pulling back the curtain on the drivers of signed earnings ... · 1 factset is a multinational...

Pulling Back the Curtain on the Drivers of Signed Earnings Announcement Returns

John R. M. Hand Henry Laurion UNC–Chapel Hill University of Colorado Boulder [email protected] [email protected] Alastair Lawrence Nicholas Martin London Business School UNC–Chapel Hill [email protected] [email protected]

ABSTRACT

Since 1990, the number of one-quarter-ahead financial statement items forecasted by analysts and firm managers that are captured in I/B/E/S and FactSet online data feeds has soared from 1 to 29 and 0 to 21, respectively. We propose that this shift in data capture enables us as researchers to pull back the curtain on the drivers of signed returns at earnings announcements more powerfully than ever before. We offer support for our view by annually estimating regressions of earnings announcement returns on the increasingly rich and diverse set of analyst and guidance forecast errors in I/B/E/S and FactSet, and show that the adjusted R2s of regressions that include all available financial item forecast surprises are up to six times larger than those that contain Street earnings surprise alone. Most of the increase in explanatory power comes from analyst and guidance surprises about firms’ top lines, P&L subtotals, and cash flows—revenues, EBITDA, EBIT, pre-tax income, and operating and free cash flows—rather than from balance sheet items or expenses such as SG&A, depreciation, R&D or income taxes. We also document marked time-series trends in the coefficients on analyst and guidance forecast errors and adjusted R2 and conclude that they reflect both the increase in our ability as researchers to better see what the market is seeing when it sets stock prices at earnings announcements, and changes in the economic relations among returns and accounting information.

April 7, 2019

Keywords: Analyst and guidance forecast errors, FactSet, I/B/E/S, information content

JEL Classifications: G12, G17, M41

Data Availability: Data are available from the sources cited in the text.

We appreciate the helpful comments of two anonymous reviewers, Mark Bradshaw, Andrew Alford, Ray Ball, Robert Bushman, Emmanuel De George, Travis Dyer, Petri Ferreira, Mustafa Gultekin, Peter Joos, Jim Ryans, Richard Sloan, Ahmad Tahoun, Eli Talmor, Irem Tuna, and workshop participants at the University of Connecticut, IESE, INSEAD, London Business School, Ohio State University, and UNC–Chapel Hill.

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I. INTRODUCTION

Ever since the seminal studies of Ball and Brown (1968) and Beaver (1968), empirical financial

accounting researchers have sought to document, measure and understand the magnitude and drivers

of the information content of earnings and earnings announcements. However, while it is clear that

earnings surprises are causal drivers of earnings announcement returns, the measured explanatory

power of the relation has been small, on the order of 2% to 5% depending on the samples and regression

specifications used. This has led some researchers to throw substantial shade on the usefulness of

accounting information to capital market participants (e.g., Lev, 1989).

In this paper we put forward a less pessimistic view of the explanatory power of accounting

numbers at earnings announcements by introducing a new data set to the literature: the union of

I/B/E/S’ Summary History and FactSet’s Standard DataFeed Estimates online data feeds, henceforth

the IUF data feed.1 As of mid-2016 the IUF data feed contained 29 different quarterly financial

statement items forecasted by analysts, and 21 financial statement items managers provide quarterly

guidance about. The goal of our paper is to exploit the richness of the IUF data feed and assess whether,

how and why our understanding of the magnitude and drivers of the information content of accounting

data released at earnings announcements warrants being seen in a new light. Our current analysis

ignores 131 key performance indicators (KPIs) that are available through FactSet and I/B/E/S.

We begin our analysis with the assumption that analysts, managers and investors have always

had strong incentives to forecast more than bottom line net income and EPS, and have indeed done so.

Not only are detailed forecasts of the line items in all three major financial statements required for

proper DCF valuation, but the SEC requires public companies to disclose granular actuals financial

statements in their 10-K and 10-Q filings, making highly dimensioned line item forecasts and forecast

surprises a reality for market participants. For example, since the 1970s Value Line analysts have

published their forecasts of 22-23 quarterly and annual financial items, spread across all three

statements, every 13 weeks for approximately 1,700 stocks that Value Line deems to be of interest to

institutions. In contrast, researchers have rarely gone beyond including GAAP or Street earnings when

explaining variation in earnings announcement returns. As such, we propose that prior literature has

understated the information content of accounting data at earnings announcements because market

1 FactSet is a multinational financial data and software company that was founded in 1977 and went public in 1996. Revenues in its most recent fiscal year ended 8/31/17 were $1.22 billion. I/B/E/S (Institutional Brokers’ Estimates System) was founded by Lynch, Jones & Ryan and Technimetrics and began collecting earnings estimates for US companies in 1976. Barra bought I/B/E/S in 1993, then sold it to Primark in 1995. Thomson Financial (now Thomson Reuters) purchased Primark in 2000. We focus on dissemination through FactSet and I/B/E/S because they are the largest online providers of analyst forecast data feeds to US capital markets.

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participants have in reality been repricing firms’ equity at earnings announcements using a far richer

and more detailed set of analyst and management guidance surprises than researchers have employed.

We test our proposition by estimating annual cross-sectional regressions of signed earnings

announcement stock returns on analyst and guidance forecast errors for all the non-KPI financial

statement items contained in the IUF data feed over the period 1990-2016. Because I/B/E/S and

FactSet have added items to the IUF data feed in a temporally staggered manner, the time-series of

adjusted R2 in our regressions allows us as researchers to gradually “pull back the curtain” on the

drivers of earnings announcement returns, and to do so in a more powerful way than previously.

In our results we find that the adjusted R2s of regressions that include the set of IUF analyst

and management guidance forecast surprises for financial statement items are up to six times larger

than those of regressions that contain Street earnings surprise alone. Most of the increase in

explanatory power comes from analyst and guidance surprises about firms’ top lines, P&L subtotals,

and cash flows—revenues, EBITDA, EBIT, pre-tax income, and operating and free cash flows—rather

than from balance sheet items or expenses such as SGA, depreciation, R&D and income tax. We also

show that the multiples on guidance surprises for Street earnings and sales revenue are on average at

least double those on analyst forecast surprises, indicating that investors place much more weight on

new accounting-based information from managers about their firm’s expected future performance than

on the resolution of uncertainty about actual firm performance in the most recently completed quarter.

We then document and evaluate time-series trends in the estimated coefficients on IUF analyst

and guidance surprises and the regression adjusted R2s. We propose that if there is no change in the

set of analyst and management guidance surprises that are available to market participants at earnings

announcements, and no change in the economic relations among announcement returns and analyst

and management guidance surprises, then two testable predictions arise. First, when Street earnings

surprise is the sole explanatory variable in the annual regressions, the coefficient on Street earnings

surprise will show no upward or downward trend over time. This is because with an unchanging

correlation structure between earnings announcement returns and surprises, while it will be the case

that the coefficient on Street earnings surprise will be biased because it carries on it the correlations

with the many surprises that are omitted, the bias will be constant over time. The second prediction is

that when analyst and guidance surprises are included as they become available over time in the IUF

data feed, to the extent that such surprises have explanatory power, the estimated coefficient on Street

earnings surprise will decline as the omitted correlated variable bias on Street earnings surprise falls.

We find results that are inconsistent with the first prediction, but consistent with the second.

Specifically, we observe a reliable downward trend in the estimated coefficient on Street earnings

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surprise over time as IUF analyst and guidance surprises are included in the annual regressions, and a

reliable upward trend in the estimated coefficient on Street earnings surprise when it is the only

independent variable. This leads us to infer that while the IUF data feed enables researchers to better

see what the market sees at earnings announcements, the IUF data feed also makes visible otherwise

hidden changes in the underlying economic relations between accounting-based drivers of firms’ stock

returns and firms’ economic characteristics.

Our study contributes to the accounting literature in several ways. First, we introduce the IUF

data feed to empirical accounting research and highlight the rich and diverse data that it contains.

Second, through the results we present, we offer a less pessimistic perspective on the information

content of earnings than has historically been assessed. Third, we add to the insights of other recent

work that has shed new light on the information content of earnings. Our study complements Beaver

et al. (2017, 2018) who document and analyze a doubling over the past 25 years in mean abnormal

squared returns and mean abnormal volume at earnings announcements. Fourth, we report the first

results we are aware of about the relative importance to investors of management guidance (of the

future) versus analyst forecast errors (of the past). Fifth, by studying the IUF data feed we add to the

emerging literature in on the transmission and dissemination of information in financial markets.

Lastly, we believe we point the way to the increasingly multidimensional and granular data that are

demanded by and supplied to market participants, and although likely with a lag, that also will be

available to researchers. I/B/E/S and FactSet data are the tip of a rapidly growing iceberg of big-data

sets of analyst forecasts, as evidenced by the data feeds of firms like Visible Alpha LLC. A fintech

company formed by five of the world’s largest investment banks to create a common language and

platform for their and other brokers’ analyst financial models, as of 12/31/18, Visible Alpha’s

worldwide Insights platform is used by 150+ asset managers and contains 150,000+ current and

historical broker Excel models for 9,800+ companies contributed by 100+ research providers. The

typical firm in Visible Alpha’s data feed has GAAP and non-GAAP income statement, balance sheet,

and cash flow statement forecasts for 200+ different line items going out quarterly for two years and

annually for seven years, plus detailed forecast data on geographic breakdowns, expense models,

segment information, and KPIs such as operational metrics, product-level sales, pricing, and margins.

Such remarkable richness leads us to propose that the higher explanatory power of accounting

information at earnings announcements that we document may well even still be markedly understated,

and that future research will likely benefit by making use of data feeds such as Visible Alpha’s.

The remainder of our paper proceeds as follows. In section II we describe the IUF union of the

I/B/E/S and FactSet analyst and management forecast data feeds. Then in section III we present

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descriptive statistics on the number and types of analyst and management forecasts in the IUF data

feed, highlighting the changes that have occurred over time. In section IV, we report the results of

tests that assess the economic importance of these changes, especially in terms of providing us as

researchers a way to pull back the curtain on the drivers of signed stock returns at earnings

announcements. We discuss our results in section V and provide concluding remarks in section VI.

II. IUF DATA FEED

We created the IUF data feed by uniting I/B/E/S’ Summary History and FactSet’s Standard

DataFeed Estimates online data feeds. We use these data feeds as our proxy for the one-quarter-ahead

analyst and management’s future forecasts that are available to investors in real-time, machine-

readable online form because I/B/E/S and FactSet are the two largest electronic financial data

providers. As such, we expect that we will reduce inferential risks due to problems of selection bias

and incomplete data. While selection bias could occur if a small data provider chooses not to include

in its data feed certain analyst forecasts, we think this issue is addressed by using data feeds from large

data providers, both because large data providers face strong incentives to satisfy their clients’ demands

for as much information as possible, and because large data providers are large by virtue of having

bought smaller rivals and then likely having integrated those rivals’ nonoverlapping data into their own

data feeds.2 Problems from incomplete data could arise if the data-provider industry is highly

fragmented and each provider has a distinct subset of data that it would not share or sell to other

providers. We propose that this issue is addressed by our pooling the data feeds from the two largest

data providers.3

We purchased access to FactSet’s Standard DataFeed Estimates and I/B/E/S’s Summary

History dataset. FactSet’s DataFeed consists of 194 different analyst forecast Measures, which we

classify into 156 unique Items across five Categories (13 income statement; 6 cash flow statement; 10

balance sheet; and 127 KPIs), and data is available on 110 different management guidance Measures,

2 A key part of FactSet’s strategy has been to combine the disparate databases of many smaller data vendors that it has acquired with its own databases. See https://en.wikipedia.org/wiki/FactSet. 3 FactSet and I/B/E/S analyst forecast data feeds differ in how the data are collected. I/B/E/S data are supplied to it by analysts, while FactSet’s data are primarily gathered manually from analysts’ PDF reports by FactSet employees. This means that the databases are subject to different sources of bias and/or error. I/B/E/S history data constitute at root a voluntary disclosure that for a variety of strategic or other reasons may not exactly reflect the contents of analysts’ PDF reports or full Excel-based financial models. However, the strengths of the I/B/E/S approach are that there is less ambiguity about what analysts are forecasting (since they supply it directly to I/B/E/S in a standardized manner), and analysts can supply I/B/E/S with better information than they disclose in their PDF reports. In contrast, since FactSet estimates are manually extracted from analysts’ reports, analysts are not able to choose to supply different information in their reports versus their database feeds. Potentially offsetting this advantage is the risk that FactSet employees may misinterpret analysts’ PDFs and/or incorrectly enter the data they contain.

https://en.wikipedia.org/wiki/FactSet

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covering 87 unique Items across four Categories (13 income statement; 6 cash flow statement; 2

balance sheet; and 66 KPIs). The I/B/E/S dataset contains 29 analyst forecast Measures, which we

classify into 16 unique Items in four Categories (7 income statement; 3 cash flow statement; 2 balance

sheet; and 4 KPIs), and data is available on 14 different management guidance Measures, covering 11

unique Items across three Categories (7 income statement; 2 cash flow statement; and 2 KPIs). When

the I/B/E/S and FactSet data feeds are combined to form the IUF data feed, we obtain a total of 223

Measures that we classify into 160 unique Items (13 income statement; 6 cash flow statement; 10

balance sheet; and 131 KPIs).4 We detail our classification of these Measures into Items and

Categories in appendix A.

In our main analyses we limit our attention to non-KPI items in the IUF data feed. We do so

to most directly connect ourselves with the extant literature, because the non-KPI items are all financial

statement line items or subtotals and prior work has focused on financial statement line items. In this

regard, for analyst forecasts we use the consensus one-quarter-ahead analyst forecasts in the IUF data

feed that are closest to firms’ quarterly earnings announcement dates. For each firm’s earnings

announcement in 1990-2016, we assemble a table of all Measures that have at least one analyst forecast

prior to the announcement.5 When there are multiple consensus periods for the same Measure, we

keep the latest consensus period prior to the earnings announcement. For each Measure, we have data

from either FactSet or I/B/E/S on the number of analysts forecasting the Measure, the median forecast

and the actual. For each Item, we take the FactSet or I/B/E/S Measure with the largest number of

analysts forecasting the Measure.6 For guidance we have the actual guidance reported by the company

and the analyst consensus for that Measure and time period, based on the last consensus period prior

to the earnings announcement. Per appendix A, there is a maximum of 75 Measures representing 29

unique Items that could possibly be forecasted for any given earnings announcement. We then define

a variety of variables based on this table of Measures, the details of which are shown in appendix B.

In figure 1 we show the entrance of items into the IUF data feed over time. Panel A presents

by date ordering the first appearance of each of the 26 non-KPI items forecasted by analysts and the

percentage of analyst-covered firms for which the item is forecasted, subject to the percentage being

sufficiently material, which we define to be at least 5%. Likewise, panels B and C show the first

4 We do not undertake separate analysis on FactSet and I/B/E/S data, because each dataset has been built up over time as FactSet and Thomson Reuters have acquired smaller data providers and almost certainly backfilled the purchased data provider’s forecasts into their own primary datasets. 5 We require that the analyst consensus period begin no earlier than the first day of the quarter forecasted and no later than the earnings announcement date, and that the earnings announcement date be within 150 days of the quarter-end. 6 Of the non-KPI item observations that we use in our analysis, 60% are from FactSet and 40% are from I/B/E/S. All KPI items are from FactSet because we did not have access to I/B/E/S KPIs.

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appearance of each of the three quarterly and five annual horizon management guidance items and the

percentage of analyst-covered firms for which the item is forecasted, as long as the percentage is at

least 5%. Panel A shows that before 1998 the only one-quarter-ahead consensus analyst forecast item

that was captured and disseminated by FactSet and/or I/B/E/S data feed was Street earnings. However,

starting in 2002 there is a marked and steady increase in the number of forecasted financial statement

items in FactSet’s and/or I/B/E/S’s data feed. Panels B and C show that both quarterly and annual

guidance items have been almost exclusively income statement focused, and in contrast to analyst

forecasts, have seen no new additions in the data feed since 2007. In all panels the predominant pattern

is that the percentage of analyst-covered firms for which the forecasted item is present in the IUF data

feed reaches a steady-state level in two to three years.

In figure 2 we present how we arrive at the main sample we use to assess the magnitude and

drivers of the information content of accounting data released at earnings announcements in light of

the IUF data feed. We start with all firm-quarters in the Compustat Fundamentals Quarterly database

with fiscal years 1990:Q1-2016:Q2. We then require that the three-day abnormal stock return ABRET

centered on a given firm-quarter’s earnings announcement can be calculated, which yields sample [1]

that comprises 603,735 earnings announcements. The hill-shaped trajectory over calendar time of

sample [1] matches the well-known increase and then decrease in the number of publicly traded US

firms centered on and around the Internet boom in 2000.

Our primary dataset is sample [2C], the subset of sample [1] that has one-quarter-ahead

consensus analyst forecasts in either the I/B/E/S Summary History data feed or in the FactSet Standard

DataFeed Estimates, and where there is at least one Measure for which a consensus analyst forecast

surprise is calculable.7 In defining surprises this way, we assume that the pertinent FactSet or I/B/E/S

reported actual is available to investors in the 3-day announcement window. To the extent this is not

the case (e.g., for certain specific balance sheet line items), the coefficient estimates from our

regressions will be biased toward zero, assuming rational pricing occurs at earnings announcements.8

Sample [2C] consists of 383,596 firm-quarter earnings announcements. We refer to firms in

sample [2C] as analyst-covered firms. We note that sample [2C] contains forecasts from only I/B/E/S

prior to the start of FactSet coverage in 2002:Q3, at which point the union of I/B/E/S and FactSet

7 We calculate analyst forecast surprises by taking the difference between the pertinent FactSet or I/B/E/S reported actual value and the mean analyst forecast, then dividing that by market cap just prior to the announcement. 8 The next version of the paper will use Compustat’s Snapshot database to assess the availability of item actuals.

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results in more observations than either data feed alone. Samples [2A] and [2B] comprise 193,983

FactSet and 380,291 I/B/E/S firm-quarter earnings announcements, respectively.9

III. DESCRIPTIVE STATISTICS ON THE NUMBERS AND TYPES OF ANALYST AND MANAGEMENT FORECASTS IN THE IUF DATA FEED

Forecasts In figure 3, panel A, we display on a quarterly basis the total number of consensus analyst

forecasts (left-hand axis) and management guidance forecasts (right-hand axis) across all forecasted

financial statement items in the IUF data feed. The plot shows a striking increase in each series, starting

in 2002 and 2000, respectively. The total number of quarterly consensus analyst forecasts increases

from 7,185 in 1990:Q1 to 296,597 in 2016:Q2, while the number of quarterly management guidance

forecasts increases from zero in 1999:Q4 to 5,283 in 2016:Q2.

For any quarter, the number of analyst forecasts can be decomposed into the number of covered

firms multiplied by the mean number of analysts per covered firm multiplied by the mean number of

forecasts made per analyst, while the number of management guidance forecasts can be decomposed

into the number of covered firms multiplied by the mean number of guidance forecasts per covered

firm. We present the time series of these components in figure 3, panel B. The greatest impact on the

total number of analyst forecasts comes from the increase in the number of forecasts made per analyst,

followed by the number of covered firms, then the number of analysts per covered firm. From 1990:Q1

to 2016:Q2 the number of forecasts per analyst increases almost tenfold, linearly increasing from 1.0

to 9.0, while the number of analysts per firms doubles from 4.4 to 8.5 and the number of covered firms

just more than doubles from 1,663 to 3,897. In contrast, the number of guidance items forecasted by

management rises from zero in 1999:Q4 to 1.4 in 2016:Q2, but has been flat at that level since 2007.

Forecasted items

In figure 4 we transition the total number of forecasts and the number of forecasts per analyst

shown in figure 3 to a per-firm basis. We do so to prepare for our regression analysis which is at the

analyst-covered firm level. Panel A of figure 4 shows that the mean number of analyst forecasted

items per covered firm in the IUF data feed displays the same sharply upward-kinked pattern as in

panel A of figure 3. The series starts at a mean of 1.0 in 1990:Q1 and grows almost linearly to 15.0

9 We caution against reading figure 2 as suggesting that FactSet adds very little beyond I/B/E/S, or vice versa. This is because we use historical data feeds as of early 2017, and both FactSet and I/B/E/S regularly add to their data feeds forecasts that were available in real time from other vendors’ data feeds, but not from their own.

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by 2016:Q2, with the latter figure being two thirds higher than the mean number of forecasts per analyst

because the typical covered firm has more than one analyst and analysts typically do not forecast for

exactly the same set of financial statement items. The mean number of guidance forecasts per firm

remains identical to that shown in panel B of figure 3 for obvious reasons.

Similarly upward-kinked patterns are seen for items across income statements, balance sheets

and cash flow statements in panel B. However, panel B also reveals that income statement items

dominate items in other statements, especially for analyst forecasts. As of 2016:Q2, the mean number

of income statement category items (9.2) far exceeds the mean number of balance sheet (3.2) and cash

flow statement (2.7) forecasts, and the mean number of income statement guidance items (0.44) is

double that of cash flow statement guidance items (0.21). This leads us to conjecture that if I/B/E/S

and FactSet are supplying their clients with what their clients find to be economically most valuable,

then analysts and management forecasts of income-statement-related items will be substantially more

valuable than those of non-income-statement-related items.

IV. REASSESSING THE INFORMATION CONTENT OF ACCOUNTING DATA AT EARNINGS ANNOUNCEMENTS BY USING THE IUF DATA FEED

Having shown that the number of and richness in analyst and management guidance forecasts

and forecast surprises that are now available to researchers via the IUF data feed has dramatically

increased since 2000, we turn to assessing whether, how and why our understanding of the size and

drivers of the information content of accounting data at earnings announcements warrants revision.

We center our analysis and the interpretation of our results on the assumption that analysts,

managers and investors have always had strong incentives to forecast more than bottom line net income

and EPS, and have indeed done so. Not only are detailed forecasts of the line items in all three major

financial statements required for proper DCF valuation, but the SEC requires public companies to

disclose granular actuals financial statements in their 10-K and 10-Q filings, making highly

dimensioned line item forecasts and forecast surprises a reality for market participants. For example,

since the 1970s Value Line analysts have published their forecasts of 22-23 quarterly and annual

financial items, spread across all three statements, every 13 weeks for approximately 1,700 stocks that

Value Line deems to be of interest to institutions.10

10 The items that Value Line Investment Survey has created and maintained its Estimates and Projections File, a commercially available machine-readable database, are sales, earnings, dividends, CAPEX, operating margin, depreciation, income tax rate, working capital, long-term debt, return on equity, and return on total capital. Despite these data, we use the IUF data feed in our analysis for three reasons. First, Value Line’s forecasts are for annual periods, not quarterly periods (e.g., current-year EPS, or one-year-ahead sales revenue). There are therefore no quarterly line item surprises to calculate at a firm’s first-, second-, or third-quarter earnings announcements. Second,

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In contrast to the data available to and used by capital market participants in the real world,

researchers have rarely gone beyond including GAAP or Street earnings when explaining variation in

earnings announcement returns. For example, since 1968 we find just 11 papers that have examined

analysts’ revenue forecasts, and nine that have studied analysts’ cash flow forecasts.11 As such, we

propose that prior literature has understated the information content of accounting data at earnings

announcements because market participants have in reality been repricing firms’ equity at earnings

announcements using a far richer and more detailed set of analyst and management guidance surprises

than researchers have employed.

We test our proposition by estimating annual cross-sectional regressions of signed earnings

announcement stock returns on analyst and guidance forecast errors for all the non-KPI financial

statement items contained in the IUF data feed over the period 1990-2016. Because I/B/E/S and

FactSet have added items to the IUF data feed in a temporally staggered manner, the time-series of

adjusted R2 in our regressions allows us as researchers to gradually “pull back the curtain” on the

drivers of earnings announcement returns, and to do so in a more powerful way than previously.

Descriptive statistics

In table 1 we present descriptive statistics for the non-missing values of analyst and guidance

surprises at firms’ earnings announcements in our 1990:Q1-2016:Q2 data window. All variables are

first expressed in dollars, then scaled by the market value of the firm’s common equity just prior to the

earnings announcement. To mitigate the effect of outliers, we winsorize all independent surprise

variables at +/- 10% (of market cap).

Our dataset is defined as all publicly traded US firms in the window 1990:Q1-2016:Q2 with a

non-missing ABRET and a Street earnings surprise in the IUF data feed (n = 383,596). Reflecting the

ramp-up in forecasts and surprises captured in the IUF data feed over time, table 1 shows that of the

each stock in the Value Line universe of 1,700 has its forecasts updated on a set schedule only every 13 weeks. Value Line’s forecasts are therefore likely staler than are those of FactSet and I/B/E/S. Third, quantitative equity hedge funds trade far more on quarterly signals than they do on annual signals. This makes FactSet’s and I/B/E/S’s continuously updated online one-quarter-ahead consensus analyst forecast data feeds much more appealing to them than Value Line’s staler annual horizon forecasts. 11 Revenue forecasts: Bradshaw et al. (2016), Clark and Elgers (1973), Ertimur et al. (2003), Ertimur et al. (2011), Jegadeesh and Livnat (2006), Jones (2007), Keung (2010), Rees and Sivaramakrishnan (2007), Schreuder and Klaassen (1984), Swanson et al. (1985), and Trueman et al. (2001). Cash flow forecasts: Brown and Christensen (2014), Call et al. (2009, 2013), DeFond and Hung (2003, 2007), Givoly et al. (2009), McKinnis and Collins (2011), Mohanram (2014), and Radhakrishnan and Wu (2014). We identified four papers outside of the top five accounting journals: Brown et al. (2013), Lerman et al. (2007), Pae and Yoon (2011) and Yoon and Pae (2013). We also identified three recent working papers: Calegari et al. (2016), Givoly et al. (2017), and Ohlson et al. (2016). Also, Givoly et al. (2017) explore the information content of I/B/E/S’s KPI analyst forecasts (which we do not have for our study).

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33 non-Street earnings surprises, only Sales and GAAP earnings are non-missing less than 50% of the

time. The mean and median percentages of non-missing are 18% and 12%, respectively.

Regression results

In table 2 we report the results of annual cross-sectional OLS regressions of ABRET, the 3-day

abnormal stock returns at firms’ earnings announcements, on consensus analyst forecast surprises and

management guidance surprises for forecasted Income Statement, Cash Flow Statement and Balance

Sheet Items. Each annual regression contains up to four earnings announcements with its associated

ABRET and surprise data per firm. Missing values are addressed by dummy variables: if the forecast

error for a given item is missing, we set a missing-value dummy for that variable equal to 1, zero

otherwise. Slope coefficients on missing-value dummies are estimated but not reported, as is the

regression intercept. t-statistics are in parentheses.

In panel A the sole independent variable is Street earnings surprise, while in panel B, all n =

34 analyst and guidance surprises are included as they sequentially become available in the IUF data

feed. For ease of viewing, in panel B we report only the estimated coefficients and associated t-

statistics for the subset of surprises for which the mean t-statistic over the 1990-2016 window is greater

than or equal to 1.95, and we also color code analyst surprise variables in grey versus guidance surprise

variables in yellow.

We highlight the following aspects of the results in panels A and B. First, the adjusted R2s of

the regressions in panel B are up to 6.5X those in panel A (8.8% versus 1.4% in 2012), and on average

are 4X as large (starting in 1998 when Sales enters the IUF data feed). We plot the two series, along

with the adjusted R2s from intermediate regressions that include all analyst forecast surprises but do

not include any guidance surprises, in panel A of figure 5. We interpret the levels of adjusted R2s that

arise when we exploit the IUF data feed as being inconsistent with the historical view that accounting

data is of little use to capital market participants when such usefulness is judged by its information

content at earnings announcements (e.g., Lev, 1989).

Second, the estimated coefficient on Street surprise is always reliably positive in both panels,

and in panel B its mean t-statistic is the largest of all explanatory variables. Third, of the full set of 33

analyst and guidance surprises apart from Street earnings in panel B, 12 are significant in that their

estimated coefficients have a mean t-statistic over time of at least 1.95. Of these 12, seven pertain to

analyst surprise items (out of 25 included in the regressions), and five pertain to guidance surprise

items (out of eight included in the regressions)—two at the quarterly forecasting horizon and three at

the annual horizon. Fourth, all but two of the significant surprises pertain to income statement items—

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revenues, EBITDA, EBIT, pre-tax income, and operating and free cash flows. Only two are from the

cash flow statement, and none relate to firms’ balance sheets (out of the six balance sheet surprises that

were included in the regressions). Fifth, the mean coefficient estimates across all surprises are positive,

as would be expected given the nature of the line items they represent. Lastly, going back to panel A

of figure 1, none of the 13 surprises added to the IUF data feed since the Great Recession in 2008

display incremental explanatory power—including SG&A, depreciation, R&D and income taxes.

Next, we interpret time-series the regression results shown in table 2 within the following

framework. We propose that if there is no change in the set of analyst and management guidance

surprises that are available to market participants at earnings announcements, and no change in the

economic relations among announcement returns and analyst and management guidance surprises, then

two testable predictions arise. First, when Street earnings surprise is the sole explanatory variable in

the annual regressions, the coefficient on Street earnings surprise will show no upward or downward

trend over time. This is because with an unchanging correlation structure between earnings

announcement returns and surprises, while it will be the case that the coefficient on Street earnings

surprise will be biased because it carries on it the correlations with the many surprises that are omitted,

the bias will be constant over time. The second prediction is that when analyst and guidance surprises

are included as they become available over time in the IUF data feed, to the extent that such surprises

have explanatory power, the estimated coefficient on Street earnings surprise will decline as the

omitted correlated variable bias on Street earnings surprise falls.

Evidence bearing on these predictions can be found by comparing the estimated coefficients

on Street earnings surprise across panels A and B over time. For visual accessibility, we also plot the

two series in panel B of figure 5, together with their estimated linear trend lines. Since the t-statistic

on the time-trend slope coefficient for Street surprise alone is 5.3, while the coefficient on Street

surprise in the presence of all other surprises is -6.2, the time trends in the estimated coefficients on

Street surprise are inconsistent with the first prediction but are consistent with the second. The strong

differences in the signs of the time-trends in the two sets of estimated coefficients on Street earnings

surprise leads us to conclude that not only does the IUF data feed increase our ability as researchers to

better see what the market is seeing when it sets stock prices at earnings announcements—based on

the declining coefficients on Street earnings surprise and the 12 significant non-Street earnings item

surprises—but the IUF data feed also reveals changes in the economic relations among returns and

accounting information—since otherwise the coefficient on Street earnings surprise when included

alone in the regression would remain flat over time. The latter inference is strengthened by noting

from the far rightmost column of panel B that of the 11 non-Street earnings analyst forecast surprises,

12

two have estimated coefficients with a reliably increasing time-trend (one-quarter ahead Street

guidance surprise and EBITDA) but none have a reliably decreasing time-trend.

We conduct two additional analyses based on the regressions in table 2. First, we compare the

estimated coefficients of three key financial statement items across analyst forecast surprises versus

management guidance surprises—Street earnings, GAAP earnings, and Sales. We do so because these

are the main forecasts issued by management and overall, management on average tend only to forecast

one of the foregoing variables and not all three. In panels A, B and C of figure 6 we plot the estimated

coefficients and the intra-panel differences between them. Consistent with Street earnings being seen

by market participants as more value-relevant than GAAP earnings, the sizes of the coefficients on

Street earnings surprises are markedly larger than those on GAAP earnings surprises for both analyst

and management guidance (eyeball comparison of panels A and B). It is also strongly the case for

Street earnings and Sales, but not for GAAP earnings, that management guidance surprises evoke a

stronger per-unit reaction than do analyst forecast surprises. This leads us to infer that investors place

much more weight on new accounting-based information from managers about their firm’s expected

future performance than on the resolution of uncertainty about actual firm performance in the most

recently completed quarter.

Second, as our last analysis, we put forward and test the hypothesis that since data providers

are plausibly assumed to be profit-maximizing, I/B/E/S and FactSet will sequenced the collection,

inclusion and dissemination of items in their data feeds in order of their value to capital market clients.

Using the t-statistic on the estimated coefficient of an item’s forecast surprise in table 2 as our measure

of its value to investors, we therefore predict a negative relation between the absolute t-statistic of an

item in table 2 and the date on which the item first appears in the IUF data feed. In support of this

prediction, in figure 7 we plot the relation between the average absolute t-statistic of each item over

1990-2016 and the first date the forecasted item is in the IUF data feed. The Pearson correlation

between the two variables is −0.75 (p < 0.001), and the t-statistic on the coefficient estimated on First

Date in a regression of abs{t-stat} on First Date is −6.4. We view this evidence as consistent with

I/B/E/S and FactSet identifying which analyst-forecasted items are most valuable to investors and

collecting, including and disseminating those sooner via their data feeds.

Robustness tests and limitations Robustness tests We undertook several robustness tests. First, we re-estimated the annual regressions after non-

parametrically recasting all regression variables into ranks (annually), and into 1/0 meet/beat-or-miss

13

form based on whether the surprise was positive or zero (1) or negative (0). In both approaches we

arrived at similar inferences to those in our parametric approach, with the exception that the adjusted

R2s in the non-parametric regressions are typically an average of 6% (ranks) and 4% (1/0) higher, with

the consequence that non-parametric regressions lead to pulling back the curtain by including all

financial statement surprises in the IUF data feed on average doubling (rather than increasing six fold)

the adjusted R2 relative to Street earnings surprise being the sole explanatory variable.

Limitations of our analysis

We acknowledge that the release by the firm of the actual values of its current-quarter financial

items, the revising by analysts of their forecasts, and the issuing of guidance by managers, is not all

that transpires during the earnings announcement window. During the post-announcement conference

call, management may provide soft information, and we do not capture or control for this..

V. DISCUSSION We have documented that over the past 25 years there has been a huge upsurge in the quantity

and richness of the financial forecasts made by both analysts and managers that are captured and

disseminated by I/B/E/S’ Summary History and FactSet’s Standard DataFeed Estimates data feeds.

We have also shown that taking this richness into account in academic research—pulling back the

curtain on the data the market actually sees—makes a big difference in measuring and understanding

the magnitude and drivers of the information content of earnings and earnings announcements.

This said, we argue that the I/B/E/S and FactSet data we study is the tip of a rapidly growing

domain of big-data-type analyst forecasts and associated forecast errors. In our study we focus on only

the one-quarter-ahead forecasting horizon. However, as highlighted by Hand and Martin (2017), there

are many reasons for attention to be paid also to longer horizon forecasts. Normative theory in financial

statement analysis and valuation calls for the creation of detailed sets of forecasted income statements,

balance sheets, and cash flow statements over multiyear horizons (Penman, 2012; Lundholm and

Sloan, 2013; www.wallstreetprep.com; www.trainingthestreet.com). Our study could therefore be

extended to include the revisions in longer-term revenues, EPS, cash flows, and other detailed financial

items forecasts that analysts and managers make at the earnings announcements.

It also seems likely that continued innovations in fintech—information technology applied to

finance—will lead to investors having data feeds that contain individual and consensus analyst

forecasts of fully detailed GAAP and non-GAAP financial statements over horizons ranging from one

quarter to ten years ahead. A step in this direction has already been taken by Visible Alpha LLC, a

http://www.wallstreetprep.com/

http://www.trainingthestreet.com/

14

fintech company formed by five of the world’s largest investment banks to create a common language

and platform for their and other brokers’ analyst financial models. As of 12/31/18, Visible Alpha’s

worldwide Insights platform is used by 150+ asset managers and contains 150,000+ current and

historical broker Excel models for 9,800+ companies contributed by 100+ research providers. The

typical firm in Visible Alpha’s data feed has GAAP and non-GAAP income statement, balance sheet,

and cash flow statement forecasts for 200+ different line items going out quarterly for two years and

annually for seven years, plus detailed forecast data on geographic breakdowns, expense models,

segment information, and KPIs such as operational metrics, product-level sales, pricing, and margins.

Such remarkable richness leads us to propose that the higher explanatory power of accounting

information at earnings announcements that we document may well even still be markedly understated,

and that future research will likely benefit by making use of data feeds such as Visible Alpha’s..

Lastly, we have limited our analyses to the current-quarter earnings announcement. This leaves

open the question of the degree to which market efficiency holds with respect to the increasing flood

of financial statement forecasts being supplied to investors. There are many analyses that future

research could undertake to determine whether this deluge is making markets more or less efficient.

While the accepted inference from positive associations between new information and price/volume

changes is that the latter imply that the former are helping investors make better investing decisions,

the anomalies and behavioral finance literatures typically take a more skeptical view. We propose that

the stream of new forecasted items for which there are high-quality, machine-readable data provides

fresh opportunities for efficient-market proponents and opponents to test their theories.

VI. CONCLUSIONS The goal of our paper has been to exploit the richness of the IUF data feed and assess whether,

how and why our understanding of the magnitude and drivers of the information content of accounting

data released at earnings announcements warrants being seen in a new light.

We show that since 1990, the number of one-quarter-ahead financial statement items forecasted

by analysts and firm managers that are captured in I/B/E/S and FactSet online data feeds has soared

from 1 to 29 and 0 to 21, respectively. We proposed that this shift in data capture enables us as

researchers to pull back the curtain on the drivers of signed returns at earnings announcements more

powerfully than ever before. We offered support for our view by annually estimating regressions of

earnings announcement returns on the increasingly rich and diverse set of analyst and guidance forecast

errors in I/B/E/S and FactSet, and showed that the adjusted R2s of regressions that include all available

financial item forecast surprises are up to six times larger than those that contain Street earnings

15

surprise alone. Most of the increase in explanatory power comes from analyst and guidance surprises

about firms’ top lines, P&L subtotals, and cash flows—revenues, EBITDA, EBIT, pre-tax income, and

operating and free cash flows—rather than from balance sheet items or expenses such as SG&A,

depreciation, R&D or income taxes. We also documented marked time-series trends in the coefficients

on analyst and guidance forecast errors and adjusted R2 and concluded that they reflect both the

increase in our ability as researchers to better see what the market is seeing when it sets stock prices at

earnings announcements, and changes in the economic relations among returns and accounting

information

Based on our analyses, we conclude that the capture and dissemination of analyst and

managers’ forecasts of an increasingly numerous and diverse set of financial items by data providers

such as I/B/E/S and FactSet has increased the informativeness of accounting-based information in US

equity markets. Like those of Beaver et al. (2017, 2018), Ball and Shivakumar (2008) and Shao et al.

(2018), our results contribute fresh insights to the debate about the value relevance of accounting

information. We also add to the emerging literature in accounting and finance that examines the

transmission of information in financial markets by entities such as the business press, Standard &

Poor’s, Dow Jones Newswires, Twitter, EDGAR, I/B/E/S, and First Call. Lastly, by alerting

accounting researchers to the explosion of items that sell-side equity analysts forecast and that are

available through online data feeds such as those of I/B/E/S and FactSet, we hope to encourage future

studies that can exploit the increasing availability of analyst forecasts of detailed financial statements

and financial items.

16

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19

APPENDIX A

Listing of the full set of data items in the FactSet, I/B/E/S, and union of I/B/E/S and FactSet data feeds. Panel A reports the financial statement Measures for which FactSet and I/B/E/S disseminate analyst consensus forecasts. FactSet Measures are listed in the left-hand section along with their FactSet codes and the number of firm-quarters for which there is a consensus surprise for each Measure (n = 57). The middle section presents I/B/E/S Measures along with their I/B/E/S codes and the number of firm-quarters for which there is a consensus surprise for each Measure (n = 18). In the right-hand section, the FactSet and I/B/E/S Measures are manually consolidated into 26 unique financial statement Items, and we report the number of firm-quarters for which there is a consensus surprise for each Item. We exclude Items that are not broadly available, defined as being present for less than 5% of analyst-covered firms in any given year. For example, deferred revenue consensus analyst forecasts are available for fewer than 5% of analyst-covered firms in every year, and are therefore not included. Panel B reports the key performance indicator (KPI) items. Panel A: Financial statement items

FACTSET MEASURE FACTSET CO DE I/B/E/S MEASURE I/B/E/S CO DE CATEGO RY ITEM # ITEM NAME1 Inventories INVENTORIES 12,235 BS ITEM 1 Inventories 12,2352 Current Assets CURRENT_ASSETS 20,257 BS ITEM 2 Current Assets 20,2543 Total Goodwill GW_TOT 31,315 BS ITEM 3 Total Goodwill 31,3124 Total Assets ASSETS 51,809 BS ITEM 4 Total Assets 51,7665 Current Liabilit ies CURRENT_LIABILITIES 20,361 BS ITEM 6 Current Liabilit ies 20,3596 Net Debt NDT 28,224 1 Net Debt NDT 51,910 BS ITEM 8 Debt 57,8497 Total Debt TOTAL_DEBT 10,1848 Shareholder's Equity SH_EQUITY 57,998 BS ITEM 9 Shareholder's Equity 57,9559 Book Value Per Share BPS 45,807 2 Book Value Per Share BPS 86,894 BS ITEM 10 Book Value Per Share 91,905

10 Tangible Book Value Per ShareBPS_TANG 17,13111 Sales SALES 165,176 3 Revenue (Non Per Share) SAL 230,642 IS ITEM 11 Sales 236,98312 Revenue REV_TOT 1,85513 Net Sales NET_SALES 1,51314 Consolidated Sales SALES_C 1915 Cost of Goods Sold (Imputed) COGS1 66,352 4 Cost of Goods Sold (Imputed) COGS2 88,830 IS ITEM 12 Cost of Goods Sold 98,14616 Selling, General and Administr SGA 48,286 IS ITEM 13 Selling, General and Administr 71,33717 General and Administrative G_A_EXP 36,47618 Sales and Marketing S_M_EXP 19,35919 Research and Development RD_EXP 29,207 IS ITEM 15 Research and Development 29,20420 EBITDA EBITDA 91,183 5 EBITDA (Non Per Share) EBT 122,399 IS ITEM 16 EBITDA 133,675

6 EBITDA Per Share EBS 34,23921 EBITDA Adjusted EBITDA_ADJ 25,17922 EBITDA Reported EBITDA_REP 14,70923 Funds From Operations FFO 6,519 7 Funds From Operations FFO 8,04124 Adjusted Funds From Operatio AFFO 4,68625 EBITA EBITA 1,42226 EBITDAR EBITDAR 1,09127 Depreciation and Amortizatio DEPR_AMORT 43,311 IS ITEM 17 Depreciation and Amortizatio 43,30028 EBIT EBIT 125,096 8 EBIT (Non Per Share) EBI 118,868 IS ITEM 18 EBIT 176,423

9 Operating Profit (Non Per ShaOPR 100,39429 EBIT Adjusted EBIT_ADJ 22,67530 EBIT Reported EBITR 17,37731 EBIT Consolidated EBIT_C 2332 Interest Expense INT_EXP 51,808 IS ITEM 19 Interest Expense 51,721

# firm-quarters in

Sample

# firm-quarters

in Sample

UNIO N O F FACTSET AND I/B/E/S # firm-quarters

in Sample

20

APPENDIX A (continued)

Panel B: KPI items

33 Pre-Tax Income PTI 131,350 10 Pre-tax Profit (Non Per SharePRE 175,205 IS ITEM 20 Pre-Tax Income 187,80934 Pre-Tax Profit Reported PTIAG 26,15235 Pre-Tax Profit Adjusted PTPA 25,83436 Consolidated Pretax Income PTI_C 2437 Tax Expense TAX_EXPENSE 41,473 IS ITEM 21 Tax Expense 41,45438 Earnings Per Share EPS 185,462 11 Earnings Per Share EPS 375,307 IS ITEM 22 Street Earnings 383,59639 Net Profit Adjusted NETBG 79,975

12 EPS - Before Goodwill EBG 3,53313 Cash Earnings Per Share CSH 90

40 EPS Non-GAAP EPS_NONGAAP 69,81441 EPS Excluding Exceptionals EPS_EX_XORD 16,48242 Reported EPS EPS_GAAP 91,859 14 GAAP EPS GPS 157,533 IS ITEM 23 GAAP Earnings 207,04343 Net Profit NET 137,591 15 Net Income (Non Per Share) NET 197,66144 Net Income Reported BFNG 45,76345 Consolidated Net Income NET_C 2646 Consolidated EPS EPS_C 2547 Diluted Reported EPS EPSRD 2248 EPS - ex. Extraordinary ItemsEPSAD 1049 Cash Flow Per Share CFPS 40,430 16 Cash Flow Per Share CPS 59,187 CFS ITEM 24 Cash Flow From Operations 82,11550 Cash Flow From Operations CF_OP 50,05751 Capital Expenditure CAPEX 58,071 17 Capital Expenditure (Non Per CPX 69,780 CFS ITEM 25 Capital Expenditure 77,45152 Maintenance CAPEX MAINT_CAPEX 4,04753 Free Cash Flow FCF 42,489 CFS ITEM 26 Free Cash Flow 44,51354 Free Cash Flow Per Share FCFPS 24,82455 Cash Flow From Investing CF_INV 36,743 CFS ITEM 27 Cash Flow From Investing 36,72856 Cash Flow From Financing CF_FIN 34,590 CFS ITEM 28 Cash Flow From Financing 34,57557 Dividends Per Share DPS 55,497 18 Dividends Per Share DPS 83,251 CFS ITEM 29 Dividends Per Share 88,830

19 Net Asset Value (Non Per Share)

NAV 40,034 KPI ITEM 30 Net Asset Value (Non Per Share)

40,020

20 Enterprise Value (Non Per Share)

ENT 675 KPI ITEM 31 Enterprise Value (Non Per Share)

675

21 Return on Assets ROA 17,975 KPI ITEM 32 Return on Assets 17,97422 Return on Equity ROE 31,696 KPI ITEM 33 Return on Equity 31,693

58 Organinc Growth ORGANICGROWTH 1,252 KPI ITEM 34 Organic Growth 1,25259 Airlines; Available seat km AVAILABLESEATKM 371 KPI ITEM 35 Airlines; Available seat km 37160 Airlines; Load Factor LOADFACTOR 358 KPI ITEM 36 Airlines; Load Factor 35861 Airlines; Operating Expenses

per ASKOPEX_ASK 342 KPI ITEM 37 Airlines; Operating Expenses

per ASK342

62 Airlines; Operating Expenses per ASK excluding fuel costs

OPEX_ASK_XFUEL 173 KPI ITEM 38 Airlines; Operating Expenses per ASK excluding fuel costs

173

63 Airlines; Passenger revenue per ASK

PASS_REV_ASK 296 KPI ITEM 39 Airlines; Passenger revenue per ASK

296

'--- END OF I/B/E/S data ---

21


64 Airlines; Passenger revenue per RPK

PASS_REV_RPK 305 KPI ITEM 40 Airlines; Passenger revenue per RPK

305

65 Airlines; Revenue passenger REV_PASSENGER 0 KPI ITEM 41 Airlines; Revenue passenger 066 Airlines; Revenue passenger

kmREVPASSENGERKM 349 KPI ITEM 42 Airlines; Revenue passenger

km349

67 Airlines; Total Revenue per ASK

TOT_REV_ASK 331 KPI ITEM 43 Airlines; Total Revenue per ASK

331

68 Banking; Non-Performing Assets

ASSETS_NONPERF 1,287 KPI ITEM 44 Banking; Non-Performing Assets

1,286

69 Banking; Risk Weighted Assets

ASSETS_RISK_WGHT 1,415 KPI ITEM 45 Banking; Risk Weighted Assets

1,415

70 Banking; Average Earning Assets

AVG_EARN_ASSETS 1,955 KPI ITEM 46 Banking; Average Earning Assets

1,955

71 Banking; T ier 1 Capital Ratio

CAP_RATIO_TIER1 1,770 KPI ITEM 47 Banking; T ier 1 Capital Ratio

1,770

72 Banking; T ier 1 Common Capital Ratio

COMCAP_RATIO_TIER1

1,112 KPI ITEM 48 Banking; T ier 1 Common Capital Ratio

1,112

73 Banking; Cost to Income Ratio

COST_INCOME 5,095 KPI ITEM 49 Banking; Cost to Income Ratio

5,093

74 Banking; Deposits DEPS 5,201 KPI ITEM 50 Banking; Deposits 5,20175 Banking; Average Deposits DEPS_AVG 1,062 KPI ITEM 51 Banking; Average Deposits 1,06276 Banking; Income from Fees

& CommissionsINC_FEES 5,457 KPI ITEM 52 Banking; Income from Fees

& Commissions5,457

77 Banking; Trading Income INC_TRADING 736 KPI ITEM 53 Banking; Trading Income 73678 Banking; Net Interest

IncomeINT_INC 8,301 KPI ITEM 54 Banking; Net Interest

Income8,299

79 Banking; Net Interest Margin INT_INC_MARGIN 2,335 KPI ITEM 55 Banking; Net Interest Margin 2,335

80 Banking; Net Loans LOAN_NET 5,705 KPI ITEM 56 Banking; Net Loans 5,70581 Banking; Average Net Loans LOAN_NET_AVG 1,865 KPI ITEM 57 Banking; Average Net Loans 1,865

82 Banking; Non-Performing Loans

LOAN_NONPERF 3,219 KPI ITEM 58 Banking; Non-Performing Loans

3,219

83 Banking; Provisions for Loans

LOAN_PROV 8,169 KPI ITEM 59 Banking; Provisions for Loans

8,169

84 Banking; Net Charge-Offs NET_CHARGE_OFFS 1,483 KPI ITEM 60 Banking; Net Charge-Offs 1,48385 Banking; Operating Expense OPER_EXP 9,332 KPI ITEM 61 Banking; Operating Expense 9,33186 Computer Hardware; Total

Addressable MarketTAM 11 KPI ITEM 62 Computer Hardware; Total

Addressable Market11

87 Education; New Student Enrollment

STUDENTENROLL_NEW

75 KPI ITEM 63 Education; New Student Enrollment

75

88 Education; Total Student Enrollment

STUDENTENROLL_TOT

111 KPI ITEM 64 Education; Total Student Enrollment

111

89 Financial Data Provider; Annual Subscription Value

ASV 69 KPI ITEM 65 Financial Data Provider; Annual Subscription Value

69

90 Home Builders; Backlog Average Price

BACKLOG_AVG_PRICE

254 KPI ITEM 66 Home Builders; Backlog Average Price

254

22


91 Home Builders; Backlog Units

BACKLOG_UNITS 288 KPI ITEM 67 Home Builders; Backlog Units

288

92 Home Builders; Backlog Value

BACKLOG_VALUE 313 KPI ITEM 68 Home Builders; Backlog Value

313

93 Home Builders; Deliveries Average Price

DELIV_PRICE 284 KPI ITEM 69 Home Builders; Deliveries Average Price

284

94 Home Builders; Deliveries Units

DELIVERIES_UNITS 318 KPI ITEM 70 Home Builders; Deliveries Units

318

95 Home Builders; Financial Services

FIN_SERVICES 192 KPI ITEM 71 Home Builders; Financial Services

192

96 Home Builders; Home Sales HOME_SALES 265 KPI ITEM 72 Home Builders; Home Sales 26597 Home Builders; Land Sales LAND_SALES 210 KPI ITEM 73 Home Builders; Land Sales 21098 Home Builders; New Orders

Average PriceNEW_ORD_PRICE 223 KPI ITEM 74 Home Builders; New Orders

Average Price223

99 Home Builders; New Orders Units

NEW_ORDERS_UNITS 291 KPI ITEM 75 Home Builders; New Orders Units

291

100 Home Builders; New Orders Value

NEW_ORDERS_VALUE 662 KPI ITEM 76 Home Builders; New Orders Value

662

101 Hospitals; Provision for Bad Debt

BAD_DEBT_PROV 104 KPI ITEM 77 Hospitals; Provision for Bad Debt

104

102 Hospitals; Other Operating Expaness

OTHER_OPEX 102 KPI ITEM 78 Hospitals; Other Operating Expaness

0

103 Hospitals; Salaries and Benefits

SAL_BENEFITS 367 KPI ITEM 79 Hospitals; Salaries and Benefits

367

104 Hospitals; Same Store Adjusted Admissions

SS_ADJ_ADM 42 KPI ITEM 80 Hospitals; Same Store Adjusted Admissions

42

105 Hospitals; Same Store Admissions

SS_ADM 40 KPI ITEM 81 Hospitals; Same Store Admissions

40

106 Hospitals; Same Store Revenue per Adjusted Admissions

SS_REV_PER_ADJ_ADM

40 KPI ITEM 82 Hospitals; Same Store Revenue per Adjusted Admissions

40

107 Hospitals; Supplies Cost SUPPLIES 77 KPI ITEM 83 Hospitals; Supplies Cost 77108 Hotels; Occupancy Rate -

DomesticOCCUPY_RATE_DOM 4 KPI ITEM 84 Hotels; Occupancy Rate -

Domestic4

109 Hotels; Occupancy Rate - International

OCCUPY_RATE_INTL 5 KPI ITEM 85 Hotels; Occupancy Rate - International

5

110 Hotels; Occupancy Rate - Total

OCCUPY_RATE_TOT 238 KPI ITEM 86 Hotels; Occupancy Rate - Total

238

111 Hotels; Revenue per Available Room - Domestic

REV_PER_ROOM_DOM

8 KPI ITEM 87 Hotels; Revenue per Available Room - Domestic

8

112 Hotels; Revenue per Available Room - International

REV_PER_ROOM_INTL

13 KPI ITEM 88 Hotels; Revenue per Available Room - International

13

113 Hotels; Revenue per Available Room - Total

REV_PER_ROOM_TOT 302 KPI ITEM 89 Hotels; Revenue per Available Room - Total

302

23


114 Hotels; Daily Room Rate - Domestic

ROOM_RATE_DAILY_DOM

6 KPI ITEM 90 Hotels; Daily Room Rate - Domestic

6

115 Hotels; Daily Room Rate - International

ROOM_RATE_DAILY_INTL

3 KPI ITEM 91 Hotels; Daily Room Rate - International

3

116 Hotels; Daily Room Rate - Total

ROOM_RATE_DAILY_TOT

172 KPI ITEM 92 Hotels; Daily Room Rate - Total

172

117 Insurance; Book Value Per Share Excl AOCI

BVPS_EXCL_AOCI 188 KPI ITEM 93 Insurance; Book Value Per Share Excl AOCI

188

118 Insurance; Book Value Per Share Incl AOCI

BVPS_INCL_AOCI 171 KPI ITEM 94 Insurance; Book Value Per Share Incl AOCI

171

119 Insurance; Underlying Combined Ratio

COMB_RATIO_UND 83 KPI ITEM 95 Insurance; Underlying Combined Ratio

83

120 Insurance; Combined Ratio COMBINED_RATIO 865 KPI ITEM 96 Insurance; Combined Ratio 865121 Insurance; Embedded Value EMBEDDED_VALUE 0 KPI ITEM 97 Insurance; Embedded Value 0122 Insurance; Gross Premiums

WrittenGROSS_PREM_WRITTEN

812 KPI ITEM 98 Insurance; Gross Premiums Written

812

123 Insurance; Net Investment Income

INVEST_INC 1,654 KPI ITEM 99 Insurance; Net Investment Income

1,654

124 Insurance; Net Premiums Earned

PREM_EARN 1,224 KPI ITEM 100 Insurance; Net Premiums Earned

1,224

125 Insurance; Net Premiums Written

PREM_WRITTEN 1,024 KPI ITEM 101 Insurance; Net Premiums Written

1,024

126 Insurance; Underwriting Income

UW_INCOME 99 KPI ITEM 102 Insurance; Underwriting Income

99

127 Mining; Cash Cost CASH_COST 440 KPI ITEM 103 Mining; Cash Cost 440128 Multifinancial; Assets Under

ManagementAUM 386 KPI ITEM 104 Multifinancial; Assets Under

Management386

129 Multifinancial; Assets Under Management Average

AUM_AVG 173 KPI ITEM 105 Multifinancial; Assets Under Management Average

173

130 Multifinancial; Long Term Flows

LT_FLOWS 21 KPI ITEM 106 Multifinancial; Long Term Flows

21

131 Multifinancial; Net Flows NETFLOWS 172 KPI ITEM 107 Multifinancial; Net Flows 172132 Oil/Gas; Chemicals Income -

DomesticCHEM_DOM 0 KPI ITEM 108 Oil/Gas; Chemicals Income -

Domestic0

133 Oil/Gas; Chemicals Income - International

CHEM_INTL 0 KPI ITEM 109 Oil/Gas; Chemicals Income - International

0

134 Oil/Gas; Chemicals Income CHEM_OPINC 106 KPI ITEM 110 Oil/Gas; Chemicals Income 106135 Oil/Gas; Debt-Adjusted Cash

FlowDACF 50 KPI ITEM 111 Oil/Gas; Debt-Adjusted Cash

Flow50

136 Oil/Gas; Upstream Income - Domestic

E_P_DOM 83 KPI ITEM 112 Oil/Gas; Upstream Income - Domestic

83

137 Oil/Gas; Upstream Income - International

E_P_INTL 83 KPI ITEM 113 Oil/Gas; Upstream Income - International

83

138 Oil/Gas; Upstream Income E_P_OPINC 283 KPI ITEM 114 Oil/Gas; Upstream Income 283139 Oil/Gas; Exploration

ExpensesEXPL_EXP 1,017 KPI ITEM 115 Oil/Gas; Exploration

Expenses1,017

24


140 Oil/Gas; OPEX per Unit OPEX_UNIT 1,316 KPI ITEM 116 Oil/Gas; OPEX per Unit 1,314141 Oil/Gas; Production per day -

Natural GasPROD_DAY_GAS 1,918 KPI ITEM 117 Oil/Gas; Production per day -

Natural Gas1,910

142 Oil/Gas; Production per day - Oil & NGLs

PROD_DAY_OIL 1,695 KPI ITEM 118 Oil/Gas; Production per day - Oil & NGLs

1,687

143 Oil/Gas; Production per day PRODPERDAY 2,743 KPI ITEM 119 Oil/Gas; Production per day 2,727144 Oil/Gas; Downstream Income

- DomesticR_M_DOM 0 KPI ITEM 120 Oil/Gas; Downstream Income

- Domestic0

145 Oil/Gas; Downstream Income - International

R_M_INTL 0 KPI ITEM 121 Oil/Gas; Downstream Income - International

0

146 Oil/Gas; Downstream Income R_M_OPINC 204 KPI ITEM 122 Oil/Gas; Downstream Income 204

147 Oil/Gas; Realized Price - Natural Gas

REAL_PRICE_GAS 1,700 KPI ITEM 123 Oil/Gas; Realized Price - Natural Gas

1,699

148 Oil/Gas; Realized Price - Oil & NGLs

REAL_PRICE_OIL 1,723 KPI ITEM 124 Oil/Gas; Realized Price - Oil & NGLs

1,722

149 Oil/Gas; 1P Proved Reserves RSV_1P 0 KPI ITEM 125 Oil/Gas; 1P Proved Reserves 0150 Oil/Gas; 2P Proved and

Probable ReservesRSV_2P 0 KPI ITEM 126 Oil/Gas; 2P Proved and

Probable Reserves0

151 Oil/Gas; 3P Proved Probable and Possible Reserves

RSV_3P 0 KPI ITEM 127 Oil/Gas; 3P Proved Probable and Possible Reserves

0

152 Reits; Net Asset Value Per Share

NAVPS 0 KPI ITEM 128 Reits; Net Asset Value Per Share

0

153 Reits; Net Asset Value Per Share - Next Twelve Months

RNAVPS 4,136 KPI ITEM 129 Reits; Net Asset Value Per Share - Next Twelve Months

4,063

154 Retail; Net sales per square foot

SALES_RSF 1,263 KPI ITEM 130 Retail; Net sales per square foot

1,263

155 Retail; Same Stores Sales - Total

SAMESTORESALES 4,336 KPI ITEM 131 Retail; Same Stores Sales - Total

4,336

156 Retail; Selling Space - Total SELL_SP 2,015 KPI ITEM 132 Retail; Selling Space - Total 2,015157 Retail; Selling Space -

DomesticSELL_SP_D 489 KPI ITEM 133 Retail; Selling Space -

Domestic489

158 Retail; Selling Space - International

SELL_SP_I 24 KPI ITEM 134 Retail; Selling Space - International

24

159 Retail; Same Stores Sales - Domestic

SSS_D 1,242 KPI ITEM 135 Retail; Same Stores Sales - Domestic

1,242

160 Retail; Same Stores Sales - International

SSS_I 240 KPI ITEM 136 Retail; Same Stores Sales - International

240

161 Retail; Number of Stores Closed - Total

ST_CL 394 KPI ITEM 137 Retail; Number of Stores Closed - Total

394

162 Retail; Number of Stores Closed - Domestic

ST_CL_D 4 KPI ITEM 138 Retail; Number of Stores Closed - Domestic

4

25


163 Retail; Number of Stores Closed - International

ST_CL_I 0 KPI ITEM 139 Retail; Number of Stores Closed - International

0

164 Retail; Number of Stores at Period End - Total

ST_END 3,421 KPI ITEM 140 Retail; Number of Stores at Period End - Total

3,421

165 Retail; Number of Stores at Period End - Domestic

ST_END_D 1,091 KPI ITEM 141 Retail; Number of Stores at Period End - Domestic

1,091

166 Retail; Number of Stores at Period End - International

ST_END_I 296 KPI ITEM 142 Retail; Number of Stores at Period End - International

296

167 Retail; Number of Stores Opened - Domestic

ST_OPN_D 198 KPI ITEM 143 Retail; Number of Stores Opened - Domestic

198

168 Retail; Number of Stores Opened - International

ST_OPN_I 2 KPI ITEM 144 Retail; Number of Stores Opened - International

2

169 Retail; Number of Stores Relocated - Total

ST_RLOC 27 KPI ITEM 145 Retail; Number of Stores Relocated - Total

27

170 Retail; Number of Stores Relocated - Domestic

ST_RLOC_D 0 KPI ITEM 146 Retail; Number of Stores Relocated - Domestic

0

171 Retail; Number of Stores Relocated - International

ST_RLOC_I 0 KPI ITEM 147 Retail; Number of Stores Relocated - International

0

172 Retail; Number of Stores Opened - Total

STOREN_OPENED 833 KPI ITEM 148 Retail; Number of Stores Opened - Total

833

173 Social Media/Games; Daily Active Users

DAU 27 KPI ITEM 149 Social Media/Games; Daily Active Users

27

174 Social Media/Games; Monthly Active Users

MAU 37 KPI ITEM 150 Social Media/Games; Monthly Active Users

37

175 Social Media/Games; Monthly Unique Users

MUU 13 KPI ITEM 151 Social Media/Games; Monthly Unique Users

13

176 Telecom; Access Lines ACCESS_LINES 64 KPI ITEM 152 Telecom; Access Lines 64177 Telecom; Average Revenue

Per UserARPU 261 KPI ITEM 153 Telecom; Average Revenue

Per User260

178 Telecom; Churn CHURN 180 KPI ITEM 154 Telecom; Churn 179179 Telecom; Cost per Gross Add CPGA 0 KPI ITEM 155 Telecom; Cost per Gross Add 0

180 Telecom; Gross Adds GROSS_ADDS 133 KPI ITEM 156 Telecom; Gross Adds 133181 Telecom; Minutes of Use MOU 0 KPI ITEM 157 Telecom; Minutes of Use 0182 Telecom; Net Adds NET_ADDS 268 KPI ITEM 158 Telecom; Net Adds 268183 Telecom; Subscriber

Acquisition CostSAC 41 KPI ITEM 159 Telecom; Subscriber

Acquisition Cost41

184 Telecom; Number of Subscribers

SUBSCRIBERS_NB 378 KPI ITEM 160 Telecom; Number of Subscribers

378

26

APPENDIX B

Definitions of abnormal stock returns, and non-KPI variables in the IUF data feed

Subscripts

m An element of the set of 75 database Measures (57 in FactSet, 18 in I/B/E/S) listed in appendix A. Each Measure is an element of one and only one Item.

i An element of the set of 26 researcher-defined Items listed in appendix A for the union of I/B/E/S and FactSet. Each Item is a set of one or more database Measures.

c Either an element of the set of three researcher-defined Categories listed. The thhree Categories are Income Statement, Cash Flow Statement, and Balance Sheet.

t Fiscal period end. Variable Definitions (listed alphabetically) ABRETt Abnormal stock return at earnings announcement for period t. Equal to:

𝑅𝑅𝑅𝑅𝑅𝑅 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅[−1,+1] − 𝛼𝛼�𝐸𝐸𝐸𝐸 − �̂�𝛽𝐸𝐸𝐸𝐸 ∗ 𝑀𝑀𝑅𝑅𝑅𝑅𝑀𝑀𝑅𝑅𝑅𝑅 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅[−1,+1]

where Raw Return[-1,+1] and Market Return[-1,+1] are the 3-day raw return and value-weighted market returns surrounding the earnings announcement for period t; 𝛼𝛼�𝐸𝐸𝐸𝐸, �̂�𝛽𝐸𝐸𝐸𝐸, and �̂�𝜇𝐸𝐸𝐸𝐸 are estimates from a regression model that uses 3-day cumulative, nonoverlapping returns observations during the trading-day period [-130,-10), (+10,+130] relative to the earnings announcement day:

𝑅𝑅𝑅𝑅𝑅𝑅 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 = 𝛼𝛼𝐸𝐸𝐸𝐸 + 𝛽𝛽𝐸𝐸𝐸𝐸 ∗ 𝑀𝑀𝑅𝑅𝑅𝑅𝑀𝑀𝑅𝑅𝑅𝑅 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 + 𝜇𝜇𝐸𝐸𝐸𝐸

This variable is winsorized at the 2nd and 98th percentiles.

Earnings Announcementt

Defined as the 3-day window <-1, +1>, where day <0> is the report date of quarterly earnings (Compustat: rdq) for period t.

Forecast Surprisem,t

The forecast error in dollars scaled by the Market Value of equity at the end of the day prior to the earnings announcement window. The forecasts are taken from the latest consensus period prior to the earnings announcement for period t and winsorized at +/- 10 percent. Equal to

�𝑀𝑀𝑅𝑅𝑅𝑅𝑀𝑀𝑅𝑅𝑅𝑅𝑅𝑅𝑚𝑚,𝑡𝑡 − 𝑀𝑀𝑅𝑅𝑀𝑀𝑀𝑀𝑅𝑅𝑅𝑅 𝐹𝐹𝐹𝐹𝑅𝑅𝑅𝑅𝐹𝐹𝑅𝑅𝑀𝑀𝑅𝑅𝑚𝑚,𝑡𝑡�𝑀𝑀𝑅𝑅𝑅𝑅𝑀𝑀𝑅𝑅𝑅𝑅 𝑉𝑉𝑅𝑅𝑉𝑉𝑅𝑅𝑅𝑅

.

Management Guidance Surprisem,t

Actual management guidance in dollars for Measure m in period t+1 reported at the earnings announcement minus the median analyst consensus forecast in dollars for Measure m in period t+1 as of just prior to the earnings announcement, scaled by Market Value. This variable is winsorized at +/- 10 percent.

�𝐴𝐴𝐹𝐹𝑅𝑅𝑅𝑅𝑅𝑅𝑉𝑉 𝐺𝐺𝑅𝑅𝑀𝑀𝑀𝑀𝑅𝑅𝑅𝑅𝐹𝐹𝑅𝑅𝑚𝑚,𝑡𝑡+1 − 𝑀𝑀𝑅𝑅𝑀𝑀𝑀𝑀𝑅𝑅𝑅𝑅 𝐴𝐴𝑅𝑅𝑅𝑅𝑉𝑉𝐴𝐴𝑀𝑀𝑅𝑅 𝐹𝐹𝐹𝐹𝑅𝑅𝑅𝑅𝐹𝐹𝑅𝑅𝑀𝑀𝑅𝑅𝑚𝑚,𝑡𝑡+1�𝑀𝑀𝑅𝑅𝑅𝑅𝑀𝑀𝑅𝑅𝑅𝑅 𝑉𝑉𝑅𝑅𝑉𝑉𝑅𝑅𝑅𝑅

27

FIGURE 1

Dates of first appearance of non-KPI analyst forecast and management guidance surprises in the union of the I/B/E/S and FactSet databases, and the annual percentage of analyst-covered

firms for which each surprise is present (subject to the percentage being at least 5%)

Panel A: One-quarter-ahead analyst-forecast surprises

Panel B: One-quarter-ahead management guidance surprises

Panel C: One-year-ahead management guidance surprises

Item 1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

Tax Expense 40 74 76 77Current Assets 26 35 35 34Inventories 8 23 24 23Current Liabilities 27 35 35 34Depreciation and Amortization 9 53 55 55 55 55Interest Expense 9 51 54 55 56 56 57Research and Development 23 26 27 26 28 30 30Shareholder's Equity 23 46 47 50 51 52 55 55Total Assets 21 41 42 45 46 47 49 48SG&A Expense 11 59 63 65 65 66 68 69Total Goodwill 12 24 25 28 29 30 30 26Cash Flow From Financing 12 20 24 25 28 28 29 30 30Cash Flow From Investing 13 21 26 27 29 30 30 32 31Free Cash Flow 6 17 27 31 32 35 35 35 36 36Capital Expenditure 7 33 39 43 50 53 56 56 56 56 55Gross Income 14 52 52 59 62 66 69 66 65 65 65Cash Flow From Operations 8 9 10 14 20 35 44 52 53 56 55 58 60 59Debt 7 8 11 14 18 21 28 33 36 38 39 39 42 43Book Value Per Share 22 30 33 35 37 39 46 48 50 51 51 50 52 52Pre-Tax Income 30 59 71 78 83 86 85 86 88 90 92 91 91 92 93EBIT 12 51 62 71 78 80 79 80 84 88 90 90 92 93 93GAAP Earnings 41 73 83 88 93 94 95 94 94 96 98 97 98 99 99EBITDA 8 22 31 37 53 59 62 65 71 75 77 76 76 77 76Dividends Per Share 10 20 25 20 28 33 32 39 46 50 54 55 54 56 56Sales 15 35 46 52 63 81 88 91 94 95 96 97 97 97 97 96 97 98 99Street Earnings 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100

Legend: Income statement Balance sheet Cash flow statement

Item 1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

GAAP Earnings 6 9 11 6 5 6 7 7 7 7 7 7 7Sales 6 12 17 17 18 17 16 15 16 17 18 17 17 16 15Street Earnings 13 16 19 20 19 19 16 15 13 14 14 15 15 14 14 13

Item 1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

Capital Expenditure 13 20 23 24 26 27 25 23 23 22EBITDA 6 6 6 8 9 10 11 12 14 15GAAP Earnings 8 13 15 10 9 11 12 14 13 13 12 12 15Sales 11 17 19 21 23 22 19 22 24 24 24 24 25 25Street Earnings 5 13 19 23 25 26 27 25 23 21 23 24 25 25 24 23 24

28

FIGURE 2

Coverage by the FactSet and I/B/E/S data feeds of public firms’ earnings announcements

Legend:

Sample [1] Firm-quarter earnings announcements at which an abnormal stock return (ABRET) and market value (MVE) can be computed [N = 603,735].

Sample [2A] Subset of [1] with FactSet coverage [N = 193,983]. Sample [2B] Subset of [1] with I/B/E/S coverage [N = 380,291]. Sample [2C] Subset of [1] with either I/B/E/S or FactSet coverage [N = 383,596].

This figure shows the number of publicly traded US firms in total. and with analyst coverage in the Factset and/or I/B/E/S data feeds. Analyst coverage in a given quarter is defined as the firm’s having at least one consensus forecast and its actual available for at least one item at the one-quarter-ahead forecasting horizon. Data are 1990:Q1–2016:Q2.

0

1000

2000

3000

4000

5000

6000

7000

8000

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

Num

ber

of F

irms

[1] [2A] [2B] [2C]

29

FIGURE 3

Consensus analyst and guidance forecasts in the union of the I/B/E/S and FactSet data feeds

Panel A: Total number of analyst and management guidance forecasts, by quarter

Panel B: Number of analyst-covered firms, mean number of analysts per firm, and mean

number of forecasts per analyst, by quarter

0

1,000

2,000

3,000

4,000

5,000

6,000

0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

Num

ber o

f gui

danc

e fo

reca

sts

Tota

l num

ber

of a

naly

st fo

reca

sts

Number of analyst forecasts Number of guidance forecasts

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

5,000

0

1

2

3

4

5

6

7

8

9

10

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

Num

ber o

f ana

lyst

-cov

ered

firm

s

Mea

n nu

mbe

r of a

naly

sts p

er c

over

ed fi

rmM

ean

num

ber o

f for

ecas

ts p

er a

naly

stN

umbe

r of g

uida

nce

fore

cast

s per

cov

ered

firm

Mean number of forecasts per analystMean number of analysts per firmNumber of guidance forecasts per covered firmNumber of analyst-covered firms

30

FIGURE 4

Mean number of financial statement items in the union of I/B/E/S and FactSet data feeds with

analyst consensus or management guidance surprises available at earnings announcements

Panel A: Mean number of forecasted financial statement items per analyst-covered firm

Panel B: Mean number of forecasted financial statement items per firm, by Item category

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

0

2

4

6

8

10

12

14

16

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

Mea

n N

umbe

r of G

uida

nce

Item

Sur

pris

es

Mea

n N

umbe

r of A

naly

st F

orec

aste

d Ite

m S

urpr

ises

Mean number of Analyst Forecast Surprises per covered firmMean number of Management Guidance Surprises per covered firm

0

1

2

3

4

5

6

7

8

9

10

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

Analyst I/S Analyst B/S Analyst CFS Guidance I/S Guidance CFS

31

FIGURE 5

Time-series evolution of estimated coefficients on Street earnings surprise and regression adjusted R2s, based on the increasingly rich sets of IUF data feed item forecast surprises

Panel A: Adjusted R2 based on increasingly rich sets of IUF data feed item forecast surprises

Panel B: Estimated coefficients on Street earnings alone versus Street earnings when the full set of analyst and management guidance item forecast errors are included

0%

1%

2%

3%

4%

5%

6%

7%

8%

9%

10%

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

Adj. R2 {Street earnings alone}

Adj. R2 {Street earnings + all IUF data feed Item surprises}

Adj. R2 {Street earnings + all IUF data feed Item surprises except Guidance}

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

Street Earnings alone Street Earnings Linear (Street Earnings alone) Linear (Street Earnings) multivariate multivariate

t-stat{slope} = 5.3, p < 0.01

t-stat{slope} = -6.2, p < 0.01

32

FIGURE 6

Comparisons of the estimated coefficients on key items across analyst forecast surprises and management guidance surprises, in regressions that include all surprises (per table 2, panel B).

t-stat{difference} is within-panel, for guidance surprise less analyst surprise coefficients.

Panel A: Street earnings

Panel C: GAAP earnings

Panel B: Sales

0

1

2

3

4

5

6

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

Street earnings Street earnings (GQ) Street earnings less Street earnings (GQ)

-1.0

-0.5

0.0

0.5

1.0

1.5

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

GAAP earnings GAAP earnings (GQ) GAAP earnings less GAAP earnings (GQ)

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

Sales Sales (GQ) Sales less Sales (GQ)

t-stat{difference} = 4.1, p < 0.01

t-stat{difference} = 7.1, p < 0.01

t-stat{difference} = 1.2

33

FIGURE 7

Association between the timing of the initial inclusion of an item in the IUF data feed and its

economic importance to investors

Pearson correlation (abs{t-stat}, First Date) = -0.75 (p < 0.001), n = 34 forecasted items OLS regression: abs{t-stat} = 772 – 0.38 * First Date (-6.4) The dashed line displays the OLS trendline. First the first year the item appears in the IUF data feed, subject to being forecasted by at least 5% of analyst-covered firms. While one-quarter-ahead forecasted Street earnings were available to I/B/E/S subscribers prior to 1/1/1990, for simplicity we begin at 1990 because 1/1/1990 is the start of our data analysis window.

0

1

2

3

4

5

6

7

8

9

10

1990 1995 2000 2005 2010 2015

Aver

age

abs{

t-sta

tistic

}

First year forecasted item appears in IUF data feed (5% or more of analyst-covered firms)

34

TABLE 1

Descriptive statistics for analyst and guidance surprises at quarterly earnings announcements

This table shows descriptive statistics for all variables, where surprises are all defined as a percentage of market value of equity just prior to the earnings announcement, and trimmed at +/-10% by year. The data are sample [2C] in Figure 2, span 1990:Q1–2016:Q2, and pertain to only non-missing surprises.

ITEM Analyst forecast Item N

% non-miss.

(% n-m) Min. Mean Median Max. Std.Dev. Mean[abs]U_3DAY ABRET [-1,+1] (%) 383,596 100% -102% 0.06% -0.05% 603% 9.2% 5.9%ITEM22 Street Earnings 383,596 100% -10.0% -0.11% 0.00% 10.0% 1.8% 0.6%ITEM11 Sales 236,993 62% -10.0% 0.01% 0.05% 10.0% 2.9% 1.5%ITEM29 Dividends Per Share 88,839 23% -10.0% 0.00% 0.00% 10.0% 0.5% 0.0%ITEM16 EBITDA 133,999 35% -10.0% -0.06% 0.02% 10.0% 1.9% 0.8%ITEM23 GAAP Earnings 207,078 54% -10.0% -0.14% 0.02% 10.0% 1.9% 0.7%ITEM18 EBIT 176,463 46% -10.0% -0.11% 0.03% 10.0% 2.0% 0.9%ITEM20 Pre-Tax Income 187,832 49% -10.0% -0.15% 0.03% 10.0% 2.2% 1.0%ITEM10 Book Value Per Share 92,371 24% -10.0% -0.16% 0.00% 10.0% 3.2% 1.6%ITEM8 Debt 58,095 15% -10.0% 0.27% 0.10% 10.0% 5.1% 3.7%ITEM24 Cash Flow From Operations 82,866 22% -10.0% -0.02% 0.01% 10.0% 2.4% 1.2%ITEM12 Gross Income 98,149 26% -10.0% 0.01% 0.01% 10.0% 2.6% 1.3%ITEM25 Capital Expenditure 77,452 20% -10.0% -0.03% -0.02% 10.0% 1.8% 0.7%ITEM26 Free Cash Flow 44,513 12% -10.0% -0.06% 0.01% 10.0% 2.7% 1.5%ITEM27 Cash Flow From Investing 36,860 10% -10.0% -0.17% 0.00% 10.0% 2.9% 1.5%ITEM28 Cash Flow From Financing 34,691 9% -10.0% -0.02% 0.00% 10.0% 3.4% 2.0%ITEM3 Total Goodwill 31,312 8% -10.0% 0.16% 0.00% 10.0% 2.5% 1.0%ITEM13 SG&A Expense 71,455 19% -10.0% 0.03% 0.00% 10.0% 0.8% 0.3%ITEM4 Total Assets 51,769 13% -10.0% 0.23% 0.15% 10.0% 5.5% 4.1%ITEM9 Shareholder's Equity 57,977 15% -10.0% -0.19% 0.03% 10.0% 3.6% 2.2%ITEM15 Research and Development 29,821 8% -10.0% -0.03% 0.00% 10.0% 0.7% 0.3%ITEM19 Interest Expense 51,724 13% -10.0% 0.02% 0.00% 10.0% 0.4% 0.1%ITEM17 Depreciation and Amortization 43,307 11% -10.0% 0.00% 0.00% 10.0% 0.6% 0.2%ITEM6 Current Liabilities 20,418 5% -10.0% 0.21% 0.09% 10.0% 3.5% 2.2%ITEM1 Inventories 12,235 3% -10.0% 0.19% 0.01% 10.0% 2.4% 1.2%ITEM2 Current Assets 20,312 5% -10.0% -0.17% -0.07% 10.0% 4.1% 2.8%ITEM21 Tax Expense 41,478 11% -10.0% -0.05% 0.00% 10.0% 1.1% 0.3%

ITEM Quarterly guidance (GQ) Item N % n-m Min. Mean Median Max. StdDev. Mean[abs]ITEM22 Street Earnings (GQ) 39,109 10% -10.0% -0.10% -0.01% 10.0% 0.7% 0.2%ITEM11 Sales (GQ) 36,223 9% -10.0% -0.51% -0.09% 10.0% 2.6% 1.4%ITEM23 GAAP Earnings (GQ) 14,590 4% -10.0% -0.17% -0.03% 10.0% 1.0% 0.3%

ITEM Annual guidance (GA) Item N % n-m Min. Mean Median Max. StdDev. Mean[abs]ITEM22 Street Earnings (GA) 58,420 15% -10.0% -0.13% 0.00% 10.0% 1.1% 0.4%ITEM11 Sales (GA) 47,299 12% -10.0% -0.93% -0.15% 10.0% 4.1% 2.7%ITEM23 GAAP Earnings (GA) 24,682 6% -10.0% -0.39% -0.07% 10.0% 1.9% 0.9%ITEM16 EBITDA (GA) 16,798 4% -10.0% -0.53% 0.00% 10.0% 2.6% 1.4%ITEM25 Capital Expenditure (GA) 35,336 9% -10.0% 0.18% 0.00% 10.0% 2.8% 1.4%

TABLE 2. Annual OLS regressions of abnormal stock returns at firms’ earnings announcements (ABRET) on consensus analyst forecast

surprises and management guidance surprises for forecasted Income Statement, Cash Flow Statement and Balance Sheet Items. Intercepts are estimated but not reported; t-statistics are in parentheses; and Slope t-stat is the t-statistic on the time-trend of estimated coefficients

Panel A: Regression results when sole independent variable is consensus Street Earnings surprise

Panel B: Regression results when all n = 34 consensus analyst forecast surprises (grey) and management guidance surprises (yellow) are included as explanatory variables, but where only estimated coefficients with an absolute t-statistic > 1.95 are shown.

Item Surprise 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Avg.Street Earnings 0.55 0.47 0.55 0.45 0.62 0.49 0.54 0.42 0.42 0.49 0.57 0.55 0.57 0.49 0.46 0.55 0.50 0.60 0.69 0.81 0.68 0.75 0.63 0.73 0.61 0.76 0.80 0.58 5.3 (alone) (14.3) (10.5) (12.9) (12.0) (16.6) (14.3) (15.0) (12.0) (10.7) (11.4) (12.3) (11.2) (10.2) (11.0) (11.1) (14.8) (13.7) (16.6) (15.4) (19.2) (19.2) (17.9) (14.2) (16.3) (15.1) (14.1) (14.0) (13.9)# obs. 6,678 7,457 8,547 10,099 11,964 13,096 14,819 15,942 16,598 16,879 16,084 14,541 14,450 14,494 15,146 15,959 16,247 16,315 15,904 15,333 15,467 15,044 14,556 14,845 15,738 15,909 15,485 14,207Adj. R2 3.0% 1.5% 1.9% 1.4% 2.3% 1.5% 1.5% 0.9% 0.7% 0.8% 0.9% 0.8% 0.7% 0.8% 0.8% 1.4% 1.1% 1.6% 1.5% 2.3% 2.3% 2.1% 1.4% 1.8% 1.4% 1.2% 1.2% 1.4%

Slope t-stat

Item Surprise 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Avg.Street Earnings 0.55 0.47 0.55 0.45 0.62 0.49 0.54 0.42 0.41 0.47 0.53 0.52 0.49 0.29 0.23 0.23 0.17 0.31 0.20 0.27 0.32 0.33 0.22 0.29 0.25 0.32 0.28 0.38 -6.2 (multivariate) (14.3) (10.5) (12.9) (12.0) (16.6) (14.3) (15.0) (12.0) (10.5) (11.0) (11.5) (10.4) (8.6) (6.4) (5.3) (5.9) (4.4) (7.7) (3.9) (5.4) (8.1) (7.0) (4.3) (5.9) (5.5) (5.2) (4.3) (8.8)Sales 0.15 0.16 0.28 0.08 0.22 0.22 0.27 0.30 0.27 0.30 0.34 0.17 0.23 0.25 0.27 0.32 0.30 0.32 0.18 0.24 2.2

(3.2) (4.3) (8.3) (2.5) (6.3) (7.6) (9.4) (11.3) (9.4) (8.4) (10.0) (5.5) (8.1) (7.3) (8.3) (8.4) (7.6) (7.6) (4.3) (7.3)Street Earnings 1.66 0.64 -0.20 0.61 0.48 0.78 0.73 0.74 1.20 0.73 0.74 0.91 1.38 0.54 0.18 1.09 0.78 0.76 0.2 (GA) (6.6) (3.6) (-0.9) (4.3) (3.9) (7.3) (6.8) (6.5) (6.5) (3.5) (4.7) (6.4) (9.3) (3.6) (1.4) (5.9) (4.3) (4.9)Street Earnings 0.97 0.98 0.50 0.42 0.73 0.51 0.79 1.41 3.07 0.86 0.99 2.54 3.74 3.20 5.31 2.18 1.76 4.1 (GQ) (4.6) (3.5) (2.7) (2.1) (4.1) (2.3) (3.5) (4.0) (5.4) (2.1) (3.9) (7.8) (7.8) (6.8) (7.4) (3.6) (4.5)Pre-Tax Income 0.20 0.20 0.15 0.30 0.22 0.20 0.26 0.37 0.43 0.45 0.33 0.18 0.15 0.22 0.18 0.26 0.3

(2.8) (2.7) (2.2) (4.9) (3.5) (3.5) (5.2) (7.9) (9.6) (9.2) (7.2) (3.3) (2.8) (4.5) (3.5) (4.9)EBIT 0.23 0.27 0.29 0.42 0.41 0.27 0.02 0.31 0.20 0.03 0.20 0.35 0.28 0.04 0.27 0.24 -1.1

(2.8) (3.6) (4.2) (6.6) (6.4) (4.4) (0.3) (5.5) (3.7) (0.6) (3.8) (5.6) (4.5) (0.8) (4.9) (3.8)GAAP Earnings 0.00 0.27 0.24 0.14 0.24 -0.03 0.28 0.27 0.07 0.01 -0.03 0.08 0.09 0.18 0.15 0.13 -0.7

(-0.1) (4.0) (3.5) (2.2) (3.9) (-0.6) (5.8) (5.2) (1.5) (0.2) (-0.6) (1.4) (1.7) (3.3) (2.4) (2.3)EBITDA 0.17 0.07 0.09 0.28 0.17 0.41 0.25 0.40 0.24 0.40 0.25 0.58 0.43 0.33 0.31 0.29 3.1

(1.5) (1.0) (1.2) (4.0) (2.4) (6.2) (4.3) (6.4) (4.3) (7.3) (5.0) (9.3) (7.3) (5.8) (5.2) (4.7)Sales 0.14 0.90 0.76 0.75 0.63 0.50 0.43 0.56 0.63 0.52 0.58 0.61 0.82 0.63 0.76 0.61 0.8 (GQ) (1.2) (10.9) (10.8) (11.2) (9.1) (6.3) (5.8) (7.8) (10.3) (8.8) (10.8) (8.2) (11.6) (7.4) (7.8) (8.5)CFlow OPS 0.10 -0.06 0.08 0.06 0.22 0.21 0.09 0.10 0.04 0.14 0.08 0.21 0.20 0.16 0.12 1.8

(1.1) (-0.5) (0.6) (0.5) (2.9) (3.5) (1.6) (2.7) (0.9) (3.6) (1.9) (5.5) (4.3) (3.2) (2.3)Sales 0.22 0.17 0.26 0.29 0.29 0.26 0.21 0.15 0.12 0.19 0.28 0.17 0.13 0.24 0.21 -1.1 (GA) (4.3) (3.9) (6.3) (7.3) (7.4) (5.7) (5.2) (4.9) (3.6) (6.1) (8.0) (4.9) (3.2) (5.3) (5.4)Free Cash Flow 0.21 0.15 0.07 0.20 0.25 0.17 0.20 0.25 0.24 0.14 0.19 0.8

(1.9) (2.2) (1.2) (4.4) (5.1) (3.9) (4.1) (5.2) (4.4) (2.2) (3.5)EBITDA 0.43 0.46 0.75 0.32 0.37 0.18 0.44 0.38 0.48 0.58 0.44 -0.1 (GA) (3.9) (3.6) (6.3) (4.0) (4.2) (2.4) (5.6) (4.8) (5.6) (6.5) (4.7)# obs. 6,678 7,457 8,547 10,099 11,964 13,096 14,819 15,942 16,598 16,879 16,084 14,541 14,450 14,494 15,146 15,959 16,247 16,315 15,904 15,333 15,467 15,044 14,556 14,845 15,738 15,909 15,485 14,207Adj. R2 3.0% 1.5% 1.9% 1.4% 2.3% 1.5% 1.5% 0.9% 0.7% 0.9% 1.7% 1.2% 1.4% 4.0% 4.3% 7.0% 6.2% 6.1% 6.5% 8.5% 8.9% 8.1% 8.8% 9.4% 7.9% 6.1% 5.6% 4.3%

Slope t-stat

pulling back the curtain on the drivers of signed earnings ... · 1 factset is a multinational...

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