.at 5%), boom years (sig. at 1%) and other years as well (sig. at 1%). The paired
148 ESSAYS ON SEGMENT REPORTING AND VALUATION
Table 3.8: EBIT sample, cross-sectional approachBUS sample GEO sample
N diff. t-value N diff. t-valueTotal sample of forecasts 795 0,1813*** 4,7426 597 0,0484 0,7410
(0,0382) (0,0654)SFAS 14 period (1987-1999) 420 0,1687*** 5,604 490 0,0588 0,7386
(0,0301) (0,0796)SFAS 131 period (2001-2014) 324 0,1803** 2,1303 84 0,0019 0,1370
(0,0846) (0,0140)Crisis years 94 0,1789** 1,7667 47 -0,0599 -0,0599
(0,1013) (0,0388)Boom years 166 0,3638*** 5,131 162 0,1960 0,8693
(0,0709) (0,2254)Other years 535 0,1252*** 2,5497 388 -0,0000 -0,0008
(0,0491) (0,0353)
Notes: The table shows the number of observations (N) and the mean difference (diff.)between totals-based and segment-based relative (absolute) errors (RAEc
i,t+1 − RAEsi,t+1)
of earnings before interest and taxes (EBIT) forecasts using the cross-sectional approach.BUS refers to the line-of-business sample under SFAS 14 and the operating segment sampleunder SFAS 131. Standard errors in brackets. A positive difference means that segment-based forecasts are more accurate than totals-based ones. Stars indicate the significance levelof these differences being higher than zero. *significant at 10% level; **significant at 5%level; ***significant at 1% level. Crisis years are defined as years when the US GDP growthwas negative (1980, 1982, 1991, 2008, 2009), boom years are years when the US GDP growthwas over 4% (1983, 1984, 1985, 1988, 1994, 1997, 1998, 1999, 2000) and normal growth yearsare all the other years in the sample.
t-tests reported for the geographical disclosure sample show that the relativeabsolute forecast errors are not significantly lower for segment-based forecaststhan those for consolidated-based ones either for the whole sample or for anyof the subsamples.
Pre-tax income (PI) sample
The coefficients for the business segment-based Pre-tax Income forecasts areestimated in the following regression:
Earningsj,t = β0 +β1∗Atj,t−1 +β2∗Salesj,t−1 +β3∗Earningsj,t−1 +εj,t. (3.22)
Similarly, I estimate the consolidated-based Pre-tax Income forecasts for thebusiness segment sample using the following regression:
SEGMENT REPORTING AND EARNINGS FORECASTING 149
Table 3.9: Pre-tax income sample, cross-sectional approachBUS sample GEO sample
N diff. t-value N diff. t-valueTotal sample of forecasts 411 0,3043*** 2,9950 480 0,1609*** 4,0544
(0,1016) (0,0397)SFAS 14 period (1987-1999) 354 0,3516*** 2,9854 468 0,1656*** 4,0451
(0,1178) (0,0409)SFAS 131 period (2001-2014) 53 0,0116 0,9545 12 N/A N/A
(0,0122) N/ACrisis years 42 0,1806 1,2308 34 0,1678*** 2,6785
(0,1467) (0,0627)Boom years 95 0,1218 1,2107 135 0,0011 0,0287
(0,1006) (0,0378)Other years 274 0,3865*** 2,6376 311 0,2295*** 3,9417
(0,1465) (0,0582)
Notes: The table shows the number of observations (N) and the mean difference (diff.)between totals-based and segment-based relative (absolute) errors (RAEc
i,t+1 − RAEsi,t+1)
of Pre-tax Income forecasts using the cross-sectional approach. BUS refers to the line-of-business sample under SFAS 14 and the operating segment sample under SFAS 131. Stan-dard errors in brackets. A positive difference means that segment-based forecasts are moreaccurate than totals-based ones. Stars indicate the significance level of these differences be-ing higher than zero. *significant at 10% level; **significant at 5% level; ***significant at1% level. Crisis years are defined as years when the US GDP growth was negative (1980,1982, 1991, 2008, 2009), boom years are years when the US GDP growth was over 4% (1983,1984, 1985, 1988, 1994, 1997, 1998, 1999, 2000) and normal growth years are all the otheryears in the sample.
Earningsi,t = β0 +β1 ∗Ati,t−1 +β2 ∗Salesi,t−1 +β3 ∗Earningsi,t−1 +εi,t. (3.23)
The coefficients for the geographical segment-based Pre-tax Income fore-casts are estimated in the following regression:
Earningsj,t = β0+β1∗Atj,t−1+β2∗Salesj,t−1+β3∗Earningsj,t−1+β4∗gj,t−1+εj,t.
(3.24)I estimate the geographical consolidated-based Pre-tax Income forecasts us-
ing the following regression:
150 ESSAYS ON SEGMENT REPORTING AND VALUATION
Earningsi,t = β0+β1∗Ati,t−1+β2∗Atgri,t−1+β3∗Salesi,t−1+β4∗Earningsi,t−1+εi,t.
(3.25)Table (3.9) presents the results using the economy-wide cross-sectional ap-
proach on the BUS and GEO Pre-tax income samples. The forecast errors aresignificantly lower for segment-based forecasts compared to consolidated-basedforecasts for the total BUS sample (significant at the 1% level), meaning thatsegment-based forecasts are significantly better than consolidated-based ones.This result holds for the SFAS 14 period as well. However, operating segment-based forecasts are not better for the SFAS 131 period. Furthermore, segment-based forecasts are not better than consolidated-based ones when forecastingboom and crisis years. Finally, segment-based forecasts are significantly betterthan total-based ones for ‘other’ years.
The results of the investigations on the geographic disclosure sample showthat segment-based forecasts are significantly better than consolidated-basedones on the whole sample (sig. at 1%). The findings are similar for the SFAS 14period and there are not enough forecasts for the SFAS 131 period to test thedifference. Segment-based forecasts significantly outperform total-based oneswhen it comes to forecasting crisis years or ‘other’ years (sig. at the 1% level),but not for boom years.
Net income (NI) sample
I only make net income forecasts when investigating the research question onthe geographical disclosure sample, as there are too few firms disclosing netincome for the business segments.
The coefficients for the geographical segment-based Net income forecastsare estimated in the following regression:
Earningsj,t = β0+β1∗Atj,t−1+β2∗Salesj,t−1+β3∗Earningsj,t−1+β4∗OMj,t−1+εj,t.
(3.26)I estimate the geographical consolidated-based Net income forecasts using
the following regression:
Earningsi,t = β0+β1∗Ati,t−1+β2∗Salesi,t−1+β3∗Earningsi,t−1+β4∗gi,t−1+εi,t.
(3.27)
SEGMENT REPORTING AND EARNINGS FORECASTING 151
Table 3.10: Net income sample, cross-sectional approachGEO sample
N diff. standard error t-valueTotal sample of forecasts 375 0,0014 0,0030 0,4601SFAS 14 period (1987-1999) 361 -0,0006 0,0016 -0,3854SFAS 131 period (2001-2014) 12 N/A N/A N/ACrisis years 26 N/A N/A N/ABoom years 114 -0,0014 0,0022 -0,6566Other years 235 0,0028 0,0046 0,6069
Notes: The table shows the number of observations (N) and the mean difference (diff.)between totals-based and segment-based relative (absolute) errors (RAEc
i,t+1 − RAEsi,t+1)
of Net Income forecasts using the cross-sectional approach, together with standard errors ofthe mean and t-values. A positive difference (diff.) means that segment-based forecasts aremore accurate than totals-based ones. Stars indicate the significance level of these differencesbeing higher than zero. *significant at 10% level; **significant at 5% level; ***significantat 1% level. Crisis years are defined as years when the US GDP growth was negative (1980,1982, 1991, 2008, 2009), boom years are years when the US GDP growth was over 4% (1983,1984, 1985, 1988, 1994, 1997, 1998, 1999, 2000) and normal growth years are all the otheryears in the sample.
Table (3.10) presents the results using the economy-wide cross-sectionalapproach on the GEO Net income sample. The results of the investigationsshow that segment-based earnings forecasts are not better than consolidated-based ones, either for the total sample, or for the SFAS 14 period. There arenot enough observations to draw conclusions from the t-tests for the SFAS 131period. Segment-based forecasts are not better when it comes to forecastingboom years or other years. There are not enough observations to test the dif-ference between forecast errors when forecasting earnings in crisis years.
Income before extraordinary items (IB) sample
I only make income before extraordinary items forecasts - similarly to net in-come forecasts - when investigating the research question on the geographicaldisclosure sample, as there are too few firms disclosing income before extraor-dinary items for the business segments.
The coefficients for the geographical segment-based Income before extraor-dinary items forecasts are estimated in the following regression:
152 ESSAYS ON SEGMENT REPORTING AND VALUATION
Earningsj,t = β0+β1∗Atj,t−1+β2∗Salesj,t−1+β3∗Earningsj,t−1+β4∗OMj,t−1+εj,t.
(3.28)I estimate the geographical consolidated-based Income before extraordinary
items forecasts using the following regression:
Earningsi,t = β0+β1∗Ati,t−1+β2∗Salesi,t−1+β3∗Earningsi,t−1+β4∗gi,t−1+εi,t.
(3.29)
Table 3.11: Income before extraordinary items sample, cross-sectional ap-proach
GEO sampleN diff. standard er-
rort-value
Total sample of forecasts 283 0,4492* 0,3001 1,4970SFAS 14 period (1987-1999) 276 0,1546*** 0,0368 4,2057SFAS 131 period (2001-2014) 5 N/A N/A N/ACrisis years 22 N/A N/A N/ABoom years 83 0,1853** 0,0953 1,9449Other years 178 0,6109 0,4751 1,2858
Notes: The table shows the number of observations (N) and the mean difference (diff.)between totals-based and segment-based relative (absolute) errors (RAEc
i,t+1 − RAEsi,t+1)
of Income before extraordinary items forecasts using the cross-sectional approach, togetherwith standard errors and t-values. A positive difference (diff.) means that segment-basedforecasts are more accurate than totals-based ones. Stars indicate the significance level ofthese differences being higher than zero. *significant at 10% level; **significant at 5% level;***significant at 1% level. Crisis years are defined as years when the US GDP growth wasnegative (1980, 1982, 1991, 2008, 2009), boom years are years when the US GDP growthwas over 4% (1983, 1984, 1985, 1988, 1994, 1997, 1998, 1999, 2000) and normal growthyears are all the other years in the sample.
Table (3.11) presents the results using the economy-wide cross-sectional ap-proach on the GEO Income before extraordinary items sample. The relativeabsolute forecast errors are significantly higher for consolidated-based forecastsas compared to segment-based ones for the total sample (sig. at 10%), meaningthat segment information-based forecasts are significantly better than consol-idated information-based ones. Segment-based forecasts significantly outper-form total-based ones during the SFAS 14 period (sig. at the 1% level) andthere are not enough forecasts to compare for the SFAS 131 period. Similarly,
SEGMENT REPORTING AND EARNINGS FORECASTING 153
there are too few observations available for crisis years. Segment-based fore-casts significantly outperform firm-based ones (sig. at 5%) when forecasts aremade for boom years, while for other years the difference is not significant.
Overview of results from the cross-sectional approach
This section provides an overview of the the results from the different earningssamples using the cross-sectional approach. Table (3.12) includes an overviewof the cross-sectional model based results per earnings type disclosed for thebusiness and the geographic segment sample. As discussed above, the EBIT andOIADP and the IB and NI samples have many overlapping observations. Theoverlap can result in the same variables being included in the actual regressionsfor estimating coefficients (as it did with EBIT and OIADP for the businesssample), which in turn can cause similar results for the subsamples. The tableshows that segment-based forecasts are significantly better than consolidatedones for most of the business subsamples. The difference of 0.35 for the PI BUSsample in the SFAS 14 period suggests that segment-based PI margin forecastsare on average 35 percentage points better than consolidated-based ones.
The results are mixed for the geographic disclosure sample. Segment-basedEBIT and OIADP forecasts are not better than consolidated-based ones. Segment-based IB forecasts outperform consolidated-based ones for the total sample (sig.> 5%) and the statistical significance of the result is higher for the SFAS 14 pe-riod. The macro sample split suggests that the result is driven by forecastsmade for boom years. In contrast to IB forecasts, NI forecasts show no signifi-cant difference depending on the information set used for forecasting. Finally,segment-based PI forecasts are significantly better than consolidated based onesfor the total sample (sig.> 1%) as well as for the SFAS 14 period (sig.> 1%).While there are too few observations under the SFAS 131 period, the macrosample split suggests that the difference is driven by forecasts made for crisisyears and other years, the difference in both subsamples being significant at the1% level.
Tab
le3.
12:
Ove
rvie
wo
fre
sults
by
ea
rnin
gs
disc
lose
d,c
ross
-se
ctio
na
lap
pro
ac
hBu
sine
sssa
mpl
eG
eogr
aphi
csa
mpl
eE
BIT
OIA
DP
PIE
BIT
IBN
IO
IAD
PPI
Tota
lsam
ple
offo
reca
sts
0,18
***
0,17
***
0,30
***
0,05
0,45
*0,
001
-0,0
4***
0,16
***
SFA
S14
peri
od(1
987-
1999
)0,
17**
*0,
15**
*0,
35**
*0,
060,
155*
**-0
,001
-0,0
5***
0,16
6***
SFA
S13
1pe
riod
(200
1-20
14)
0,18
**0,
18**
0,01
0,00
2N/A
N/A
0,00
8*N/A
Cri
sisy
ears
0,18
**0,
17**
0,18
-0,0
6N/A
N/A
-0,1
9***
0,16
8***
Boom
year
s0,
36**
*0,
346*
**0,
10,
196
0,18
5**
-0,0
01-0
,04*
**0,
001
Oth
erye
ars
0,12
5***
0,11
5***
0,15
***
-0,0
000,
610,
003
-0,0
250,
23**
*
Not
es:T
heta
ble
show
sthe
mea
ndi
ffer
ence
betw
een
tota
ls-b
ased
and
segm
ent-b
ased
rela
tive
abso
lute
fore
cast
erro
rs(RAE
c i,t+
1−RAE
s i,t+
1)f
orth
edi
ffer
ente
arni
ngsd
iscl
osed
onth
ese
gmen
tlev
elus
ing
the
cros
s-sec
tiona
lapp
roac
hfo
rthe
busi
ness
and
theg
eogr
aphi
csam
ples
epar
atel
y.EB
ITis
earn
ings
befo
rein
tere
stsa
ndta
xes,
OIA
DP
isop
erat
ing
inco
mea
fter
depr
ecia
tion,
PIis
pre-
tax
inco
me,
IBis
inco
meb
efor
eext
raor
dina
ryite
ms.
NIi
snet
inco
me.
Apo
sitiv
edi
ffer
ence
mea
nsth
atse
gmen
t-bas
edfo
reca
stsa
rem
ore
accu
rate
than
tota
ls-b
ased
ones
.Sta
rsin
dica
teth
esi
gnifi
canc
ele
velo
fthe
sedi
ffer
ence
sbei
nghi
gher
than
zero
.N/A
indi
cate
ssam
ples
izeu
nder
50on
whi
chte
stsa
reno
tcon
duct
ed.
*sig
nific
anta
t10%
leve
l;**
sign
ifica
ntat
5%le
vel;
***s
igni
fican
tat1
%le
vel.
Cri
sis
year
sar
ede
fined
asye
ars
whe
nth
eU
SG
DP
grow
thw
asne
gativ
e(1
980,
1982
,199
1,20
08,2
009)
,boo
mye
ars
are
year
sw
hen
the
US
GD
Pgr
owth
was
over
4%(1
983,
1984
,198
5,19
88,1
994,
1997
,199
8,19
99,2
000)
and
norm
algr
owth
year
sar
eal
lthe
othe
rye
ars
inth
esa
mpl
efo
rw
hich
fore
cast
sare
mad
e.
SEGMENT REPORTING AND EARNINGS FORECASTING 155
3.6.3 Segment reporting characteristics
In order to investigate whether certain segment reporting characteristics are as-sociated with better earnings forecasts, I regress the segment-based forecast er-rors on the segment reporting characteristics. I do this for the pooled segment-based earnings forecasts of the firm-based approach and the different earningssubsamples from the cross-sectional approach, both for the business and thegeographical sample. The correlations between the variables are presented inTable (3.3).
Table (3.13) presents the regression results. The results for most of thecharacteristics investigated are mixed. However, for most of the subsamplesthe coefficients of the number of segments disclosed variable (Nrseg) are sig-nificantly negative, suggesting that disclosing more segments results in lowersegment-based forecast errors.
Furthermore, the coefficient of the proprietary cost proxy variable (Max-margind) is significantly positive for most of the subsamples, suggesting thatproprietary information is associated with higher segment-based forecast er-rors. Similarly, the coefficient for RelsdROA is significantly positive for severalsubsamples, suggesting that disclosing segments with different return on assetscharacteristics is not associated with better earnings forecasting either. Finally,the tests provide weak evidence that incurring agency costs through disclosingweakly performing segments in geographic segment disclosures is associatedwith better earnings forecasts. The agency cost proxy variable (Minmargind) issignificantly negatively associated with segment-based forecast errors for threeof the subsamples investigated, suggesting that revealing low profitability seg-ments are associated with better segment-based earnings forecasts.
Tab
le3.
13:
Seg
me
nt-
ba
sed
fore
ca
ste
rrors
an
dse
gm
en
tre
po
rtin
gc
ha
rac
teris
tics
Busi
ness
sam
ple
Geo
grap
hic
sam
ple
Firm
-sp.
Cro
ss-se
ctio
nal
Firm
-sp.
Cro
ss-se
ctio
nal
All
EBI
TO
IAD
PPI
All
EBI
TIB
NI
OIA
DP
PIC
onc
0.23
70.
166
0.16
50.
088
0.65
5***
0.46
2**
-0.4
21**
-0.4
50**
0.46
4**
-0.2
33Sd
sale
sgr
-0.0
00-0
.000
-0.0
000.
000
0.00
00.
000
0.00
0*0.
000
0.00
00.
000
Rel
sdsa
les
-0.7
74**
-0.5
08*
-0.5
03*
-0.4
19-0
.895
***
-0.7
04**
*0.
322*
*0.
361*
*-0
.706
***
-0.3
88N
rseg
-0.1
40**
*-0
.049
*-0
.048
*-0
.064
**-0
.054
***
-0.0
48**
*-0
.030
*-0
.026
-0.0
48**
*-0
.108
***
Rel
sdR
OA
N/A
0.00
00.
000
0.00
1***
N/A
0.00
1***
0.00
1*0.
001*
0.00
1***
0.00
0M
axm
argi
nd0.
002
-0.0
00-0
.000
0.00
2***
0.15
3***
0.04
1***
0.42
6***
0.42
3***
0.04
1***
0.28
2***
Min
mar
gind
-0.0
000.
000
0.00
00.
000
-0.0
00-0
.000
-0.0
10**
-0.0
08*
-0.0
00-0
.033
***
R-sq
uare
d0.
020
0.00
50.
005
0.18
70.
053
0.06
40.
491
0.47
60.
064
0.05
3
Not
es:
The
tabl
esh
ows
the
coef
ficie
nts
from
regr
essi
on(3
.13)
.Fi
rm-sp
.re
fers
toth
efir
m-sp
ecifi
cap
proa
ch.
BUS
refe
rsto
the
line-
of-b
usin
esss
ampl
eun
der
SFA
S14
and
the
oper
atin
gse
gmen
tsam
ple
unde
rSF
AS
131.
The
vari
able
Rel
sdR
OA
isno
tinc
lude
din
the
firm
-spec
ific
appr
oach
asas
setv
alue
swer
eno
tuse
dfo
rfo
reca
stin
gea
rnin
gs.
SEGMENT REPORTING AND EARNINGS FORECASTING 157
3.7 Summary and conclusions
Previous research documents that earnings forecasts based on both segmentlabel information and quantitative segment information can outperform earn-ings forecasts based on consolidated financial numbers only. However, recentpapers have provided evidence that label information might not be useful forforecasting earnings, especially when segment disclosures are provided accord-ing to the management approach. The introduction of the management ap-proach with SFAS 131 has arguably led to lower comparability of segmentsacross firms. However, FASB argued that information provided under SFAS131 should be more relevant compared to disclosures under the industry ap-proach of SFAS 14. This paper investigates whether segment disclosures arerelevant for earnings forecasting without considering within-industry compa-rability. Additionally, I investigate whether certain segment reporting charac-teristics are associated with lower segment-based earnings forecast errors.
The study investigates the research questions using a firm-based time se-ries model and an economy-wide cross-sectional model. These models cap-ture important characteristics of the information reported, such as time-seriestrends and economy-wide cross-company comparability. I compare segmentinformation-based forecasts with consolidated information-based forecasts. Fore-casts errors proxy for the difficulty of forecasting. Lower segment-based fore-cast errors (compared to consolidated-based ones) suggest that segment infor-mation contains additional, relevant information compared to the consolidatedinformation only.
The sample of firms provided business or geographic segment informationover the period 1977 to 2014. I investigate the research questions on the line-ofbusiness and geographic segment disclosure sample separately.
My findings for the firm-specific approach reinforce the results reported inSilhan (1983), namely that quantitative segment disclosures do not improve theaccuracy of sales forecasts. The results from earnings forecasting using the firm-specific approach suggest that segment based earnings forecasts are not moreaccurate than consolidated-based ones. However, the findings for the cross-sectional model show that segment based forecasts are better than consolidatedbased forecasts for several subsamples.
The cross-sectional model builds on the comparability of segment informa-tion being reported across firms. The findings from the cross-sectional model
158 ESSAYS ON SEGMENT REPORTING AND VALUATION
suggest that the introduction of the “management approach” in SFAS No. 131did not increase the usefulness of segment information for forecasting pur-poses. Segment based earnings forecasts outperform consolidated-based onesfor more of the subsamples during the SFAS No. 14 period (the “industry ap-proach”), as compared to during the management approach. All in all, my re-sults indicate that quantitative segment disclosures can improve earnings fore-casts.
The forecasting results for the segment reporting characteristics are notin line with a priori expectations. Even though the results show that moresegments being reported is associated with better segment-based earnings fore-casts, the other characteristics could not be linked to any improvements in theforecasting.
The paper contributes to the literature on segment reporting. It shouldbe of interest to standard setters and regulators. The management approachin SFAS 131 requires that the segments should be presented in line with theinternal organization of the business operations. This firm adaptation in thesegment definition often leads to lower cross-company comparability of seg-ments. The paper shows that there is a potential to make better segment-basedforecasts than total-based ones when segments are presented in a consistentway. In order to obtain results of this kind, firms should present the samesegment structure over time.
There are a number of limitations to the empirical investigation of this pa-per. The segment database appears to contain data recording errors. I adressthis issue through considering extreme segments only in a few subsamples.Moreover, a change in the reporting structure of the segments leads to the ob-servation being deleted in the firm-specific approach. Similarly, if mergers andacquisitions result in the creation of new segments, forecasts for the follow-ing two years cannot be made with the firm-specific approach. These issuescause more limitations after the introduction of SFAS 131 and therefore, thereare a limited amount of forecasts for the corresponding period using the firm-specific approach.
A further limitation of the paper is that the simple structure of the predic-tion models cannot capture all the information contained in segment disclo-sures. However, if this simplistic methodology manages to show evidence onprediction improvements, it can reasonably be assumed that better forecastswould be possible with more sophisticated prediction models.
SEGMENT REPORTING AND EARNINGS FORECASTING 159
Due to the sample selection criteria, the findings are not representative forthe whole population of listed multi-segment US firms with sales over 2 mil-lion USD. As I dropped all observations with incomplete disclosure, the find-ings are only representative for companies with complete segment reporting.Furthermore, as I could not make forecasts for firms that have changed theirsegment reporting, the sample is not representative for firms which have madeacquisitions or divestitures and therefore changed their segment reporting, orfor firms that have changed their segment reporting for other reasons.
An interesting area for future research would be to investigate the condi-tions under which segment information is especially useful for earnings fore-casting. Segments and companies differ in several characteristics such as finan-cial risk, capital intensity, industry competitiveness and future prospects. Aquestion would then be whether such differences can lead to segment informa-tion being more useful for earnings forecasting.
160 ESSAYS ON SEGMENT REPORTING AND VALUATION
Appendix 3.A
Table 3.14: Variable definitionsEarningsi,t Earnings of the company measured in million USD. It can refer to EBIT, IB, NI, OIADP or
PI depending on the earnings disclosed by the firm on the segment level.EBIT Earnings before interests and taxes, Compustat item ebit.IB Income before extraordinary items, Compustat item ib.NI Net income, Compustat item ni.OIADP Operating income after depreciation, Compustat item oiadp.PI Pre-tax income, Compustat item pi.Att Total assets measured in million USD.Atgrt Growth in total assets calculated Att
Att−1.
ROAt Return on assets calculated as EarningstAtt−1
.OMt Operating margin calculated as earnings divided by sales, Earningst
Salest.
Salest Reported sales in million USD.gt Growth in sales in percentages, measured as ( Salest
Salest−1− 1) ∗ 100.
omchgt Change in operating margin measured as OMt −OMt−1.RAEsi,t Relative absolute segment-based forecast error for firm i, year t. Calculated as the absolute
value of the difference between the earnings forecast and the actual outcome divided by theabsolute value of the outcome, see formula (3.11).
RAEci,t Relative absolute consolidated-based forecast error for firm i, year t. Calculated as the abso-lute value of the difference between the earnings forecast and the actual outcome divided bythe absolute value of the outcome, see formula (3.10).
Conci,t Segment reporting concentration variable. Calculated as∑Jj=1 s
2j,i,t−1, where sj,i,t−1 =
Salesj,i,t−1
Salesi,t−1.
Sdsalesgri,t Standard deviation of segments’ sales growths within the firm, calculated as σg,si,t =√1J∗∑Jj=1(gj,i,t − gj,i,t)2, where gj,i,t =
∑Jj=1 gj,i,t
J.
Relsdsalesi,t Relative standard deviation of the reported segments’ sales, calculated as√1J∗∑J
j=1(Salesj,i,t−Salesj,i,t)2
Salesj,i,t, where Salesj,i,t =
∑Jj=1 Salesj,i,t
J.
Nrsegi,t Number of reported segments.RelsdROAi,t Relative standard deviation of the reported segments’ return on assets (ROA), calculated as√
1J∗∑J
j=1(ROAj,i,t−ROAj,i,t)2
ROAj,i,t, where ROAj,i,t =
∑Jj=1 ROAj,i,t
J.
Maxmargindi,t The difference between the margin of the most profitable segment reported and the companyaverage, calculated as maxj(OMj,i,t −OMj,i,t).
Minmargindi,t The difference between the consolidated margin and the margin of the least profitable seg-ment reported, calculated as−maxj(OMj,i,t −OMj,i,t).
Notes: This table contains the definitions of the variables. The variable subscripts i, j and t refer to the firms, segmentsand years, respectively. Variables in the table defined with only t subscript can refer to segment-level or firm levelmeasures (i.e. Total assets can either be Ati,t, total assets of firm i in time t or Atj,t, total assets of segment j in timet).
Chapter 4
Paper III: Heterogeneous investorbeliefs and value creation through
equity carve-outs
Abstract. This paper provides novel empirical evidence in support of the het-erogeneous beliefs theory for corporate restructuring. The heterogeneous be-liefs literature implies that a conglomerate can maximize shareholder value byseparating the company into its parts and selling them to the investors whovalue the attributes of the different parts the most. Building on this theory, Ipresent a new, competing hypothesis to explain the positive abnormal returnson equity carve-out (ECO) announcements. I use a sample of 102 US equitycarve-outs between 1980 and 2016 to investigate the explanation that firms cangenerate value for current shareholders in ECOs through selling parts of thecompany to optimistic shareholders. I document a decrease in valuation levelfor the parent company from the pre-announcement to the post-completionperiod, inconsistent with both previous hypotheses and consistent with theheterogeneous beliefs explanation. Moreover, I document that the change invaluation is significantly higher when the ECO operates in a different indus-try compared to the parent firm, in line with the divestiture gains hypothesisand the heterogeneous beliefs hypothesis. This paper contributes with a new,heterogeneous beliefs based hypothesis to explain the positive announcementreturns for equity carve-outs. It also contributes to the literature on equitycarve-outs by documenting empirical evidence consistent with this theory andinconsistent with previous explanations in the literature. The findings in this
162 ESSAYS ON SEGMENT REPORTING AND VALUATION
paper also extend the literature on diversification discount and is relevant forsum-of-parts valuation.
4.1 Introduction
Investors disagree about the future performance of companies which leads todifferent value estimates. Miller (1977) referred to this as heterogeneous beliefsand presents a theory implying that under such circumstances a conglomeratecan maximize shareholder value through taking the company to its parts andselling them to investors whom value the attributes of the different parts themost. In this paper, I provide a heterogeneous beliefs-based explanation for thepositive announcement returns of equity carve-outs.
In an equity carve-out (ECO) the parent company separates (carves out)the operation of a subsidiary, lists the shares of the carved-out subsidiary ona stock exchange, sells some of the shares on the market and retains the restof the shares in the subsidiary. The parent firm usually retains control overthe carved-out entity.1 Early research on equity carve-outs documents positiveannouncement returns for the parent company. Previous studies present twomain competing hypotheses to explain the positive announcement returns.
First, the asymmetric information hypothesis in Nanda (1991) argued thatwhen firms raise financing through equity carve-outs instead of issuing equityin the parent firm, they signal that the stock of the parent is undervalued. Themarket reacts on this signal positively leading to the increase of the parent’s
1In this paper I refer to the divesting firm as the parent both when I discuss stock price or
valuation effects and when I discuss financial performance. This is imprecise from a legal
point of view for two main reasons. First, if the divesting firm does not retain control over
the ECO firm, it is no longer the parent. Second, when I discuss the financial performance
of the divesting firm, it is the performance of the whole group of legal entities that is of
interest and not the parent firm as a separate legal entity. Similarly, I refer to the carved-out
part as the subsidiary which is also imprecise. The divested part might not be a subsidiary
any longer after the ECO from a legal point of view. Also, when I discuss the financial
performance of the subsidiary it is also the financial performance of the carved-out entity
as a group that is of interest and not only the legal entity the shares of which are listed.
Even though referring to the divesting firm as the parent and the carved-out operations as
the subsidiary is not precise, it is common in previous literature.
HETEROGENEOUS BELIEFS AND EQUITY CARVE-OUTS 163
share price.Second, the divestiture gains hypothesis in Schipper and Smith (1986) ar-
gued that the subsidiary will be more competitive in its own industry followingthe carve-out. In particular, they argued that the positive return on carve-out announcement could be due to information about positive net presentvalue (NPV) projects, perceived decreased likelihood that future positive NPVprojects will be foregone, expected improvement in asset management by themanagers due to the revision of managers responsibilities or better incentivecontracts.
Previous studies provided mixed empirical evidence with respect to thesetwo explanations. Hulburt et al. (2002) found that rivals of the parent compa-nies have negative stock price reactions upon the announcement of the ECO,suggesting that ECOs are associated with expected better operating perfor-mance, consistent with the divestiture gains hypothesis. In a related study,Powers (2003) showed that the operating performance of the subsidiary peaksin the carve-out year and deteriorates significantly afterwards, which is incon-sistent with the divestiture gains hypothesis. Dasilas and Leventis (2018) doc-umented positive initial return for the ECO firm which reverses into severelosses in the months coming, inconsistent with the divestiture gains hypoth-esis. Dereeper and Mashwani (2018) found that analyst disagreement and an-alyst following of the parent firm increases after the ECO, inconsistent withthe asymmetric information hypothesis.
Given the inconclusive evidence presented so far in the literature, I presentin this paper an additional hypothesis built on the heterogeneous beliefs theoryand investigate whether companies can create value for current shareholdersthrough selling a part of the company to those investors who value its attributesthe most.
Miller (1977) argued that when investors are presented with the same infor-mation, they interpret it differently and therefore, they arrive at different valueestimates. This difference in opinions about the value estimates is referred toas heterogeneous beliefs. He argued that the stock of any company will bebought by those investors who have higher value estimates for the company,while other investors, including those with the mean value estimate, wouldforgo the investment opportunity.2
2I refer to investors with high value estimates as ‘optimistic investors’ following Miller (1977),
164 ESSAYS ON SEGMENT REPORTING AND VALUATION
The model by Miller (1977) suggested that heterogeneity in beliefs can pro-vide an explanation for the diversification discount of conglomerates. He ar-gued that diversification reduces divergence in beliefs as investors are usuallyoptimistic about one or some, but not all parts of the company. His modelof heterogeneous beliefs suggested that a conglomerate would trade at a lowerprice compared to what the sum of its parts would be worth separately. He ar-gued that in some cases splitting up conglomerates could maximize shareholdervalue as the parts could be sold to those investors who value their attributes themost.3
Jiao et al. (2013) were the first to provide empirical evidence in supportof the connection between diversification discount and heterogeneous beliefs.They documented that diversified firms have lower divergence in beliefs com-pared to focused firms. They also found, using a sample of merger deals, thatthe heterogeneity of investor beliefs for the bidder decreases subsequent to thetakeover if the bidder was a focused firm and diversifies with the takeover. Fur-thermore, the reduction in heterogeneity of beliefs is higher for diversifyingmergers than for non-diversifying ones. The authors relate the heterogeneity
even though investors might have high value estimates for other reasons than optimism.
Miller (1977) defined these investors as the ones who value the attributes of the company themost. Being optimistic about the future of the company or its industry is only one of the
reasons why investors might have higher value estimates. Other reasons can be investor
preference towards a certain type of firms, such as high growth companies, companies that
pay dividends, turnover candidates, high-risk companies, companies in industries in which
the investor specializes in, etc.3The reasoning is as follows: under homogeneous expectations (i.e. when all investors have
the same beliefs and value estimates) the company with three parts will have a value that
equals the sum of the values of the separate entities. However, under heterogeneous beliefs
the price of the parts sold separately will be set by the investors most optimistic about the
specific part. These investors are probably not so optimistic about the other parts. There-
fore, an investor who has to own all the parts (the whole conglomerate) is willing to pay
less for the conglomerate as a whole than what the different optimistic investors would pay
for the parts separately. This is similar to closed-end investment funds and listed investment
companies. In these funds, the investor has to own a whole basket of shares and is willing
to pay less for it compared to the net asset value of the fund. See also the numerical example
in Appendix 4.A for an illustration.
HETEROGENEOUS BELIEFS AND EQUITY CARVE-OUTS 165
in beliefs to the valuations the firms trade at. They found lower heterogeneityin beliefs to be associated with lower market values, providing an explanationfor the diversification discount.
While Jiao et al. (2013) provided empirical evidence for the impact of diver-sification on heterogeneity in beliefs and valuation levels, my paper investigatesthe Miller (1977) model’s prediction in the opposite direction, the possibilityto unlock shareholder value through splitting up the firm.
I apply the heterogeneous beliefs model to present a competing hypothesisproviding an explanation for the positive announcement returns upon ECOannouncements. The original theory provides predictions for split-ups underheterogeneous beliefs. I build on these predictions to provide a theory that canbe applied to equity carve-outs. In contrast to the split-up of the firm discussedby Miller, the divesting firm following the ECO retains partial ownership inthe subsidiary. However, the logic of the theory can be applied to equity carve-outs as well.
When investors are presented with the possibility to buy a part of the firm(a segment) separately, Miller (1977) argued that the market price of the seg-ment will be higher than the value estimates of current investors of the firm.I apply this prediction for equity carve-outs where the divesting firm retainsinterest in the subsidiary. In the ECO, the parent sells shares in the ECO firmto outside investors. Because these investors pay a price for the ECO firm’sshares that is higher than the value estimate of current investors, this resultsin an indirect gain for shareholders of the parent. Upon an ECO announce-ment, current shareholders expect that new owners will pay a high price forthe carved-out part and therefore adjust their valuation (with the expected gainon the shares sold) upwards. This explains the positive announcement returnson equity carve-outs.
Upon the listing of ECO firm shares, investors of the parent, who hadhigh value estimates for the ECO part of the group and lower value estimatesfor the rest of the firm, are given the opportunity to own the ECO firm sep-arately. Previous investors of the firm, who have high value estimates for theECO firm, sell their interest in the parent and buy the shares of the ECO firmseparately.4 Such investors exit the investor pool of the parent firm which leads
4Vijh (1994) presented evidence consistent with this prediction. The paper documented a
market anomaly of positive abnormal returns on spin-off ex-dates. As this date is known in
166 ESSAYS ON SEGMENT REPORTING AND VALUATION
to lower market value - as their shares will be sold to the investors with the next-highest value estimates whom by definition had a lower value estimate of thefirm compared to the market price. The heterogeneous beliefs theory predictsa lower valuation level for the parent share after the restructuring.
The equity carve-out provides an opportunity for shareholders with highvalue estimates in the ECO firm to invest in the firm separately. Firms of-ten argue that the carve-out helps the subsidiary with obtaining financing forfuture projects. Looking at the combined operations after the restructuring(that is, the entirety of the previous operations, the group and the divestedpart together), the heterogeneous beliefs theory expects the operations to befinanced at a higher valuation level compared to the group before the restruc-turing. This is due to the ECO firm obtaining financing from investors whohave higher value estimates than previous investors in the parent firm. Thetheory predicts that the rest of the original group will have similar valuationlevels as investors who valued the rest of the firm at a higher level compared tothe ECO part will still have to own part of the carved-out entity.
All in all, the heterogeneous beliefs theory predicts positive announcementreturns upon equity carve-outs, lower valuation levels for the parent firm af-ter the restructuring and higher valuation level for the combined operationsafter the restructuring, compared to the valuation levels before the carve-outannouncement. The theory attributes the valuation effects to the differencein investor preferences between the carved-out part and the rest of the firm.I expect investor preferences to be more different across the parts of the firmwhen these parts are more dissimilar.
The evidence in previous literature is inconclusive with respect to the ex-planation of positive announcement returns upon equity carve-outs. There-fore, I provide a heterogeneous beliefs theory-based hypothesis to explain theannouncement returns. In order to distinguish between the heterogeneous be-liefs hypothesis and the previous two hypothesis discussed in the literature,I investigate the changes in the valuation of the parent firm (and the com-bined operations) from pre-announcement to post-completion levels. Both the
advance, investors should not be able to realize abnormal returns through buying the share
before the ex-date and selling the two parts on the next day. The paper argued that this
phenomenon is due to the separate firms attracting different types of investors who prefer
buying shares in the separate entities.
HETEROGENEOUS BELIEFS AND EQUITY CARVE-OUTS 167
asymmetric information hypothesis and the divestiture gains hypothesis pre-dict higher valuation for the parent firm after the completion of the restruc-turing. The asymmetric information hypothesis implies that the firm was un-dervalued before the announcement and after reading the signal, the marketcorrects for this undervaluation. The divestitures gains hypothesis posits thatthere are operational gains from the ECO which leads to higher valuations.In contrast, the heterogeneous beliefs theory predicts that the valuation of theparent will decrease as those investors of the parent firm who had higher valueestimates for the ECO firm can sell their shares in the parent firm to other in-vestors (with lower value estimates by definition) and buy shares in the ECOfirm separately.
In the main test, I investigate the change in valuation levels of the parentfirm from pre-announcement levels to post-completion levels to distinguishbetween the heterogeneous beliefs hypothesis and the other two explanationsestablished in the literature. In additional tests I compare the valuation levelsof combined operations, the whole of the subsidiary and the parent from be-fore to after the restructuring. All three hypotheses expect the valuation levelof the combined operations to be higher after the restructuring. Then I in-vestigate the change valuation levels when the ECO and the parent operatesin different industries. The heterogeneous beliefs hypothesis and the divesti-tures gains hypothesis expect more positive value effects from unrelated ECOscompared to related ECOs. Finally, I investigate the change in excess valuedepending on the difference between the financial performance of the parentand the ECO firm. Here, the heterogeneous beliefs hypothesis predicts morepositive changes in valuation levels for firms with different financial perfor-mance while the other two hypotheses do not predict any difference betweenthe subsamples. Finally, in an additional analysis, I investigate associations be-tween heterogeneous beliefs measures and valuation levels. Previous findingsin the heterogeneous beliefs literature predict excess heterogeneity in beliefs tobe positively related to valuation levels.
I use three-day abnormal returns for the announcement return investiga-tions. I use the excess value (diversification discount) measure established inprevious literature (Berger and Ofek, 1995; Jiao et al., 2013), and investigatechanges in excess values from pre-announcement levels to post-completion lev-els.
I collect equity carve-out announcements from the SDC Platinum database
168 ESSAYS ON SEGMENT REPORTING AND VALUATION
for companies listed in the US between 1976 and 2017. For all the parent com-panies and the carve-out firms, I obtain financial information from CompustatAnnual Fundamentals and segment data from Compustat Historical Segmentsdatabase. Furthermore, I obtain stock price data from CRSP. I verify all eventswith the Factiva database and collect variables missing from the SDC Platinumdatabase.
I document a 2.1% (0.8%) mean (median) abnormal return upon the ECOannouncement, significant at the 5% (1%) level. I find an insignificant decreaseof 6 percentage points in the excess value of the parent company following theECO on the full sample. After excluding events where the size of the ECO(measured by sales) is less than 2% of the group (14 observations), the decreasein excess values is 9 percentage points and significant at the 10% level.5 Thedecrease in excess value is inconsistent with the two previous hypotheses inthe literature and consistent with the heterogeneous beliefs explanation.
Next, I find an insignificant decrease in the excess value of the original op-erations, the parent company before the announcement and the combined op-erations of the parent and the ECO in the post-completion period. This resultis not consistent with any of the three hypotheses.
While I document no significant differences in announcement returns be-tween related and unrelated ECOs, I document a weakly significant differencein the change of the excess value of the combined operations between relatedand unrelated ECOs. The change in excess values is negative for related ECOsand positive for unrelated ECOs. The economically significant difference of19.6 percentage points in the change in excess values is statistically significantat the 10% level. This finding is consistent with the divestiture gains hypothesisand the heterogeneous beliefs hypothesis but is not predicted by the informa-tion asymmetry hypothesis. Finally, I find no significant difference in the an-nouncement returns and the changes in excess values between ECOs where thefinancial performance of the parent and the subsidiary is similar and different.
My results from the additional analysis investigating the association be-tween heterogeneous beliefs measures and excess values are inconsistent withJiao et al. (2013). I document for both the parent and the ECO that higher lev-els of heterogeneity in beliefs are associated with lower excess values. Further-
5The results are similar in magnitude and statistical significance for excluding observations
where the size of the ECO (measured by sales) is less than 1% or 3% of the group as well.
HETEROGENEOUS BELIEFS AND EQUITY CARVE-OUTS 169
more, I document that the decrease in the excess values for parent companiesfollowing equity carve-outs are associated with a contemporaneous increase inheterogeneity in beliefs measures. This result is consistent with the findingsin Dereeper and Mashwani (2018), who documented an increase in informa-tion asymmetry (analyst disagreement and analyst following) following equitycarve-outs.
This paper contributes to two main streams of literature. First, I con-tribute to the literature on heterogeneous beliefs. Building on the model ofMiller (1977), to the best of my knowledge, I present the first hypothesis forexplaining valuation effects of equity carve-outs. I also provide the first empir-ical investigation of the heterogeneous beliefs theory with respect to corporaterestructuring and, in particular, equity carve-outs. Second, I also contributeto the literature on equity carve-outs. I provide a novel, alternative explana-tion for the positive announcement returns documented for carve-outs. Thispaper presents empirical evidence consistent with the prediction of the hetero-geneous beliefs explanation but inconsistent with previous explanations in theliterature.
The rest of the paper is organized as follows. Section 4.2 reviews the rele-vant literature. Section 4.3 outlines the research question. Section 4.4 presentsthe methodology. Section 4.5 presents the data and sample selection. Section4.6 and 4.7 respectively present the descriptive statistics and the empirical re-sults and Section 4.8 concludes. Appendix 4.A presents an illustrative exampleon how heterogeneous beliefs affect the segment-based valuation of firms.
4.2 Literature review
4.2.1 Conglomerates and the diversification discount
The rationale in corporate diversification is that firms expect incremental valueincreases from acquiring other firms. Lewellen (1971) identifies two main typesof sources of such value increases. First, there are operating sources such aseconomies of scale, increasing market power through larger market share, theacquisition of complementary research or technology or acquiring manage-ment skills. Second, there are potential financial sources of value gains, whichcan be acquiring firms for a price under their intrinsic values, utilizing unuseddebt capacity of the target firm or decreasing the variability of total corporate
170 ESSAYS ON SEGMENT REPORTING AND VALUATION
earnings.Despite these potential benefits from diversification, a vast amount of pa-
pers provides empirical evidence suggesting that diversified firms have lowermarket valuation compared to the sum of the imputed values of the differentparts. Lang and Stulz (1994); Berger and Ofek (1995) provided early empiricalevidence on the phenomenon of the diversification discount. Lang and Stulz(1994) used industry Tobin’s q values and Berger and Ofek (1995) used indus-try sales, earnings and asset multiples to value parts of the conglomerate. Theiranalyses suggested that the sum of the parts’ imputed value exceeds the marketvalue of the conglomerate.
Other papers investigated the reason why conglomerates trade at lowerprices compared to their imputed value. Campa and Kedia (2002) argued thatthe lower valuation of diversified firms is not due to destruction of value throughdiversification. They argued that firms self-select into diversification and thatone should take into account the firm-specific characteristics driving the deci-sion to diversify. For example, firms facing technological changes that affecttheir technological advantages and firms in industries that have lower growthprospects are more likely to diversify. These firms would have lower valua-tion even without diversification. They found that after taking into accountself-selection (using fixed-effects and Heckman-type self selection models), thediversification discount disappears. Their results implied that diversificationdid not destroy value. Villalonga (2004) used propensity score matching andfound in line with the self-selection argument that diversification did not de-stroy value.
However, diversification comes with potential agency costs, managers mightuse funds inefficiently to build empires through which they can increase theirstatus and compensation (Jensen, 1986; Cronqvist et al., 2001; Campa and Ke-dia, 2002; Villalonga, 2004). Rajan et al. (2000) provided a theoretical modelshowing that inefficient investment through cross-subsidization in conglomer-ates can lead to the diversification discount. Their empirical findings reinforcedthe prediction of the model.
4.2.2 Corporate restructuring
In order to eliminate negative synergies in conglomerates, to decrease agencycosts and to maximize shareholder value, firms can restructure their operationsthrough divesting assets (John and Ofek, 1995; Comment and Jarrell, 1995).
HETEROGENEOUS BELIEFS AND EQUITY CARVE-OUTS 171
Previous research on corporate restructuring focused on three main types ofdivestment through which firms can increase focus and improve the efficiencyof operations. When firms decide to totally relinquish their ownership theycan conduct asset sell-offs or spin-offs. Alternatively, firms might decide toretain partial ownership through an equity carve-out (ECO) (Prezas and Si-monyan, 2015).
In all three types of transactions, the firm separates the operations to be di-vested into a subsidiary. In an asset sell-off, the firm negotiates the sales of thesubsidiary in a private transaction and sells the subsidiary (usually) for cash.Firms can also divest part of the operations through a spin-off. In this case,instead of selling the subsidiary to a private buyer, the firm lists the shares ofthe subsidiary on the stock market and distributes the shares of the subsidiaryamong its own shareholders as a (usually tax-free) dividend on a pro rata basis.In contrast to an asset sell-off, firms do not receive any cash (or other consider-ations) from a spin-off. Moreover, while shareholders of the divesting firm donot retain control over the assets following an asset sell-off, they retain controlover the spin-off firm, as its shares are distributed proportionally among theshareholders of the divesting firm.
Finally, a firm can retain partial control over the subsidiary through an eq-uity carve-out (ECO). In an ECO, the firm separates part of its operations intoa subsidiary, lists the subsidiary (henceforth the carve-out firm or ECO firm) onthe stock market as a standalone firm and sells some shares of the ECO firm tooutside investors. Following an ECO, the divesting firm usually retains con-trol over the subsidiary. Similarly to asset sell-offs, the divesting firm usuallyreceives cash from the ECO transaction.
Previous studies found positive abnormal returns upon the announcementsof corporate restructuring (Vijh, 1994; Slovin et al., 1995; Krishnaswami andSubramaniam, 1999; Prezas and Simonyan, 2015). The choice between differ-ent types of restructuring has been associated with different characteristics ofthe divesting firm and the operations to be divested. Frank and Harden (2001)provided evidence suggesting that firms that carve out are more likely to bein need of cash and to have lower marginal tax rates compared to firms thatspin off their assets. They also found that carve-out firms have higher growthand are more profitable than spin-off firms. Furthermore, carve-outs are morelikely than spin-offs if the subsidiary is operating in a related industry. Theyalso found that firms with high pre-divestment growth are more likely to carve
172 ESSAYS ON SEGMENT REPORTING AND VALUATION
out (than spin off) their subsidiary and they argued it suggests that carve-outsare a financing mechanisms for high growth, cash constrained firms.
Prezas and Simonyan (2015) found that undervalued firms are more likelyto conduct carve-outs than selling off their assets. Moreover, they documentedthat carve-outs are more likely during periods of investor optimism. Khanand Mehta (1996) provided an analytical model predicting that the reason fordivestment is low margins and high costs and that the type of divestment de-pends on the operating risk of the asset being divested. Their model predictedthat firms are more likely to carve-out assets (compared to selling off) whenthe operating risk of the asset being divested is high. They provided empiricalevidence supporting this prediction.
Slovin et al. (1995) examined stock price reaction of industry peers of carve-out firms (N=36), spin-off firms (N=41) and sold-off subsidiaries (N=203) ona sample of observations between 1981 and 1990. They found a significantlynegative price reaction of 1% for rivals in case of equity carve-outs (which issimilar to rival price reaction for normal IPOs), positive price reaction for spin-offs and neutral price reaction for asset sell-offs. They conjectured that man-agers conduct a carve-out when outside investors are likely to price the sharehigher than the value perceived by the managers.
4.2.3 Value creation through equity carve-outs
Equity carve-outs are costly transactions. Firms often argue that the restruc-turing benefits both entities through increased focus or higher flexibility inthe future for the carve-out firm.6 Whether such benefits outweigh the costsof restructuring is not clear. Previous studies on equity carve-outs documentedpositive announcement returns for the parent company. These papers pre-sented two main competing hypotheses to explain the positive announcementreturns.
6For example, NovoNordisk chief executive Mads Ovlisen said upon carving out 50%
of Zymogenetics Inc. in 2000 that "the restructuring is intended to allow each busi-
ness better focus and will let ZymoGenetics raise more money than Novo alone could
provide". https://www.nytimes.com/2000/10/23/business/zymogenetics-will-become-
independent-of-novo-nordisk.html, retrieved 2019-03-02.
HETEROGENEOUS BELIEFS AND EQUITY CARVE-OUTS 173
A) Asymmetric information hypothesis
First, the asymmetric information hypothesis by Nanda (1991) posited thatwhen parent firms raise financing through equity carve-outs, instead of issuingtheir own shares, they signal that their stock is undervalued - at least comparedto the subsidiary. Upon the announcement of the carve-out the market getsthis signal and the share price of the parent firm increases.
B) Divestiture gains hypothesis
Second, the divestiture gains hypothesis by Schipper and Smith (1986) impliedthat the information released upon ECO announcements or the separate fi-nancing possibility can reveal positive information about the future of the sub-sidiary. The release can reveal current positive NPV projects that the firmneeds financing for, the better financing opportunities might imply decreasedprobability that future positive NPV projects will be foregone or the separatelisting might lead to expected improved asset management by the managersdue to the revision of managers responsibilities or better incentive contracts(Schipper and Smith, 1986).
Schipper and Smith (1986) discussed several potential sources for divesti-ture gains. Other papers investigated these possible sources in more detail[e.g. Comment and Jarrell (1995); Daley et al. (1997); Allen and McConnell(1998)]. Hulburt et al. (2002) provided a good overview of the extension ofthe divestiture gains hypothesis following the seminal paper by Schipper andSmith (1986). Hulburt et al. (2002) argued that the divestiture gains hypothesisis in essence a collection of four independent but related hypotheses.
First, the financing and investment strategy hypotheses suggest that ECOscreate value if the proceeds are used to pay down debt (Allen and McConnell,1998) or are retained in the business and used efficiently (Schipper and Smith,1986). Second, the contracting efficiency hypothesis suggests that carve-outscan result in more efficient contracting between shareholders and the manage-ment and this can lead to better operating performance (Schipper and Smith,1986). Third, the incentive alignment hypothesis suggest better contractingat the subsidiary level due to being listed on the stock market (Allen and Mc-Connell, 1998). Finally, the corporate focus hypothesis argues that managersare more suited to the management of the core business and therefore, im-proved focus leads to value gains [e.g. Comment and Jarrell (1995); Daley et al.
174 ESSAYS ON SEGMENT REPORTING AND VALUATION
(1997)].
Previous results from empirical investigations
Numerous studies have attempted to distinguish between these possible expla-nations. Hulburt et al. (2002) analyzed the stock price reaction of rivals ofcarve-out parents on carve-out announcements during the 1980s and 1990s inorder to distinguish the information asymmetric information and the divesti-ture gains hypothesis. They argued that the divestiture hypothesis suggests(operating) gains for the parent and would be consistent with negative stockreturns for the rivals. On the other hand, the asymmetric information hy-pothesis suggests that the parent company (and its industry) is undervaluedand would be consistent with positive price reaction for rivals upon announce-ment. They found evidence consistent with the divestiture gains hypothesis,that rivals of carve-out parents experience negative returns around the carve-out announcements. They also found that parent firms and their carved-outsubsidiaries both experienced improvements in their operating performance(adjusted for previous operating performance).
In a related study, Powers (2003) presented evidence that is somewhat con-trary to Hulburt et al. (2002). They showed that carve-out operating perfor-mance peaks at listing and then significantly deteriorates (which is inconsistentwith the divestiture gains hypothesis). He also found that when parents sell alarger portion of the firm, the post-issue operating performance and long-termexcess returns of the subsidiary is lower suggesting that parents use their in-formation advantage and conduct carve-outs in order to sell overvalued equity.However, he found that long-term excess returns of subsidiaries, calculated asthe difference of the subsidiary and the CRSP value-weighted index returnshave a positive relationship with the percentage of ownership sold. This sug-gests that investors adjust their valuation, to some extent, as they buy the sub-sidiary share cheaper when the parent sells a larger part of the shares in thecarve-out.
Dasilas and Leventis (2018) presented similar findings from a sample of 60equity carve-outs in Europe where the parent retained controlling interest inthe subsidiary. They documented significant positive announcement returnsof around 1.6% for the parent firm and positive initial return for the subsidiaryupon listing. However, they also found that after the positive initial returnupon listing, the shares of ECO firms produced severe losses in the subsequent
HETEROGENEOUS BELIEFS AND EQUITY CARVE-OUTS 175
months (on average), also inconsistent with the divestiture gains hypothesis.Allen and McConnell (1998) presented evidence consistent with the finan-
cial strategy hypothesis (divestiture gains hypothesis). They argued that man-agers value control over assets and they only divest through an equity carve-outwhen the firm is capital constrained. They found support for this hypothesisshowing that firms conducting carve-outs have poor operating performanceand high leverage before the carve-out. They documented that when proceedsfrom the ECO are used to pay down debt, the average 3-day excess return of6.6% upon announcement is greater than the average excess return of -0.01%for carve-outs where the company uses the funds for investment purposes.
The evidence presented so far in the literature is not conclusive as to howequity carve-outs create value for shareholders of the divesting firm. In thispaper, I suggest another hypothesis built on the heterogeneous beliefs literatureto explain this phenomenon.
4.2.4 The heterogeneous beliefs theory
The key assumption of the standard capital asset pricing model from the 1960sis homogeneous expectations (Sharpe, 1964). Under homogeneous expecta-tions, when investors are presented with an identical set of information, allinvestors have identical estimates of the expected returns and the probabil-ity distribution of security returns. The investor behavior has been discussedby several authors [e.g. Lintner (1965)] and the optimal portfolio strategy isdescribed as a combination of the market portfolio and risk-free bonds [e.g.Hakansson (1971)].
Miller (1977) argued that it is implausible to assume that investors haveidentical expectations in a world of uncertainty and forecast difficulties. Hepresented a theoretical model of divergence of opinions. In this model, in-vestors are presented with the same information, but they disagree about thefuture performance of the company and therefore have different value esti-mates. The theory implies that the stock will be bought by the investors whomhave the highest value estimate for the company. Furthermore, as the entiresupply of the security can be absorbed by a minority of potential investors,the price of the stock will be above the median estimate of investors, as mostinvestors (including those with the median value estimate) will forgo the in-vestment opportunity. His model shows that holding the average value esti-mate fixed, the larger the disagreement (the heterogeneity of beliefs) among
176 ESSAYS ON SEGMENT REPORTING AND VALUATION
investors, the higher the share price becomes. The same phenomenon can ex-plain the poor long-term returns for IPO firms, as the prices of new issuesare set by an optimistic minority of investors. As the uncertainty around thenewly listed firm resolves (as is usually the case over time), the divergence inbeliefs decrease and the stock price decreases to a level closer to the appraisalof the typical investor. In Appendix 4.A, I provide a simplified numerical ex-ample for the intuition behind the heterogeneous beliefs idea, how it affectsthe segment-by-segment valuation of a company and how spin-offs can unlockvalue for the shareholders.
Diether et al. (2002) provided empirical evidence in support of the diver-gence of opinions argumentation. They found that firms with higher disper-sion in analyst forecasts earn lower future returns. These results are consistentwith the model of Miller (1977) where heterogeneous beliefs increase stockprices (and decrease subsequent returns).
The Miller (1977) model also provided an explanation for the diversifica-tion discount of conglomerates. As a multi-segment company is more diver-sified and has more stable income streams than the parts of it have separately,there is less divergence of opinions about its future performance. He arguedthat diversification reduces divergence in beliefs as investors are usually opti-mistic about one or some (but not all) industries a company operates in. Hismodel of heterogeneous beliefs suggested that a conglomerate trades at a lowerprice compared to what the sum of its parts would be worth separately.
Jiao et al. (2013) were the first to provide empirical evidence supporting theconnection between diversification discount and heterogeneous beliefs. Theydocumented that diversified firms have lower divergence of beliefs comparedto focused firms. They also found, using a sample of merger deals, that theheterogeneity of investor beliefs for the bidder decreases after the takeover ifit was a focused firm that became diversified with the takeover. Furthermore,the reduction in heterogeneity of beliefs is higher for diversifying mergers thanfor non-diversifying ones. The authors also related the heterogeneity in beliefsto the valuation the firms trade at. They used the excess value measure fromBerger and Ofek (1995), the market value of firm assets divided by the imputedvalue of the firm, where the imputed value of the firm is the sum of the imputedvalue of the firms’ segments calculated as the sales of the segment multiplied bythe median market value to sales ratio in the segments’ industries. They foundthat the contemporaneous excess value of a diversified firm relative to its fo-
HETEROGENEOUS BELIEFS AND EQUITY CARVE-OUTS 177
cused peers is positively related to its excess heterogeneity (meaning that lowerheterogeneity in beliefs is associated with lower market values thus providingan explanation for the diversification discount).
Miller (1977) argued that splitting a company and selling the parts to theinvestors who value their characteristics the most can be value-enhancing dueto the higher divergence of beliefs the different parts have on their own. Heprovided an example of a company with three different segments, one high-growth division, one money-losing division and a normal profit division. Heargued that this company probably trades at normal earnings multiples. How-ever, if the company were split up into three parts the sum of the parts couldbe higher. The money losing division probably decreases the value of a com-pany for current investors through lowering the consolidated profits but thispart could be separated and sold as a turnaround case for investors. The divi-sion with great prospects could be sold to investors interested in potential highgrowth, while the rest of the company could be traded at a normal multiple.Assuming different investors are interested in different types of investments,the sum of the three parts could be higher than that of the conglomerate.
In this paper, I use this split-up argumentation of Miller (1977) to develop ahypothesis providing an explanation for the positive announcement return onequity carve-out announcements. Miller (1977) argued that managers can un-lock value for the shareholders through taking the firm to its parts and sellingthe parts separately. In an equity carve-out, the parent firm does not divest thesubsidiary in its entirety. However, ECOs provide a unique setting for testingthe split-up argument of Miller (1977). After an ECO, the shareholders of theparent will still own a (large) part of the ECO firm. After a spin-off, sharehold-ers can own the parent and the subsidiary separately. In contrast, following anequity carve-out only the ECO firm can be owned separately. Using the ECOsetting I can observe the valuation of a diversified firm (the parent) and a partof it (the ECO firm) at the same time.
I also provide an empirical investigation for this heterogeneous beliefs hy-pothesis explaining the positive announcement returns. Furthermore, to thebest of my knowledge, mine is the first study to empirically investigate corpo-rate restructuring from a heterogeneous beliefs point of view.
178 ESSAYS ON SEGMENT REPORTING AND VALUATION
4.3 Research question development
The literature review on the value creation of equity carve-outs above showsthat previous papers provide some support for both the asymmetric informa-tion hypothesis and the divestiture gains hypotheses, but also that other pa-pers present empirical evidence inconsistent with the hypotheses. Therefore,there is no conclusive evidence in the literature for the value creation of equitycarve-outs. Here I put forward a heterogeneous beliefs hypothesis to explainthe positive abnormal returns upon equity carve-out announcements.
4.3.1 The heterogeneous beliefs hypothesis
Miller (1977) argued that splitting up a conglomerate can result in higher share-holder value because the parts of the company can be sold to the investors whovalue the particular parts of the business the most. I build on this argumentto present the heterogeneous beliefs hypothesis explaining positive announce-ment returns on equity carve-outs.
The Miller (1977) model implied that in an ECO, the investors buyingshares in the ECO firm have higher value estimates about the ECO firm com-pared to most of the current owners of the parent. The intuition is as follows.Some current investors who are optimistic about the ECO firm and expect thelisting price to be below their value estimates might adjust their valuation ofthe parent share downwards. Other, potential investors whose value estimatesfor the parent share were just below the market price may adjust their valua-tion of the parent shares upwards if they expect the parent to sell the shares inthe ECO firm at a price higher than its intrinsic value. If the potential exitingshareholders can not be replaced by potential entering shareholders without adecrease in the market price (that is, if potential and current shareholders don’texpect the listing terms of the ECO firm to be good enough), the ECO destroysvalue for current shareholders. Given that the parent can decide if and whento carve out the ECO (similarly to the private owners who can decide if andwhen to conduct an IPO), it is reasonable to expect that firms only conduct anECO when the terms are satisfactory.
Investors who buy the shares in the ECO firm pay more for the shares thanthe value estimate of most of the parent company shareholders. These currentowners of the parent give up a share of their interest in the ECO firm for a
HETEROGENEOUS BELIEFS AND EQUITY CARVE-OUTS 179
price higher than their value estimate. This way they realize an indirect profitas the parent firm receives a cash sum for the shares sold in the ECO whichis higher than current owners’ value estimate for the interest disposed. There-fore, upon the announcement of the ECO most of the current shareholdersexpect to realize this indirect profit after the listing of the ECO firm. They ad-just their valuation of the parent shares upwards, leading to an increase in theshare price of the parent company upon ECO announcement. Once again, asfirms self-select into the decision and the timing of the ECO, it is not unrea-sonable to assume that firms carve out operations that can be sold for a pricethat is not below the estimate of most current shareholders.
Next, I develop the longer-term predictions of the heterogeneous beliefshypothesis for the valuation of the firms after the ECO. After the announce-ment of the carve-out there is a period of restructuring (operational changes,contract writing) before the listing of the shares in the subsidiary. This pe-riod lasts several months and the operations of the firm can change during theperiod. Moreover, firms provide quarterly updates that can also affect the mar-ket’s perception about the firm. During this period, it is difficult to differen-tiate the valuation effects that are specifically attributable to the ECO. There-fore, after looking at the announcement effect, I analyze the relative valuationlevel (diversification discount) at two points in time, before the announcementof the carve-out and after the listing of the subsidiary shares. I look at changesin valuation levels to investigate the effect of the equity carve-out on the differ-ent operations.
In particular, I look at two different valuation effects. First, the valua-tion effect of the carve-out on the parent company’s shares. This investigationsheds light on the changes in valuation level for the shareholders of the parentcompany. Next I discuss the valuation effect on the whole original group, i.e.the combined operations of the two entities. This latter question is of interestbecause it provides information on the financing possibilities of the originaloperations of the firm. Managers sometimes claim that the ECO transactionwill result in better opportunities for both firms in terms of financing newprojects. The market valuation of the old operations after the ECO is of in-terest because it investigates whether the market finances the operations at ahigher level following a restructuring.
Upon the listing of the subsidiary shares, investors can own shares in theECO firm separately. Some owners of the parent company may have a value
180 ESSAYS ON SEGMENT REPORTING AND VALUATION
estimate for the ECO firm that is higher than the listing price. Upon listing,these owners will sell their shares in the parent firm and buy shares in the ECOfirm separately. This is similar to the observations of Vijh (1994), who docu-mented a market anomaly of positive abnormal returns on spin-off ex-dates.The author conjectured that this is due to the separate firms attracting differ-ent types of investors who prefer buying shares in the separate entities.
Similarly, after the listing of ECO shares some previous shareholders buyshares in the ECO firm and sell their parent shares. This way they exit the in-vestor pool for parent shares and their shares will be sold to investors with thenext-highest value estimates. These new investors by definition have a lowervalue estimate of the firm. The heterogeneous beliefs hypothesis therefore ex-pects the valuation of the parent firms’ shares to decrease after the completionof the equity carve-out.
The hypothesis predicts the valuation level of the combined original opera-tions after the carve-out (that is, the valuation level of the parent firm combinedwith the divested part) to be higher than before the announcement. After theECO, owners with high value estimates for the ECO firm have the possibilityto invest in that firm separately. This way the relative valuation of the ECOfirm is expected to increase following the ECO. I do not expect the relativevaluation of the rest of the firm to change following the ECO as investors whoprefer to own the non-divested part of the group cannot buy shares in that partseparately, but have to co-own it with the non-divested part of the ECO firm.
RQ: Can the heterogeneous beliefs theory provide an explanation forthe positive announcement returns of equity carve-outs?
To the best of my knowledge, this is the first paper to empirically investi-gate the heterogeneous beliefs hypothesis for ECO value creation. However,some previous papers already present evidence consistent with my hypothesis.
Bayar et al. (2011) present a theoretical model implying that managers chooseto finance new project by equity carve-outs (in contrast to integrating the projectin the current firm) when the optimism of the marginal outsider financing theproject is higher than that of the managers and when this incremental opti-mism is higher about the new project than that about the whole firm. Theyprovide a rationale for "negative stub values", that is, when the parent firm’smarket value is lower than the market value of its holdings in the carve-outfirm (e.g. the carve-out of Palm from 3Com). They show that such cases arepossible given that the market value of the two firms are determined by differ-
HETEROGENEOUS BELIEFS AND EQUITY CARVE-OUTS 181
ent investor groups. Their model has several predictions. Projects with greateruncertainty, those about which outsiders are more optimistic and projects ap-pealing to an investor base different from current investors are more likely tobe implemented outside the firm under a new venture. However, new projectsthat are closely related to or that has greater synergies with the firm’s existingprojects are more likely to be implemented inside the firm.
Bayar et al. (2011) predict that negative stub values are more likely if het-erogeneity in investor beliefs about the new project is high, investors are moreoptimistic about the new project and when the investor base for the new andthe old projects are different (low correlation of investor beliefs). Their modelpredicts that the turnover in the subsidiary’s shares in cases of negative stubvalues will be much higher than the turnover of the parent’s shares given thatthe trade is generated by differences in beliefs. This prediction is consistentwith the empirical findings of Lamont and Thaler (2003). Lamont and Thaler(2003) analyze negative stub value cases during the dot-com bubble (1998-2000)in the US. Their sample consists of six negative stub value carve-outs where theparent companies announced their intention to distribute the subsidiary shareslater in the form of a tax-free spin-off. They argue that the law of one price isviolated due to the combination of two factors, short-sale constraints and thepresence of investors who are irrational, woefully uninformed, endowed withstrange preferences or for some other reasons, willing to own overpriced assets.The authors present arbitrage opportunities and argue that these investors wereirrational.
Parent firms in my paper do not intend to distribute the subsidiary sharesto the parent shareholders, so the arbitrage strategies presented by Lamont andThaler (2003) are not present. Hence, my paper does not assume investors tobe irrational. On the contrary, it assumes investors to be rational but to haveheterogeneous beliefs.
4.3.2 Testable empirical hypotheses
The heterogeneous beliefs hypothesis provides an additional explanation forpositive announcement returns of equity carve-outs. In order to distinguishthis hypothesis from the asymmetric information hypothesis and the divesti-ture gains hypothesis, I investigate the longer-term valuation effects of ECOannouncements.
The heterogeneous beliefs hypothesis predicts the valuation of the parent
182 ESSAYS ON SEGMENT REPORTING AND VALUATION
firm to decrease as 1) parent shareholders do not adjust their value estimateof the ECO firm upwards due to other investors paying a higher price for theshare, and 2) some parent shareholders with high value estimates for the ECOfirm will sell their shares in the parent to own the ECO firm separately. How-ever, it predicts the valuation of the original operations (the parent and theECO firm operations combined) to increase as a result of the restructuring asother, more optimistic investors also finance part of the original operations ata price higher than the value estimate of the shareholders of the parent. More-over, the Miller (1977) theory predicts the value effects to be more pronouncedwhen the ECO firm and the rest of the operations are more different.
In particular, I conduct the following four empirical tests to distinguish theheterogeneous beliefs hypothesis from the asymmetric information hypothesisand the divestiture gains hypothesis. The hypotheses are stated in the followingalternative forms.
H1: The valuation level of the parent share is lower following theequity carve-out.
The information asymmetry hypothesis predicts increase in the valuationof the parent due to the ECO signaling that the shares in the parent firm areundervalued. After receiving this signal, the market adjusts the price of theparent upwards and thus, the diversification discount decreases, and the eval-uation of the parent increases. The divestiture gains hypothesis also suggestshigher valuation following the ECO. The hypothesis expects operational orfinancing gains from the restructuring which results in higher valuation.
The heterogeneity in beliefs hypothesis predicts a decrease in the excessvalue of the parent as some investors of the parent whom were mainly inter-ested in the ECO part of the firm sells their shares in the parent and buys theECO share separately, in this way reducing the market price (and valuation) ofthe parent.
In the next test, I compare the valuation of the combined operations of theparent and the divested part, before and after the restructuring. The combinedoperations is defined as the group before the restructuring and the combinationof the parent firm and the divested part in the ECO firm after the restructuring.This way I can investigate whether managers can enhance the valuation of thetotal operations of the group through the restructuring.
H2: The valuation of the combined operations is higher after the eq-uity carve-out.
HETEROGENEOUS BELIEFS AND EQUITY CARVE-OUTS 183
The information asymmetry hypothesis suggests an increase in the valu-ation of the combined operations. The ECO signals that the parent share isundervalued and therefore, its valuation increases. The divestiture gains hy-pothesis also suggests increase in the valuation of the combined operations dueto improved future operating performance.
The heterogeneous beliefs hypothesis suggests an increase in the excessvalue of the combined operations. The ECO presents the opportunity forinvestors with high value estimates about the ECO firm to purchase its sharesseparately, therefore increasing the valuation level of the combined operations.
In the next two hypotheses, I compare parts of the sample to test theseeffects across different subgroups of reorganizations. First, I look at the relat-edness of the carved-out operations and the rest of the divesting firm.
H3: The increase in valuation of the combined operations is higherwhen the ECO firm operates in a different industry compared to the restof the group.
The information asymmetry hypothesis implies that the parent is signalingthat its stock is undervalued when conducting an ECO. Therefore, it expectsno difference in the increase in valuations based on the relatedness of the parentand the ECO firm. The divestiture gains hypothesis (in particular, the corpo-rate focus hypothesis) expects the increase in valuation to be higher when thetwo operations are more unrelated.
The heterogeneous beliefs theory predicts that the increase in valuation ishigher when the investors have more different value estimates which is morelikely if the two operations operate in unrelated businesses.
H4: The increase in valuation of the combined operations is largerwhen there is larger difference between the operating performance of theECO firm and the rest of the divesting group.
The information asymmetry hypothesis predicts no difference in the in-crease in valuations based on the similarity of the operating performances ofthe firms. The divestiture gains hypothesis does not predict that the changein valuation is related to the similarity of the operating performances of thefirms either. The heterogeneous beliefs hypothesis suggests that the increasein excess value is higher when the ECO is more different compared to the par-ent firm. Table (4.1) summarizes the predictions by the different theoreticalhypotheses for the four empirical tests.
In an additional analysis, I investigate associations between heterogeneous
184 ESSAYS ON SEGMENT REPORTING AND VALUATION
Table 4.1: Summary of predicted relationships by the hypothesesEmpirical hypothesis Het.B Asym.Inf Div.G.H1: ∆ parent EV - + +H2: ∆ in EV of the combined operations + + +H3: ∆ in EV higher for unrelated ECO + 0 +H4: ∆ in EV higher for more different ECO + 0 0
Notes: This table presents a summary of the different empirical hypotheses and the outcomepredicted by the different theoretical hypotheses. Het.B refers to the heterogeneous beliefshypothesis. Asym.Inf. refers to the asymmetric information hypothesis. Div.G. refers to thedivestitures gains hypothesis. EV stands for Excess value and is the valuation level variableused in the study. ∆ EV refers to change in valuation following the ECO. The ‘+’ sign indi-cates that the hypothesis predicts positive association. ‘-’ indicates that the hypothesis predictsnegative association. ‘0’ indicates that the theoretical hypothesis does not have any directionalprediction.
beliefs measures and valuation levels. Jiao et al. (2013) find that lower valua-tion levels are associated with lower heterogeneity in beliefs. Moreover, theydocument that the decrease in valuation following diversifying mergers are as-sociated with a decrease in heterogeneous beliefs. Previous findings in the het-erogeneous beliefs literature predict higher levels of heterogeneity in beliefs tobe positively related to valuation levels.
4.4 Methodology
I measure the abnormal announcement return of the equity carve-out as thethree-day abnormal return of the parent’s stock around the announcement day[t-1;t+1]. The abnormal daily return is calculated as the raw return of the par-ent’s stock less the daily return of the S&P 500 stock index. Previous studiesuse different time windows for measuring announcement returns. Schipperand Smith (1986) uses [t-4;t] as the event window (t being the date the ear-liest announcement about the subsidiary equity offering appears in the WallStreet Journal), arguing that the news coverage of the announcement might bedelayed and the WSJ coverage date t might not correspond to the actual an-nouncement date. Vijh (1994); Allen and McConnell (1998) use [t-1;t], Slovinet al. (1995) uses [t;t+1] and Prezas and Simonyan (2015) uses [t-1;t+1] and fiveother event windows between [t-3;t+3]. I mitigate the issue raised in Schipperand Smith (1986) by manually investigating the articles about the announce-
HETEROGENEOUS BELIEFS AND EQUITY CARVE-OUTS 185
ments in Factiva to see which date the firms announced the carve-outs. Duringthe data collection, I found that some announcement dates in SDC platinumdatabase is one day earlier than the first news in the Factiva database. For oth-ers, the time of the announcement (first news covering the event on Factiva)on the announcement day is after the closing of the market. I use the [t-1;t+1]event window in order to make sure that I capture the announcement returnsfor all these cases.
For investigating the long-term value effects of ECOs, I calculate excess val-ues for the parent, the ECO firm and the combined operations. I calculate ex-cess values at two different points in time, once before the announcement of thecarve-out and once after the completion of the ECO. The pre-announcementvalues are calculated using the last reported annual filings before the announce-ment date given that the end of the financial year is at least 40 days before theECO announcement. The enterprise value is calculated using the share priceand shares outstanding 60 days after the end of the last financial year. If thatday is after the announcement day, I use the share information from the daybefore the announcement day.
I calculate the post-completion excess values by using the financial figuresfrom the first annual filing after the listing of the ECO firm (both for the parentand the ECO firm). I calculate the market value of equity by using the shareprice and shares outstanding 60 days after the end of the financial year.
First, I use the excess value calculation formula of diversified firms follow-ing the diversification discount literature Berger and Ofek (1995); Jiao et al.(2013). I calculate excess value as the natural logarithm of the ratio of firm’senterprise value to its imputed value. The enterprise value of the firm is themarket value of its net operating assets calculated as the sum of the market valueof equity and book value of net debt. The imputed value of the firm is calcu-lated as the sum of its segments’ imputed values and the segment imputed valuesare calculated as the product of segment sales and the median sales-to-enterprisevalue ratio in the primary SIC industry of the segment among single-segmentfirms - similarly to Jiao et al. (2013):
EV = ln(Enterprisevalue
IV
), (4.1)
where
IV =
n∑j=1
∗SEGSALEj(Enterprisevalue
SALE)SSj
). (4.2)
186 ESSAYS ON SEGMENT REPORTING AND VALUATION
EV stands for excess value; IV stands for imputed value; Enterprisevalueis the market value of the assets for the firm calculated as the sum of the marketvalue of equity and book value of debt for the firm. SALE refers to net sales(sale item in Compustat) and (EnterprisevalueSALE )SSj is the median Enterprisevalue
to sale ratio of the single-segment firms operating in the segments’ primary SICindustry j.
I use this formula to calculate the excess value of the ECO firm and the ex-cess value of the parent before the restructuring if the parent owned all theshares from the ECO before the announcement. In cases when the parentowned less than 100% of the shares in the ECO before the announcement,I calculate the imputed value of the ECO firm before the announcement andsubtract the imputed value of the non-owned proportion of the ECO from theparent imputed value.
Pre-announcement excess value formula:
EVpar,pre = ln( EnterprisevalueparIVpar − IVECO ∗ (1− spre)
), (4.3)
where subscript par refers to the parent firm, subscript ECO refers to the carve-out firm and spre is the ratio of the parent’s ownership in the ECO before theannouncement. In cases when the ECO is a wholly owned subsidiary of theparent, this expression collapses to (4.1).
Furthermore, when calculating the excess value of the parent after the re-structuring, I adjust the excess value calculation for the ECO ownership. Incases when the parent owns more than 50 % of the ECO firm, I deduct the theimputed value of the non-controlling interest in the ECO similarly to (4.3).
Post-completion excess value formula for retained control:
EVpar,post = ln( EnterprisevalueparIVpar − IVECO ∗ (1− spost)
), (4.4)
where spost is the ratio of the parent’s ownership in the ECO after the comple-tion of the restructuring.
If the parent retains less than 50% of the ownership, it does not consolidatethe ECO and therefore, the sales of the ECO will not appear in the parentfilings. Therefore, I deduct the market value of the shares in the ECO fromthe enterprise value of the parent, as the imputed value calculated using thesegment filings excludes the imputed value of the ownership in the ECO.
Post-completion excess value formula when the control is not retained:
HETEROGENEOUS BELIEFS AND EQUITY CARVE-OUTS 187
EVpar,post = ln(Enterprisevaluepar −MVECO ∗ spost
IVpar
). (4.5)
I also calculate the excess value of the combined operations for the post-completion period. It is used to investigate whether the market finances theold operations after the restructuring at a lower discount. I calculate the excessvalue of the combined operations as the ratio of the enterprise value of theoriginal operations divided by the imputed value of the original operations.When the parent retains control over the ECO, its imputed value is for thewhole combined operations. In case the parent does not consolidate the ECOafter the restructuring, the imputed value of the combined operations is thesum of imputed values of the parent and the ECO.
Post-completion excess value of the combined operation for retained control:
EVops,post = ln(Enterprisevaluepar +MVECO ∗ (1− spost)
IVpar
), (4.6)
where the subscript ops refers to the combined operations.Post-completion excess value of the combined operation when the control is not
retained:
EVops,post = ln(Enterprisevaluepar +MVECO ∗ (1− spost)
IVpar + IVECO
). (4.7)
In an additional analysis, I investigate associations between heterogeneousbeliefs measures and excess values. I construct the measures of excess hetero-geneity in investor beliefs following Jiao et al. (2013). These variables measurethe excess level of investors’ heterogeneity in believes about the firm comparedto the imputed value of the standalone counterparts. I construct the variablessimilarly to the excess value of the firm. I take the natural logarithm of theratio of the heterogeneity of investor beliefs about the firm and the imputedheterogeneity of beliefs based on the segments of the firm. The imputed valueof heterogeneity in beliefs is calculated as segment-sales weighted average ofthe median heterogeneity in beliefs among the single-segment firms in the seg-ments’ primary SIC industry. First, the excess idiosyncratic volatility is calcu-lated as:
188 ESSAYS ON SEGMENT REPORTING AND VALUATION
Exc_V ol = ln(
σreturn∑nj=1
SEGSALEjSALE (σreturn)SSj
), (4.8)
where Exc_V ol stands for excess idiosyncratic volatility, σreturn is the standarddeviation of the daily, market-adjusted stock returns in a 90-day window; and(σreturn)SSj is the median σreturn of the single-segment firms operating in thesegment’s primary SIC industry j. I expect a higher level of excess idiosyncraticvolatility to indicate a higher level of heterogeneity in investor beliefs followingprevious empirical literature in the area (Jiao et al., 2013; Diether et al., 2002;Boehme et al., 2006).
The second variable for excess heterogeneity in beliefs is the excess sharetrading turnover,
Exc_Turnover = ln(
Turnover∑nj=1
SEGSALEjSALE (Turnover)SSj
), (4.9)
where Exc_Turnover stands for excess share trading turnover, Turnover is the90-day average of the ratio of daily trading volume to the shares outstandingand (Turnover)SSj is the median Turnover of the single-segment firms operatingin the segment’s primary SIC industry j.
Apart from the excess value and excess heterogeneity variables, I also fol-lowing the heterogeneous beliefs literature (Diether et al., 2002; Jiao et al.,2013) for constructing control variables. I calculate Size as the logarithm ofthe market value of equity, market-to-book (MTB) as the market value of assetsdivided by the book value of assets, long-term debt ratio (LTDebt) as the ra-tio of long term debt to book value of assets, operating performance (ROA) asoperating income before interest and taxes scaled by total assets and capital ex-penditures ratio (CAPEX) as the ratio of capital expenditures divided by totalsales.
4.5 Data and sample
I obtain the initial sample of equity carve-outs from the Security Data Cor-poration’s (SDC) spin-off database. The initial sample consists of 1568 eventsannounced and completed in the US between 1980 and 2017. I extract data onfinancial statement information from Compustat Annual Fundamentals, onsegment data from Compustat Historical Segments, and on stock prices and
HETEROGENEOUS BELIEFS AND EQUITY CARVE-OUTS 189
trading volumes from the Center for Research in Security Prices (CRSP). Ta-ble (4.2) presents the sample development.
Table 4.2: Sample developmentECO observations
Original sample 1568less unlisted parent (or not in Compustat) -1033less couldn’t match parent with CRSP -43less data unavailable for subsidiary -112less different end of financial period -78less data available only for pre-announcement or post-completion period
-88
Initial sample for hand-collecting post-IPO owner-ships
214
less events that could not be verified from Factiva -68less shares distributed later in spinoff -14less the EV the parent is not within +/- 1.386 -30Full sample 102
Notes: This table presents the sample development. Following Berger andOfek (1995); Jiao et al. (2013), I exclude observations where the absolute valueof the excess value calculated is over 1.386, meaning that the imputed value ofthe firms are less than 1/4th or more than 4 times of the enterprise value eitherin the pre-announcement or the post-completion period.
The database includes equity carve-outs where the parent is a private com-pany. I require the parent company to have financial information in Com-pustat, this leads to dropping 1033 observations (in most cases the parent isa private company), resulting in 535 events. For additional 43 events, thereis no share price data available for the parent in CRSP. I exclude another 112cases due to financial data for the ECO firm is not available from Compustat.I require the parent and the subsidiary to have the same financial year end af-ter the restructuring to enable the calculation of excess value for the originaloperations. This requirement results in dropping another 78 events. In an-other 88 cases, the data is not available both for the pre-announcement and thepost-completion period. These requirements result in an initial sample of 214equity carve-out events.
190 ESSAYS ON SEGMENT REPORTING AND VALUATION
The information about percentage of shares owned by the parent beforeand after the restructuring is missing for 121 observations. Since I need thisinformation to calculate the excess value of the parent in the post-completionperiod, I search the event included in the initial sample in Factiva to collect in-formation about the ownership retained by the parent in the ECO firm and toverify the observations (following previous studies on equity carve-outs usingSDC Platinum). I could not verify 68 observations and for 14 more events, theshares in the ECOs were later distributed as tax-free dividends. Consequently,this results in a sample of 132 observations. Following the diversification dis-count literature Berger and Ofek (1995); Jiao et al. (2013), I exclude observa-tions where the absolute value of the excess value calculated is over 1.386, mean-ing that the imputed value of the firms are less than 1/4th or more than 4 timesof the enterprise value either in the pre-announcement or the post-completionperiod, resulting in a full sample of 102 observations.
Table (4.3) presents the yearly distribution of equity carve-outs, the offersize, the book value of total assets of the ECO firms in million dollars and theaverage post-IPO ownership. Most carve-outs were conducted in 1996 with 12transactions. There is a slightly increasing trend in the size of the ECO, butno specific trend in the post-IPO ownership.
HETEROGENEOUS BELIEFS AND EQUITY CARVE-OUTS 191
Table 4.3: Sample distributionIPO year No. Mean
Offer Size(MUSD)
ECO Assets(MUSD)
Post-IPO Owner-ship(%)
1980 1 11.0 19.7 67.01981 2 19.4 39.6 42.01982 1 15.3 50.6 73.01983 3 57.5 103.1 68.21984 1 6.0 21.9 53.51985 2 420.8 3,442.2 63.01986 6 29.7 93.7 80.91987 6 109.3 341.2 51.51988 2 26.6 246.9 74.31989 2 40.6 13.0 63.01990 1 20.3 41.2 73.01991 1 9.0 43.6 73.01992 4 37.8 33.3 41.91993 6 352.4 9,478.8 65.71994 2 16.0 22.3 73.91995 2 78.0 234.0 56.41996 10 69.6 194.4 69.51997 6 108.8 305.6 54.11998 1 70.0 166.8 80.01999 5 1,011.5 3,126.0 66.92000 7 356.3 1,638.9 75.52001 1 221.0 1,373.7 83.02002 1 107.3 228.6 34.72003 1 475.0 1,030.7 0.02005 1 51.9 300.7 54.02006 1 173.3 329.7 79.52007 1 957.0 1,146.0 89.02009 1 720.0 1,301.9 80.02012 1 262.5 546.4 64.72013 13 486.0 1,710.4 63.42014 7 463.5 3,062.8 61.62015 2 738.3 4,256.4 57.32016 1 1,050.0 7,867.8 70.0Total 102 280.0 1,622.1 64.1
Notes: This table presents the sample distribution by the year of the IPO. No. is the number ofECO-s listed in the year. Offer Size is the proceeds from the ECO in million dollars. ECO Assetsit the book value of total assets of the ECO in the pre-announcement period in million dollars.Post-IPO Ownership is the proportion of ECO shares owned by the (pre-announcement) parentcompany after the restructuring.
192 ESSAYS ON SEGMENT REPORTING AND VALUATION
4.6 Descriptive statistics
The following section presents the descriptive statistics. Table (4.4) presentsfinancial numbers and ratios for the parent firms and the ECOs in the pre-announcement period. The mean of total assets for the parent firm is 20635million dollars, and the mean total assets for the ECO firm is 1622 milliondollars. Similarly, sales and EBIT is also higher for the parent firm.
The ECO firms are more profitable on average (ROA=0.081) compared tothe parent (ROA=0.063), but the difference is not significant at conventionallevels. The ECO firms also have higher sales growth on average but the differ-ence is not significant, either. Notably, this figure is missing for more than halfof ECO firms because its calculation requires two restated years of financialinformation from the ECO.
The carve-out firms have higher capital expenditure intensity on average(sig. 1%), while it has significantly less leverage, measured as the long term debtto total assets ratio (significant at the 1% level).
Table (4.5) presents the descriptive statistics around the restructuring event.The mean 3-day abnormal announcement return is 2.1% for the full sample,significantly different from zero at the 5% level in line with previous literature.The mean excess value of the firm before the announcement is -0.002 whichcorresponds to a diversification discount of 0.2%. The median excess value of0.005 indicates a diversification premium of 0.5%. The descriptive statisticsof the final sample shows that the post-completion excess value of the parentis negative, but not significantly different from zero. The excess value of thewhole operations (parent and ECO together) is also insignificantly negative,while the ECO is traded at a significantly positive excess value (both mean andmedian significant at the 1% level).
HETEROGENEOUS BELIEFS AND EQUITY CARVE-OUTS 193
Table 4.4: Descriptive statistics, parent and ECO financial numbers and ratios,pre-announcement period
variable N mean sd min p25 median p75 maxAssetsParent 102 20 635 79 003 10.9 294.7 2 339 18 367 755 233AssetsECO 102 1 622 5 505 1.099 39.7 126.8 1 103 52 098diff. 19013 Wilcoxont-stats p-value (0.015)** Sign rank p-value (0.00)***
SalesParent 102 6 031 11835.5
20.1 2474 1 194 6 613 67 425
SalesECO 102 816 2 339 0.0 32.5 77.6 509.8 20 228diff. 5214 Wilcoxont-stats p-value (0.000)*** Sign rank p-value (0.000)***
EBITParent 101 938.7 2 383 -200.9 8.2 104.5 875 19 769EBITECO 102 56.2 222.8 -1 425 3.1 8.5 60.5 847diff. 882 Wilcoxont-stats p-value (0.000)*** Sign rank p-value (0.00)***
ROAParent 102 0.063 0.059 -0.082 0.025 0.066 0.101 0.235ROAECO 102 0.081 0.241 -1.865 0.028 0.079 0.152 0.813diff. -0.018 Wilcoxont-stats p-value (0.43) Sign rank p-value (0.007)***
SalesgrowthParent 98 1.47 1.654 0.512 1.043 1.154 1.367 16.663SalesgrowthECO 35 2.355 5.655 0.466 1.021 1.217 1.623 34.564diff. -0.708 Wilcoxont-stats p-value (0.49) Sign rank p-value (0.027)**
CAPEXParent 100 0.065 0.080 0.000 0.017 0.046 0.081 0.529CAPEXECO 101 0.090 0.125 0.000 0.017 0.045 0.103 0.684diff. -0.025 Wilcoxont-stats p-value (0.002)*** Sign rank p-value (0.018)**
LTDParent 102 0.280 0.189 0.000 0.142 0.276 0.372 0.929LTDECO 102 0.148 0.200 0.000 0.000 0.043 0.244 0.710diff. 0.132 Wilcoxont-stats p-value (0.000)*** Sign rank p-value (0.000)**
Notes: The table presents the descriptive statistics about selected financial measures for the parent (Parent) and thesubsidiary (ECO) at the end of last financial year before the carve-out announcement. Assets refers to total assets inmillion dollars. EBIT is earnings before interests and taxes in million dollars. ROA is calculated as EBIT divided byAssets. Sales is net revenues in million USD. Salesgrowth is the ratio of current sales to lagged sales. CAPEX ismeasured as capital expenditures divided by sales. LTD refers to long-term debt divided by total assets. T-statistics arepresented for the hypothesis of equal means. Results for Wilcoxon matched-pairs signed-ranks test are presented withthe null hypothesis of the equality of distributions.
194 ESSAYS ON SEGMENT REPORTING AND VALUATION
Table 4.5: Descriptive statistics about the restructuring, market reaction andexcess values
Descriptive statistics of the samplevariable N mean sd min p25 median p75 max% owned before ECO 102 96.9 9.15 52.6 100 100 100 100% owned after ECO 102 64.1 22.9 0 54 70.9 80.2 94.5
3-day announcement return 98 2.1% 8.8%p -15.3% -1.5% 0.8% 4.6% 70.9%t-statistics p-value (0.0206)** Sign rank p-value (0.0089)***
pre_Excess value 102 -0.002 0.572 -1.367 -0.454 0.005 0.371 1.238t-statistics p-value (0.97) Sign rank p-value (0.92)
post_Excess value 102 -0.061 0.547 -1.381 -0.427 -0.055 0.315 1.104t-statistics p-value (0.26) Sign rank p-value (0.35)
post_ECO Excess value 102 0.801 1.612 -2.148 -0.359 0.373 1.832 5.360t-statistics p-value (0.000)*** Sign rank p-value (0.000)***
post_Op excess value 102 -0.018 0.594 -1.376 -0.393 0.021 0.281 1.816t-statistics p-value (0.75) Sign rank p-value (0.656)
Notes: Panel A presents the descriptive statistics of the whole sample and Panel B presents the descriptive statisticsof the restricted sample. 3-day announcement return is the three-day abnormal announcement return for the parentcompany on the day of the carve-out announcement with the S&P500 return used as the benchmark. The t-test teststhe hypothesis that the mean announcement return equals zero. The Wilcoxon Sign test tests the hypothesis that themedian announcement return is zero. % owned before ECO and % owned after ECO is the percentage of equity inthe ECO firm owned by the parent before and after the carve-out, respectively. pre_Excess value is the excess valueof the parent firm in the pre-announcement period. post_Excess value is the excess value of the parent firm in thepost-completion period. post_ECO Excess value is the excess value of the carve-out firm after the ECO. post_Op excessvalue refers to the excess value of the whole of the parent and the carve-out in the post-completion period.
HETEROGENEOUS BELIEFS AND EQUITY CARVE-OUTS 195
4.7 Empirical results
Table (4.6) presents the empirical results for the four testable hypotheses forthe full and the restricted sample separately. The restricted sample excludesevents where the ECO is a very small part of the original group (less than 2%measured by sales) as in these cases the value effect predicted by the heteroge-neous beliefs hypothesis is very small. The results and their significance levelare similar for using a 1% or 3% cutoffs as well. At the 4% cutoff, the mag-nitude of the differences are similar but it becomes statistically insignificant.Furthermore, at that level more than 25% of the observations are excluded.The median ECO sales to parent sales ratio is 13%.
Panel A presents the change in the excess value of the parent firm from thepre-announcement to the post-completion period. The results show that thechange in the excess value of the parent is negative and insignificant. Excludingcases where the ECO is very small compared to the parent, the results showthat the excess value of the parent decreases by 8.6% (e−0.09 = 0.9139) follow-ing the restructuring, significant at the 10% level. This result is in contrastwith the predictions by the information asymmetry and the divestiture gainshypotheses and is consistent with the heterogeneous beliefs hypothesis.
Panel B presents the result for the change in the excess value of the wholeoperations; it compares the parent excess value in the pre-announcement pe-riod to the combined parent and ECO excess value in the post-completion pe-riod. The results show that the change in the excess value of the combinedoperations is negative and insignificant, which does not support any of theproposed hypotheses.
Panel C presents the results for the announcement return and the changein excess value by relatedness. The parent and the ECO are related if their mainindustry of operations are in the same 2-digit SIC industry. The announcementreturn tests can indicate whether investors expect higher gains from one type ofrestructuring over the other. The announcement returns are not significantlyassociated with the relatedness of the parent and the ECO, inconsistent withthe divestiture gains hypothesis and the heterogeneous beliefs hypothesis. Thechange in the excess value of combined operations is significantly higher at the10% level for unrelated carve-outs for both the full and the restricted samples,consistent with the heterogeneous beliefs hypothesis and the divestiture gainshypothesis.
196 ESSAYS ON SEGMENT REPORTING AND VALUATION
Panel D presents the sample split by difference in financial performance be-tween the parent and the ECO, measured as the absolute value of the differenceof their EBIT to total assets ratio. Observations with below-median absolutedifference are classified as ‘similar’. The results show that the announcementreturns are higher for the observations with above-median difference in finan-cial performance, but the difference is insignificant. The change in excess valuefor the combined operations is lower for firms with different financial perfor-mance and the difference is also insignificant.
Table 4.6: Empirical resultsPanel A. Hypothesis 1, Change in parent excess value
Sample N pre post diff. p-valueExcess value (EV) Full 102 -0.002 -0.061 -0.059 (0.23)
Restricted 88 0.015 -0.074 -0.090 (0.08)*Panel B. Hypothesis 2, Change in the excess value of the whole operations
Sample N pre post diff. p-valueOps. EV Full 102 -0.002 -0.018 -0.016 (0.78)
Restricted 88 0.016 -0.045 -0.061 (0.275)Panel C. Hypothesis 3, Sample split by relatedness using 2-digit SIC industry classification
Sample N related unrelated diff. p-valueAbn. ann. return Full 48;50 2.6% 1.6% 0.98% (0.58)
Restricted 43;42 2.6% 1.5% 1.07% (0.60)∆ Ops. EV Full 52;50 -0.112 0.083 0.196 (0.091)*
Restricted 46;42 -0.149 0.036 0.185 (0.096)*Panel D. Hypothesis 4, Sample split by difference in financial performance (ROA)
Sample N similar different diff. p-valueAnn. return Full 48;50 1.2% 2.9% +1.7%p (0.33)
Restricted 42;43 0.96% 3.2% +2.2%9 (0.27)∆ Ops. EV Full 51;51 0.073 -0.106 -0.179 (0.12)
Restricted 44;44 -0.023 -0.099 -0.076 (0.50)
Notes: The table presents the empirical results for the four testable hypotheses for the full and the restricted sampleseparately. Panel A presents the change in the excess value of the parent firm from the pre-announcement to thepost-completion period. Panel B presents the result for the change in the excess value of the whole operations, itcompares the parent excess value in the pre-announcement period to the combined parent and ECO excess value inthe post-completion period. Panel C presents the results on a sample split using 2-digit SIC industry classification.The related sample includes observations where the ECO and the parent have their main operations in the same 2-digitSIC industry. Ann.return stands for announcement returns. Panel D presents a sample split based on the differencein financial performance between the parent and the ECO in the pre-announcement period. Similar performanceindicate below-median absolute difference in the ECO and parent ROA. p-values of t-tests are presented for each testwhere the null hypothesis is equivalence of means.
Previous literature documents future returns being negatively related toproxies used as risk measures under homogeneous expectations models (Jiaoet al., 2013) - such as analyst disagreement (Diether et al., 2002) or trading vol-ume (Lee and Swaminathan, 2000). Jiao et al. (2013) find that lower excess val-ues are associated with lower heterogeneity in beliefs using excess idiosyncratic
HETEROGENEOUS BELIEFS AND EQUITY CARVE-OUTS 197
volatility and excess trading volume as a proxy. Furthermore, they documentthat the decrease in excess values following diversifying mergers are associatedwith a decrease in heterogeneous beliefs. These results are also inconsistentwith using excess trading volume and idiosyncratic volatility as risk measures.
In an additional analysis, I investigate associations between heterogeneousbeliefs measures and excess values. First, I present findings from univariate re-gressions in Table (4.7). The dependent variable in the first three pairs of uni-variate regressions (level regressions) are the pre-announcement excess valueof the parent, the post-completion excess value of the parent and the post-completion excess value of the ECO, respectively, while the independent vari-able is one of the excess heterogeneity measures for the same firm in the sameperiod. In the last pair of univariate regressions, the dependent variable is thechange in excess values from the pre-announcement to the post-completionperiod and the independent variable is the change in the excess heterogeneitymeasure between the two periods.
The results from the levels regressions show no association between excessvalues and excess heterogeneity measures. The findings from the changes re-gressions show that changes in excess heterogeneity measures are negatively re-lated to changes in excess value measures, and not in line with the expectationsfrom the heterogeneous beliefs literature and somewhat unexpected, given theevidence documented by Jiao et al. (2013).
Table 4.7: Univariate regressions - excess value and heterogeneous beliefsmeasures
Panel A: Heterogeneous beliefs and excess valueexcess volatility excess turnover
EV measure N coeff. p-value R-squared N coeff. p-value R-squared
pre_EVParent 94 -0.006 (0.935) 0.00 94 -0.027 (0.605) 0.00post_EVParent 96 -0.102 (0.119) 0.03 96 -0.064 (0.201) 0.02post_EVECO 98 -0.054 (0.756) 0.00 96 0.080 (0.572) 0.00∆EV _Parent 94 -0.393 (0.000)*** 0.12 94 -0.241 (0.001)*** 0.13
Notes: The table presents univariate regressions. The dependent variables are the excess value measures and theindependent variables are the excess heterogeneity measures for the same firm in the same period. For the changein parent EV measure, the dependent variable is the change in the EV of the parent from the pre-announcement tothe post-completion period and the independent variable is the change in the excess heterogeneity measure from thepre-announcement to the post-completion period.
Next, I present findings from the linear regression models similar to Jiaoet al. (2013) in Table (4.8). The dependent variable in the regressions are ex-cess value measures for a given firm (Parent or ECO) and measurement period
198 ESSAYS ON SEGMENT REPORTING AND VALUATION
(Pre-announcement or post-completion) in regressions (1-6), and changes inexcess values from the pre-announcement to the post-completion period in re-gressions (7) and (8). The independent variables are the excess heterogeneitymeasures corresponding to the same firm measured in the same period (regres-sions 1-6) and measured as the difference between the pre-announcement andthe post-completion period (regressions 7-8). I use control variables similar toJiao et al. (2013).
My results suggest that excess values are negatively related to heteroge-neous beliefs measures both in the pre-announcement and in the post-completionperiod. Moreover, the change in excess values from the pre-announcement pe-riod to the post-completion period is negatively related to the changes in het-erogeneity in beliefs measures, a result that is unexpected given the findings ofJiao et al. (2013). The results suggest that the decrease in parent excess valuesco-exist with an increase in excess heterogeneity of beliefs. This finding thoughis consistent with Dereeper and Mashwani (2018) who document that there ishigher information asymmetry for the parent firm after an ECO (measuredusing analyst disagreement and analyst following).
My findings, regarding the association between excess value measures andexcess heterogeneity measures in corporate restructuring, in particular, equitycarve-outs, are inconsistent with those of Jiao et al. (2013) for mergers. Forboth the parent and the ECO I document that higher levels of heterogeneity inbeliefs are associated with lower excess values. Furthermore, I document thatthe decrease in the excess value for parent companies following equity carve-outs are associated with a contemporaneous increase in heterogeneity in beliefsmeasures. This result is consistent with Dereeper and Mashwani (2018) whodocument an increase in information asymmetry (analyst disagreement andanalyst following) following equity carve-outs.
Tab
le4.
8:Li
ne
arr
eg
ress
ion
mo
de
ls-
exc
ess
valu
ea
nd
he
tero
ge
ne
ous
be
liefs
me
asu
res
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Dep
.var
:Exc
essv
alue
.(Fi
rm)
Pare
ntPa
rent
Pare
ntPa
rent
EC
OE
CO
Pare
ntPa
rent
(Per
iod)
Pre
Pre
Post
Post
Post
Post
Cha
nge
(∆)
Cha
nge
(∆)
Exc
essi
dios
yncr
atic
vola
tility
-0.1
78**
-0.2
40**
*-0
.405
***
-0.3
03**
*(0
.075
6)(0
.064
8)(0
.113
)(0
.084
9)E
xces
stur
nove
r-0
.217
***
-0.1
61**
*-0
.171
*-0
.169
***
(0.0
587)
(0.0
540)
(0.0
939)
(0.0
568)
Size
0.03
430.
0582
**0.
0115
0.02
410.
209*
**0.
186*
**0.
164
0.08
76(0
.025
3)(0
.025
6)(0
.025
4)(0
.027
8)(0
.062
2)(0
.066
0)(0
.126
)(0
.128
)M
arke
t-to-
book
0.17
4***
0.17
1***
0.31
9***
0.27
6***
0.05
46**
*0.
0540
***
0.20
9***
0.22
1***
(0.0
487)
(0.0
457)
(0.0
921)
(0.0
933)
(0.0
0629
)(0
.006
67)
(0.0
498)
(0.0
508)
EBI
T-to
-ass
ets
-2.5
08**
-3.0
86**
*-0
.758
-0.9
35-2
.125
***
-2.1
06**
-2.4
95**
-2.5
21**
(0.9
88)
(0.9
65)
(0.9
66)
(1.0
21)
(0.7
74)
(0.8
74)
(1.1
47)
(1.1
71)
Long
-term
debt
0.52
0*0.
533*
0.56
7**
0.51
8*0.
786
0.86
90.
558
0.65
3(0
.295
)(0
.282
)(0
.272
)(0
.278
)(0
.580
)(0
.627
)(0
.543
)(0
.552
)C
apex
0.52
40.
668*
*0.
387*
**0.
348*
**0.
124*
**0.
115*
**0.
111
0.24
1(0
.329
)(0
.319
)(0
.123
)(0
.125
)(0
.041
3)(0
.043
6)(0
.525
)(0
.535
)C
onst
ant
-0.5
12**
-0.5
59**
*-0
.562
***
-0.6
07**
*-0
.665
*-0
.576
0.01
860.
0077
2(0
.199
)(0
.191
)(0
.189
)(0
.186
)(0
.356
)(0
.377
)(0
.046
6)(0
.045
0)O
bser
vatio
ns91
9193
9397
9590
90R
-squa
red
0.25
00.
312
0.24
90.
286
0.65
90.
628
0.43
70.
460
Not
es:T
hist
able
repo
rtst
here
sults
from
the
regr
essi
onso
fexc
essv
alue
sand
exce
sshe
tero
gene
ities
.The
depe
nden
tvar
iabl
eis
the
leve
lofe
xces
sva
lue
for
the
pare
ntin
regr
essi
ons1
-2(m
easu
red
inth
epr
e-an
noun
cem
entp
erio
d)an
d3-
4(m
easu
red
inth
epo
st-c
ompl
etio
npe
riod
),th
ele
velo
fex
cess
valu
efo
rth
eE
CO
inre
gres
sion
s5-6
(pos
t-com
plet
ion
peri
od)a
ndch
ange
inex
cess
valu
efo
rth
epa
rent
inre
gres
sion
s7-8
(cha
nge
from
the
pre-
anno
unce
men
tto
the
post
-com
plet
ion
peri
od).
The
inde
pend
entv
aria
bles
inea
chre
gres
sion
corr
espo
nds
toth
esa
me
firm
and
sam
epe
riod
asth
ede
pend
ent
vari
able
.In
regr
essi
ons
7-8
the
inde
pend
ent
vari
able
sar
eth
ech
ange
inth
eva
riab
les
from
the
pre-
anno
unce
men
tto
the
post
-co
mpl
etio
npe
riod
.E
xces
sva
lue,
exce
ssid
iosy
ncra
ticvo
latil
ityan
dex
cess
turn
over
are
the
log
ratio
sof
the
mar
ketv
alue
ofas
sets
,the
stan
dard
devi
atio
nof
mar
ket-a
djus
ted
stoc
kre
turn
sand
trad
ing
volu
me/
outs
tand
ing
shar
es,r
espe
ctiv
ely
over
the
corr
espo
ndin
gsa
les-w
eigh
ted
aver
age
ofse
gmen
tim
pute
dva
lues
.The
segm
enti
mpu
ted
valu
esar
em
edia
nsof
segm
enti
ndus
trie
s.Si
zeis
the
natu
rall
ogar
ithm
ofm
arke
tval
ueof
equi
ty.
Mar
ket
tobo
okis
the
the
ratio
ofm
arke
tva
lue
toth
ebo
okva
lue
ofas
sets
.E
BIT
-to-A
sset
sis
the
ratio
ofE
BIT
divi
ded
byth
ebo
okva
lue
ofas
sets
.Lon
g-te
rmde
btis
the
ratio
oflo
ng-te
rmde
btsc
aled
byth
ebo
okva
lue
ofas
sets
.Cap
exis
the
amou
ntof
capi
tale
xpen
ditu
ress
cale
dby
sale
s.St
anda
rder
rors
are
prov
ided
inpa
rent
hese
s.**
*p<
0.01
,**
p<0.
05,*
p<0.
1
200 ESSAYS ON SEGMENT REPORTING AND VALUATION
4.8 Summary and conclusions
Consistent with previous literature, I document positive announcement re-turns for equity carve-outs. I present a new hypothesis, which builds on theheterogeneous beliefs theory, to explain this phenomenon. In order to distin-guish the heterogeneous beliefs hypothesis from the previous two hypothesesin previous literature, I developed four, empirically testable hypotheses. Table(4.9) presents the empirically testable hypotheses, the results predicted by thedifferent hypotheses and the results from my empirical tests. For H1, the sig-nificantly negative change in excess values is consistent with the heterogeneousbeliefs hypothesis but inconsistent with the asymmetric information hypoth-esis and the divestiture gains hypothesis. The heterogeneous beliefs hypothe-sis predicts lower valuation levels for the parent firm after the ECO, becausesome previous investors sell their shares in the parent firm to own the ECOseparately. The other two hypothesis predicted positive valuation effects formECOs due to signalling or operational or financing gains.
The empirical result from H2 shows insignificant change in the excess valueof the combined operations, and is inconsistent with all three hypotheses. Thechange in excess value is higher for unrelated ECOs, and is consistent withthe heterogeneous beliefs hypothesis and the divestiture gains hypothesis. Thefindings from H4 show that the change in excess value is not significantly dif-ferent when the financial performance of the ECO is more different from theparent, amd is inconsistent with the heterogeneous beliefs hypothesis. For thetwo tests where there was a significant change in excess value (difference inchanges in excess value between the subsamples), the findings are consistentwith the heterogeneous beliefs hypothesis. For the other three tests, the em-pirical results were not significant.
The additional tests on the association between heterogeneity in beliefsvariables and excess values are unexpected, given the heterogeneous beliefs the-ory for both the levels and the changes regressions. I documented a negative as-sociation between excess values and excess heterogeneity, which is inconsistentwith previous findings in the heterogeneous beliefs literature (Jiao et al., 2013),but reinforces the findings of Dereeper and Mashwani (2018) who documentincreased information asymmetry for the parent following equity carve-outs.
In this paper I, provide a new, competing hypothesis by using the heteroge-neous beliefs theory by Miller (1977) for explaining the positive announcement
HETEROGENEOUS BELIEFS AND EQUITY CARVE-OUTS 201
Table 4.9: Summary of predicted relationships by the hypotheses and empiricaltest results
Empirical hypothesis Het.B Asym.Inf Div.G. ResultH1: ∆ parent EV - + + -H2: ∆ in EV of the combined operations + + + 0H3: ∆ in EV higher for unrelated ECO + 0 + +H4: ∆ in EV higher for more different ECO + 0 0 0
Notes: This table presents a summary of the different empirical hypotheses, the outcomepredicted by the different theoretical hypotheses and the result of the empirical tests. Het.Brefers to the heterogeneous beliefs hypothesis. Asym.Inf. refers to the asymmetric informationhypothesis. Div.G. refers to the divestitures gains hypothesis. EV refers to Excess value. ∆
EV refers to change in excess value from the pre-announcement to the post-completion period.The ‘+’ sign indicates that the hypothesis predicts positive association. ‘-’ indicates that thehypothesis predicts negative association. ‘0’ indicates that the theoretical hypothesis does nothave any directional prediction or that the empirical results are not significant.
returns upon ECO announcements. I present evidence consistent with the het-erogeneous beliefs explanation and inconsistent with the previous asymmetricinformation hypothesis and the divestiture gains hypothesis. Moreover, to thebest of my knowledge, this is the first paper to investigate corporate restructur-ing from a heterogeneous beliefs point of view. The association documentedbetween excess values and excess heterogeneity extends the literature debatingthe use of such measures for risk proxies, and provides evidence contrary toprevious findings in the literature.
202 ESSAYS ON SEGMENT REPORTING AND VALUATION
Appendix 4.A
Table 4.10: Variable definitionsAssets Book value of total assets in million dollars.CAPEX Capital expenditures ratio, calculated as the ratio of capital expenditures divided by sales.EBIT Earnings before interests and taxes in million dollars.Enterprisevalue The market value of net operating assets calculated as the sum of the market value of equity
and book value of net debt, in million dollars.EV, Excess value The natural logarithm of the ratio of firm’s enterprise value to its imputed value, calculated
as ln(Enterprisevalue
IV
).
EVpar,post Excess value of the parent firm, after the restructuring, calculated as
ln(
Enterprisevaluepar
IVpar−IVECO∗(1−spost)
).
Exc_Turnover Excess share trading turnover, calculated as ln(
Turnover∑nj=1
SEGSALEjSALE
(Turnover)SSj
).
Exc_Vol Excess idiosyncratic volatility, calculated as ln(
σreturn∑nj=1
SEGSALEjSALE
(σreturn)SSj
).
IV, Imputed value The imputed value of the firm is calculated as the sum of its segments’ imputed values, and thesegment imputed values are the product of segment sales and the median sales-to-enterprisevalue ratio in the primary SIC industry of the segment among single-segment firms, calcu-
lated as∑nj=1 ∗SEGSALEj(
EnterprisevalueSALE
)SSj
), where Enterprisevalue
SALE
SS
jis the me-
dian Enterprisevalue to sale ratio of the single-segment firms operating in the segments’primary SIC industry j.
LTDebt Long-term debt ratio, calculated as the ratio of long-term debt to total assets.MTB Market-to-book ratio, calculated as the market value of assets divided by the book value of
assets.MVECO Market value of the ECO firm’s equity in million dollars.Offer size The proceeds from the ECO transaction in million dollars (selling price of the share times
number of shares sold).par, ECO, ops (sub-scripts)
The par subscript refers to the parent firm, the ECO subscript refers to the carve-out firm(in the post-completion period) and the ops subscript refers to the whole operations (that isthe parent firm before the announcement and the combination of the parent firm and thecarved-out part in the post-completion period).
Post-IPO Owner-ship
The proportion of ECO shares owned by the (pre-announcement) parent company after therestructuring.
pre, post The pre subscript refers to the pre-announcement period, the post subscript refers to the post-completion period.
ROA Operating performance measure, calculated as operating income before interest and taxesscaled by total assets.
spre, spost The ratio of the parent’s ownership in the ECO before the announcement (pre), and after thecompletion of the restructuring (post).
SALE SalesSEGSALEj Sales in segment j.σreturn Standard deviation of the daily, market-adjusted stock returns in a 90-day window.(σreturn)SSj The median σreturn of the single-segment firms operating in the segment’s primary SIC in-
dustry jSize Natural logarithm of the market value of equity, ln(MVE).Turnover 90-day average of the ratio of daily trading volume to the shares outstanding.(Turnover)SSj The median Turnover of the single-segment firms operating in the segment’s primary SIC
industry j.
Notes: This table presents the definitions of the different variables in the paper.
HETEROGENEOUS BELIEFS AND EQUITY CARVE-OUTS 203
Appendix 4.B
Table 4.11: List of equity carve-outsAnnouncement date Listing date ECO Parent1980-05-29 1980-07-14 General Defense Corp Clabir Corp1981-05-05 1981-05-06 Wometco Cable TV Inc Wometco Enterprises Inc1981-06-09 1981-07-30 Grant Industries Inc Buildex Inc1982-09-20 1982-10-27 Sizzler Restaurants Intl Inc Collins Foods International1983-06-13 1983-11-09 Levitt Corp Starrett Housing Corp1983-06-28 1983-09-14 First Data Resources American Express Co1983-07-26 1983-09-19 Pearle Health Services GD Searle & Co1984-09-17 1984-11-21 Robert Bruce Industries Savoy Industries Inc1985-08-09 1985-09-19 All American Gourmet Co General Host Corp1985-09-11 1985-10-24 Fireman’s Fund Corp American Express Co1986-05-09 1986-06-25 Newmont Gold Co Newmont Mining Corp1986-05-23 1986-07-09 Genmar Industries Inc Minstar Inc1986-08-20 1986-11-24 Victoria Creation Inc United Merchants & Mnfrs Inc1986-09-11 1986-11-06 BHA Group Inc Standard Havens Inc1986-10-01 1986-10-08 Gruen Marketing Corp Jewelcor Inc1986-11-07 1986-12-19 MTech Corp MCorp1986-11-25 1987-02-10 Crown Brands Inc H.H.R. Food Industries1987-02-05 1987-03-19 Petrolane Partners LP Texas Eastern Corp1987-02-12 1987-03-26 Applied Bioscience Intl IMS International Inc1987-03-27 1987-05-05 Envirosafe Services Inc IU International Corp1987-05-27 1987-09-23 Entree Corp Farm House Foods Corp1987-08-19 1987-10-01 IBP Inc Occidental Petroleum Corp1988-06-28 1988-08-11 Kinder-Care Learning Centers Kinder-Care Inc1988-10-27 1989-01-19 Thermo Cardiosystems Inc Thermedics (Thermo Electron)1988-10-28 1988-12-16 ERC Environmental and Energy ERC International Inc1989-10-26 1989-12-15 Home Nutritional Services Inc Healthdyne Inc1990-05-03 1990-06-12 NSC Corp (Waste Management) OHM Corp1991-09-09 1991-12-06 Jimbo’s Jumbos Inc Chock Full O’Nuts Corp1991-10-09 1992-03-03 Vitalink Pharmacy Services Inc Manor Care Inc1992-01-17 1992-03-31 Eskimo Pie Corp Reynolds Metals Co1992-05-22 1992-07-29 RHI Entertainment Inc New Line Cinema Corp1992-10-13 1992-11-20 USA Classic Inc Orbit International Corp1993-03-17 1993-06-03 Allstate Corp Sears Roebuck & Co1993-04-06 1993-06-22 Healthdyne Technologies Inc Healthdyne Inc1993-05-07 1993-07-16 Steck-Vaughn Publishing Corp National Education Corp1993-06-18 1993-08-26 Simmons Outdoor Corp Noel Group Inc1993-08-06 1993-09-30 Kentucky Electric Steel Inc NS Group Inc1993-08-13 1993-10-27 Paul Revere Corp Textron Inc1994-01-07 1994-02-23 FinishMaster Inc Maxco Inc1994-04-22 1994-06-10 ThermoLase Corp ThermoTrex Corp1995-08-01 1995-09-22 Midwest Express Holdings Inc Kimberly-Clark Corp1995-10-23 1995-12-13 Ascent Entertainment Group Inc COMSAT Corp1996-03-29 1996-06-27 Trex Medical Corp ThermoTrex Corp1996-04-05 1996-06-13 Southern Pacific Funding,OR Imperial Credit Industries Inc1996-04-17 1996-06-10 Thermo Optek Corp Thermo Instrument Systems Inc1996-05-03 1996-08-09 National Processing Inc Natl City Corp, Cleveland, Ohio
(cont.)
204 ESSAYS ON SEGMENT REPORTING AND VALUATION
Table 4.11: (continued)Announcement date Listing date ECO Parent1996-06-13 1996-10-04 Integrated Living Communities Integrated Health Services Inc1996-07-24 1996-09-18 Thermo BioAnalysis Thermo Instrument Systems Inc1996-07-29 1996-11-05 Symons International Group Inc Goran Capital Inc1996-08-01 1996-10-31 Depuy Inc (Corange Ltd) Johnson & Johnson1996-08-14 1996-10-31 Donnelley Enterprise Solutions RR Donnelley & Sons Co1996-08-26 1996-10-25 Metris Cos Inc Fingerhut Cos Inc1997-04-15 1997-06-20 Metrika Systems Corp Thermo Instrument Systems Inc1997-06-02 1997-08-06 Eagle Geophysical Inc Seitel Inc1997-07-16 1997-09-16 NewCom Inc Aura Systems Inc1997-08-12 1997-10-07 Logility Inc American Software Inc1997-10-17 1997-12-11 Thermo Vision Thermo Optek Corp1997-11-06 1997-12-18 Dollar Thrifty Automotive Grp Chrysler Corp1998-03-19 1998-06-18 Unigraphics Solutions Inc Electronic Data Systems Corp1999-01-08 1999-03-31 Pepsi Bottling Group Inc PepsiCo Inc1999-02-09 1999-07-30 Digex Inc Intermedia Communications Inc1999-03-31 1999-06-23 TD Waterhouse Group Inc Toronto-Dominion Bank1999-04-21 1999-07-28 American National Can Group Pechiney SA1999-10-18 1999-12-16 Xpedior Inc PSINet Inc1999-11-12 2000-02-15 Savvis Communications Corp CenturyLink Inc2000-01-12 2000-02-10 Digex Inc Intermedia Communications Inc2000-01-13 2000-03-23 inSilicon Corp Phoenix Technologies Ltd2000-03-08 2000-06-15 Osca Inc (Great Lakes Chem) SPX Corp2000-05-12 2000-10-18 Monsanto Co Pharmacia Corp2000-06-05 2000-09-22 INRANGE Technologies Corp SPX Corp2000-07-26 2000-11-10 Luminent Inc MRV Communications Inc2001-02-20 2001-06-14 FMC Technologies Inc FMC Corp2001-09-10 2002-02-01 ZymoGenetics Inc Novo Nordisk A/S2003-08-04 2003-10-31 Overnite Corp Union Pacific Corp2004-06-29 2005-01-28 National Interstate Corp American Financial Group Inc2005-09-21 2006-01-26 Chipotle Mexican Grill Inc McDonald’s Corp2007-04-26 2007-08-14 VMware Inc EMC Corp2008-09-15 2009-02-11 Mead Johnson Nutrition Co Bristol-Myers Squibb Co2012-02-13 2012-06-27 EQT Midstream Partners LP Eqt Corp2012-02-20 2013-02-01 Zoetis Inc Pfizer Inc2012-02-27 2013-03-28 Pinnacle Foods Inc Blackstone Group LP2012-06-10 2013-09-26 Covisint Corp Compuware Corp2012-07-19 2013-01-18 SunCoke Energy Partners LP SunCoke Energy Inc2012-08-08 2013-01-18 CyrusOne Inc Cincinnati Bell Inc2012-09-05 2013-04-19 Blackhawk Network Holdings Inc Safeway Inc2012-09-28 2013-01-17 CVR Refining LP CVR Energy Inc2012-12-14 2013-04-19 SeaWorld Entertainment Inc Blackstone Group LP2013-01-07 2013-08-09 QEP Midstream Partners LP QEP Resources Inc2013-02-07 2013-08-01 Sprouts Farmers Market LLC Apollo Global Management LLC2013-04-04 2013-07-18 UCP Inc PICO Holdings Inc2013-05-13 2013-08-13 Independence Realty Trust RAIT Financial Trust2013-06-06 2013-07-17 NRG Yield Inc NRG Energy Inc2013-08-08 2014-04-11 Enable Midstream Partners LP CenterPoint Energy Inc2014-02-06 2014-11-05 Antero Midstream Partners LP Antero Resources Corp2014-03-28 2014-10-15 Dominion Midstream Partners LP Dominion Resources Inc2014-04-29 2014-07-30 Westlake Chemical Partners LP Westlake Chemical Corp2014-04-30 2014-06-27 NextEra Energy Partners LP NextEra Energy Inc2014-08-28 2014-10-15 Great Western Bancorp Inc National Australia Bank Ltd2014-09-28 2015-02-06 Columbia Pipeline Partners LP NiSource Inc2014-10-01 2015-03-12 Summit Materials Inc Blackstone Group LP2014-11-06 2014-12-17 Rice Midstream Partners LP Rice Energy2015-10-01 2016-04-20 MGM Growth Properties LLC MGM Resorts International
Notes: This table contains the list of equity carve-outs studied in this paper. Dates are in YYYY-MM-DD format.
HETEROGENEOUS BELIEFS AND EQUITY CARVE-OUTS 205
Appendix 4.C - Illustrative example
This section illustrates the effect of heterogeneous beliefs on the market priceof the conglomerate and the different parts separately. It is a simplified examplebased on the model of Miller (1977).
Assume a group consists of three separable segments, A, B and C. Thereare five investors on the market with limited capital to invest, and in order toclear the supply of shares for any company on the market, two investors needto invest in the project. Furthermore, there is no short selling.7 The meanvalue estimates of the investors are 50, 30 and 40, respectively.
Under homogeneous expectations, all investors estimate the value of thesegment based on the information available and arrive at 50, 30 and 40 respec-tively. Then, the group trades at a price of 120 (50+30+40). If one splits thegroup into its parts, the parts will sell for 50, 30 and 40 separately, the valueestimate by the investors.
Now assume that investors have heterogeneous beliefs with respect to thevalue of the different parts of the firm. The value estimates of the investorsare presented in Table (4.12). The value estimate of the group by the differentinvestor is the sum of their value estimates for the different segments. Themean value estimates for segment A, B and C are still 50, 30 and 40, respectively,however, the value estimates of the different investors differ. As two investorsare needed to buy the whole supply of shares, the market price of any entitywill equal the value estimate of the investor with the second largest one. Thisleads to the following result. Compared to a market price of 50, 30 and 40 forthe three different parts of the firm under homogeneous believes, the marketprice of the different parts are 60, 35 and 56, respectively. Also, the wholegroup would trade at 130 compared to the 120 under homogeneous beliefs and
7Investors have limited capital to invest, as otherwise the market price of any venture would
be set by the value estimate of the most optimistic investor. The supply of shares on the
market can be cleared by two investors as Miller (1977) argues that in order to finance any
venture, less than half of the potential investors are enough to clear the market (that is, he
argues that the investor with the mean value estimate will not invest in the firm because
there will be enough investors with above-mean value estimates to buy the whole supply
of shares). No short selling is there to simplify the example, in order for the Miller (1977)
model to be applicable it is enough if short selling is costly.
206 ESSAYS ON SEGMENT REPORTING AND VALUATION
Table 4.12: The value estimates of the different investors for the different partsof the firm under heterogeneous beliefs
Investors A B C Group;∑
(A+B+C)Investor I. 30 25 56 111Investor II. 40 30 72 142Investor III. 50 35 8 93Investor IV. 60 40 24 124Investor V. 70 20 40 130Market price 60 35 56 130Mean value estimate 50 30 40 120Rel.sd value estimates 31.62% 26,35% 63,25% 15,65%Market price to mean est. ratio 1,2 1,17 1,4 1,083
Notes: This table presents the value estimates of the five investors for the different segmentsseparately, as well as the value estimates for the group (that is the sum of their value estimatesfor the segments. The market price is the second highest value estimate for the entity. Inevery column, bold indicates the two highest value estimates. The investors with these valueestimates are the ones who buy the shares of the company and the market price is the lowervalue estimate of the two. ‘Rel.sd’ is the relative standard deviation of value estimates calculatedas the standard deviation of industry value estimates divided by the mean value estimate.
151, the sum of what the three parts would trade for separately. Also, if thegroup - trading at 130 - would decide to spin off segment C, the price of Cwould be 56 and the price of the rest of the firm would be 908, resulting inunlocking a value of 16 for the original investors (90+56-130=16).
The table provides some other practical insights into the heterogeneousbeliefs literature. The relative standard deviation of value estimates show howheterogeneous beliefs are about the value of the different parts. Miller (1977)argues that the higher the heterogeneity in beliefs, the higher the share pricebecomes compared to the mean value estimate. The last row of the table showthe ratio of market price to mean value estimate. The ratio is the highest forC, where the heterogeneity in beliefs is the highest. The market price for Cseparately would be 56, 40% over the mean value estimate.9 Among the parts
8The sum of A+B value estimates for the five investors are 55, 70, 85, 90 and 100 respectively.9It is also interesting to observe that the relative difference between the market price of A
and C is lower than the difference in mean value estimates. This is similar to what one can
observe nowadays with respect to General Motors and Tesla. The two firms have similar
market capitalization, even though in 2016 GM delivered more than 1000 times more vehi-
cles, recorded more than 20 times more revenue than Tesla and recorded a net income of 10
HETEROGENEOUS BELIEFS AND EQUITY CARVE-OUTS 207
of the firm, the market price is closest to the mean estimate for B with a 17%difference in values. B is also the one with the lowest heterogeneity in beliefs.
Another prediction of the Miller (1977) model - which has also been shownempirically by Jiao et al. (2013) - is that diversification reduces heterogeneity inbeliefs. The table shows that the group as a whole has lower heterogeneity inbeliefs compared to the value-weighted average heterogeneity in beliefs for itssegments (in fact, it has lower heterogeneity in beliefs than any of its segments).This also leads to lower ratio of market price to mean value estimate. The lowerheterogeneity in beliefs can be linked to the diversification discount.
bn USD (on 166 bn revenues) compared to a loss reported by Tesla. The small difference
in market valuation can be explained by heterogeneous beliefs as the price of both firms are
driven by the few most optimistic investors. While Tesla’s investors are really optimistic
compared to the mean investor, there is less heterogeneity in beliefs about the future per-
formance of GM and so GM is priced less above the mean value estimate. Source of data:
http://nordic.businessinsider.com/tesla-value-vs-ford-gm-chart-2017-4; 20-06-2018.
208 ESSAYS ON SEGMENT REPORTING AND VALUATION
Bibliography
AIMR. Financial Reporting in the 1990s and Beyond. Association for Invest-ment Management and Research, 1993.
Elio Alfonso, Dana Hollie, and Shaokun Carol Yu. Managers’ Segment Fi-nancial Reporting Choice: When Aggregated Segment-Level Earnings Dif-fer from Firm-Level Earnings? SSRN Electronic Journal, 5 2011.
Jeffrey W. Allen and John J. McConnell. Equity Carve-Outs and ManagerialDiscretion. The Journal of Finance, 53(1):163–186, 2 1998.
Paul André, Andrei Filip, and Rucsandra Moldovan. Segment DisclosureQuantity and Quality under IFRS 8: Determinants and the Effect on Fi-nancial Analysts’ Earnings Forecast Errors. The International Journal of Ac-counting, 51(4):443–461, 2016.
Anil Arya, Hans Frimor, and Brian Mittendorf. Discretionary disclosure ofproprietary information in a multisegment firm. Management Science, 56(4):645–658, 2010.
Ramji Balakrishnan, Trevor S. Harris, and Pradyot K. Sen. The PredictiveAbility of Geographic Segment Disclosures. Journal of Accounting Research,28(2):305, 23 1990.
Bruce A. Baldwin. Segment Earnings Disclosure and the Ability of SecurityAnalysts to Forecast Earnings Per Share. The Accounting Review, 59(3):376–389, 1984.
Sasson Bar-Yosef and Itzhak Venezia. Experimental study of implications ofSFAS 131: The effects of the new standard on the informativeness of segmentreporting. Discussion Papers, 2004.
Onur Bayar, Thomas J. Chemmanur, and Mark H. Liu. A theory of equitycarve-outs and negative stub values under heterogeneous beliefs. Journal ofFinancial Economics, 100(3):616–638, 6 2011.
BIBLIOGRAPHY 209
Bruce K. Behn, Nancy B. Nichols, and Donna L. Street. The predictive abilityof geographic segment disclosures by us companies: Sfas no. 131 vs. sfas no.14. Journal of International Accounting Research, 1(1):31–44, 2002.
Daniel A. Bens and Steven J. Monahan. Disclosure quality and the excess valueof diversification. Journal of Accounting Research, 42(4):691–730, 2004.
Daniel A. Bens, Philip G. Berger, and Steven J. Monahan. Discretionary Dis-closure in Financial Reporting: An Examination Comparing Internal FirmData to Externally Reported Segment Data. The Accounting Review, 86(2):417–449, 3 2011.
Philip G. Berger and Rebecca Hann. The Impact of SFAS No. 131 on Infor-mation and Monitoring. Journal of Accounting Research, 41(2):163–223, 52003.
Philip G. Berger and Rebecca N. Hann. Segment Profitability and the Pro-prietary and Agency Costs of Disclosure. The Accounting Review, 82(4):869–906, 7 2007.
Philip G. Berger and Eli Ofek. Diversification’s effect on firm value. Journalof Financial Economics, 37(1):39–65, 1 1995.
Anne Beyer, Daniel A. Cohen, Thomas Z. Lys, and Beverly R. Walther. Thefinancial reporting environment: Review of the recent literature. Journal ofAccounting and Economics, 50(2-3):296–343, 2010.
David Blackwell. Equivalent comparisons of experiments. The Annals of Math-ematical Statistics, pages 265–272, 1953.
David Blackwell and Meyer Girshick. Theory of Games and Statistical Decisions.John Wiley and Sons, New York, 1954.
Belen Blanco, Juan M. Garcia Lara, and Josep A. Tribo. Segment Disclosureand Cost of Capital. Journal of Business Finance & Accounting, 42(3-4):367–411, 4 2015.
Rodney D. Boehme, Bartley R. Danielsen, and Sorin M. Sorescu. Short-SaleConstraints, Differences of Opinion, and Overvaluation. Journal of Finan-cial and Quantitative Analysis, 41(02):455, 6 2006.
210 ESSAYS ON SEGMENT REPORTING AND VALUATION
Christine A. Botosan and Mary S. Harris. Motivations for a change in disclo-sure frequency and its consequences: An examination of voluntary quarterlysegment disclosures. Journal of Accounting Research, 38(2):329–353, 2000.
Christine A. Botosan and Mary Stanford. Managers’ Motives to Withhold Seg-ment Disclosures and the Effect of SFAS No. 131 on Analysts’ InformationEnvironment. The Accounting Review, 80(3):751–772, 7 2005.
Martin Bugeja, Robert Czernkowski, and Daryl Moran. The Impact of theManagement Approach on Segment Reporting. Journal of Business Finance& Accounting, 42(3-4):310–366, 4 2015.
Kees Camfferman and Stephen Zeff. ‘the apotheosis of holding company ac-counting’: Unilever’s financial reporting innovations from the 1920s to the1940s. Accounting, Business & Financial History, 13(2):171–206, 2003.
Jose Manuel Campa and Simi Kedia. Explaining the Diversification Discount.The Journal of Finance, 57(4):1731–1762, 8 2002.
Tyrone M. Carlin and Nigel Finch. Discount Rates in Disarray: Evidence onFlawed Goodwill Impairment Testing. Australian Accounting Review, 19(4):326–336, 12 2009.
Peter F. Chen and Guochang Zhang. Heterogeneous Investment Opportu-nities in Multiple-Segment Firms and the Incremental Value Relevance ofSegment Accounting Data. The Accounting Review, 78(2):397–428, 4 2003.
Peter F. Chen and Guochang Zhang. Segment Profitability, Misvaluation, andCorporate Divestment Corporate Divestment. The Accounting Review, 82(1):1–26, 2007.
Young Jun Cho. Segment Disclosure Transparency and Internal Capital Mar-ket Efficiency: Evidence from SFAS No. 131. Journal of Accounting Research,53(4):669–723, 2015.
Daniel W. Collins. Predicting Earnings with Sub-Entity Data: Some FurtherEvidence. Journal of Accounting Research, 14(1):163–177, 1976.
Robert Comment and Gregg A. Jarrell. Corporate focus and stock returns.Journal of Financial Economics, 37(1):67–87, 1 1995.
BIBLIOGRAPHY 211
Teresa L. Conover and Wanda A. Wallace. Equity market benefits to disclosureof geographic segment information: An argument for decreased uncertainty.Journal of International Accounting, Auditing and Taxation, 4(2):101–112, 11995.
Denis Cormier and Michel Magnan. Corporate environmental disclosurestrategies: determinants, costs and benefits. Journal of Accounting, Auditing& Finance, 14(4):429–451, 1999.
Denis Cormier and Michel Magnan. Environmental reporting management:a continental european perspective. Journal of Accounting and Public Policy,22(1):43–62, 2003.
Henrik Cronqvist, Peter Högfeldt, and Mattias Nilsson. Why agency costsexplain diversification discounts. Real Estate Economics, 29(1):85–126, 2001.
Lane Daley, Vikas Mehrotra, and Ranjini Sivakumar. Corporate focus andvalue creation evidence from spinoffs. Journal of Financial Economics, 45(2):257–281, 8 1997.
Apostolos Dasilas and Stergios Leventis. The performance of European equitycarve-outs. Journal of Financial Stability, 34:121–135, 2 2018.
Efthimios G. Demirakos, Norman C. Strong, and Martin Walker. What Val-uation Models Do Analysts Use? Accounting Horizons, 18(4):221–240, 122004.
Sebastien Dereeper and Asad Iqbal Mashwani. Equity carve-outs, divergenceof beliefs and analysts’ following. Research in International Business and Fi-nance, 43:58–67, 1 2018.
Karl B. Diether, Christopher J. Malloy, and Anna Scherbina. Differences ofOpinion and the Cross Section of Stock Returns. The Journal of Finance, 57(5):2113–2141, 10 2002.
Ronald A. Dye. Classifications Manipulation and Nash Accounting Standards.Journal of Accounting Research, 40(4):1125–1162, 9 2002.
David Easley and Maureen O’hara. Information and the Cost of Capital. TheJournal of Finance, 59(4):1553–1583, 8 2004.
212 ESSAYS ON SEGMENT REPORTING AND VALUATION
Peter Easton. Estimating the Cost of Capital Implied by Market Prices andAccounting Data. Foundations and Trends R© in Accounting, 2(4):241–364,2007.
Pamela Edwards and Richard A. Smith. Competitive disadvantage and vol-untary disclosures: the case of segmental reporting. The British AccountingReview, 28(2):155–172, 6 1996.
Michael Ettredge, Soo Young Kwon, and David Smith. Competitive harmand companies’ positions on sfas no. 131. Journal of Accounting, Auditing &Finance, 17(2):93–109, 2002a.
Michael Ettredge, Soo Young Kwon, and David Smith. Security market effectsassociated with sfas no. 131: reported business segments. Review of Quanti-tative Finance and Accounting, 18(4):323–344, 2002b.
Michael L. Ettredge, Soo Young Kwon, David B. Smith, and Paul A. Zarowin.The Impact of SFAS No. 131 Business Segment Data on the Market’s Abil-ity to Anticipate Future Earnings. The Accounting Review, 80(3):773–804, 72005.
Michael L. Ettredge, Soo Young Kwon, David B. Smith, and Mary S. Stone.The effect of sfas no. 131 on the cross-segment variability of profits reportedby multiple segment firms. Review of Accounting Studies, 11(1):91–117, 2006.
Patricia M. Fairfield, Sundaresh Ramnath, and Teri Lombardi Yohn. DoIndustry-Level Analyses Improve Forecasts of Financial Performance? Jour-nal of Accounting Research, 47(1):147–178, 3 2009.
Pablo Farías and Ricardo Rodríguez. Segment disclosures under ifrs 8’s man-agement approach: has segment reporting improved? Spanish Journal ofFinance and Accounting/Revista Espanola de Financiacion y Contabilidad, 44(2):117–133, 2015.
FASB. Statement of Financial Accounting Standards No. 14: Financial Report-ing for Segments of a Business Enterprise, 1976.
FASB. SFAS 131 : Disclosures about Segments of an Enterprise and RelatedInformation, 1997.
BIBLIOGRAPHY 213
Gerald A. Feltham and James A. Ohlson. Valuation and Clean Surplus Ac-counting for Operating and Financial Activities. Contemporary AccountingResearch, 11(2):689–731, 3 1995.
George Foster. Security price revaluation implications of sub-earnings disclo-sure. Journal of Accounting Research, pages 283–292, 1975.
Kimberley E Frank and J William Harden. Corporate restructurings: A com-parison of equity carve-outs and spin-offs. Journal of Business Finance & Ac-counting, 28(3-4):503–529, 2001.
M. Gietzmann and J. Ireland. Cost of Capital, Strategic Disclosures and Ac-counting Choice. Journal of Business Finance & Accounting, 32(3-4):599–634,4 2005.
Dan Givoly, Carla Hayn, and Julia D’Souza. Measurement Errors and Infor-mation Content of Segment Reporting. Review of Accounting Studies, 4(1):15–43, 1999.
Benjamin Graham and David L. Dodd. Security analysis : principles and tech-nique. McGraw-Hill, 1951.
Magee Marilyn Greenstein and Heibatollah Sami. The Impact of the SEC’sSegment Disclosure Requirement on Bid-Ask Spreads. The Accounting Re-view, 69(1):179–199, 1994.
Nils H. Hakansson. Capital Growth and the Mean-Variance Approach to Port-folio Selection. The Journal of Financial and Quantitative Analysis, 6(1):517,1 1971.
Mary Stanford Harris. The Association between Competition and Managers’Business Segment Reporting Decisions. Journal of Accounting Research, 36(1):111, 21 1998.
David C Hayes. The contingency theory of managerial accounting. The Ac-counting Review, 52(1):22, 1977.
Rachel M. Hayes and Russell Lundholm. Segment Reporting to the CapitalMarket in the Presence of a Competitor. Journal of Accounting Research, 34(2):261, 23 1996.
214 ESSAYS ON SEGMENT REPORTING AND VALUATION
Niclas Hellman, Jordi Carenys, and Soledad Moya Gutierrez. Introducingmore ifrs principles of disclosure–will the poor disclosers improve? Account-ing in Europe, 15(2):242–321, 2018.
Don Herrmann and Wayne Thomas. Segment reporting in the EuropeanUnion: Analyzing the effects of country, size, industry, and exchange list-ing. Journal of International Accounting, Auditing and Taxation, 5(1):1–20, 11996.
Don Herrmann and Wayne B. Thomas. An Analysis of Segment Disclosuresunder SFAS No. 131 and SFAS No. 14. Accounting Horizons, 14(3):287–302,9 2000.
David Hirshleifer and Siew Hong Teoh. Limited attention, information dis-closure, and financial reporting. Journal of Accounting and Economics, 36(1-3):337–386, 2003.
Dana Hollie and Shaokun Carol Yu. Do Reconciliations Of Segment EarningsAffect Stock Prices? - ProQuest. Journal of Applied Business Research, 28(5):1085–1106, 2012.
Ole-Kristian Hope, Tony Kang, Wayne B. Thomas, and Florin Vasvari. Pricingand mispricing effects of sfas 131. Journal of Business Finance & Accounting,35(3-4):281–306, 2008.
Kewei Hou, Mathijs A. Van Dijk, and Yinglei Zhang. The implied cost ofcapital: A new approach. Journal of Accounting and Economics, 53(3):504–526, 2012.
Heather M Hulburt, James A Miles, and J Randall Woolridge. Value creationfrom equity carve-outs. Financial Management, pages 83–100, 2002.
L. Peter Jennergren and Kenth Skogsvik. The Abnormal Earnings GrowthModel, Two Exogenous Discount Rates, and Taxes. Journal of Business Fi-nance & Accounting, 38(5-6):505–535, 6 2011.
Michael C. Jensen. Agency costs of free cash flow, corporate finance, andtakeovers. The American Economic Review, 76(2):323–329, 1986.
Jie Jiao, Bin Qiu, and An Yan. Diversification and heterogeneity of investorbeliefs. Journal of Banking & Finance, 37(9):3435–3453, 9 2013.
BIBLIOGRAPHY 215
Sven-Erik Johansson, Tomas Hjelström, and Niclas Hellman. Accounting forgoodwill under ifrs: A critical analysis. Journal of International Accounting,Auditing and Taxation, 27:13–25, 2016.
Kose John and Eli Ofek. Asset sales and increase in focus. Journal of FinancialEconomics, 37(1):105–126, 1995.
L. Todd Johnson and Kimberley R. Petrone. Commentary: Is goodwill anasset? Accounting Horizons, 12(3), 1998.
D.J. Johnstone. Information and the Cost of Capital in a Mean-Variance Ef-ficient Market. Journal of Business Finance & Accounting, 42(1-2):79–100, 12015.
A. Qayyum Khan and Dileep R. Mehta. Voluntary Divestitures and theChoice Between Sell-Offs and Spin-Offs. The Financial Review, 31(4):885–912, 11 1996.
William R Kinney. Predicting earnings: entity versus subentity data. Journalof Accounting Research, pages 127–136, 1971.
Richard Frank Kochanek. Segmental Financial Disclosure by Diversified Firmsand Security Prices. The Accounting Review, 49(2):245–258, 1974.
Tim. Koller, Marc H. Goedhart, David. Wessels, and McKinsey and Company.Valuation : measuring and managing the value of companies. John Wiley &Sons, 2010.
S.P. Kothari, Karthik Ramanna, and Douglas J. Skinner. Implications forGAAP from an analysis of positive research in accounting. Journal of Ac-counting and Economics, 50(2-3):246–286, 12 2010.
Wenchao Kou and Simon Hussain. Predictive gains to segmental disclosurematrices, geographic information and industry sector comparability. TheBritish Accounting Review, 39(3):183–195, 2007.
Sudha Krishnaswami and Venkat Subramaniam. Information asymmetry, val-uation, and the corporate spin-off decision. Journal of Financial Economics,53(1):73–112, 7 1999.
216 ESSAYS ON SEGMENT REPORTING AND VALUATION
Richard Lambert, Christian Leuz, and Robert E. Verrecchia. Accounting In-formation, Disclosure, and the Cost of Capital. Journal of Accounting Re-search, 45(2):385–420, 5 2007.
Richard A. Lambert, Christian Leuz, and Robert E. Verrecchia. Informationasymmetry, information precision, and the cost of capital. Review of Fi-nance, 16(1):1–29, 2011.
Owen A. Lamont and Richard H. Thaler. Can the Market Add and Subtract?Mispricing in Tech Stock Carve-outs. Journal of Political Economy, 111(2):227–268, 4 2003.
Larry H.P. Lang and Rene M. Stulz. Tobin’s q, corporate diversification, andfirm performance. Journal of Political Economy, 102(6):1248–1280, 1994.
Charles M.C. Lee and Bhaskaran Swaminathan. Price Momentum and TradingVolume. The Journal of Finance, 55(5):2017–2069, 10 2000.
Edith Leung and Arnt Verriest. The Impact of IFRS 8 on Geographical Seg-ment Information. Journal of Business Finance & Accounting, 42(3-4):273–309, 4 2015.
Wilbur G. Lewellen. A pure financial rationale for the conglomerate merger.The Journal of Finance, 26(2):521–537, 5 1971.
John Lintner. Security prices, risk, and maximal gains from diversification.The Journal of Finance, 20(4):587–615, 12 1965.
Gerald J. Lobo, Sung S. Kwon, and Gordian A. Ndubizu. The Impact of SFASNo. 14 Segment Information on Price Variability and Earnings Forecast Ac-curacy. Journal of Business Finance & Accounting, 25(7-8):969–985, 9 1998.
Annie S. Mcgowan and Valaria P. Vendrzyk. The Relation between Cost Shift-ing and Segment Profitability in the Defense-Contracting. The AccountingReview, 77(4):949–969, 2002.
Edward M Miller. Risk, uncertainty, and divergence of opinion. The Journalof Finance, 32(4):1151–1168, 1977.
BIBLIOGRAPHY 217
Rucsandra Moldovan. Post-implementation reviews for iasb and fasb stan-dards: A comparison of the process and findings for the operating segmentsstandards. Accounting in Europe, 11(1):113–137, 2014.
Richard D. Morris. Signalling, Agency Theory and Accounting Policy Choice.Accounting and Business Research, 18(69):47–56, 12 1987.
Stewart C. Myers. Interactions of corporate financing and investment deci-sions—implications for capital budgeting. The Journal of Finance, 29(1):1–25,1974.
Vikram Nanda. On the Good News in Equity Carve-Outs. The Journal ofFinance, 46(5):1717–1737, 12 1991.
Nancy B. Nichols, Donna L. Street, and Sidney J. Gray. Geographic segmentdisclosures in the United States: reporting practices enter a new era. Journalof International Accounting, Auditing and Taxation, 9(1):59–82, 1 2000.
Nancy B. Nichols, Donna L. Street, and Sandra J. Cereola. An analysis of theimpact of adopting IFRS 8 on the segment disclosures of European blue chipcompanies. Journal of International Accounting, Auditing and Taxation, 21(2):79–105, 2012.
Nancy B. Nichols, Donna L. Street, and Ann Tarca. The Impact of SegmentReporting Under the IFRS 8 and SFAS 131 Management Approach: A Re-search Review. Journal of International Financial Management & Accounting,24(3):261–312, 9 2013.
James A. Ohlson. Earnings, Book Values, and Dividends in Equity Valuation.Contemporary Accounting Research, 11(2):661–687, 3 1995.
James A. Ohlson and Beate E. Juettner-Nauroth. Expected EPS and EPSGrowth as Determinantsof Value. Review of Accounting Studies, 10(2-3):349–365, 9 2005.
Richard F. Ortman. The Effects on Investment Analysis of Alternative Report-ing Procedure for Diversified The Effects on Investment Analysis of Alter-native Reporting Procedure for Diversified Firms. The Accounting Review,50(2):298–304, 1975.
218 ESSAYS ON SEGMENT REPORTING AND VALUATION
Jong Chool Park. The effect of sfas 131 on the stock market’s ability to predictindustry-wide and firm-specific components of future earnings. Accounting& Finance, 51(2):575–607, 2011.
Stephen H. Penman. Financial statement analysis and security valuation, vol-ume 3. McGraw-Hill, New York, 2007.
Stephen H. Penman and Theodore Sougiannis. A Comparison of Dividend,Cash Flow, and Earnings Approaches to Equity Valuation. ContemporaryAccounting Research, 15(3):343–383, 9 1998.
Eric A. Powers. Deciphering the Motives for Equity Carve-Outs. Journal ofFinancial Research, 26(1):31–50, 3 2003.
Jenice Prather-Kinsey and Gary K. Meek. The effect of revised ias 14 on seg-ment reporting by ias companies. European Accounting Review, 13(2):213–234, 2004.
Annalisa Prencipe. Proprietary costs and determinants of voluntary segmentdisclosure: evidence from Italian listed companies. European Accounting Re-view, 13(2):319–340, 7 2004.
Gary John Previts, Robert J. Bricker, Thomas R. Robinson, and Stephen JYoung. A content analysis of sell-side financial analyst company reports.Accounting Horizons, 8(2), 1994.
Alexandros P. Prezas and Karen Simonyan. Corporate divestitures: Spin-offsvs. sell-offs. Journal of Corporate Finance, 34:83–107, 10 2015.
Raghuram Rajan, Henri Servaes, and Luigi Zingales. The Cost of Diversity:The Diversification Discount and Inefficient Investment. The Journal of Fi-nance, 55(1):35–80, 2 2000.
Jagjit Singh Saini and Don Herrmann. Cost of equity capital, informationasymmetry, and segment disclosure. Advances in Quantitative Analysis ofFinance and Accounting, (11):143–173, 2013.
Katherine Schipper and Abbie Smith. A comparison of equity carve-outs andseasoned equity offerings: Share price effects and corporate restructuring.Journal of Financial Economics, 15(1-2):153–186, 1 1986.
BIBLIOGRAPHY 219
David Schröder and Andrew Yim. Industry effects in firm and segment prof-itability forecasting. Contemporary Accounting Research, 35(4):2106–2130,2018.
Thomas William Scott. Incentives and disincentives for financial disclosure:Voluntary disclosure of defined benefit pension plan information by cana-dian firms. Accounting Review, pages 26–43, 1994.
William F. Sharpe. Capital asset prices: a theory of market equilibrium underconditions of risk. The Journal of Finance, 19(3):425–442, 9 1964.
Ronald E. Shrieves and John M. Wachowicz Jr. Free cash flow (FCF), eco-nomic value added (EVA), and net present value (NPV): A reconciliation ofvariations of discounted-cash-flow (DCF) valuation. Engineering Economist,46(1):33–53, 2001.
Peter A Silhan. The effects of segmenting quarterly sales and margins on ex-trapolative forecasts of conglomerate earnings: Extension and replication.Journal of Accounting Research, pages 341–347, 1983.
Kenth Skogsvik. Conservative accounting principles, equity valuation and theimportance of voluntary disclosures. The British Accounting Review, 30(4):361–381, 1998.
Kenth Skogsvik. A tutorial on residual income valuation and value added val-uation. SSE/EFI Working Paper Series in Business Administration No 1999:10,2002.
Myron B. Slovin, Marie E. Sushka, and Steven R. Ferraro. A comparison ofthe information conveyed by equity carve-outs, spin-offs, and asset sell-offs.Journal of Financial Economics, 37(1):89–104, 1995.
Donna L. Street and Nancy B. Nichols. LOB and geographic segment disclo-sures: an analysis of the impact of IAS 14 revised. Journal of InternationalAccounting, Auditing and Taxation, 11(2):91–113, 2002.
Donna L. Street, Nancy B. Nichols, and Sidney J. Gray. Segment Disclosuresunder SFAS No. 131: Has Business Segment Reporting Improved? Account-ing Horizons, 14(3):259–285, 9 2000.
220 ESSAYS ON SEGMENT REPORTING AND VALUATION
Siva Swaminathan. The Impact of SEC Mandated Segment Data on Price Vari-ability and Divergence of Beliefs. The Accounting Review, 66(1):23–41, 1991.
James D. Thompson. Organizations in action: Social science bases of adminis-trative theory. Transaction publishers, 1967.
Pontus Troberg, Juha Kinnunen, and Harri J Seppänen. What drives cross-segment diversity in returns and risks? evidence from japanese and us firms.The International Journal of Accounting, 45(1):44–76, 2010.
Senyo Tse. Attributes of industry, industry segment and firm-specific informa-tion in security valuation. Contemporary Accounting Research, 5(2):592–614,3 1989.
Robert E. Verrecchia. Discretionary disclosure. Journal of Accounting andEconomics, 5:179–194, 1983.
Anand M. Vijh. The Spinoff and Merger Ex-Date Effects. The Journal of Fi-nance, 49(2):581–609, 6 1994.
Belen Villalonga. Does diversification cause the "diversification discount"? Fi-nancial Management, pages 5–27, 2004.
Qian Wang and Michael Ettredge. Discretionary allocation of corporate in-come to segments. Research in Accounting Regulation, 27(1):1–13, 4 2015.
Qian Wang, Michael Ettredge, Ying Huang, and Lili Sun. Strategic revelationof differences in segment earnings growth. Journal of Accounting and PublicPolicy, 30(4):383–392, 7 2011.
Jens Wüstemann and Sonja Wüstemann. Why consistency of accounting stan-dards matters: A contribution to the rules-versus-principles debate in finan-cial reporting. Abacus, 46(1):1–27, 2010.
Haifeng You. Valuation-driven profit transfer among corporate segments. Re-view of Accounting Studies, 19(2):805–838, 6 2014.