Whisper forecasts and earnings management

Whisper Forecasts and Earnings Management

A DISSERTATION
SUBMITED TO THE FACULTY OF THE GRADUATE SCHOOL
OF THE UNIVERSITY OF MINNESOTA
BY:

Arnoldo Jose Rodriguez

IN PARTIAL FULFILMENT OF THE REQUIREMENTS
FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY

Judy A. Rayburn

April, 2005

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.


UMI Number: 3165898

INFORMATION TO USERS

The quality of this reproduction is dependent upon the quality of the copy
submitted. Broken or indistinct print, colored or poor quality illustrations and
photographs, print bleed-through, substandard margins, and improper
alignment can adversely affect reproduction.
In the unlikely event that the author did not send a complete manuscript
and there are missing pages, these will be noted. Also, if unauthorized
copyright material had to be removed, a note will indicate the deletion.

®

UMI
UMI Microform 3165898
Copyright 2005 by ProQuest Information and Learning Company.
All rights reserved. This microform edition is protected against
unauthorized copying under Title 17, United States Code.

ProQuest Information and Learning Company
300 North Zeeb Road
P.O. Box 1346
Ann Arbor, Ml 48106-1346

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.


UNIVERSITY OF MINNESOTA

This is to certify that I have examined this copy o f a doctoral Dissertation by

Arnoldo J. Rodriguez

And have found that it is complete and satisfactory in all respects,
and that all revisions required by the final
examining committee have been made

Judy A. Rayburn

Signature

4

Y t

Date

GRADUATE SCHOOL

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.


Acknowledgments

I gratefully acknowledge useful comments by and discussions with John
Dickhaut, Judy Rayburn, Pervin Shroff, Susan Watts, Marc Bagnoli, Gerard
McCollough, Tong Lu, Jack Stecher, Luis Sanz, Niels Kettelhohn, Nicolas
Marin, Esteban Brenes, Carlos Quintanilla and participants at the XI Latin
American Congress o f Internal Auditors.

i

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.


Abstract

This paper examines the recent market phenomenon of whisper forecasts of
earnings and whether their appearance affected investors, managers and market
behavior. We explore the relation between official earnings per share analysts’

forecasts, realized earnings, and whisper forecasts. We find that the mean
analysts’ forecast error for a sample of growth firms has increased over time and
shows a pessimistic bias. When whisper forecasts are used as earnings estimates,
no bias is evident and the mean whisper error is significantly lower than the mean
analysts’ forecast error. The unexpected component of earnings better explains
abnormal returns around the earnings announcement date when whisper forecasts
are used as earnings expectations instead of analysts’ forecasts. In view of recent
evidence, that managers manipulate earnings to meet or just beat earnings
thresholds, we test whether managers regard whisper forecasts as a relevant
threshold to meet or beat. We find that firms that were able to meet or just beat
the whisper forecast reported higher abnormal accruals when compared with a
cross-section of firms in the same industry. This finding is consistent with the
hypothesis that whisper forecasts were not only a more accurate predictor of
earnings, but also a market relevant threshold that provoked accrual manipulation
by managers to reach aggressive unofficial estimates.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.


Table of Contents

Page
I.

Introduction

2

II.

Background and Hypotheses Development

6

HI.

Sample Selection and Description

16

IV.

Research Design and Methods

17

V.

Results

21

VI.

Concluding Remarks

28

iii

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.


List of Tables

Page
Table 1.

Comparative Firm Characteristics

35

Table 2.

Separation of the Groups

36

Table 3.

Historical Comparison of Earnings Performance

37

Table 4.

Results of the Kotmogorov Smirnov Two Sample Test

38

Table 5.

Comparison of Analysts’ Forecast Errors for the Sample of Growth
Firms

39

Comparison of Analysts’ and Whisper Forecast Errors for the
Sample of Firms for which Whisper Forecasts are Available

41

Table 7.

Explanatory Power of Forecast Error and Whisper Error

43

Table 8.

Mean Cumulative Abnormal Returns by Group

44

Table 9.

Earnings Management Discretionary Accruals by Group

45

Table 10.

Earnings Management Discretionary Accruals by Group

46

Table 6.

iv

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.


List of Figures

Figure 1.

Comparison of the Whisper Forecast, the Analysts’ Forecast and Reported
Earnings for Quarters when both Analysts’ Forecasts and Whisper Forecasts
are Available

Figure 2.

Distribution of Forecast Errors and Whisper Errors

v

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.

I. Introduction
“Whisper forecasts of earnings” or “whisper numbers” are unofficial estimates of
earnings made available to the general public prior to the release of quarterly earnings.
Whisper forecasts first appeared in the mid-nineties. The business press argues that
whisper forecasts gained popularity when the official analysts’ forecasts, especially for
growth firms, were believed to have downward bias. The hypothesis was that this
downward bias was induced by analysts to maintain good client-relations by offering an
“easy to beat” earnings benchmark 1
‘j

This hypothesis is consistent with the following anecdotal observation. :
“Analysts, investors, and corporate managements observed that companies that exceeded
expectations outperformed in the short run, while those that met expectations did not.
This led companies to under-promise and over-deliver, and led analysts to be
conservative in their published reports in order to be able to write that the company was
doing better than expected and therefore its stock should outperform. Another factor in
the whisper forecasts game was that prices were so expensive that even the most obtuse
analyst could understand that prices could not be justified by any reasonable set of
published estimates, but only by results that were better than expected (exactly how much
better they had to be to underpin valuations was ignored)."

‘Empirical evidence (for example, Myers and Skinner (1998)) suggests that companies, especially in
growth industries, that meet or beat analysts’ expectations are priced at a premium, whereas those that
disappoint suffer disproportionately in the market (Skinner and Sloan(2001)). The unprecedented increase
in stock-based compensation, especially for growth firms, also provides managers with strong personal
incentives to meet or just beat analysts’ expectations (Gaver, Gaver, and Austin (1995)).
2 www.ragingbull.com. Michael Pearson,: “Are whispers for real”, 1999.

2

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.


Research by Bagnoli, Beneish, and Watts (1999) documents that whisper forecasts
are more accurate predictors of quarterly earnings than analysts’ forecasts, even after
controlling for the differential timing o f the release of these numbers. They further show
that trading strategies based on the relation between analysts’ forecasts and whisper
forecasts earn significant excess returns. If whisper forecasts better reflect market
expectations, as the evidence seems to suggest, we argue that managers will treat these
forecasts as their quarterly earnings target. Previous literature on whisper forecasts
focused on the accuracy and pre-eamings announcement returns for firms for which a
whisper number was available, but they left unanswered questions, e.g., how the market
reacts to whisper forecasts around earnings announcements, whether their presence
affects managers’ behavior, and if so, which methods were used by managers to meet
earnings targets . This paper tests whether a significant percentage of firms meet or just
beat the whisper forecast of quarterly earnings. It also tests whether these firms engage in
earnings manipulation to meet or just beat the whisper forecast threshold.
In a sample of growth firms, we find that the mean optimistic bias in analysts’
forecasts declined over our sample period, 1990-2000. Consistent with popular press
explanations for the appearance of whisper forecasts on the investment scene, the mean
and median analysts’ forecast error for this sample in fact shows a pessimistic bias over
the later sub-period, 1997-2000. On the other hand, for a sample of 140 growth firms for
which whisper forecasts are available during the period 1997-2000, no mean bias in the
whisper forecast is evident. Interestingly, we find no significant difference in the mean
bias in analysts’ forecasts over the sub-period 1990-96 and the whisper forecast over the
Prior research by DeGeorge, Patel, and Zeckhauser (1998) provides evidence consistent with managers
attempting to meet or just beat analysts’ quarterly earnings forecasts.

3

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.


sub-period 1997-2000. This evidence indicates that whisper forecasts during the late
nineties were more in line with analysts’ forecasts during the early nineties (in terms of
the systematic forecast bias.) On the other hand, over the sub-period 1997-2000, for
quarters for which both analysts’ and whisper forecasts are available, we find significant
pessimistic bias in analysts’ forecasts in contrast to the unbiased whisper forecasts.
Consistent with previous research, we find that whisper forecasts are on average more
accurate than analysts’ forecasts, as indicated by the mean absolute and squared forecast
errors. The improved accuracy of whisper forecasts relative to analysts’ forecasts holds
even after controlling for the timing advantage of whispers4. Whisper forecasts are more
accurate than the mean o f analysts’ forecasts released within a period of thirty days
preceding the earnings announcement.
We find that for 84.2% of the sample firms the whisper forecast is greater than or
equal to the analysts’ forecast. We find that 24% of our sample firms meet or just beat
(by one cent) the whisper forecast, while 22% of firms meet or just beat analysts’
forecast5. Thus, it appears that for a significant number o f firms, managers may have
been using the whisper forecast as an earnings threshold rather than the analyst forecast.
The result is consistent with recent academic findings that managers try to avoid negative
news related to earnings announcements6. Additional results indicate that managers of
firms that meet or just beat the whisper forecast may engage in earnings manipulation.
4 It was possible to collect and send whisper forecasts up to the day prior to the earnings announcement.
We use the mean forecast in the First Call Database as die analysts’ forecast. Therefore, there is a timing
advantage for whisper forecasts relative to analysts’ forecasts.
5 The cases when the analysts’ forecast and the whisper forecast were separated by less than 1 cent are
excluded from this analysis and represent about 14.5% o f the sample. The reason to do this is that under the
1 cent difference, it is not feasible to distinguish which o f the two earnings forecasts firms were trying to
meet or beat.
6 Brown (2001), Burgsthaler and Eames (2000).

4

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.


We find that abnormal accruals for firms that meet or just beat the whisper forecast are
significantly higher than those for an industry-matched control group. Consistent with
previous literature, we also find that firms that meet or just beat the analysts’ forecast,
show higher abnormal accruals than the industry matched control group.
Consistent with anecdotal evidence, we also find that unexpected earnings based on
whisper forecasts are more closely associated with abnormal returns during earnings
announcement periods than are unexpected earnings based on analysts’ forecasts. The
result is not sensitive to different specifications of unexpected earnings based on analysts’
forecasts and whisper forecasts. Additionally, firms that were able to meet or beat the
whisper number show higher positive abnormal returns than the control groups.
Consistent with whisper forecasts being a relevant market threshold, firms that were able
to beat the analysts’ forecast but not the whisper forecast show negative abnormal
returns. Firms that were not able to meet the whisper had negative abnormal returns
similar to those of firms that missed both the whisper and analysts’ forecast.
The results of this paper provide insights into effects of whisper forecasts on
investors, firms, and markets. Healy and Whalen (1999) criticize the lack of academic
research to address specific questions about methods, circumstances and opportunities for
earnings management. This paper is relevant to accounting literature because it addresses
1) earnings management behavior in the presence of more aggressive earnings targets, 2)
the value relevance o f biased versus unbiased earnings forecasts, and 3) the emergence of
new thresholds based on unofficial information, and their impact on the behavior of
managers and investors.

5

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.


Section II defines whisper forecasts and motivates the hypotheses. Section III
describes the whisper database used to conduct the tests. Section IV discusses the
research design. Section V describes the results followed by a summary and conclusions
in Section VI.

II. Background and Hypotheses Development

II. 1 Whisper forecasts. Analysts’ Behavior and Earnings Management

Academics and financial press writers define whisper forecasts as:
■ Unofficial estimates of earnings communicated to and among investors before a
company releases its quarterly earnings (Thestreet.com)
■ Real-time market estimate of earnings per share (Fool.com)
■ Investors’ assessment of a company’s true earnings potential (whispemumbers.com).

Financial market observers report that whisper forecasts have existed since 1990.
Before the mid-1990’s, the numbers were generated by sell-side analysts for preferred
(wealthy) clients as a value-added service to remove the apparent bias in earnings
estimates. Consistent with this statement is the apparent loss of trust that individual
investors have in analysts’ opinions and estimates in recent times (attributable to the
conflict of interest that is present inside some financial supermarket companies). Mike
Thompson (director of research for BullDogResearch.com), refers to analysts’ opinions
as “clearly providing a service and a marketing function to the investment-banking side
o f their businesses”.
6

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.


During the 1995-2001 period, analysts appeared to be unwilling to downgrade or to
set aggressive earnings targets for companies that might have turned to their brokerages
for future investment banking. In the case of private clients, analysts are motivated to
increase clients’ returns. One way of achieving the latter objective is to take a
conservative approach to their public earnings expectations (pessimistic bias) since
companies they recommend to clients will report earnings that beat the analysts’ estimate.
Whisper forecasts may be the reaction by investors who were now able to
communicate electronically (‘whisper’) the unbiased earnings potential of firms at a very
low cost. The whispers quickly spread across the Internet. On average, the whispers were
more accurate than analysts’ estimates (FirstCall, I/B/E/S). The accuracy o f whispers was
validated by specialized websites, such as www.whispemumbers.com, as well as
academic research (Bagnoli et al. 1999), and consequently, whisper forecasts started to
gain credibility among the investment community (CNBC, Bloomberg, CNNFN). By the
mid 1990’s, whisper forecasts were commonly used by investors. As the Internet began
its global development and expanded around 1997 it was common to observe financial
reporters announce whisper forecasts in parallel with the firm’s earnings estimates.
Because whisper forecasts are unofficial earnings estimates, their validity and
informational properties were questioned. Bagnoli et al. (1999) documented basic
characteristics of whisper forecasts and some whisper content not contained in analysts’
estimates (First Call). Specifically, Bagnoli et al. (1999) design tested the information
content and the probability of achieving abnormal returns from trading strategies based
on whisper forecasts; they measured returns from the date the whisper forecasts were
disclosed to the date that analysts’ quarterly earnings were made public. They found that

7

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.

whisper forecasts contain some eamings-relevant information not contained in the First
Call analysts’ forecasts. They also found that by following a trading strategy based on
whisper forecasts, analysts’ estimates, and their pre-announcement locations, an investor
could attain additional returns by buying/selling stocks according to whether the whisper
was above or below the analysts’ estimate. The authors did not examine how markets
reacted to the earnings announcement in the presence of whisper forecasts or if managers
engaged in earnings management practices to reach a whisper threshold (Bagnoli et al.
1999).
Related research has shown that managers manipulate earnings to exceed thresholds.
DeGeorge, Patel and Zeckhauser (1999) found that, for both market and psychological
reasons, managers try to beat earnings thresholds. One threshold managers try to reach,
with or without earnings management, is the quarterly earnings estimates issued by
analysts. DeGeorge et al. (1999), and Burgsthaler and Eames (2001) provide evidence
that firms manage earnings upward, particularly to meet analysts’ expectations. Kasznik
(1999) found that managers use unexpected accruals to manage earnings upward when
firms are in danger of falling short of managers’ earnings forecasts.
Prior research assessed the impact of missing earnings expectations. Companies that
are able to meet or exceed certain thresholds and avoid earnings disappointments are
priced at a premium, while those that disappoint —especially growth firms —suffer
disproportionately. (Myers and Skinner (1998), Skinner and Sloan (2001).) Collins and
Kothari (1989) show that market reaction to earnings announcements is greater for
growth firms (Note that whisper forecasts of earnings were circulated mostly for growth
firms.) The increased use of executive options, especially for high-tech growth firms is a

8

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.

related issue. The reward to senior executives depends on stock-price performance or
earnings, or both (Healy, 1985, and Gaver, Gaver and Austin, 1995). For example, higher
earnings imply higher management bonuses because annual bonuses are either a direct
function of annual earnings or are a direct function of common stock value, which is
related to reported earnings (Gaver, Gaver, and Austin, 1995; Healy, 1985).
In summary, there exists a group of growth companies whose managers hold a large
amount o f equity linked to stock market performance of their shares and financial
markets that react disproportionately to earnings disappointments from these firms. We
hypothesize that this observation provides an incentive for managers to engage in
earnings management to beat a threshold . We now turn to an analysis of these
thresholds.
Analysts have incentives to bias forecasts to benefit private clients, maintain good
informational relations with companies, and promote investment-banking activities with
firms (Francis and Philbrick (1993)). Whisper forecasts may be a consequence of the
relationship between companies and the analysts that follow them. Companies try to
manage expectations downward and analysts may be rewarded when companies they
recommend beat estimates. Thus, whisper forecasts are a reaction of investors to the
observed pessimistic bias of analysts’ estimates.
This raises two important questions: 1) How value-relevant are analysts’ and
whisper earnings forecasts to investors? and 2) Are whisper forecasts relevant enough to
investors that managers engage in earnings management to meet or beat this “new”
threshold?

9

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.


II.2 Accuracy, bias, and relevance of analysts’ forecasts of earnings when compared with

whisper forecasts o f earnings

II.2a Comparison of analysts’ forecast errors and whisper forecast errors

To test the most general hypothesis that whisper forecasts were relevant to
investors, we examine the change in analysts’ earnings forecast errors and whisper
forecast errors during the sample period (1990-2000).
Specifically, we compare the distribution of analysts’ forecast error for the period
1990-1996 (called Period I or pre-whisper period) and the period 1997-2000 (Period II or
whisper period) to the distribution of whisper forecast errors for 1997-2000.
If whisper forecasts are relevant and accurate there has to be a change in the accuracy
of analysts’ estimates that justifies the existence of whisper forecasts. For Period I or the
pre-whisper period, analysts’ forecasts for firms included in the sample were neutral or
slightly pessimistic. If there was indeed a shift in the analysts’ bias due to the incentives
previously discussed, we expect the mean analysts’ forecast error to increase from Period
I to Period II, the period where whisper forecasts became available for these firms . The
hypothesis is

H I (a) Analysts’forecast error increases from Period I to Period II.

10

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.


If the whisper number becomes a substitute for the analysts’ forecast, then whisper
forecast errors should approximate the characteristics of analysts’ forecast errors before
whispers. Thus,

HI (b) The characteristics o f whisper forecast errors in Period II are not significantly

different from those o f analysts’forecast errors in Period I.

II.2b Comparison o f the overall distribution of analysts’ forecast errors and whisper
forecast errors

The shape of the distribution of analysts’ forecast errors before whisper forecasts has
the common characteristics of distributions that reflect earnings management. A whispererror distribution similar to the distribution of analysts’ forecast errors before whispers is
consistent with evidence that the firms that are part of that distribution also were involved
in eamings-management practices. This test is also a test of the relevance of whisper
forecasts; if it is possible to replicate the previous distribution o f analysts’ forecast errors
with the distribution o f whisper errors, this is evidence not only that investors were more
accurate in setting the whisper number, but also that companies could have moved their
attention to focus on the unofficial earnings forecast (whispers), as proposed in the
DeGeorge, Patel and Zeckhauser (1999) framework. Thus,

H l(ci) The distribution o f analysts’forecast errors in Period I is not different from the
distribution o f whisper errors in Period II.

11

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.


If whisper forecasts were relevant and if companies were pursuing them, the
distribution of analysts’ forecast errors would shift to the right as a consequence of more
aggressive whisper earnings estimates. Thus,

H l(cii) The distribution o f analysts’forecast errors in Period I is different from the
distribution o f analysts’forecast errors in Period II.

11.2c Conflicts of interest, bias, and accuracy o f whisper forecasts

If whisper forecasts were relevant to investors as a mechanism to remove the bias
in analysts’ estimates, we should expect that whisper forecasts are more accurate and less
biased than analysts’ forecasts. The possibility that whispers have a timing advantage
over analysts’ estimates may confound the results. Therefore, for tests of this hypothesis,
separate results will be presented for a sub-sample of analysts’ estimates. The sub-sample
includes only analysts’ estimates that were released within a period of 30 days preceding
the earnings announcement.
Thus, to test for a shift in bias and accuracy of analysts’ forecasts that occurred
during the 1990’s, we hypothesize the following

H I (di) The bias and inaccuracy o f analysts ’forecasts are greater in Period II than in
Period I.

Hl(dii) The bias and inaccuracy o f whisper forecasts are smaller than that o f analysts’
forecasts.

II. 3 Value Relevance of Whisper forecasts

12

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.


II.3a Abnormal Returns and Unexpected Earnings

According to DeGeorge, Patel and Zeckhauser (1999), “executives focus on
thresholds, because the parties concerned with the firm do.” One of the relevant parties is
the investor, both individual and institutional. If abnormal returns can be better explained

by a simple statistic based on whisper forecasts (such as whisper errors) rather than on
analysts’ forecast errors, it will be evidence that whisper forecasts were more closely
related to true market expectations about earnings than were the analysts’ forecasts. Thus,
the test will add validity to the concept of whisper forecasts as a piece of information
relevant for markets and for valuation purposes. If whisper forecasts are not able to
explain abnormal returns, their relevance will be questionable. Thus,

H2 (a) Abnormal returns are better explained by whisper forecast errors than by
analysts' forecast errors.

II.3b Investors’ Reaction to Earnings Announcements

Whisper forecasts are generally speaking more optimistic relative to analysts’
estimates. In order to isolate the effect that the whisper number has on investors’ and
managers’ behavior, we divide the sample into nine different groups. The classification of
groups will be discussed in detail later. The justification for dividing the sample is to
facilitate the analysis of both the returns and the earnings management hypotheses. Based
on empirical and anecdotal evidence, if whisper forecasts are relevant thresholds, we

13

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.


should expect firms that meet or beat the whisper number to have positive abnormal
returns. A confounding factor is that whisper forecasts and analysts’ forecasts are highly
correlated. Therefore, for a number of observations, it would be hard to disentangle the
effect on abnormal returns of beating the whisper versus beating the estimate. The
proposed separation deals with that problem since it splits the sample on earnings
performance based on analysts’/whisper estimates conditional on whether the whisper

was similar (within +-1 cent), smaller, or larger than the analysts’ estimate. Evidence
suggests that managers have strong incentives to meet a threshold in order to shelter their
stock price, especially in growth firms. If whisper forecasts represent “true” market
expectations, we should expect that even if firms were able to beat the analysts’ estimates
but not the whispers, firms would be penalized by the market. If whisper forecasts are
relevant to the market, we predict that:

H2 (bi) Whisper errors are positively correlated with abnormal returns independently
o f the sign o f analysts’forecast errors.

H2 (bii) Analysts’forecast errors are positively correlated with abnormal returns
independently o f the sign o f whisper errors.

H2 (ci) Groups with positive unexpected earnings based on whisper forecasts have
higher abnormal returns than groups with positive unexpected earnings based on
analysts ’forecasts.

H2 (cii) Groups with negative unexpected earnings based on whisper forecasts have
more negative abnormal returns than groups with negative unexpected earnings based
on analysts’forecasts.

II.4 Managers’ reaction to whisper forecasts

14

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.


Discretionary Accruals

DeGeorge, Patel and Zeckhauser (1999) showed that, “executives focus on
thresholds, because the parties concerned with the firm do”. We assume that if
markets/investors also care about whisper forecasts, it is likely that firms/managers care
about whisper forecasts. The DeGeorge et al. (1999) paper also suggested that in the
presence of a relevant threshold, companies manage earnings to meet or beat the
threshold.
To test whether companies manage earnings to beat the whisper estimate we identify
companies that we suspect will engage in such practices. As in DeGeorge et al. (1999),
we assume that companies that were able to meet or beat the estimate likely managed
earnings. To test whether firms were using discretionary accruals to meet or beat whisper
earnings forecasts, I use the same grouping of firms that is used for the returns analysis to
study earnings management. Firms with a 0 or 1 cent whisper forecast error are suspected
of using discretionary accruals to avoid earnings disappointments. Formally, the two
hypotheses are:

H3 (ai) Abnormal discretionary accruals fo r firms that ju st meet or beat the whisper
earnings forecast are higher than those o f their industry peers.

H3 (aii) The proportion ofpositive/negative abnormal accruals is higher fo r groups
that ju st meet/beat the whisper forecast than fo r other groups.

15

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.


III. Sample Selection and Description

I manually created the whisper-numbers database from a set of diverse sources.
Most important were the web sites that specialize in collecting the whisper forecasts. To

create a database, several such web sites were visited and all the whisper forecasts related
to the relevant sample were collected for the period between the fourth quarter of 1996
and the fourth quarter of 2000. Several message boards were visited, and with the aid of
specialized search engines, nearly 3 million messages were browsed and searched using
the keyword “whisper”. When a hit was obtained, the message was then read and if there
was a whisper forecast for a specific company, it was included in the database. Also,
specialized websites provided an average whisper forecast for companies from the
selected sample. For each company quarter, the database contained quarterly whisper
forecasts, the analysts’ earnings estimates from Zack’s Investment Services, and the
stock-price movements the day after the earnings releases. The search yielded 544
whisper forecasts for about 150 companies. The median whisper number frequency per
firm was close to 4. The companies in the sample are above average in terms of size,
assets, sales, and market capitalization.
The analysts’ estimates of earnings per share were collected from the FirstCall
Earnings Database and dates were collected from the I/B/E/S databases. Accounting and
financial data was collected from the Compustat Industrial Quarterly file. Data related to
returns was collected from the Center for Research on Security Prices (CRSP) daily stock
file.

16

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.


The “selected sample” includes firms that are members of one or more of the
following market indices: the Nasdaq 100, the Philadelphia Stock Exchange Technology
Index (PSETI) or the ISDEX Internet Index. The name “whisper companies” refers to
companies for which a whisper number was found for the 1997-2000 period. “Rest of the
companies” refers to the rest of the companies on the FirstCall Database and the
Compustat Industrial Quarterly Database. Table 1 lists descriptive characteristics of each

of these groups.

IV. Research Design and Methods

The non-parametric test (Kolmogorov-Smimov two-sample test) is used to test
whether or not two samples may reasonably be assumed to come from the same
population. The procedure estimates a difference (D):

D = max \F(x)-G(x)\

for all x values. The null hypothesis that the two distributions are identical is rejected at
the p level of significance if the computed value of D exceeds a certain amount.
We compare the ability of the forecast error and the whisper error to explain
abnormal returns around earnings announcements. We calculate market adjusted
Cumulative Abnormal Returns (CAR) for the 3-day window centered on the date of the
earnings announcement. CAR is defined as the difference between the firm’s security
return minus the return on a value weighted portfolio.

17

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.


For the earnings management hypothesis, we use quarterly data to estimate
discretionary accruals. Quarterly data provides a sharper focus on the event when
compared with yearly data, increasing the likelihood of detecting earnings management. I
use the modified Jones Model to detect earnings management because Jeter and
Shivakumar (1999) found the model to be well-specified for both annual and quarterly
data. The times-series version of the model estimates firm-specific parameters using data
from periods before the event period and the cross-sectional version uses parameters to

estimate each period for each firm in the event sample using contemporaneous
accounting data of firms in the same industry. Because whisper forecasts were available
on a quarterly basis, the detection of earnings management is done with quarterly data.
The cross-sectional model is selected, not only because of the evidence that it is wellspecified, but also because the requirement of time-series data would have substantially
reduced our sample size.
The cross-sectional version of the model was also selected because the abnormal
accruals detected should be interpreted as industry-relative abnormal accruals. There is a
caveat when dealing with the cross-sectional version of the model. Jeter and Shivakumar
(1999) argue that if an industry enjoys favorable economic conditions and if firms enjoy
smooth reported earnings, then the actual abnormal accruals for the firms in the industry
will be negative (in fact, our results show this characteristic). The cross-sectional model
is unlikely to capture all the negative abnormal accruals because earnings management is
contemporaneously correlated across firms in the sample. Only those firms whose
accruals are negative relative to the industry benchmark are identified as earnings
managers.

18

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.