IBES Reported Actual EPS and Analysts’ Inferred Actual EPS

7/27/2019 IBES Reported Actual EPS and Analysts Inferred Actual EPS

1/55Electronic copy available at: http://ssrn.com/abstract=1732573

I/B/E/S Reported Actual EPS and Analysts Inferred Actual EPS

Lawrence D. Brown

Temple University

[email protected]

Stephannie LarocqueUniversity of Notre Dame

[email protected]

Forthcoming in The Accounting Review

December 1, 2012

We thank Brad Badertscher, Rajiv Banker, Sudipta Basu, Jeff Burks, William Buslepp, Angie

Davis, Gus De Franco, Peter Easton, Harry Evans (the Editor), Tom Frecka, Yu Gao, Leo Guo,

Mo Khan, Jevons Lee, Greg McPhee, Jeff Miller, Ken Njoroge, Kyle Peterson, Eric Press, Jom

Ruangprapun, Phil Shane, Pervin Shroff, and Ling Zhou, two anonymous reviewers, and

seminar participants at the 2012 Financial Accounting and Reporting Section Meeting

(Chicago), the 2011 AAA Annual Meeting (Denver), Temple University, Tulane University,

University of Minnesota, University of Notre Dame, University of Oregon, and Washington

University (St. Louis) for their helpful comments and suggestions. We gratefully

acknowledge Thomson Reuters for providing I/B/E/S analyst earnings forecast data. Any

errors remain our responsibility.


2/55Electronic copy available at: http://ssrn.com/abstract=1732573


Abstract

Users of I/B/E/S data generally act as if I/B/E/S reported actual earnings represent theearnings analysts were forecasting when they issued their earnings estimates. For

example, when assessing analyst forecast accuracy, users of I/B/E/S data compare

analysts forecasts of EPS with I/B/E/S reported actual EPS. I/B/E/S states that it

calculates actuals using a majority rule indicating that its actuals often do not

represent the earnings that all individual analysts were forecasting. We introduce a

method for measuring analyst inferred actuals, and we assess how often I/B/E/S actuals

do not represent analyst inferred actuals. We find that I/B/E/S reported Q1 actual EPS

differs from analyst inferred actual Q1 EPS by at least one penny 39 percent of the time

during our sample period, 36.5 percent of the time when only one analyst follows the

firm (and hence this consensus forecast is based on the majority rule), and 50 percent

of the time during the last three years of our sample period. We document two adverse

consequences of this phenomenon. First, studies failing to recognize that I/B/E/S EPS

actuals often differ from analyst inferred actuals are likely to obtain less accurate

analyst earnings forecasts, smaller analyst earnings forecast revisions conditional on

earnings surprises, greater analyst forecast dispersion, and smaller market reaction to

earnings surprises than do studies adjusting for these differences. Second, studies failing

to recognize that I/B/E/S EPS actuals often differ from analyst inferred actuals may make

erroneous inferences.

Keywords: I/B/E/S reported actual EPS, analyst inferred actual EPS, accuracy, revisions,dispersion, and surprises.


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I. Introduction

Many studies use actual EPS figures reported by I/B/E/S when examining analyst

forecast accuracy, analyst forecast revisions conditional on observing earnings surprises,

and the capital market reaction to earnings surprises.1

Researchers conducting these studies

implicitly assume that I/B/E/S reported actual earnings represent the earnings forecasted by

individual analysts when they issued their earnings estimates. I/B/E/S uses the majority

rule so its single reported actual for a given firm-time period does not always represent the

earnings numbers that all analysts were forecasting for all firm-time periods. Our study is

the first to examine how often I/B/E/S reported actuals do notrepresent earnings numbers

that individual analysts were forecasting and how ignoring this difference (hereafter DIFF

phenomenon) impacts the results of studies. We conduct our examinations by introducing a

method to infer the earnings that individual analysts were forecasting for a given firm-time

period (hereafter the analysts inferred actuals) which we illustrate with the following

example.

Firmjreports its first quarter EPS for fiscal year 2005 at 10 AM on April 12th

. I/B/E/S

reports firm jsactual first quarter EPS as $0.25 at 11 AM on April 12th

. Analysts A and B

issued forecasts of firmjs second, third and fourth quarter earnings of 2005, and its annual

earnings for 2005 at noon on April 14th

. More specifically,

(1) Both analysts forecasted EPS of $0.25 for Q2, $0.25 for Q3, $0.25 for Q4.(2) Analyst A forecasted $1.00 for the full fiscal year.

1 See Ramnath, Rock, and Shane (2008) for summaries of studies published after 1990, and Brown (2007)for abstracts of articles using analyst earnings data. For a recent article in each area, see Ertimur, Sunder,and Sunder (2007) for accuracy, Gleason and Lee (2003) for revision, and Hugon and Muslu (2010) formarket reaction to earnings surprise.


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(3) Analyst B forecasted EPS of $1.05 for the full fiscal year.To determine if the I/B/E/S Q1 actual EPS represents analyst As(Bs) inferred Q1 EPS, we

compare analyst As (Bs) forecast for the fiscal year with the sum of the analyst s forecast

for the remainder of the year and the I/B/E/S Q1 actual EPS. The numbers do add up for

analyst A (i.e., the $1.00 forecast for the full fiscal year equals the sum of analyst A s

forecast of $0.75 for the remainder of the year and the I/B/E/S Q1 actual EPS of $0.25) but

the numbers do notadd up for analyst B (i.e., analyst Bs $1.05 forecast for the full fiscal

year is five cents greater than the sum of his forecast of $0.75 for the remainder of the year

and the I/B/E/S Q1 actual EPS of $0.25). Thus, we infer that analyst A was aiming at the

same target that I/B/E/S reported as the Q1 actual but that analyst B was aiming at a

different target than the one I/B/E/S reported as the Q1 actual. We measure analyst As

inferred Q1 EPS as $0.25, and analyst Bs inferred Q1 EPS as $0.30.2

Our primary analysis

uses a simple algorithm. If the absolute value of the difference between the I/B/E/S

reported actual EPS and the analysts inferred actual EPSis at least one cent, we say that the

DIFFphenomenon exists and we set DIFF= 1. Otherwise, we say that the DIFFphenomenon

does not exist and we set DIFF= 0.

We find that DIFF= 1 for Q1 EPS 39 percent of the time during our 13 year sample

period, 1996-2008, and 50 percent of the time for the last three years of our sample period,

2006-2008. We also find that DIFF= 1 36.5 percent of the time when a single analyst follows

a given firm in a given year. Because this sole forecast is the mean, median and modal

consensus forecast, our evidence suggests: (1) I/B/E/S actuals often are not based on a

2 According to an I/B/E/S official, I/B/E/S applies a 4 leverage policy, such that it includes brokersannual estimates provided that the sum of the quarterly EPS estimates are within $0.04 of the brokersannual EPS estimate. In our sample, we find that 88 percent of analysts fiscal-year estimates are within|$0.04| of the I/B/E/S Q1 actual plus their forecast for the remainder of the year.


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majority rule, and (2) the DIFF phenomenon applies to consensus forecasts as well as

individual forecasts.3

We show that the DIFF phenomenon is more likely to pertain to

analysts with certain characteristics that the literature has shown to describe inaccurate

analysts, namely analysts who forecast infrequently, follow many firms, and are employed

by smaller brokerage houses. We also show that the DIFF phenomenon is more likely to

pertain both to firms reporting non-operating or extraordinary items and those followed by

more analysts; for the three industries, energy, transportation and utilities; and for the most

recent years. We show that studies ignoring the DIFF phenomenon are likely to

underestimate analyst forecast accuracy, analyst earnings forecast revisions conditional on

earnings surprises, and market reactions to earnings surprises, and to overestimate analyst

dispersion. We provide three ways for researchers to mitigate the measurement error

problems associated with the DIFF phenomenon, and we provide an example of how a

recent study may have reached some different inferences if it had omitted observations

where DIFF= 1.

We present our research questions in section II. We discuss our sample selection

procedures and report the prevalence of DIFF = 1 in section III. We examine factors

associated with the DIFFphenomenon and the adverse consequences of not controlling for

it in sections IV and V, respectively. We provide a replication of prior research in section VI,

robustness tests in section VII, and conclusions in section VIII. Appendix 1 contains

definitions of variables used in our study. Appendix 2 provides additional insight on how we

estimate the DIFFphenomenon.

3 We focus on individual analyst forecasts throughout the study. Nonetheless, in untabulated results, wefind that DIFF = 1 two-thirds of the time for the median consensus forecast so the DIFFphenomenonapplies to the consensus as well as to individual forecasts.


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II. Research questions

Because we are the first to identify the DIFFphenomenon, we examine the extent to

which researchers should be concerned about it. We first examine its prevalence. If the DIFF

phenomenon occurs rarely, it is of little concern to researchers. We find that it occurs often.

Second, we examine if its occurrence is random or systematic. If its occurrence is

systematic, it is more likely to impact the results of studies whose samples are heavily

weighted in the systematic factors. We show that it the DIFF phenomenon is systematic.

Third, we examine the magnitude of the adverse consequences to results of studies that

ignore it. If the magnitudes of the coefficients of studies that ignore it are not materially

affected, researchers failure to consider it is of little consequence. We find that the

coefficients of studies failing to control for the DIFF phenomenon are materially affected

both statistically and economically. Fourth, we examine if there are ways to mitigate its

adverse effects. If it is possible to mitigate its adverse effects, researchers should attempt to

do so. We identify three ways to mitigate the DIFFphenomenons adverse effects, enabling

researchers to conduct studies with more reliable coefficient estimates. Finally, we examine

whether a published study may have reached different conclusions if it excluded

observations where DIFF = 1. Failure to find such a study suggests that attempting to

mitigate the effects of the DIFFphenomenon may not be important. We identify a published

study subject to one of the systematic factors related to the DIFF phenomenon, the

temporal effect, and show that the temporal result described by the researchers may have

been an artifact of increased temporal prevalence of the DIFFphenomenon.

In order to determine whether the Q1 I/B/E/S actual EPS differs from the analysts

inferred Q1 EPS, we compare Q1 I/B/E/S actual EPS to the inferred Q1 analyst EPS for all


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analysts in the I/B/E/S database possessing requisite information. Prior to the Q1 earnings

announcement, an analyst forecasts earnings of Q1 and FY (the fiscal year).4

After Q1

earnings are announced, the same analyst forecasts earnings of Q2, Q3, Q4, and FY. We

refer to the analysts forecasts prior to (after) the Q1 earnings announcement as the first

(second) forecast, and we measure the inferred Q1 EPS by subtracting the summation of the

second forecast of Q2, Q3, and Q4 earnings from the second forecast of FY earnings. Our

first task is to determine how often DIFF= 1. Our first research question (RQ1) is:

RQ1: How often does theDI FF phenomenon occur?

After providing evidence that the frequency ofDIFF= 15 is pervasive in our sample,

we investigate if the DIFFphenomenon is random or systematic. We consider four possible

factors that DIFF = 1 may be related to: analyst effects, firm effects, industry effects, and

year (temporal) effects. Our second research question is:

RQ2: I s the frequency ofthe DIFF phenomenon systematical ly related to

analyst, fi rm, i ndustry and/or year f ixed effects?

After showing that the frequency of the DIFFphenomenon is related to all four fixed

effects, we examine the severity of the consequences to the results of four common types

of studies which ignore the DIFFphenomenon.6

Our third research question is:

4 Prior to the Q1 earnings announcement, analysts may forecast earnings for other periods but we requireonly their forecasts of Q1 and the fiscal year (FY). Our method does not pertain to longer-term earnings

forecasts such as long-term growth forecasts.5 When we examine individual analysts, the term DIFF= 1 refers to all analyst-firm-years whose inferredQ1 differs from the I/B/E/S Q1 actual by at least one penny. When we examine firms, the term DIFF= 1refers to all firms followed by at least one analyst whose inferred Q1 differs from the I/B/E/S Q1 actual byat least one penny.6 We do not query whether any effects exist because DIFF= 1 guarantees that earnings forecast accuracy(i.e., adding measurement error reduces unsigned earnings forecast accuracy), earnings forecast revisioncoefficients and market reaction to earnings surprise coefficients are affected (i.e., adding measurementerror to an independent variable mitigates the magnitude of the variables coefficient estimate). Our task isto explore the economic significance ofDIFF= 1s adverse effects. For simplicity, we only consider the


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RQ3: For studies of forecast accuracy, forecast revision, forecast dispersion,

and the market reaction to earnings sur prises, what are the consequences to

researchers of ignori ng the DIFF phenomenon?

We find that ignoring the DIFFphenomenon has a material effect on the magnitudes

of coefficients of the four types of studies delineated in RQ3. We next examine if it is

possible to mitigate its adverse effects. Our fourth research question is:

RQ4: Can researchers miti gate the adverse eff ects of the DI FF phenomenon to

provide more reliable estimates of their variables of interest for studies of

earnings forecast accuracy, earnings forecast revision, earnings forecast

dispersion , and the market reaction to earni ngs sur pri ses?

We provide three ways for researchers to mitigate the DIFFphenomenons adverse

effects. We next examine if an extant study may have arrived at some different inferences if

it had omitted observations where the DIFFphenomenon exists. Our final research question

is:

RQ5: Would some in ferences in the extant li terature possibly dif fer if

researchers omitted observations wherethe DIFF phenomenon exists?

We discuss a published study that may have reached some different inferences if it omitted

observations subject to the DIFFphenomenon.

III. Sample selection and evidence regarding the prevalence ofDIFF= 1

We extract from the I/B/E/S earnings detail file 74,009 U.S. firm-years with available

reporting dates for fiscal year t-1 (FYt-1) and the first and second quarters of fiscal year t(Q1t

and Q2t) for the 13 years, 1996 to 2008. Consistent with Payne and Thomas (2003) we use

direct effects of theDIFFphenomenon. We do not consider its indirect effects on studies whose focus is onvariables that do not incorporate I/B/E/S actual EPS but which include these data in their control variables.


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unadjusted I/B/E/S data.7

We require reporting dates for FYt-1, Q1t, and Q2t because we

examine analyst forecasts made: (1) after the year t-1 report but before the first quarter of

year treport; and (2) after the first quarter of year treport but before the second quarter of

year treport. We delete 7,858 firm-years for which we cannot obtain actual earnings from

I/B/E/S for FYt (EPSt(FYt)) and for Q1t (EPSt(Q1t)), leaving us with 66,151 firm-years.8

We also

delete 5,470 firm-years without share prices as of the end of year t-1 available from CRSP,

resulting in 60,681 firm-years.

For these 60,681 firm-years, we search the I/B/E/S detail file for analysts who, no

earlier than the day following the firms release of Q1t earnings, issued EPS forecasts for

Q2t, Q3t, Q4t, and FYt on the same day. We do not take into account announcement times in

selecting the sample. We limit the sample to the 32,019 analyst-firm-year observations

where analysts issued a Q1t forecast after the release of FYt-1 earnings but before the

release of Q1t earnings. We use these 32,019 analyst-firm-years to examine the influence of

the DIFF phenomenon on analyst forecast accuracy of Q1t earnings.9

36 percent (64

percent) of analyst-firm-years are followed by only (more than) one analyst. Figure 1

presents an example of the timeline used to select our sample.

[INSERT FIGURE 1 HERE]

We form four sub-samples from the 32,019 analyst-firm-years to examine the

impact of the DIFF phenomenon on: (1) analyst forecast accuracy of FYt (24,427 analyst-

firm-years with FYt forecasts made after the release of FYt-1 earnings but before the release

7 In untabulated results, we omit observations with either stock splits or stock distributions between whenanalysts made their forecasts and the firm announced its earnings. The results, available on request, aresimilar to those reported herein.8 We require the reporting dates for year t-1 and the second quarter of year tbut we do not require theactual earnings numbers for these dates.9 These data are used in panels B, C and D of Table 3 (see Section IV). The other panel of Table 3 andsubsequent tables use smaller sample sizes as the tests have additional data restrictions.


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of Q1t earnings); (2) analyst earnings forecast revisions of Q2-Q4 (21,197 analyst-firm-years

with Q2t, Q3t, and Q4t forecasts made after release of FYt-1 earnings but before the release

of Q1t earnings); (3) analyst forecast dispersion in Q1t (FYt) earnings forecasts (7,152

(5,169)) firm-years with multiple analysts following the firm); and (4) capital market reaction

to Q1 earnings surprises (18,533 firm-years with non-missing CRSP stock return data). Table

1 outlines our sample selection method.

[INSERT TABLE 1 HERE]

We examine analysts annual earnings forecasts made after Q1 earnings are known.

In untabulated results, we find DIFFequals 1 for 12,530 or 39 percent of the analyst-firm-

years. Figure 2 presents the distribution of the difference between the analysts inferred Q1

EPS and the I/B/E/S Q1 actual. The median difference of 0.00 and the mean difference of --

0.003 are insignificantly different from zero, indicating that we cannot reject the null

hypothesis that, on average, I/B/E/S reported Q1 EPS reliably estimate analysts inferred Q1

EPS. The standard deviation of the distribution is 0.250, revealing that the I/B/E/S actual

differs from the analysts inferred actual by less than the absolute value of one-half cent

approximately 95 percent of the time (i.e., two standard deviations on either side of the

mean). Skewness is +73.778, indicating that the distribution of the difference between the

analysts inferred Q1 EPS and the I/B/E/S Q1 actual is skewed right. Kurtosis is +9,570,

indicating that the distribution is leptokurtic (i.e., it has fat tails).

[INSERT FIGURE 2 HERE]

IV. Factors associated with the DIFFphenomenon

In order to determine if the DIFFphenomenon is systematic or random, we estimate

the following fixed effects model using the logistic regression:


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DIFFijkt =i+ j+k + t+ ijkt (1)

We consider four factors: (1) analyst effects (i), (2) firm effects (j), (3) industry effects (k),

and (4) year effects (t). The dependent variable, DIFFijktequals 0 when analyst ifollowing

firm j in I/B/E/S industry k in year tmakes an annual earnings forecast for year tafter the

release of Q1 EPS that is within one penny of the sum of the I/B/E/S Q1 actual EPS and the

analysts EPS forecast of the remainder of year t and 1 otherwise. The model residual is

represented by ijkt. Following OBrien (1990), we estimate separate fixed effects models

where each model estimates only one fixed effect.10

Following Hamilton (1992), we evaluate

the log-likelihood of each logistic model to determine if the fixed effect helps explain the

probability that DIFF= 1.

Our estimations require multiple observations of: (1) analysts for the analyst model;

(2) firms for the firm model; (3) industries for the industry model; and (4) years for the year

model. Table 2 presents the results. Table 2 indicates that the log-likelihood ratio is -

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when we include only analyst fixed effects,-10,335.4 when we include only firm

fixed effects, -19,025.0 when we include only industry fixed effects, and -19,097.2 when we

include only year fixed effects. The log-likelihood ratio is -2,835.7 when we include all four

fixed effects. In sum, the DIFFphenomenon is systematically associated with analyst, firm,

industry, and year effects.


To learn more about these fixed effects, we investigate each one separately. Panel A

of Table 3 presents the association ofDIFFwith five analyst-level variables commonly used

10OBrien (1990) considered analyst effects, firm effects, and year effects. We extend her analysis byadding industry effects.11

The 2 for each model is significant at the 0.00 level.


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in the literature (Mikhail, Walther, and Willis 1997; Clement 1999; Jacob, Lys, and Neale

1999; Clement and Tse 2005), namely firm experience (FEXP), forecast frequency (FREQ),

number of firms covered (NFIRMS), brokerage size (BSIZE), and forecast horizon (HORIZON).

Our results reveal that the DIFFphenomenon is least likely to pertain to analysts who work

for larger brokerage houses, forecast more frequently, and follow fewer firms.12

Panel B shows that the frequency ofDIFFincreases monotonically as the number of

analysts following the firm increases, ranging from 36.5 percent when one analyst follows

the firm to 44.8 percent when five or more analysts follow the firm.13

We examine a second

firm effect in untabulated analysis: the presence or absence of non-operating or

extraordinary items. We test the correlation between DIFFand NONOP (NONOP = 1 when

the absolute value of the difference between Q1 Compustat operating earnings and Q1

Compustat earnings after extraordinary items is one penny or more and 0 otherwise). The

correlation is positive and significant (0.056, p < 0.001), suggesting that firms reporting Q1

EPS with non-operating or extraordinary items are more likely to be subject to the DIFF

phenomenon, and that some analysts following firms with such transitory earnings items

aim at a different earnings target than the earnings reported as the I/B/E/S actual.14

Panel C provides pooled descriptive statistics on the frequency of DIFF= 1 for each

of the 11 industries (industry definitions obtained from I/B/E/S): Basic Industries (Basic),

Capital Goods (Capital), Consumer Durables (ConsDur), Consumer Non-Durables

12 Our brokerage house result is consistent with Ljungqvist et al. (2009), who find that changes to theI/B/E/S database of analyst recommendations occur more often for relatively larger brokerage houses.13 It is logical that as the number of analysts following a firm increases, it is more likely that at least oneanalyst aims at a different earnings target than the other analysts.14 It is intuitively appealing that firms reporting non-operating or extraordinary items have an increasedprobability that at least one analyst is aiming at a different earnings target than other analysts (e.g., someanalysts may consider the non-reporting items as transitory and ignore them; other analysts may considerthe non-reporting items as recurring and include them).


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(ConsNonDur), Consumer Services (ConsSvc), Energy (Energy), Finance (Finance), Health

Care (Health), Technology (Technol), Transportation (Transp), and Public Utilities (Utility).

The frequency with which DIFF = 1 ranges from around 32 percent for the capital goods

industry to 45 percent or more for the utility, transportation, and energy industries.15

Panel D examines year effects by estimating the following specification for the full

sample of 32,019 observations:

Pr(DIFFijkt=1) = a + b * Yeart+ eijkt (2)

To facilitate interpretation of our results, we set the first year of our sample (1996) equal to

one, the second year equal to two, and so on. Our results reveal that the frequency of DIFF

= 1 in the I/B/E/S earnings data base has increased dramatically over time. Untabulated

results reveal that DIFF = 1 50 percent of the time in the last three years of our sample

(2006-2008).16


In sum, the DIFF phenomenon arises more often for: (1) analysts who forecast

infrequently, follow more firms, and are employed by smaller brokerage houses; (2) firms

with non-operating or extraordinary items and those with greater analyst following; (3) the

energy, transportation and utility industries; and (4) in more recent years.

15 We investigate why theDIFFphenomenon is more pronounced in these industries. We find that, relativeto the ten other industries in our sample, the energy industry has the highest analyst following and theutility industry has the highest overall percentage of non-operating earnings and extraordinary items.16 This temporal increase is not surprising given the increased temporal prevalence of non-operating andextraordinary items.During our 1996-2008 sample period, non-operating and extraordinary items increasedfrom 22 percent (in 1996) to 41 percent (in 2008) of firms. To ascertain whether the increase in DIFFovertime is due entirely to an increased prevalence of non-operating and extraordinary items, we re-estimateequation (2) excluding firms with earnings that contain extraordinary items. We obtain similar inferencessuggesting the temporal effect is incremental to the non-operating and extraordinary item effects.


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V. Adverse consequences of the DIFFphenomenon

In the following analyses, we provide univariate results for the full sample, the DIFF

= 0 and DIFF = 1 sub-samples, and the difference between these sub-samples. In

multivariate analyses, we document the severity of the consequences for studies of forecast

accuracy, forecast revision, forecast dispersion, and market reaction to earnings surprise

which ignore the DIFFphenomenon. To mitigate the impact of outliers, we winsorize the

distributions of accuracy, revisions, and earnings forecasts in the dispersion analysis, and

the distribution of earnings surprises in the top and bottom 1 percent of observations.

Earnings forecast accuracy

Table 4 presents separate results for modeling the accuracy ofanalysts forecasts of

both first-quarter and annual EPS. For brevity, we only discuss first-quarter results in the

text, but inferences are similar for the annual results. We define Accuracyijkt as the absolute

value of the difference between I/B/E/S actual earnings and analyst is last EPS estimate for

firm jin industry kduring year tbefore the release of Q1 EPS, scaled by lagged stock price

(i.e., stock price as of the end of fiscal year t-1) so that a smaller value indicates greater

accuracy. We expect analyst forecast accuracy to be lower when the DIFF phenomenon

exists. Panel A presents pooled accuracy results for the full sample. Earnings forecast

accuracy averages 0.0042 across all analyst-firm-years, averaging 0.0037 and 0.0049 for the

DIFF= 0 and DIFF= 1 analyst-firm-years, respectively. The order of magnitude of unsigned

error for DIFF = 1 is nearly one-third larger than when DIFF = 0. This magnitude is both

economically meaningful and statistically significant (t-statistic = 13.65).



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Panel B provides results of a multivariate analysis to examine the relation between

forecast accuracy and the DIFFphenomenon based on the following model:

Accuracyijkt = 0 + 1 DIFFijkt+ 2FEXPijkt+ 3FREQijkt+ 4NFIRMSijkt+ 5BSIZEijkt

+ 6HORIZONijkt+ 7NONOPjkt+i+ j+k+ t+ eijkt

(3)

We control for five analyst effects: firm experience (FEXP), forecast frequency

(FREQ), number of firms followed (NFIRMS), brokerage size (BSIZE), and forecast horizon

(HORIZON). We also control for instances where firms have non-operating or extraordinary

items on their income statements (NONOP) along with analyst, firm, industry, and year fixed

effects. Based on prior research (Mikhail et al. 1997; Clement 1999; Jacob et al. 1999;

Clement and Tse 2005), we expect analysts who are more experienced, forecast more often,

and work for larger brokerage houses to be more accurate, and analysts who follow more

firms, make forecasts over longer time horizons, and forecast firms reporting non-operating

or extraordinary items to be less accurate. Most of the coefficients on the control variables

have their expected signs. The main difference between our result and findings of the

extant literature (Mikhail et al. 1997; Clement 1999; Jacob et al. 1999; Clement and Tse

2005) is that we find firm experience to be positive.17

Consistent with our findings in the univariate analyses, the DIFF phenomenon is

associated with significantly less accurate forecasts. To assess the economic meaning of our

results, we compare the coefficient on DIFF (1) with the intercept (0), which represents

the accuracy of analysts with inferred Q1 EPS within one penny of the I/B/E/S Q1 actual EPS

after controlling for the other factors in the model. Analysts one-quarter-ahead forecasts

17 Untabulated results reveal that the coefficient on firm experience is positive and significant in a minorityof years in our sample so we do not consider the anomalous finding regarding firm experience to be veryreliable.


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are only one-half as accurate when DIFF= 1 versus when DIFF= 0, and DIFFs coefficient of

0.0004 is identical to that ofNONOP, the other indicator variable.18

Earnings forecast revisions

Table 5 presents results relating to the revision of analysts EPS forecasts for the last

nine months of the fiscal year after the Q1 EPS is reported. Panel A estimates the following

equation:

REV_9Mijkt = a + b * SURPijkt+ eijkt (4)

The dependent variable represents the revision of analyst is EPS estimate for firm jfor Q2

through Q4 in industry k in year t, defined as the difference between the analysts first

forecast of Q2 through Q4 issued after the release of Q1 earnings but before the release of

Q2 earnings and the same analysts last forecast of Q2 through Q4 earnings issued after the

release of year t-1 earnings but before the release of Q1 earnings, scaled by lagged stock

price. SURPijkt is defined as the difference between the I/B/E/S actual Q1 earnings and the

last Q1 forecast issued by analyst ibefore the release of Q1 earnings, scaled by lagged stock

price. We expect the beta coefficient for the DIFF = 0 sub-sample to be higher than the beta

coefficient for the DIFF = 1 sub-sample because measurement error in SURP reduces the

magnitude of the coefficient towards zero.

Table 5 Panel A presents pooled revision results for the sub-sample of 21,197

analyst-firm-years with forecasts of Q1, Q2, Q3, and Q4 EPS made by the same analyst on

the same day both after the release of FY t-1 earnings but before the release of Q1 earnings,

and after the release of Q1 earnings. The revision slope coefficient of 0.91 for all analyst-

18The ratio of accuracy whenDIFF= 1 versus 0 equals 2 [(0.0004+0.0004)/0.0004], indicating that theformer is only one-half as accurate as the latter.


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firm-years reveals that for every $1.00 ofSURP for Q1, on average, analysts revise their EPS

forecasts of the remainder of the year in the same direction as the surprise by $0.91 (t-

statistic = 56.43).19

As in Table 4, we partition our sample into DIFF = 0 and DIFF = 1. The

forecast revision coefficient for the DIFF= 0 sub-sample is significantly larger than for the

DIFF= 1 sub-sample (1.09 versus 0.75, t-statistic of the difference = -10.70). In other words,

for each dollar ofSURP in Q1, analysts revise their EPS forecasts for the remainder of the

year by an average of $1.09 (t-statistic = 50.15) when DIFF= 0. In contrast, when DIFF= 1,

for each dollar ofSURP in Q1, analysts revise their EPS forecasts for the remainder of the

year by an average of only $0.75 (t-statistic = 31.18). The revision coefficient is 46 percent

higher when the DIFF phenomenon does not exist than when it does exist, a magnitude

which is both economically relevant and statistically significant (t-statistic = -10.70).


Panel B provides results which control for NONOP along with analyst, firm, industry,

and year fixed effects. More specifically, we estimate the following model:

REV_9Mijkt = 0+ 1 DIFFijkt+ 2SURPijkt+ 3 DIFFijkt* SURPijkt+ 4NONOPjkt+i

+ j+k+ t+ eijkt

(5)

We have no priors regarding DIFFs main effect (1). Given that analysts revise their

forecasts of future quarters in the direction of their most recent forecast errors (Brown and

Rozeff 1979), we expect analyst forecast revisions to be positively related to SURP (2). We

expect the revision coefficient on SURP to be smaller when DIFF = 1 than when DIFF = 0 (3)

because SURP measures the analysts inferred earnings surprisewith error when DIFF = 1. As

19 For simplicity, we use the term analyst-firm-year in lieu of the more correct but more cumbersomeanalyst-firm-industry-year both in the text and the tables.


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16

non-operating and extraordinary items are more transitory than operating earnings (Elliott

and Hanna 1996), we expect analysts to revise their forecasts of future earnings to a lesser

extent conditional on SURP when it contains a non-operating or extraordinary item

component (4).

The main effect of DIFF is small in magnitude and statistically insignificant

(coefficient = 0.0002, t-value = 0.87). As expected, the coefficient on SURP is positive and

significant (0.8636, t-value = 31.26). Most importantly for our purposes, the coefficient on

DIFF * SURP is negative and significant (-0.3256, t-value = -8.44). Thus, when the DIFF

phenomenon is absent, analysts revise their earnings forecasts for the remainder of the

year 61 percent more than when the DIFF phenomenon is present, a magnitude which is

both statistically and economically relevant.20,

21

Earnings forecast dispersion

Table 6 presents results for the dispersion of analysts first-quarter and annual EPS

estimates. For brevity, we only discuss the first-quarter results in the text but the annual

results are qualitatively similar. We expect mean dispersion to be smaller when all the

analysts are shooting at the earnings target that I/B/E/S reports as its actual ( DIFF = 0).

Panel A presents estimates of dispersion as a pooled average of the standard deviation of

analysts EPS estimates for each firmjin industry kwith multiple analyst following in a given

quarter of year t. Mean dispersion (STD (Q1))jkt for these 7,152 firm-years is 0.0463. Mean

dispersion is 0.0445 for firm-years with DIFF = 0 and 0.0569 for firm-years with DIFF= 1.

20 61 percent equals 0.8636/ (0.8636-0.3256). If analysts learn over time, it is possible that our revision testsare affected by changing expectations relative to when the I/B/E/S actual earnings number is issued. Toaddress this concern, we re-estimate equation (5) using the sample of analyst-firm-years that issue revisionsnearer to and further from the earnings announcement date. We obtain similar inferences in both sub-samples.21 In untabulated results, we addDIFF*NONOPto equation 5 and obtain qualitatively similar significantcoefficients forSURPandDIFF* SURP.


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Thus, mean dispersion is 28 percent greater than for firm-years with DIFF= 1 than with DIFF

= 0.22


Panel B presents results which control for firms earnings stability (STABLE), NONOP,

and fixed effects for firm, industry and year. More specifically, we estimate the model:

STD (Q1)jkt = 0+ 1 DIFFjkt+ 2STABLEjkt+ 3NONOPjkt+ j+k+ t+ ejkt (6)

There is likely to be less agreement among analysts when DIFF = 1 than when DIFF =

0 so we expect the coefficient on DIFF (1) to be positive. As there is likely to be more

agreement among analysts when earnings are stable, we expect the coefficient on STABLE

(2) to be negative. Because there is likely to be less agreement among analysts when firms

report non-operating or extraordinary items, we expect NONOP to have a positive

coefficient (3).

The coefficients on the two control variables are insignificant.23

More importantly,

dispersion is greater when DIFF = 1 than when DIFF= 0 (2 = 0.0037, t-value = 3.41). To

determine the economic importance of our results, we compare the 1 coefficient with the

intercept (0) which represents dispersion for firms followed by analysts who all agree with

the I/B/E/S Q1 actual. Analyst dispersion is 25 percent (.0037/.0151) greater when DIFF= 1

for at least one analyst following the firm, a magnitude which is both economically

meaningful and statistically significant.

22 Analyst dispersion may reflect both differences of opinion regarding an earnings number defined in acertain way (e.g., all analysts forecasts include restructuring charges) and differences of opinion regardingan earnings number defined in different ways (e.g., some analysts forecasts exclude restructuring chargeswhile other analysts forecasts include restructuring charges). We do not attempt to distinguish betweenthese differences.23 They are both significant for the analysis of annual forecasts but they do not have the expected sign.


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Market reaction coefficients

Table 7 provides results from estimating the market reaction to the first quarter

earnings surprise. Panel A presents results based on the following specification:

CARjkt = a + b * SURPjkt+ ejkt (7)

CARjkt is the two-day (-1, 0) cumulative return minus the cumulative value-weighted market

return related to the announcement of Q1 earnings for firm j in in industry k in year t.

SURPjkt is defined as the I/B/E/S actual Q1 EPS minus the last Q1 estimate issued by an

analyst following firmjbefore the release of Q1 earnings, scaled by lagged stock price.24

We

expect the beta coefficient for the DIFF= 0 sub-sample to be higher than that for the DIFF=

1 sub-sample because measurement error in SURP drives the beta coefficient towards zero.

Using the entire sample for which we can calculate CAR, b in equation (7) is estimated to be

1.435 (t-statistic = 19.78), conforming to the well-documented evidence that abnormal

returns are positively and significantly related to quarterly earnings surprises (Brown and

Kennelly 1972; Foster 1977). As in Tables 4 to 6, we partition our sample into DIFF= 0 and

DIFF= 1 sub-samples. Columns 2 and 3, respectively, include firm-years for which DIFF= 0

and DIFF= 1 for at least one analyst following the firm.25

The beta coefficient for the market

reaction coefficient for the DIFF= 0 sub-sample has nearly twice the magnitude of the DIFF

24 When we examine individual analysts, the term SURPijkt= 1 refers to the difference between the I/B/E/S

actual Q1 earnings and the last Q1 forecast issued by analyst i for firmj in industry kduring yeartbeforethe release of Q1 earnings, scaled by lagged stock price. When we examine firms, the term SURPjkt = 1refers to the difference between the I/B/E/S actual Q1 earnings and the last Q1 forecast issued by an analystfollowing firmj in industry kduring yeartbefore the release of Q1 earnings, scaled by lagged stock price.For the latter, we use the last forecast in lieu of the consensus forecast because it more closely representsmarket expectations (Brown and Kim 1991). Thus, the analyst in this regression may either be an analystfor whom DIFF= 1 orDIFF= 0, so our results probably understate the magnitude of the impact of theDIFFphenomenon.25 For simplicity, we use the term firm-year in lieu of the more correct but more cumbersome -firm-industry-year both in the text and the tables.


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= 1 sub-sample (1.987/1.036). This economically substantial magnitude is also statistically

significant (t-statistic = -6.48).


Panel B provides results which control for STABLE, NONOP, and fixed effects for firm,

industry, and year. More specifically, we estimate the following model:

CARjkt = 0+ 1 DIFFjkt+ 2SURPjkt+ 3 DIFFjkt* SURPjkt+ 4STABLEjkt

+ 5NONOPjkt+ j+k+ t+ ejkt

(8)

Firms with stable earnings should have higher valuation multipliers (Beaver 1998) so

we expect 4 to be positive. The market should react less to firms earnings with non-

operating or extraordinary items because their earnings are less permanent (Elliott and

Hanna 1996) so we expect 5 to be negative. Scores of studies have shown earnings

surprises to be value-relevant (Ball and Brown 1968; Foster 1977; Beaver et al. 1979), so we

expect 2 to be positive. We have no priors for the main effect of DIFFwhich is measured by

the sign of 1, but we do have a prior for the coefficient on the interaction of DIFF with

SURP(3), our primary variable of interest. The positive relation between market reaction

and earnings surprise should be attenuated when DIFF= 1, so we expect 3 to be negative.

As expected, there is a positive market reaction to earnings surprises for firm-years

when DIFF = 0 (2 = 2.1867, t-value = 13.11). The coefficients on the control variables

STABLE and NONOP are insignificant. Most importantly for our purposes, the positive

reaction to earnings surprises is substantially mitigated when DIFF= 1 (3 = -0.6026, t-value

= -2.74). To determine the economic significance of our results, we compare the ratio of the

coefficient ofDIFF * SURP to that ofSURP. The market reaction to earnings surprise is 28


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percent greater when DIFF = 0 than when DIFF = 1, suggesting that our results are both

economically meaningful and statistically significant.

VI. Replication of prior research

One issue that Heflin and Hsu (2008) examine in their study is the Impact of non-

GAAP disclosure regulations on the probability that disclosed earnings meet or beat

forecasted earnings. They find that non-GAAP disclosure regulations made effective in early

2003 are associated with a reduction in the probability that firms disclosed earnings that

meet or beat analysts earnings forecasts. We have shown in Table 3, Panel D that the DIFF

phenomenon has become more pervasive over time so it is possible that Heflin and Hsus

results were attributable to a relatively larger portion of their sample being subject to the

effects of the DIFF phenomenon after non-GAAP disclosure regulations became effective

than before that time (i.e., more observations exhibited the DIFF phenomenon due to the

temporal effect we documented above).

To investigate the possible impact of the temporal increase in the DIFFphenomenon

on the Heflin and Hsu (2008) results, we construct a data set similar to theirs for the same

March 2000 to February 2005 period. The first column of Table 8 is our replication of Table

5 in Heflin and Hsu (2008) using a sample of 35,142 firm-quarters.26

We use variable

definitions that are similar to theirs. NGREGq equals 1 if quarter q is the first quarter of 2003

or later and zero otherwise. POST_01q equals one if quarter q is the first quarter of 2002 or

later and zero otherwise. SOXq equals one if quarter q is the third quarter of 2002 or later

and zero otherwise. SPECjq equals one if firm jreports a special item in quarter q and zero

otherwise. LOSSjq equals one if firm js GAAP earnings in quarter q are less than zero, and

26Our sample is qualitatively similar to Heflin and Hsu (2008)s sample of 38,886 firm-quarters.


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zero otherwise. UPEARNjq equals one if firmjs GAAP earnings in quarter q are greater than

or equal to earnings in the same quarter from the previous year, and zero otherwise.

GROWTHjq is firm js quarter q sales growth over the previous years same quarter sales .

BTMjq is the ratio of book value of equity over market value of equity for firm jin quarter q.

LEVjq equals total liabilities divided by book value of shareholders equity. N_ANLSTjq is the

number of analysts following firm jin quarter q. FCSTDjq represents the standard deviation

of the most recent analysts forecasts for firmjin quarter q. TECHj equals one if firmjis in a

high-tech industry and zero otherwise. RETjq is firmjs raw return over quarter q. INDPRDq is

the percent change in the quarterly seasonal adjusted industrial production, and QTR4q

equals one if quarter q is a fourth quarter and zero otherwise.


Similar to Heflin and Hsu (2008), we obtain positive coefficients on POST_01, SPEC,

UPEARN, GROWTH, TECH, and RET in the first column of Table 8, negative coefficients on

SOXand FCSTD, and, most importantly, a negative coefficient for their primary variable of

interest, NGREG.27

The second column of Table 8 limits the sample to those 3,820 firm-

quarters used in Column 1 which we identify as DIFF= 1. We find a negative and significant

coefficient on NGREG in this sub-sample. The third column of Table 8 limits the sample to

those 11,931 firm-quarters used in Column 1 which we identify as DIFF = 0. We do not

obtain a significant coefficient on NGREG in this sub-sample, and the magnitude of the

coefficient on NGREG in column three is 94.2 percent smaller than that in column two.

Overall, our results suggest that Heflin and Hsu (2008) may have reached a different

27 In Table 8, with the exception of the coefficient on SOX, all these variables coefficients are significant.


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conclusion if they had confined their sample to observations that were not subject to the

DIFFphenomenon.

VII. Robustness tests

Tests using the magnitude of the DIFF phenomenon (MDIFF)

Thus far we have used a zero-one indicator variable to proxy for the DIFF

phenomenon based on whether there is at least a penny difference between the analysts

inferred EPS and the I/B/E/S actual EPS. This approach has two disadvantages: (1) it ignores

the magnitude of the DIFF phenomenon; and (2) all cutoffs are arbitrary. We repeat our

multivariate analyses of accuracy, revision, dispersion, and market reaction for Q1 using the

following continuous (magnitude) measure of the DIFFphenomenon (MDIFF)28

:

MDIFFijkt =Analyst Inferred EPS (Q1)ijktEPS (Q1)ijkt (9)

Analyst Inferred EPS (Q1) is the difference between the analysts forecast of the year minus

the forecast of Q2 + Q3 + Q4, conditional on Q1 having been reported; and EPS is Q1

earnings as reported by I/B/E/S.

We employ the following multivariate regression model, which is a modification of

equation (3), to examine accuracy:

Accuracyijkt = 0+ 1|MDIFFijkt|+ 2FEXPijkt+ 3FREQijkt+ 4NFIRMSijkt+ 5BSIZEijkt

+ 6HORIZONijkt+ 7NONOPjkt+i+ j+k+ t+ eijkt

(10)

Equation (10) differs from equation (3) only in that it uses MDIFFin lieu ofDIFF. Panel A of

Table 9 indicates that the 1 coefficient on |MDIFF| is positive and significant (coefficient =

0.1748; t-value = 38.48). Moreover, MDIFFhas the largest t-value of any of the independent

28 When we examine individual analysts in Equation 10, the term MDIFFijkt refers to the difference betweenanalyst is inferred EPS and the I/B/E/S Q1 actual for firm j in industry k in year t. In Equation 11,MDIFFjktrefers to the mean value ofMDIFFijkt for firmj in industry kin yeart.


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variables, indicating it has the most incremental power of any of the independent variables

for explaining the variability in analyst forecast accuracy.29

Equation (10) has over six times

the explanatory power of equation (3), 8.1 percent versus 1.3 percent, revealing that it is

more powerful than the DIFF indicator variable model for the purpose of explaining the

variability in analyst forecast accuracy.


We use the following multivariate regression, which is a modification of equation

(6), to examine analyst forecast dispersion:

STD (Q1)jkt = 0+ 1|MDIFFjkt|+ 2STABLEjkt+ 3NONOPjkt+ j+k+ t+ ejkt (11)

Equation (11) differs from equation (6) only in that it uses MDIFFin lieu ofDIFF. Panel A of

Table 9 indicates that the 1 coefficient on |MDIFF| is positive and significant (coefficient =

0.1985, t-value = 3.58) with the largest t-value of any of the independent variables,

indicating it provides the greatest incremental power for explaining the variability in

earnings forecast dispersion. Equation (11) has similar explanatory power to equation (6),

2.3 percent versus 2.3 percent, revealing that it is as powerful as the DIFFmodel.

We employ the following multivariate regression, which is a modification of

equation (5), to examine analyst forecast revisions:

REV_9Mijkt =1QN1ijkt+ 2QN2ijkt+ 3QN3ijkt+ 4QN4ijkt+ 5QN5ijkt

+ 6SURPijkt* QN1ijkt+ 7SURPijkt* QN2ijkt+ 8SURPijkt* QN3ijkt

+ 9SURPijkt* QN4ijkt+ 10SURPijkt* QN5ijkt+ 11NONOPjkt+i+ j+k

+ t+ eijkt

(12)

29 Recall thatNONOPhad a higher t-value thanDIFFin the estimation of equation (3) in Table 4 Panel B.


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Equation (12) differs from equation (5) by interacting SURPijkt with the quintile ranking of

the absolute value ofMDIFFijkt, denoted by the indicator variables QN1ijkt through QN5ijkt.30

While the focal point of equation (5) is the interaction between DIFFand SURP, we are also

interested in how the coefficient on SURP changes as we move from the lowest (QN1) to the

highest (QN5) quintile of the absolute value ofMDIFF. We expect the analyst to revise his

earnings forecast of the remainder of the year in the direction ofSURP. Thus, we expect the

coefficient on SURP to be positive. We also expect the coefficient on SURP to decrease as

we move to higher levels ofMDIFFbecause increased measurement error drives coefficient

estimates to zero. Consistent with our expectations, Panel B of Table 9 reveals that the

coefficient ofSURP decreases nearly monotonically as the quintile rank ofMDIFFincreases.

We run the following modification of equation (8) to examine the market reaction to

earnings surprise:

CARjkt =1QN1jkt+ 2QN2jkt+ 3QN3jkt+ 4QN4jkt+ 5QN5jkt

+ 6SURPjkt* QN1jkt+ 7SURPjkt* QN2jkt+ 8SURPjkt* QN3jkt

+ 9SURPjkt* QN4jkt+ 10SURPjkt* QN5jkt+ 11STABLEjkt+ 12NONOPjkt

+ j+k+ t+ ejkt

(13)

Similar to equation 12, equation (13) differs from equation (8) by interacting SURPjkt with

the quintile ranking of the absolute value of MDIFFjkt denoted by the Indicator variables

QN1jktthrough QN5jkt. While equation (8) focuses on the interaction term between DIFFand

SURP, the focus of equation (13) is on how the coefficient on SURP changes as we move

from the lowest (QN1) to the highest (QN5) quintile of the absolute value ofMDIFF. Capital

30 Unlike accuracy and dispersion, the results for revisions and market reaction cannot easily be comparedusing MDIFF versus DIFF since the MDIFF formulations have more explanatory variables than theirDIFFcounterparts.


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markets should react in the direction of the earnings surprise (SURP) so we expect SURPs

coefficient to be positive. We also expect SURPs coefficient to decrease as we move to

higher levels ofMDIFFbecause increased measurement error drives the coefficient estimate

more to zero. Consistent with our expectations, the coefficient ofSURP in Panel B of Table 9

decreases monotonically as the amount of the measurement error in SURP, as proxied by

the quintile of the absolute value ofMDIFF, increases.

Tests using a common sample

Our main tests for accuracy, revision, dispersion, and market reaction have required

varying sample sizes, as described in section III. In untabulated tests, we re-estimate each of

our four main tests using a common sample size. Inferences for the accuracy, revision, and

dispersion tests are unchanged. For the market reaction test, as in Table 7, we continue to

find a negative coefficient on DIFF * SURP but that coefficient is no longer significantly

different from zero.

VIII. Conclusions

We examine the prevalence of, factors associated with, and adverse consequences

of I/B/E/S reported actual EPS differing from the earnings analysts forecast when they issue

their earnings estimates, which we refer to as the DIFF phenomenon. We find that the

I/B/E/S reported actual Q1 EPS differs from the analysts inferred actual Q1 EPS by at least

one cent 39 percent of the time during our sample period and 50 percent of the time in the

last three years of our sample period. The DIFF phenomenon arises less often for higher-

quality analysts (those employed by larger brokerage houses, who forecast more often, and

who follow fewer firms); for firms not reporting non-operating or extraordinary items and

for firms followed by fewer analysts; and for the energy, transportation and utility


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industries. When the DIFF phenomenon occurs, analysts are likely to make less accurate

earnings forecasts, analysts forecast revision coefficients are likely to be smaller, analysts

are likely to make more disparate earnings forecasts, and capital markets are likely to react

less to firms earnings surprises.The economic magnitude of the DIFF phenomenon is

reflected by the fact that when DIFF= 1, analysts absolute forecast errors are nearly twice

as large; analyst forecast revision coefficients are 38 percent smaller; analyst forecast

dispersion is over 25 percent larger; and capital markets react nearly 28 percent less to

earnings surprises.

We provide three ways for users of I/B/E/S reported actual EPS data who examine

forecast accuracy, forecast revision, forecast dispersion, and market reaction to earnings

surprise to increase the power of their tests. The first method is to use an indicator variable

if the absolute value of the difference between the I/B/E/S actual and the analysts inferred

actual is at least one penny.31

A second approach is to use the absolute value of the

magnitude of the difference between the I/B/E/S actual and the analysts inferred actual .

The third approach is to omit cases where the absolute value of the difference between the

I/B/E/S actual and the analysts inferred actual is substantial (e.g., at least one penny).

We examine whether ignoring the DIFF phenomenon might have affected the

interpretation of some results of prior studies. We replicate Heflin and Hsus (2008)

investigation of the impact of non-GAAP disclosure regulations on the probability that

disclosed earnings meet or beat forecasted earnings. We obtain results similar to theirs

when we either use a sample similar to theirs or when we use a sub-sample of firm-quarters

for which the DIFFphenomenon is present, but we obtain much different results when we

31Researchers requiring individual analysts forecasts can estimate the analysts inferred actual for three ofthe four fiscal quarters.


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use a sub-sample of firm-quarters for which the DIFF phenomenon is absent. More

specifically, the magnitude of their coefficient of interest is 94.2 percent lower for the

subsample ofDIFF= 0 (DIFF phenomenon is absent) than for the sub-sample of DIFF = 1

(DIFFphenomenon is present), suggesting they may have made different inferences if they

omitted observations subject to the DIFFphenomenon.

We show that the DIFFphenomenon occurs 36.5 percent of the time when only one

analyst follows a firm, nearly as often as the 39 percent of the time shown for our entire

sample. When only one analyst follows a firm, this analyst is the mean, mode, median,

majority, and the consensus forecast. This finding has two important implications for

researchers and other users of I/B/E/S earnings data. First, I/B/E/S reported actual EPS often

do not appear to adhere to the majority rule. Second, the DIFFphenomenon pertains to

both consensus earnings forecasts and individual analyst estimates.

Our study is related to Payne and Thomas (2003) (hereafter PT) but the two studies

differ in three major respects. First, PT examined the effect of rounding in the I/B/E/S

earnings database, while we examine the effect of differences between I/B/E/S reported

actual earnings and analysts inferred actual earnings. Second, PT suggested researchers use

non-split adjusted I/B/E/S earnings data, while we provide three ways for users of I/B/E/S

EPS data who examine forecast accuracy, forecast revisions, forecast dispersion, or market

reactions to earnings surprises to increase the power of their tests by taking the DIFF

phenomenon into consideration. Third, PT indicated that research conclusions of studies

using split-adjusted I/B/E/S earnings data are most likely to be affected in samples with

larger firms, higher price to book firms and better performers, while we find that research

conclusions of studies using I/B/E/S earnings data ignoring the DIFFphenomenon are most


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likely to be affected if they focus on low-quality analysts (those who work for smaller

brokerage houses, forecast less frequently, and follow many firms), on firms followed by

many analysts and those reporting non-operating earnings or extraordinary items, on the

energy, utility and transportation industries, or on samples using earnings data from recent

years.

Our study is also related to Ljungqvist, Malloy, and Marston (2009) (hereafter LMM)

but these two studies differ in three major respects. First, LMM find discrepancies in the

I/B/E/S recommendations data base while we find differences between I/B/E/S actual

earnings and analyst inferred actual earnings. Second, LMM suggest that discrepancies in

the recommendations data base have been mitigated in recent years, but we find that the

DIFFphenomenon has become much more prevalent in recent years. Third, LMM conclude

that researchers cannot address most of the problems they identify, whereas we provide

researchers with three ways to address the DIFFphenomenon.

We close with some caveats and suggestions for future research. Our findings and

interpretations of our findings are based on the validity of our procedure for estimating

analyst inferred actual earnings. Future research may wish to examine the validity of our

procedure, and to try to derive a more reliable procedure than the one we have introduced

to the literature. Moreover, we have confined our analyses to a particular expectations data

base with a focus on earnings. Future research may wish to determine the pervasiveness

and the adverse consequences of differences between data-provider actuals and analyst

inferred actuals in other sources of U.S. earnings expectations data (Zacks, First Call),

international earnings expectations data, and in non-earnings expectations data (e.g. cash

flows, revenues, dividends).


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APPENDIX 1: Variable Definitions of Terms

\

Variable Definition

Accuracy (FY)ijkt=

1

)()(

jkt

ijktjkt

price

FYAFFYEPS

Accuracy (Q1)ijkt=

1

))1()1(

jkt

ijktjkt

price

QAFQEPS

AF (FY)ijkt=

Analyst is forecast offirmjin industry kin fiscal year tEPS obtained

from I/B/E/S

AF (Q1)ijkt=

Analyst is forecast of firmjin industry kof Q1 EPS for year tobtained

from I/B/E/S

BSIZEijkt = Analyst is brokerage size, calculated as the number of analysts

employed by the brokerage house of analyst ifollowing firmjin industry

k in year t minus the minimum number of analysts employed by

brokerage houses of all analysts following firm j in year t, with this

difference scaled by the range of brokerage house sizes for all analysts

following firmjin year t

BTMjq = book value of equity (SEQQ from Compustat) over market value of

equity obtained from CRSP for firmjin quarter q

CARjkt = Two-day (-1,0) cumulative return minus cumulative value-weighted

market return related to the announcement of Q1 earnings for firm jin

industry kin year twhere 0 is the Q1 earnings announcement day

DIFFijkt = 1 when analyst ifollowing firmjin industry khas an annual forecast for

year t made after the release of Q1 EPS in year t that exceeds the

absolute value of the sum of the I/B/E/S Q1 actual EPS and the analysts

EPS forecast of the remainder of year tby at least one penny (See also

Appendix 2)

DIFFjkt = 1 when at least one analyst following firm j in industry khas an annual

forecast for year t made after the release of Q1 EPS in year t that

exceeds the absolute value of the sum of the I/B/E/S Q1 actual EPS andthe analysts EPS forecast of the remainder of year t by at least one

penny (See also Appendix 2)

EPS (FY)jkt = Actual annual EPS for firm j in industry k in fiscal year tobtained from

I/B/E/S

EPS (Q1)jkt = Actual Q1 EPS for firmjin industry kin fiscal year tobtained from I/B/E/S


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FCSTDjq = The standard deviation of the most recent analysts forecasts for firmjin

quarter q

FEXPijkt = Analyst is firm experience, calculated as the number of prior forecasting

years for analyst i following firm j in industry k in year t minus the

minimum number of prior forecasting years for all analysts following firm

in year t, with this difference scaled by the range of prior forecasting

years for all analysts following firmjin year t

FREQijkt = Analyst is forecast frequency, calculated as the number of firm j

forecasts made by analyst ifollowing firm jin industry kin year tminus

the minimum number of firmjforecasts for all analysts following firmjin

year t, with this difference scaled by the range of number of firm j

forecasts issued by all analysts following firmjin year t

GROWTHjq = Firm js quarter q sales growth over the previous years same quarter

sales (SALEQ from Compustat)

HORIZONijkt = Analyst is horizon, calculated as the number of days from the year t

forecast date (following the release of Q1 earnings) to the earnings

announcement date for analyst following firm j in industry k in year t

minus the minimum forecast horizon for all analysts who follow firm jin

year t, with this difference scaled by the range of forecast horizons for all

analysts following firmjin year t

INDPRDq = Quarter q percentage change in the quarterly seasonal adjusted U.S.

industrial production, obtained fromwww.federalreserve.gov

LEVjq = Total liabilities (DEBTQ from Compustat) divided by book value of

shareholders equity (SEQQ from Compustat) for firmjin quarter q

LOSSjq = 1 if firmjs GAAP earnings in quarter q (EPSFIQ from Compustat) are less

than zero

MDIFFijkt = Analyst inferred actual IBES actual, scaled by pricet-1, where the analyst

inferred actual is the difference between analyst is forecast for the fiscal

year tand the sum of the analysts forecasts for Q2, Q3, and Q4 of the

same year for firmjin industry k

MDIFFjkt = Difference between the inferred EPS and the I/B/E/S Q1 actual, scaled by

pricet-1, for the analyst who issued the last Q1 estimate for firm j in

industry kbefore the release of Q1 earnings in year t

N_ANLSTjq = number of analysts following firmjin quarter q

NFIRMSijkt = The number of firms followed, calculated as the total number of firms

followed by analyst i following firm j in industry k in year t minus the

minimum number of firms followed by all analysts covering firmjin year

t, with this difference scaled by the range of the number of firms
http://www.federalreserve.gov/http://www.federalreserve.gov/http://www.federalreserve.gov/http://www.federalreserve.gov/


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followed by all analysts covering firmjin year t

NGREGq = 1 if quarter q is the first quarter of 2003 or later

NONOPjkt = 1 when the absolute value of the difference between Q1 EPS Compustat

operating earnings (OEPSXQ from Compustat) and Compustat earnings

after extraordinary items (EPSFIQ from Compustat) exceeds one pennyfor firmjin industry kin year t

POST_01q = 1 if quarter q is the first quarter of 2002 or later

Pricejkt-1 = Stock price for firmjin industry kas of the end of fiscal year t-1 obtained

from CRSP

QN1 QN5 = Indicator variables that = 1 if the absolute value ofMDIFFis in the first

fifth quintile

QTR4q = 1 if quarter q is a fiscal fourth quarter (i.e. if FQTR from Compustat = 4)

RETjq = firm js raw return over quarter q, compounded using daily returns

obtained from CRSP

REV_9Mijkt = The difference between analyst is first estimate of Q2 through Q4 for

firmjin industry kissued after the release of Q1 earnings and the same

analysts last estimate of Q2 through Q4 issued before the release of Q1

earnings during year t, scaled by stock price as of the end of fiscal year t-

1 obtained from CRSP

SOXq = 1 if quarter q is the third quarter of 2002 or later

SPECjq = 1 if firmjreports a special item (SPIQ from Compustat) in quarter q

STABLEjkt = I/B/E/S stability measure, defined by I/B/E/S as a gauge of annual EPS

growth consistency over the past 5 years for firmjin industry kin year t

STDjkt = Standard deviation of analysts EPS estimates for firmjin industry k, for

a given quarter or year t, for those firms with >1 analyst following in our

sample

SURPijkt =

1

)1()1(

jkt

ijktjkt

price

QAFQEPS

SURPjkt =

1

)1()1(

jkt

jktjkt

price

QAFQEPS

TECHj = 1 if firmjis in a high-tech industry and zero otherwise

UPEARNjq = 1 if firm js GAAP earnings in quarter q (EPSFIQ from Compustat) are

greater than or equal to earnings in the same quarter from the previous

year


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APPENDIX 2: Calculation of the DIFFvariable

Assume:

EPS (Q1) = Actual Q1 EPS according to I/B/E/S

AF(FY/Q1) = Analysts forecast of annual EPS according to I/B/E/S issued after the quarter 1

earnings announcement

AF ((Q2+Q3+Q4)/Q1) = Analysts forecast of EPS for the remainder of the fiscal year issued

after the quarter 1 earnings announcement

We define theAnalysts Inferred EPS (Q1) = AF(FY/Q1)AF((Q2+Q3+Q4)/Q1)

MDIFF= |Analyst Inferred EPS (Q1) EPS (Q1)|

DIFF= 1 ifMDIFF0.01

and

DIFF= 0 ifMDIFF


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References

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Beaver, W. 1998. Financial Reporting: An Accounting Revolution, Third Edition, Prentice Hall,

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returns and the magnitude of earnings forecast errors.Journal of Accounting

Research 17 (2): 316-340.

Brown, L. (editor). 2007. Thomson Financial Research Bibliography. First Edition. Thomson.

New York.

Brown, L. and K. Kim. 1991. Timely aggregate analyst forecasts as better proxies for market

earnings expectations.Journal of Accounting Research 29 (2): 382-385.

Brown, L. and M. Rozeff. 1979. Adaptive expectations, time-series models, and analyst

forecast revision.Journal of Accounting Research 17 (2): 341-351.

Brown, P. and J. Kennelly. 1972. The informational content of quarterly earnings: Anextension and some further evidence.Journal of Business 45 (3): 403-415.

Clement, M. 1999. Analyst forecast accuracy: Do ability, resources and portfolio complexity

matter?Journal of Accounting and Economics 27 (3): 285-303.

Clement, M. and S. Tse. 2005. Financial analyst characteristics and herding behavior in

forecasting.Journal of Finance 60 (1): 307-341.

Elliott, J. and J. Hanna. 1996. Repeated accounting write-offs and the information content of

earnings.Journal of Accounting Research 34 (Supplement): 135-155.

Ertimur, Y., J. Sunder, and S. Sunder. 2007. Measure for measure: The relation between

forecast accuracy and recommendation profitability of analysts. Journal of

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Foster, G. 1977. Quarterly accounting data: Time series properties and predictive-abilityresults. The Accounting Review52 (1): 1-21.

Gleason, C. and C. Lee. 2003. Analyst forecast revisions and market price discovery. The

Accounting Review78 (1): 193-225.

Greene, W. 2003. Econometric Analysis, Fifth Edition. Pearson Education, Upper Saddle

River, New Jersey.

Hamilton, L. 1992. Regression with Graphics. Wadsworth, Inc.

Heflin, F. and C. Hsu. 2008. The impact of the SECs regulation of non -GAAP disclosures.

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Accounting and Economics 50 (1): 42-57.

Jacob, J., T. Lys, and M. Neale. 1999. Expertise in forecasting performance of securityanalysts.Journal of Accounting and Economics 28 (1): 51-82.

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1935-1960.

Mikhail, M., B. Walther, and R. Willis. 1997. Do security analysts improve their performance

with experience?Journal of Accounting Research 35 (Supplement): 131-157.

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Payne, J. and W. Thomas. 2003. The implications of using stock-split adjusted I/B/E/S data in

empirical research. The Accounting Review78 (4): 1049-1067.

Ramnath, S., S. Rock, and P. Shane. 2008. The financial analyst forecasting literature:

taxonomy with suggestions for future research. International Journal of Forecasting

24 (1): 34-75.


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FIGURE 1

Timeline

FIGURE 2

Distribution of Differences between I/B/E/S Q1 Actual EPS and Analysts Inferred Q1

EPS

This figure presents the distribution of the difference between the analysts inferred Q1 EPS and the

I/B/E/S Q1 actual for the 32,019 analyst-firm-years in our sample. Variable definitions are in Appendix 1.


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TABLE 1

Sample Selection Procedure for the Main Analysis

Criteria Analyst-firm-years

Firm-years

Firm-years:

U.S. firm-years with FYt-1, Q1t, and Q2t reporting dates available from I/B/E/S for

1996 to 200874,009

Keep: Firm-years with I/B/E/S actual Q1t and FYt earnings 66,151

Keep: Firm-years with year t-1 prices available from CRSP 60,681

Analyst-firm-years:

Analyst-firm-years with Q2t, Q3t, Q4t, and FYt EPS forecasts issued on the same

day after the release of Q1t earnings83,789 33,650

Keep: Analyst-firm-years with Q1t EPS forecasts issued on any day after the

release of FYt-1 earnings but prior to the release of Q1t earnings (Table 4)32,019 18,533

Analyst-firm-years with 1 analyst following in our sample 11,381 11,381

Analyst firm-years with >1 analyst following in our sample (Table 6) 20,638 7,152

Sub-samples:

Analyst-firm-years with FYt EPS forecasts issued after the release of FYt-1

earnings but prior to the release of Q1t earnings (Table 4)24,427 15,060

Analyst-firm-years with Q2t, Q3t, and Q4t EPS forecasts issued after the release

of FYt-1 earnings but prior to the release of Q1t earnings (Table 5)21,197 14,065

Analyst-firm-years with FYt EPS forecasts issued after the release of FYt-1

earnings but prior to the release of Q1t earnings, with > 1 analyst following

(Table 6)

14,537 5,169

Firm-years with non-missing CARjkt (Table 7) 18,533

This table summarizes the procedure used to select the sample for Tables 4, 5, 6, and 7. Variable

definitions are in Appendix 1.


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TABLE 2

Estimation of Model with Analyst, Firm, Industry, and Year Fixed Effects

Analysts Firms I/B/E/S Industries Years Full Model1

Logit Model (1) (2) (3) (4) (5)

N 3,710 4,327 11 13 28,584

Log-likelihood -7,723.5 -10,335.4 -19,025.0 -19,097.2 -2,835.7

Prob > 2 0.00 0.00 0.00 0.00 0.00

1Sample sizes and full-model log-likelihood are reported.

This table estimates the following equation using a logit specification:

DIFFijkt= i+ j+ k+ t+ ijkt

Observations in which the analyst or firm appears only once in the full sample are deleted in order to

estimate the fixed effects models. Variable definitions are in Appendix 1.


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TABLE 3

Investigation of Analyst, Firm, Industry, and Year Effects

Panel A

Analyst Effects

Intercept

(1)

FEXPijkt

(2)

FREQijkt

(3)

NFIRMSijkt

(4)

BSIZEijkt

(5)

HORIZONijkt

(6)

% Concordant

Number of

analyst-firm-

years

Pr(DIFFijkt)=1 0.5059

(


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TABLE 3 (continued)

Panel D

Year Effects

Total

a -0.586***

b 0.018***

% Concordant 48.1

# analyst-firm-years 32,019

This table investigates the effects of analysts, firms, industries, and years on the DIFFindicator variable.

Panel A presents results based on estimating the following equation using a logit specification for the sub-

sample of analyst-firm-years for which the analyst variables could be estimated (p-values are in

parentheses):

Pr (DIFFijkt=1) = 0+ 1FEXPijkt+ 2FREQijkt + 3NFIRMSijkt+ 4BSIZEijkt+ 5HORIZONijkt + eijkt

Panel B provides the number and percent of analyst-firm-years for DIFF = 1 for one, two, three, four, and

five or more analysts following the firm.

Panel C provides the number and percent of analyst-firm-years for DIFF = 1 for each I/B/E/S industry and

for the full sample.

Panel D presents results of estimating the following specification for each I/B/E/S industry and for the full

sample:

Pr (DIFFijkt=1) = a + b*Yeart + eijkt

Results are pooled. Variable definitions are in Appendix 1.


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TABLE 4

Analysts Earnings Forecast Accuracy

All observations DIFFijkt = 0 DIFFijkt = 1

(1) (2) (3) (3) (2)

A. Analyses without control variables

Accuracy (Q1)ijkt 0.0042 0.0037 0.0049 0.0012***

(13.65)

N 32,019 19,489 12,530

Accuracy (FY)ijkt 0.0378 0.0349 0.0423 0.0074***

(7.95)

N 24,427 14,761 9,666


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TABLE 4 (continued)

B. Analyses with control variables

Dependent variable is: Predicted sign Accuracy(Q1)ijkt Accuracy(FY)ijkt

Intercept ? 0.0004*

(1.76)

0.0099***

(4.34)

DIFFijkt + 0.0004***

(4.62)

0.0022***

(2.92)

FEXPijkt - 0.0005***

(3.42)

0.0058***

(3.91)

FREQijkt - -0.0002

(-1.13)

0.0019

(1.16)

NFIRMSijkt + 0.0006***

(3.43)

0.0008

(0.42)

BSIZEijkt - -0.0002

(-0.73)

-0.0045*

(-1.79)

HORIZONijkt + 0.0013***

(6.49)

0.0037*

(1.88)

NONOPjkt + 0.0004***

(5.34)

0.0018**

(2.43)

Analyst Effects Yes Yes

Firm Effects Yes Yes

Industry Effects Yes Yes

Year Effects Yes Yes

Adj. R2

0.013 0.021

Number of Analyst-firm-years 19,765 15,444


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TABLE 4 (continued)

This table estimates the accuracy of analysts EPS estimates. Accuracyis defined as the absolute value of

the difference between actual Q1 (FY) earnings and the analysts last EPS estimate of Q1 (FY) preceding

the release of Q1 EPS, scaled by lagged stock price. Both actual earnings and analysts earnings forecasts

are obtained from I/B/E/S. Stock price is obtained from CRSP. DIFFequals zero when analyst i following

firm j has an annual forecast for year t made after the release of Q1 EPS in year t that is within one penny

of the sum of the I/B/E/S Q1 actual and the analysts forecast of the remainder of year t . DIFFequals one

when analyst i following firm j has an annual forecast for year t made after the release of Q1 EPS in year t

that is not within one penny of the sum of the I/B/E/S Q1 actual and the analysts forecast of the

remainder of year. Panel A presents univariate results. Panel B presents multivariate results which control

for analyst characteristics, NONOP, analyst effects, firm effects, industry effects, and year effects using the

following specification:

Accuracyijkt = 0+ 1DIFFijkt+ 2FEXPijkt+ 3 FREQijkt+ 4NFIRMSijkt+ 5BSIZEijkt+ 6HORIZONijkt

+ 7NONOPjkt + i+ j+ k+ t + eijkt

Results are pooled. T-statistics are in parentheses. ***, **, and * denote significance at the 1%, 5%, and

10% levels, based on one-tailed tests, respectively. All variable definitions are in Appendix 1.


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TABLE 5

Analysts Earnings Forecast Revisions

All observations DIFFijkt = 0 DIFFijkt= 1(1) (2) (3) (3) (2)


b 0.9079***

(56.43)

1.0941***

(50.15)

0.7497***

(31.18)

-0.3440***

(-10.70)

Adj. R2

0.131 0.168 0.100

N 21,197 12,446 8,751


Dependent variable is: Predicted sign REV_9Mijkt

Intercept ? -0.0002

(-0.34)

DIFFijkt ? 0.0002

(0.87)

SURPijkt + 0.8636***

(31.26)

DIFFijkt * SURPijkt - -0.3256***

(-8.44)

NONOPjkt - 0.0001

(0.47)

Analyst Effects Yes

Firm Effects Yes

Industry Effects Yes

Year Effects Yes

Adj. R2

0.096

Number of Analyst-firm-years 14,464


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TABLE 5 (continued)

This table estimates the revision of analysts EPS forecasts. The revision of the analysts EPS estimate for

Q2 through Q4 (REV_9M) is defined as the difference between the analysts first 9M estimat e after the

release of Q1 earnings and the same analysts last 9M estimate before the release of Q1 earnings, scaled

by lagged stock price, where the 9M estimate is the summation of the estimates of Q2, Q3, and Q4.

Earnings surprise (SURP) is defined as the difference between the I/B/E/S Q1 actual earnings and the

same analysts last Q1 estimate before the release of Q1 earnings, scaled by lagged stock price . DIFF

equals zero when analyst i following firm j has an annual forecast for year t made after the release of Q1

EPS in year t that is within one penny of the sum of the I/B/E/S Q1 actual and the analysts forecast of the

remainder of year t. DIFFequals one when analyst i following firm j has an annual forecast for year t made

after the release of Q1 EPS in year t that is not within one penny of the sum of the I/B/E/S Q1 actual and

the analysts forecast of the remainder of year.Panel A uses the following specification:

REV_9Mijkt = a + b*SURPijkt + eijkt

Results for the intercepts are excluded for simplicity. The final column compares the slope coefficients incolumns 2 and 3.

Panel B presents results which control for NONOP, and analyst, firm, industry, and year effects:

REV_9Mijkt = 0+ 1DIFFijkt+ 2SURPijkt+ 3DIFFijkt * SURPijkt+ 4NONOPjkt + i+ j+ k+ t + eijkt


10% levels, based on one-tailed tests, respectively. All variable definitions are in Appendix 1.


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TABLE 6

Dispersion of Analysts Earnings Forecasts

All observations DIFFjkt = 0 DIFFjkt = 1

(1) (2) (3) (3) (2)


STD (Q1) jkt 0.0463 0.0445 0.0569 0.0125***

(4.51)

N 7,152 6,075 1,077

STD (FY) jkt 0.1932 0.1864 0.2335 0.0471***

(3.53)

N 5,169 4,426 743


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TABLE 6 (continued)


Dependent variable is: Predicted sign STD(Q1)jkt STD(FY)jkt

Intercept 0.0151***

(4.47)

0.0669***

(4.85)

DIFFjkt + 0.0037***

(3.41)

0.0117***

(2.85)

STABLEjkt - 0.000001

(0.55)

0.00002***

(4.14)

NONOPjkt + 0.0005

(0.43)

-0.0098**

(-2.38)

Firm Effects Yes Yes

Industry Effects Yes Yes

Year Effects Yes Yes

Adj. R2

0.023 0.043

Number of Firm-years 13,986 11,460

This table estimates the dispersion of analysts EPS estimates. Dispersion is defined as the average of the

standard deviation of analysts EPS estimates for a given interval for those firms with >1 analyst following

in our sample. DIFF equals one when at least one analyst following firm j in industry k has an annualforecast for year tmade after the release of Q1 EPS in year tthat is not within one penny of the sum of

the I/B/E/S Q1 actual and the analysts forecast of the remainder of year, and equals zero otherwise.

Panel A presents univariate results without control variables. Panel B presents results which control for

STABLE, NONOP, and firm, industry, and year effects, using the following specification:

STD (Q1) jkt = 0+ 1DIFFjkt+ 2STABLEjkt+ 3NONOPjkt+ j+ k+ t + ejkt


10% levels, based on one-tailed tests, r

Documents

IBES Reported Actual EPS and Analysts’ Inferred Actual EPS