Whisper forecasts and earnings management

... summary and conclusions in Section VI II Background and Hypotheses Development II Whisper forecasts Analysts’ Behavior and Earnings Management Academics and financial press writers define whisper forecasts. .. the Whisper Forecast, the Analysts’ Forecast and Reported Earnings for Quarters when both Analysts’ Forecasts and Whisper Forecasts are Available Figure Distribution of Forecast Errors and Whisper. .. 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

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

UMI Number: 3165898

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Arnoldo J Rodriguez

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and that all revisions required by the final examining committee have been made

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

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

Table of Contents

I Introduction

Page2

List of Tables

Page

Table 5 Comparison of Analysts’ Forecast Errors for the Sample of Growth

Table 6 Comparison of Analysts’ and Whisper Forecast Errors for the

Sample of Firms for which Whisper Forecasts are Available 41Table 7 Explanatory Power of Forecast Error and Whisper Error 43

Distribution of Forecast Errors and Whisper Errors

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)).

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 o f 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 o f 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

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 o f 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.

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 o f whisper forecasts on investors, firms, and markets Healy and Whalen (1999) criticize the lack o f 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 o f 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

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”

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 o f 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 o f 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

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 o f 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

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 o f 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 o f 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?

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

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,

H I (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 whisper- error 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 o f 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.

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 o f 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.

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

II 3 Value Relevance of Whisper forecasts

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 o f 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 o f 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

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

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 firm s 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.

III Sample Selection and Description

I manually created the whisper-numbers database from a set o f 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

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 o f 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

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 o f 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 well- specified, 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

Each firm’s quarterly accruals relative to the same quarter for the previous year

are estimated by

Acct = ACA it - ACASH it - A CL it - DEP it

ACASH it = Change in cash over quarter t

A CL u - Change in current liabilities excluding the current portion of long-term debt over quarter t

DEP u = Depreciation during quarter t

All changes are calculated relative to the same quarter o f the previous year to control for seasonality in accruals Accruals are calculated for all firms in the sample and for all firms on the CompuStat Full Coverage file

The two-digit SIC code ensures a reasonable match on industry accruals (Jeter and Shivakumar (1999)) The sample contains firms from 18 SIC codes The number of firms for each SIC group ranged from 30 to 1,028; the mean was 160 firms and the median was

97 for each quarter ranging from the fourth quarter of 1998 to the fourth quarter of 2000

Failure to meet data requirements to run the regression reduced the whisper/quarter observations from 544 to 504 The regression was estimated using the following specification:

Accit / ait-i = Pi (1 / ait-i) + P2 (Arevit/ ait.i) + P3 (gppe it / ait-0+ sit

Accit / ait-i = Accruals at time t scaled by the beginning of quarter total assets Arevjt / ait-i = Change in revenue with respect to the same quarter o f the previous year