tak jurnal schneider

Volume 5, Number 2 ISSN 1096-3685

ACADEMY OF ACCOUNTING ANDFINANCIAL STUDIES JOURNAL

An official Journal of theAllied Academies, Inc.

Phil Little, Western Carolina UniversityAccounting Editor

Denise Woodbury, Weber State UniversityFinance Editor

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Academy of Accounting and Financial Studies Journal, Volume 5, Number 2, 2001

ACADEMY OF ACCOUNTING ANDFINANCIAL STUDIES JOURNAL

CONTENTS

LETTER FROM THE EDITORS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v

CLASSIFICATION OF FINANCIAL INSTRUMENTS WITHCHARACTERISTICS OF BOTH DEBT AND EQUITY:EVIDENCE CONCERNING CONVERTIBLEREDEEMABLE PREFERRED STOCK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1Mark G. McCarthy, East Carolina UniversityDouglas K. Schneider. East Carolina University

AUDITOR CONCENTRATION WITHIN CLIENT INDUSTRIES . . . . . . . . . . . . . . . . . . . . . 15James H. Scheiner, Northern Michigan UniversityClark M. Wheatley, Florida International University

THE USEFULNESS OF ACCOUNTING INFORMATIONIN ASSESSING SYSTEMATIC RISK:A RE-EXAMINATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35Ronald J. Woan, Indiana University of Pennsylvania

COMPENSATION OF INVESTMENT COMPANY ADVISORS:AN EMPIRICAL INVESTIGATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51Denise Woodbury, Weber State UniversityKyle Mattson, Weber State University

EARNINGS MANAGEMENT USING PENSIONRATE ESTIMATES AND THE TIMING OFADOPTION OF SFAS 87 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69Marianne L. James, California State University, Los Angeles

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EARNINGS RESPONSE TO AUDITOR SWITCHESUSING A MULTI-TIERED AUDITOR CLASSIFICATION . . . . . . . . . . . . . . . . . . . . 85Ronald A. Stunda, Birmingham-Southern CollegeDavid H. Sinason, Northern Illinois University

ACCOUNTING FOR ACQUISITIONS AND FIRM VALUE . . . . . . . . . . . . . . . . . . . . . . . . . 99Oliver Schnusenberg, St. Joseph's UniversityW. Richard Sherman, St. Joseph's University

LEASE FINANCIAL STATEMENT ACCOUNTINGPRACTICES TYPES AND NUMBERS FOR HONG KONG . . . . . . . . . . . . . . . . . . . 117Gary A. Miller, Texas A&M International University

COMPREHENSIVE INCOME REPORTING CONCERNS . . . . . . . . . . . . . . . . . . . . . . . . . . 133R. David Mautz, Jr., North Carolina A&T State UniversityIda Robinson-Backmon, University of Baltimore

THE UK INVESTOR AND INTERNATIONAL DIVERSIFICATION . . . . . . . . . . . . . . . . . 143Michael E. Hanna, University of Houston-Clear LakeJoseph P. McCormack, University of Houston-Clear LakeGrady Perdue, University of Houston-Clear Lake

A NEW STOCK OPTION PLAN AND ITS VALUATION . . . . . . . . . . . . . . . . . . . . . . . . . . 155Anthony Yanxiang Gu, State University of New York, Geneseo

THE IMPACT OF THE AMERITRADE ONLINEINVESTOR INDEX ON THE AUTOCORRELATIONSAND CROSS-CORRELATIONS OF MARKET RETURNS . . . . . . . . . . . . . . . . . . . 165Thomas Willey, Grand Valley State University

LOAN PRICING: A PRICING APPROACH BASED ON RISK . . . . . . . . . . . . . . . . . . . . . . 175James B. Bexley, Sam Houston State UniversityLeroy W. Ashorn, Sam Houston State UniversityJoe F. James, Sam Houston State University

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LETTER FROM THE EDITORS

Welcome to the Academy of Accounting and Financial Studies Journal. The Academy ofAccounting and Financial Studies is an affiliate of the Allied Academies, Inc., a non profitassociation of scholars whose purpose is to encourage and support the advancement and exchangeof knowledge, understanding and teaching throughout the world. The AAFSJ is a principal vehiclefor achieving the objectives of the organization. The editorial mission of this journal is to publishempirical and theoretical manuscripts which advance the discipline.

Dr. Philip Little, Western Carolina University, is the Accountancy Editor and Dr. DeniseWoodbury, Weber State University is the Finance Editor. Their joint mission is to make the AAFSJbetter known and more widely read.

As has been the case with the previous issues of the AAFSJ, the articles contained in thisvolume have been double blind refereed. The acceptance rate for manuscripts in this issue, 25%,conforms to our editorial policies.

The established mission of fostering a supportive, mentoring effort on the part of the refereeswhich will result in encouraging and supporting writers. Phil and Denise will continue to welcomedifferent viewpoints because in differences we find learning; in differences we developunderstanding; in differences we gain knowledge and in differences we develop the discipline intoa more comprehensive, less esoteric, and dynamic metier.

Information about the Allied Academies, parent organization of the AAFS, the AAFSJ, andthe other journals published by the Academy, as well as calls for conferences, are published on ourweb site. In addition, we keep the web site updated with the latest activities of the organization.Please visit our site and know that we welcome hearing from you at any time.

Phil Little, Western Carolina University

Denise Woodbury, Weber State University

www.alliedacademies.org

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MANUSCRIPTS

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CLASSIFICATION OF FINANCIAL INSTRUMENTSWITH CHARACTERISTICS OF BOTH DEBT AND

EQUITY: EVIDENCE CONCERNING CONVERTIBLEREDEEMABLE PREFERRED STOCK

Mark G. McCarthy, East Carolina UniversityDouglas K. Schneider. East Carolina University

ABSTRACT

This study examines the market perception of a compound financial instrument, convertibleredeemable preferred stock (CRPS). CRPS has the form of preferred stock, but also possesses aredemption feature and a conversion feature. Accounting for such instruments is the subject of apending exposure draft by the Financial Accounting Standards Board. Current accounting rulesfor CRPS require that it be excluded from equity, but not classified as debt. A sample of firmsreporting CRPS for fiscal years 1991 through 1995 is examined using a levels approach. Thefindings suggest that the market perceives CRPS as debt in two out of the five years under study.In the other three years the evidence is less convincing, raising the question as to whether currentaccounting rules classify CRPS according to how it is perceived by investors. The results wouldappear to support the FASB's decision to consider a new accounting standard for instruments withcharacteristics of both debt and equity. This study is timely and sheds light on an issue underdeliberation by accounting standard setters.

INTRODUCTION

Compound financial instruments are securities that have characteristics of both debt andequity. This study addresses one type of compound financial instrument called convertibleredeemable preferred stock (CRPS). Specifically, investor perception of CRPS is examined andbased on the findings, implications are discussed in regard to accounting for CRPS.

CRPS is a type of preferred stock that contains a debt-like redemption feature requiring theissuer to pay the holder the par value for the preferred stock at a specified redemption date. Theredemption feature is similar to the maturity value and date of a debt instrument. In addition, CRPSpossesses a conversion feature that allows the holder the option to convert CRPS into commonstock, similar to the conversion feature available on convertible debt instruments. In spite of thesubstance of CRPS, it has the form of "preferred stock" and is accounted for in the U.S. accordingto current accounting rules for redeemable stock. Examples of recent issuances of CRPS includea $15 million issuance by Frontline Communications Corporation and Mpower CommunicationsCorporation's $207 million issuance, both in February 2000. In the United States (U.S.), redeemablepreferred stock is currently accounted for as 'temporary equity'. According to the Securities andExchange Commission's (SEC) Accounting Series Release No. 268, "Presentation in Financial

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Statements of Redeemable Preferred Stocks" (Securities and Exchange Commission 1979),redeemable preferred stock should be reported below debt but above stockholders' equity on thebalance sheet. Since redeemable preferred stock is not considered debt, the distributions related toredeemable preferred stock are treated as dividends and are not reported on the income statement.

Research investigating CRPS is both timely and relevant. In the U.S., accounting forcompound financial instruments, such as CRPS, is under deliberation by standard setters. A decadeago the Financial Accounting Standards Board (FASB), the U.S. body that issues accountingstandards, issued a Discussion Memorandum entitled "Distinguishing between Liability and EquityInstruments and Accounting for Instruments with Characteristics of Both." It discussed alternativesfrom current reporting standards for compound financial instruments (FASB, 1990). In 1997 theFASB formally added redeemable preferred stock and convertible debt instruments to its activeagenda (FASB 1997).

More recently, March 2000, the FASB announced its tentative decisions on how to approachaccounting for compound instruments (FASB 2000). As it relates to CRPS, the FASB wouldclassify it as a liability because, in the absence of the holder exercising a right to convert thesecurity, the issuer is required to settle the obligation by transferring assets, e.g., cash. In essence,the FASB considers CRPS to be a debt instrument. If CRPS is in substance a debt instrument, asstated in the previous sentence, then taking in to account its conversion feature, CRPS should beconsidered a convertible debt instrument. The FASB also announced in March 2000 that it wouldrequire the proceeds from a convertible debt issuance to be segmented into separate debt and equitycomponents. The equity component represents the value of a call option giving the right of a holderto exchange convertible debt for shares of the issuing firm's stock.

The tentative decision by the FASB mentioned in the preceding paragraph (FASB 2000) fora new accounting standard applicable to CRPS is in contrast to the current standard that requiresCRPS to be excluded from both debt or equity and reside in 'temporary equity' (Securities andExchange Commission 1979). In so far as that CRPS is in substance a convertible debt instrument(as the FASB's tentative proposal would suggest), the current accounting rule requires thatconvertible debt be classified as entirely debt, without any separation of the instrument into debt andequity components. The FASB issued an exposure draft for new accounting standards for compoundinstruments applicable to CRPS in October 2000, with the expectation that the new standard wouldbe effective for fiscal years beginning after June 15, 2002.

Due to the anticipated issuance of an exposure draft and the deliberations in response to theexposure draft, it would appear that research addressing the market perception of a compoundfinancial instrument would be of significant interest to the financial reporting community. Thisstudy empirically examines the market perception of CRPS for fiscal years 1991 through 1995 byemploying a levels approach research design. The findings of this study suggest that CRPS isperceived as debt for sample firms, consistent with the FASB's tentative approach to such financialinstruments. The results of this study provide insights into investor perception of a compoundfinancial instrument and challenge current accounting rules for CRPS. Such insights should bepertinent to the FASB's deliberation on a new accounting standard for financial instruments.

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RESEARCH DESIGN AND MODEL DEVELOPMENT

The objective of this study is to provide evidence concerning the market perception of CRPSto the equity value of a firm using a levels approach. Landsman (1986) uses balance sheet elementsto explain the variation in the market value of stockholders' equity. The market value ofstockholders' equity, ME, is given by:

ME = B 1 MA + B 2 ML + u (1)

where MA equals the market value of a firm's assets and ML represents the market value of a firm'sliabilities. Landsman uses the model in equation (1) with book values for assets and liabilities totest the market's perception of firms' pension assets and liabilities. Ayers (1998) uses the samemodel with assets and liabilities to test the value relevance of firms' net deferred tax liabilities, netpension liabilities, and post-retirement benefits.

Regressions similar to equation (1) above have been used in other studies to examine therelationship between stock market valuation and the accounting treatment for various balance sheetitems and disclosures. Barth (1991) and Gopalakrishnan and Sugrue (1993) estimate regressionsto examine the accounting treatment for pension fund assets and liabilities. In other studiesemploying different versions of the model, Harris and Ohlson (1987) investigate the accountingtreatment for oil and gas properties, Shevlin (1991) examines accounting treatment for research anddevelopment limited partnerships and Barth (1994) examines the accounting treatment for holdinggains and losses on investment securities held by banks. Barth, Beaver and Landsman (1996)examine the value relevance of fair value disclosure of banks under Statement of FinancialAccounting Standard (SFAS) No. 107 and Amir (1996) examines the value relevance of financialdisclosures under SFAS No. 106. Hughes (2000) a explored non-financial measure, air pollution,in the electric utility industry and its association with the market value of the firm.

Ohlson (1995), however, models the value of a firm with the inclusion of an income variablein addition to the balance sheet. This is based on Ohlson's conclusion that the market value of afirm is a function of both the balance sheet and income statement.

In this study, the hypothesis testing examines the relationship between stock prices andCRPS as a separate independent variable. The following regression equation is estimated to testhow the market perceives CRPS:

ME i = B 0 + B 1 ASSET i + B 2 LIAB i + B 3 CRPS i + B 4 NI i + u (2)

where: ME = market value of common stock at fiscal year-end,ASSET = book value of assets,LIAB = book value of liabilities,CRPS = book value of convertible redeemable preferred stockNI = net income before extraordinary items 1

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ADDITIONAL PROCEDURES

Several econometric problems may be encountered with regression equations employing thelevels approach used in this study. Two of these potential problems are addressed here:heteroscedasticity and multicollinearity. Heterocedasticity occurs since large firms tend to havelarge errors and small firms usually have small errors. The result produces the understatement ofstandard errors resulting in t-statistics that are overstated. T-statistics which are greater than theirtrue values can lead to concluding that a variable is significant in explaining the variation of thedependent variable when in fact it is not significant. There are methods to mitigate the effects ofheteroscedasticity. One common technique is deflation. For each firm observation in the samplethis involves adjusting all variables in the regression model by a deflator. In this study, eachvariable is deflated through division of the number of common shares outstanding.

The estimates of regression coefficients are unbiased in the presence of multicollinearity.However, there are several potential problems including the imprecision of estimation (highsampling variances) and a high degree of sensitivity of the estimates of the coefficients to particularsets of sample data. The existence of multicollinearity may create the potential for drawingmisleading inferences from the t-statistics. Specifically, even though the t-statistics are unbiased(given there are no other econometric problems), it is hard to determine if the sampling variancesare large because of multicollinearity, or whether the variance of the true population is large. In themodel employed in this study, CRPS, the variable of primary interest, is not suspected ofmulticollinearity. It is expected that ASSET and LIAB will be highly correlated. Therefore, themodel is also estimated in a net form where assets and liabilities are combined to form a singlevariable, "net assets". Multicollinearity should be mitigated by use of the reduced model whereassets and liabilities are combined.

SAMPLE SELECTION

To address the research issue in this study a sample is constructed of firms reporting CRPSfor the fiscal years 1991 through 1995. The Compustat data base provides all the necessary data.

Compustat does not report a specific data item for CRPS. However, it reports a data itemfor convertible preferred stock (without regard to whether it is redeemable) and a separate data itemfor redeemable preferred stock (without regard to whether it is convertible). Amounts of preferredstock that are both convertible and redeemable are determined by identifying firms where all of thefirms' preferred stock is both redeemable and convertible. Thus, firms are identified that report thesame dollar amounts for the Compustat data items for preferred stock (data item A130), redeemablepreferred stock (data item A175) and convertible preferred stock (data item A214).

The integrity of the study would be compromised if observations of convertible preferredstock included non-redeemable preferred. To further confirm that all the convertible preferred stockis redeemable, another Compustat data item, nonredeemable preferred stock (data item A209), iscollected. Nonredeemable preferred stock is found to equal zero for each firm in the sample, thusconfirming that all convertible preferred stock is also redeemable preferred stock.

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Thus, firms included in this study are those reporting a positive net income beforeextraordinary items and equal dollar amounts for preferred stock (data item A130), redeemablepreferred stock (data item A175) and convertible preferred stock (data item A214). One otherscreening criterion is that each firm must report at least $1,000 of market value. (Market value isthe Compustat 'concept' MKVALF, consisting of data item A199 multiplied by data item A25).Market value is the dependent variable in this study.

This screening process generated the number of firms used in the regression models in thisstudy as described in the following table. The results of the model are discussed in the next section.

Year Number of Firms

1991 41

1992 45

1993 42

1994 50

1995 53

Total 231

RESULTS

Table 1 presents summary statistics for all of the deflated variables, dependent andindependent, used in the regression model for the years 1991-1995. CRPS, the variable of primaryinterest, ranges from $0.01 per share to $17.64 per share across all five years. The mean of CRPSranges from $1.14 per share in 1992 to $2.25 per share 1994, while the median of CRPS ranges from$0.55 per share to $0.95 per share.

Table 2 presents summary statistics for the sample of firms related to the mean and medianpercentage of CRPS to total liabilities and to total assets. Both percentages increase from the firstyear, 1991, to the last year, 1995. The mean CRPS/LIAB ratio percentages range from 10.1% in1992 to 16.4% in 1995. The lowest mean CRPS/ASSET ratio percentage is 6.3% in 1992 andreaches a maximum of 10.5% in 1995. The median CRPS/LIAB ranges from 5.6% in 1992 to 10.5%in 1995. The median CRPS/ASSET ranges from 3.6% in 1992 to 5.4% in 1995. In each year themean is greater than the median.

Since multicollinearity is a potential problem with the regression model, several diagnosticsare used to check for its presence. The Pearson correlation coefficients are presented in Table 3.As expected, the correlation between ASSET and LIAB is greater than 0.97 in all of the yearsexamined suggesting a high degree of multicollinearity among these variables. The correlationbetween the variable of interest, CRPS, and the other independent variables varies over the five-yearsample period. The year with the highest correlations is 1993 where the correlation between CRPSand the independent variables AT and LT are .7159 and .6854 respectively. In 1995 the correlationbetween CRPS and the other balance sheet variables is not significant.

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TABLE 1DESCRIPTIVE STATISTICS FOR VARIABLES USED IN ESTIMATING THE REGRESSION MODEL

(Deflated - $ Per Share)

YEAR

VARIABLE MEAN MEDIAN MINIMUM MAXIMUM

1991N=41

MEASSETLIABCRPSNI

11.0919.7713.151.370.67

8.2613.727.380.620.51

0.460.770.200.010.01

38.3793.1570.199.734.31

1992N=45

MEASSETLIABCRPSNI

11.7224.2116.881.140.69

10.3716.4612.350.550.49

0.310.570.200.010.01

37.60136.39107.64

9.212.99

1993N=42

MEASSETLIABCRPSNI

13.2124.8517.731.630.93

10.4014.8712.830.600.49

1.531.450.250.010.01

57.70118.52101.0612.804.21

1994N=50

MEASSETLIABCRPSNI

13.6037.6029.602.251.08

11.8129.6916.170.790.75

1.620.920.390.010.01

55.25181.76170.3717.644.59

1995N=53

MEASSETLIABCRPSNI

15.5536.0927.951.650.97

12.0018.0311.370.950.72

0.040.690.270.010.01

65.37460.54426.1116.414.84

ME: Market Value of Common Stockholders' EquityASSET: Book Value of Total AssetsLIAB: Book Value of Total LiabilitiesCRPS: Convertible Redeemable Preferred StockNI: Net Income or Loss Before Extraordinary Items

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TABLE 2: CONVERTIBLE REDEEMABLE PREFERRED STOCK AS A PERCENTAGE OFLIABILITIES AND ASSETS

YEAR

RATIO MEAN MEDIAN STD DEV

1991N=41

CRPS/LIABCRPS/ASSET

11.9%7.6%

6.7%4.6%

13.58.1

1992N=45

CRPS/LIABCRPS/ASSET

10.1%6.3%

5.6%3.6%

14.28.3

1993N=42

CRPS/LIABCRPS/ASSET

12.9%7.0%

7.3%4.3%

15.37.8

1994N=50

CRPS/LIABCRPS/ASSET

12.5%9.1%

5.7%4.1%

17.915.9

1995N=53

CRPS/LIABCRPS/ASSET

16.4%10.5%

10.5%5.4%

20.114.6

ASSET: Book Value of Total Assets LIAB: Book Value of Total LiabilitiesCRPS: Convertible Redeemable Preferred Stock

TABLE 3: PEARSON CORRELATION COEFFICIENTS FISCAL YEARS 1991 - 1995

1991 ME AT LT CRPS NI

MKVALF 1.0000 0.6018* 0.5475* 0.2132 0.4594*

AT 1.0000 0.9772* 0.5342* 0.2725

LT 1.0000 0.5402* 0.2399

PSTKC 1.0000 0.6202*

NI 1.0000


MKVALF 1.0000 0.5550* 0.5267* 0.1645 0.6158*

AT 1.0000 0.9874* 0.3183* 0.2136

LT 1.0000 0.2855 0.2027

PSTKC 1.0000 0.2778

NI 1.0000


MKVALF 1.0000 0.5044* 0.4053* 0.1902 0.7698*

AT 1.0000 0.9789* 0.7159* 0.7419*

LT 1.0000 0.6854* 0.7011*

PSTKC 1.0000 0.5256*

NI 1.0000

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TABLE 3: PEARSON CORRELATION COEFFICIENTS FISCAL YEARS 1991 - 1995



MKVALF 1.0000 0.4001* 0.3147* 0.0430 0.6725*

AT 1.0000 0.9820* 0.3084* 0.5424*

LT 1.0000 0.3066* 0.4859*

PSTKC 1.0000 0.2196

NI 1.0000


MKVALF 1.0000 0.3169* 0.2472 0.0375 0.6591*

AT 1.0000 0.9944* 0.0124 0.4724*

LT 1.0000 -0.0035 0.4126*

PSTKC 1.0000 0.0980

NI 1.0000

* Significant at the 0.05 level

Variance Inflation Factors (VIFs) and Condition Indices (CI) are also examined anddiscussed below, but are not shown in a table for purposes of brevity. As expected, the VIFs forASSET and LIAB range from 23 to 155 suggesting a high degree of multicollinearity (Neter,Wasserman and Kutner, 1985). The Condition Index values range from 17 to 34 suggestingmoderate dependencies (Belsley, Kuh and Welsch, 1980). However, for the variable of interest,CRPS, the greatest VIF is 2.2 in 1991 suggesting that multicollinearity may not be a problem withthis variable.

The results of the initial regressions estimated for each year are presented in Table 4. Theregression models are significant in every year with the adjusted R-square ranging from 0.4955 in1994 to .7654 in 1993. The estimated coefficients for the variables representing total assets(ASSET) and total liabilities (LIAB) are in their expected direction in every year, but not significantin all years. The income variable is significant in all five years and has a positive coefficient asexpected.

The variable of interest, CRPS, has a negative coefficient in all five years and is significantin two of them, 1991 and 1993. In the years where CRPS is not significant, 1992, 1994, and 1995,the p-values are 0.1293, 0.2876 and 0.4797. With CRPS being negative in all years and significantin two of them, there is some evidence to suggest that the market perceives the nature of theseinstruments as consisting largely of debt, but perhaps not exclusively as debt.

As discussed previously, there is a problem with multicollinearity between the ASSET andLIAB variables. The model was estimated again with these two variables netted together to forma NET variable. In these regressions the highest VIF was 2.01 and the largest CI was less than 5.These statistics suggest that multicollinearity is not an issue with this model that uses a singlecombined variable to represent ASSET and LIAB.

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TABLE 4REGRESSION MODEL RESULTS

ME i = B 0 + B 1 ASSET i + B 2 LIAB i + B 3 CRPS i + B 4 NI i + u i

Year Variable Coef. Error t Stat p Value* R 2 (N)

1991 INTERCEPTASSETLIABCRPSNI

3.75860.4361-0.2042-2.18746.8059

1.38260.20600.29670.64831.5475

2.7192.116-0.688-3.3744.204

0.01000.04130.49570.00180.0002

.5610(41)


3.49240.4400-0.3734-0.85037.0103

1.41950.21180.27580.54911.3327

2.4602.077-1.354-1.5495.260

0.01830.04430.18330.12930.0001

.5630(45)


3.13200.8422-0.9255-1.49988.5794

1.26410.17830.19480.40991.2723

2.4784.723-4.751-3.6596.743

0.01790.00010.00010.00080.0001

.7654(42)


5.01700.4325-0.4538-0.35886.0319

1.75630.16270.17640.33341.3696

2.8572.657-2.572-1.0764.404

0.00650.01090.01350.28760.0001

.4955(50)


5.80190.8960-0.9446-0.32614.4853

1.87070.22940.24120.45781.8152

3.1013.905-3.916-0.7122.471

0.00320.00030.00030.47970.0171

.5364(53)

ASSET: Book Value of Total Assets LIAB: Book Value of Total LiabilitiesCRPS: Convertible Redeemable Preferred Stock NI: Net Income Before Extraordinary Items*p-value is a two-tailed statistic

The results for these regressions are presented in Table 5. Consistent with the previousresults, CRPS was negative and significant in 1991 and 1993. The other three years were againinsignificant but did have negative coefficients. These results tend to confirm the findings in thefirst regression model of some evidence that the market perceives CRPS to be primarily a liability.However, the lack of significance of the coefficient for CRPS in three of the five years leaves openthe possibility that investors may regard CRPS as having a component other than debt, whichlogically would be an equity component. If one subscribes to this interpretation of the results, thenthe results would appear to lend support to the FASB's March 2000 proposal to consider an

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instrument such as CRPS as primarily a convertible debt instrument that should be broken up intoseparate debt and equity components.

TABLE 5REGRESSION MODEL RESULTS

ASSETS AND LIABILITIES NETTED INTO ONE VARIABLEME i = B 0 + B 1 NET i + B 2 CRPS i + B 3 NI i + u i


1991 INTERCEPTNETCRPSNI

4.42710.7610-1.62095.6987

1.40790.14380.61761.5688

3.1445.292-2.6243.633

0.00330.00010.01250.0008

.5197(41)


3.49260.5904-0.86997.0742

1.41570.12630.54721.3272

2.4674.672-1.5905.330

0.01790.00010.11960.0001

.5653(45)


2.94580.8216-1.75087.7947

1.26730.17920.36471.1297

2.3254.585-4.8006.899

0.02550.00010.00010.0001

.7612(42)


4.90730.4101-0.40015.8032

1.73170.15640.32241.2952

2.8342.622-1.2414.480

0.00680.01180.22090.0001

.5031(50)


5.95680.6953-0.24444.4686

1.92460.21220.46951.8691

3.0953.277-0.5212.391

0.00320.00190.60490.0207

.5084(53)

NET: Book value of assets less liabilities CRPS: Convertible Redeemable Preferred StockNI: Net Income before extraordinary items *p-value is a two-tailed statistic

To investigate the market perception further, all the years were pooled into one sample andthe full model was estimated again. The results from this regression are presented in Table 6.Caution must be exercised with these results since some of the same firms appear in the pooledsample more than once creating a lack of independence.

The pooled results in Table 6 show that ASSET, LIAB, and NI are significant and in theexpected direction. The variable of interest, CRPS, is negative and significant, suggesting that themarket perceives these instruments as liabilities. However, the entirely-debt conclusion based ona significant coefficient for CRPS is supported only by the pooled results and not the results forindividual years.

Finally, there may be some firms that have more liabilities than assets, i.e., a negative bookvalue, so there may be some question as to whether the model holds for these type firms. Therefore,

11


the full model and reduced model with net assets were estimated again where firms with a negativebook value or stockholders' equity were deleted. For the years 1991 through 1995 there were 2, 1,0, 3, and 4 firms deleted respectively. These results were consistent with the previous findings inTable 4. In two of the five years, 1991 and 1993, the variable of interest CRPS was negative andsignificant. For 1994 the CRPS variable was closer to being significant at the .05 level withp-values of 0.0633 for the full model and 0.0566 for the reduced model. In 1992 and 1995 the CRPSvariable was not significant. These results also suggest that the market perceives these instrumentsprimarily as liabilities, but perhaps with some other component (equity) present.

TABLE 6REGRESSION MODEL RESULTS: POOLED SAMPLE OF FIRMS FOR FISCAL YEARS 1991 - 1995

ME i = B 0 + B 1 ASSET i + B 2 LIAB i + B 3 CRPS i + B 4 NI i + u i


1991-1995 INTERCEPTASSETLIABCRPSNI

4.68930.6511-0.6825-0.65416.0537

0.70450.07810.08560.18480.6204

6.6558.337-7.974-3.5389.758

0.00010.00010.00010.00050.0001

.5676(231)

ASSET: Book Value of Total Assets LIAB: Book Value of Total LiabilitiesCRPS: Convertible Redeemable Preferred Stock NI: Net Income Before Extraordinary Items*p-value is a two-tailed statistic

IMPLICATIONS

Current U.S. accounting rules for CRPS place it in 'temporary equity,' excluded fromstockholders' equity and not required to be included in debt. One could argue that CRPS should beequity since it has the form of, or is at least called, 'preferred stock' and is also reported in anpseudo-equity category, albeit temporary equity. Following this reasoning, all of the CRPS issuesshould be perceived by investors as equity, i.e., a positive and significant coefficient. Yet, that isnot what the results of this study found.

A counter-argument could be made that one should not expect CRPS to be perceived asequity because it has the substance of debt, particularly in regard to the redemption feature of CRPS.If it were argued that CRPS is actually debt, then the conversion feature of CRPS would suggest thatCRPS is in substance a form of convertible debt. However, if this argument is accepted, current U.S.rules for convertible debt, Accounting Principles Board Opinion No. 14 (APBO 14) (AICPA 1969),require that all issues of convertible debt be classified as entirely debt until conversion. APBO 14does not allow a convertible debt issue to be classified as equity prior to conversion, regardless ofthe conversion features. Nor does it allow a portion of the proceeds at issuance to be allocated toequity, a proposal contained in the most recent FASB decision on how to account for convertibledebt.

Accordingly, even if one argues that CRPS is in substance convertible debt, an ex anteexpectation conforming to APBO 14 is that all issues of CRPS should be perceived as debt. Our

12


findings do show some support for CRPS being primarily debt. However, the lack of significancein three of the five years raises the questions of whether an equity component may be present, atleast in the years that the CRPS coefficient was not significant. Regardless of whether CRPS ispresumed to be debt, or consisting of separate debt and equity components, current U.S. accountingrules do not appear to adequately account for CRPS, at least based on investors' perception.

CONCLUSIONS

In summary, this study can be said to provide at least some evidence that current accountingrules for CRPS are at variance with investor perception of CRPS. The importance of these findingsto financial reporting is the suggestion that new standards are needed for at least one compoundinstrument, CRPS. Perhaps a future area of research would be to repeat the tests and designpresented in Table 5 for other compound instruments. If other such studies' results present findingssimilar to these findings, then additional evidence would exist to support the need for changes inreporting standards for compound instruments.

ENDNOTES

1 Only those firms that reported a positive net income before extraordinary items are included in the sample.Barth, Beaver and Landsman (1992) examined the market valuation of pension cost components using a levelsapproach. They only included items on the income statement as independent variables in trying to explain themarket value of a firm. Barth et al. included only those firms that reported a positive net income. The equationmodel is not expected to hold for firms reporting a net loss. Firms reporting losses were also eliminated fromthe sample in Barth, Beaver and Stinson (1991) and Barth, Beaver, and Wolfson (1990).

REFERENCES

American Institute of Certified Public Accountants (1969). Accounting for convertible debt and debtissued with stock purchase warrants. APB Opinion No. 14. New York, NY: AICPA.

Amir, E. (1996). The effect of accounting aggregation on the value-relevance of financialdisclosures: The case of SFAS No. 106. The Accounting Review (October), 573-90.

Ayers, B.C. (1998). Deferred tax accounting under SFAS No. 109: An Empirical Investigation ofits Incremental Value-Relevance Relative to APB No. 11. The Accounting Review (April),195-212.

Barth, M.E. (1991). Relative measurement errors among alternative pension asset and liabilitymeasures. The Accounting Review (July), 433-63.

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Barth, M.E. (1994). Fair value accounting: evidence from investment securities and the marketvaluation of banks. The Accounting Review (January), 1-25.

Barth, M.E., W.H. Beaver, & W.R. Landsman (1992). The market valuation implications of netperiodic pension cost components. Journal of Accounting and Economics (Vol. 15), 27-62.

Barth, M.E., W.H. Beaver, & W.R. Landsman (1996). Value-relevance of banks' fair valuedisclosure under SFAS No. 107. The Accounting Review (October), 513-37.

Barth, M.E., W.H. Beaver, & C.H. Stinson (1991). Supplemental data and the structure of thriftshare prices. The Accounting Review (Vol. 66), 56-66.

Barth, M.E., W.H. Beaver, and M.A. Wolfson. 1990. Components of Bank Earnings and theStructure of Bank Share Prices. Financial Analysts Journal (May/June), 53-60.

Belsley, D.A. E. Kuh, & R.E. Welsh (1980). Regression diagnostics: Identifying influential dataand sources of collinearity. (John Wiley & Sons).

Financial Accounting Standards Board (1990). Distinguishing between Liability and EquityInstruments and Accounting for Instruments with Characteristics of Both. DiscussionMemorandum. Stamford, CT: FASB (August).

Financial Accounting Standards Board (1997). Financial Accounting Series, No. 172-A, StatusReport, No. 287 (April 22).

Financial Accounting Standards Board (2000). Project Updates: Board Agenda Projects; Liabilitiesand Equity (April 28). http://www.rutgers.edu/Accounting/raw/fasb/tech/index.html.

Gopalakrishnan, V.& T.F. Sugrue (1993). An empirical investigation of stock market valuation ofcorporate projected pension liabilities. Journal of Business Finance & Accounting(September), 711-24.

Harris, T.S. & J.A. Ohlson (1987). Accounting disclosures and the market's valuation of oil and gasproperties. The Accounting Review (October), 651-70.

Hughes, K.E., II (2000). The value relevance of nonfinancial measures of air pollution in the electricutility industry. The Accounting Review (April), 209-28.

Landsman, W. (1986). An empirical investigation of pension fund property rights. The AccountingReview (October), 662-91.

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Neter, J., W. Wasserman & M.H. Kutner (1985). Applied Linear Statistical Models, Second Edition(Richard D. Irwin).

Ohlson, J.A. (1995). Earnings, book values, and dividends in security valuation. ContemporaryAccounting Research (Spring), 648-76.

Securities and Exchange Commission, Accounting Series Release No. 268: "Presentation inFinancial Statements of Redeemable Preferred Stocks," SEC Docket, August, 1979,1411-19.

Shevlin, T. (1991). The valuation of R&D firms with R&D limited partnerships. The AccountingReview (January), 1-21.

15


AUDITOR CONCENTRATIONWITHIN CLIENT INDUSTRIES

James H. Scheiner, Northern Michigan UniversityClark M. Wheatley, Florida International University

ABSTRACT

Auditors have increasingly emphasized industry specialization in their audit, managementadvisory, tax, and litigation services as a tool for obtaining new clients and serving existing ones.This research explores auditor concentration within industries over a twenty-year period. We findthat increases in concentration came at the expense of non Big-6 auditors. In contrast to previousstudies, however, our results demonstrate that firms with the largest market share in a particularindustry at the beginning of the period, did not necessarily maintain or increase that market sharethrough the end of the period. Furthermore, this study indicates that auditor concentrationresearch, which relies solely upon metrics such as the "four-firm concentration ratio," may providebiased or misleading results.

INTRODUCTION

Mergers, acquisitions and bankruptcies in the recent past have significantly altered thecomposition of business in the United States. This time period has witnessed similar changes in theaccounting profession as well. In essence, these changes have left fewer client firms with a longfinancial history, contracting with fewer accounting firms. To increase share in an industry, anauditor must obtain new clients from new businesses, from established businesses or by being onthe "winning side" in a merger. Whether this decline in the population of potential auditors is goodor bad for the business community depends in part on the level of competition in the accountingindustry.

Increasingly, auditors have emphasized industry specialization in their audit, managementadvisory, tax, and litigation services as a tool for obtaining new clients and serving existing ones.Specialization by auditors is promoted as leading to greater efficiency and hence lower audit feesfor client companies. As regards management advisory services, specialization may enhance theservice firm's competence thus increasing the value of its services. Similar benefits may be realizedin both tax and litigation services.

Specialization may, however, result in fewer firms providing services to any one particularindustry. Such increases in auditor concentration might therefore have anti-competitiveconsequences - especially as regards consulting services (Minyard and Tabor, 1991).

The impact of this potential problem is compounded in specific industries by mergers andbankruptcies which have reduced the number of industry firms. A good example of this is the oiland gas industry. In such industries, client firms that desire to engage both specialists and different

16


auditors/consultants than those engaged by their competitors, must balance any benefits accruingfrom auditor specialization with the potential detriments of increased concentration.

The only scenarios in which this complication would not result, would be those where either(1) no audit firms are specialists, or (2) all audit firms are specialists. Assuming that specializationis not costless, competition between auditors would induce specialization (eliminating the firstalternative), until only those accounting firms able to operate with some competitive advantagewithin an industry would remain (eliminating the second alternative). The result of specializationtherefore, is that client firms would be forced to choose from a smaller pool of "efficient"audit/consulting providers.

This article examines changes in auditor concentration for publicly held entities by industryand year, from 1977 through 1996. We look at three traditional measures of concentration, proposea new measure of concentration and assess the competitiveness of the audit profession in variousindustries.

PRIOR RESEARCH

Auditor specialization or concentration has been examined in a number of papers.Eichenseher and Danos (1981) using 1977 data for 4,900 firms, found significant concentrationwithin industry groupings. Danos and Eichenseher (1982) examined changes in auditorconcentration during the 1970s and found that gains in market share occurred in regulated industrieswhile declines occurred in nonregulated industries. Kwon (1966) presented evidence that inconcentrated industries, clients will demand audit firms different from competitors' auditors. Hoganand Jeter (1997) examined changes in concentration and market share from 1976 to 1993 and foundthat concentration levels have increased over time. They also found that firms classified as marketleaders increased market share over time at the expense of firms with a smaller market share. Weextend these investigations, by exploring auditor concentration within the professed specializationareas of the Big-6 accounting firms, and suggest an alternative approach for gauging industryconcentration.

METHODOLOGY AND SAMPLE SELECTION

Auditor concentration has been measured in prior studies (Scheiner and Wheatley (1998),Hogan and Jeter (1997) and Danos and Eichenseher (1982)): following three metrics (1) numberof firms in an industry audited by a public accounting firm, (2) the square root of total assets of firmsin an industry audited by a public accounting firm, and (3) the square root of net sales of firms inan industry audited by a public accounting firm. The second measure is based on Simunic's (1980)conclusion that audit fees vary linearly with the square root of a client's total assets.

The first measure (N), which uses the number of firms audited by a single public accountingfirm, is computed using equation 1.

N i = n i / SUM i ( n i ) (1)

17


where n i is the number of industry firms audited by auditor i. The second measure (TA), which uses the square root of the total assets of clients audited

by a single public accounting firm, is computed using equation 2.

TA i = SUM j ( Square Root ( A i j ) ) / SUM i j ( SUM ( Square Root ( A i j ) ) ) (2)

where A i j is the total assets of client firm j for auditor i. The third measure (NS), which uses the square root of the net sales of clients audited by a

single public accounting firm, is computed using equation 3.

NS i = SUM j ( Square Root ( S i j ) ) / SUM i j ( SUM ( Square Root ( S i j ) ) ) (3)

where S i j is the net sales of client firm j for auditor i.Consistent with prior research, we measure concentration using three and four firm

concentration ratios by summing the largest values for each measure for each year. Note however,that when an accounting firm audits one very large client, measures (2) and (3) may skew the results.

Since the time period covered by our study predates the merger of Price Waterhouse withCoopers & Lybrand, those firms are evaluated as separate entities. Auditors are thus classified asone of the individual Big-6 firms or as Non Big-6. Hence there are seven possible classifications.(Firms audited by Deloitte, Haskins and Sells and Touche Ross were combined during the entiretime period as were firms audited by Ernst & Whinney and Arthur Young).

Data for this study was gathered from the Standard and Poor's Compustat PC-Plus Databasepublished in 1997. Compustat company data "is derived from publicly traded companies,specifically those trading on the New York Stock Exchange, American Stock Exchange, NASDAQ,Over-the Counter, Toronto Stock Exchange, Quebec Stock Exchange, Montreal Stock Exchange,and wholly-owned subsidiaries of companies that are required to file with the SEC." Sample firmswere identified by searching the Active database for those firms with auditor data for the period1977 through 1996. Hence firms that were taken over, have gone out of business or been delistedare not included in this sample.

This method of sample selection may lead to a survivorship bias. We chose however, toexamine survivor firms because obtaining or having clients that are either bought-out or fail is, inthe long term, an inappropriate expenditure of effort by the auditor. In addition, since survivorauditors are used in this and previous studies to represent the supply side of the audit market, it isconsistent to use survivor firms to represent the demand side. Indeed, it is just this "survivorship"approach that Danos and Eichenseher (1982) assume implies a competitive advantage. This reducesthe number of firms in the earlier years as compared to the later years examined.

The requirements for data on assets, auditor, industry and sales resulted in a sample of85,574 firm-year observations for the 20-year period analyzed. The number of sample observationsavailable was lowest for 1977 (1700) and greatest for 1995 (8555). The data available given therequirements for auditor, sales and total asset information is summarized by year in Table 1.

Firms were classified into industry groups by standard industrial classification code. Eightindustry groupings were created using the following schedule: agricultural and natural resources (0

18


- 1499); manufacturing (1500-3999); transportation and public utilities (4000-4799 & 4900-4999);communications (4800-4899); wholesale trade (5000-5199); retail trade (5200-5999); finance,insurance and real estate (6000-6799); and services (7000-9999).

Table 1Firm-Year Sample Observations

Year Auditor Net Sales Total Assets

1977 1714 1711 1700

1978 1903 1901 1878

1979 1967 1964 1962

1980 2099 2096 2088

1981 2210 2206 2195

1982 2594 2591 2585

1983 2825 2824 2816

1984 3027 3027 3018

1985 3425 3424 3417

1986 3834 3832 3816

1987 4140 4136 4118

1988 4363 4361 4335

1989 4599 4596 4565

1990 4955 4951 4923

1991 5406 5406 5361

1992 6032 6031 5985

1993 6741 6739 6685

1994 7572 7568 7518

1995 8611 8606 8555

1996 8901 8901 8054

Total 85,574

We chose this broad based method of classification, rather than classification based ontwo-digit SIC codes (as in Hogan and Jeter, 1997), because the various Big-6 firms themselves usethese broad classifications in describing their areas of specialization. Hogan and Jeter, by matchingareas of claimed specialization to two-digit SIC codes, found that the Big-6 specialized in only 57%of the possible industry classifications, and that within that 57% there was considerable overlap.

19


Since this result is counterintuitive to the basic precepts of business competition, and since we couldfind no evidence that the Big-6 define their areas of specialization in such a narrow fashion, weemploy the broader classification groupings used in Warren (1980) and Rose-Green, et al(1998).Table 2 depicts the specialization areas claimed by the various Big-6 firms, and thecorresponding industry groupings. Four of the Big-6 claimed specialization in all eight industrygroups. Ernst & Young claimed specialization in six of the groups, while Price Waterhouse claimedspecialization in seven of the eight.

Table 2Areas of Specialization

Industry Areas (per firms) SIC Code AA CL EY DT KPMG PW

Communications and Entertainment 4 X X X X X X

Consumer Products 2,5,6 X X X X X X

Energy 1 X X X X X X

Financial Services 7 X X X X X X

Government NA X X X X

Healthcare 8 X X X X X X

Manufacturing 2 X X X X X X

Real Estate 7 X X X X X

Transportation 3 X X X X

Notes: An "X" indicates that the firm reports a specialization in that industryAA = Arthur Andersen CL = Coopers & LybrandEY = Ernst & Young DT = Deloitte & ToucheKPMG = KPMG Peat Marwick PW = Price Waterhouse

RESULTS

Table 3 presents the change in non Big-6 firms between 1977 and 1996. In all eight industrygroups there is a decline in the percentage of audits by non Big-6 firms. In only the communicationsindustry using total assets and net sales is there an increase for non Big-6 firms.

Table 4 presents the four firm (and three firm) concentration ratios by industry by year.While there is some variability among years, the concentration ratios based on number of clientsexhibit very little change over the period. The TA and NS measures show a slightly greater amountof variation however, particularly in respect to industry 4 (communications and entertainment).

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Table 3: Percentage of Industry Audits by Non Big-Six FirmsPanel A: Number (N) of Client Firms

Industry 1977 1996 Average Change 1997 to 1996

1 0.353 0.186 0.284 -0.17

2 0.195 0.151 0.178 -0.04

3 0.096 0.069 0.064 -0.03

4 0.132 0.112 0.142 -0.02

5 0.273 0.226 0.225 -0.05

6 0.256 0.108 0.174 -0.15

7 0.217 0.145 0.235 -0.07

8 0.308 0.191 0.270 -0.12

All Firms 0.215 0.154 0.193 -0.06

Panel B: Total Assets (TA) of Client Firms


1 0.147 0.046 0.115 -0.10

2 0.110 0.043 0.079 -0.07

3 0.044 0.010 0.021 -0.03

4 0.019 0.038 0.025 0.02

5 0.130 0.091 0.096 -0.04

6 0.119 0.030 0.068 -0.09

7 0.079 0.031 0.070 -0.05

8 0.143 0.061 0.110 -0.08

All Firms 0.099 0.039 0.070 -0.06

Panel C: Net Sales (NS) of Client Firms


1 0.120 0.038 0.097 -0.08

2 0.108 0.044 0.078 -0.06

3 0.044 0.012 0.025 -0.03

4 0.017 0.037 0.023 0.02

5 0.137 0.092 0.096 -0.04

6 0.126 0.028 0.071 -0.10

7 0.128 0.033 0.073 -0.09

8 0.131 0.060 0.101 -0.07

All Firms 0.105 0.042 0.071 -0.06

Notes: 1 = Agricultural & Natural Resources, 2 = Manufacturing, 3 = Transportation and Public Utilities, 4 =Communications, 5 = Wholesale Trade, 6 = Retail Trade, 7 = Finance, Insurance and Real Estate, 8 = Services

21


Table 4Industry

Year 1 2 3 4 5 6 7 8

Panel A: Four-Firm Concentration Ratio (Number of Clients) by Industry and Year

1977 0.51 0.59 0.75 0.72 0.57 0.59 0.65 0.55

1978 0.48 0.59 0.74 0.71 0.61 0.60 0.64 0.51

1979 0.50 0.60 0.77 0.71 0.61 0.61 0.62 0.51

1980 0.53 0.60 0.81 0.69 0.59 0.61 0.61 0.52

1981 0.53 0.60 0.81 0.70 0.60 0.60 0.60 0.52

1982 0.51 0.61 0.75 0.72 0.62 0.61 0.60 0.52

1983 0.51 0.61 0.75 0.74 0.60 0.61 0.58 0.49

1984 0.53 0.60 0.75 0.75 0.58 0.61 0.59 0.50

1985 0.52 0.60 0.76 0.76 0.57 0.61 0.59 0.55

1986 0.52 0.59 0.77 0.73 0.58 0.63 0.62 0.57

1987 0.56 0.61 0.77 0.71 0.61 0.65 0.62 0.61

1988 0.55 0.61 0.78 0.72 0.62 0.67 0.62 0.60

1989 0.60 0.61 0.77 0.70 0.62 0.66 0.60 0.59

1990 0.60 0.62 0.76 0.68 0.63 0.69 0.61 0.62

1991 0.59 0.62 0.75 0.67 0.62 0.70 0.61 0.61

1992 0.59 0.61 0.74 0.68 0.61 0.69 0.61 0.58

1993 0.60 0.60 0.73 0.70 0.61 0.67 0.61 0.58

1994 0.60 0.60 0.73 0.69 0.58 0.64 0.64 0.58

1995 0.60 0.60 0.72 0.70 0.57 0.66 0.64 0.58

1996 0.62 0.61 0.73 0.73 0.57 0.69 0.66 0.62

Average 0.55 0.60 0.76 0.71 0.60 0.64 0.62 0.56

Panel B: Four-Firm Concentration Ratio (Total Assets) by Industry and Year

1977 0.70 0.66 0.77 0.90 0.73 0.69 0.81 0.70

1978 0.68 0.66 0.78 0.91 0.74 0.69 0.83 0.65

1979 0.68 0.68 0.79 0.89 0.75 0.71 0.83 0.66

1980 0.68 0.68 0.84 0.89 0.77 0.70 0.83 0.68

1981 0.68 0.68 0.82 0.88 0.76 0.70 0.84 0.68

1982 0.68 0.67 0.81 0.88 0.77 0.71 0.84 0.66

1983 0.67 0.67 0.80 0.87 0.76 0.71 0.82 0.65

1984 0.65 0.67 0.80 0.89 0.74 0.71 0.80 0.65

1985 0.66 0.66 0.78 0.89 0.74 0.71 0.80 0.64

1986 0.68 0.66 0.78 0.85 0.75 0.72 0.80 0.69

1987 0.72 0.65 0.77 0.83 0.74 0.74 0.79 0.72

1988 0.66 0.66 0.79 0.83 0.75 0.75 0.80 0.73

1989 0.72 0.67 0.79 0.81 0.76 0.74 0.77 0.72

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Table 4Industry

Year 1 2 3 4 5 6 7 8


1990 0.70 0.67 0.77 0.79 0.76 0.77 0.76 0.74

1991 0.69 0.68 0.77 0.80 0.77 0.77 0.76 0.73

1992 0.70 0.68 0.77 0.80 0.77 0.76 0.76 0.72

1993 0.72 0.68 0.77 0.78 0.76 0.75 0.75 0.71

1994 0.71 0.68 0.75 0.78 0.72 0.74 0.77 0.71

1995 0.68 0.68 0.74 0.74 0.73 0.75 0.77 0.71

1996 0.70 0.69 0.77 0.79 0.72 0.75 0.77 0.72

Average 0.69 0.67 0.78 0.84 0.75 0.73 0.80 0.69

Panel C: Four-Firm Concentration Ratio (Net Sales) by Industry and Year

1977 0.70 0.66 0.80 0.91 0.71 0.68 0.76 0.71

1978 0.72 0.67 0.80 0.91 0.75 0.68 0.82 0.66

1979 0.68 0.68 0.82 0.90 0.77 0.68 0.82 0.68

1980 0.68 0.68 0.86 0.89 0.77 0.68 0.83 0.70

1981 0.69 0.69 0.84 0.89 0.77 0.69 0.84 0.70

1982 0.69 0.67 0.79 0.88 0.77 0.71 0.83 0.66

1983 0.68 0.67 0.78 0.87 0.76 0.69 0.81 0.65

1984 0.67 0.67 0.78 0.89 0.75 0.70 0.80 0.66

1985 0.68 0.67 0.78 0.89 0.75 0.70 0.79 0.65

1986 0.71 0.66 0.77 0.87 0.76 0.71 0.80 0.70

1987 0.74 0.65 0.78 0.85 0.75 0.73 0.79 0.73

1988 0.70 0.66 0.79 0.84 0.74 0.74 0.79 0.73

1989 0.74 0.67 0.78 0.83 0.76 0.73 0.75 0.73

1990 0.73 0.67 0.77 0.81 0.75 0.75 0.75 0.75

1991 0.71 0.68 0.77 0.81 0.76 0.76 0.75 0.75

1992 0.71 0.68 0.77 0.81 0.76 0.75 0.74 0.73

1993 0.73 0.68 0.77 0.80 0.75 0.75 0.75 0.73

1994 0.73 0.68 0.77 0.80 0.72 0.74 0.77 0.71

1995 0.69 0.67 0.76 0.78 0.73 0.74 0.77 0.71

1996 0.71 0.68 0.77 0.81 0.72 0.75 0.78 0.72

Average 0.71 0.67 0.79 0.85 0.75 0.72 0.79 0.70

Panel D: Three-Firm Concentration Ratio (Number of Clients) by Industry and Year

1977 0.40 0.46 0.60 0.65 0.44 0.49 0.52 0.45

1978 0.38 0.46 0.59 0.63 0.50 0.49 0.51 0.41

1979 0.41 0.47 0.62 0.63 0.50 0.52 0.50 0.41

1980 0.43 0.47 0.63 0.62 0.47 0.50 0.47 0.42

1981 0.43 0.47 0.65 0.62 0.50 0.50 0.47 0.42

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Table 4Industry

Year 1 2 3 4 5 6 7 8


1982 0.41 0.48 0.64 0.65 0.52 0.51 0.48 0.41

1983 0.41 0.49 0.65 0.65 0.50 0.51 0.46 0.38

1984 0.43 0.48 0.64 0.57 0.49 0.49 0.48 0.39

1985 0.42 0.48 0.64 0.65 0.46 0.48 0.47 0.44

1986 0.41 0.48 0.64 0.63 0.47 0.51 0.50 0.45

1987 0.43 0.48 0.64 0.59 0.49 0.53 0.50 0.47

1988 0.42 0.48 0.65 0.59 0.50 0.55 0.48 0.47

1989 0.48 0.48 0.64 0.58 0.49 0.55 0.47 0.47

1990 0.48 0.49 0.62 0.56 0.49 0.56 0.49 0.49

1991 0.48 0.49 0.62 0.56 0.47 0.55 0.48 0.49

1992 0.47 0.48 0.61 0.56 0.46 0.54 0.48 0.46

1993 0.47 0.47 0.60 0.58 0.46 0.52 0.48 0.47

1994 0.48 0.47 0.60 0.57 0.44 0.50 0.50 0.47

1995 0.49 0.48 0.58 0.57 0.44 0.51 0.51 0.48

1996 0.50 0.49 0.60 0.59 0.44 0.54 0.53 0.50

Average 0.44 0.48 0.62 0.61 0.48 0.52 0.49 0.45

Panel E: Three-Firm Concentration Ratio (Total Assets) by Industry and Year

1977 0.59 0.53 0.61 0.85 0.59 0.58 0.73 0.55

1978 0.57 0.53 0.62 0.85 0.61 0.56 0.75 0.51

1979 0.57 0.54 0.63 0.83 0.62 0.58 0.74 0.51

1980 0.56 0.54 0.66 0.83 0.64 0.58 0.74 0.52

1981 0.57 0.55 0.67 0.82 0.66 0.58 0.75 0.53

1982 0.57 0.54 0.69 0.81 0.66 0.59 0.73 0.51

1983 0.58 0.54 0.67 0.79 0.65 0.59 0.72 0.51

1984 0.55 0.54 0.67 0.82 0.64 0.58 0.70 0.52

1985 0.55 0.53 0.66 0.80 0.64 0.57 0.67 0.53

1986 0.56 0.53 0.66 0.75 0.64 0.58 0.66 0.58

1987 0.58 0.52 0.66 0.73 0.63 0.60 0.65 0.60

1988 0.55 0.52 0.67 0.71 0.64 0.61 0.65 0.60

1989 0.59 0.53 0.68 0.70 0.64 0.63 0.62 0.59

1990 0.57 0.53 0.65 0.69 0.61 0.63 0.63 0.62

1991 0.56 0.54 0.65 0.70 0.62 0.64 0.63 0.62

1992 0.56 0.53 0.65 0.70 0.64 0.63 0.61 0.60

1993 0.58 0.53 0.64 0.65 0.62 0.61 0.60 0.60

1994 0.57 0.53 0.62 0.65 0.58 0.60 0.61 0.59

1995 0.53 0.53 0.61 0.61 0.57 0.59 0.61 0.58

24

Table 4Industry

Year 1 2 3 4 5 6 7 8


1996 0.56 0.54 0.63 0.67 0.56 0.59 0.62 0.60

Average 0.57 0.53 0.65 0.75 0.62 0.60 0.67 0.56

Panel F: Three-Firm Concentration Ratio (Net Sales) by Industry and Year

1977 0.59 0.52 0.65 0.83 0.57 0.57 0.63 0.57

1978 0.60 0.53 0.66 0.83 0.64 0.56 0.69 0.53

1979 0.56 0.54 0.68 0.82 0.65 0.56 0.69 0.54

1980 0.57 0.55 0.68 0.82 0.65 0.56 0.69 0.55

1981 0.60 0.55 0.71 0.81 0.69 0.57 0.69 0.55

1982 0.58 0.54 0.68 0.80 0.67 0.58 0.70 0.53

1983 0.57 0.54 0.67 0.79 0.67 0.57 0.68 0.52

1984 0.55 0.54 0.66 0.82 0.65 0.58 0.67 0.52

1985 0.56 0.53 0.67 0.81 0.64 0.57 0.64 0.53

1986 0.57 0.53 0.65 0.78 0.64 0.58 0.65 0.58

1987 0.60 0.52 0.66 0.76 0.64 0.59 0.64 0.60

1988 0.58 0.52 0.68 0.74 0.63 0.61 0.64 0.61

1989 0.60 0.53 0.68 0.73 0.63 0.62 0.61 0.61

1990 0.58 0.53 0.65 0.71 0.61 0.62 0.60 0.63

1991 0.58 0.53 0.65 0.72 0.62 0.63 0.61 0.63

1992 0.57 0.53 0.64 0.71 0.63 0.61 0.60 0.62

1993 0.59 0.53 0.64 0.67 0.62 0.60 0.58 0.61

1994 0.58 0.53 0.63 0.68 0.59 0.59 0.60 0.60

1995 0.54 0.52 0.62 0.65 0.57 0.59 0.61 0.58

1996 0.56 0.53 0.64 0.71 0.56 0.59 0.62 0.60

Average 0.58 0.53 0.66 0.76 0.63 0.59 0.64 0.58

Notes: 1 = Agricultural & Natural Resources, 2 = Manufacturing, 3 = Transportation and Public Utilities, 4 =Communications, 5 = Wholesale Trade, 6 = Retail Trade, 7 = Finance, Insurance and Real Estate, 8 = Services

Table 5 summarizes the changes in the concentration ratios between 1977 and 1996. Fornumber of clients [N], Transportation and Public Utilities was the only industry to have a decreasein its four firm concentration ratio. For total assets [TA], Communications had the largest decrease(12.2%) followed by Finance, Insurance and Real Estate (4.9%) and Wholesale Trade with (1.4%).Two industries had no change. For net sales [NS], Communications (11%) and Transportation andPublic Utilities (3.8%) had decreases. Hence while the number of clients increased, their size asmeasured by assets or sales did not increase proportionally. This is consistent with auditorsobtaining more and smaller client firms.

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Table 5: Four Firm Concentration Ratios 1977 and 1996

1977 1996 Percentage Change

Number of Clients

Agriculture & Natural Resources 51.0% 62.0% 21.6%

Manufacturing 59.0% 61.0% 3.4%

Transportation & Public Utilities 75.0% 73.0% -2.7%

Communications 72.0% 73.0% 1.4%

Wholesale Trade 57.0% 57.0% 0.0%

Retail Trade 59.0% 69.0% 16.9%

Finance, Insurance & Real Estate 65.0% 66.0% 1.5%

Services 55.0% 62.0% 12.7%

Total Assets



Transportation & Public Utilities 77.0% 77.0% 0.0%

Communications 90.0% 79.0% -12.2%

Wholesale Trade 73.0% 72.0% -1.4%

Retail Trade 69.0% 75.0% 8.7%

Finance, Insurance & Real Estate 81.0% 77.0% -4.9%

Services 70.0% 72.0% 2.9%

Net Sales



Transportation & Public Utilities 80.0% 77.0% -3.8%

Communications 91.0% 81.0% -11.0%

Wholesale Trade 71.0% 72.0% 1.4%

Retail Trade 68.0% 75.0% 10.3%

Finance, Insurance & Real Estate 76.0% 78.0% 2.6%

Services 71.0% 72.0% 1.4%

Rather than examining a four or three firm concentration ratio in more detail, as was donein previous studies, we examine the distribution of clients compared to a uniform distribution. Thismethod was selected because of the high concentration of clients among the Big-6 firms, whichmight mask increasing or decreasing concentration if three or four firm concentration ratios wereused. For example, concentration ratios of more than 50% have typically been considered indicators

26


of high industry concentration. If however, Big-6 firms accounted for 90% of the number of clientsin an industry, one would expect a four firm concentration ratio of 60% even if the industry was notconcentrated (90% / 6 firms = 15% per firm, 15% x 4 firms = 60%).

To further investigate concentration andits change between 1977 and 1996, Figure 1 ispresented. Figure 1 presents the actuals for eachof the Big-6 firms for 1977 and 1996. It alsopresents the level that each Big-6 firm shouldhave if there was no concentration (total Big-6share/6) for 1977 and 1996. Also presented is aprojection for 1996 based upon the 1977 shareplus the average of the decrease in non Big-6share between 1977 and 1996. The final estimatethat is presented is a projection for 1966 basedupon 1977 share plus a weighted average of 1977market share and the decrease in non Big-6 share.Since the results for NS mirror those of TA, onlyN and TA are presented for economy's sake.Also, since the second projection is substantiallyidentical to the first projection, only the firstprojection is discussed in the text.

In Figure 2 - Panel A, Agricultural andNatural Resources market share is presentedusing number of clients. Market share using totalassets is presented in Panel B. As derived fromTable 3, Big-6 firms market share in this industryincreased from 65% to 82%. Hence the averagemarket share of each Big-6 firm should haveincreased from approximately 10.8% to 13.6%.In 1977 and 1996 three of the Big-6 were abovethe median (two using client size). One firm(D&T) had a decrease in number of client firmsand an increase in client size. Only two firmsexperienced increases above the estimate basedupon a uniform distribution. KPMG and E&Yeach increased more than 5% in both number ofclients and size.

Figure 3 presents the results for the Communications industry. Big-6 firms market share inthis industry increased only slightly from 87% to 88.7%. Hence the average market share of eachBig-6 firm should have increased only slightly from 14.5% to 14.8%. In 1977, two (two) of theBig-6 were above the median and in 1996 three (two) were above the median for number of clients(client size). Two firms (C&L and PW) had a decrease in number of clients and client size (despite

27


a claim of specialization). Four firms increased above the estimate based upon the uniformdistribution.

Figure 4 presents the results for the Finance, Insurance and Real Estate industry. Big-6 firmsmarket share in this industry increased from 78% to 85.5%. Hence the average market share of eachBig-6 firm should have increased from approximately 13% to 14.3%. In 1977 three (two) of theBig-6 were at or above the median and in 1996 three were at or above the median (three using clientsize). Three firms (C&L, E&Y, and KPMG) had an increase in number. Only D&T had a decreaseusing both number and client size. Three firms (C&L, E&Y and KPMG) increased above the

28


projection based upon the uniform distribution for number of clients and client size. In addition, PWand AA experienced an increase based upon firm size.

The results for the Manufacturing industry are presented in Figure 5. Big-6 firms marketshare in this industry increased from 80% to 85%. Hence the average market share of each Big-6firm should have increased from approximately 13.2% to 14.3%. In 1977 three (four) of the Big-6were at or above the median and in 1996 two were at or above the median (three using client size).Four firms (AA, C&L, E&Y, and KPMG) had an increase in number. Only PW had a decreaseusing both number and client size. Three firms (AA, E&Y and KPMG) increased above the estimatebased upon the uniform distribution for number of clients and client size (C&L, E&Y, and KPMG).

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In Figure 6 - Panel A, Retail market share is presented using number of clients and marketshare using total assets is presented in Panel B. Again, as derived from Table 3, Big-6 firms marketshare in this industry increased from 74.3% to 89%. Hence the average market share of each Big-6firm should have increased from approximately 12.4% to 14.8%. In 1977 three (two) of the Big-6were at or above the median and in 1996 four were at or above the median (four using client size).Four firms (AA, C&L, E&Y, and PW) had an increase in number. Only DT had a decrease usingboth number and client size. Four firms (AA, C&L, KPMG and PW) increased above the estimatebased upon the uniform distribution for number of clients and client size (AA, C&L, E&Y, andKPMG).

30


The results for the Service industry are presented in Figure 7. Big-6 firms market share inthis industry increased from 69.3% to 81%. Hence the average market share of each Big-6 firmshould have increased from approximately 11.6% to 13.5%. In 1977 three (four) of the Big-6 wereat or above the median and in 1996 three were at or above the median (three using client size).Three firms (C&L, E&Y, and KPMG) had an increase in number. Four firms (AA, C&L, E&Y, andKPMG) had an increase based on size. Three firms (C&L, E&Y, and KPMG) increased above theestimate based upon the uniform distribution for number of clients and client size.

F igure 8 presents the results for the Transportation industry. Big-6 firms market share in this industryincreased from only slightly from 90.4% to 93.1%. Hence the average market share of each Big-6

31


firm should have increased from approximately 15.1% to 15.5%. In 1977 three (two) of the Big-6were at or above the median and in 1996 one was at or above the median (two using client size).Two firms (AA and C&L) had an increase in number and based on size. Two firms increased abovethe estimate.

Figure 9 presents the results for the Wholesale Trade industry. Big-6 firms market share inthis industry increased from 72.7% to 77.5% (87.0 to 90.9%) using the N (TA) measure. Hence theaverage market share of each Big-6 firm should have increased from approximately 12.1% to 12.9%(14.5 to 15.2%). In 1977 four (three) of the Big-6 were at or above the median and in 1996 three(four) were at the median or above.

Table 7Summary of Changes in Actual versus Forecast Proportion of Audits by Industry and Big-6 Auditor

Industry AA C&L E&Y D&T KPMG PWNumber of ClientsAgriculture & Natural Resources + + ++ - ++ +Manufacturing ++ + ++ - ++ -Transportation & Public Utilities ++ ++ - - - -Communications ++ - ++ ++ ++ -Wholesale Trade ++ ++ - ++ + ++Retail Trade ++ ++ + - ++ ++Finance, Insurance & Real Estate - ++ ++ - ++ -Services - ++ ++ - ++ -Summary: ++ 5 5 5 2 6 2

+ 1 2 1 1 1- 2 1 2 6 1 5

Total AssetsAgriculture & Natural Resources - - ++ ++ ++ +Manufacturing + ++ ++ - ++ -Transportation & Public Utilities ++ + - - - -Communications ++ - ++ ++ ++ ++Wholesale Trade - + - ++ - ++Retail Trade + ++ ++ - ++ -Finance, Insurance & Real Estate ++ ++ ++ - ++ ++Services + ++ ++ ++ - -Summary: ++ 3 4 6 4 5 3

+ 3 2 1- 2 2 2 4 3 4

Notes: ++ increase above the 1996 estimate + increase - decrease

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Using number of clients, AA, E&Y, D&T and KMPG were above the average in 1997, butonly AA, E&Y and DT remained above in 1996. Using client size, three firms (AA, E&Y andKPMG) were above the average in 1977. In 1996 four firms (AA, E&Y, DT and PW) were abovethe estimate.

Table 7 presents a summary of changes by industry based upon three classifications: anincrease above the projection, some increase, or a decline. For number of clients there appears tobe a marked distinction among the firms, those with five or more of the eight industries having anincrease over the 1996 estimate and those with two industries having an increase over the 1996estimate. The latter group includes D&T and PW.

However, if total assets is still a valid proxy for audit fees, the changes are not as striking.Only one firm had an increase in six industries (E&Y), one in five industries (KPMG), two in fourindustries (D&T and C&L) and two in three industries (AA and PW).

CONCLUSIONS

During the time period under study, concentration increased substantially at the expense ofnon Big-6 firms. Non Big-6 auditors' market share declined substantially during the period. In onlyone industry do non Big-6 firms now audit more than 20% of the firms (wholesale trade), and theirmarket share consists principally of the smaller firms when measured by asset size. Overall, duringthis period, four of the Big-6 (AA, C&L, E&Y and KPMG) had gains in market share using numberof clients. However C&L did not grow as much as the other three (which increased more than theexpectation of uniform growth during the period).

In contrast to previous studies, our results demonstrate that firms that had the largest marketshare in a particular industry did not necessarily maintain that market share. For example in five ofthe industries, Agricultural & Natural Resources, Communications, Finance, Services and WholesaleTrade, the leading firm in the industry in 1977 was replaced by another firm by 1996. Further, thisstudy indicates that auditor concentration research, which relies solely upon metrics such as the"four-firm concentration ratio", may provide biased or misleading results.

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REFERENCES

Danos, P. and J. Eichenseher. 1982. Audit Industry Dynamics: Factors Affecting Changes inClient-Industry Market Shares. Journal of Accounting Research (Autumn): 604-616.

Eichenseher, J. and P. Danos. 1981. The Analysis of Industry-Specific Auditor Concentration:Towards and Explanatory Model. The Accounting Review (July): 479-492

Hogan, C. and D. Jeter. 1999. Industry Specialization by Auditors. Forthcoming in Auditing: AJournal of Practice and Theory (Spring).

Minyard, D. and R. Tabor. 1991. The Effect of Big Eight Mergers on Auditor Concentration.Accounting Horizons (December): 79-90.

Rose-Green, E., J. Scheiner and C. Wheatley. 1998. Audit Quality in the Post Merger Era. WorkingPaper, Florida International University.

Scheiner, J. and C. Wheatley. 1998. Auditor Concentration in the Oil and Gas Industry. PetroleumAccounting and Financial Management Journal (Spring): 1-13.

Simunic, D. 1980. The Price of Audit Services: Theory and Evidence. Journal of AccountingResearch (Spring): 161-190.

Warren, Carl S. 1980. Uniformity of Auditing Standards: A Replication. Journal of AccountingResearch. (Spring): 312-324.

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35


THE USEFULNESS OF ACCOUNTING INFORMATIONIN ASSESSING SYSTEMATIC RISK:

A RE-EXAMINATION

Ronald J. Woan, Indiana University of Pennsylvania

ABSTRACT

This study documents the potential statistical problems in using accounting risk measuresin assessing a firm's systematic risk. It is found that all three problems: measurement error,omission of variables and multicollinearity exist in this area of research. To underscore the seriousnature of the problems, two most important empirical studies in this area of research are replicatedwith new data: the Beaver, Kettler and Scholes' study and the Eskew's study. In both cases, theresults are inconsistent with their findings. An alternative model, LISREL, is recommended for thisarea of research.

INTRODUCTION

The objectives of this paper are 1) to reiterate the common problems in the application of theconventional regression model in empirical accounting research and provide some evidence thatthese problems do exist in accounting data and 2) to underscore these problems, two of the oft-citedearlier studies of the usefulness of accounting information in assessing systematic risk are partiallyreplicated based on a new data set to see whether their results may be cross-validated and if not,why. But, first, some justification is given here as to why research in this area is important.

Portfolio theory tells us that systematic risk is the only risk for which investors, holdingdiversified portfolios, need to be concerned about (e.g., Sharpe 1964). Thus, rational risk averseinvestors will hold well diversified portfolios. Systematic risk is an important economic decisionvariable for most investors because it is well known that most people are risk averse. The accountingliterature also stresses the importance of risk assessment. It is stated in ASOBAT (AAA 1966, 4 and19) that one of the objectives of accounting is to provide information for "making decisionsconcerning the use of limited resources" and it continues, "accounting information is the chief meansof reducing uncertainty under which external users act". AICPA's Trueblood Report (1973, 13)states that, "the basic objective of financial statements is to provide information useful for makingeconomic decisions". Also, the FASB (1983, par. 34) states that the objective of financial reportingis "to provide information that is useful to present and potential investors and creditors and otherusers in making rational investment, credit, and similar decisions". Hence, investigation into theusefulness of accounting information is an important area of research and is consistent with thepronouncements and viewpoints expressed by the above groups concerned with financial reporting.

Capital market based accounting research has been widely accepted for this purpose, as isevidenced by the voluminous publications in prestigious accounting and finance journals. In fact,

36


the article by Ball and Brown (1968), which served to introduce the capital market-basedmethodology in the accounting literature, was recently named the recipient of the AmericanAccounting Association's newly established 'Seminal Contribution to Accounting LiteratureAwards.' This noteworthy designation underscores the important contribution that capitalmarket-based research has provided to the body of knowledge in accounting.

Since systematic risk is theoretically an important decision variable for investors, creditorsand managers, the ability of accounting information to assess systematic risk is of particularimportance. Since the publication of Ball and Brown (1969) numerous research studies in this areahave been conducted and published (e.g., Ang et al. 1984; Beaver et al. 1970; Bildersee 1975;Ben-Zion and Shalit 1975; Bowman 1979; Breen and Lerner 1973; Brenener and Smidt 1978; Elgers1980; Elgers and Murray 1982; Eskew 1979; Hill and Stone 1980; Lev 1974; Lev and Kunitzky1974; Logue and Merville 1972; Mandelker and Rhee 1984; Melicher 1974; Melicher and Rush1974; Rosenberg and Mckibben 1973; Thompson 1976; White 1972). The results have been mixedand inconsistent.

The remainder of this paper is divided into four sections. Section I discusses some potentialproblems facing empirical researchers in this area of study. Section II describes the sample selectioncriteria and the resulting sample. Section III presents the results of partial replications and someextensions of the oft-cited studies by BKS and Eskew. Section IV presents the concluding remark.

SOME PROBLEMS RELATED TO THE STUDIES OF THE ASSOCIATION BETWEENTHE SYSTEMATIC RISK AND ACCOUNTING RISK MEASURES

The FASB recognizes the limitations of accounting information. In particular, it

acknowledges that "the information often results from approximate, rather than exact, measures."and thus, "despite the aura of precision that may seem to surround financial reporting in general andfinancial statements in particular, with few exceptions the measures are approximations, which maybe based on rules and conventions, rather than exact amounts" (FASB 1983, par. 20). That is to say,it acknowledges potential measurement errors in accounting information.

The potential determinants of systematic risk are not accounting data per se. Rather, thedeterminants are the financial constructs, such as growth, operating leverage, profitability, liquidity,efficiency, etc., resulting from the management's operating, investing and financial decisions, whichaccounting data attempt to measure. These constructs are widely accepted risk measures about a firmand their surrogates have been somewhat successfully used in default predictions (Altman et al.1977; Deakin 1972) and bond ratings (Copeland and Ingram 1982; Pinches et al. 1977; Watson etal. 1983). These surrogates are usually obtained from publicly available accounting data.Consequently, identification of the determinants of systematic risk from commonly acceptedaccounting risk measures has been actively studied in both the finance and accounting literature.Unfortunately, the results have so far been inconclusive, and mostly conflicting. Sound guidance in1) the selection of variables to be incorporated (omitted variables), 2) dealing with measurementerrors, and 3) coping with multicollinearity has been lacking and has contributed to the inability toreach consistent conclusions in previous studies. A brief summary of the major consequences ofthese problems is given below (e.g., Jensen 1967; Judge, Griffithhs, Hill, Lutkepohl and Lee 1985):

37


1 Omitting variables that are correlated with the independent variables in a regression equation will causebiased estimates of the error variance and the regression coefficients of the remaining variables. In the caseof two correlated independent variables, for instance, the bias caused by leaving out one variable will beequal to the product of the partial regression coefficient of the omitted variable had it been in the equationand the simple regression coefficient of the omitted variable on the remaining independent variable.

2 Measurement errors in variables have much more complicated consequences in regression than eitheromitted variables or multicollinearity problems. In the simple case of a pair of variables, it is well knownthat measurement errors will attenuate the correlation between the two variables. But, in multiple regressionwhere there is more than one independent variable involved, measurement errors in the independentvariables have a much more complicated impact on the partial regression coefficients. The biases may beupward or downward depending on the interrelationship among the observed variables, measurementerrors, and the true scores. Wickens (1972) showed that it is better to include variables with measurementerrors than omit them in a multiple regression model.

3 The well known multicollinearity problem in regression tends to cause unstable parameter estimates. Theestimates are generally highly correlated with large variances making it very difficult to isolate the effectsattributable to the individual variables. Furthermore, it can cause serious numerical inaccuracies in theparameter estimates, as demonstrated in Wampler (1970). However, estimates remain unbiased providedthe model is not misspecified and numerical accuracy is not sacrificed.

4 Finally, all three problems could result in estimates having signs opposite to the true ones. Hence, the resultfrom regression or correlation analysis could be sample sensitive and hard to interpret.

From the discussion above, one can see that any application of regression techniquesdemands careful thought on the part of the analyst. This is especially true in the study of therelationship between market determined risk measures and accounting data based risk measuressince the relevant measures are of the ex ante type, which are difficult to obtain. Furthermore, therisk measures in accounting applications are generally abstract concepts, which have no unique welldefined measures. Various ex post proxies have generally been employed in empirical research.Measurement errors and multicollinearity have thus become thorny issues. Most of the variableschosen to be included in the models examined generally lack theoretical support (see, e.g., thediscussion given in BKS article). Furthermore, important variables were omitted in many of thestudies. For example, operating leverage and financial leverage, which can theoretically be linkedto systematic risk (Galon 1981; Galon and Gentry 1982; Subramanyan and Thomadakis 1980;Hamada 1972), were either excluded or omitted from BKS and Eskew's studies. This perhaps helpsto explain the inconsistent findings. So far, none of the previous studies seem to have been replicatedto cross-validate earlier findings. Each research study uses a different, even though sometimesoverlapping set of accounting risk measures. Most of the researchers were somewhat aware ofmeasurement error, omitted variables and multicollinearity problems. But, none has faced theproblem seriously, let alone come up with a reasonable solution. In fact, most researchers seemedto have been content with casually mentioning these problems and went on to report their findingsas if these problems did not interfere with their results. If such studies cannot be cross-validated bya new set of data, they will be of little or dubious value. Cross-validation will lend some credenceto empirical studies and thus, help theoretical development. So, it seems to be reasonable to

38


undertake a replication of earlier works. In the following section, the findings from partialreplications of the BKS and Eskew studies will be reported.

DESCRIPTION OF SAMPLE

The Standard and Poor's Industrial Compustat 1985 tape was used to obtain the necessaryfinancial data, and the University of Chicago Center for Research on Security Prices (CRSP) 1985tape was consulted for the relevant market determined systematic risk. The CRSP monthly securityreturn tape was used to estimate each firm's market determined systematic risk measure. Of the3,211 firms listed on the tape, only 574 firms had a complete twenty-year database (January 1966? December 1985) of monthly returns. The Compustat tape has a total of 2,322 firms listed, of which875 firms use the calendar year as the fiscal year. Of these 875, firms 395 firms are also listed onthe CRSP tape. Therefore, 395 firms constitute the basic sample used in this study.

REPLICATIONS

In this section, the essential portions of the studies by BKS and Eskew, two of the mostoft-cited studies, are replicated using the new data set described in the previous section.

Beaver, Kettler and Scholes' Study

355 of the 395 firms in the basic sample have complete twenty years data on the Compustattape for all the variables examined by BKS. This twenty-year period is broken down into twosub-periods with ten years each. The summary statistics of the two periods' systematic risks arepresented in Table 1.

Table 1: Summary Statistics for Distribution of Estimates of Systematic Risk (beta)

Mean Standard Deviation Range

Period one (66-75) .805 .299 .077 to 0.97

Period two (76-85) .810 .346 .160 to 2.20

Correlation for the two period betas:.69108

Compared to the BKS sample (BKS 1970, Tables 1 and 2, 665) it can be seen that the meansof the systematic risks using the same equally weighted market index as employed by BKS aresmaller than those reported by BKS (.805 and 0.810 vs. .991 and .987). Also, the product momentcorrelation for the two time periods is .69, which is about 16 percent higher than the .59 reportedby BKS. Together with the mean of the logarithm of the size variable, as presented in Table 2, onecan see that, compared to the BKS sample (BKS 1970, Table 3, 667), the current sample consistsof larger, less risky firms.

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Table 2: Summary Statistics for Accounting Risk Measures

Mean Standard Deviation

Period one two one two

Dividend payout .485 .663 .198 1.587

Growth .104 .085 .043 .048

Leverage .480 .478 .155 .126

Liquidity 2.100 1.910 1.145 1.255

Size 6.250 7.200 1.324 1.352

Earnings variability .060 .092 .066 .149

Accounting beta 1.043 .991 1.481 2.498

These firms exhibit a higher growth rate in assets, much less liquidity and higher leverage.Also, the second period has much greater earnings variability and dividend payout; the accountingbetas for both periods are much larger with somewhat larger cross-sectional standard deviations thanthe BKS sample. These findings are not surprising, since the second period covers the recessionalperiod of the late seventies and early eighties. Due to the oft mentioned reluctance on the part ofmanagement to reduce dividends, the mean payout ratio could be larger simply because the earningsare smaller as a result of recession. Inspection of Table 3 reveals that all but two of the inter-periodcorrelations for the accounting risk measures are similar in magnitude to those reported by BKS(BKS 1970, Table 4, 668).

Table 3: Inter-period Correlations for Accounting Risk Measures

Dividend payout .059

Growth .243

Leverage .784

Liquidity .811

Size .963

Earnings variability .480

Accounting beta .299

The two notable differences are dividend-payout, which has a much lower inter-correlation,and accounting beta, which has a much higher inter-correlation than those reported by BKS. Thelower inter-period correlation for the payout is probably due to the recession affecting the businessin a non-homogeneous way, whereas the higher inter-period correlation for the accounting betasupports the claim that the current sample consists of more stable firms. The non-homogeneousimpact of recession on business earnings is also suggested by the larger average earnings variability

40


for the second period. Of course, these results could also be due solely to sample variation. Resultsfrom the multiple regression analysis are presented in Table 4.

Table 4: Multiple Regression Summary

coefficients t-statistics


Dividend payout -.575 -.082 -8.477 -.810

Growth -.779 -.692 -2.692 -1.990

Leverage .393 -.286 3.526 -1.822

Liquidity .056 .202 3.699 1.203

Size -.040 -.052 -4.224 -3.920

Earnings variability 2.606 .674 12.479 5.032

Accounting beta -.051 .023 -5.414 3.055

R-square: .499 F(7, 347)-statistics: 49.4 (period one)R-square: .286 F(7, 347)-statistics: 19.8 (period two)

The coefficients for all the variables except liquidity, accounting beta and, leverage have thecorrect signs as predicted by the theoretical and analytical results of Myers and Thurnbull (1977),Subramanyam and Thomadakis (1980), Ang, Peterson and Peterson (1984). The growth variable hasa negative coefficient for both periods conforming to the results of Myers and Thurnbull. Thisnegative relationship also holds for the simple correlation for both periods. This contradicts most,if not all empiricists' ad hoc arguments, which assert that, ceteris paribus, growth firms should beriskier (BKS 1970, 660). For period one, all variables are statistically significant (at .05 level ofsignificance), whereas for period two, leverage, liquidity, and dividend payout are not significant.The coefficients for liquidity and accounting beta have the wrong sign for period one, and for periodtwo, the coefficients for liquidity and leverage have the wrong sign even though they are notstatistically significant. Such inconsistent findings have also been reported in past empirical studies(e.g., Breen and Lerner 1973; Ang, Peterson and Peterson 1984). Simple correlations between thesystematic risk and the accounting risk measures are presented in Table 5.

Compared to the BKS result (BKS 1970, Table 5, 669) the accounting beta, size and liquidityhave much higher correlation with systematic risk, and growth and leverage have much lowercorrelations with systematic risk. One must be careful to avoid potentially misleading inferencesbased solely on analyses of simple correlation coefficients. For example, financial leverage isnegatively correlated with systematic risk for both periods while dividend payout is positivelycorrelated with the systematic risk for period two. These results appear, on the surface, to becounterintuitive. One possible source of this apparent inconsistency is the violation of the ceterisparibus assumption made in theoretical and analytical arguments. In multiple regression analysis,the relationship between the dependent and the independent variables is dealt with in a partial

41


fashion, i.e., the regression coefficient for an independent variable has the same sign as the partialcorrelation coefficient between the independent variable in question and the dependent variable withall the other independent variables held constant. Thus, as long as there are no interactive effectsamong the independent variables, multiple regression analysis is more in line with theoretical andanalytical assumptions. Of course, the model must be free of misspecifications for properinterpretation.

Table 5: Contemporaneous Correlation between Accounting Risk measures and Systematic Risk

Period one two

Dividend payout -.329 .024

Growth -.095 -.226

Leverage -.001 -.119

Liquidity .232 .241

Size -.305 -.301

Earnings variability .535 .388

Accounting beta .190 .361

The results of the BKS model, i.e. using accounting risk measures to reduce measurementerror of period one's beta, are provided in Table 6.

Table 6: BKS Model with New Data Set

Coefficients t-statistics

Dividend payout -.511 -7.744

Growth -.833 -2.704

Earnings variability 2.275 11.843

R-square: .391 F(3, 351)-statistics: 74.97

The multiple correlation (.63) is only slightly less than the .67 reported by BKS (BKS 1970,672). Also, growth has a negative coefficient as opposed to the positive one reported by BKS.Another interesting similarity between the two results is that the magnitude of the coefficients(ignoring the sign difference for the growth variable) is very similar. Table 7 provides theforecasting results of various models.

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Table 7: Analysis of Forecast Errors

Mean Square Error Mean Absolute Error

Naive Model .070 .202

BKS Model .081 .233

The naive forecasting model, which uses period one betas as forecasts of their correspondingperiod two counterparts, produces forecasts with mean square error of .07 and mean absolute errorof .202. The respective values given by BKS are .093 and .239 (BKS 1970, Table 7, 677). The BKSmodel produces forecasts having mean square error of .081 and mean absolute error of .233; thecorresponding results given by BKS are .089 and .23 (BKS 1970, Table 7, 677). From these results,it can be seen that the instrumental variables approach actually increases forecast errors for thecurrent sample. This is in direct contrast to the results provided by BKS. One possible explanationis that the current sample consists of more stable firms as mentioned before, and the inter-periodcorrelation for the systematic risk is much higher. Of course, besides the possible impact of samplingvariation, measurement errors and omitted variable problems are other possible causes of thiscontradictory finding. Notice that the improvement of mean square error from .093 to .089 and ofmean absolute error from 0.239 to 0.230 as reported by BKS was not impressive and it might haveoccurred simply by chance.

Eskew Study

Eskew used the CRSP value weighted index as a surrogate for the market portfolio return.By using this index, the un-weighted arithmetic mean of the firm's betas is no longer one, as Eskewseemed to have implied in his article (1979, 108 and 117). Table 8 provides the summary statisticsfor the systematic risk.

Table 8Summary Statistics for Distribution of Estimates of Systematic Risk (beta)

Mean Standard Deviation Range

Period one 1.067 .331 .180 to 2.23

Period two .963 .384 .230 to 2.28

Correlation for the two period betas: .62389

The mean of the betas is 1.07 and .96 for periods one and two respectively. They are slightlysmaller than those given by Eskew (Eskew 1979, Table 3, 115). Note that Eskew broke his sampleinto three six year sub-periods. The multiple regression result is presented in Table 9.

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Table 9: Multiple Regression Summary

Coefficients t-statistics


Dividend payout -.749 -.009 -9.369 -.748

Growth .387 -.567 1.136 -1.385

Leverage .180 -.507 1.367 -2.741

Liquidity .052 .023 2.866 1.147

Size -.015 -.017 -1.334 -1.077

Earnings variability 2.761 .649 11.225 4.116

Accounting beta -.074 .026 -6.712 2.933

R-square: .434 F(7, 347)-statistics: 38.0 (period one)R-square: .200 F(7, 347)-statistics: 12.4 (period two)

Some interesting points are worth noting when Table 9 is compared to Table 4. The onlydifference between the two tables is the market determined systematic risk. In Table 4, thedependent variable is estimated from the market model by using the CRSP equally weighted marketindex, whereas, in the current table the market index used is the value weighted index. There aredifferences in the significant variables as well as some of the signs of the coefficients. For periodone, the significant variables are dividends payout, earnings variability, accounting beta andliquidity, and for period two, the significant variables are earnings variability, leverage andaccounting beta. The coefficients for accounting beta, liquidity and growth have the wrong signs forperiod one; only the coefficients for leverage and liquidity have the wrong signs for period two.Again, this is consistent with the inconclusive results reported in the literature. Table 10 presentsthe contemporaneous correlation between the accounting risk measures and the systematic riskestimated from the CRSP value weighted market index.

Table 10: Contemporaneous Correlation between Accounting Risk Measures and Systematic Risk

Period one two

Dividend payout -.417 .021

Growth -.091 -.175

Leverage -.076 -.157

Liquidity .227 .208

Size -.196 -.169

Earnings variability .415 .323

Accounting beta .093 .323

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Again, care must be exercised in interpreting the simple correlation coefficients as discussedabove.

Using the SPSS stepwise regression procedure with F set to enter and to remove at 3.5 andtolerance level set at .9, Eskew selected growth, size, and earning's variability as the three significantindependent variables. Notice that dividends payout, which was used by BKS was no longersignificant and was replaced by a size variable. The results of the Eskew's model are presented inTable 11.

Table 11: Eskew Model with New Data Set

Coefficient t-statistics

Size -.044 -3.716

Growth 1.269 3.452

Earnings variability 2.144 8.966

R-square: .226 F(3, 351)-statistics: 34.23

The signs of the coefficients are identical to those reported by Eskew (1979, Table 2, 113).But, the current R square (.23) is slightly less than that reported by Eskew (.27). Notice that this ismuch smaller than the squared inter-period correlation (.39) of the systematic risks. The model isalso fitted to the systematic risk estimated from the equally weighted market index. The R?squareis .36 and growth has an insignificant, though negative coefficient. From this, it appears that theusefulness of accounting information is sensitive to the choice of market index. Elgers and Murray(1982) reached a similar conclusion. The results of various forecasting models are presented in Table12.

Table 12: Analysis of Forecast Errors

Mean Square Error Mean Absolute Error

Naive Model .109 .266

Eskew Model .140 .311

The naive model produces forecasts having mean square error of .109 and mean absoluteerror of .266; the corresponding values given by Eskew are .145 and .2995 (Eskew 1979, Table 4,115). Using the three variables selected by Eskew as instrumental variables to reduce themeasurement error for period one's betas, the resulting forecasts have a mean square error of .141and mean absolute error of .311; the corresponding values given by Eskew are 0.0952 and 0.2315respectively (Eskew 1979, Table 5, 115). As in the case of the replication of BKS, using accountingvariables as instrumental variables actually increases the mean square error and mean absolute errorof the forecasts. This is in direct contrast to the results given in Eskew's article, where he showedthat accounting variables improved the forecast.

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When Eskew's stepwise regression approach was employed, it selected three accounting riskmeasures: dividend payout, earnings variability, and liquidity as the three significant variables forbetas estimated from CRSP value weighted market index. These three variables were then used asinstrumental variables for period one's beta. This model produces forecast errors having mean squareerror of .106 and mean absolute error of 0.265. This is a negligible improvement over the naiveforecast. On the other hand, the same method applied to the betas estimated from CRSP equallyweighted index, selects earnings variability, dividend payout, size, accounting beta and growth asthe significant variables. However, the resulting model produces forecast errors having mean squareerror of .096 and mean absolute error of .249, which are worse than those produced by a naivemodel.

CONCLUDING REMARKS

Portions of the results of the two replications are inconsistent with those reported by BKSand Eskew. The instrumental variables approach used to reduce measurement error of the periodone's betas for forecasting the second period's betas not only fails to improve forecasting accuracy,it actually increases the mean square error and mean absolute error for the current sample.Apparently, the usefulness of the accounting risk measures in improving forecast accuracy dependson the market index as well as the sample and time period studied. Also, by the nature of theinstrumental variables approach, bias is not affected. This disturbing finding could potentially arisefrom measurement errors, multicollinearity and omitted variables problems. Users of accountinginformation should be cautioned against using the research results of a particular study. Withoutsound theoretical guidance, any empirical study must be replicated to cross-validate the results.There are some preliminary piecewise theoretical advances in this area of research (Galai andMasulis 1976; Goldenberg and Chiang 1983; Hamada 1972; Myers and Thurnbull 1977; Senbet andThompson 1982; Subramanyam and Thomadakis 1980). To cope with measurement error andmulticollinearity problems, the linear structural equation model (LISREL) suggested by Joreskogand Sorbom (1982) or the partial least squares model developed by Wold (1982) may be viablealternatives to the problem-ridden linear regression model as is commonly employed.

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Breen, W. J., & Lerner, E. M. (1973). Corporate financial strategies and market measures of risk andreturn. Journal of Finance (May), 339-52.

Brenner, M., & Smidt, S. 1978. Asset characteristics and systematic risk. Financial Management(Winter), 33-39.

Copeland, R. M. & Ingram, R. W. (1982). The association between municipal accountinginformation and bond rating changes. Journal of Accounting Research 20 (Autumn), 275?89.

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Deakin, E. B. (1972). A discriminant analysis of predictors of business risk. Journal of AccountingResearch 10 (Spring), 167-79.

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Elgers, P. & Murray, D. (1982). The impact of the choice of market index on the empiricalevaluation of accounting risk measures. The Accounting Review (April), 358-75.

Eskew, R. K. (1979). The forecasting ability of accounting risk measures: Some additional evidence.

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Subrahmanyam. M. G. & Thomadakis, S. B. (1980). Systematic risk and the theory of firm. TheQuarterly Journal of Economics (May), 437-51.

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Wampler, R. H. (1970). A report on the accuracy of some widely used least squares computerprograms. Journal of the American Statistical Association, 540-65.

Watson, C. J., Stock, J. D., & Watson, K. D. (1983). Multivariate normality and a bond ratingdecision model. Decision Science 14(Fall), 513-26.

White, R. (1972). On the measurement of systematic risk. Unpublished Ph. D. Dissertation, M.I.T.

Wickens, M. R. (1972). A note on the use of proxy variables. Econometrica (July), 759?61.

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51


COMPENSATION OF INVESTMENT COMPANYADVISORS: AN EMPIRICAL INVESTIGATION

Denise Woodbury, Weber State UniversityKyle Mattson, Weber State University

ABSTRACT

Data about the compensation contracts of open-end investment companies is collected andempirically examined. As expected, compensation is always a function of the NAV of the investmentcompany and, in general, compensation is (weakly) monotonically decreasing in the marginal ratepaid on increasing levels of NAV. Less often, the compensation contract is also contingent on theincome of the company. In the cases where the method of calculating the income proportion ofcompensation is identifiable, the compensation is (weakly) monotonically decreasing in the marginalrate paid on increasing levels of income. Interestingly, however, the definition of "cash income"varies across investment companies. In addition, open-end investment companies pay additionalcompensation (or reduce the compensation paid) as a function of excess expenses, a group fee,and/or performance relative to a benchmark component.

As expected, compensation is contingent (either explicitly or implicitly) on performance.Counter to intuition and the agency framework model developed, the less risky the portfolio(measured by purpose, composition, or size), the more sensitive is the compensation contract toperformance.

BRIEF REVIEW OF THE AGENCY MODEL

Agency models generally address the problem of moral hazard. This problem occurs whena decision made by one individual, striving to satisfy personal desires, affects the welfare of others.Furthermore, those so affected cannot observe or directly control the choice made by the individual.

In an agency model, the principal is the owner of a business who, for some reason, choosesnot to be directly involved in the management of the firm; instead, the principal hires an agent anddelegates decision-making authority for the enterprise to that agent. The owner of the firm mightchoose not to be involved in the operation of the business because of a desire not to expend effortor because the agent has a comparative advantage (either in resources or ability) in the managementof the firm or because of a desire to diversify personal and capital resources. If the principal andagent have different goals, if it is too costly to monitor the behavior of the agent, and if both of theparties seek to meet their own goals, the agent won't act in the best interests of the principal. If theagent has decision-making authority and the agent's choices are not easily observed and controlled,the choices he or she makes are unlikely to be consistent with the objectives of the principal.

Suppose the agent expends effort to improve the outcome of the business. The principalreceives the net profit of the operation of the business less the fee paid to the agent. Thus, theprincipal may receive the benefit of the agent's expenditure of effort through the increase in the

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profitability of the enterprise. However, the agent is assumed to have some, as yet unspecified, levelof disutility associated with the expenditure of effort. Because the agent may or may not, dependingon the compensation scheme, receive the full benefit of any increase in effort, the agent may choosea level of effort less than the principal would like. The problem of the principal is to motivate theagent to act in a manner that will be mutually satisfying.

The contract that leads to the highest level of expected utility for the principal may berelatively simple or may be quite complex. If the actions of the agent are perfectly and costlesslyobservable, the principal can merely reward the agent for the "correct" action and punish the agentfor the "incorrect" action. However, it is usually the case that the actions or effort of the agent arenot perfectly and costlessly observable. In this case, the incentives for the agent must be aligned insuch a way that the agent works in a manner desired by the principal.

Problems arise when the effort or actions of the agent are not perfectly and costlesslyobservable and the incentives of the agent do not lead to behavior that is consistent with the desiresof the principal. This is the case which is typically modeled in the principal-agent literature. Moralhazard problems may be characterized by a divergence of incentives between the two parties andasymmetric information.

One possible solution for this problem is to make compensation contingent on the variableof concern for the principle, which in this case is the outcome of the business venture. The agent willexpend effort just as if he or she were the principal if the fee paid to the agent is a residual claim onthe performance of the company. Such a contract aligns the incentives of the agent with those of theprincipal by having the agent bear the consequences of his/her choice of effort. However, if the risktolerance of the principal differs from that of the agent, this contract may not be efficient; it mayresult in the relatively risk-averse party to the contract bearing all of the risk of the enterprise.

Both effort and risk bearing are important in the design of compensation contracts. Payingthe agent a residual claim induces effort, but allocates risk to the party for whom the assumption ofrisk may be most costly. Compensating the agent with a fixed fee will provide improved risk sharingif the agent has lower risk tolerance than the principal. It does not, however, provide the agent withincentive to expend effort.

The optimal scheme would be expected to balance these two forces by compensating theagent through the use of both a fixed fee payment and a partial claim on the outcome of theenterprise. The exact form of an optimal contract would, therefore, depend on the relative degreesof risk aversion of the parties to the contract, on the relation between the outcome of the businessventure and the effort of the agent, and on the degree to which the actual effort of the agent may beobserved.

SUMMARY OF HYPOTHESES IDENTIFIED BY WOODBURY & NEAL (1999)

Compensation contracts might potentially be designed to alleviate, at least in part, the agencyproblem. Features of these compensation contracts might include a portion of compensation, whichis a function of the objective of the individual fund, as well as of the restrictions placed on theconstituent assets of the fund.

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In general, compensation would be expected to be a positive function of the outcomes ofNAV and of income. However, the sensitivity to outcome might be expected to be lower if theportfolio is more risky, given the likely relative risk aversions of the parties to the contract. Thus,compensation would be less sensitive to outcome for investment companies with a purpose ofgrowth or for investment companies whose portfolio compositions reduce the agent's ability todiversify the portfolio.

Further, compensation should be concave in outcome, whether that outcome is a function ofwealth or of income. This concavity might be due to transactions costs and a margin of inefficiencyin the market, which may be exploited by the investment advisors who are risk averse; thecompensation contract would be concave to balance the incentive and risk imposition aspects of theagency problem. It might be a function of the Grinblatt & Titman (1989) argument that considerscompensation contracts as options on the difference between the value of the portfolio and the valueof the benchmark portfolio and yields concavity in the valuation function. Alternatively, it mightbe a result of the U.S. tax code under which investors face convex tax schedules and thereforeconcave after-tax returns and would want their investment advisors to face similar concavecompensation schedules to better align their incentives.

Shareholders seem to indicate their degree of risk aversion, or alternatively, their desiredlevel of risk, to the fund manager, through the statement of purpose. The compensation contract ofthe advisor might reasonably be designed to reinforce the stated objective of the investmentcompany.

Additionally, the stated composition of the fund might be expected to reflect the desires ofthe shareholders. In this statement, the documentation of the fund indicates the proportion of thefund which may be invested in particular instruments (e.g., common stocks, preferred stocks, andbonds), the extent to which the fund may be leveraged, and the allowed risk level of the constituentinvestments (e.g., no more than 50 percent of the fund invested in securities rated lower than "A"by a particular rating service).

Given the statements of purpose and composition, risk and return do appear to be concernsof investors. Additionally, the form of the increase in wealth appears to concern the investor. Onecharacteristic of the form of income emphasized in the objective's statement is often current income.Growth is a second characteristic of the form of increases in wealth often emphasized in theobjective's statement. A third characteristic in some objective statements is a tax-free realizationof returns. Other concerns are also expressed in this way. Given this range of stated objectives ofthe various investment companies, risk level, increases in wealth, and the form of the increase allappear to be issues of concern to the investors of investment companies. The problem of theprincipal appears to lie in determining how to motivate the manager to act in a manner which wouldbe mutually satisfying to both the agent and the principal. A compensation contract that providessuch incentives ties the compensation of the agent to the "outcome" of the period or of multipleperiods (e.g., compensation by the week, month, or quarter but on at an annualized rate) .

Finally, the effect of size on compensation is ambiguous. As the size of the fund increases,and therefore the ability of the manager to exploit the margin of inefficiency in the market improves,the sensitivity of the compensation of the agent should increase, reflecting the greater benefit to be

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shared. Conversely, if the diversification of the portfolio is more limited or if the form of investmentis more limited, compensation may be less sensitive to outcome in larger funds.

OPEN-END INVESTMENT COMPANY SAMPLE

Although managerial compensation contracts are not typically publicly available, in certaininstances the contractual terms are available. If an investment company is managed by an outsideentity (an investment advisor), the Securities and Exchange Commission (SEC) requires that thecontract between the investment advisor and the investment company must be approved annuallyby the shareholders. To obtain this approval, management must publish a summary of the exactform of compensation; this compensation is usually stated in terms of observable variables.

Initial identification of the sample of open-end investment companies is obtained from theWeisenberger's Mutual Fund Panorama (Panorama). The Panorama provides a listing of the mutualfunds registered for sale in the U.S. as well as some basic information about each of the funds. ThePanorama identifies both the primary objective and the investment policy of the fund. It alsospecifies total net asset and the net asset value per share. The stock of open-end investmentcompanies is not traded in a secondary market, hence traditional stock market returns are notavailable. Alternatively, we may examine net income (dividends and interest received), net realizedand unrealized gain or loss, dividends paid, change in NAV, or change in the number of investmentcompany shares outstanding as a measure or indication of return on an investment in an open-endinvestment company.

Two years of data for this sample were collected. The years selected were 1980 and 1985,which were chosen for access to the Weisenberger and Moody's information necessary for theanalysis.

Additional detail is available for many of the investment companies in Weisenberger'sInvestment Companies 1981 (for the 1980 data) and Investment Companies 1986 (for the 1985data). From the Weisenberger's publication we obtain the total net assets of the mutual fund, theincome dividend paid, the capital gains distributed, the name of the investment advisor, and asummary of the advisor's compensation contract, where possible.

Additional, as well as confirmatory, detail is available from the appropriate issue of Moody'sBank and Finance Manual (Moody's). From Moody's we obtain a summary of the compensationcontract of the investment advisor, the number of shareholders invested in the fund, and a statementof the Net Assets of the mutual fund as well as an identification of the securities which comprise theinvestment portfolio.

When the compensation contract is available for a specific company from bothWeisenberger's and Moody's, the Moody's information is used to verify the accuracy of theWeisenberger's report. The eight discrepancies observed are a consequence of annualizing a feewhich is stated on a less than annual basis.

The sample from the Panorama contains 659 open-end companies in 1980 and 1356companies in 1985. Only a clear description of the compensation contract from eitherWeisenberger's or Moody's is required for inclusion in the sample. These criteria resulted in 437

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observations for 1980 and 540 observations for 1985. Unfortunately, it also resulted in 23observations which were only partially useful because of a lack of additional information.

CHARACTERIZATION OF THE COMPANIES IN THE SAMPLE

Tables one and two show the primary objective and the investment policy, respectively, ofthe companies for the 1980 and 1985 samples for which this information was available. From Table1 we see that Growth, Income, and Income plus Growth are the three most common stated purposesfor 1980. Further, Growth has nearly twice the number of observations of Income and three timesthe incidence of Income combined with Growth. Growth and Income, with similar incidence ofobservation, still, however, dominate the sample with more than three times the number ofobservations of either Tax Avoidance or Income plus Growth in 1985. A chi-square test fordifferences in purpose by year is significant at a 1 percent level, with a chi-square statistic of 33.327.

Table 1: Frequency Table of Fund Purpose by the Year of Observation

Purpose 1980 Percent 1985 Percent

Growth 200 46 196 36

Income 105 24 184 34

Tax-Avoidance 19 4 59 11

Income and Growth 72 17 67 13

Income and Security 13 3 11 2

Growth and Security 1 0 0 0

Income, Growth, and Security 27 6 23 4

TOTAL 437 100 540 100

From Table 2 we see that Common Stock as a required portfolio composition dominates boththe 1980 and 1985 samples. For 1980, the next most frequently cited compositions are Bond andDiversified/Flexible but these combined represent only one-fourth of the observations. By 1985,the incidence of Diversified/Flexible has fallen significantly whereas bond funds represent nearly20 percent of the observations and money market funds are now 13 percent of the sample.Information about the portfolio of securities, including the number of securities held in the portfolioholdings, is available. Thus, the portfolio investment policy and the fund's stated purpose appearto indicate the risk characteristics of the investment company. A chi-square test of the relationshipbetween composition by year is also significant at the 1 percent level with a chi-square statistic of53.819.

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Table 2: Frequency Table of Portfolio Composition by the Year of Observation

Composition 1980 Percent 1985 Percent

Diversified / Flexible 50 12 37 7

Bonds 57 13 104 19

Specialized 14 3 12 2

Common Stock 251 57 251 47

International 9 2 11 2

Balanced 21 5 18 3

Bonds and Preferred 9 2 8 1

U.S. Government Securities 0 0 14 3

Money Market Funds 25 6 72 13

Tax-Free Money Market Funds 0 0 10 2

Preferred 1 <1 3 1

TOTAL 437 100 540 100

To demonstrate the relationship between purpose and composition, Table 3 provides afrequency table of the stated fund purpose by the required portfolio composition for both years ofthe sample. Several of the pairs of purpose and composition seem to dominate. For instance, theportfolio of Common Stock is concentrated primarily in Growth funds with Income & Growth adistant second. Also, the required portfolio composition of Bonds has roughly equal (and dominant)representation in stated fund purpose of Income and of Tax Avoidance. A chi-square test of purposeby composition yields a chi-square statistic of 1478.775 which is significant at the 1 percent level.

To reduce the number of categories, we reclassify the portfolio composition variable intothree mutually exclusive categories: diversified, limited diversification, and no diversification. Wealso reclassify the fund purpose into four categories which are not mutually exclusive: growth,income, security, and tax-free. Table 4 provides the frequency table for the sample using thisclassification scheme. Table 4 indicates that funds that include Income in their stated purpose tendto allow only limited diversification. Funds that include Income in their stated purpose are fairlyequally divided between full diversification and limited diversification with very few observationsof no diversification. All open-end funds in the sample with a purpose of Tax Avoidance allow onlylimited diversification. Chi-square tests of growth by classification of composition, income byclassification of composition, security by classification of composition, and tax avoidance byclassification of composition are all significant at the 1 percent level.

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Table 3: Frequency Table of Fund Purpose by Portfolio Composition for the Entire Sample(G = Growth; I = Income; S= Security; TF=Tax-Free)

Composition G I TF I&G I&S G&S I,G&S Total

Diversified / Flexible 11 47 0 18 3 1 7 87

Bonds 0 74 78 3 6 0 0 161

Specialized 16 6 0 3 1 0 0 26

Common Stock 354 30 0 105 3 0 10 502

International 14 1 0 4 0 0 1 20

Balanced 1 3 0 6 0 0 29 39

Bonds and Preferred 0 12 0 0 2 0 3 17

U.S. Government Securities 0 13 0 0 1 0 0 14

Money Market Funds 0 89 0 0 8 0 0 97

Tax-Free Money Market Funds 0 10 0 0 0 0 0 10

Preferred 0 4 0 0 0 0 0 4

TOTAL 396 289 78 139 24 1 50 977

Table 4: Frequency Table of Classification of Required Portfolio Composition (mutually exclusivecategories) by Classification of Fund Purpose (not mutually exclusive categories) for the Entire Sample

Fund Purpose Includes Diversified Limited Diversification No Diversification

Income 210 252 40

Growth 73 475 38

Security 48 24 3

Tax Avoidance 0 78 0

Number of Observations 223 684 70

CHARACTERIZATION OF THE COMPENSATION CONTRACTS

Stock pyramid schemes, self-dealing transactions, and outright embezzlement were just a fewof the abuses perpetrated against investors by the organizers of early mutual funds (Baumol, et.al.,1990). Lack of information available to the public contributed to the self-seeking behavior of thesemanagers. These abuses led to the passage of the Investment Company Act of 1940 (the 1940 Act).

The Act established an oversight structure for the investment company industry. Legislatorshoped to minimize the opportunity for mutual fund providers to ignore the interests of shareholdersand to practice selfish behavior to the detriment of the investors. The 1940 Act requires that every

58


investment company distribute, at least semiannually, a report of the company's activities to theshareholders. These reports must contain a variety of financial information as well as informationabout the portfolio and any portfolio activity over the time period. The 1940 Act also requires thatthe written compensation contract be precisely described, be renewed at least annually, beterminable without penalty and with up to 60 days' notice by the board of directors or by theshareholders, and be terminable automatically if it is assigned. In addition to the reportingrequirements, the 1940 Act gave the SEC the power to intervene on behalf of a fund's shareholders;in particular, the Act allowed the SEC to sue for "gross misconduct or abuse of trust" on the part ofthe investment advisor.

Mutual funds were a relatively new investment vehicle in the 1930s. As the popularity ofmutual funds grew, Congress became more conscious of the regulation of the investment companyindustry. The findings of several reports commissioned by Congress in the 1960s indicated thatbecause a particular fund is organized and managed by its investment adviser, it cannot sever its tiesto its adviser without losing its unique character. Thus the likelihood of changing management isreduced and one of the mechanisms of market discipline is circumvented. Another major concerncited by the reports was a potential conflict of interest between shareholders and fund management.It was suggested that the 1940 Act did not sufficiently control for these problems.

The 1970 Amendments were passed to try to control for the potential conflicts of interest andfor the possible absence of arm's-length bargaining. Among other effects, the 1970 Amendmentsexplicitly specified the fiduciary duty of the investment adviser and provided a mechanism for thechallenge of an advisory fee contract by either the SEC or the shareholders with the burden of proofresting on the plaintiff. The purpose of this legislation has been to protect small investors in mutualfunds against abuses by a fund's investment adviser. One way to effect this protection has been torequire public dissemination of information.

This section provides a simple characterization of the extant compensation contracts ofinvestment companies. The compensation of the adviser can be determined by examining the annualreport of the investment company in which the compensation package and the amount andcomposition of the remuneration for the prior year is generally reported. The following is anexample of the detail of the investment advisor contract of an open-end company, Bond Fund ofAmerica, Inc., for 1985, as provided by Weisenberger (1986):

Investment Adviser: Capital Research and Management Co. Compensation to the Adviser is at an annual rate of0.30% on the first $60 million of net assets and 0.21% on assets over $60 million computed and paid on a monthlybasis, plus 3% of the first $450,000 of monthly gross investment income and 2.25% on the excess over $450,000.

The actual compensation contracts of investment company advisors can be expressed as afunction of NAV, of some measure of fund income, of some measure of fund performance, of agroup fee, and a negative function of excess expenses:

C(V,W,X, Y, Z) = k + G(V) +H(W) + I(X) + J(Y) + K(Z)with: G(0), H(0), I(0), J(0), K(0) = 0 and V, W, X, Y, Z > 0.

59


Here k is a constant, V is the NAV, W is a measure of fund income, X is a measure of fundperformance relative to an explicit benchmark index, Y is the total assets managed by the investmentadvisor, Z is a measure of "excess expenses," and C is total compensation.

Fixed Fee Component - k

The constant, k, can be positive, zero, or negative. For example, a positive fixed fee mightbe used to provide a minimum level of expected income to managers who expend effort and facerisk. In contrast, a negative fixed fee might be used to provide increased risk-bearing and henceincreased incentives for the investment advisor. The fixed fee component was zero in 966 of the 977cases where compensation contracts were identifiable, with only eleven observations of a nonzerofixed fee component. These eleven nonzero fixed fees occurred among 10 of the open-endinvestment companies, with two of the observations in 1985 and the other nine observations in 1980.The values of the nonzero fixed fees ranged from -$350,000 to $1,948,000. Observations of suchfixed fee components indicates a lower bound on compensation for most investment companies. Forcompanies without excess expense penalties or compensation for performance relative to abenchmark, the lower bound is attained when a loss of 100% of the assets of the investmentcompany occurs in one year. The likelihood of a total loss of assets before shareholders eitherdischarge the manager or redeem their proportionate share of the investment company is low.

NAV Component - G(V)

In every case for which the compensation package is available, the compensation contractpays the advisor a percentage of the fund's NAV. For a given contract the percentage paid oftenvaries in NAV. Moreover, G(V) is always piecewise linear. As shown in Table 5, the observednumber of intervals for the sample ranges from one to eleven with marginal rates ranging from zeroto two percent. Four hundred sixty-six of the 954 observations have only one NAV interval whereasthe remaining 488 contracts have two or more NAV intervals. The final interval always has infinitelength with any preceding intervals of discrete length.

The pattern for these NAV schedules is, in general, (weakly) monotonically decreasing inthe marginal rate paid on increasing levels of NAV. When an observed NAV schedule is notmonotonically decreasing, it starts at zero, after which the marginal rate becomes positive at somespecified percent and is then monotonically decreasing. In 950 of the 954 cases for which the NAVportion of the compensation contracts are available, the last marginal rate being paid on NAV isgreater than zero, but in four cases the last marginal rate being paid on NAV is zero. Thus most, butnot all, of the investment company advisors are being compensated for increased size of the firm inall cases for all possible values of NAV. Ironically, those observations for which the last marginalrate is zero are all investment companies with a stated purpose of growth / capital appreciation.Table 6 provides statistics for the operating rate and the marginal elasticity of NAV compensation.

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Table 5: Descriptive Statistics Characterizing NAV Components for the Entire Sample* in millions

NAV Interval 1 2 3 4 5 6 7 8 9 10 11

Mean Marginal Rate .0060 .0052 .0044 .0040 .0037 .0036 .0031 .0030 .0030 .0028 .0025

Median Marginal Rate .0055 .0050 .0045 .0039 .0068 .0035 .0032 .0030 .0030 .0028 .0025

Maximum Marginal Rate .0200 .0175 .0100 .0091 .0066 .0050 .0035 .0033 .0030 .0028 .0025

Minimum Marginal Rate .0000 .0000 .0000 .0005 .0015 .0025 .0025 .0028 .0030 .0028 .0025

Mean Discrete Interval Length* 165 178 271 432 528 735 231 100 100 100 100

Median Discrete Interval Length* 100 150 250 250 250 250 250 100 100 100 100

Maximum Discrete Interval Length* 1500 1300 4000 5000 5000 5000 250 100 100 100 100

Minimum Discrete Interval Length* 1 3 25 50 50 100 100 100 100 100 100

Number of Observations of Infinity 466 157 184 101 32 4 2 7 0 0 1

Number of Observations 954 488 331 147 46 14 10 8 1 1 1

Table 6: Descriptive Statistics on Elasticity and the Operating Marginal Rate for the Entire Sample

Elasticity Operating Rate

Number of Observations 953 954

Mean .970528 .005585

Median 1 .0050

Maximum 6 .02

Minimum .52831 0

The length of the intervals also varies both within plans and across plans. The greatestlength of an interval for a closed end investment company is infinite since all of the compensationcontracts provide for some percentage of NAV "in excess thereof." The minimum length for aninterval is $1,000,000. The largest finite (discrete) interval is $5,000,000,000.

Income Component - H(W)

Another component of compensation found in these contracts is some specified percentageof cash income. For the open-end investment companies, only 41 of the 977 companies have acompensation contract explicitly contingent on income. The percentages paid ranged from one tosix percent. However, in 6 of the 15 cases in which the rates paid on income were discernable, thecompensation based on income was in a graduated from with decreasing percentages paid onincreased income, in a manner similar to the compensation paid on the NAV of the investmentcompany. The number of intervals ranged up to three.

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In a manner similar to the pattern of NAV compensation, as the level of income increasesthe marginal rate paid on income decreases (or remains constant) in a piecewise fashion. Thedefinition of "cash income" does differ across funds. In most cases cash income is designated asdividends and interest received in cash less interest paid by the investment company. However, ina few cases cash income is defined as interest and dividends received in cash with no mention ofinterest paid by the investment company.

Performance Relative to a Benchmark - I(X)

In 24 of the 977 open-end investment companies the managers were compensated for theperformance of their investment company relative to some index, commonly the S&P 500. Ten ofthe companies have contracts that evaluate performance relative to a benchmark during both 1980and 1985. Eight additional companies had such contracts in 1980 while six additional contractswere observed in 1985. Of these 34 observations, 31 are for common stock portfolios and 27 ofthese companies have stated growth as the sole purpose of their fund. Four more have growth as oneof their principle, though not sole, purposes.

Group Fee Component - J(Y)

In 35 of the 977 open-end contracts, the investment advisor is compensated by each of theinvestment companies under their portfolio umbrella for the total assets being managed.Twenty-four of these contracts are observed in 1980 and 11 in 1985 with no overlap in the sampleof companies. Recall that the same companies are not necessarily included in the sample duringboth 1980 and 1985. Thus, the lack of overlap does not necessarily mean that the company changedtheir contract over time.

Excess Expenses Component - K(Z)

In 19 observations (for 17 companies, with two of the companies observed in both 1980 and1985) the investment companies explicitly reduced the compensation of their advisor by somemeasure of excess expenses. For example, ten of these observations were for 1980 and 9 cases wereobserved for 1985. Again, to the extent that expenses reduce the NAV of the fund, open-endinvestment companies implicitly reduce managerial compensation. The excess expenses for whichopen-end companies are explicitly penalized include interest on debt used to finance the portfolioof the investment company, the investment advisor's fee above some specified level, and transactionscosts above some specified level.

Summary of the Characteristics of the Compensation Contracts

All of the contracts in the sample are contingent on NAV. Some, but not most, compensationcontracts are contingent on some measure of income. A few of the contracts contains a nonzerofixed fee component. Some are negatively related to excess expenses, some are contingent on a

62


group fee, and some are compensated for performance relative to a benchmark. All of the observedcontracts are weakly positively related to NAV and to income, with weak concavity in both NAVand income. There are up to 11 intervals in the NAV component of compensation and up to threeintervals in the Income component of compensation.

EMPIRICAL EXAMINATION OF COMPENSATION CONTRACTS

Compensation and Performance: A Positive Relationship

Agency theory suggests that the manager, through an expenditure of effort, may be able toimprove performance, on average; however, the agent has a personal disincentive to expend thateffort. A fundamental prediction of agency models that exploit compensation to help alleviate theagency conflict is that compensation should be a positive function of performance. Further, to theextent that the manager is able to improve performance, the compensation should be positivelycontingent on performance, rather than be merely a function of performance.

Observable compensation contracts of investment advisors can depend on performance intwo ways. First, compensation can depend on performance relative to some benchmark. Suchperformance compensation is observable in 34 of the 977 companies in the sample. For each ofthese 34 contracts, the performance compensation clause demonstrates positive dependence.

Second, compensation depends on performance through the NAV portion of thecompensation. That is, the typical compensation contract contains a portion of compensation whichis contingent on the NAV of the investment company. To the extent that the proportion paid onNAV is positive at higher levels of NAV, the manager is rewarded for growth of the investmentcompany.

As Table 5 shows, performance compensation, as a function of a compensation componentstated in terms of NAV, is apparent in all of the compensation contracts in the data set. Further, allof the compensation contingent on NAV is non-negative. However, some intervals exist, for somecompanies, over which the compensation rate is zero. These intervals are always either at the startof the NAV compensation (providing a kind of cushion for the shareholders over which nocompensation is paid) or at the end of the compensation contract (reducing the incentive to expendeffort as the growth of assets exceeds some fixed point). Only one company in the sample actuallyoperates over an interval where the marginal NAV compensation is zero.. Only nine open-endcompanies exist that have a contractual marginal rate of zero over any NAV interval.

Compensation and Performance: Concavity

We not only expect compensation to be positively related to performance, but also to beconcave in performance. Concavity is predicted for three reasons. First, concavity might followfrom a declining marginal ability to shift the distribution of portfolio returns. Second, Grinblatt &Titman assert that adverse risk incentives of performance based fees mean that penalties for poorperformance should be at least as great as the rewards for good performance. Third, convexity inthe U.S. tax code and thus concavity in after-tax income, could lead fund holders to face managers

63


with concave compensation schemes. Note that these explanations are not mutually exclusive, andmay be reinforcing.

Thirty-four of the 977 observations in the data set contain a compensation component forperformance relative to a benchmark. For each of these, the performance component is weaklyconcave in performance relative to a specified, observable benchmark. For 33 of the 34 companies,the performance is strictly linear in performance relative to the specified benchmark. Thisbenchmark is usually the S&P 500 Index but may be any publicly observable index or combinationof indices.

Consider compensation which is a function of NAV. Performance compensation, as afunction of NAV, is almost always weakly concave in performance. For only 5 of the 540companies in the 1985 sample, 2 of which are managed by the same investment advisor, nocompensation is paid on the initial discrete interval of NAV but compensation on NAV in excessof the initial interval is weakly concave in performance. Table 5 shows that the mean marginal ratepaid on NAV also weakly declines. Again, evidence about the median and maximum marginal ratesalso shows a similar pattern. The minimum marginal rate paid on NAV intervals also shows theeffect of not only contracts which pay zero compensation on the initial interval but even on the finalinterval. There are 466 of the 954 (204 of 414 and 262 of 540 for 1980 and 1985) with only oneNAV interval. Further, 157 contracts have two intervals and 184 have three intervals, while 147have four or more intervals specified in their contracts. When examined by year, the evidenceremains consistent. The length of the intervals over which the NAV compensation is paid varieswidely; the minimum length is $1,000,000 and the maximum discrete interval length is$5,000,000,000, while the maximum interval length is, of course, infinity.

Thus, concavity of individual plans is manifested. One question that arises is whether theconcavity ever comes into play. That is, are investment company managers ever subject to changesin interval and corresponding changes in marginal operating compensation rates? To respond to thisquestion, we examined the companies which were in the sample in both 1980 and 1985 to determinewhether they operated in a different NAV compensation interval between the two years. As shownin Tables 7 and 8, nearly 40 percent of the 124 companies changed interval over this five yearperiod, with the largest change in either direction being three intervals. This indicates that theadvisors are subject to the concavity of marginal returns on NAV and the incentives of advisorswould be better aligned with the desires of the owners of the funds.

Compensation and Required Portfolio Composition

To the extent that the agent may negate or otherwise neutralize the risk imposed by acompensation contract by trading in his/her personal portfolio, the effectiveness of incentivecontracting is reduced. This same principle can be applied to the situation in which the risk can beneutralized, not in his/her personal portfolio, but in the portfolio he/she advises and through whichhe/she is compensated. Further, to the extent that the restrictions placed on the asset mix availableto the manager limits the ability of agents to exercise their expertise in the management of theportfolio or to trade on any information they may have, we might expect the compensation to be lesssensitive to performance. An agent who manages a portfolio that can be fully diversified might have

64


compensation more sensitive to outcome than would be the compensation of an investment advisorof a relatively limited portfolio. That is, the increased risk should imply less sensitivity toperformance whereas a greater ability to shift the distribution of returns should imply a greatersensitivity of compensation to performance.

Table 7: Frequency Table of Interval Changes

Number of Intervals Changed Frequency Proportion of Observations

-3 1 .8%

-2 1 .8%

-1 5 4.0%

0 76 61.3%

1 34 27.4%

2 6 4.8%

3 1 .8%

TOTAL 124 100%

Table 8: Descriptive Statistics of Interval Changes

Mean .314516

Median 0

Maximum 3

Minimum -3

Table 9 shows a variety of compensation rate measures for the sample as a whole as well aspartitioned on stated composition and on class of composition. Surprisingly, all of the measuresprovided have weakly higher compensation rates, in both mean and median, for the last contract rate,the average NAV rate paid based on their contract and NAV, and operating marginal rate, asdiversification becomes more limited. That 18 of the 19 companies that have performancecompensation relative to a benchmark are common stock funds is also interesting, because we haveno reason to expect that common stock funds would explicitly compensate for performance whileother funds would not.

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Table 9: Descriptive Statistics for the Entire Sample

MeanLast

ContractRate (#)

MedianLast

ContractRate (#)

MeanAverage

NAVCompen-

sationRate

Paid (#)

MedianAverage

NAVCompen-

sationRate

Paid (#)

MeanNAVplus

Perform-ance

Rate* (#)

MedianNAVplus

Perform-ance

Rate* (#)

MeanOperatingMarginalRate (#)

MedianOperatingMarginalRate (#)

By Composition

Diversified .0050(83)

.0050(83)

.0060(83)

.0060(83)

N/A N/A .0058(83)

.0060(83)

Bonds .0046(157)

.0050(157)

.0050(157)

.0050(157)

N/A N/A .0049(157)

.0050(157)

Specialized .0056(25)

.0050(25)

.0069(25)

.0063(25)

N/A N/A .0066(25)

.0063(25)

Common Stock .0054(489)

.0050(489)

.0063(489)

.0061(489)

.0082(18)

.0082(18)

.0060(489)

.0060(489)

International .0058(20)

.0055(20)

.0064(20)

.0070(20)

N/A N/A .0062(20)

.0067(20)

Balanced .0041(38)

.0040(38)

.0050(38)

.0050(38)

N/A N/A .0048(38)

.0050(38)

Bonds & Preferred .0043(17)

.0050(17)

.0053(17)

.0050(17)

N/A N/A .0052(17)

.0050(17)

US Government Securities .0047(14)

.0048(14)

.0049(14)

.0043(14)

N/A N/A .0050(14)

.0048(14)

Money Market Funds .0041(97)

.0040(97)

.0044(97)

.0050(97)

.0125(1)

.0125(1)

.0044(97)

.0050(97)

Tax-Free Mon Mkt Funds .0043(10)

.0050(10)

.0043(10)

.0050(10)

N/A N/A .0043(10)

30050(10)

Preferred .0033(4)

.0028(4)

.0044(4)

.0040(4)

N/A N/A .0042(4)

.0039(4)

By Class of Composition

Diversified .0044(218)

.0045(218)

.0051(218)

.0050(218)

.0125(1)

.0125(1)

.0050(218)

.0050(218)

Limited Diversification .0052(667)

.0050(667)

.0059(667)

.0055(667)

.0082(18)

.0080(18)

.0057(667)

.0050(667)

No Diversification .0053(69)

.0050(69)

.0060(69)

.0054(69)

N/A N/A .0058(69)

.0050(69)

Entire Sample .0050(954)

.0050(954)

.0058(954)

.0050(954)

.0084(19)

.0080(19)

.0056(954)

.0050(954)

The results are inconsistent with our explanation that the manager of a diversified portfoliowould need compensation which is more contingent on outcome to provide incentives to work. One

66


possible explanation would be that opportunities for profitable trading are more limited forwell-diversified portfolios than for portfolios that are less diversified. In terms of the agencyframework, the ability of the agent to shift the distribution of outcomes to the right is far less fordiversified portfolios.

Another possible explanation is that the manager is compensated for risk by higher expectedlevels of compensation. In the sample, less diversified portfolios are generally compensated at ahigher rate per dollar of NAV. This weakly supports the contention of the relationship betweenincreased risk and increased compensation. However, those funds that are either diversified or donot allow diversification compensate, both in terms of NAV and of total reported compensation, ata higher level than do firms that allow only limited diversification. Thus, evidence in this area isinconclusive.

Compensation and Size

Another explanation might be that all funds are actually diversified, either generally orwithin asset class, and so the classification scheme being employed is not informative. Indeed, allof these portfolios are fairly large. The observable NAVs of the funds range from $1,200,000 to$11,875,300,000, with 91 companies with NAV less than $10,000,000 and 62 companies with NAVgreater than $1,000,000,000.

To the extent that a manager may more easily diversify away any unsystematic risk in alarger portfolio, even if the security types in which the portfolio may be invested are limited, largerportfolios might need more risk-contingent compensation to induce effort. However, examinationof the data indicates that both the mean and median last contract rate, both the mean and medianaverage NAV compensation rate paid, and both the mean and median operating marginal rate areall weakly decreasing with increasing NAV size. Median NAV plus performance is also weaklydecreasing with increasing size while mean NAV plus performance is weakly decreasing only untilthe last NAV quartile when it increases slightly.

Compensation and Fund Purpose

Perhaps a better indicator of the systematic risk of the portfolio is the stated fund purposeof the investment company. Intuition suggests that "growth" funds are probably invested in riskiersecurities than are other funds. Examination of the data indicates that for all variables except NAVcompensation plus performance, companies with a fund purpose that includes growth pay highermarginal compensation than do companies with a fund purpose that excludes growth. For thecombined forms of performance compensation (i.e., performance relative to a benchmark plus NAVcompensation), mean compensation increases when the purpose excludes growth but mediancompensation decreases. However, caution must be used in interpreting this fact; only 2 of the 19companies that compensate for performance relative to a benchmark have a stated purpose thatexcludes growth. Sixteen of these 17 companies that include growth in their stated purpose havegrowth as the sole stated purpose.

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Thus, counter to the intuition that more risk should imply less sensitivity to performance, astated purpose of growth appears to be associated with greater sensitivity of compensation toperformance (i.e., both performance relative to a benchmark and NAV compensation). This mayindicate that the manager of a growth fund has a greater ability to induce first-order stochasticallydominating shifts in the distribution of portfolio returns than does the manager of an income fund.

Finally, those companies which include growth in their stated purpose have lower averagelevels of compensation ($1,113,608) than do either funds that include income in their stated purpose($1,600,850) or funds that include security in their stated purpose ($1,428,169).

Income versus Performance

Stated fund purpose can indicate a desire for "growth" or for "income." It can also indicatea combination of these two as well as security or tax avoidance. To the extent that investors in suchfunds have made explicit their desire for a particular form of return, we would expect to observecompensation which is a function of that form of return. That is, income funds might be moredominantly rewarded for income while growth funds might be more dominantly rewarded for NAVperformance.

All compensation contracts in the sample include NAV contingent compensation of somekind. Of the 31 compensation contracts that have an income component to their compensation, 15have income as their sole stated purpose and 14 more have income in combination with growth orgrowth and security. Only 2 of the companies that compensate on income exclude income fromtheir stated purpose. Of the 34 companies that compensated explicitly based on performance, 27have growth as their sole stated purpose and 4 of the remaining 7 include growth as one, though, notsole, explicit purpose. Only 3 exclude growth from their stated purpose.

Summary of Empirical Results

In examining the extant managerial compensation contracts of investment advisors we cansee that compensation is generally contingent on performance, through compensation as a functionof NAV if not through performance relative to a benchmark; further, compensation is at least weaklyconcave in performance. Further, the majority of those funds that include compensation forperformance relative to a benchmark in their compensation packages have growth as one of theirstated purposes. Furthermore, funds that state that they are interested in income have many moreincidents of explicit income compensation than do funds with stated purposes that do not includeincome.

In addition, counter to intuition, the less risky the portfolio as measured by purpose,composition, or size, the more sensitive is the compensation contract to performance. It is alsopossible that the negative relationship between risk and sensitivity of compensation to performanceis due to limited opportunities for profitable trading when the portfolio is large, well diversified, andrelatively risk free.

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CONCLUSION

Among the proposed relationships tested in this paper are that compensation should beperformance contingent and that the contingency should be concave in performance. In general,these relationships are borne out. All of the contracts are contingent on performance, either throughan NAV contingent component or through compensation for performance relative to a benchmark.Further, 972 of the 977 open-end observations are weakly concave in performance.

The investment companies differ by stated purpose and by portfolio composition.Investment companies which have compensation contingent on income tend to have income as oneof their stated purposes. Funds that have compensation contracts with both NAV contingentcompensation and compensation for performance relative to a benchmark tend to have growth asone of their stated purposes. Further, larger portfolios tend to be less performance contingent at themargin than smaller portfolios. Perhaps the inability of the manager to diversify the portfolio ispositively related to his/her ability to shift the distribution of outcomes. However, in general,counter to the intuition of the standard agency model, the greater the ability of the manager todiversify the fund's investment portfolio the less performance contingent is the compensation.

REFERENCES

Baumol, W.J., S.M. Goldfeld, L.A. Gordon, and M.F. Koehn, 1990, The economics of mutual fundmarkets: Competition versus regulation, Kluwer Academic Publishers, Boston.

Grinblatt, M. and Titman, S. (1989) Adverse risk incentives and the design of performance-basedcontracts, Unpublished manuscript (University of California at Los Angeles).

Moody's Investors Service, 1981, Moody's Bank and Finance Manual, New York.

Moody's Investors Service, 1986, Moody's Bank and Finance Manual, New York.

Weisenberger Financial Services, 1981, Investment Companies 1981, New York.

Weisenberger Financial Services, 1986, Investment Companies 1986, New York.

Woodbury, D. and Neal, W. (1999) Fund advisor compensation: An application of agency theory,Academy of Accounting and Financial Studies Journal, 3, 73-83.

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EARNINGS MANAGEMENT USING PENSIONRATE ESTIMATES AND THE TIMING OF

ADOPTION OF SFAS 87

Marianne L. James, California State University, Los Angeles

ABSTRACT

Concerns about earnings management and its effect on the usefulness of financial reportingcontinue to be pervasive and have prompted the Securities and Exchange Commission (SEC) toincrease its enforcement activities. Flexibility in applying accounting provisions frequently providesopportunities for earnings management. Statement of Financial Accounting Standards No. 87,Employers’ Accounting for Pensions, (SFAS 87), provides extensive flexibility in choosing requiredpension estimates, and in timing the adoption of the standard. Several studies suggested that earlyadopters of SFAS 87 were motivated by its positive income effect.

This study investigated the relationship between the timing of adoption of SFAS 87 andearnings management using pension rate estimates subsequent to adoption. Study of 1,035 firmsfound that early adopters tended to use higher estimates of the rate-of-return on pension plan assets(ROR) subsequent to adoption, than on-time adopters. In the absence of higher actual returns, thesefindings suggest that early adopters were using the ROR estimates to facilitate earningsmanagement subsequent to adoption. This may provide important information for the FinancialAccounting Standards Board in assessing the effect of multi-year transition periods on earningsmanagement.

INTRODUCTION

Concerns about earnings management continue to be pervasive, and prompted the SEC toincrease its regulatory enforcement activities to both prevent and detect earnings management. Forexample, in 1998, the SEC informed 150 firms that it may be reviewing their earnings reports anddirected its Division of Corporate Finance to appoint a special task force to investigate potentialearnings management problems (MacDonald, 1999, A2-6). In several of his speeches, Arthur Levitt,until recently chair of the SEC, and other SEC officials, such as former Chief Accountant, L.E.Turner have spoken out against earnings management that firms practice within the boundaries ofGenerally Accepted Accounting Principles (GAAP).

The potential for earnings management tends to arise when accounting standards provide forflexibility, require extensive estimates, and when long time horizons are involved. Accounting fordefined benefit pensions combines all three of these criteria: it requires extensive estimates (e.g., oflongevity, employee turn-over rates, and pension rates), permits flexibility in applying some of itsprovisions (e.g., in choosing pension rates), and typically involves an unusually long time horizon(i.e., employees for whom pensions benefits are currently accrued may not retire for severaldecades). In addition, SFAS 87, like a number of other recent accounting standards, provided for

70


a long, multi-year transition period. In fact, the standard permitted firms to choose an adoption datefrom among three years.

Pension accounting evolved from the initial cash-as-you-go-basis to accrual accounting.SFAS 87 represents the newest in a series of accounting standards and was expected to improve therelevance and reliability of financial reporting related to defined benefit pension plans. However,Ali and Kumar (1993) found that the new standard provides more earnings managementopportunities, than were available under the prior authoritative standard, APBO 8, Accounting forthe Cost of Pension Plans. These enhanced earnings management opportunities arise in part fromthe provision that permits the discount rate to differ from the ROR, and due to income smoothingincentives (Ali & Kumar, 1993).

Prior research also found independent evidence of (1) an association between adoptiontiming of SFAS 87 and increased earnings at time of adoption (e.g., Langer & Lev, 1993; Tung &Weygandt, 1994), and (2) manipulation of pension rates subsequent to adoption (Blankley, 1992;Blankley & Swanson, 1995; Amir & Benartzi, 1998). Both of these findings are consistent withearnings management. A review of the related literature yielded no studies investigating thepotential relationship between early adoption of the pension accounting standard and choice ofpension rate estimates subsequent to adoption.

The purpose of this study was to investigate the relationship between timing the adoption ofSFAS 87 and firms' choice of pension rate estimates subsequent to adoption. A sample of firms thatadopted SFAS 87 were identified from the Standard and Poor COMPUSTAT industry files, andrelated financial statement data and disclosures were extracted. The relationships between adoptiontiming and pension rate estimates (expected ROR on pension plan assets, discount rate, and salarytrend rate) were evaluated through multiple regression analyses.

This study found evidence of a significant positive relationship between early adoption ofSFAS 87 and the ROR estimates utilized by the sample firms subsequent to adoption for each of thefour years tested. That is, early adopters tended to use higher ROR estimates than on-time adopters,and these were not supported by higher actual returns. These findings are consistent with earningsmanagement and thus may provide an alternate or additional explanation for the adoption timingdecision. Thus, while previous studies linked SFAS 87 adoption timing to earnings managementonly at time of adoption, this study linked adoption timing to earnings management subsequent toadoption. These findings are important, as they may assist the Financial Accounting Standards Board(FASB) in assessing whether the intended purpose of long multi-year transition periods for standardsrequiring extensive estimates is being achieved.

REVIEW OF LITERATURE

Schipper defines "Earnings Management" as a "purposeful intervention in the externalfinancial reporting process, with the intent of obtaining some private gain..." (1989, 92). Theimplication of this definition is that earnings may reduce the representational faithfulness and thusthe usefulness of financial reporting. Arthur Levitt, in his now famous speech “The NumbersGame” asserted that earnings management “if not addressed soon, will have adverse consequencesfor America’s financial reporting system” (Levitt, SEC, 1998). Firms that manage earnings usually

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do not restrict their efforts to a single accrual, instead they tend to use a portfolio of accruals,including pension accruals (Zmijewski and Hagerman, 1981). Using a portfolio approach, somefirms manage earnings only when perceived necessary, or when a special opportunity arises (e.g.,a new earnings management tool becomes available; or the adoption timing of a new standard). Forothers, earnings management may be a more ongoing practice (Schipper, 1989).

Standards with long transition periods may provide a one-time earnings managementopportunity by permitting firms to choose the timing of the effect of the change on the financialstatements. Standards may also provide earnings management opportunities by providing flexibilityin choosing extensive required estimates.

During the past few decades FASB has issued a number of accounting standards with long,multi-year transition periods and flexibility in choosing extensive required estimates. For example,the transition period was four years for SFAS 106, Employers' Accounting for PostretirementBenefits Other than Pensions, and three years for SFAS 109, Accounting for Taxes. SFAS 87requires extensive estimates, permits considerable flexibility in choosing those estimates, andprovided for a three-year transition period.

The primary purpose of long transition periods for new accounting standards is to assist firmsin generating reliable estimates. The purpose stated in SFAS 87 is: "to give time for employers andtheir advisors to assimilate the requirements...[,] to obtain the information required....[and] torenegotiate or to obtain waivers of provisions of some legal contracts" (FASB 1985, par. 259-260).

A number of studies have examined the timing of adoption of accounting standards with longtransition periods, including the adoption of SFAS 87. Some of the major studies are discussed inthe following paragraphs.

Recent studies found evidence that income smoothing, a form of earnings management, wasassociated with the adoption timing of new accounting standards. For example, Gujarathi andHoskin (1992) found evidence of income smoothing in their study of 284 early adopters of SFAS96, Accounting for Income Taxes. Their study showed that some entities that experienced significantincome-increasing effect from the standard’s early adoption, would otherwise have reported a lossor no earnings increase. Their follow-up study (Gujarathi & Hoskin,1995) provided additionalevidence suggesting that the long transition period was utilized by early adopters to "present theirfinancial picture in the best light" (p. 28), and that those firms with the largest positive income effecton adoption tended to adopt at the earliest possible date.

Studies on early adoption of SFAS 52 also found evidence of income smoothing. Forexample, Ayres (1986) investigated the relationships between choice of adoption date and proximityof debt and dividend-constraints, control of entities, size, and earnings. They found that earlyadopters tended to experience lower rates of income growth prior to adoption of the Standard, thatthey tended to be smaller, closer to debt and dividend-constraint violations, and tended to havesmaller percentages of stock owned by directors and managers, than late adopters.

A number of studies also examined adoption timing of SFAS 87 and found evidenceconsistent with earnings management. SFAS 87 was issued in December 1985; its effective date,the date for which adoption was required, was for fiscal years ended after Dec. 15, 1986. Thus, formost firms on-time adoption was for their 1987 fiscal period. However, since early adoption of the

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standard was permitted, firms could choose to adopt the standard for fiscal years ending in 1985,1986, or 1987.

Fifty-one percent of the entities surveyed in Accounting Trends and Techniques thatdisclosed defined benefit pension plans early-adopted SFAS 87 during 1985 and 1986 (based oninformation reported by the AICPA, 1987, p. 274). Most of these early adopters reported increasedincome from the adoption. A number of studies found a positive association between the positiveincome effect and the timing of adoption. For example, Tung and Weygandt (1994) investigatedwhether debt contracting and/or political cost influenced adoption timing, and found that earlyadopters reported significantly higher increases in income than on-time adopters, and that of 1,011early adopters only 35 reported decreased income associated with their adoption of SFAS 87.

Langer and Lev (1993) investigated both compliance costs and investor perceptions andfound that the motive to increase earnings consistently discriminated between early and late (on-time) adopters of SFAS 87; these results also held true regardless of the quarter during which thestandard was adopted. Other studies such as Norton (1989), Stone & Ingram (1988), and Senteney& Strawser (1990) also found that early adopters reported income increases at time of adoption. Norton (1989) found that early adopters also had better funded pension plans, and that thosefirms that adopted during the earliest possible year had better funded plans than those that adoptedlater; i.e., 1985 adopters had better funded plans than 1986 adopters, and 1986 adopters had betterfunded plans than 1987 adopters. Thus, early adopters generally were not required to recordadditional minimum liabilities that would have increased debt-to-equity ratios.

SFAS 87 not only provided a one-time opportunity for earnings management at time ofadoption, but also provides continued opportunities for earnings management subsequent to adoptionthrough the choice of extensive required estimates, including pension rate estimates. SFAS 87requires that firms estimate three pension rates: the discount rate, the expected ROR on pension planassets, and the salary trend rate, and utilize them in calculating pension expense and pension benefitobligations. The standard does not prescribe specific pension rates or set specific limits, and insteadprovides suggested guidelines. For example, it emphasizes (FASB 1985, par. 43) that the "best"estimate for each future event should be given for each rate.

Choice of these three rates tends to significantly affect pension expense and the pensionbenefit obligations. The ROR estimate is utilized in calculating the return on plan assets andpension expense, with a higher ROR estimate typically resulting in lower expense and higherincome. The discount rate also affects the interest component of pension expense, the pensionbenefit obligations reported in the financial notes, and the minimum pension liability. The salarytrend rate affects the current service cost component of pension expense and the projected benefitobligation disclosed in the notes.

Firms typically engage actuaries to assist in deriving estimates, including pension rateestimates. However, actuaries’ primary responsibility is to ensure that pension plans are adequatelyfunded (Fogarty & Grant, 1995), and management ultimately decides which actuary to hire, andwhat pension rate estimates to use in accounting for defined benefit pension plans. Thus, firms haveconsiderable flexibility in choosing estimates, and as a result, the rates utilized by firms varyconsiderably. To illustrate, information reported by Accounting Trends and Techniques for fiscalyear 1992 indicated that six percent of the entities disclosing discount rates utilized rates of 7.5

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percent or less, while 18 percent utilized rates of 9.0 percent or greater (AICPA, 1993, p. 300).Furthermore, for fiscal year 1994, 19 percent utilized rates of 7.5 percent or less, while 7 percentutilized rates of 9 percent or more (AICPA, 1995, p. 342).

Pension rates provide an excellent tool for earnings management, as rates can be chosen touniformly increase or decrease income, or one rate can be chosen to offset the effect of another. Forexample, the salary trend rate can be decreased, along with an increase in the estimated ROR onpension plan assets (both decreasing expense); or an increase in the salary trend rate can be offsetpartially or totally by an increase in the expected ROR. While SFAS 87 was issued to improve the relevance and reliability of financial reporting,Ali and Kumar (1993) found that this new standard provides more opportunities for earningsmanagement than APBO 8 did. Some of these opportunities may arise from a provision that permitsthe interest rate used for calculating the return on pension plan assets to differ from that used forcalculating the interest cost component of pension expense; i.e., the ROR and the discount rate donot have to be the same (Ali & Kumar, 1993).

Several other studies found that the extensive pension rate estimates required under SFAS87 may provide opportunities for earnings management. For example, Mittelstaedt (1989) andThomas (1989) found that prior to pension plan terminations, actuarial assumptions, (such as thediscount rate), were frequently manipulated. Ghicas (1990) found that firms tended to increasediscount rates to reduce funding requirements in periods of cash shortages. Blankley (1992) andBlankley and Swanson (1995) linked the choice of pension rates to the opportunistic behavior ofmanagers. Amir and Benartzi (1998) found that the actual ROR on pension plan assets was notassociated with the estimated ROR.

Another recent study examined rate choices under SFAS 106, Employers Accounting forPostretirement Benefits Other than Pensions, which is very similar to and deals with similar isuesas SFAS 87. Amir and Gordon (1996) investigated cross-sectional variations in firms' choice of thediscount rate and healthcare cost trend rates, and found that firms that chose more conservativeestimates tended to have lower debt-to-equity ratios, smaller postretirement benefit obligations, moreextreme (high or low) earnings, and more significant postretirement plan amendments. They alsofound that investors based their valuations of an entity on reported, instead of actual rates; theauthors interpreted this as another incentive for using less conservative estimates that facilitateearnings management goals. This last finding also supports the contention that using pension ratesfor earnings management (which is investigated in this study) also would not be detected easily.

HYPOTHESES DEVELOPMENT

Firms that manage earnings usually do not restrict their efforts to a single accrual, insteadthey tend to use a portfolio of accruals (Zmijewski & Hagerman, 1981). Several studies (e.g., Tung& Weygandt, 1994, Senteney & Strawser, 1990, and Langer & Lev, 1993) showed that most earlyadopters of SFAS 87 increased income upon adoption. This suggests that early adoptions may havebeen motivated by a desire to manage income.

However, SFAS 87 can be used to manage income not only upon adoption, but alsosubsequent to adoption through the choice of pension rate estimates. Ali and Kumar (1993) found

74


that SFAS 87 provides more opportunities for earnings management than the previous accountingstandard did. Because pensions typically have long time horizons, such earnings managementcannot be detected easily.

Firms that were motivated to early-adopt a standard that increases opportunities for earningsmanagement may be more likely to utilize this new standard to manage earnings subsequent toadoption. The following hypotheses were tested to investigate the relationships between the timingof adoption of SFAS 87 and pension rate estimates subsequent to adoption.

H1: Firms that adopted SFAS 87 early choose different ROR estimates than firms that adopted on the effectivedate.

H2: Firms that adopted SFAS 87 early choose different discount rates than firms that adopted on the effectivedate.

H3: Firms that adopted SFAS 87 early choose different salary trend rates than firms that adopted on the effectivedate.

For testing Hypotheses H1, H2, and H3, the independent variable of interest was the adoption year,and the dependent variables were the pension rates. All variables are defined in the methodologysection.

METHODOLOGY

A sample of 1,035 firms that adopted SFAS 87 and disclosed pension rate and other financialinformation for the 1991-1994 fiscal periods were chosen from the COMPUSTAT industry files.The relationships between adoption timing and pension rate estimates were evaluated utilizingmultiple linear regression. The years 1991-1994 were chosen as study period to avoid compoundingof too many additional factors with that of interest. Thus, the period surrounding the initial adoptionwas excluded, and the analysis restricted to four years to avoid the influence of new factors on therates chosen (i.e., economic changes, new accounting standards and regulation that may affectchoice of rates).

The dependent variables were the pension rates used and disclosed by the firms; that is, theROR for testing H1, the discount rate for testing H2, and the salary trend rate for testing H3. Theindependent variable for testing all three hypotheses was the adoption year of SFAS 87(YRADOPT). A dichotomous classification, similar to that used in most studies on timing choice(e.g., Tung & Weygandt,1994; Langer & Lev, 1993, Senteney & Strawser, 1990), was chosen; avalue of one (1) was assigned to the independent variable for firms that adopted SFAS 87 early (infiscal years 1985 or 1986), and a value of zero (0) for firms that adopted on the effective date.

Other factors may have influenced the timing of adoption, and may influence the choice ofpension rate estimates. A number of studies have investigated these factors (e.g., Tung &Weygandt, 1994, Stone & Ingram, 1988, Senteney & Strawser, 1990, Norton, 1989). The variablesfound significant in one or more of pertinent prior studies were included as control variables; thesewere measured in the year prior to the year tested and scaled by beginning-of-year total assets. Thecontrol variables were:

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1 Earnings variable: Prior-year earnings may create earnings expectations and influence subsequent yearaccruals and pension rate choices. Previous SFAS 87 studies (e.g., Tung & Weygandt, 1994, and Langer& Lev, 1993) found that earnings influenced adoption timing. In this study, earnings were measured byincome before extraordinary items (EARN).

2 Funding: Senteney and Strawser (1990) and Norton (1989) found that firms with well-funded plans tendedto adopt SFAS 87 early. Funding status also may affect choice of pension rates, particularly choice of thediscount rate, which can influence the funding status. The unfunded or over funded accumulated pensionbenefit obligation was included as a control variable (FUND).

3 Debt Contracting: The more likely an entity is to violate a debt-covenant ratio, the less likely it is to makeincome-decreasing accounting choices (Watts & Zimmerman 1986, 216). Tung and Weygandt (1994) foundthe debt/equity ratio (a measure of leverage frequently used as proxy for debt-covenant tightness) to besignificantly higher for early adopters of SFAS 87, than for on-time adopters. In this study, the debt/equityratio (DE) was utilized to measure leverage.

4 Pension Benefit Obligation: The size of the pension benefit obligation may affect the interest component ofpension expense, and may influence the choice of pension rates, particularly the discount rate. Amir andGordon (1996) found that the relative size of the postretirement benefit obligation affects firms' selectionof health care and discount rates. This study used the scaled accumulated pension benefit obligation tomeasure the pension benefit obligation (ABO).

5 International or Domestic firm: The model included a dichotomous variable to control for potentialdifferences between domestic and foreign firms. A value of 0 was assigned to domestic firms and a valueof 1 to foreign firms (FOREIGN).

6 Size variable: Firm size may influence firms' desire to increase/decrease accruals (Watts & Zimmermann,1986). The most frequently used measures of size are the natural logs of sales revenue and total assets; bothhave been found to be valid measures of size (e.g., Tung & Weygandt, 1994). In this study, size wasmeasured by the natural logarithm of total assets (SIZE).

The statistical model tested for hypotheses H1, H2, and H3 was:

H1 H2 H3RORit, DISCit, SALit = bo + b1xit1 + b2xit2 + b3xit3 + b4xit4 + b5xit5 + b6xit6 + b7xit7 + eit,where xit1 was the independent variable (YRADOPT), and xit2 ... xit7 were the control variables(EARN, FUND, DE, ABO, FOREIGN, and SIZE); eit = stochastic error term.

Since the year of adoption (YRADOPT) was the variable of interest, this study tested Ho: b1 = 0,against H1: b1 … 0 for Hypotheses H1, H2, and H3.

EMPIRICAL RESULTS

Of the 1035 sample firms, 490 (47 percent) adopted the new pension accounting standardprior to its effective date. Some firms were omitted from the final sample due to missing data. Themeans, medians, and standard deviations of the pension rates, and the sample sizes used in theanalysis are presented in Table 1. Table 2 presents the mean, median, and standard deviations forthe control variables.

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Table 1: Descriptive Statistics - Pension Rates in Percentage

Pension Rate 1991 1992 1993 1994

RORMean MedianStd. DeviationSample Size

9.029.001.29743

8.969.001.34757

8.678.831.25766

8.789.001.21752

Discount RateMean MedianSt. DeviationSample Size

8.33 8.50 0.65724

8.178.210.62756

7.447.500.52759

8.138.250.56749

Salary Trend RateMeanMedianStandard DeviationSample Size

5.635.951.05668

5.445.501.03681

4.965.000.98676

4.955.000.98655

The mean estimated RORs utilized by the firms were 9.02, 8.96, 8.67, and 8.78 for years1991, 1992, 1993, and 1994, respectively. The rates ranged from a minimum of 0 to a maximumof 13 percent.

The mean discount rates were 8.33, 817, 7.44, and 8.13 for years 1991, 1992, 1993, and1994, respectively. The decrease between 1991 and 1993 was consistent with the overall decreasein interest rates during this period. The rates ranged from a minimum of 4.2 to a maximum of 13percent.

For all years tested, the mean estimated RORs exceeded the mean discount rates.Differences in these two rates may facilitate smoothing of periodic pension expense and the pensionliability/asset, since a higher (lower) ROR decreases (increases) periodic pension expense, while alower (higher) discount rate increases (decreases) the present value of the pension benefit obligationand may increase (decrease) pension expense. The standard deviations for the estimated RORs werehigher than those for the discount rate, indicating more extreme inter-firm variations in the estimatedROR, than in the discount rate.

Table 2: Descriptive Statistics - Control Variables Scaled by Total Assets

Variable 1991 1992 1993 1994

EARNMeanMedian Std. Deviation.

0.030.030.03

0.020.020.03

0.020.020.03

0.030.030.03

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FUNDMeanMedian Std. Deviation

0.030.000.06

0.030.020.06

0.020.010.07

0.020.010.06

DEMeanMedianStd. Deviation

2.181.533.44

2.301.583.80

2.281.702.53

2.231.632.47

ABOMeanMedianStd. Deviation

0.120.080.13

0.130.080.17

0.140.080.17

0.150.090.19

SIZEMeanMedianStd. Deviation

6.826.851.89

6.906.901.91

6.876.971.91

6.927.031.91

YRADOPT = Adoption yearEARN = Income before extraordinary items, divided by total assetsFUND = Accumulated pension benefit obligation minus pension plan assets, divided by total assetsDE = Total debt divided by total equityABO = Accumulated pension benefit obligation divided by total assetsSIZE = Natural Log of Total AssetsFOREIGN = Domestic or Foreign Company: for all years tested, the mean percentage of foreign

companies was 3.58 percent.

The mean salary trend rates were 5.63, 5.44, 4.96, and 4.95 for the years 1991, 1992, 1993,and 1994, respectively. The year-to-year decreases were consistent with overall lower rates of salaryand wage increases during this period of low inflation.

Table 3 presents Pearson correlation matrixes for the pension rates in 1991-1994. Since thesample sizes were large for each year, the coefficients of correlation obtained between the discountrate and the estimated ROR were highly significant. This suggests that estimated ROR and discountrates were correlated.

However, further analysis showed that the coefficients of correlation between the estimatedand the actual RORs were not significant, and positive for some years and negative for others.These findings are consistent with those of Amir and Benartzi (1998) who also did not find asignificant correlation between actual and estimated RORs, and interpreted their findings as apotential indication of earnings management.

Table 3: Correlation Matrix Using Pooled 1991-1994 Pension RatesNumbers are Pearson Correlations

DISC 1991 DISC 1992 DISC 1993 DISC 1994 EROR 1991 EROR 1992 EROR 1993 EROR 1994

DISC 1991 1 0.84 0.48 0.40 0.47 0.48 0.43 0.40

DISC 1992 1 0.53 0.47 0.37 0.43 0.40 0.39

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DISC 1993 1 0.25 0.17 0.20 0.24 0.22

DISC 1994 1 0.25 0.30 0.34 0.42

EROR 1991 1 0.91 0.78 0.62

EROR 1992 1 0.88 0.75

EROR 1993 1 0.88

EROR 1994 1


AROR 1991 0.03 0.00 0.13 -0.11 0.04 -0.06 -0.13 -0.14

AROR 1992 -.032 -0.01 -0.14 0.09 -0.05 -0.06 -0.06 -0.07

AROR 1993 -.041 -0.04 -0.03 0.03 -0.12 -0.15 0.04 0.05

AROR 1994 -.036 -0.05 -0.13 0.05 -0.05 -0.05 0.01 0.02

SAL 1991 0.09 0.06 0.09 0.04 0.01 -0.01 0.02 0.05

SAL 1992 0.01 0.08 0.06 0.02 -0.02 -0.02 0.01 0.04

SAL 1993 -0.15 -0.13 0.17 -0.14 -0.13 -0.13 -0.08 -0.07

SAL 1994 -0.11 -0.06 0.25 -0.04 -0.09 -0.09 -0.05 -0.05

AROR1991 AROR1992 AROR1993 AROR1994 SAL 1991 SAL 1992 SAL 1993 SAL 1994

AROR 1991 1 -0.06 0.28 -0.22 -0.00 -0.01 -0.01 -0.05

AROR 1992 1 0.11 0.13 0.25 0.02 -0.00 0.00

AROR 1993 1 0.01 0.00 0.02 -0.05 -.05

AROR 1994 1 -0.07 -0.05 -0.05 -.01

SAL 1991 1 0.81 0.63 0.61

SAL 1992 1 0.75 0.69

SAL 1993 1 0.78

SAL 1994 1

DISC = Discount rate used in calculating pension benefit obligations and interest component of expenseEROR* = Estimated ROR used in calculating pension expenseAROR* = Actual ROR achieved SAL = Estimated salary trend rate used in calculating projected pension benefit obligation and service

component of pension expense*E and R was added to the ROR variable to distinguish between the estimated ROR (which is used in calculatingpension expense), and the actual ROR achieved by the pension plan assets.

Hypotheses H1, H2, and H3 tested the effects of SFAS 87 adoption timing on the pensionrate estimates chosen by the firms subsequent to adoption.

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Table 4: Relationship Between Estimated ROR and Choice of Adoption Date of SFAS 87 Model: RORit = bo + b1xit1 + b2xit2 + b3xit3 + b4xit4 + b5xit5 +b6xit6 + b7xit7 + eit

Year YradoptB1

EarnB2

FundB3

De B4

AboB5

ForeignB6

SizeB7

SampleSize

R2

Adj R2

1991coeff.t-stat.p-val.

0.43 6.45 0.00

0.771.580.11

-4.43-7.52 0.00

-0.00-0.05 0.96

1.194.600.00

-0.26-1.25 0.21

0.105.480.00

743 0.150.15


0.36 5.58 0.00

0.551.260.21

-3.26-6.94 0.00

-0.00-0.34 0.74

1.074.710.00

-0.25-1.18 0.24

0.11 6.190.00

757 0.150.14


0.35 5.74 0.00

0.871.850.07

-2.75-5.71 0.00

-0.00-0.33 0.74

0.612.920.00

-0.71-3.650.00

0.126.910.00

766 0.150.14


0.26 4.28 0.00

0.291.050.29

-2.11-4.89 0.00

-0.00 0.19 0.85

0.432.240.03

-0.90-4.40 0.00

0.105.880.00

752 0.120.11

YRADOPT = Adoption year (1=early, 0=on-time); EARN = Income before extraordinary items, divided by total assets; FUND= accumulated pension benefit obligation minus pension plan assets, divided by total assets; DE = total debt divided by totalequity;; ABO = accumulated benefit obligation divided by total assets; FOREIGN = Domestic or Foreign Company; SIZE =Natural Log of Total Assets

Hypothesis H1 tested the effect of adoption timing on the ROR chosen by sample firms forfiscal periods 1991-1994. Regression analysis showed that the coefficients for YRADOPT werepositive and highly statistically significant for each of the years 1991-1994 (p-values <0.01). Theseresults suggest that early adopters tended to utilize higher ROR estimates, than on-time adopters.

Higher ROR estimates decrease pension expense and increase reported income; this mayindicate an earnings management strategy if the higher ROR estimates are not supported by higheractual returns. To further investigate this, the relationship between the timing of adoption and actualRORs earned by the pension plan assets was analyzed. This analysis showed that the pension plansof early adopters did not tend to earn higher actual RORs, than those of on-time adopters for any ofthe years tested.

Hypothesis H2 tested the relationship between adoption timing and the estimated discountrate chosen by the firms subsequent to adoption.

Regression analysis found that the adoption year (YRADOPT) was statistically significantfor years 1991 and 1992 (p-values < 0.01), but not for 1993 and 1994. The coefficients werepositive, which means that early adopters tended to use significantly higher discount rate estimatesduring 1991 and 1992, than on-time adopters. Higher discount rates reduce the present value of thereported pension benefit obligations, which tends to reduce the minimum pension liability that mustbe recognized by firms sponsoring the pension plans, and may reduces the interest cost componentof pension expense. Because of the typically high magnitude of the pension benefit obligations of

80


many plans, even a small difference in the discount rate can significantly change reported expensesand liabilities.

Table 5: Relationship Between Discount Rate and Choice of Adoption Date of SFAS 87 Model: DISCit = bo + b1xit1 + b2xit2 + b3xit3 + b4xit4 + b5xit5 +b6xit6 + b7xit7 + eit

Year YradoptB1

EarnB2

FundB3

De B4

AboB5

ForeignB6

SizeB7

SampleSize

R2

Adj R2


0.183.970.00

0.180.550.58

-0.00-1.67 0.95

-0.01-0.97 0.33

0.553.150.00

0.201.330.18

0.053.650.00

724 0.0620.053


0.112.530.01

0.050.190.85

-0.00-1.61 0.11

0.000.240.81

0.412.820.01

0.191.390.16

0.042.810.01

756 0.0360.027


0.041.120.26

0.260.990.32

0.00-0.81 0.42

-0.00-0.77 0.44

0.353.050.00

0.433.830.00

-0.02-1.660.10

759 0.0400.032


0.030.740.46

-0.29 1.71 0.09

-0.00-2.18 0.03

0.000.460.64

0.332.870.00

-0.49-3.820.00

0.948.470.00

749 0.1120.104

YRADOPT = Adoption year (1=early, 0=on-time); EARN = Income before extraordinary items, divided by total assets; FUND= accumulated pension benefit obligation minus pension plan assets, divided by total assets; DE = total debt divided by totalequity; ABO = accumulated benefit obligation divided by total assets; FOREIGN = Domestic or Foreign Company; SIZE =Natural Log of Total Assets.

Hypothesis H3 tested the relationship between adoption timing and the salary trend ratesubsequent to adoption.

Regression analysis showed that the adoption year (YRADOPT) was not statisticallysignificant for any of the years tested. Thus, the estimated salary trend rates utilized by the earlyadopters was not significantly different from those utilized by the on-time adopters.

Significant Control Variables

Funding status (FUND) was significantly related to the ROR chosen by firms subsequent toadoption (p-values <0.01). The regression coefficients were negative, which suggests that firmswith underfunded pension plans were more likely to choose higher ROR estimates than those thathad well-funded plans. Funding status did not appear to effect choice of the discount and salarytrend rates.

The relative size of the accumulated pension benefit obligation (ABO) was statisticallysignificantly related to all three pensions rates. The coefficients were positive for the ROR and thediscount rate hypotheses, and negative for the salary trend rate hypothesis. The positive coefficientfor the ROR regression suggests that firms with relatively high pension benefit obligations chose

81


higher RORs, which decrease pension expense and increase income. The negative coefficient forsalary trend rate suggests that firms with relatively large pension benefit obligations choose lowersalary trend rates, which decrease the projected pension benefit obligation and pension expense. Thepositive coefficient for ABO in the discount rate regression suggests that firms with relatively largepension benefit obligations tend to choose higher discount rates, which tends to decrease the vested,accumulated, and projected pension benefit obligations and may also decrease pension expense forpension plans with long time horizons.

Table 6: Relationship Between Estimated Salary Trend Rate and Choice of Adoption Date of SFAS 87 Model: SALit = bo + b1xit1 + b2xit2 + b3xit3 + b4xit4 + b5xit5 +b6xit6 + b7xit7 + eit

Year YradoptB1

EarnB2

FundB3

De B4

AboB5

ForeignB6

SizeB7

SampleSize

R2

Adj R2


0.070.960.34

0.310.600.55

-0.00-1.21 0.23

0.00-0.28 0.78

-0.45-1.47 0.14

0.411.740.08

0.041.670.10

668 0.020.00


0.010.160.87

1.242.990.00

-0.00-0.59 0.56

0.00 0.87 0.38

-0.48-2.08 0.04

0.592.730.01

0.010.540.59

681 0.030.02


0.691.130.26

1.262.510.01

0.000.480.63

0.00-0.08 0.94

-0.59-2.65 0.01

0.733.400.00

-0.04-2.31 0.02

676 0.040.03


0.071.130.26

1.413.830.00

0.002.120.03

0.000.080.94

-0.58-2.74 0.01

0.471.950.05

-0.02-1.24 0.22

655 0.050.04

YRADOPT = Adoption year (1=early, 0=on-time); EARN = Income before extraordinary items, divided by total assets; FUND= accumulated pension benefit obligation minus pension plan assets, divided by total assets; DE = total debt divided by totalequity; ABO = accumulated benefit obligation divided by total assets; FOREIGN = Domestic or Foreign Company; SIZE =Natural Log of Total Assets.

Firm size was significantly related to the estimated ROR and the discount rates chosensubsequent to adoption. The coefficients were positive which suggests that large firms tended to usehigher ROR estimates and higher discount rates than small firms. Size was not significantly relatedto salary trend rate choice.

Thus, firms with underfunded pension plans, and large firms were more likely to choosehigher ROR estimates. Firms with relatively large pension plans tended to use higher ROR anddiscount rate estimates, and lower salary trend rates. The other control variables (EARN, DE,FOREIGN) were not consistently statistically significant in any of the regression analyses.

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CONCLUSIONS

Several studies suggested that firms that early-adopted SFAS 87 were motivated by theincome increasing effect at time of adoption of the new pension standard (e.g., Langer and Lev,1993; Tung & Weygandt, 1994). However, SFAS 87 can be used to manage earnings not only uponadoption, but also subsequent to adoption through the choice of pension rate estimates (the ROR,discount rate, and salary trend rate). This study investigated the relationship between adoptiontiming and choice of pension rate estimates subsequent to adoption.

This study found that early adopters tended to use higher ROR estimates than on-timeadopters for all the years tested. In addition, early adopters tended to use higher discount rates duringtwo of the years tested. In the absence of higher actual returns, this suggests that early adopterswere utilizing the ROR estimates to manage earnings.

These findings may provide an additional explanation for the timing of adoption of SFAS87. The findings may also provide useful input to FASB for assessing (1) whether its intendedpurpose for long multi-year transition periods is being achieved, and (2) whether future accountingstandards requiring extensive estimates should combine the flexibility of choice of estimates withthe flexibility of choice of the adoption year.

Acknowledgment

I am grateful to the members of my dissertation committee for their guidance,especially to Professor Harvey Hendrickson, my major professor, to whose memorythis paper is dedicated, and Professor Arun Prakash, who also provided usefulsuggestions on this paper.

REFERENCES

Ali, A., and K.R. Kumar (1993). Earnings Management Under Pension Accounting Standards:SFAS 87 Versus APB 8. Journal of Accounting, Auditing & Finance, 8(4), 427-446.

American Institute of Certified Public Accountants (1966, November). Accounting for the Cost ofPension Plans. Accounting Principles Board Opinion No. 8.

American Institute of Certified Public Accountants (1986, 1987, 1988,1991, 1992, 1993, 1994,1995). Accounting Trends and Techniques.

Amir, E, & Gordon, E. (1996, Summer). Firms' Choice of Estimation Parameters: EmpiricalEvidence from SFAS 106. Journal of Accounting, Auditing and Finance, 11(3), 427-448.

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Amir, E., & S. Benartzi, S. (1998). The Expected Rate of Return on Pension Funds and AssetAllocation as Predictors of Portfolio Performance. The Accounting Review, 73(3), 335-352.

Ayres, F. L. (1986). Characteristics of Firms Electing Early Adoption of SFAS 52. Journal ofAccounting and Economics, 64(3), 143-158.

Blankley, A. I. (1992). Incentives in Pension Accounting: An Empirical Study Investigation ofReported Rate Estimates. Dissertation, Texas A&M University.

Blankley, A.I., & E.P. Swanson (1995). A Longitudinal Study of SFAS 87 Pension RateAssumptions. Accounting Horizons, 9(4), 1-21.

Financial Accounting Standards Board (1981, December). Foreign Currency Translation. Statementof Financial Accounting Standards No. 52.

Financial Accounting Standards Board (1985, December). Employers' Accounting for Pensions.Statement of Financial Accounting Standards No. 87.

Financial Accounting Standards Board (1987, December). Accounting for Income Taxes. Statementof Financial Accounting Standards No. 96.

Financial Accounting Standards Board (1990, December). Employers' Accounting forPostretirement Benefits Other Than Pensions. Statement of Financial Accounting StandardsNo. 106.

Financial Accounting Standards Board (1992, February). Accounting for Income Taxes. Statementof Financial Accounting Standards No. 109.

Fogarty, T. J., & J. Grant (1995) Impact of Actuarial Profession on Financial Reporting. AccountingHorizons, 9(3), 23-33.

Ghicas, D. C. (1990). Determinants of Actuarial Cost Method Changes for Pension Accounting andFunding. The Accounting Review, 65(2), 384-405.

Gujarathi, M. R., & R.E. Hoskin (1992). Evidence of Earnings Management by the Early Adoptersof SFAS 96. Accounting Horizons, 6(4) 18-31.

Gujarathi, M. R., & R.E. Hoskin (1995). Managers' Use of Adoption Timing and Choice ofTransition Method as a Strategic Financial Reporting Opportunity: Additional Evidencefrom the Early Adopters of SFAS 96. Working Paper. Bentley College, University ofConnecticut.

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Langer, R., & B. Lev (1993). The FASB's Policy of Extended Adoption for New Standards: AnExamination of FAS No. 87. The Accounting Review, 68(3), 515-533.

Levitt, A. (1998, Sept. 28). The Numbers Game. Remarks of the Chairman of the Securities andExchange Commission. Retrieved August 10, 2001 from www.sec.gov/news/speech/speecharchive/1998/spch220.txt.

MacDonald, E. (1999, January 22). SEC Weighs Wide Review of Write-Offs. The Wall StreetJournal, A2-6.

Mittelstaedt, H. F. (1989). An Empirical Analysis of the Factors Underlying the Decision to RemoveExcess Assets from Overfunded Pension Plans. Journal of Accounting and Economics.(November), 11(4), 399-418.

Norton, C. (1989). Transition to New Accounting Rules: The Case of FAS 87. Accounting Horizons,3(4), 40-48.

Schipper, K. (1989). Commentary on Earnings Management. Accounting Horizons, 3(4), 91-101.

Senteney, D. L., & J.R. Strawser (1990). An Investigation of the Association Between FinancialStatement Effects and Management's Early Adoption of SFAS 87. Review of Business andEconomic Research, 25(2), 12-22.

Stone, M.S., and R. Ingram (1988). The Effect of Statement No. 87 on the Financial Reports ofEarly Adopters. Accounting Horizons, 2(3), 48-61.

Thomas, J. K. (1989). Why Do Firms Terminate Their Overfunded Pension Plans. Journal ofAccounting and Economics, 11(4), 361-398.

Tung, S. S., & J.J. Weygandt (1994). The Determinants of Timing in the Adoption of NewAccounting Standard: A Study of SFAS No. 87, Employers' Accounting for Pensions.Journal of Accounting, Auditing, and Finance, 9(2), 325-337.

Watts, R. L., & J.L. Zimmerman (1986). Positive Accounting Theory. Englewood Cliffs, N.J.:Prentice-Hall, p. 216.

Zmijewski, M. E., & R.J. Hagerman (1981). An Income Strategy Approach to the Positive Theoryof Accounting Standard Setting/Choice. Journal of Accounting and Economics, 3 (1), 129-149.

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EARNINGS RESPONSE TO AUDITOR SWITCHESUSING A MULTI-TIERED AUDITOR CLASSIFICATION

Ronald A. Stunda, Birmingham-Southern CollegeDavid H. Sinason, Northern Illinois University

ABSTRACT

Previous studies have provided evidence indicating that the securities market values auditsperformed by large audit firms more then audits performed by smaller audit firms. This may be dueto a perception that large audit firms provide higher quality audits or that large audit firms providegreater insurance to investors in the event of a loss. Findings in this study are based on studiesbetween big-five (big-six, big-eight) audit firms and smaller audit firms. While the market mayvalue big-five audit firms, it would be unreasonable to expect the market to treat all other auditfirms equally. This study provides evidence that the market reacts differently to earnings when anaudit firm of a different classification is associated with the financial information provided by thefirm and is not limited to changes from or to the big-five audit firms.

INTRODUCTION

The fact that large audit firms enjoy a reputation as the premier quality auditors has beenaccepted in audit research as a valid construct. The recognition of this size relationship has beenoperationalized in research as a two-class system consisting of the "quality" big-five auditors andall other audit firms. While firms like Arthur Andersen and KPMG enjoy international reputationswhich are shared by only a select group of firms, other firms like McGladrey & Pullen, BDOSeidmans and Grant Thornton, enjoy national reputations that are not shared by smaller regionalfirms. Therefore, it may be logical to expect investors to find value in a change from a smallregional firm to a non-big-five firm with a national reputation. This study evaluates the marketreaction to auditor switches using a multi-tiered classification based on the number of audit clientsfor each audit firm. This market reaction may be the result of an increase in information quality oran increase in the insurance provided by the larger audit firm.

The recognition of a different market reaction to distinct classifications of auditors isimportant for two reasons. First, audit researchers have dichotomized audit quality between big-fiveaudit firms and all other audit firms. This classification has been utilized in many studies thatinvestigate auditor-client relationships. If the market views auditors as a multi-classification andreacts differently to each classification, this information may facilitate future research inauditor-client relationships. Second, if the market views auditors as a multi-classification, clientsmay need to give more consideration to the markets perceptions when selecting an auditor.

The remainder of the paper is organized in five sections. The first section outlinessignificant prior research in the area of auditor switching. The next section provides the theoreticaldevelopment of the hypotheses being tested. This is followed by the section that describes the

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sample selection. The next section provides the discussion of the results. This is followed by theConclusions of the study.

PRIOR RESEARCH

Previous studies regarding changes in auditors have focused on the reasons companieschange auditors and the market reaction to changes in auditor type (i.e. big-five and non-big-five). Most of this research utilizes a dichotomous variable, where 1 is assigned to big-five firms and 0to non-big-five firms, as either the dependent or independent variable. As an independent variable,this classification is usually used as a proxy for audit quality.

Francis and Wilson (1988) tested whether a positive relationship exists between a firm'sagency costs and its demand for a quality-differentiated audit. The authors utilized two models:

1 a brand name model where the dependent variable of big-eight/non-big-eight, and

2 a continuous size model where the dependent variable was defined as the natural logarithm of the ratio ofcombined sales of the public companies audited by the new auditor to that of the old auditor in the year ofthe auditor change.

Results of the brand name model supported the agency cost relationship, however, the results of thecontinuous size model did not support the hypothesis.

Johnson and Lys (1990) evaluate whether changes in clients' financing, investing, andoperating characteristics are related to auditor changes. They evaluate auditor changes between andamong big-eight audit firms and non-big-eight firms. The authors used a cross-sectional regressionand logit analysis to study the relation between relative audit firm size and the change in clientcharacteristics for the period 1973-1982. They maintain that auditor changes are a response toshifts in the client's financing and operating characteristics that result in an auditor-client mismatch. The authors also presented an event study that evaluated common stock returns at the time of theauditor change. The results of the event study provide no statistical evidence of a market reactionto auditor changes.

In a 1993 article on perceived audit quality, Teoh and Wong provided an analysis of themarket reaction to firms changing from big-eight to non-big-eight or non-big-eight to big-eight.This study evaluated market reaction to earnings during a period prior to the change in auditors withthe period subsequent to the change in auditors. The results of this part of Teoh and Wong studywere inconclusive with regards to a market reaction to changes in auditor.

Krishnan (1994) examined auditor switching as a function of auditor conservatism. Theauthor concluded that switching is triggered by conservative treatment rather than by the issuanceof qualified opinions. Krishnan used an ordered probit regression that includes a dichotomousindependent variable BIG6. This variable is not defined as to its representation in the equation, butappears to proxy for the quality of the auditor. Krishnan et. al. (1996) indicate that auditorswitching is more likely to occur when the auditor issues a qualified opinion, however, the authors

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find no support that a change in auditor influences the opinion provided. In the research, theauthors use an independent variable BIG6 to proxy for auditor quality and reputation. This paper extends the market reaction to auditor switching studies of Johnson and Lys(1990) and Teoh and Wong (1993) by looking at a multi-tiered classification scheme based on thenumber of clients for an auditor.

THEORETICAL DEVELOPMENTS

Audit Quality

The users of financial information desire an independent audit as means of monitoringfinancial information to ensure that information is reliable. Information reliability incorporates thecharacteristics of precision and bias. Precision implies that stated measurement methods wereproperly applied, while bias indicates that the measurement results were correctly displayed(Kinney, 2000). The users of financial information require a quality audit to ensure that numbersare precise (within the confines of materiality) and free from bias.

The quality of an audit may be defined as the market-assessed joint probability that anauditor will discover a financial reporting impression or bias, and report the situation to theinformation users (DeAngelo, 1981). Although audit quality is not directly observable, usersdevelop proxies that they believe are associated with audit quality (Wilson and Grimlund 1990;Palmrose 1991). One such proxy is audit firm size.

It has been stated in many studies that large audit firms provide a higher quality audit thensmaller firms (DeAngelo 1981; Chow and Rice 1982; Schwartz and Menon 1985). According toDeAngelo (1981)

….the larger the auditor as measured by the number of clients, the less incentive the auditor hasto behave opportunistically and the higher the perceived quality of the audit.

In addition, large audit firm investments in specialized resources such as training and technologyyield economies of scale and scope for audit services (Johnson and Lys 1990). If investors believethat large audit firms provide a better quality audit then smaller audit firms, and that this qualityresults in improved reliability of financial information, changes to larger audit firms could lead toa positive share price reaction around the announcement of the auditor change.

Insurance Hypothesis

Prior research indicates that investors perceive auditors as providing a type of implicitinsurance to users and investors (Hill et. al. 1993). The auditors are deemed to be a "deep pocket"because CPA firms often carry malpractice insurance or, in many cases, are the only solventdefendant in a lawsuit. Therefore, the auditor is considered a potential indemnifier to investors andcreditors if a loss is experienced. Menon and Williams (1994) assert that the legal right to seek

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indemnification from an auditor for losses sustained is assigned a value by investors and is acomponent of the stock price of publicly traded companies. The insurance hypothesis indicates thatinvestors will react to changes that may affect their ability to collect damages from the auditor. Changes to larger, more solvent audit firms could lead to a positive share price reaction for auditclients around the announcement of the auditor change.

The Timing of the Market Reaction

Many auditor switching studies have used the announcement of a change in auditor as theevent date. Studies such as Smith (1988), Mangold (1988), Teoh (1989), and Johnson and Lys(1990) do not find a market reaction to the announcement of an auditor switch. Fried and Schiff(1981) find a negative cumulative abnormal return for a 21-week period following the filing of an8-K report for an auditor change. Wells and Louder (1997) find evidence that the market views anauditor resignation as bad news and a resultant negative price reaction occurs.

It should be noted that at the announcement date of the change in auditor, the successorauditor has not yet performed any work for the client. Legally the successor auditor cannot be heldresponsible for the work performed or information provided by the predecessor auditor. Therefore,should the investors find fault in published financial information, it is still the predecessor auditorthat is liable. This may lead to a perceived difference in the risk of relying on financial informationassociated with the successor auditor. In addition, when a new auditor is announced, investors mayanticipate an audit of higher quality from the successor auditor. However, until informationprepared or audited by the new firm is made available to the market, there is no "product" that themarket can assess. This may lead to a perceived difference in the reliability of the financialinformation associated with the successor auditor. For these reasons, the market may reactdifferently to earnings announcements when a new auditor is associated with the financialstatements. This reaction may be in addition to any reaction that the market has to theannouncement of the auditor change.

Hypothesis Development

The theory that large audit firms provide higher quality audits and that large audit firmsprovide greater insurance protection for investors and creditors does not indicate that sizeadvantages are limited to the big-five audit firms. Indeed, it is only the perpetuation of priormethodology that has resulted in an audit quality proxy as a dichotomy between big-five firms andall other firms providing audit services. The following hypotheses will be tested to determine ifthere are cumulative abnormal returns at the earnings announcement date when clients changeauditor class in a multi-tiered classification system.

H1: Clients that change from a non-big-five audit firm to big-five audit firm experience positive cumulativeabnormal returns in the market place.

H2: Clients that change from a big-five audit firm to a smaller audit firm experience negative cumulativeabnormal returns in the market place.

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If the market place perceives a quality difference in a big-five auditor, the results of H1 should bestatistically significant with a positive sign, while the results of H2 should be statistically significantwith a negative sign.

H3: Clients that change from one big-five audit firms to another non-big-five audit firm experience cumulativeabnormal returns in the market place.

H4: Clients that change from a non-big-five to another non-big-five audit firm experience cumulative abnormalreturns in the market place.

Hypotheses 3 and 4 are designed to indicate that the market does not react to auditor changes whenauditor changes do not involve changes in auditor class. The results of the testing of thesehypotheses should not be statistically different from zero.

H5: Clients that change to a larger audit firm experience positive cumulative abnormal returns in the marketplace.

H6: Clients that change to a smaller audit firm experience negative cumulative abnormal returns in the marketplace.

Hypotheses 5 and 6 are designed to test whether the market reacts to changes in audit firm size whenthe auditor is not a big-five auditor.

SAMPLE SELECTION

Six regressions are estimated with samples of firms obtained from the COMPUSTATindustrial tapes, which include firms listed on the New York Stock Exchange (NYSE), the AmericanStock Exchange (AMEX), and the National Association of Security Dealers Automated Quotations(NASDAQ). The sample is selected from files of the 1999 annual industrial tapes, and is limited tofirms with earnings information in each year of the period 1989-1998. As a measure of unexpectedearnings, we use consensus analysts' forecast, therefore, we require the sample firms to be followedby the Institutional Brokers Estimate System (IBES) similar to Baginski, Hassell and Waymire(1994) and Stunda (1996). We also require that firms have daily stock returns data available on tapefrom the Center for Research in Security Prices (CRSP) during the period under study.

The first sample is a control sample that contains industry-matched pairs of firms to controlfor differences in the information environment. Industry matching is accomplished by matchingfour-digit, three-digit, and two-digit SIC codes for each firm audited by one of the big-five firmsversus a firm in the same industry audited by non-big-five firm. For the period under study, 1,485firms were observed that met the sample criteria.

The second sample contains firms that have switched from a non-big-five firm to a big-fiveaudit firms during the study period. The sample is selected from COMPUSTAT and must meet thedata availability criteria. For the study period, 147 firms were observed which met the sampleparameters.

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The third sample contains firms that have switched from one of the big-five firms to anon-big-five audit firms during the study period. For the study period, 42 firms were observedwhich met the sample criteria.

The fourth sample contains firms that have switched from a big-five audit firm to anotherbig-five audit firms during the study period. For the study period, 26 firms were observed whichmet the sample criteria.

The fifth sample contains firms that have switched from a non-big-five audit firm to anothernon-big-five audit firm during the study period. For the study period, 31 firms were observed whichmet the sample criteria.

The sixth sample stratifies firms that have switched auditors during the study period. Fourgroups of audit firms are created using the number of clients identified for each firm . This meansof classification is appropriate, since this study utilizes many smaller audit firms. Information, suchas total audit firm revenue, is not readily available for such clients. Information concerning thenumber of COMPUSTAT clients for an audit firm is objective and measurable for all firms.Group 1 consists of the five largest firms (big-five). These firms average more than 2,000 clientsas reported on COMPUSTAT for the years 1989-1998. Group 2 consists of audit firms with anaverage number of clients between 500 and 2,000 as reported on COMPUSTAT for the years1989-1998. These firms proxy for the large national firms. Group 3 consists of audit firms withan average number of clients between 200 and 400 as reported on COMPUSTAT for the years1989-1998. These firms proxy for the large regional audit firms. Group 4 consists of audit firmswith less than 200 clients as reported on COMPUSTAT for the years 1989-1998 of small regionalfirms. These cut-offs are arbitrary in nature, but based on the data appear reasonable.

audit group # of audit firms # of sample firms

1 (Big 5) 5 535

2 (non-Big 5 national) 6 326

3 (large regional) 16 418

4 (small regional) 10 206

Total 37 1,485

Dummy variables are utilized and an analysis is made of sample firms that switch as follows:

Change from group 1 auditors to group 2 auditorsChange from group 1 auditors to group 4 auditorsChange from group 2 auditors to group 4 auditorsChange from group 2 auditors to group 1 auditorsChange from group 3 auditors to group 1 auditorsChange from group 4 auditors to group 2 auditors

Change from group 1 auditors to group 3 auditorsChange from group 2 auditors to group 3 auditorsChange from group 3 auditors to group 4 auditorsChange from group 3 auditors to group 2 auditorsChange from group 4 auditors to group 3 auditorsChange from group 4 auditors to group 1 auditors

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Methodology

The first regression assesses the relative information content of unexpected earnings inmatched pair samples of firms in comparable industries audited by big-five and non-big-five auditfirms. This regression is run as a control from which subsequent regressions will be compared. Thefollowing model is used to evaluate information content:

CARit = a+b1UEit+b2DitUEit+b3MBit+b4DitMBit+b5LMVit+b6DitLMVit+b7Nit+b8DitNit +b9Bit+b10DitBit+eit [1]

Where: CARit = Cumulative abnormal return for firm i, time ta = Intercept termUEit = Unexpected earnings forecast for firm i, time tDit = Dummy variable, 1 for NB5 client, 0 for B5 clientMBit = Market value to book value as a proxy for growth and persistenceLMVit = Natural log of market value as a proxy for firm sizeNit = Number of analysts’ forecasts included in IBES as a proxy for noise in predisclosure environmentBit =Market value slope coefficient as a proxy for systematic riskeit = Error term for firm i, time t

The coefficient “a” measures the intercept. The coefficient “b1” is the earnings responsecoefficient (ERC) for all firms in the sample (both big-five and non-big-five clients). Thecoefficient b2 represents the incremental ERC. Therefore, b2 captures the difference in theinformation content for firms that are big-five clients versus those who are not. The remainingcoefficients are contributions to the ERC for all firms in the sample. To investigate the effects ofthe information content of unexpected earnings, there must be some control for variables shown byprior studies to be determinants of the ERC. For this reason, variables represented by thesecoefficients are included in the study.

Unexpected earnings (UEit) is measured as the difference between the actual earnings andthe security market participants’ expectations for earnings proxied by consensus analysts’ forecastas per IBES. The unexpected earnings are scaled by the firm’s stock price 180 days prior to theforecast:

UEit = Actual Earnings – Expected Earnings / Price

For each disclosure sample, an abnormal return (ARit) is generated for event days –1, 0, +1,where day 0 is defined as the date of the earnings disclosure identified by the Dow Jones NewsRetrieval Service (DJNRS). The market model is utilized along with the CRSP equally-weightedmarket index and regression parameters are estimated between days –290 and –91. Abnormalreturns are then summed to calculate a cross-sectional cumulative abnormal return (CARit).

Regressions two through five address the switching of client firms among and between auditfirm groupings. These switches are observed as follows:

1) NB5 to B5 2) B5 to NB5 3) B5 to B5 4) NB5 to NB5

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The following model is used to evaluate information content among the switched groups:

CARit = a+b1UEit+b2MBit+b3LMVit+b4Nit+b5Bit+eit [2]

Variables and model parameters used are the same as those utilized in equation 1, except forthe elimination of the dummy variable. The above equation is run four times, substituting switchedgroups in each run.

For regression six, the following equation is used:

CARit = a+b1UEit+b2UEit+b3UEit+b4UEit+b5UEit+b6UEit+b7UEit+b8UEit+b9UEit +b10UEit+b11UEit+ b12UEit+b13MBit+b14LMVit+b15Nit+b16Bit+eit [3]

Where: b1 = Variable for change from group 1 auditors to group 2 auditorsb2 = Variable for change from group 1 auditors to group 3 auditorsb3 = Variable for change from group 1 auditors to group 4 auditorsb4 = Variable for change from group 2 auditors to group 3 auditorsb5 = Variable for change from group 2 auditors to group 4 auditorsb6 = Variable for change from group 3 auditors to group 4 auditorsb7 = Variable for change from group 2 auditors to group 1 auditorsb8 = Variable for change from group 3 auditors to group 2 auditorsb9 = Variable for change from group 3 auditors to group 1 auditorsb10= Variable for change from group 4 auditors to group 3 auditorsb11= Variable for change from group 4 auditors to group 2 auditorsb12= Variable for change from group 4 auditors to group 1 auditorsb13= Market value to book value as a proxy for growth and persistenceb14= Natural log of market value as a proxy for firm sizeb15= Number of analysts’ forecasts included in IBES as proxy for noise in predisclosure environmentb16 = Market value slope coefficient as a proxy for systematic riskAll parameters are the same as used in the first two regression equations.

DISCUSSION OF RESULTS

Table 1 provides the results from the first regression of matched pair firms. As can be seen,none of the variables contained in the regression are significant in explaining the CAR. This issimilar to results found by Teoh and Wong (1993).

Table 2 provides results from the first switch sample where client firms switched from non-big-five audit firms to big-five audit firms. As can be seen from the table, the unexpected earningsvariable is positively significant in providing information content relative to CAR. The implicationis that unexpected earnings contain information content for firms switching to big-five auditors, andthis information is positively correlated. This result confirms hypothesis 1 and indicates that themarketplace adds value to a publicly traded stock when the company changes to a big-five auditor.

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Table 1: Summary of Pair-Matched Samples 1989-1998n= 1,485 client firms

CARit = a+b1UEit+b2DitUEit+b3MBit+b4DitMBit+b5LMVit+b6DitLMVit+b7Nit+b8DitNit +b9Bit+b10DitBit+eit

Mean Median

Variable B5 NB5 B5 NB5 Coeff t-statistic p-value

UE -0.0286 -0.0180 -0.0009 -0.0011 -0.1103 -0.6475 0.4362

MB 2.7190 2.4881 1.6021 1.7872 0.0829 0.2183 0.5768

LMV 4.1911 4.2832 4.2901 4.0098 -0.0541 -0.3389 0.4976

N 5.2218 5.0190 4.0000 3.0000 0.0423 2.9090 0.1586

B 1.3856 1.3249 1.2019 1.2001 -0.0218 -1.1391 0.4027

Table 2: Summary of Client Firms Switching From NB5 to B5 Audit Firmsn = 147

CARit = a+b1UEit+b2MBit+b3LMVit+b4Nit+b5Bit+eit

Variable Mean Median Coeff. t-statistic p-value

UE 0.0329 0.0190 0.0921 2.3284 0.0219

MB 2.2802 1.2081 0.0538 0.4492 0.6304

LMV 4.5890 4.2891 -0.0322 -0.1938 0.7984

N 4.5890 4.0000 0.0725 1.5947 0.2609

B 1.2819 1.1947 0.0198 1.1149 0.2531

Table 3 provides results from the second switch sample where client firms switched frombig-five audit firms to non-big-five audit firms. As can be seen from the table, the unexpectedearnings variable is negatively significant in providing information content relative to CAR. Theimplication is that unexpected earnings contain information content for firms switching to non-big-five auditors, and this information is negatively correlated. These results support hypothesis 2 andindicate that the marketplace reduces value to the stock when a change from a big-five auditor ismade.

Tables 4 and 5 provide results from the remaining two switch samples between big-five firmsand non-big-five firms respectively. As expected, no significant results were noted in these switchsamples. While the lack of significance cannot imply the acceptance of the alternative hypothesisthat the marketplace adds no value to changes among the big-five or among the non-big-fiveauditors, it is comforting that the results were as expected.

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Table 3: Summary of Client Firms Switching From B5 to NB5 Audit Firmsn = 42



UE -0.0217 -0.0209 -0.1053 2.7562 0.0118

MB 2.1546 1.8253 0.0486 0.5417 0.6728

LMV 4.3862 4.1170 -0.0251 -0.1764 0.8194

N 3.7652 3.0000 0.0665 1.7621 0.4153

B 1.1597 1.2089 0.0249 1.0018 0.2764

Table 4: Summary of Client Firms Switching From B5 to B5 Audit Firmsn = 26



UE 0.0568 0.0419 0.0291 0.8915 0.4956

MB 1.9976 1.9541 0.0447 0.4876 0.6219

LMV 4.1876 4.0091 -0.0210 -0.2018 0.7847

N 4.8431 4.0000 0.0655 1.6291 0.2987

B 1.3196 1.2922 0.0200 1.1551 0.3281

Table 5: Summary of Client Firms Switching From NB5 to NB5 Audit Firmsn = 31



UE 0.1521 0.1019 0.0313 0.4987 0.6636

MB 2.0198 1.9827 0.0521 0.5121 0.7147

LMV 4.2819 4.0989 -0.0198 -0.4942 0.7767

N 3.8219 3.0000 0.0715 1.8431 0.2262

B 1.4003 1.3821 0.0249 1.0089 0.3724

Table 6 provides results of firms that have switched between auditor classes for the periodunder study. As can be seen, the unexpected earnings variable is positive and significant inproviding information content relevant to CAR in switches from smaller firms to larger firms (i.e.,

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variables b7 through b12). These results are consistent with hypothesis 5 and indicate that the marketadds value to firms that switch to a larger firm, even if the larger firm is not a big-five audit firm.This result may be associated with an audit quality or an insurance factor associated with a largeraudit firm.

Table 6: Summary of Client Firms Switching Auditor Typesn = 220

CARit=a+b1UEit+b2UEit+b3UEit+b4UEit+b5UEit+b6UEit+b7UEit+b8UEit+b9UEit+b10UEit+b11UEit+b12UEit+b13MBit+b14LMVit+b15Nit+b16Bit+eit

Variable Number * Mean Median Coeff. t-statistic p-value

b1 28 -0.1021 -0.1035 -0.09431 1.2846 0.3015

b2 11 -0.1085 -0.1062 -0.08271 1.3102 0.2795

b3 3 -0.1407 -0.1318 -0.0915 1.4519 0.2102

b4 8 -0.1182 -0.1168 -0.0622 1.3422 0.2820

b5 0 N/A N/A N/A N/A N/A

b6 3 -0.1201 -0.1159 -0.0449 1.1950 0.3102

b7 87 0.0739 0.0801 0.0591 2.3515 0.0211

b8 10 0.0901 0.0889 0.0774 2.2412 0.0372

b9 51 0.0995 0.1010 0.0820 2.3802 0.0146

b10 6 0.0840 0.0809 0.0338 2.1921 0.0486

b11 9 0.1014 0.1041 0.0516 2.2056 0.0437

b12 4 0.0785 0.0798 0.0249 2.3601 0.0203

b13 2.1608 2.1554 0.0418 0.6072 0.7519

b14 4.1005 4.1001 -0.0302 0.2019 0.8209

b15 3.5821 3.5618 0.0467 1.8721 0.2398

b16 1.5109 1.5007 0.0128 1.1526 0.4001

* Number of switches in the Group

The variables b1 through b6 (excluding b5 which did not include any samples) representswitches from larger audit firms to smaller audit firms. While the signs for these switches arenegative, as expected, none of the coefficients are statistically significant at any of the conventionallevels. It should be noted that many of these groups contained few, if any, audit switches. Whereonly a few firms exist, a contrary reaction to even one switch can greatly skew the data. Table 3

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presented the results for switches from big-five auditors (group1) to non-big-five auditors (groups2, 3, and 4 combined). Using that grouping, the changing from a large auditor to a smaller auditorwas statistically significant. The limited number of firms in these groups is a limitation of this studyand may account for the lack of statistical significance for variables b1 through b6.

CONCLUSION

It has been noted in many studies that the financial statement users value large audit firmsbecause they perceive these firms as either providing a higher quality audit or greater insurance inthe event of a financial loss. These studies have resulted in audit quality as a dichotomous variablewhere big-five auditors represent the quality firm. This study provides some evidence that themarket does value a larger audit firm, even if that firm is not a big-five firm.

REFERENCES

Baginski, S., J. Hassell, and G. Waymire, 1994, Some Evidence on the News Content of PreliminaryEarnings Estimates, The Accounting Review, January, pp. 265-271

Chow, C. W. and S. J. Rice, 1982, Qualified Audit Opinions and Auditor Switching, AccountingReview, April, pp. 326-335

DeAngelo, Linda, 1981, Auditor Size and Audit Quality, Journal of Accounting and Economics, pp.183-199

Francis, J. R. and E. R. Wilson, 1988, Auditor Changes: A joint Test of Theories Relating to AgencyCosts and Auditor Differentiation, Accounting Review, October, pp. 663-683

Fried, D. and A. Schriff, 1981, CPA Switches and Associated Market Reactions, The AccountingReview, April, pp. 326-341

Hill, J. W., M. Metzger, and J. Schatzberg, 1993, Auditing’s Emerging Legal Peril Under theNational Surety Doctrine: A Program for Research, Accounting Horizons, March, pp. 12-28

Johnson, W. B. and T. Lys, 1990, The Market for Audit Services: Evidence from Voluntary AuditorChanges, Journal of Accounting and Economics, January, pp. 281-308

Kinney, Willian R., 2000, Information Quality Assurance and Internal Control for ManagementDecision Making, Irwin McGraw-Hill:Boston

Krishnan, J., 1994, Auditor Switching and Conservatism, Accounting Review, January, pp. 200-215

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Krishnan, J., J. Krishnan, and R.G. Stephens, 1996, The Simultaneous Relation Between AuditorSwitching and Audit Opinion: An Empirical Analysis, Accounting and Business research,3, pp. 224-236

Mangold, N. R., 1988, Changing Auditors and the Effect on Earnings, Auditors’ Opinions, andStock Prices, UMI Research Press

Menon, K. and D. Williams, 1994, The Insurance Hypothesis and the Market Prices, AccountingReview, April, pp. 327-342

Palmrose, Z. , 1991, An Analysis of Auditor Litigation Disclosures, Auditing: A Journal of Theoryand Practice, Supplement, pp. 54-76

Shwartz, K. B. and K. Menon, 1985, Auditor Switches by Failing Firms, Accounting Review, April,pp. 248-261

Smith, D.B., 1988, An Investigation of Securities and Exchange Commission Regulation of AuditorChange Disclosures: The Case of Accounting Series Release No. 165, Journal of AccountingResearch, Spring, pp/ 134-145

Stunda, R., 1996, The Credibility of Management Forecasts During Corporate Mergers andAcquisitions, The American Academy of Accounting and Financial Studies Journal,December, 352-358

Teoh, S.H., 1989, Auditor Independence, Dismissal Threats, and the Market Reaction to AuditorSwitches, Journal of Accounting Research, 30, pp. 1-25

Teoh, S. H. and T. J. Wong, 1993, Perceived Auditor Quality and the Earnings ResponseCoefficient, Accounting Review, April, pp. 346-366

Wells, D.W. and M.L. Loudder, 1997, The Market Effects of Auditor Resignations, Auditing: AJournal of Theory and Practice, Spring, pp: 138-144

Wilson, T. and R. Grimlund, 1990, An Examination of the Importance of an Auditor’s Reputation,Auditing: A Journal of Theory and Practice, Spring, pp. 43-59

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ACCOUNTING FOR ACQUISITIONS ANDFIRM VALUE

Oliver Schnusenberg, St. Joseph's UniversityW. Richard Sherman, St. Joseph's University

ABSTRACT

The accounting of an acquisition is based on either the pooling or the purchase method.Pooling (or pooling of interests) treats the combined companies (acquirer and acquired) as thoughthey had always operated as one company. The purpose of this study is to investigate the extent towhich the accounting method used affects the value of the acquiring firm. One argument is that theaccounting method used should not affect this value inasmuch as the accounting method does notdirectly affect cash flow. Our sample consists of 146 pooling and 46 purchase announcements from1981 to 1995. Results indicate that valuation effects are more favorable for acquisitions using thepurchase method in the eleven-day period surrounding the announcement and for at least six monthsfollowing the announcements. These results stand even after conducting a cross-sectional analysisthat controls for the firms' price/earnings ratio, size, market parameter estimates, earningssurprises, and leverage. These results suggest that market participants value the added flexibilityand indirect tax benefits that are provided by the purchase method of accounting as opposed to thehigher reported future earnings associated with the pooling method.

INTRODUCTION

The accounting of an acquisition is based on either the pooling or the purchase method.Pooling (or pooling of interests) treats the combined companies (acquirer and acquired) as thoughthey had always operated as one company. Consequently, the financial statements of the newcompany merely reflect the consolidation of statements of the two previously separate entities. Incontrast, purchase accounting revalues the assets and liabilities of the acquired company at theircurrent fair market values with the possible difference between the acquisition price and the marketvalue of the acquired company's net identifiable asset (i.e. goodwill) being amortized over a periodnot to exceed 40 years. This amortization creates an expense that reduces reported earnings of theacquiring firm.

The purpose of this study is to investigate the extent to which the accounting method usedaffects the value of the acquiring firm. One argument is that the accounting method used should notaffect this value inasmuch as the accounting method does not directly affect cash flow. However,there are other arguments relating to valuation effects from future cash flows and/or indirect cashflows that provide a rationale for how and why the choice of accounting method can impact value.

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ADVANTAGES OF POOLING

Research by Ball & Brown (1968), Gonedes (1975), Hoskins, Hughes & Ricks (1986), andothers has shown that reported earnings can partially drive stock prices. To the extent that theaccounting method affects future earnings, the valuation effects may be more favorable for acquirersusing the pooling method.

Earnings for pooling firms are generally higher for a number of reasons. The first is due tothe way in which the acquired firm's earnings are folded into the new entity's reported earnings.Under pooling, the net earnings for the entire year of acquisition are carried into the merged firm'sincome statement; under purchase accounting, only the income earned by the acquired firm after theacquisition date are reported by the acquiring firm. Depending on when during the year theacquisition takes place, this difference may be more (late in the year) or less (early in the year)significant in the reported earnings for the first year.

Pooling would also result in higher earnings reporting for reason related to the treatment ofthe acquired firm's assets and liabilities after acquisition. The tax aspects of mergers and acquisitionsare extraordinarily complex but can be roughly divided between tax-free reorganizations and taxableacquisitions. In general, tax-free reorganizations under IRC Section 368 will be accounted for underthe "pooling of interest" method. Taxable acquisitions, with re-valuation of assets to their fair marketvalue (an election under IRC Section 338 is available for stock acquisitions), are usually reportedusing the "purchase" method of accounting. The discussion of the reporting aspects of pooling vs.purchase accounting assumes a parallel to the tax consequences of tax-free reorganizations vs.taxable acquisitions. It further assumes that market participants implicitly understand the relevanttax consequences related to the method of accounting disclosed in the acquisition announcement.Under pooling, the valuation of these assets and liabilities remains unchanged from how theyappeared on the pre-acquisition balance sheet of the acquired firm. Under purchase accounting, theassets and liabilities of the acquired firm are restated at their current market values. Because thereis no re-valuation (i.e., "write-up") of the acquired firm's assets under pooling, depreciation expensesafter the acquisition are generally lower (with the resultant reported earnings being generally higher)than the depreciation that would taken for the same acquisition under purchase accounting.

Related to this non-revaluation of assets is the fact that there is no possibility for therecognition of "goodwill" (i.e. the difference between the purchase price and the market value of theacquired firm's net identifiable assets) from an acquisition under pooling. Had a firm recognizedgoodwill upon acquisition (as would have been the case under purchase accounting), it would berequired to amortize (i.e., expense) this intangible asset, which, in turn, would negatively impactreported earnings. In fact, under the Exposure Draft on a proposed Statement, BusinessCombinations and Intangible Assets, issued by the FASB in September, 1999, not only would thepurchase method be required for all business combinations but any goodwill that is recognized asa result of the acquisition would not be subject to amortization. Instead, goodwill would be reviewedfor impairment (i.e., its fair value is less than its carrying amount) on a regular basis (FASB, 1999).As a final consequence of non-revaluation of assets, pooling firms will generally report higher gains(or lower losses) upon the disposal of the assets of the acquired firm due to the lower basis of these

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assets. In contrast, because of the revaluation to current market values at the time of acquisition, theacquired firm's assets lose whatever pre-acquisition gains which are inherent in them.

As a result of these accounting differences, a company using pooling would be expected toreport higher earnings after the acquisition than a company using the purchase method. (Also seeHerz & Abahoonie (1990) for a more detailed discussion of earnings differentials between thepurchase and pooling methods.)

If market participants do indeed use reported earnings in assessing a firm's value, one wouldexpect abnormal returns associated with the pooling method to be larger than those associated withthe purchase method of accounting for acquisitions. This leads to the following null hypothesis:

HO: Abnormal returns surrounding the announcement of a pooling acquisition are largerthan those associated with a purchase acquisition.

ADVANTAGES OF THE PURCHASE METHOD

While its use will generally result in lower reported earnings, the purchase method ofaccounting does offer some advantages over pooling. As shown in Appendix A, firms must meettwelve criteria in order to be allowed to report an acquisition under the pooling method. (APBOpinion 16) A firm may, therefore, be restricted from restructuring in order to meet these criteria.For example, because a company utilizing the pooling method must agree not to enter into otherfinancial arrangements for the benefit of the former stockholders of a combining company, anexchange of equity securities would be prohibited. Furthermore, the company also must agree notto dispose of a significant part of the assets of the combining companies. To the extent that thetwelve criteria (none of which apply to the purchase method of accounting) can be perceived asrestricting a firm's future financing or operating flexibility, market participants may penalize firmsusing the pooling method. Therefore, acquisitions reported under the purchase method may resultin more favorable valuation effects than those reported under pooling.

The choice of accounting for an acquisition may also affect future indirect cash flows of afirm. As was discussed in the previous section, purchase accounting requires the revaluation of theacquired firm's assets to their market values as of the date of acquisition. This results in higherexpenses being reported for the depreciation of the assets and for the amortization of the goodwillthat may result from the acquisition. However, as Bittker & Eustice (1994) note, "as is often thecase, an acquirer's desires to increase book income generally are at odds with its desires to reducetax income". Inasmuch as both depreciation (Internal Revenue Code Sections 167 & 168) andamortization of goodwill (Internal Revenue Code Section 197) are tax deductible, the purchasemethod may be preferable over pooling because it reduces future cash tax outflows resulting fromrelatively high future earnings.

Another tax-related indirect cash flow relates to the method of payment. Due to the fact thatpooling requires stock-for-stock acquisitions, no recognition of gain or loss is possible for taxpurposes. No such restriction applies to purchase accounting.

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These potential advantages of the purchase method form the basis for the alternativehypothesis (relative to HO) that acquisitions using the purchase method of accounting should resultin more favorable valuation effects.

HA: Abnormal returns surrounding the announcement of a purchase acquisition are largerthan those associated with a purchase acquisition.

LITERATURE REVIEW

Studies investigating the stock returns associated with purchase and pooling acquisitionannouncements are relatively few. Two studies measured the abnormal stock price movements overa long window surrounding acquisition announcements and compared the movements of thesub-sample that was identified as using purchase accounting to the sub-sample using pooling. Hong,Kaplan & Mandelker (1978) examined 138 pooling and 62 purchase method mergers from1954-1964. The accounting method associated with these mergers was identified using proxystatements issued in connection with the mergers. The authors found no reaction associated withpooling announcements but did find significant cumulative abnormal returns during the twelvemonths preceding the announcement of a purchase and that these abnormal returns were maintainedfor eight subsequent months. The authors conclude that the pooling of interests method does not leadto abnormal stock price behavior for acquiring firms but offer no explanation for the large abnormalreturns associated with the purchase method of accounting for acquisitions.

A related study by Davis (1990) examined 108 pooling and 69 purchase acquisitions overthe period of 1971-1982. The accounting method associated with these acquisitions was identifiedin Mergers & Acquisitions. Davis' findings were similar to those reported by Hong et al. Purchaseacquisitions exhibited positive and statistically significant abnormal returns while poolingacquisitions exhibited largely positive but statistically insignificant returns over a period from 26weeks before the announcement to 26 weeks after the effective date of the acquisition.

Other empirical studies have addressed the reasons for choosing one method over the other.Nathan & Dunne (1991) studied 30 purchase and 291 pooling acquisitions that occurred between1963 and 1985. Using the firms' proxy statements to identify the accounting method used, theyfound that the purchase/pooling choice was influenced by goodwill, acquirer leverage, and theissuance of APB Opinion No. 16.

Using the Wall Street Journal to identify the type of accounting used, Robinson & Shane(1990) investigated 59 pooling and 36 purchase acquisitions taking place 1972-1982. They foundthat bid premiums are generally larger for firms using the pooling method. The authors conclude thatsince the costs associated with structuring an acquisition to qualify for pooling are greater than thosefor the purchase method, acquirers will only structure an acquisition as a pooling of interests if theperceived benefits are greater than those that would be achieved under purchase accounting.

Finally, Haw, Jung, & Ruland (1994) found that analysts' forecast accuracy decreased greatlyafter mergers in general but even more so for firms who used the purchase method due to the factthat purchase accounting interrupts the past time series of earnings.

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SAMPLE SELECTION

Both Hong et al. (1978) and Davis (1990) investigated the extent to which abnormal returnswere associated with the "announcement" of the method by which the acquisitions would beaccounted - pooling or purchase. Yet both studies identified this accounting method by sources notdirectly associated with the announcement date (i.e. by proxy statement or by information publishedin Mergers & Acquisitions). While these sources identify the accounting method accurately,information of the method used is not necessarily available to market participants at the time thatthe merger is announced. Thus, these two studies implicitly assume that the market participantscould, at the time of the merger announcement, properly guess whether pooling or purchaseaccounting would be used. However, Hong, et al., and Davis were investigating mergers thatoccurred in 1954-1964 and 1971-1982, respectively. The method of accounting was rarely disclosedat the time of that the merger was announced until the 1990s.

To avoid this flaw in the previous studies and ensure that the accounting method was knownby market participants, the sample of this study was created by identifying all acquisitions in whichthe method of accounting was stated at the time of the acquisition announcement. The Wall StreetJournal Index and the Lexis/Nexis database were used to search for acquisitions during 1981-1985for which there were corresponding announcements of the method being used to account for themerger. (Huang & Walking (1987) investigated acquisition attempts that were resisted and notresisted, using a sample based on announcements in the Wall Street Journal. As we do in our study,they argue that sample selection bias is avoided because only information known at the time of theacquisition announcement is used.) This resulted in an initial sample of 229 acquisitions (173pooling and 56 purchase). This sample was screened by removing any companies for which thestock price data were not consistently reported over a period of 240 days before the acquisitionannouncement and 5 days after the announcement. Thirty-seven companies were removed for thisreason, leaving a total of 192 acquisitions (146 pooling; 46 purchase) that could be assessed.

The sample is segmented by year in Table 1. While 76% of the acquisition reflected the useof pooling, purchase accounting was announced more frequently in the early 1980s and was moreevenly distributed throughout the period studies than is the case for pooling. Furthermore, as wasnoted previously, the choice of accounting method in the acquisition announcement was notgenerally disclosed until the 1990s. 92% of all the relevant announcements (i.e., those whichdisclosed the method that was being used to account for the merger) were in the 1990-1995 period,with most of these announcements occurring in 1994 and 1995.

As shown by the descriptive statistics provided in Table 2, firms undertaking poolingacquisitions are, on average, larger than those firms using the purchase method of accounting.Pooling firms also experienced a higher growth rate, a higher return on assets (ROA), and also paidhigher taxes as a percentage of before-tax income than those using purchase accounting. However,those firms using the purchase method showed higher returns on equity (ROE) than those usingpooling.

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Table 1: Distribution of Pooling and Purchase Acquisitions Over Time

Acquisitions Involving the PoolingMethod

Acquisitions Involving thePurchase Method

Total Acquisitions

1981 1 0 1

1982 0 0 0

1983 0 1 1

1984 0 2 2

1985 2 1 3

1986 2 3 5

1987 0 3 3

1988 0 2 2

1989 6 2 8

1990 7 0 7

1991 12 3 15

1992 25 2 27

1993 17 6 23

1994 32 11 43

1995 42 10 52

Total 146 46 192

Table 2: Summary Information for Pooling and Purchase Acquisitions

Pooling Sample Purchase Sample Total Sample

Mean Asset Size of Acquirer ($million) 8,758 6,053 8,053

Mean Taxes Paid as % of EBT in Year Before Acquisition 34.86% 32.58% 34.26%

Three-Year Growth Rate of Acquirer (Sales) 20.15% 19.18% 19.90%

Mean ROA of Acquirer 3.18% 4.24% 3.45%

Mean ROE of Acquirer 10.40% 10.12% 10.33%

METHODOLOGY

Valuations effects are measured for all firms contained in the sample. The abnormal returnof each acquirer is estimated with prediction errors using the procedure of Mikkelson & Patch

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(1988). The alpha and beta of each acquirer are derived over an estimation period of from t = -240to t = -20 by applying the market model to returns of the University of Chicago's Center forResearch in Security Prices (CRSP). CRSP calculates the raw return as:

Rit = (Pt + Dt)/Pt-1 - 1,

where Rit = the rate of return on security i for event day t,Pt = last sale price or closing bid/ask average on day tDt = cash adjustment for day t, andPt-1 = last sale price or closing bid/ask average at time of last available price less than t, and

The intercept and beta resulting from application of the market model are then used long withthe actual market movement over an examination period to derive an expected return. The predictionerror (PE) for each acquirer is measured as:

PEit = Rit – (ai – biRmt), (1)

where Rmt = the rate of return on the S&P 500 index on event day t, andai, bi = ordinary-least-squares estimates of the intercept and slope of the market model regression

from the estimation period.

The mean of prediction errors for all acquirers that announced the use of pooling is derivedfor each day within the examination period. The same process is used to derive the mean predictionerrors for acquirers that announced the use of the purchase method. The primary focus of theexamination window is on the two-day window (t-1, t), in which day t serves as the announcementdate. Because wire services may have reported the news of the acquisition before stock tradingclosed on the day prior to the announcement, day t-1 is also assessed in order to determine if themarket response (if any) could have occurred on this day.

Test for significance are based on Mikkelson and Partch’s (1988) Z-statistic, which for eachday is calculated as:

Zt = 3 SPEit/N½, (2)

where SPEit = ARit/Sit, and Sit is calculated according to Mikkelson and Partch (1988).

Two day-interval prediction errors are tested for significance using the following Z-statistic:

Zt = 1/N * 3 ASCARi, (3)

where ASCARi is the standardized cumulative abnormal return defined by Mikkelson and Partch (1988). Thedenominator required for this calculation is the variance of the cumulative prediction error of firm i.

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RESULTS

Stock Price Reaction in the Announcement Period

Results for various intervals are presented in Table 3. As mentioned above, the two-day (t= -1, 0) prediction errors are of primary interest. The mean two-day PE for acquisitions in whichpooling was announced is -0.49% (Z = -7.54). The mean PE for purchase acquisitions is 0.31% (Z= 1.27). A Z-test was applied to test for a significant difference in the two sub-samples for thecumulative abnormal returns over the [-1, 0] interval. The difference of 0.80% is significant witha Z-statistic of 3.39 (p = 0.00069). The results support HA and suggest that the market respondsmore favorably to acquisition announcements involving the purchase method of accounting. Theresults are attributed to the greater restructuring flexibility and the indirect tax benefits afforded bypurchase accounting.

Table 3: Summary of CARs Over Various Intervals for Pooling vs. Purchase Methods of Accounting for anAcquisition (Test-Statistic in Parentheses)

Interval CAR of Acquisitions Using thePooling Methoda

CAR of Acquisitions Using thePurchase Methodb

Test for Difference

[-5,+5] -1.57% (-3.60)*** 3.31% (2.34)** 4.88% (1.15)

[-3,0] -0.62% (-4.19)*** 2.24% (2.82)*** 2.86% (2.26)**

[-1,0] -0.49% (-7.54)*** 0.31% (1.27) 0.80% (3.39)***

[0,180] -15.97% (-5.09)*** -2.42% (-0.40) 13.55% (2.12)**

[0,360] -35.81% (-5.60)*** -17.26% (-1.82)* 18.55% (1.62)*

* Significant at the 10% level** Significant at the 5% level*** Significant at the 1% levela The number of pooling firms utilized over the five intervals are 146, 146, 146, 108, and 87, respectively.b The number of purchase firms utilized over the five intervals are 46, 46, 46, 37, and 27, respectively.

Long-Term Effects

Valuation effects are also measured over an extended period in order to estimate long-termperformance following acquisitions. Unfortunately, in order to retain a relatively large sample, theinitial sample was screened to retain only those firms with returns data available for up to five daysfollowing the acquisition announcement. Consequently, investigation of long-term valuation effectsresults in additional sample attrition due to missing returns data. As shown in Table 3, 108 poolingfirms and 37 purchase firms had available data for intervals [0, 180]. This number drops to 37pooling and 27 purchase firms with available data for intervals [0, 360].

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The results summarized in Table 3 show that regardless of whether pooling or purchaseaccounting is used, acquirers suffer large negative returns in the year following the acquisitions.Pooling firms experience significant negative abnormal returns of 35.81% over the 360-day periodafter the merger (t = -1.82; p < 0.0005); firms announcing the use of purchase accounting experiencesignificant abnormal returns of 17.26% over the same time period (t = -1.82; p = 0.0808). Thedifference (18.55%) in these negative returns is marginally significant (t = 1.62; p = 0.1114). Thus,even when assessing extended periods, acquisitions using the purchase method are associated withrelatively more favorable valuation effects than those acquirers using pooling.

Hong et al. (1978) found that purchase firms maintained their positive cumulative abnormalreturns for a period of eight months following the announcement. While the negative abnormalreturns of -2.42% experienced by purchase firms in our study are not significant (t = -0.40; p =0.6894), the returns of pooling firms (-15.97%) are highly significant (t m= -5.09; p < 0.00005), withthe 13.55% difference in returns between the two sub-samples being significant at the 5% level (t= 2.12; p = 0.0361).

Cross-Sectional Analysis

Since the two sub-samples may have distinct characteristics, a complimentary cross-sectionalanalysis is conducted to control for these while testing whether valuation effects are related to thetype of accounting method announced. Previous research has identified the following variables forwhich the accounting method may be proxying: Price: Earnings (P/E) ratio, size, market modelparameter estimates, earnings surprise, and leverage. (See, among others, Basu (1983); Banz (1981);Beaver, Clark & Wright (1979); Bernard (1979); Elgers & Clark (1979); Hagerman & Shah (1984);and Reinganum (1981).) Each of these variables is discussed below.

Price/Earnings Ratio

In an efficient capital market, all available information is rapidly reflected in security prices.However, previous studies have found that the efficient market hypothesis may be violated in certaincircumstances. For example, Basu (1977; 1983) found that low P/E ratios may be an indicator offuture stock price performance. Low P/E ratios may lead to exaggerated investor optimism regardingfuture growth in earnings and dividends, thereby leading to higher future returns for low P/E stocks.Because the two sub-samples in our study may exhibit different P/E ratios, this ratio was includedas a control variable.

Size

In addition to proxying for a firm's P/E ratio, the abnormal negative returns found in ourstudy may be attributable to firm size rather than to the accounting method used. Prior researchindicates that there may be a 'size effect' - i.e., small firms' stocks experience, on average, higherrisk-adjusted returns than large firms' stocks. (For a more detailed discussion, see Banz (1981) andReinganum (1981).) This finding may be due to the higher risk associated with small firms, which,

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in turn, may be a result of the limited information available about small firms. A size variable wasincluded in our cross-sectional analysis to control for a potential 'size effect'.

Size may also be proxying for an income-reducing policy. For example, Watts &Zimmerman (1978) believe that larger firms tend to lobby for accounting standards that reducereported income. Consequently, the size variable included here may be proxying for an incomedeflating policy rather than for the accounting method itself. It follows that since the purchase firmsin our sub-sample are, on average, smaller than those in the pooling sub-sample, the pooling firmswould be expected to pursue an income reducing policy to a greater extent than the purchase firms.Therefore, choosing the pooling method of acquisition accounting may have negative valuationconsequences for these firms inasmuch as pooling will result in higher future earnings relative topurchase accounting.

Market Model Parameter Estimates

Elgers & Clark (1980) found important differences in risk effects across merger types. Morespecifically, conglomerate (as opposed to smaller) mergers are often undertaken due to a riskmotive. This difference is somewhat puzzling in light of the capital asset pricing model (CAPM).Unless investors are constrained from achieving the same risk diversification effects by revisingtheir own portfolios, there should be no economic reward from mergers undertaken to create riskshifts. In order to control for risk differences as well as for a disproportionate ratio of conglomeratesin one of our samples, the beta of the estimation period is included in the cross-sectional analysis.

Furthermore, Blume (1971; 1975; 1979) suggests that market model parameter estimates maybe non-stationary over time, viz. that the market model parameter are mean-reverting over time. Inother words, any abnormal returns calculated using the market model parameter estimates of anestimation period could be attributable to the parameter estimates themselves if the parameters arenon-constant. (For example, non-constant parameters could occur if the target's risk level is differentfrom the acquirer's risk level inasmuch s the acquirer's operations include the target's operationsfollowing the acquisition.) To control for this possibility, our estimated alpha parameter is alsoincluded in the cross-sectional analysis.

Earnings Surprise

As noted previously, accounting research indicates that positive earnings forecast errors areassociated with positive stock returns and, conversely, for negative earnings forecast errors. To theextent that the announced method of acquisition accounting (or the acquisition itself) wasanticipated, future earnings may substantially differ from forecasted earnings. Therefore, in orderto test whether the abnormal returns associated with the announcement of purchase and poolingmethods as opposed to other variables for which the accounting method may be proxying, it is alsonecessary to control for earnings surprises in a cross-sectional analysis. It is important to note thatthis variable directly accomplishes the purpose of testing the null hypothesis (H0) as the relativelylow future earnings of firms using the purchase method are the primary reason for expecting higher

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abnormal returns for firms using the pooling method. Thus, if this variable is insignificant inexplaining the abnormal returns, H0 is rejected.

Leverage

Davis (1990) found that purchase firms have significantly higher leverage than pooling firms.This finding may be explained by Crawford (1986), who finds that firms using the purchase methodare more likely than pooling firms to have debt covenants based on assets or intangible assets ratherthan on earnings. In this context, the higher abnormal returns experienced by purchase firms maybe attributable to the higher leverage of these firms rather than to the accounting method itself. Tocontrol for this, leverage is included as a control variable in this cross-sectional analysis.

Accounting Method

To directly test whether the abnormal returns are significantly related to the accountingmethod employed as opposed to any of the control variables described above, the cross-sectionalanalysis employs an indicator variable to identify the accounting method used. If the coefficient ofthis variable is significant after controlling for the other variables, then it is likely that the higherabnormal returns of purchase method firms are due to the higher organizational flexibility andindirect tax cash flows which the purchase method offers.

Cross-Sectional Model

The resulting cross-sectional model was applied to both the purchase and poolingsub-samples and is stated below:

ASCARi = a0 + a1ALPHAi + a2BETAi + a3EARSURi + a4LEVi + a5PEi + a6SIZEi + a7POOLi + ei, (4)

whereASCARi = the cumulative standardized [-1,0] prediction error for firm i,ALPHAi , BETAi = the market model parameter estimates for firm i,EARSURi = earnings surprise for firm i, defined as the change in EPS in the announcement year

divided by share price at the beginning of the announcement year; thus, the surpriserepresents the abnormal earnings based on a naïve random walk earnings forecastmodel,

LEVi = leverage of firm i, defined as the ratio of total liabilities to total assets,Pei = the price/earnings ratio of firm i, defined at the end of the year prior to the

announcement year,SIZEi = firm i’s total assets,POOLi = a dummy variable equal to unity if firm i uses the pooling method and zero

otherwise, andei = error term.

Correlations of Variables Used in the Cross-Sectional Model

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The correlation coefficients for all pairs of independent variables are disclosed in Table 4.The ALPHA, BETA, LEV, and SIZE exhibit some degree of correlation. Consequently, thesignificance tests for these variables may be biased. However, the maximum variance inflation factor(VIF) of the full model is only 1.4, which, according to Neter, Kutner, Nachtsheim & Wasserman(1996) does not present a serious multi-collinearity problem. Variance inflation factors for theindividual variables measure how much the variances of the estimated regression coefficients areinflated as compared to when the predictor variables are not linearly related. However, in order toaccount for potential multi-collinearity effects, our original model is supplemented with threereduced models.

Table 4: Correlation Coefficients of Independent Variablesa

ALPHA BETA EARSUR POOL LEV PE

BETA 0.4448

EARSUR 0.1471 -0.0201

POOL 0.1527 0.1839 -0.0889

LEV -0.2034 -0.3084 0.1733 0.0544

PE 0.0539 -0.0506 -0.0573 0.1409 0.02

SIZE -0.1863 0.017 0.0178 0.0772 0.3133 0.0032a ALPHAi, BETAi = the market model parameter estimates for firm i,EARSURi = earnings surprise for firm i, defined as the change in EPS in the announcement year divided by shareprice at the beginning of the announcement year.POOLi = a dummy variable equal to unity if firm i uses the pooling method and zero otherwise.LEVi = leverage of firm i, defined as the ratio of total liabilities to total assets,PEi = the price/earnings ratio of firm i, defined at the end of the year prior to the announcement year,SIZEi = firm i’s total assets

Cross-Sectional Results

Results from the cross-sectional analysis are summarized in Table 5. The first row presentsthe results of the full model. Model 2 excluded the PE variable due to its extreme insignificance (p= 0.9693). Models 3 and 4 additionally exclude variables BETA and SIZE to counteract anypotential multi-collinearity problems. Once a variable was omitted, it did not re-enter thecross-sectional model.

The POOL variable is of primary importance in this analysis. If this variable exhibits asignificant coefficient, the accounting method itself explains a portion of the distribution ofabnormal returns associated with the acquisition announcement. As shown in Table 5, the POOLvariable is significant in all models examined, with a maximum p-value of 0.0314. Note that the sizeof the coefficient of this variable is very close to the difference over the [-1, 0] interval found in

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stock price reaction summarized in Table 3. (While the Z-statistic used in our original analysis wascomputed using standardized values (in accordance with Mikkelson & Partch (1988)), the 0.08%difference shown in Table 3 was computed using the raw cumulative abnormal returns.Consequently, it is possible for the coefficient in the cross-sectional model to be larger than thisdifference because the standardized cumulative abnormal returns are used as the dependent variablein this cross-sectional analysis.) These findings lend support to the results in the previous section.Apparently, in addition to any indirect tax benefits that may accrue under purchase accounting,market participants highly value that purchase method firms are not constrained by the samerequirements set out in APB Opinion No. 16 for pools.

Table 5: Cross-Sectional Analysis for Valuation Effects of Pooling and Purchase Acquisitions(t-Statistic Parentheses)a

Model Intercept ALPHA BETA EARSUR LEV PE SIZE INDIC Adj. R2 F

Full Model(N = 119)

-0.836(-0.93)

409.94(2.23)**

-0.537(-1.27)

-4.179(-1.48)

2.904(2.06)**

-0.000091(-0.04)

-0.000096(-1.89)*

-0.893(-1.68)*

7.40% 2.35**

Model 2(N = 119)

-0.834(-0.93)

409.34(2.25)**

-0.535(-1.27)

-4.172(-1.49)

2.904(2.07)**

--- -0.000096(-1.90)*

-0.896(-1.71)*

8.22% 2.76**

Model 3(N = 119)

-1.402(-1.81)*

314.30(1.89)*

--- -4.079(-1.45)

3.429(2.55)**

--- -0.000108(-2.17)**

-0.989(-1.90)*

7.72% 2.97**

Model 4(N = 119)

-1.243(-1.58)

365.382(2.18)**

--- -4.064(-1.42)

2.631(2.00)**

--- --- -1.087(-2.06)**

4.71% 2.46**

* Significant at the 10% level** Significant at the 5% levela ALPHAi, BETAi = the market model parameter estimates for firm i,EARSURi = earnings surprise for firm i, defined as the change in EPS in the announcement year divided by shareprice at the beginning of the announcement year.POOLi = a dummy variable equal to unity if firm i uses the pooling method and zero otherwise.LEVi = leverage of firm i, defined as the ratio of total liabilities to total assets,PEi = the price/earnings ratio of firm i, defined at the end of the year prior to the announcement year,SIZEi = firm i’s total assets

Three other variables - the market model parameter (ALPHA), the firm's leverage (LEV),and firm size (SIZE) - are significantly related to the [-1, 0] standardized cumulative abnormalreturns. The significance of ALPHA suggests that the market model parameters may be non-constantover time as discussed by Blume (1979). The significance of leverage is similar to the findings byDavis (1990). In other words, purchase firms tend to exhibit higher leverage than pooling firms,lending further support to Crawford's (1986) notion that firms using the purchase method may have

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debt covenants based on net assets or intangibles rather than on income. (The coefficient of thisvariable is highly positive. When this variable is examined separately for the two sub-samples, it issignificant for the purchase group but not for the pooling firms. The difference (see Model 4) ishighly significant (t = 2.39; p = 0.0183).) Consequently, even though their future income is expectedto be relatively low relative to that reported by pooling firms, purchase firms are more likely toemploy debt in their capital structure.

The last significant variable in the cross-sectional analysis is firm size as measured by totalassets. There are two possible explanations for the negative coefficient of this variable. First, largerfirms seem to experience lower abnormal returns than smaller firms, lending support to the sizeeffect (i.e., smaller firms exhibit higher returns than larger firms regardless of the time periodexamined) documented in the previous literature. Consequently, the large returns for relatively smallacquirers may be driven by the size anomaly.

On the other hand, since pooling firms are larger than purchase firms, previous literaturesuggests that they would pursue an income reducing policy. However, as we have seen, the poolingmethod will generally result in the reporting of higher earnings than would be the case underpurchase accounting causing market participants to interpret the use of pooling as suboptimal. Giventhe contradictory results, it is reassuring that the difference in size between the two sub-samples isnot significant (t = 0.61; p = 0.5446). (Model 3 was used to test for this difference because the SIZEvariable is omitted in Model 4.) Thus, it appears that the significance of the SIZE variable isattributable to a general size effect rather than to pooling firms not pursuing an income reducingpolicy.

SUMMARY AND CONCLUSION

The purchase method of accounting can reduce future reported earnings as a consequenceof increased expenses for amortization of goodwill and depreciation of tangible assets. However,as a trade-off, this method of accounting for acquisitions allows more restructuring flexibility thandoes the pooling method and can also provide greater indirect tax benefits. Given these conflictingadvantages of the two methods, the objective of this study is to determine whether and how thevaluation effects of acquisition announcements are conditioned on the type of accounting methodemployed. The sample used is pure inasmuch as it focuses solely on acquisitions in which themethod of accounting was disclosed at the same time as the acquisition announcement, eliminatingthe need to assume that market participants have other information except what was contained inthe acquisition announcement itself.

Valuation effects are found to be more favorable for acquisitions using the purchase methodin the eleven-day period surrounding the announcement and for at least six months following theannouncements. These results stand even after conducting a cross-sectional analysis that controlsfor the firms' price/earnings ratio, size, market parameter estimates, earnings surprises, and leverage.

These results suggest that market participants value the added flexibility and indirect taxbenefits that are provided by the purchase method of accounting as opposed to the higher reportedfuture earnings associated with the pooling method.

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REFERENCES

APB Opinion No. 16, Business Combinations.

Ball, R. & P. Brown (1968). An Empirical Evaluation of Accounting Income Numbers. Journal ofAccounting Research, Autumn, 159-178.

Banz, R. W. (1981). The Relationship Between Return and Market Value of Common Stocks.Journal of Financial Economics, March, 3-18.

Basu, S. (1977). Investment Performance of Common Stocks in Relation to their Price-EarningsRatios: A Test for Market Efficiency. Journal of Finance, June, 663-682.

Basu, S. (1983). The Relationship Between Earnings Yield, Market Value and Return for NYSECommon Stocks. Journal of Financial Economics, June, 129-156.

Beaver, W. H., R. Clarke & W. F. Wright (1979). The Association Between Unsystematic SecurityReturns and the Magnitude of Earnings Forecast Errors. Journal of Accounting Research,Autumn, 316-321.

Bernard, V. L. (1986). Unanticipated Inflation and the Value of the Firm. Journal of FinancialEconomics, March, 285-321.

Bittker, B. I. & J. S. Eustice (1994). Federal Taxation of Corporations and Shareholders, 6th

Edition. Boston: Federal Tax Press.

Blume, M. E. (1971). On the Assessment of Risk. Journal of Finance, March, 1-11.

Blume, M. E. (1975). Betas and Their Regression Tendencies. Journal of Finance, June, 785-795.

Blume, M. E. (1979). Betas and Their Regression Tendencies: Some Further Evidence. Journal ofFinance, March, 265-267.

Davis, M. L. (1990). Differential Market Reaction to Pooling and Purchase Methods. TheAccounting Review, July, 696-709.

Davis, M. L. (1991). APB 16: Time to Reconsider. Journal of Accountancy, October, 99-107.

Elgers, P. T. & J. J. Clark (1980). Merger Types and Shareholder Returns: Additional Evidence.Financial Management, Summer, 66-72.

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FASB (1999). Exposure Draft of Statement of Financial Accounting Standard: BusinessCombinations. Stamford, CT: FASB.

Gonedes, N. J. (1975). Risk, Information and the Effects of Special Accounting Items on CapitalMarket Equilibrium. Journal of Accounting Research, Autumn, 220-256.

Hagerman, R. L. & P. Shah (1984). The Association Between the Magnitude of Quarterly EarningsForecast Errors and Risk-Adjusted Stock Returns. Journal of Accounting Research, Autumn,526-540.

Hagerman, R. L. & M. E. Zmijewski (1979). Some Economic Determinants of Accounting PolicyChoice. Journal of Accounting and Economics, August, 141-161.

Haw, I. M., K. Jung & W. Ruland (1994). The Accuracy of Financial Analysts' Forecasts AfterMergers. Journal of Accounting, Auditing, and Finance, Summer, 465-486.

Herring, C. B. & F. Norris (1990), Merger Mania. The National Public Accountant, June, 38-42.

Herz, R. H. & E. J. Abahoonie (1990). Innovations to Minimize Acquisition Goodwill. Mergers &Acquisitions, March/April, 35-40.

Hong, H, R. S. Kaplan & G. Mandelker (1978). Pooling vs. Purchase: The Effects of Accountingfor Mergers on Stock Prices. The Accounting Review, January, 31-47.

Hoskin, R. E., J. Hughes & W. Ricks (1986). Evidence on the Incremental Information Content ofAdditional Firm Disclosures Made Concurrently With Earnings. Journal of AccountingResearch, Supplement, 1-32.

Huang, Y. & R. A. Walking (1987). Target Abnormal Returns Associated With AcquisitionAnnouncements: Payment, Acquisition Form, and Managerial Resistance. Journal ofFinancial Economics, December, 329-349.

Mikkelson, W. H. & M. M. Partch (1986). Valuation Effects of Security Offerings and the IssuanceProcess. Journal of Financial Economics, January/February, 31-60.

Mikkelson, W. H. & M. M. Partch (1988). Withdrawn Security Offerings. Journal of Financial andQuantitative Analysis, June, 119-133.

Nathan, K. & K. M. Dunne (1991). The Purchase-Pooling Choice: Some Explanatory Variables.Journal of Accounting and Public Policy, Winter, 309-323.

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Neter, J., M. H. Kutner, C. J. Nachtsheim & W. Wasserman (1996). Applied Linear StatisticalModels, 4th Edition. Chicago: Richard D. Irwin, Inc.

Reinganum, M. R. (1981). Misspecification of Capital Asset Pricing: Empirical Anomalies Basedon Earnings' Yields and Market Values. Journal of Financial Economics, March, 19-46.

Robinson, J. R. & P. B. Shane (1990). Acquisition Accounting Method and Bid Premia for TargetFirms. The Accounting Review, January, 25-48.

Watts, R. L. & J. L. Zimmerman (1978). Towards a Positive Theory of the Determination ofAccounting Standards. The Accounting Review, January, 112-134.

Zmijewski, M. E. & R. L. Hagerman (1981). An Income Strategy Approach to the Positive Theoryof Accounting Standard Setting/Choice. Journal of Accounting and Economics, August, 129-149.

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APPENDIX: TWELVE CONDITIONS FOR USE OF POOLING (AS PER APB OPINION NO. 16)

1 Each of the combining companies is autonomous and has not been a subsidiary or division of anothercorporation within two years before the combination is initiated.

2 Each of the combining companies is independent of other combining companies. While joint ventures arepermissible, independence is generally interpreted as 10% or less ownership of voting stock at any timebetween the initiation and consummation date of the combination.

3 The combination is effected in a single transaction or is completed in accordance with a specific plan withinone year after the plan is initiated.

4 The acquiring company offers and issues only common stock with rights identical to those of the majority ofits outstanding common stock in exchange for substantially all of the voting stock of the acquired company."Substantially all" means at least 90% of the shares outstanding and is measured as of the date ofconsummation.

5 Within the period beginning two years before the plan's initiation and ending at the date of consummation, noneof the combining companies change the equity interest of voting common stock in contemplation of effectingthe combination.

6 Each of the combining companies reacquires voting common stock only for purposes other than businesscombinations and no company reacquires more than a normal number of shares between the plan's initiationand combination.

7 The proportionate share of ownership of each shareholder remains the same after the combination as it wasbefore the combination.

8 The voting rights to which the common stock ownership interests in the resulting corporation are not restricted.9 The combination is resolved at the date of the plan's consummation and no provisions of the plan relating to

the issuance of securities or other compensation are pending.10 The combined company does not agree to retire or reacquire, directly or indirectly, all or part of the common

stock issued to effect the combination.11 The combined company does not enter into other financial arrangements for the benefits of former stockholders

of the combining company (e.g. guaranty of loan secured by the stock of the combined company) which ineffect negates the exchange of equity securities.

12 The combined company does not intend or plan to dispose of a significant part of its assets within two yearsafter the combination. Disposals in the ordinary course of business and to eliminate duplicate facilities or excesscapacity are permissible.

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LEASE FINANCIAL STATEMENT ACCOUNTINGPRACTICES TYPES AND NUMBERS FOR HONG KONG

Gary A. Miller, Texas A&M International University

ABSTRACT

The purpose of this research study was to investigate existing lease disclosure practices forfinancial statements in Hong Kong. Part of the purpose of this project is descriptive. The studyclassified, summarized and analyzed the lease accounting practices for a sample of Hong Kongcompanies.

Annual reports for a year ending during 1996 were examined to gather information aboutthe accounting practices for leases for financial reporting in Hong Kong. The fifty companies wereselected in a systematic random selection process. The fifty companies selected representapproximately nine percent of all the companies traded on the Hong Kong Stock Exchange for 1996.

Some of the major conclusions from this study include the following:

1 Most Hong Kong companies are involved in lease transactions as either lessees or lessorsor both.

2 The dollars committed to lease transactions are significant.3 The profit and loss effect is, also, significant.

INTRODUCTION

International leasing markets are rapidly becoming a single global market. Salameh, GeneralManager for Hewlett Packard for Asia Pacific, Central and Eastern Europe, has recently stated thatglobalization of financing services is driven by natural globalization of accounts and partners(Salameh, 1997). Leasing activities have become "big" business. Leasing volume in the AsiaPacific area was approximately US $111 billion in 1995. Total worldwide activity exceeded US$409 billion in the same year. Four of the twenty-two largest leasing companies in the world werelocated in Asia in 1995. Orix Corporation in Asia was the third largest with leasing volume greaterthan US $8.9 billion. Leasing is one of the fastest growing segments of the US financial servicesindustry. The US $147 billion in new capital leases written in 1995 was a significant 5.3% increaseover the 1994 total (McConville, 1996).

Most large institutional users are not satisfied with the existing levels of general disclosuresby multinational firms (Taylor, 1995). This study will examine the lease accounting practicesincluding footnote disclosures in Hong Kong. The accounting treatment for leases can be consideredpart of the general issue, the controversial use of off-balance sheet activities. The flexibility allowedcompanies under the existing general rules for leases may allow different companies to treat similarevents in different ways. This inconsistent treatment may make financial comparisons very difficult.For example, certain financial measures such as solvency and debt ratios may be effected. Some

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companies may interpret accounting events in such a manner that the events are not reflected on thecompany's balance sheet. In some cases, information about an event may be disclosed in a footnotedepending on certain criteria. Related accounting areas including contingencies may be effected bythe procedures for leases.

The treatment for leases has been particularly controversial. Some leases, if the lease meetscertain rules, are classified as finance leases. In that case, an asset and liability would be includedin the lessee's balance sheet. If the lease is classified as an operating lease instead of a finance lease,then only a lease or rental charge reduces the profit and loss and no balance sheet accounts areeffected. Some Hong Kong companies disclose information about leases only in a footnote and,therefore, these transactions can be classified as off-balance sheet financing. A discussion paper,Accounting for a New Approach, has been published by the respective accounting standards boardin Australia, Canada, New Zealand, the UK, US, and the IASC proposes that all finance leases andmost, if not all, operating leases would qualify as assets and liabilities (McGregor, 1996).

In Hong Kong, different groups are concerned about lease accounting practices. Manycompanies have been involved as lessees in Hong Kong. For example, Cathay Pacific has used amixture of UK, US, German, French, Hong Kong and Swedish leases to finance its' aircraft fleet.Cha, Securities and Futures Commission (SFC) executive director, has stated it is important to adoptinternational standards as part of the SFC and stock exchange's commitment to maintaining HongKong's competitiveness (Ibison, 1995).

Based on the latest survey of members of The Hong Kong Equipment Leasing Association(HKELA), members' leased equipment assets in China amounted to US $386 million. There arethirty-six full and eleven associate members in the HKELA. Lessors do face some difficultiesconducting leasing business in China because the finance lease has not been given substantialattention by the Chinese Government. Predictions are that the China market will continue to growin the future.

Review of Leasing In Selected Asian Countries

Other Asian countries have substantial leasing industries. For example, Indonesia had 254leasing companies in 1995 that had total lease contracts of $8,498,020 (million Rp). The averagelease contract per company was $33,456 million Rp (Indonesian Ministry of Finance). The SpecialAccounting Standard for Lease Accounting, No. 30, that provides the guidance to classify leases wasissued in 1990.

In Malaysia, only 53 companies were classified as pure leasing companies. In recent years,more companies are using hire purchase contracts for the financing of assets. Leasing accounts foronly 2% to 5% of total gross capital formation in Malaysia. In 1995, new leasing business totaledRM 2,007 million.

In the Philippines, only companies registered under Republic Act 5980 are allowed toconduct leasing activities. As of March 1997, there were 167 registered finance companies with 27actively offering financial leasing services. Only one company is, solely, engaged in leasing. Theleasing industry is not fully developed in the Philippines.

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In India, there are over 3000 companies that are involved in some type of leasing activity.The 500 largest leasing companies contribute approximately Rs. 9000 crones worth of equipmentleasing. However, this accounts for only 5% of the fixed asset acquisition for the industrial sectorin the country. Internationally the average percentage is approximately 22%. The most popularusers of lease financing have been medium and small-scale companies.

Lease Practices

Accounting guidance for lease contracts is provided by HKSSAP No. 14, Accounting forLeases and Hire Purchase Contracts, that was issued in August 1988 by the HKSA. The definitionof a finance lease is a lease that transfers substantially all the risks and rewards of ownership of anasset to the lessee. An operating lease is a lease other than a finance lease (HKSA, 1988).

Criteria such as length of useful life and the existence of a bargain purchase are provided todetermine if a lease should be classified as a finance or operating lease. Many companies includean explanation in a footnote. The actual narratives vary somewhat, but the basic description is thesame.

In a hire purchase, the customer generally becomes the legal owner of the asset assuming theterms of the original agreement are met by the lessee. Disclosure requirements are, also, includedin the standard.

Some have criticized the existing lease accounting practices. The IASC had been requestedto examine some of the lease reporting issues. However, recently, the International AccountingStandards Committee (IASC) decided not to instigate a major project related to lease accounting.At present, the UK and Australia have completely different views, but both believe changes needto be made. The US seems to fall between the two (Accountancy, 1997). Others, also, haveexpressed concerns about lease accounting reporting practices. McGregor, executive director of theAustralian Research Foundation, has stated existing lease accounting standards are deficient(McGregor, 1996). He believes a discussion paper, Accounting for a New Approach, published bythe respective accounting standards board in Australia, Canada, New Zealand, the UK, US and theIASC could solve some of the problems. Based on the proposed practices, all finance leases andmost, if not all, operating leases would qualify for recognition as assets and liabilities (McGregor,1996). At the present time, the rules related to the classification of finance and operating leases canin many cases be circumvented if desired. In most cases, companies would prefer to have a leaseclassified as an operating lease instead of a capital or finance lease.

PURPOSE

The purpose of this research study was to investigate existing lease disclosure practices forfinancial statements in Hong Kong. Part of the purpose of this project is descriptive. The studyclassified, summarized and analyzed the lease accounting practices for a sample of Hong Kongcompanies. In a recent study, Barth and Murphy (1994) developed a framework to analyze therequired footnotes including lease disclosures for companies in the United States. Another studyuses a similar approach to examine the situation in Hong Kong, also, including lease disclosures

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(Miller, 1997). Lease disclosures met two main purposes, disaggregation of unrecognized items andprovided information on future cash inflows and outflows. In the next section, the methodology forthis study is described.

METHODOLOGY

Annual reports for a year ending during 1996 were examined to gather information about theaccounting practices for leases for financial reporting in Hong Kong. The fifty companies wereselected in a systematic random selection process. The fifty companies selected representapproximately nine percent of all the companies traded on the Hong Kong Stock Exchange. SeeTable 1 for list of companies included in the study.

Table 1: LeasesNumber Company Year Type

1 Acme Landis Holdings Dec-96 22 Allan International Holdings Mar-96 43 Bossini International Holdings Mar-96 24 Burwill Holdings Jun-96 45 Chaifa Holdings Mar-96 36 China Investments Holdings Dec-96 27 Cheerful Holdings Dec-96 28 Dynamic Holdings Jun-96 29 Daido Concrete Apr-96 3

10 East Asiatic Company (HK) Dec-96 211 Esprit Asia Holdings Jun-96 112 First Sign International Holdings Jun-96 213 Fairyoung Holdings Dec-96 214 Great Eagle Holdings Sep-96 215 Goldlion Holdings Mar-96 216 Harbour Centre Development Dec-96 217 Hong Kong Ferry (Holdings) Dec-96 218 Indesen Industries Mar-97 219 IDT International Mar-96 120 Jusco Stores (HK) Feb-96 121 Johnson Electric Holdings Mar-96 522 Kwong Sang Hong International Dec-96 223 Kingfook Holdings Mar-96 124 Le Saunda Holdings Feb-96 2

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Table 1: LeasesNumber Company Year Type


25 Lane Crawford International Mar-96 226 Midland Realty (Holdings) Dec-96 227 Min Xin Holdings Dec-96 228 Ngai Hing Hong Jun-96 129 Nority International Group Dec-96 230 Oriental Press Group Mar-96 231 Orient Power Holdings Dec-96 432 Pacific Concord Holdings Dec-96 233 Peregrine Investment Holdings Dec-96 234 QPL International Holdings Apr-96 135 Rivera (Holdings) Mar-96 236 RPJ Electronics Jun-96 137 S.Megga International Holdings Jun-96 438 Same Time Holdings Mar-96 139 Tem Fat Hing Fung (Holdings) Apr-96 240 Topstyle International Holdings Mar-96 141 USI Holdings Dec-96 242 Universal Appliances Dec-96 343 Varitronix International Dec-96 244 Vanda Systems & Communications Mar-96 345 Wang On Group Mar-96 346 Wong's Kong King International (Holdings) Dec-96 447 Yanion International Holdings Dec-96 148 Yaohan HK Mar-96 149 Yeebo International (Holdings) Mar-96 150 YGM Trading Mar-96 1

RESULTS

All fifty companies had, at least, one example of a finance or operating lease. Forconvenience, companies were classified into five groups.

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Type 1

Type 1 companies were involved as both lessees and lessors. As a lessee, these companieshad both finance and operating leases. As a lessor, these companies had only operating leases.Thirteen companies were classified as Type 1 companies (See Table 2 for list)

Table 2: Type 1

No. Company Lessee Profit & Loss Account Lessor Profit & Loss Account

Finance Lease OperatingLease

Operating Lease

Depreciation Interest Rentals Gross RentalIncome

Ongoings Net RentalIncome

1 Esprit Asia Holdings 1011 230 408673 N/A N/A N/A

2 IDT International 1240 193 14188 N/A N/A N/A

3 Jusco Stores (HK) 3849 715 109004 76394 0 76394

4 Kingfook Holdings 486 92 50880 3276 0 3276

5 Ngai Hing Hong 2845 984 1325 550 0 550

6 QPL InternationalHoldings

36550 18645 15379 3197 -350 2847

7 RPJ Electronics 1170 257 4012 750 -194 556

8 Same Time Holdings 1101 1404 2442 N/A N/A N/A

9 TopstyleInternationalHoldings

20 10 4804 4461 -253 4208

10 Yanion InternationalHoldings

1541 416 11008 N/A N/A N/A

11 Yaohan HK 29618 5726 417561 20216 0 20216

12 Yeebo International(Holdings)

1050 436 3729 N/A N/A N/A

13 YGM Trading 700 603 127248 7534 -837 6697

Average 6245 2285 90019 14547 -204 14343

All figures shown above are in HK thousand dollars.

Depreciation for the lessee finance leases, on average, was HK $6.3 million for the thirteencompanies with a low of HK $20,000 to a high of HK $36.6 million. Interest expense for the financeleases was HK $2.3 million on average. All amounts are denominated in Hong Kong dollars. Theoperating lessee lease rental was an average $90 million. For the lessor transactions, the thirteencompanies had an average net rental income of $14.3 million. See Table 2 for additional details.

For the lessee operating leases, details about the obligations under finance or hire purchasecontracts is grouped during the following time categories, 1 year, 1-2 years, 2-5 years, current

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liabilities and after 1 year in Table 3. Information about lessee operating lease commitments is, also,provided.

Table 3: Type 1

No Company Finance Lease Balance Sheet Operating Lease Balance Sheet

Obligations under Finance Leases & HirePurchase Contracts

Operating Leases Commitments

1 year 1-2 years 2-5 years CurrentLiability

after 1year

1 year 2-5 years > 5 years Total

1 Esprit Asia Holdings 1599 609 0 -1599 609 29673 152132 13497 195302

2 IDT International 3117 2589 0 -3117 2589 2114 8170 828 11112

3 Jusco Stores (HK) 2537 370 0 -2537 370 19756 55101 23925 98782

4 Kingfook Holdings 528 348 0 -528 348 7127 37518 0 44645

5 Ngai Hing Hong 4002 4180 483 -4002 4663 25 229 1288 1542

6 QPL International Holdings 101553 75275 110857 -320453 626149 10278 14191 0 24469

7 RPJ Electronics 415 33 0 -415 33 1048 0 707 1755

8 Same Time Holdings 3026 2359 0 -3026 2359 40 453 1635 2128

9 Topstyle International Holdings 19437 0 0 -19437 0 163 491 1608 2262

10 Yanion International Holdings 1159 777 0 -1159 777 546 2660 7298 10504

11 Yaohan HK 44198 72858 0 -44198 72858 1458 201907 247021 450386

12 Yeebo International (Holdings) 490 16 0 -490 16 908 1128 249 2285

13 YGM Trading 822 822 702 -822 1524 22652 74824 7569 105045

Average 14068 12325.85 8619 -30906 54792 7368 42216 23510 73094

All figures shown above are in HK thousand dollars.Note:For Obligations under Finance Leases & Hire Purchase Contracts1 year Amount payable within one year1-2 years Amount payable for more than one year, but not exceeding two years2-5 years Amount payable for more than two years, but not exceeding five yearsCurrent Liabilities Amount repayable within one year, shown under current liabilities after 1 year

Amount repayable after one yearFor Operating Lease Commitments1 year Annual commitments payable under non-cancellable operating leases which expire within one year in one

year2-5 years Annual commitments payable under non-cancellable operating leases which expire in second to the fifth

years inclusively> 5 years Annual commitments payable under non-cancellable operating leases which expire over five years

The average total operating lease commitments for the thirteen companies was $73.1 million.

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Type 2

Type 2 companies had only lessee and lessor operating leases. Twenty-six companies hadeither operating leases as a lessee or lessor. All companies had, at least, one lessee operating lease.Six companies had only lessee operating leases. See Table 4 for details such as rentals (lessee),Gross rental income, Ongoings and net rental income for lessors.

Table 4: Type 2

No. Company Lessee Profit &Loss Account

Operating Lease

Lessor Profit & Loss Account Operating Lease

Rentals Gross RentalIncome


1 Acme Landis Holdings 6668 816 0 816

2 Bossini International Holdings 230894 222 0 222

3 China Investments Holdings 3035 6925 -2112 4813

4 Cheerful Holdings 3975 N/A N/A N/A

5 Dynamic Holdings 1410 62024 -1584 60440

6 East Asiatic Company (HK) 52144 N/A N/A N/A

7 First Sign International Holdings 2009 N/A N/A N/A

8 Fairyoung Holdings 8275 1747 -1112 635

9 Great Eagle Holdings 2914 748 -25 723

10 Goldlion Holdings 12485 16877 -348 16529

11 Harbour Centre Development 140100 100300 -12600 87700

12 Hong Kong Ferry (Holdings) 18572 116422 -14042 102380

13 Indesen Industries 1058 N/A N/A N/A

14 Kwong Sang Hong International 1795 58633 -7965 50668

15 Le Saunda Holdings 142226 677 -116 561

16 Lane Crawford International 223100 45700 -10300 35400

17 Midland Realty (Holdings) 88202 932 0 932

18 Min Xin Holdings 357 3350 -295 3055

19 Nority International Group 4248 N/A N/A N/A

20 Oriental Press Group 1648 68879 -1237 67642

21 Pacific Concord Holdings 9506 76311 -262 76049

22 Peregrine Investment Holdings 78312 32863 -2876 29987

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Table 4: Type 2

No. Company Lessee Profit &Loss Account

Operating Lease

Lessor Profit & Loss Account Operating Lease

Rentals Gross RentalIncome



23 Rivera (Holdings) 201 2222 0 2222

24 Tem Fat Hing Fung (Holdings) 6505 4978 -339 4639

25 USI Holdings 7800 56600 -600 56000

26 Varitronix International 332 N/A N/A N/A

AVERAGE 40299 32861 -2791 30071


Table 5: Type 2

No. Company Operating Lease Balance Sheet Commitments


1 Acme Landis Holdings 5147 575 0 5722

2 Bossini International Holdings 76990 182832 0 259822

3 China Investments Holdings 0 2611 0 2611

4 Cheerful Holdings 45 4904 0 4949

5 Dynamic Holdings 866 0 0 866

6 East Asiatic Company (HK) 9017 28071 0 37088

7 First Sign International Holdings 220 1856 523 2599

8 Fairyoung Holdings 0 183 8093 8276

9 Great Eagle Holdings 449 1348 0 1797

10 Goldlion Holdings 11 6573 2822 9406

11 Harbour Centre Development 2400 44800 0 47200

12 Hong Kong Ferry (Holdings) 2565 1474 0 4039

13 Indesen Industries 695 900 0 1595

14 Kwong Sang Hong International 167 1707 0 1874

15 Le Saunda Holdings 33054 97018 0 130072

16 Lane Crawford International 12700 200900 10100 223700

17 Midland Realty (Holdings) 21269 88595 0 109864

18 Min Xin Holdings 124 88 0 212

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Table 5: Type 2

No. Company Operating Lease Balance Sheet Commitments



19 Nority International Group 180 2887 3156 6223

20 Oriental Press Group 0 5494 1001 6495

21 Pacific Concord Holdings 1804 7969 1520 11293

22 Peregrine Investment Holdings 30592 37599 6522 74713

23 Rivera (Holdings) 99 0 0 99

24 Tem Fat Hing Fung (Holdings) 558 4415 0 4973

25 USI Holdings 0 2900 800 3700

26 Varitronix International 0 485 0 485

Average 7652 27930 1328 36911

All figures shown above are in HK thousand dollars.Note:For Commitments1 year Annual commitments payable under non-cancellable operating leases which expire within one

year within one year2-5 years Annual commitments payable under non-cancellable operating leases which expire in second to

the fifth years inclusively> 5 years Annual commitments payable under non-cancellable operating leases which expire over five years

For lessee operating leases, the twenty-six companies had an average rental expensedisclosed in their respective profit and loss statement of $40.3 million for 1996. The average rentalincome for lessor operating leases was $30.0 million (See Table 4 for additional details). Footnotedisclosures information is provided in Table 5. The average total lease commitment was $36.9million for the twenty-six companies. The average commitments payable within one year was $7.7million.

Type 3

Type 3 companies were similar to Type 1 as these companies, also, had lessee finance andoperating leases and lessor operating leases. The profit and loss information that was provided suchas depreciation and interest for lessee finance leases was similar, but the format for the footnotedisclosures for the obligations under finance leases and hire purchase contracts was somewhatdifferent (See Table 6). For these companies, the average depreciation and interest for finance leaseswas $1.4 million and $870,000 respectively. Lessee operating lease rentals was an average of $6.3million. Lessor net rental income was $472,000.

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Table 6: Type 3

No Company LesseeProfit & Loss Account

LessorProfit & Loss Account

Operating LeaseFinance Lease OperatingLease



1 Chaifa Holdings 144 31 12276 N/A N/A 432

2 Daido Concrete 3507 2888 4029 N/A N/A N/A

3 Universal Appliances 2742 1313 9710 N/A N/A 584

4 Vanda Systems & Communications 102 5 4286 N/A N/A 399

5 Wang On Group 330 111 1125 N/A N/A N/A

Average 1365 870 6285 472


Table 7: Type 3

No. Company Finance Lease Balance SheetObligations under finance leases & hire purchase contracts

Operating Lease Balance SheetOperating Leases Commitments

1 year 2-5years

Min.LeasePmts

Finance Charge

NetTotal

Curr Liab

LTportion

1 year 2-5years

> 5years

Total

1 Chaifa Holdings 383 510 893 -118 775 -313 462 4495 5068 0 9563

2 Daido Concrete 19421 13417 32838 -2371 30467 -17942 12525 1539 2490 0 4029

3 UniversalAppliances

33650 50895 84545 -11206 73339 -26881 46458 3123 1864 1236 6223

4 Vanda Systems &Communications

123 158 281 -22 259 -114 145 1069 1066 64 2199

5 Wang On Group 211 319 530 -73 457 -169 288 451 1198 9548 11197

Average 10758 13060 23817 -2758 21059 -9084 11976 2135 2337 2169.6 6642

All figures shown above are in HK thousand dollars.For Obligations under Finance Leases & Hire Purchase Contracts1 year Amount payable within one year2-5 years Amount payable in two years to five years time inclusivelyMin. Lease Pmts Total minimum lease paymentsFinance Charges Future finance chargesNet Total Total net lease payablesCurr Liab Portion classified as current liabilitiesLT Portion Long term portion of lease payablesFor Operating Lease Commitments1 year Annual commitments payable under non-cancellable operating leases which expire within one year2-5 years Annual commitments payable under non-cancellable operating leases which expire in second to the fifth

years inclusively> 5 years Annual commitments payable under non-cancellable operating leases which expire over five years

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The details for lessors were 1 year, 2-5 years, Min. lease payment, finance charges, net total,current liabilities and long term portion (See Table 7). Five companies were classified as Type 3companies. The average total operating lease commitments for the five companies was $6.6 million.

Net total obligation under finance and hire purchase contracts was an average $21.1 million.Average total operating lease commitments was greater than $6.6 million.

Type 4

Type 4 companies, also, had lessee finance and operating leases and lessor operating leases.Five companies were classified under this category (See Table 8).

Table 8: Type 4

No Company LesseeProfit & Loss Account

LessorProfit & Loss Account

Finance Lease OperatingLease

Operating Lease



1 Allan International Holdings 1974 307 3689 252 -13 239

2 Burwill Holdings 2726 1367 11083 16547 -286 16261

3 Orient Power Holdings 3875 808 21458 N/A N/A N/A

4 S.Megga International Holdings 971 262 2193 4410 -53 4357

5 Wong's Kong King International(Holdings)

1313 519 8033 5937 -854 5083

Average 2172 653 9291 6787 -302 6485


Again, the only difference was that for the lessee, only the term, finance lease, was usedinstead of both the terms, finance and hire contract. The difference is somewhat subtle. Please seediscussion in introduction section. Average depreciation was $2.2 million. The average net rentalincome for lessor operating leases was $6.5 million. Lease balance sheet disclosures are presentedin Table 9.

Type 5

Only one company had only lessor finance leases. For this company, interest income wasdisclosed in the profit and loss statement. In the footnotes, details such as lease receivable andunearned income were provided (See Table 10). Gross rental income was $13.7 million.

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Table 9: Type 4

No Company Finance Lease Balance Sheet Operating Lease Balance Sheet

Long Term LiabilitiesObligations under finance leases

Lease Commitments

1 year 1-2 years 2-5 years Total 1 year 2-5 years > 5 years Total

1 Allan International Holdings 0 1589 2654 4234 0 2506 0 2506

2 Burwill Holdings 0 3397 6327 9724 2947 164 0 3111

3 Orient Power Holdings 5667 2856 1511 10034 1270 5502 8875 15647

4 S.Megga International Holdings N/A N/A N/A N/A 2337 0 0 2337

5 Wong's Kong King International(Holdings)

0 1603 0 1603 91 5505 0 5596

Average 1416.75 2361 2623 6399 1329 2735 1775 5839

All figures shown above are in HK thousand dollars.Note:For Obligations under Finance Leases 1 year Amount payable within one year1-2 years Amount payable for more than one year, but not exceeding two years2-5 years Amount payable for more than two years, but not exceeding five yearsFor Operating Lease Commitments1 year Annual commitments payable under non-cancellable operating leases which expire within one year2-5 years Annual commitments payable under non-cancellable operating leases which expire in second to the fifth years

inclusively> 5 years Annual commitments payable under non-cancellable operating leases which expire over five years

Table 10: Lessor Type 5

Company Finance LeaseBalance Sheet

Finance Lease Balance Sheet

Interest &DividendIncome

Long Term Receivables

Interest fromFinance Lease

Lease Rev. Unearned Inc. REUFL due 1 yr. Total

Johnson Electric Holdings 9812 306919 -82264 224655 -6764 217891

All figures shown above are in HK thousand dollars.Note:For Long Term ReceivablesLease Rev. Lease ReceivablesUnearned Inc. Unearned IncomeREUFL Amount receivables from employees under finance leasesDue 1 yr. Amount due within one year included in other debtors

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SUMMARY AND CONCLUSIONS

Lease accounting in Hong Kong will remain a major issue. Many companies are involvedin leasing activities. At present, the accounting rules are being examined by accounting rulesmakers such as FASB in the US and IASC. For the US, it has been estimated that 80 percent ofcorporations lease assets each year and that in the aggregate, the corporations lease more than US$100 billion in plant and equipment (Nevitt and Fabozzi, 1988). Heng, general manager of AICconferences, has stated that leasing has proven to be a very effective financial instrument in thestimulation and facilitation of new investments in the Asia area. Enormous amount of capitalinvestments and funding is needed to keep up Asia's pace of economic growth. Ever-changing fiscaljurisdictions worldwide and increasing demands for large complex transactions will continue to offernew challenges to leasing professionals including accountants (Heng, 1997). For example, athree-day conference (Leasing & Asset Finance Asia 1997) was held in September 1997. Part ofthe conference was an update on the latest accounting issues for international transactions.

Some of the major conclusions from this study include the following:

1 Most Hong Kong companies are involved in lease transactions as either lessees and lessors or both.

2 The dollars committed to lease transactions are significant.

a The average total operating lease commitments for lessees are $40.3 million.

b The average amount payable within one year for lessee finance leases was 12.8 million.

3 The profit and loss effect is, also, significant.

a Average depreciation for lessees is $ 4.3 million.

b Interest expense for lessees is on average $ 1.6 million.

c The average lessee annual rentals are $ 54.3 million.

b Net rental income for lessors is an average of $ 15.2 million

If the accounting rules are changed in the future and almost all lease transactions were to beclassified as finance leases, the financial statement effect would be dramatic. Total assets andliabilities would be increased. In most cases, financial ratios such as debt and return calculationswould be affected. Further research needs to be conducted to determine if the Hong Kong stockmarket would then be affected.

It is hoped this study will provide a starting point for evaluating any future changes in leaseaccounting in Hong Kong. As discussed, part of the objective of this study was descriptive. Datahas been collected and analyzed to provide information about existing lease accounting practices.Any classification changes would result in significant financial statement effects.

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REFERENCES

Anonymous (1997). Board ducks leases issue. Accountancy, February, 8.

Barth, M. and Murphy, C. (1994). Required Financial Statement Disclosures: Purposes, Subject,Number, and Trends. Accounting Horizons, December.

Hong Kong Society For Accountants (1988). Accounting for Leases and Hire Purchase Contracts,September.

Ibison, D. (1995). Tighter rules of disclosure find strong support, South China Morning Post,October 27.

McConville, D. (1996). Leasing leaps forward as favored financing tool. Corporate Cashflow,January.

McGregor, W. (1996). Lease accounting: righting the wrongs. Accountancy, September, 76.

Miller, G. (1998). Financial Statement Disclosures- Purposes, Subject and Number The Hong KongExperience. working paper.

Nevitt, P. and Fabozzi, F. (1988). Equipment Leasing, Dow Jones, Homewood.

Salameh, C. (1997). speech presented at Leasing & Asset Finance Asia '97, 10 September.

Taylor and Associates (1995). Full Disclosures 1994: An International Study of DisclosurePractices, London.

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133


COMPREHENSIVE INCOME REPORTING CONCERNS

R. David Mautz, Jr., North Carolina A&T State UniversityIda Robinson-Backmon, University of Baltimore

ABSTRACT

Statement of Financial Accounting Standards No. 130, Reporting Comprehensive Income,requires presentation of comprehensive income as part of a complete set of financial statements.However, researchers, members of the financial community, and even some members of theFinancial Accounting Standards Board, have expressed concerns about the effectiveness of the newstandard. This article reports survey results that confirm and amplify several concerns. Inparticular, accounting academics and financial executives are concerned that the reportingstandards allow too much latitude and are likely to lead to confusion among financial statementreaders. Respondents also express concern about the cost of preparing comprehensive incomedisclosures and the potential for management to downplay poor results by reporting comprehensiveincome in the stockholders' equity statement. The Financial Accounting Standards Board is urgedto reduce the reporting alternatives available to companies and to undertake a review of theburgeoning array of performance measures reported in financial statements.

INTRODUCTION

In June 1997, the Financial Accounting Standards Board issued Statement of FinancialAccounting Standards No. 130, Reporting Comprehensive Income, which requires presentation ofcomprehensive income as part of a complete set of financial statements. The objectives of the newstandard include providing a comprehensive framework for presenting all non-owner changes inequity and raising the visibility of items previously reported only as adjustments to equity. Thisarticle summarizes recent research and reports on a survey of academic accountants and practicingfinancial professionals. The results suggest that the FASB should consider refining SFAS No. 130to insure that the new disclosures succeed in expanding users' focus beyond the traditional bottomline.

COMPREHENSIVE INCOME: A LITTLE HISTORY

Accountants, managers and standard setters have debated which items should be includedin income, and which should be reported as direct adjustments to equity. At one extreme, a "currentoperating performance" definition of income includes only operating items. Non-operating resultsare reported as direct adjustments to retained earnings. Under an "all-inclusive" definition, onlyinvestments by owners and dividends are excluded from net income. The general trend among U.S.standard setters has been to favor an all-inclusive definition of income.

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Statement of Financial Accounting Concepts No. 6, Elements of Financial Statements,provided the foundation for SFAS No. 130 with this definition of an all-inclusive measure termedcomprehensive income:

…the change in equity [net assets] of a business enterprise during a period from transactions andother events and circumstances from non-owner sources. It includes all changes in equity duringa period except those resulting from investments by owners and distributions to owners (FASB,1985, para. 70).

Statement of Financial Accounting Concepts No. 5, Recognition and Measurement inFinancial Statements of Business Enterprises, asserted that comprehensive income should bereported as part of a complete set of financial statements. No such requirement was initiallyimposed. However, the list of non-owner changes in equity excluded from net income grew toinclude unrealized gains and losses arising from investments in marketable securities, foreigncurrency translation, futures contracts, and employers' pension liabilities. SFAS No. 130 was issuedin 1997 to provide framework for reporting these and other unrealized gains and losses.

EARLY MISGIVINGS ABOUT SFAS NO. 130

Even as the FASB required disclosure of comprehensive income, some questioned whetherthe reporting and display requirements of SFAS No. 130 would achieve the Board's objectives. Intheir dissenting opinion, Board members Cope and Foster expressed dissatisfaction that net incomemay be displayed more prominently in the financial statements than other components ofcomprehensive income. The primary objectives of reporting comprehensive income, include raisingthe visibility of other items of comprehensive income relative to net income and combating users'fixation on net income and earnings per share.

Research findings have also raised questions about comprehensive income reportingrequirements. Dhaliwal, et al. (1999) find no evidence that comprehensive income adds to theinformation conveyed by net income. Hirst & Hopkins (1998) report that comprehensive incomecan help analysts detect attempts to manage reported earnings through judicious management of themarketable equity securities portfolio. However, the disclosures are fully effective only whenreported in a separate statement of comprehensive income or combined with the income statement.Maines & McDaniel (2000) report that display format display format has no apparent impact oninvestors' acquisition or evaluation of unrealized gain information reported as part of othercomprehensive income. However, investors place significant weight on their assessments when theinformation is reported in a statement of comprehensive income, but not when the information ispresented in a statement of stockholders' equity.

The reporting practices of companies who adopted SFAS No.l30 early raise additionalconcerns. Campbell, et al. (1999) review the annual reports of 73 companies that adopted SFAS No.130 before the required implementation date. They find that more than half reported comprehensiveincome in the statement of stockholders' equity. The impact of comprehensive income among thesecompanies was material and negative. Companies whose comprehensive income was materially

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positive were more likely to either prepare a combined statement of income and comprehensiveincome or present a separate comprehensive income statement.

The early evidence suggests that display format matters, and that companies use format tomanipulate the prominence of comprehensive income in the financial statements. These issues willaffect even more companies as the application of SFAS No. 133, Accounting for DerivativeInstruments and Hedging Activities, gives rise to more components of other comprehensive income(Jones & Wilson, 2000). Businesses are also concerned with the potential reporting burden of SFASNo. 130. One recent article explained how companies can restructure their marketable equitysecurities portfolio to avoid implementing SFAS No. 130 (Godwin & Alderman, 1999).

CONCERNS EXPRESSED BY FINANCIAL PROFESSIONALS

The remainder of this article reports on a survey of practicing financial professionals andacademic accountants. These financial professionals also express concerns about the requirementsof SFAS No. 130. Survey respondents are particularly concerned that: (1) reporting requirementswill prove burdensome, (2) display format alternatives permitted under SFAS No. 130 will impairusefulness, and (3) users will be confused by the growing number of alternative "bottom lines."

Responses were obtained from 64 accounting faculty members and 111 chief financialofficers and financial analysts. Both groups were educated in relevant disciplines, possessedconsiderable professional experience, and were familiar with financial reporting practices. Averageexperience among practitioners was 23 years. More than half held advanced degrees; most had beeneducated in accounting, finance or economics. Nearly half were CPAs, and many held variousprofessional credentials including law degrees and securities licenses. All 111 responded that theyanalyze financial statements at least occasionally; the vast majority (86%) indicated that they do soregularly or often.

The accounting faculty had similar experience-21 years on average. Virtually all reportedformal education in accounting. Nearly 90% held a doctorate, and the majority were CPAs.Predictably, the faculty prepare and analyze financial statements less often.

AN OVERVIEW OF OPINIONS

Participants indicated agreement or disagreement with questions about comprehensiveincome reporting on six-point scales ranging from strongly disagree (1) to strongly agree (6).Respondents also weighed the costs and benefits of comprehensive income reporting and expressedtheir display format preferences. These responses are summarized in Table 1.

Both groups expressed moderate familiarity with SFAS No. 130. Academics were morefamiliar with, and more impressed by, comprehensive income reporting. Professors generallybelieved that comprehensive income reporting assists in predicting future cash flows. Practitionerswere less confident of predictive ability and more concerned about the potential reporting burden.Both were concerned that comprehensive income will confuse financial statement readers.With regard to overall costs and benefits, academics were again more positive. Fifty three percentof academic respondents believe that the benefits of reporting comprehensive income outweigh the

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costs. Only 17% responded that costs outweigh benefits. The pattern among practitioners wasexactly opposite. Forty-one percent responded that costs outweigh benefits. Only 27% believed thatSFAS No. 130 makes sense from a cost/benefit standpoint.

Table 1: Overall Familiarity with and Assessments of SFAS 130

(means* and standard deviations) AccountingFaculty

FinancialProfessionals

Prior to completing this questionnaire, I was very familiar withSFAS 130.*

4.33(1.40)

3.61(1.84)

Reporting comprehensive income will assist in predicting futurecash flows.*

3.45(1.37)

3.07(1.37)

Comprehensive income will cause confusion among financialstatement readers.

3.84(1.66)

3.95(1.56)

SFAS 130 places an unnecessary financial reporting burden oncompanies.*

2.39(1.43)

3.36(1.51)

I believe that the FASB should allow per share disclosures ofcomprehensive income.

3.34(1.53)

3.05(1.68)

Asterisk indicates that difference is statistically significant at < .10.

Costs v. Benefits of SFAS 130

Costs outweigh benefits 17% (10) 41% (10)

Cost and benefits are approximately equal 30 (18) 32 (31)

Benefits outweigh costs 53 (32) 27 (27)

Total 100% (60) 100% (111)

Missing/no response (4) (13)

Preferred Reporting Format

Single combined statement 49% (29) 26% (27)

Separate statements of income and comprehensive income 39 (23) 43 (46)

Report in stockholders' equity 12 (7) 31 (33)

Total 100% (59) 100% (106)


Differences of opinion were also evident in preferences for reporting format. Academicsoverwhelmingly favored reporting comprehensive income in one of the two formats preferred bythe FASB, either a combined statement with net income or a separate statement of comprehensiveincome. Only 12% favored reporting comprehensive income in the statement of stockholders'equity. Practitioners were more evenly divided, with nearly a third preferring the stockholders'equity alternative.

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EVALUATING COMPONENTS OF NET INCOME AND COMPREHENSIVE INCOME

To gain insight into the relative usefulness of various net income and comprehensive incomecomponents, respondents were asked to make six judgments about decision usefulness. Thejudgments were coded on six point scales ranging from strongly disagree (1) to strongly agree (6).The specific judgments were:

1. Conveys important economic information

2. Is relevant to many judgments and decisions

3. Is an indicator of management performance

4. Should be included in net income

5. Should be reported as a separate line item

6. Should be reported on a per share basis.

The items evaluated included four elements of net income, comprehensive income, and three of itscomponents:

Elements of Net Income:Income or Loss from Continuing OperationsGain or Loss from Discontinued OperationsExtraordinary Gain or LossCumulative Effect of an Accounting Change.

Comprehensive Income and Its Components:Foreign Currency Translation AdjustmentUnrealized Security Holding Gain or LossMinimum Pension Liability Adjustment

Table 2 reports means and standard deviations for the resulting assessments. Responses fromacademics and practitioners were qualitatively similar and are aggregated.

The first three questions measure usefulness without regard to current reporting standards.The latter three also measure usefulness, but responses to these questions may be influenced byknowledge of current GAAP. For example, the fact that income from continuing operations iscurrently reported on a per share basis may induce greater agreement with the statement that incomefrom continuing operations should be reported on a per-share basis.

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Table 2: Evaluation of Individual Net Income and Comprehensive Income Items Means and StandardDeviations--Faculty and Financial Professionals*

Conveysimportanteconomic

information

Relevant tojudgements

anddecisions

Indicator ofmgt

performance

Should beincluded innet income

Should bereported asa separateline item

Should bereported ona per-share

basis

6-Question/ 3-

Question**

Net Income 1 2 3 4 5 6

Income from ContinuingOperations

5.51 (0.84)

5.43 (0.87) 4.96 (1.10) 5.49(1.06)

5.38(1.05)

4.83(1.49)

5.27/5.30

Gain or Loss fromDiscontinued Operations

4.97(1.14)

4.52(1.30)

4.05(1.36)

4.73(1.52)

5.26(1.11)

4.29(1.63)

4.64/4.51

Extraordinary Gain orLoss

4.91(1.15)

4.37(1.42)

3.53(1.45)

4.65(1.58)

5.24(1.13)

4.34(1.66)

4.51/4.27

Cumulative Effect of anAccounting Change

4.23(1.48)

3.96(1.46)

2.97(1.46)

4.01(1.74)

4.86(1.47)

3.95(1.79)

4.00/3.72

Grand Means 4.61/4.45

Comprehensive Income

Foreign CurrencyTranslation Adjustment

4.35(1.31)

3.87(1.43)

3.03(1.47)

3.50(1.75)

4.12(1.75)

2.88(1.64)

3.63/3.75

Unrealized SecurityHolding Gain or Loss

4.53(1.24)

4.00(1.35)

3.47(1.50)

3.23(1.79)

4.04(1.82)

2.72(1.75)

3.67/4.00

Minimum PensionLiability Adjustment

4.03(1.47)

3.57(1.54)

2.96(1.44)

3.12(1.75)

3.65(1.87)

2.47(1.59)

3.30/3.52

Comprehensive Income 4.15(1.55)

3.75(1.54)

3.51(1.57)

3.29(1.80)

3.98(1.85)

3.20(1.88)

3.65/3.80

*Scaled responses where 1 = Strongly Disagree and 6 = Strongly Agree Grand Means 3.56/3.77**6-question = average across items 1-6. 3-question = average across items 1-3.

An overall usefulness score was computed for each item by averaging scores across all sixquestions. These scores are described in the "Overall" column at the right side of Table 3. Thegrand means leave no doubt that net income is the key performance measure. The mean score forall net income items on all six questions is 4.61. For comprehensive income and its components,the corresponding score is 3.56. Income from continuing operations (5.27) stands out from all otheritems. Comprehensive income (3.65) and its components receive much lower evaluations. Three-question scores are computed using only questions that could be answered independently ofcurrent reporting standards. The results are qualitatively similar. Net income (grand mean = 4.45)is more important than comprehensive income (grand mean = 3.77). Income from continuingoperations (5.30), discontinued operations (4.51), and extraordinary items (4.27) are all evaluatedmore favorably than comprehensive income or its elements. The highest scoring comprehensiveincome item is unrealized security holding gain or loss (4.00).

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A final analysis involved ranking the eight items, question-by-question. These results arereported in Table 3. Again, the academic and practitioner responses are combined.

Table 3: Question-by-Question Ranks of Net Income and Comprehensive Income Items Faculty andFinancial Professionals

Rank Conveysimportanteconomic

information

Relevant tojudgements

and decisions

Indicator ofmgt

performance

Should beincluded innet income

Should bereported as aseparate line

item

Should bereported on a

per-sharebasis

1 2 3 4 5 6

Net Income

Income from ContinuingOperations

1 1 1 1 1 1

Gain or Loss from 2 2 2 2 2 3

Extraordinary Gain or Loss 3 3 3 3 3 2

Cumulative Effect of anAccounting Change

6 5 7 4 4 4

Comprehensive Income

Foreign CurrencyTranslation Adjustment

5 6 6 5 5 6

Unrealized Security 4 4 5 7 6 7

Minimum Pension LiabilityAdjustment

8 8 8 8 8 8

Comprehensive Income 7 7 4 6 7 5

Several general conclusions are evident in Table 3. First, income from continuingoperations, discontinued operations, and extraordinary items dominate any assessment of usefulness.These items are ranked first, second or third on every dimension. The cumulative effect of a changein accounting principle is clearly less important, but respondents believe that it should continue tobe reported as a line-item component of net income and disclosed on a per-share basis.

Comprehensive income scores toward the bottom on every dimension except as an indicatorof management performance. There is also support for presenting comprehensive income on aper-share basis. Unrealized security holding gain or loss consistently ranks near the net incomecomponents in terms of usefulness. There is little support, however, for reporting it as a part of netincome or on a per-share basis. Minimum pension liability adjustments rank last on every question.

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BENEFITS, DRAWBACKS AND THE "BOTTOM LINE"

The final section of the questionnaire asked open-ended questions beginning with a requestto complete this statement: "If asked to identify a single 'bottom line' that would be useful for mostfinancial statement readers, I would select __________." The responses summarized in Table 4again confirm the dominance of net income. Over 80% named some component of net income;almost half chose operating income.

Table 4: Responses to Open-Ended Questions

Accounting Faculty Financial Professionals

Single Bottom Line

Operating income 0.51 (28) 45% (33)

Net income 18 (10) 37 (27)

Comprehensive income 26 (14) 7 (5)

EPS 5 (3) 7 (5)

Cash flow 4 (3)

Total 100% (64) 100% (111)


Principal Benefit

Disclosure/detail 34% (18) 33% (22)

Forward view 34 (18) 30 (20)

Understandability 9 (5) 16 (11)

Other 21 (11) 21 (14)

No benefit 2 (1) 0 (0)

Total 100% (53) 100% (67)


Principal Drawback

Confuse readers 50% (27) 56% (55)

Time/cost to prepare 15 (8) 18 (18)

Added complexity/irrelevance 9 (5) 10 (10)

Other 20 (11) 10 (10)

No drawback 6 (3) 6 (5)

Total 100% (54) 100% (98)


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The principal benefits identified with reporting comprehensive income fall heavily into twocategories. A third of those answering cite improvements in disclosure and detail. Typical remarksmention more detailed information for analysis, improved visibility for items that go directly toequity, and highlighting significant unrealized gains/losses on securities. A similar number assert that comprehensive income helps in understanding the economic picturesufficiently to forecast the future. Responses mention improved awareness of items that will affectincome in the future, allowing better estimates of future cash flows, and obtaining a better forwardview of company results. A minority assert that users, particularly sophisticated users, willunderstand the company's results better with the additional information. Only one response assertedthat reporting comprehensive income provides no benefit.

Academics and practitioners agree that the most likely drawback to reporting comprehensiveincome lies in its potential to confuse readers. More than half the responses make reference to thisproblem. Concerns include making management look better or worse due to items beyond theircontrol, providing excessive detail, and the proliferation of competing income numbers.Practitioners are also concerned about the time and cost necessary to prepare the new disclosures.

Finally, participants were invited to express thoughts that they would like to share withstandard setters. The single most prevalent comment from both academics (36%) and practitioners(54%) urged the FASB to simplify reporting requirements to avoid confusing financial statementreaders. Among academics, the second most common comment (27%) supported the Board's efforts,praising the comprehensive income standard. Those in practice were less enthused. Twenty-twopercent asserted that SFAS No. 130 does little or nothing to improve financial reporting. A commoncomplaint was that the final version of the standard was "watered down." One respondent assertedthat reporting comprehensive income "avoids the more important issue of what should be includedin net income."

CONCLUSIONS

Members of the financial community, accounting researchers, and even some members ofthe Financial Accounting Standards Board have expressed concerns about the effectiveness ofcomprehensive income reporting under SFAS No. 130. The alternative display formats permittedare a particular source of concern among those who fear that reporting different "classes" of incomeimpairs the effort to broaden users' focus beyond net income. This and other studies suggest thatthe concern is well founded and that companies are using display format to highlight or obscureresults. The current findings confirm that many practitioners favor presenting comprehensiveincome in the statement of stockholders' equity. There is also significant concern that users will beconfused by the growing number of alternative performance measures under the umbrella ofcomprehensive income.

For comprehensive income reporting to achieve its objectives, the FASB should considerreducing or eliminating the present latitude in display format. A comprehensive review of incomereporting should also be undertaken with the goal of assisting readers in identifying appropriateperformance measures for various types of decisions. Otherwise, complexity and confusion about

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the bottom line are likely to increase, limiting the potential of SFAS 130 to assist investors andcreditors.

REFERENCES

Campbell, L., D. Crawford and D. Franz (1999). How Companies are Complying with theComprehensive Income Disclosure Requirements. The Ohio CPA Journal, 58(1), 13-20.

Dhaliwal, D., K. Subramanyam and R. Trezevant (1999). Is Comprehensive Income Superior to NetIncome as a Measure of Firm Performance? Journal of Accounting and Economics, 26(1-3),43-67.

Financial Accounting Standards Board (1985). Statement of Financial Accounting Concepts No.6, Elements of Financial Statements. Stamford, CT, FASB.

Godwin, N. & C.W. Alderman (1999). Avoiding the Implementation Costs of SFAS No. 130. TheCPA Journal, 69(6), 52.

Hirst, D.E. & P. Hopkins (1998). Comprehensive Income Reporting and Analysts' ValuationJudgments. Journal of Accounting Research, 36, 47-75.

Jones, J. & A. Wilson (2000). The Effect of Accounting for Derivatives on Other ComprehensiveIncome. The CPA Journal, 70(3), 54-56.

Maines, L. & L. McDaniel (2000). Effects of Comprehensive-Income Characteristics onNonprofessional Investors' Judgments: The Role of Financial-Statement PresentationFormat. The Accounting Review, 75(2), 177-204.

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THE UK INVESTOR AND INTERNATIONALDIVERSIFICATION

Michael E. Hanna, University of Houston-Clear LakeJoseph P. McCormack, University of Houston-Clear Lake

Grady Perdue, University of Houston-Clear Lake

ABSTRACT

This study describes the development of the optimum investment portfolio for a UnitedKingdom-based investor who seeks to utilize the major stock market index from each of the Groupof Seven (G-7) industrialized countries to diversify a domestic equity index portfolio. Results of theanalysis based on data from the 1990s, indicate that substantial international diversification isessential if the UK investor's objective is to obtain an optimal portfolio.

INTRODUCTION

In modern portfolio theory international investing is widely accepted as an efficient meansto diversify a portfolio. A great body of academic literature has focused on the risk reductionenjoyed by an investor who is able to reduce risk with little or no negative impact on return. Todaymany modern investment strategies include international investments to take advantage of theimperfect correlation between the financial markets of an investor's home market and those of othercountries. The objective is to have gains in a foreign market to offset losses in the domestic market.To what extent should United Kingdom (UK) investors in the new millennium engage ininternational investing? This question takes on new importance in light of the growing trendtowards the use of defined contribution retirement plans in the UK. Individuals who have neverconsidered themselves as investors and who have previously relied on the state orcompany-administered pension schemes (as they are called in the UK), now face asset allocationdecisions and the risk and return implications inherent with those decisions. Given this newsituation, it is appropriate to realize that the extent a modern UK investor should engage ininternational investing will be related to the degree of risk reduction or return augmentation possiblewhen that investor adds an international asset class to the portfolio's original domestic only assetallocation.

REVIEW OF THE LITERATURE

Numerous academic studies have explored the virtues of international investing as anelement of an asset allocation strategy. Solnik, 1974, discusses the "primary motivation in holdinga portfolio of stocks is to reduce risk," and he shows that international diversification can lower thesystematic risk in a portfolio. Based on historical data a long-run allocation of 20 to 30 percent inforeign equity appears correct for an investor based in the United States, according to Clark and

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Tullis, 1999. Black and Litterman, 1991, conclude that international investing reduces the level ofrisk below that of a purely domestic portfolio. Michaud, Bergstrom, Frashure, and Wolahan, 1996,arrive at the finding that "international diversification increases return per unit of risk…"

While many studies have historically argued for international diversification, some contraryviews have occasionally emerged. Speidell and Sappenfield, (1992, and Most, 1999, expressconcern that as economies and global events tie together a shrinking world, the benefits ofinternational diversification between major markets may be fading away. Of particular importanceto UK investors, Beckers, 1999, shows that "European stocks are starting to behave more similarly."Aiello and Chieffe, 1999, find that international index funds fail to deliver a high level ofdiversification because the market indexes for the major world economies are becoming increasinglycorrelated. Sinquefield, 1996, questions if it is even still correct to use the Europe Australia Far Eastindex (EAFE) and other major indexes to diversify an S&P 500 portfolio. Sinquefield, 1996, andEaker, Grant and Woodard, 2000, contend that actively managed emerging market portfolios mayprovide greater potential for diversification than investment in developed markets.

Erb, Harvey and Viskanta, 1994, find that correlation coefficients appear to increase betweenequity markets during recessions (just when investors would want low correlation coefficients).Shawnky, Kuenzel and Mikhail, 1997, report that correlation coefficients between markets appearto increase during periods of increased market volatility. Higher correlation would imply areduction in diversification potential and thus higher portfolio risk. Although Solnik, Boucrelle, andLe Fur, 1996, find that long-term correlation between markets have not risen significantly, they dofind that the financial markets exhibit "correlation increases in periods of high market volatility."Michaud, Bergstrom, Frashure, and Wolahan, 1996, like the previous authors, find that the majormarket indexes have not experienced increased correlation coefficients.

Melton, 1996, shows that pension funds in other countries routinely have greaterinternational allocations than U.S. pension funds do. But Gorman, 1998, shows that U.S. pensionplans are moving in the direction of including international investments in their asset allocations.Thus, the proponents of international investing for its diversification benefits have swayed manypension fund managers in other countries and appear to be swaying U.S. pension fund managers.Yet questions still remain: "How should international investment be handled?" and "How muchinternational diversification is appropriate?"

METHODOLOGY AND DATA

The particular market indexes under study in this research are the Financial Times StockExchange (FTSE) index of London, the Standard & Poor's 500 index (S&P 500), the Toronto StockExchange (TSE) 300 Composite index, the Paris CAC 40, the Frankfurt DAX, the Milan MlBtel,and the Tokyo Nikkei 225. Data for the study are the 121 months of monthly equity market datafrom January 1990, through January 2000. The monthly observations for the FTSE and the sixforeign indexes are obtained from the first joint trading day of each month, as reported in The WallStreet Journal. Data on exchange rates are also collected from the Journal for the same trading dayas the market index observations, and are used to convert market return data to United Kingdompound equivalent returns.

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Geometric mean returns and standard deviations are computed from the monthly return datafor each of the seven indexes, after the data have been adjusted for exchange rates fluctuations.These computed values provide a basic risk-return comparison of the seven markets. Correlationcoefficients are also calculated to ascertain the relationship between each foreign market index andthe FTSE. The pound-adjusted variables are then utilized in the analysis to determine the efficientfrontier.

The study reported here analyzes the risk and return implications for a hypothetical UnitedKingdom investor choosing to diversify a domestic equity index portfolio by incorporatinginternational equity index components. The study utilizes the major equity market indexes of theUK and the other G-7 countries to construct an efficient frontier of portfolios. Those other sixnations were Canada, the United States, France, Germany, Italy, and Japan. Data to describe eachof the seven markets is based on monthly returns on the indexes and on monthly exchange ratesduring the 1990s. The data is used to determine the efficient frontier of portfolios for an UK-basedinvestor who sought to combine the Financial Times Stock Exchange (FTSE) index with aninvestment in one or more of the market indexes from the other G-7 industrialized nations.

Ascertaining the minimum standard deviation portfolio for each of a variety of selectedreturns develops the efficient frontier. For each new portfolio constructed in this process, theportfolio return, standard deviation, and coefficient of variation are reported. The minimumvolatility portfolio contained a relatively small UK component, and this may not be attractive tosome UK investors. The minimum weighting of the UK component of the portfolio was initiallyset to zero and gradually increased and new efficient portfolios are developed.

While the data used in this study were monthly data, the results have been converted to anannualized basis for readability.

FINDINGS

Presented in Table 1 are the geometric mean return and standard deviation of returns for eachof the seven markets. The London FTSE produced the third best performance during this timeperiod, and had the third best coefficient of variation. Of the European markets only Frankfurt hadboth a better return and a lower level of volatility. However, as is observable from the table theUnited States (US) index clearly dominates the other indexes during the period of the 1990s. TheUS market produced the highest geometric mean rate of return, and is also the least volatile (i.e., hadthe smallest standard deviation of returns) across this ten-year (121-month) period. The data showthe Frankfurt DAX had the closest comparable pound-adjusted rate of return, but the DAX has astandard deviation of returns that is about twenty percent larger than that of the S&P 500. Thestandard deviation of returns for the Toronto 300 was the second smallest in this period, but thepound-adjusted rate of return in the Canadian market index was only slightly above one-third of thatexperienced by the S&P 500. The S&P 500 index also had the lowest coefficient of variation forthis period of study, indicating it produced the lowest amount of risk relative to return.

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Figure 1

Table 1: Rates of Return, Standard Deviations, and Coefficients of Variation All Values Stated asPercentages

Market Annual Geometric MeanReturn

Standard Deviation ofReturns

Coefficient of Variation

London 10.55 21.75 2.062

Toronto 5.08 19.06 3.752

S&P 500 14.79 16.04 1.085

Paris 9.85 20.51 2.082

Frankfurt 12.36 19.20 1.553

Milan 5.72 30.46 5.325

Tokyo -3.63 28.92 -7.967

Adjusted to UK pounds, the implication of investing £1,000 in each of these markets isillustrated in Figure 1. As is clear from this figure, an investor investing in either the S&P 500 orFrankfurt DAX would have more than tripled these invested funds across this ten-year (121-month)period. Investing in the London index would have produced nearly identical results with investingin the Paris index as the funds in each more than doubled during this period. At the lower extreme,almost a third of the funds invested in the Tokyo index would have been lost.

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Table 2 provides information on the correlation between returns in each of the seven markets.All correlation coefficients are positive, indicating a clearly positive relationship between the returnsover this period in the seven financial markets. Correlation to the United Kingdom market isstrongest with the US and Paris indexes and weakest with the Milan and Tokyo indexes. Given thisinformation and all other factors being equal, one would expect the low correlation with the Tokyoand Milan markets to indicate great potential for diversification through these markets for the UKinvestor. However, results reported below show virtually nothing is gained for the UK investor byincluding the Italian market in his portfolio.

TABLE 2: Correlation coefficients for returns between indexes

London Toronto S&P 500 Paris Frank. Milan Tokyo

London 1

Toronto 0.4541 1

S&P 500 0.4808 0.7832 1

Paris 0.4781 0.5528 0.6556 1

Frank. 0.3986 0.5559 0.6035 0.7550 1

Milan 0.3630 0.4260 0.3923 0.4193 0.4409 1

Tokyo 0.2859 0.3278 0.3658 0.3735 0.2593 0.2738 1

Given the data from these seven equity markets, efficient frontier portfolios were developedutilizing several different minimum weightings for the UK market component of the portfolio.Efficient frontier portfolios were determined by including all seven indexes in the hypotheticalportfolio. Minimization of the standard deviation of the portfolio to ascertain the frontier wasperformed subject to the following constraints. The portfolio must earn a given rate of return (withseveral rates of return used to develop the frontier). Also the weighting of the indexes must sum toone and no index could be allowed to have negative weighting.

Table 3 presents the returns, standard deviations, and coefficients of variation for severalpossible portfolio combinations of the FTSE and other market indexes, where there is no minimumor maximum weighting preset for the FTSE. Figure 2 is a graphical representation of this table. Theselected returns are six percent, eight percent, 10.07 percent, 11.26 percent, and 12 percent. The10.07 percent return is chosen as one of the points to be determined on the frontier because that wasthe mean return for the UK market over the time period of this study (as reported in Table 1). Theportfolio with the 11.26 percent return is the minimum volatility portfolio for the UK investor.These five portfolios are the minimum volatility portfolios for each rate of return listed in the table.

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Table 3: Frontier Portfolios with no constraints on weightingsAll values stated as percentages

Portfolios Weight of each index in frontier portfolios

AnnualReturn

AnnualStandard Deviation

Coefficientof Variation

S&P 500 Toronto London Paris Frankfurt Milan Tokyo

6.00 15.79 2.632 7.23 34.13 14.80 8.31 14.48 1.17 19.89

8.00 15.21 1.901 24.84 21.67 15.58 3.56 17.85 0.99 15.52

10.07 14.87 1.477 41.96 9.04 16.70 0 21.24 0.44 10.62

11.26* 14.82 1.316 52.15 1.28 16.67 0 21.95 0 7.74

12.00 14.85 1.238 57.57 0 16.48 0 21.49 0 4.46

* Minimum standard deviation portfolio

The UK market across this decade had a return of 10.07 percent and a standard deviation ofreturns of 21.75 percent (as reported in Table 1). The frontier portfolio with the 10.07 percent returnreported in Table 3 has a standard deviation of 14.87 percent, indicating nearly a 1/3 reduction involatility as the UK component is reduced from 100 percent down to only 16.7 percent of therespective portfolio. In fact the weight of the UK component varies in each frontier portfolio froma maximum of 16.70 percent to only 14.80 percent, with the weight of each of the other indexes alsobeing varied as required to obtain the minimum volatility portfolio for that rate of return. That theUK component of the portfolio never exceeds 16.48 percent of any frontier portfolio is an importantpoint clearly demonstrating the significant gains from international diversification for the UKinvestor.

Clark and Tullis (1999) have suggested that a 20 to 30 percent allocation to internationalequities would be appropriate for a previously domestic equity only portfolio. However, their pointof view was from that of an American investor. The results reported here demonstrate that an UKinvestor needs to have a much larger portion of his equity portfolio allocated toward internationalinvestments.

Figure 2 is a graphic representation of the return and volatility data presented in Table 3.The further importance of international diversification becomes more evident when this figure isstudied. It becomes obvious that portfolios with returns below 11.26 percent (i.e., that of theminimum volatility portfolio) are not on the efficient frontier, but rather are on the inefficient portionof the frontier. Of the portfolios reported in Table 3, only the two portfolios with returns of 11.26and 12 percent are efficient. Both the 100 percent UK portfolio and the diversified portfolioproducing 10.07 percent are actually inefficient portfolios (as are the portfolios with eight and sixpercent rates of return). To be invested in an efficient portfolio, Table 3 makes it clear that the UKinvestor could have no more than 16.67 percent of his portfolio assets invested in the UK market.

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Figure 2

The frontier portfolio that returns 10.07 percent should be examined in contrast to theperformance of the UK market. The UK investor could have earned that level of return by investing100 percent of his assets in the UK market or by investing in the frontier portfolio that had only a16.70 percent weighting for the UK component. The obvious difference between the two portfoliosis the standard deviation of returns for each portfolio. The standard deviation for the UK-onlyportfolio is 21.75 percent, while the internationally diversified portfolio has a standard deviation of14.87 percent. The internationally diversified portfolio that is located on the frontier offers the UKinvestor nearly a 1/3 reduction in volatility with no sacrifice in return.

However, UK investors may feel the need to maintain some certain minimum amount ofinvesting in the home market. What would be the implications of such a course of action? Table4 reports the results of fixing the minimum weighting of the UK component of the investor'sportfolio at 20 percent. In this table only the portfolios with expected returns of 11.3 and 12 percentare on the efficient frontier. The other three portfolios are on the inefficient portion of the frontier.Tables 5, 6, and 7 report the results of setting the minimum weight of the UK portion of the portfolioat 40, 60 and 80 percent, respectively. Results here are consistent with the results presented in Table4. It should also be noted that when the UK weighting is set at a minimum of 60 percent, it is noteven possible to reach the efficient portion of the frontier and the 12 percent return. With the UKweighting set at a minimum of 80 percent, it is not possible to generate portfolios producing eitherthe six or 12 percent expected returns.

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Table 4Frontier Portfolios with UK weighting set at a minimum of 20 percent

All values stated as percentages


Annual Return AnnualStandardDeviation

Coefficient ofVariation


6.00 15.82 2.637 4.42 33.60 20.00 7.39 14.38 0.72 19.49

8.00 15.23 1.904 22.16 21.52 20.00 2.44 18.26 0.53 15.09

10.07 14.89 1.479 40.45 8.43 20.00 0 20.62 0 10.50

11.30* 14.83 1.312 50.91 0 20.00 0 21.40 0 7.69

12.00 14.87 1.239 55.71 0 20.00 0 20.47 0 3.82








6.00 16.48 2.747 0 30.65 40.00 0 9.94 0 19.41

8.00 15.86 1.983 12.61 18.78 40.00 0 15.22 0 13.39

10.07 15.52 1.541 30.57 5.02 40.00 0 15.94 0 8.47

11.06* 15.47 1.399 38.54 0 40.00 0 16.08 0 5.38

12.00 15.54 1.295 44.73 0 40.00 0 15.11 0 0.15


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6.00 18.12 3.020 0 16.54 60.00 0 0 0 23.46

8.00 17.33 2.166 3.01 15.4 60.00 0 10.20 0 11.40

10.07 17.01 1.689 20.52 1.73 60.00 0 11.38 0 6.37

10.82* 16.97 1.568 25.79 0 60.00 0 11.00 0 3.03

12.00†

* Minimum standard deviation portfolio† With the UK weighting set at a minimum of 60 percent, it is impossible to generate a portfolio with a 12 percent

rate of return







6.00†

8.00 19.48 2.435 0 7.85 80.00 0 0 0 12.15

10.07 19.14 1.901 10.03 0 80.00 0 6.44 0 3.53

10.58* 19.12 1.807 13.22 0 80.00 0 6.07 0 0.71

12.00†

* Minimum standard deviation portfolio† With the UK weighting set at a minimum of 80 percent, it is impossible to generate a portfolio with a 12 percent

rate of return

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CONCLUSION

Modern portfolio theory suggests that an UK investor's domestic portfolio should benefit byinvesting in other markets that are not perfectly correlated with the UK market. In this study we seethat diversification reduces risk significantly as the UK investor makes the necessary allocation toforeign markets. While during the decade of the 1990s, US investors were not significantlyrewarded for international diversification it held great potential of UK investors. They would haveexperienced significant reductions in risk coupled with no reductions in return. Data across this timeperiod using the market indexes for the United Kingdom and its G-7 partners clearly supports theusefulness of international diversification. If the goal of the investor's investment strategy is riskminimization and offsetting domestic market losses, then the investor facing an asset allocationdecision must consider the historical market patterns discussed here. The cases which are examinedin this study show international diversification (based on major market indexes) over these ten years(121 months) would have been a potent tool for UK investors as has been suggested in much of theacademic research. The risk reduction benefits of diversification were evident. Given this recenthistorical experience, the UK investor must recognize that there is sufficient reason (in terms of riskreduction) to pursue international diversification.

The results of this study might be sample specific. The complete data set of the decade from1990 through 2000 is used in describing the relationship between the United Kingdom and theremaining G-7 markets. However, it may be difficult to extrapolate the findings of this research toany other markets, specific stocks in these markets, or to other time periods. Nevertheless, investorstypically obtain reasonable expectations of the potential benefits of international diversification bystudying historical relationships and the results in this study should provide information that allowsinvestors to make a more informed investment decision.

REFERENCES

Aiello, S. and Chieffe, N. (1999). International Index Funds and the Investment Portfolio.Financial Services Review, 8 (1), 27-35.

Bailey, W., and Lim, J. (1992). Evaluating the Diversification Benefits of the New Country Funds.The Journal of Portfolio Management, 8 (3), 74-80.

Beckers S. (1999). Investments Implications of a Single European Capital Market. The Journal ofPortfolio Management, 25 (3), 9-17.

Black, F., and Litterman, R. (1991). Global Portfolio Optimization. Financial Analysts Journal, 48(5), 28-43.

Clarke, R.G., and Tullis, R.M. (1999). How Much Investment Exposure is Advantageous on aDomestic Portfolio? The Journal of Portfolio Management, 25 (2), 33-44.

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Eakes, M., Grant, D., and Woodard, N. (2000). Realized Rates of Return in Emerging EquityMarkets. The Journal of Portfolio Management, 26 (3), 41-49.

Erb, C.B., Harvey, C.R., and Viskanta, T.E. (1994). Forecasting International Equity Correlations,Financial Analysts Journal, 50 (6), 39-45.

Gorman, S.A. (1998). The International Equity Commitment, The Research Foundation of theInstitute of Chartered Financial Analysts, Charlottesville, VA.

Melton, P. (1996). The Investor's Guide to Going Global with Equities, Pitman Publishing, London.

Michaud, R.O., Bergstrom, G.L., Frashure, R.D., and Wolahan, B.K. (1996). Twenty Years ofInternational Equity Investing: Still a route to higher returns and lower risks? The Journalof Portfolio Management, 23 (1), 9-22.

Most, B.W. (1999). The Challenges of International Investing Are Getting Tougher. Journal ofFinancial Planning, February, 38-40, 42-46.

Shawnky, H.A., Kuenzel, R., and Mikhail, A.D. (1997). International Portfolio Diversification: ASynthesis and Update, Journal of International Financial Markets, Institutions and Money,7, 303-327.

Sinquefield, R.A. (1996). Where Are the Gains from International Diversification? FinancialAnalysts Journal, 52 (1), 8-14.

Solnik, B.H. (1974). Why Not Diversify Internationally Rather than Domestically? FinancialAnalysts Journal, 30 (4), 48-54.

Solnik, B., Boucrelle, C., and Le Fur, Y. (1996). International Market Correlation and Volatility.Financial Analysts Journal, 52 (5), 17-34.

Speidell, L.S. and Sappenfield, R. (1992). Global Diversification in a Shrinking World. TheJournal of Portfolio Management, 19 (1), 57-67.

Wahab, M., and Khandwala, A. (1993). Why Not Diversify Internationally with ADRS? TheJournal of Portfolio Management, 19 (2), 75-82.

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155


A NEW STOCK OPTION PLAN AND ITS VALUATION

Anthony Yanxiang Gu, State University of New York, Geneseo

ABSTRACT

This new option plan allows the holder to purchase an underlying asset at a discountproportional to the asset's market price, and the proportion decreases with an employee's seniority.This option would provide strong incentives to improve executive and employee performance andloyalty. Compared to existing plans, this option would create less incentive to increase companyrisk or to reduce dividend payment, and it would not be too generous. We derive the value of thisoption for the general case of a dividend-paying stock and where the option's exercise price reflectsa time-varying discount factor. The derived value incorporates the optimal exercise time.

INTRODUCTION

Existing stock option plans and previous research on stock option plans focus on executivecompensation packages. Realizing the stronger incentives of stock options, more companies havestarted to offer stock options to their non-executive employees recently.

Existing stock options have several disadvantages while creating incentives to increase stockprice. (Smith & Watts, 1982, 1992; Agrawal & Mandelker, 1987; Lambert et al., 1989; Jensen &Murphy, 1990; DeFusco et al., 1990; Yermack, 1995; Tufano, 1996; Hall, 1998; Schrand & Unal,1998; Core & Guay, 1998; Johnson & Tian 2000). Often, shareholders complain that some optionsare too generous and do not provide the right incentives to executives. Researchers find that existingstock options, traditional or nontraditional, create strong incentives to increase company risk andto reduce dividend payments. Executives compensated with stock options may take actions toincrease company risk because an increase in stock price volatility increases the value of anexecutive stock option (Agrawal & Mandelker, 1987; Tufano, 1996; Schrand &Unal, 1998).Johnson and Tian (2000) find that five out of the six nontraditional options they examine createstronger incentives than the traditional option to increase company risk. Also, executives mayreduce dividends in order to increase their option payoffs because, ceteris paribus, dividendpayments reduce expected terminal stock prices and thus the value of call options that are notdividend protected (Lambert et al., 1989). Johnson and Tian (2000) report that three out of the sixnontraditional options create stronger incentives to reduce dividend yield.

In this paper, we present a new type of option that would create strong incentives to improveperformance and loyalty of both executives and non-executive employees, while reducing theincentive to increase stock price volatility or to reduce dividend payments. We also analyze thevalue of the option. (For valuing traditional executive stock option plans, see Smith, 1976, and forvaluing nontraditional executive stock options, see Johnson and Tian, 2000).

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This option is a non-standard, American-style call option whose exercise price is aproportion of the price of its underlying asset. The option under consideration is different fromexisting call options in that its exercise price is not a fixed price but, rather, a proportion of the priceof its underlying asset. For example, an option might give its owner the right to buy a share ofcommon stock at a price equal to 80 percent of the stock's market price at any time prior to theoption's expiration date. Since its exercise price is a portion of the underlying asset's price, theoption is always in the money as long as the value of the underlying asset is greater than zero.

A potential use for such an option may be for employers who wish to provideperformance-sensitive compensation to their employees. Companies are increasingly issuing stockand/or conventional stock options to employees in compensation contracts. An alternative, whichis a hybrid of stocks and conventional stock options, would be to compensate employees in the formof options to buy company stock at a proportional discount. Such an option is always in the money,yet its value is sensitive to the firm's performance. For a given number of underlying shares, theseproportional-exercise price options would be less expensive to the company than directly issuingstock, and, unlike standard, fixed-exercise price options, they would not lose their beneficialincentive features should the company's stock decline due to exogenous swings in the stock market.Compensation in the form of proportional-exercise price options can be customized to meet aparticular company's needs. The option's proportional exercise price can be based on an employee'sseniority, a feature that can enhance loyalty to the company and, thereby, reduce employee turnover.An exercise price of, say, 80% of the current stock price could be set for a new hire, with theexercise price being reduced by one percentage point for each additional year of employment. Thus,a company could give an employee the right to buy a certain number of shares of stock at a priceequal to 80 percent of the stock's market value if purchased during the employee's first year, 79percent if purchased during her second year, 78 percent if purchased during her third year, and soon until she retires or leaves the company. While there would be no further reduction in the option'sproportional exercise price following retirement, the option's maturity could be designed to followthe employee's retirement date at which time her tax liability on the option's capital gain may belower due to a lower post-retirement tax bracket.

The plan of the paper is as follows. In Section 2 we present a general framework for valuingan option whose exercise price equals a proportion of its underlying asset's value. We derive theoptimal time at which the option should be exercised in order to determine the option's value. Wediscuss some implications in Section 3, and provide a conclusion in Section 4.

THE MODEL

Let P be the current, date 0 price of an underlying asset. An option written on this asset isassumed to have an exercise price at any future date t$ 0 equal to XPt where Pt is the price of theunderlying asset at date t and 0< X < 1.

First consider an option on a non-dividend paying asset, such as a non-dividend paying stock.Suppose the holder of the option decides to exercise the option at some arbitrary future date, T.What would be the present value of this option? We can value the cash flows from this option usinga simple application of risk-neutral pricing developed by Cox and Ross (1976). Let r be the

157


continuously compounded risk-free interest rate for borrowing or lending between dates 0 and T.Then the present value of this option, V, is simply

(1)V e E P XP X e E P

X e Pe X P

rTT T

rTT

rT rT

= − = −

= − = −

− −

−

* *[ ] ( ) [ ]( ) ( )

11 1

where E* is the risk-neutral expected value of the payoff at date T. It is well-known that thisexpectation can be computed by assuming that the underlying asset has an expected rate of returnequal to the risk-free rate, r, rather than its true expected rate of return.

Note that for this very simple case, the option's value V = (1-X)P does not depend on whenexercise occurs, that is, the option's value does not depend on date T. What this tells us is that itdoes not matter when we decide to exercise the option. The time at which the option is exerciseddoes not affect the option's value.

Let us next consider the same type of option but on a dividend-yielding asset. Theunderlying asset is assumed to continuously pay a dividend that yields a proportion, q, of its value.For example, if the option is written on a stock index where the portfolio of stocks underlying theindex pay a 3 percent dividend, then q = .03.

If we again assume that the option is exercised at some arbitrary future date, T, then the valueof the option is

(2)V e E P XP X e E P

X e Pe X Pe

rTT T

rTT

rT r q T qT

= − = −

= − = −

− −

− − −

* *

( )

[ ] ( ) [ ]( ) ( )

11 1

Here we see that E* [PT] = Pe( r-q )T is the risk-neutral expected value of the underlying assetprice at date T. This means that the price of the asset is expected to appreciate, under therisk-neutral measure, at the rate (r-q). This occurs because, in equilibrium, the owner of the assetmust obtain a total expected rate of return equal to r, which is the case since the dividend, q, plusprice appreciation, r-q, equals q + r - q = r.

Now for the case of a dividend-yielding asset, the option's value is a function of the exercisedate, T. For q > 0, we see that the value of the asset is maximized when T = 0, implying V = (1-X)P.Hence, the optimal exercise strategy for this option on a dividend-yielding asset is to exerciseimmediately.

We now consider one additional case, that being where the proportional exercise price, X,changes over time according to some deterministic function of time, X = x(t). For example, thismight reflect the case in which an employee has the option to buy stock at a discount where thediscount changes as a function of the employee's seniority or level of promotion. In this case, thevalue of the option is similar to the previous case

158


(3)V e E P x T P x T e E P

x T e Pe x T Pe

rTT T

rTT

rT r q T qT

= − = −

= − = −

− −

− − −

* *

( )

[ ( ) ] [ ( )] [ ][ ( )] [ ( )]

11 1

Depending on the specification of x(T), it may be optimal to exercise the option immediatelyor at some future date. For example, suppose that x(t) = Ke- gt so that the exercise price is assumedto decline at rate g from its initial value of K. Then we have

(4)V Ke Pe Pe KegT qT qT q gT= − = −− − − − +[ ] [ ]( )1

Taking the derivative with respect to T gives

(5)∂∂

= − + + =− − +VT

P qe q g KeqT q g T[ ( ) ]( ) 0

or

(6)( ) ( )q g Ke qeq g T qT+ =− + −

which implies that the optimal date to exercise this option is

(7)T Kgq gq

* ln= +1

Note that T* > 0 if K(q+g)/q > 1, which occurs if g and the original K is sufficiently large.If, instead, K(q+g)/q < 1, it is optimal to exercise the option immediately. Figure 1 shows theoptimal exercise date T* with respect to different values of g, assuming q = 0.03 and K = 0.9. Itwould be optimal for the option holder to exercise immediately if the value of g is close to zero. T*

reaches its maximum value when g = 0.02, and then declines as the value of g increases. The trackof T* further implies that this option is attractive to both the employer and the employee. Companiesgenerally allow new hires to exercise their stock options after one to six years of service (vesting),with this option, employees would delay the exercise voluntarily. In the early stage of one'semployment with the company as he/she gains seniority and promotions, the employee usually doesnot need to exercise the option and the optimal exercise date is far away. One usually has gainedthe most seniority and promotions as one goes close to retirement, while the optimal exercise dateis only a few years away then one would need to exercise the option.

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There may be other factors, such as personal taxes, which might affect the optimal exercisedate. But the above analysis should clarify the most important issues regarding the valuation andoptimal exercise of this option.

IMPLICATIONS

Employers who wish to provide performance-sensitive compensation to their employees andwho are concerned with the disadvantages of existing option plans may consider the use of use thistype of option. This new type of option would create strong incentives to improve performance andto increase stock price because the value of the option increases as the underlying stock's priceincreases. The option would promote employee loyalty because the proportional exercise pricedecreases as the option owner gains seniority. The new option would not be too generous becauseit would give its owner a proportion, not all of the appreciation in the stock price based on theowner's package.

This new type of stock option would create less incentive to increase company risk (stockprice volatility) and would create less incentive to reduce dividend yield. With the proportionalexercise price, the executives would have to pay a higher price for greater volatility if they try toboost stock price to a temporarily high level at the time they are ready to exercise their options.They would also have to pay a higher price if they try to boost terminal stock prices by reducingdividends. With existing stock options, higher volatility and lower dividends do not cost theexecutives higher prices. Shareholders may further reduce the executive's incentive to increasecompany risk by relating the option's exercise price to stock price volatility. For example, they cantie the decline rate of the exercise price g to their market adjusted price volatility target:

Ig yayy +−= ,*

, σσσ

where, g",y is the part of g that is related to the company's risk (g is mainly related to seniority andpromotion). F* represents the market adjusted volatility target, F",y represents the actual volatilityduring the year, and I represents an adjustment factor at the shareholder’s discretion. Hence, theexecutives would be rewarded if they keep the company's market adjusted price volatility at orbelow the target level, or be penalized if they fail to do so. Shareholders can also reduce theexecutive's incentive to reduce dividends in a similar way, i.e., relating g to optimal dividends.

The new option would reduce the incentive of early exercise. Early exercise is common forexisting executive or employee options, about 90 percent of the options are exercised beforeexpiration (Heath, Huddart & Lang, 1999). After the exercise of an option, the option and theincentive for retention it provides ceases to exist. The new option has an exercise price that declineswith seniority, ceteris paribus, the longer the option owner waits while working for the company,the greater would be the gain. The company can also adjust the optimal exercise date by adjustingthe declining rate g. Holding other factors constant, the optimal exercise date would be over 20years from today when g = 2 percentage points as shown in Figure 1. In addition, this new option

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0

5

10

15

20

0.003 0.005 0.007 0.010 0.030 0.050 0.150 0.250 0.350 0.450

Decline Rate g

Yea

rs fr

om T

oday

Figure 1: Optimal Exercise Date

can also avoid the difficulty of determining vesting periods. Currently, vesting periods range from1 to 6 years (Huddart & Lang, 1996) among companies and it is hard to determine the optimalnumber of years. With this new option, the company may grant a new hire a justified number ofoptions at a high exercise price, say 80 percent of the current market price of the underlying stock,as a sign-on bonus without working on vesting terms.

It would be interesting to see companies adopt this option plan and test whether this newoption is superior in satisfying stockholders, i.e., greater stock price increase with lower volatility,stronger employee loyalty, and rational dividend payments.

CONCLUSION

A call option having an exercise price that is a proportion of the underlying asset's price canbe an attractive part of an employee compensation scheme. This type of option would create strongincentives to improve performance and to reduce turnover rates of all employees. It would reducethe incentive to exercise the option early. It would create lower incentives to increase company riskor to reduce dividend payment because the executives would have to pay a price if they increasecompany risk or reduce dividend payments.

We show how such options could be valued using a risk-neutral pricing approach. Ourmodel's results implies that if the exercise price of the option is a fixed proportion of the underlyingasset, it will be optimal to exercise the option immediately if the asset pays dividends but is notreceived by the option owner. However, if the proportional exercise price declines at a sufficientlyhigh rate, the optimal exercise of the option occurs at a specified future date.

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165


THE IMPACT OF THE AMERITRADE ONLINEINVESTOR INDEX ON THE AUTOCORRELATIONS

AND CROSS-CORRELATIONS OF MARKET RETURNS

Thomas Willey, Grand Valley State University

ABSTRACT

This paper investigates the value of the information contained in the Ameritrade OnlineInvestors Index (AOII) for the returns of two exchange traded funds. The AOII measures the buyingand selling decisions for a group of online investors. The returns of the funds for the Nasdaq 100and the S&P Mid-Cap 400 are examined using the quartiles of the Index. Overall, results show noinfluence on the returns from a broad market index and a negative impact from the lagged value ofthe return of the given fund. An investment strategy is suggested that incorporates short-sellingwhen low values of the AOII are found in conjunction with negative returns of a given asset.

INTRODUCTION

The predictability of market returns is a topic of great interest to practitioners and financialresearchers. If financial markets are truly efficient and follow a random-walk process, the cost ofdeveloping a forecast of future returns is an unrecoverable investment of time, energy and resources.At the other end of the efficiency spectrum, perhaps the future return on a market portfolio ofsecurities is somehow linked to readily available public information and some degree ofpredictability is attainable. In this paper, the daily returns for two exchange traded funds, the firstfor the Nasdaq 100 (Ticker: QQQ) and the second for the Standard & Poor’s Mid-Cap 400 (Ticker:MDY), are examined to measure the role an index of online investors play in determining futuremarket returns.

The explosive growth of the Internet and online trading, in conjunction with vast amountsof financial information, are some of the major forces that shape individual investor decision makingtoday. Recent papers by Miller (1988), Lakonishok and Maberly (1990) and Abraham andIkenberry (1994) investigated the way investors use information in making investment choices.Their central conclusions are that there are certain time periods where it is more costly to processand use information in buying and selling choices for investors. More specifically, Abraham andIkenberry state that increased costs to process information exist during the work week and this resultleads to increased selling and lower returns of securities on Mondays. The benefits and costs ofinformation processing by online traders are one of the primary research questions for this study.

Chordia and Swaminathan (2000) employed autocorrelations and cross-correlations andfound that returns on stocks with high trading volume can be used to predict returns of low tradingvolume stocks, regardless of the size of the firm. This paper uses a methodology implemented byPerfect and Peterson (1997) and Higgins, Howton and Perfect (2000) by investigating the

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autocorrelations and cross-correlations in the returns of two exchange traded funds (ETFs) for twomajor market indices. While the two previous articles investigated the returns of an asset on a givenday of the week, this research looks at the role a given level of buying and selling by onlineinvestors play in determining market returns. The daily autocorrelations of the QQQ will beexamined first, followed by the daily cross-correlations between the QQQ and the ETF for thebroader market index of the S&P 500. For comparison purposes, similar results are provided for theMDY. Finally, the relative strengths of the two statistical measures will be estimated jointly todetermine if the lagged returns of a given security or the cross-correlations dominate the most recentreturn of the examined stock.

DATA

One of the major online brokerage firms, Ameritrade, has began to publish the AmeritradeOnline Investor Index (AOII), a daily measure of the amount of buyer participation based on adecisions made by the firm’s online investors (Ameritrade Press Release, 12/1/1999). On everytrading day, after the U.S. markets have closed, Ameritrade posts the Ameritrade Index page on theInternet. One of the stated goals of the index is to measure the individual investment decisions ofonline investment individuals. The AOII is presented as the percent of online traders that werebuyers, and is found by dividing the number of buyers of equities by the sum of buyers and sellersof equities. For the initial day of the study, the AOII was reported as 37.68%, which indicatedapproximately 38% of all buyers and sellers would have been buying stocks and the remaining 62%would have been selling equities. The study period for the AOII data begins on February 1, 2000and ends on September 22, 2000, a total of one hundred and sixty-four daily returns. The maximumvalue for the AOII of 89.03% and therefore, the strongest bull sentiment for the study period wasApril 12. The strongest bearish value for the index of 12.27% was on May 30, which indicates thatapproximately 86% of online investors were selling securities on that day.

On the whole, online investors were net purchasers of securities with a median value of51.94% for the AOII. The total observations for the sample period were further divided intoquartiles to facilitate the use of indicator variables to represent the online buyer’s purchasingsentiments. The first quartile, from the minimum of 12.27% to 39.51%, represented the sellingsentiment of the study, while the fourth quartile, which ranged from 67.48% to the maximum of89.03%, can be thought of as the buying segment of the sample period.

The data for the market returns was derived from three exchange-traded funds (ETFs) orindex tracking stocks. These securities are a relatively new innovation for the financial markets,first introduced in 1995, but have gained a great amount of popularity in recent years. Accordingto the Wall Street Journal (January 29, 2001), the astronomical compound growth rate in ETFs wasabout 118%, from $6.8 billion in 1997 an estimated $70 billion at year-end 2000. These assets aretraded on the American Stock Exchange and have become some of the most active issues tradedthere. The most widely held and most active issues for the 2000 trading year were the Nasdaq 100tracking index, known as the Cube and the mirror of the S&P 500, referred to as Spiders (tickersymbol SPY). The Cube’s trading volume for 2000 was $6,973.8 million, trailed by $1,932.7 forthe SPY. A related ETF that tracks the S&P Mid-Cap 400 Index was chosen as a comparison index

167


to the QQQ. For the entire year of 2000, the return on the MDY was 16.3% (with a volume of$212.4 million) versus annual returns of –36.2% and –10.7% for the Cube and the Spider,respectively. The primary focus of the analysis will be for the returns of the QQQ. For comparisonpurposes, the MDY returns will be examined separately, while the percentage change in the SPYwill be used as the market return for both examined securities.

METHODOLOGY

The data analysis begins by examining the daily returns for ETFs for the Nasdaq 100, theS&P Mid-Cap 400 and the S&P 500 Stock Index. Results for the hypothesis test for the mean returndiffering from zero are also reported. After the initial analysis of daily returns, the examination ofdaily autocorrelations follows by estimating regression equations for each security (Higgins andPeterson (1999)). The autocorrelations are analyzed for patterns in the four trading quartiles for theQQQ returns and the MDY comparison returns.

Daily dummy variables are used to segment the values of the AOII into four quartiles basedon the proportion of the online investor’s buying percentage. Daily returns for each index trackingsecurity are regressed on the daily dummy variables and the lagged daily returns using the followingequation:

Rt = a1Q1t +a2Q2t +a3Q3t + a4Q4t + b1Q1tRt-1 + b2Q2tRt-1 + b3Q3tRt-1 + b4Q4tRt-1 + et (1)

Where: Rt = Daily returns for the sample at time t; Rt-1 = Daily returns for the sample at time t-1; Q1t,Q2t, Q3tand Q4t = Dummy variables for the quartiles of the AOII; and et = a random error term.

Equation 1 was estimated separately for the QQQ and the MDY securities. The model doesnot include an intercept and uses a dummy variable for each quartile of the AOII index to controlfor differences in daily average returns that may lead to spurious autocorrelations (Higgins, Howtonand Perfect (2000)). In the first equation, the beta coefficients estimate daily autocorrelation terms.The Newey and West (1987) correction for autocorrelation and heteroskedasticity in the residualterms was used to estimate the first-order autocorrelations.

The existence of cross-correlations between a broad market index, the SPY, and the QQQindex are also examined. To test this relationship, Equation 2 will be estimated by regressing dailyQQQ returns on the AOII dummy variables and the lagged market returns:

Rqqq,t = a1Q1t +a2Q2t +a3Q3t + a4Q4t + c1Q1tRspy,t-1 + c2Q2tRspy,t-1 + c3Q3tRspy,t-1 + c4Q4tRspy,t-1 + et (2)

Where: Rqqq,t = Daily returns for QQQ security at time t; Rspy,t-1 = Daily returns for SPY security at time t-1;Q1t,Q2t, Q3t and Q4t = Dummy variables for the quartiles of the AOII; and et = a random error term.

The Newey and West (1987) correction was applied to the second equation and the gammacoefficients measure the daily cross-correlations. For comparison purposes, the daily cross-correlations were also estimated for the MDY security.

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A final set of regression equations are estimated to measure which of the two hypothesizeddaily effects, autocorrelations or cross-correlations, exhibit a stronger influence on the returns of theexamined securities. If the autocorrelations dominate the cross-correlations, the current returns areshaped to a greater degree by the most recent return of the security. On the other hand, conditionsin the broader market would be more valuable to investors, if the cross-correlations showed a higheramount of influence relative to the autocorrelations. Equation 3 is used to measure the relativeinfluences of the daily autocorrelations and cross-correlations on the QQQ returns:

Rqqq,t = a1Q1t +a2Q2t +a3Q3t + a4Q4t + b1Q1tRqqq,t-1 + b2Q2tRqqq,t-1 + b3Q3tRqqq,t-1 + b4Q4tRqqq,t-1 + c1Q1tRspy,t-1 + c2Q2tRspy,t-1 + c3Q3tRspy,t-1 + c4Q4tRspy,t-1 + et (3)

Where: Rqqq,t = Daily returns for QQQ security at time t; Rqqq,t-1 = Daily returns for QQQ security at time t-1;Rspy,t-1 = Daily returns for SPY security at time t-1; Q1t,Q2t, Q3t and Q4t = Dummy variables for the quartiles ofthe AOII; and et = a random error term.

The returns on the MDY security are also examined using Equation 3 by regressing thecurrent return on the first lags of the security’s return and the broad market return of the SPY indexshares. As in the previous two equations, the Newey and West (1987) correction is used to guardagainst potential biases in the estimates. The beta coefficients estimate the daily autocorrelationsand the gamma values are measuring the cross-correlations with the SPY security. Three separatehypothesis tests will be performed to determine if the intercepts, autocorrelations and cross-correlations are jointly equal to zero.

RESULTS

Table 1 contains the statistical characteristics for the average daily percent returns for theQQQ series, the matching MDY returns and the SPY index. All of the returns are positive andstatistically different from zero on days when the AOII fell in the first quartile. This is somewhatunexpected, since the values for the online investors buying decisions in this quartile representtrading days where only a 12 % to a maximum of 40% were buying and the remaining 88% to 60%were selling. Also, during the most active buying period of the fourth quartile, when the onlinetraders were purchasing stocks 67% to 89% of the time, the returns for each of the three assets werenegative and statistically significant. Based on this initial analysis, online investors do not appearto be able to generate returns that differ from zero, instead the buying signals are associated withdrops in the market and decisions to sell correspond to positive returns.

The daily autocorrelation values are presented in Table 2. Section A contains theautocorrelation values for the QQQ security, the autocorrelations for the MDY matching sample arein Section B and the results for the SPY are shown in Section C. None of the autocorrelation termsare statistically significant for the broad market index of the SPY exchange traded fund. For theQQQ and the MDY returns, both of the first quartile autocorrelations are negative and statisticallysignificant. In comparing the QQQ to the MDY, the returns for the QQQ indicate a strongernegative relationship than the returns for the MDY. Also, the autocorrelations for the second andfourth quartiles for the QQQ are negative and statistically different from zero. The joint null

169


hypothesis that all of the coefficients are zero is rejected with a p-values of less than 5% for boththe QQQ and the MDY.

Table 1-Average Daily Percent ReturnsThis table presents the sample means for the daily returns of the index tracking securities for the Nasdaq 100 (QQQ), S&P Mid-Cap 400 (MDY) and the S&P 500 (SPY). The quartiles for the Ameritrade Online Investors Index (AOII) were used to partitionthe returns. The AOII represents the percentage of the firm’s online investors who were buyers of securities on day t. The samplesize is 164 observations. The p-value represents the results for the hypothesis test that the mean return equals zero.

Index All Days First Quartile Second Quartile Third Quartile Fourth Quartile

QQQ 0.0217 2.9618 0.8252 -0.6605 -3.0396

(p-value) (0.9336) (0.0001) (0.0181) (0.0943) (0.0001)

MDY 0.1273 1.3945 0.3953 -0.0431 -1.2373

(p-value) (0.3411) (0.0001) (0.0732) (0.8393) (0.0001)

SPY 0.0289 0.9422 0.2148 -0.0741 -0.9671

(p-value) (0.7871) (0.0001) (0.2049) (0.6646) (0.0001)

In order to investigate the possibility that online investors are incorporating other marketinformation into their investing decisions, the daily cross-correlations between the QQQ and SPYsecurities are presented in Section A of Table 3. Section B contains a similar analysis for the MDYand the broader market index security. For the QQQ, evidence exists that online investors arereacting negatively to other market conditions. The cross-correlations for the first and secondquartiles are both statistically different from zero. This result follows the findings for theautocorrelations, with once again the strongest negative coefficient found for the first quartile.Based on the hypothesis test results, the daily cross-correlations are not equal to each other. Nostatistically significant cross-correlations were found for the MDY.

The final examination of the quartiles of the online investor’s decisions is presented in Table4. The results for the autocorrelations and cross-correlations for QQQ are shown in Section A, whileSection B has the results for the MDY. For the eight possible cross-correlation terms, only thefourth quartile for QQQ exhibited statistical significance with the SPY. For the first and fourthquartiles for both the QQQ and the MDY securities, the most recent returns are negatively relatedto the lagged value of each the respective securities. Also, the second quartile for the QQQ isnegative and significantly different from zero. None of the three remaining autocorrelations werestatistically significant. The joint hypothesis tests indicate all of the autocorrelation coefficients forboth securities are not equal to zero. The practical conclusion to this result is that investors shouldevaluate the AOII, if the index increases (decreases), implement the contrarion decision is to sell(buy). In other words, online investors are not very accurate in predicting future returns in theexamined ETFs of the QQQ and the MDY.

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Table 2-Autocorrelation Patterns in QQQ, MDY and SPY Daily ReturnsThis table presents the results for the daily autocorrelation terms. Sections A, B and C contain the QQQ returns, the MDY returnsand the SPY returns. In the regression model, Rt and Rt-1 are the daily percent returns for the respective index tracking securitieson day t and day t - 1. The Q1t, Q2t, Q3t and Q4t are dummy variables that equal one when the AOII falls in a given quartile andzero otherwise. Standard errors for the regression coefficients are adjusted using the Newey and West (1987) correction. Thesample sizes for all regression models are 163. The Chi-Square value for the joint hypothesis test for the equality of thecoefficients is also presented.

Coefficient with p-value in parenthesesAOII Dummy Variable Intercepts (ai) Autocorrelation Terms (bi)Section A: Daily Autocorrelation Terms for QQQFirst Quartile 3.2826 -0.4501

(0.0001) (0.0005)Second Quartile 1.0252 -0.2729

(0.0004) (0.0151)Third Quartile -0.6748 -0.1607

(0.0663) (0.3101)Fourth Quartile -3.4535 -0.3009

(0.0001) (0.0459)Joint Test of Equality 183.8118 23.0421

(0.0001) (0.0001)Section B: Daily Autocorrelation Terms for MDYFirst Quartile 1.5242 -0.2088

(0.0001) (0.0067)Second Quartile 0.4336 -0.1273

(0.0292) (0.4359)Third Quartile -0.0514 -0.1183

(0.8028) (0.3455)Fourth Quartile -1.2911 -0.1744


(0.0001) (0.0242)Section C: Daily Autocorrelation Terms for SPYFirst Quartile 1.0068 -0.1785

(0.0001) (0.1761)Second Quartile 0.2387 -0.1821

(0.1231) (0.2518)Third Quartile -0.0886 -0.1464

(0.5892) (0.2172)Fourth Quartile -0.9219 0.1416


(0.0001) (0.2676)

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Table 3-Daily Cross-Correlations Between QQQ and MDY Daily ReturnsThis table examines the predictive ability of the lagged index tracking security for the S&P 500 for the daily returns of the Nasdaq100 and the S&P 400 Mid-Cap Index. Section A contains the cross-correlations between the QQQ returns and the S&P 500 Index.Section B presents the cross-correlations between the MDY returns and the S&P 500 Index. In the regression models below, RQQQ,tand RMDY,t are the daily percent returns on day t for QQQ sample and the MDY matching sample, respectively, and RSPY,t-1 is thereturn on day t – 1 for the S&P 500 index security. The Q1t, Q2t, Q3t and Q4t are dummy variables that equal one when the AOIIfalls in a given quartile and zero otherwise. Standard errors for the regression coefficients are adjusted using the Newey and West(1987) correction. The sample sizes for all regression models are 163. The Chi-Square value for the joint hypothesis test for theequality of the coefficients is also presented.

Coefficient with p-value in parentheses

Intercepts Cross-Correlation Terms

AOII Dummy Variable (ai) (ci)

Section A: Daily Cross-Correlations Between the QQQ and the SPY

First Quartile 3.2476 -0.7567

(0.0001) (0.0022)

Second Quartile 0.8854 -0.4591

(0.0062) (0.0967)

Third Quartile -0.6962 -0.3599

(0.0594) (0.1641)

Fourth Quartile -3.1163 -0.2406

(0.0001) (0.5542)

Joint Test of Equality 153.2097 14.4015

(0.0001) (0.0061)

Section B: Daily Cross-Correlations Between the MDY and the SPY

First Quartile 1.4358 -0.0871

(0.0001) (0.5821)

Second Quartile 0.4101 -0.1117

(0.0501) (0.6018)

Third Quartile -0.0565 -0.1365

(0.7817) (0.4212)

Fourth Quartile -1.2485 -0.0348

(0.0001) (0.8791)

Joint Test of Equality 74.3691 1.2453

(0.0001) (0.8706)

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Table 4-Daily Autocorrelation and Cross-Correlations Between QQQ and MDY Daily ReturnsThis table compares the predictive ability of daily autocorrelations and cross-correlations for the QQQ and MDY indices. SectionA contains the autocorrelations in the QQQ sample and the cross-correlations between the QQQ returns and the S&P 500 Index.Section B presents the autocorrelations in the MDY sample and the cross-correlations between the MDY returns and the S&P 500Index. In the regression models below, RQQQ,t RQQQ,t-1 RMDY,t and RMDY,t-1 are the daily percent returns on day t and day t-1 for QQQsample and the MDY matching sample, respectively, and RSPY,t-1 is the return on day t – 1 for the S&P 500 index security. TheQ1t, Q2t, Q3t and Q4t are dummy variables that equal one when the AOII falls in a given quartile and zero otherwise. Standarderrors for the regression coefficients are adjusted using the Newey and West (1987) correction. The sample sizes for all regressionmodels are 163. The Chi-Square value for the joint hypothesis test for the equality of the coefficients is also presented.

Coefficient with p-value in parentheses

Intercepts Autocorrelation Terms Cross-Correlation Terms

AOII Dummy Variable (ai) (bi) (ci)

Section A: Daily Autocorrelations and Cross-Correlations for the QQQ Returns

First Quartile 3.3099 -0.3433 -0.2767

(0.0001) (0.0114) (0.2657)

Second Quartile 1.0622 -0.3955 0.4031

(0.0002) (0.0119) (0.3607)

Third Quartile -0.6891 -0.0795 -0.2171

(0.0723) (0.7851) (0.6502)

Fourth Quartile -3.4481 -0.4783 0.7839

(0.0001) (0.0058) (0.0542)

Joint Test of Equality 189.9817 20.4103 5.9856

(0.0001) (0.0004) (0.2002)

Section B: Daily Autocorrelations and Cross-Correlations for the MDY Returns

First Quartile 1.5382 -0.5191 0.4487

(0.0001) (0.0363) (0.1593)

Second Quartile 0.4333 -0.1238 -0.0052

(0.0262) (0.5396) (0.9852)

Third Quartile -0.0553 -0.0485 -0.0896

(0.7851) (0.8569) (0.8024)

Fourth Quartile -1.2398 -0.2759 0.2592

(0.0001) (0.0496) (0.3478)

Joint Test of Equality 82.8184 8.6432 2.9251

(0.0001) (0.0707) (0.5705)

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CONCLUSION

The specific purpose of this research was to investigate the influence the readily availableAmeritrade Online Investor Index exerted on the returns of two actively traded exchange tradedfunds. For the returns of the Cube, the autocorrelations (three of the four quartiles) dominated theinfluence of the cross-correlations (one of the four quartiles) with the market index. These resultsshow that current returns react inversely to the lag of the most recent value of the same return, ratherthan other market information. For the Mid-Cap SPDR, only the first and fourth quartile’sautocorrelations were statistically significant and negative. No evidence of the influence of thereturns of the broad market index was found.

In a broader sense, this paper presents an extension of the tests for financial marketefficiency. Unlike previously documented exceptions to this core concept, such as the January effectand the day-of-the week anomalies, the information contained in the buying and selling decisionsof this group of online were not associated with positive returns in the examined assets. Instead, anactive investment strategy could be devised using short-selling of the QQQ. The decision ruleincorporates the interaction between a negative return on the Cube and the AOII ending between12% to 40%, if these conditions are met, the investor should short-sell the Nasdaq 100 fund.Otherwise, holding the current position would be the correct choice. Future research is planned totest the return generating capabilities of the proposed strategy.

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REFERENCES

Abraham, A. & D. Ikenberry (1994). The individual investor and the weekend effect. Journal ofFinancial and Quantitative Analysis, (June), 263-277.

Ameritrade Press Release (1999). Ameritrade launches online investor index: First dailymeasurement of behavior of online investors. (December 1).

Chordia, T. & B. Swaminathan (2000). Trading volume and cross-autocorrelations in stock returns.Journal of Finance, (April), 913-935.

Higgins, E. & D. Peterson (1999). Day-of-the-week autocorrelations, cross-autocorrelations, and theweekend effect. The Financial Review, (November), 159-170.

Higgins, E., S. Howton & S. Perfect (2000). The impact of the day of the week on IPO returnautocorrelation and cross-correlation. Quarterly Journal of Business and Economics,(Winter), 57-67.

Lakonishok, J. & E. Maberly (1990). The weekend effect: Trading patterns of individual andinstitutional investors. Journal of Finance, (March), 231-243.

Miller, E. (1988). Why a weekend effect? Journal of Portfolio Management, (Summer), 43-49.

Newey, W. & K. West (1987). A simple, positive, semi-definite, heteroskedasticity andautocorrelation consistent covariance matrix. Econometrica, (May), 703-708.

Perfect, S. & D. Peterson (1997). Day-of-the-week effects in the long-run performance of initialpublic offerings, Financial Review, (February), 49-70.

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LOAN PRICING: A PRICING APPROACH BASEDON RISK

James B. Bexley, Sam Houston State UniversityLeroy W. Ashorn, Sam Houston State University

Joe F. James, Sam Houston State University

ABSTRACT

Loan pricing is one of the most critical decisions facing financial institution managers.Competition has forced management to continuously review loan pricing with a "sharper pencil"in light of stiffer competition for a share of the available loan pool. If the institution is to besuccessful and ensure continued profitable existence, there must be a balance between loan losscontrol and pricing to generate profitability. This study looks at the loan pricing dilemma from arisk management perspective that minimizes the number of calculations required to arrive at the riskfactor.

INTRODUCTION

In the past, substantial lip service has been given to the impact of poor loan decisions upona bank's profitability. In light of the substantial number of bank failures and declining bank earningssuffered across the nation during the 1980s and 1990s, this lip service obviously was not heeded.When talking of a one percent loan default rate, there is a notion that one percent is not statisticallysignificant. However, the reality of a $100 million bank with a 65% loan-to-deposit ratio and a onepercent loan loss equates to a $650,000 impact. Furthermore, a $100 million bank earning a returnon average assets of 1.2% would return $1.2 million annually. Now, if there is a one percent loanloss in the $100 million bank which earned $1.2 million, it would be necessary to look at the impactof a reduction in either the loan loss reserve or the charge to earnings, to replenish the loss to thereserve. In either case, the result would be an impact on net earnings reducing the return from $1.2million to $550,000. In this example, a one percent loss from loans could cost the bank over 50%of its normal earnings!

DEALING WITH THE FOUR CATEGORIES OF RISK

In looking at the loan pricing aspect of a bank from an asset/liability standpoint or riskscenario, several concepts should be instantly considered by the bank practitioner. Prior toestablishing a strategy for developing a pricing model, much consideration should be given to thevarious means of measuring risks. Hempel, et al (1994, pp. 67-68), has developed an excellentconcept of measuring risk based on four categories of risk identified as liquidity risk, interest raterisk, capital risk, and credit risk.

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The element of liquidity risk addresses the bank's ability to consciously deal with shortfallsin the supply of money either through excess withdrawals or substantial commitments of the bank'sfunds for loans and other income-producing devices. Therefore, the liquidity of the bank isparamount to being able to stay in business given the approximate 14 to 1 leverage to capital ratioin the average bank. In considering liquidity, the following formula will give you the liquidity risk:

Liquidity Risk = Short Term Securities / Bank deposits

In recent years, most banks have purchased or developed sophisticated modeling packagesthat give a detailed picture of the various scenarios that would exist for a bank given differingeconomic conditions. In examining the interest rate risk, the concern lies with the assets of the bankthat are subject to interest sensitivity as opposed to balancing these elements with the liabilities,which are also interest-sensitive. In a perfect world, assets and liabilities would be balanced at anequal level. Needless to say, banks do not exist in a perfect world and, as a result, we find the needto constantly look at the bank's position in each scenario. To measure interest rate risk, thefollowing formula should be utilized:

Interest Rate Risk = Interest Sensitive Assets / Interest Sensitive Liabilities

In determining the make-up of interest-sensitive assets, you should include short-termsecurities and all variable rate loans. Transaction deposits, short-term time and savings deposits,and short-term borrowings should be treated as interest-sensitive liabilities.

A risk that is often taken for granted, which is critical to the foundation integrity of a bank,is that of credit risk. In looking at credit risk, we are seeking to determine the basic exposure of thebank in all areas of credit extension. In the previous example of the bank with a one percent loanloss, it becomes very clear that credit risk evaluation is essential to the viability of the bank. Theformula for credit risk is arrived at as follows:

Credit Risk = Medium Loans / Assets

Medium loans would be those loans having average loss potential as opposed to those loans ofextremely high or extremely low quality. Although, there is an element of judgment in determiningwhat are medium loans most banks have classified their levels of risk on the loan portfolio in orderto easily establish those loans with average loss potential.

Capital risk addresses how much the bank's assets may decline before the depositors,creditors, and shareholders are put at risk. The more capital the bank has, the better the cushion toabsorb loss to the bank's at-risk assets. The formula associated with capital risk is as follows:

Capital Risk = Capital / Risk Assets

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RETURN OBJECTIVES

Banks have traditionally based their pricing either on what the competition was doing or onwhat the market would bear. It has become obvious that, in the current competitive environment,those old methods will not work. Before setting pricing parameters, the bank should set some basicreturn objectives such as return on average assets, return on average equity, and net interest margin.Equally important to meeting the objectives is the establishment of a loan-to-deposit ratio.

Why should the bank be concerned with return on average assets and return on averageequity since these are in reality end-result or "big picture" considerations? The answer is verysimple. If it is not focused on the desired end-result, the bank cannot ensure a sufficient volume ofloans priced at the rate desired in order to reach its goal until it is too late to do anything about theresults. In addition, if the desired goal has been established, the bank has a yardstick against whichto measure results. The formulas for return on average assets and return on average equity are asfollows:

Return on Average Assets = Net Income / Average AssetsReturn on Average Equity = Net Income / Average Equity

The net interest margin continues to be impacted by the competition for good loans andconsiders the interest earned on loans less the interest paid for the money. The formula for netinterest margin is as follows:

Net Interest Margin = ( Interest Income - Interest Expense ) / Earning Assets

It is obvious that, as rates become more competitive, there are only so many loans to bedivided up among all of the banks and the other entities that have invaded what was for years amarket dominated by banks. At the same time, the investor has more options than ever beforeconcerning where to invest his or her money for optimum return. What we see in this picture is thebank being squeezed to pay more for its deposits and charge less for its loans. This equates to areduced net interest margin. The only way to avoid the impact of such a problem is thecompetitively price loans with deposit or fee requirements and to strategically price deposits in sucha way to avoid being the highest bidder. For example, look at deposit pricing in the market and priceat approximately 10% above the average price paid for deposits.

COMPETITION AND PRICING

For years the small-to middle-sized banks escaped the competitive pricing challenges facingthe large, regional banks. Given the competition for quality loans from within the bankingcommunity as well as the non-banking entities, bankers everywhere must now be creative and priceloans off of London Interbank Offer Rate, as well as their time-tested base or prime rates, if theyhave any hope of staying competitive. Gone are the days when a bank in some small market couldassume that it had a "lock" on a loan merely because there was not another bank within miles. Mass

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communications, computer banking, and the Internet have brought Wall Street and the world toevery Main Street, U.S.A. Koch (1995, p. 763) stated, "The fact that loan losses were so highduring the 1980s revealed that loans were not priced high enough to compensate for default risk, aswell as other risks, and the cost of operating the bank." We agree with Koch and would point outthat, since the 1980s, competition has increased. Should banks and other financial institutions failto price their loans in such a way as to ensure compensation for all of the known risks, they standto repeat the errors of the 1980s.

HISTORICAL PRICING METHODS

Banks have historically priced their loans utilizing several traditional methods. For yearsthe primary method for loan product pricing was using a variation of a bank's prime lending rate ora regional money center bank's prime rate. This method implied that the bank's best customer(whether judged by risk or deposit balances) was given the prime lending rate. All other customerswere priced either at prime or some variation of prime, plus a given percentage rate. This methoddominated loan pricing until the mid-to-late 1970s when several occurrences caused the method tolose popularity. First, the competition for quality loans drove the money center banks to look tomore exotic pricing methods to attract the large, blue-chip companies. This new methodology basedoff of LIBOR was then embraced by banks in Middle America. The second occurrence was a seriesof lawsuits challenging banks' use of the prime rate as the principal method for pricing loans.

Today, in lieu of a prime rate, banks are utilizing the term "base rate" as the rate on whichthey price their loans. While many banks continue to use the base rate or prime rate (disavowingthat it is the best or lowest rate), banks continue to search for a method of loan pricing thatincorporates risk and at the same time allows the bank to obtain a reasonable profit index.

CUSTOMER PROFITABILITY ANALYSIS

In the quest for a method of lending money that would price the product being sold by thebank similar to an industrial product, large money center banks and regional banks turned tocustomer profitability analysis as a means to include all the costs for bank loans and services as wellas a profit margin. For the most part, smaller community banks stayed with base rate pricing due tothe cost and complexity of establishing and maintaining accurate costs for products and services.This method required an accurate costing of all the bank's products, which was then applied toindividual customers on an activity or volume basis. At the same time, the customers were givencredit for balances maintained and charged for the cost of reserves and several other items.

COMPENSATING BALANCES

For years, bankers have tried to recognize the deposit balances maintained by theircommercial loan customers and give credit, either formally or informally, for those balances whensetting a rate for a loan to the customers who maintain deposit balances. This method has beenutilized by more community banks than the large money center banks or regional banks. Banks

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utilizing this method would usually establish a peg or base rate and, depending on how large abalance the customer maintained, the bank would make the loan at the peg or base rate or at a rateof some percentage over the peg or base rate. Some banks would also try to allow for risk as theyset the rate, but the calculation was less than scientific.

FEE-BASED LENDING

As competition for loans heated up, corporate financial officers saw an opportunity to playone bank against another. These corporate financiers told the banks in ever-increasing numbers thatthey preferred to pay a fee rather than be required to maintain what to them amounted tounproductive compensating balances. As Koch (1995, p. 771) pointed out in his discussion of feeincome for banks, banks developed three distinct methods of utilizing special fees in the pricing ofloans. Those methods (usually some amount less than 1%) were facility fees, commitment fees, andconversion fees. Facility fees were utilized to charge the customer a fee for making funds available,whether they were utilized or not. On the other hand, a commitment fee is charged only on thatportion of the committed funds that are not drawn down. Conversion fees were charged on thoseloans which were converted to another type of loan.

RISK-BASED PRICING

With the concentration by both regulators and bankers on risk management, the time hascome for banks to price their loans based on some measure of risk related to loan price or rewardto the bank. Several authors support this position although they arrive at the concept in differingways. Sinkey (1998 pp. 420-422) believes that you should score the creditworthiness of theborrower using a statistical model. Koch (1995, p. 778) is of the opinion that banks have generallyunderpriced loans because they have understated risk, and therefore, they should identify bothexpected and unexpected losses, incorporating both in the risk charge for a loan. While we certainlyagree that both Sinkey and Koch have developed scholarly and workable approaches to theincorporation of risk, we are more concerned with developing a pricing mechanism that can beutilized equally well by the small community bank and the large regional bank. Our method wouldassign a numeric value to the various segments of the loan portfolio to be converted to a pricingfactor that would be added to a pre-established loan rate based on conventional pricing methods.Possible risk categories to implement the start-up of a risk pricing scenario are as follows:

Risk Price Defined Risk Within Category

1 0 Cash or CD Secured, or Government Guaranteed Loan

2 +0.25% Loans Secured by Stock, Cash Value Life Insurance, or Corporate Bonds

3 +0.45% Average Risk Loans Secured by Real Estate, Receivables, etc.

4 +0.75% Above Average Risk Loans to Firms with Slightly Deteriorating Profitabilities, etc.

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We would then adjust the risk rates on a historical moving average basis that would begathered from loss experience in the various grade categories (based on the grade category the lossoriginated from not where it went to loss). After several years, a migration analysis or historicalmoving average would be used, much like that currently used on calculations of historical loan lossreserve as required under Banking Circular 201.

For example, let's assume that the bank had set a rate of base +0.50% for a loan with a riskcategory 2. In our risk pricing scenario, we would add an additional 0.25% to the conventionalpricing. Assume that, instead of category 2, the risk were category 4. We would then add 0.75%,which would make the loan price out at base rate plus 1.25%.

CONCLUSION

Regardless of the method chosen, risk must be an integral part of the loan pricing scenarioto adequately compensate the bank for credit exposure.

Utilizing the risk based pricing method, a bank over several years time would have areasonably accurate means of pricing to incorporate risk.

REFERENCES

Hempel, G., Simonson, D., & Coleman, A. (1994) Bank Management Text and Cases; 4th Edition.New York: John Wiley & Sons, Inc.

Koch, T. (1995) Bank Management; 3rd Edition. Austin: The Dryden Press; Harcourt BraceCollege Publishers.

Sinkey, Jr., J. (1998) Commercial Bank Financial Management; 5th Edition. Upper Saddle River,NJ: Prentice Hall.

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