Do Firms Use Discretionary Revenues to Meet Earnings and

Revenue Targets?

Stephen R. Stubben*

Graduate School of Business

Stanford University

February 2006

Abstract:

This paper addresses two questions related to the use of discretion over revenues. First, do firms

use discretion in revenues to manage earnings to meet earnings targets? Second, do firms

manage revenues to meet revenue targets? To answer these questions, I model a common form

of discretionary revenues and its effect on the relation between revenues and accounts receivable.

Using this model, I find that firms with earnings just above analysts’ consensus forecasts report

positive discretionary revenues. Firms with greater incentives to use discretion in revenues as

opposed to expenses (i.e., growth firms and firms with high gross margins) do so to a greater

extent than other firms. I find limited evidence that growth firms use discretionary revenues to

meet revenue forecasts.

* I would like to thank my dissertation committee of Mary Barth, Bill Beaver, and Maureen McNichols

for invaluable comments and suggestions, and Chris Armstrong, Yonca Ertimur, Fabrizio Ferri, Alan

Jagolinzer, Wayne Landsman, Dave Larcker, Nate Sharp, Mark Soliman, and workshop participants at

Stanford University and the 2005 Accounting Research Symposium at Brigham Young University. I

thank Huron Consulting Group for providing data on restatements and SEC enforcement actions.

1

1. Introduction

This paper addresses two questions related to the use of discretion in revenues. First, do

firms use discretion in revenues to manage earnings to meet earnings targets? Second, do firms

manage revenues to meet revenue targets?1

Firms have incentives to meet earnings targets, and evidence suggests that they manage

earnings to do so (Burgstahler and Dichev, 1997). Firms can manage earnings using revenues,

expenses, or both. However, earnings management using revenues is likely to be more costly

than other forms of earnings management. Earnings management using revenues is more likely

to be detected and has a greater cost given detection (Marquardt and Wiedman, 2004), which

suggests that firms might prefer to manage earnings using expenses. However, certain firms,

such as growth firms and firms with high gross margins, may manage earnings using revenues

because the potential benefits are greater. Growth firms reap greater benefits from managing

earnings using revenues because investors value revenues of growth firms significantly more

highly than expenses (Ertimur, Livnat, and Martikainen, 2003). Firms with high gross margins

reap greater benefits than other firms because each dollar of discretionary revenue has a greater

impact on earnings.

Studying revenues rather than net earnings has two advantages. First, studying earnings

components can provide insights into how earnings are managed. Revenues is an ideal

component to examine; it is the largest earnings component for most firms, and it is subject to

discretion by managers. Furthermore, evidence suggests that revenue manipulation is common

relative to other forms of earnings management. For example, Dechow and Schrand (2004, page

42) documents that over 70% of SEC Accounting and Auditing Enforcement Releases involve

1 Throughout the paper I use “revenue management” to describe the use of discretion in revenues to meet revenue

targets. “Revenue manipulation” or “discretionary revenues” alone could indicate either revenue management or

earnings management using revenues.

2

misstated revenues, and revenues are the most common type of financial statement restatement

(Turner, Dietrich, Anderson, and Bailey, 2001).

The second advantage to studying revenues is that focusing on one component of

earnings has the potential to provide more precise estimates of discretion. The aggregate accrual

models that are commonly used to study discretion in earnings have been criticized for their low

power and inaccurate estimates of discretion (e.g., Dechow, Sloan, and Sweeney, 1995; Guay,

Kothari, and Watts, 1996; McNichols, 2000; and Thomas and Zhang, 2000). Although prior

studies have examined whether firms use discretionary accruals to meet earnings benchmarks

(e.g., Burgstahler and Eames, 2002; Dechow, Richardson, and Tuna, 2003), it is possible that

biased and low-powered estimates of discretion from misspecified accrual models affect the

conclusions of these studies. For example, Dechow, Richardson, and Tuna (2003) concludes that

if firms use discretionary accruals to avoid losses, their model is not powerful enough to detect it.

A substantial amount of academic research has addressed firms’ capital market

incentives to meet earnings targets. However, for growth firms it may not be sufficient to meet

only earnings targets; revenue targets are also important. Because revenue increases are more

sustainable than cost reductions (Ghosh, Ju, and Jain, 2005), investors rely on revenues more

than expenses to evaluate growth firms’ future growth potential. For example, among growth

firms that just meet earnings forecasts, those that miss revenue forecasts have significantly

negative stock returns during the earnings announcement period (Ertimur, Livnat, and

Martikainen, 2003). Therefore, it is likely that growth firms have incentives to manage revenues

to meet revenue forecasts, in addition to meeting earnings forecasts. Consistent with this idea,

Plummer and Mest (2001) finds a discontinuity in the revenue forecast error distribution, with

more than expected small positive revenue forecast errors. However, I am not aware of any

3

study that has provided direct evidence of the use of discretionary revenues to meet revenue

forecasts.

I hypothesize that firms, particularly growth firms and firms with high gross margins, use

revenues to manage earnings to meet earnings forecasts. Specifically, I test whether firms that

report earnings equal to or slightly above the consensus forecast have positive discretionary

revenues. Among firms with small positive earnings forecast errors, I also test whether growth

firms have higher discretionary revenues than non-growth firms, and whether firms with high

gross margins have higher discretionary revenues than other firms. I also hypothesize that

growth firms manage revenues to meet revenue targets. I test whether growth firms that report

revenues equal to or slightly above the consensus forecast have positive discretionary revenues.

These tests require an estimate of discretion in revenues. I model a common form of

revenue manipulation—premature revenue recognition—and its effect on the relation between

revenues and accounts receivable. In this paper, prematurely recognized revenues are sales

recognized before GAAP criteria are met and before any cash is collected. My revenue model is

similar to existing discretionary accrual models (Jones, 1991; Dechow, Sloan, and Sweeney,

1995), but with three key differences. First, I model the receivables accrual, rather than

aggregate accruals, as a function of the change in revenues. As I argue and show, receivables

have a stronger and more direct relation with revenues than the other accrual components. Thus,

the inclusion of other accrual components leads to noisy and biased estimates of discretion.

Because I model receivables instead of aggregate accruals, the model is one of revenues rather

than earnings. Second, I model the receivables accrual as a function of the change in reported

revenues, rather than the change in cash revenues (Dechow, Sloan, and Sweeney, 1995).

Although this choice systematically understates estimates of discretion in revenues, it is less

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likely to overstate estimates of discretion for growth firms. Third, I model the change in annual

receivables as a linear function of two components of the change in annual revenues: change in

revenues of the first three quarters, and change in fourth-quarter revenues. Because sales in the

early part of the year are more likely to be collected in cash by the end of the year, these have

different implications for receivables than does a change in fourth-quarter sales. Discretion in

revenues is captured by discretionary receivables, which is the difference between the actual

change in receivables and the predicted change in receivables based on the model.

To the extent that discretionary revenues are not offset by corresponding expenses,

accrual models should detect them. Therefore, for comparison I present results using the term-

adjusted modified Jones model of aggregate accruals (Teoh, Wong, and Rao, 1998). Prior

research finds that the Jones (1991) model and its variants are misspecified for firms with

extreme performance (e.g., Dechow, Sloan, and Sweeney, 1995; McNichols, 2000). Because I

study growth firms, it is possible that the revenue and accrual models produce biased estimates

of discretion. Using simulations of manipulation (Kothari, Leone, and Wasley, 2005), I find that

the revenue model produces estimates of discretion that are well specified for growth firms. The

benchmark accrual model does not. For this reason, I also use performance-matched

discretionary accrual estimates, which are purported to be well specified in the presence of

extreme performance (Kothari, Leone, and Wasley, 2005). I also find that the revenue model is

more likely to detect revenue manipulation than the accrual model. Using a sample of firms

subject to enforcement actions by the Securities and Exchange Commission and subsequent

restatements, I find that only the revenue model detects discretionary revenues for firms that

admitted to revenue manipulation.

5

Regarding using revenues to meet earnings targets, I find the following. Discretionary

revenues are significantly positive for firms that just meet analysts’ consensus forecasts of

earnings, indicating firms use revenues to manage earnings to meet analysts’ forecasts.

Furthermore, discretionary revenues are significantly higher for growth firms and firms with

high gross margins than for other firms with small positive earnings forecast errors. Thus, firms

with greater benefits from managing earnings using revenues rather than expenses are willing to

bear the greater costs associated with this type of earnings management. The performance-

matched discretionary accrual estimates do not detect earnings management to meet earnings

forecasts.

Regarding using revenues to meet revenue targets, I find that discretionary revenues are

significantly positive for growth firms that just meet analysts’ consensus forecasts of revenue,

but not for other firms that just meet revenue forecasts. This finding suggests that growth firms

meet revenue targets by prematurely recognizing revenue. However, discretionary revenues for

growth firms with small positive revenue forecast errors are not significantly higher than those

for growth firms with small negative revenue forecast errors, which casts doubt on a revenue

management interpretation. As with earnings forecasts, the performance-matched discretionary

accrual estimates do not detect revenue management to meet revenue forecasts.

This study has implications for research on management discretion. I develop an

estimate of discretion in revenues that can be used to detect revenue management. This revenue

estimate can also be used as a measure of earnings management that is more powerful and less

biased than estimates from accrual models. Even though it does not detect discretionary

expenses, it detects earnings management (via revenues) to meet earnings forecasts where the

benchmark accrual model does not.

6

The paper continues as follows. The motivation and hypotheses are discussed in section

2. The research design is presented in section 3. Section 4 details the data and descriptive

statistics, section 5 evaluates the revenue and accrual models, and section 6 discusses the primary

results. Finally, section 7 concludes.

2. Motivation and Hypotheses

Discretion in revenues can be used to achieve two financial reporting goals. First, firms

can use revenues to manage earnings to meet earnings targets (i.e., earnings management).

Second, firms can manage revenues to meet revenue targets (i.e., revenue management).

2.1 Earnings Management using Revenues

Burgstahler and Dichev (1997) argues that firms have incentives to meet earnings

benchmarks. They provide evidence of earnings management by documenting a higher than

expected frequency of firms with zero or small earnings and increases in earnings in cross-

sectional distributions. Dechow, Richardson, and Tuna (2003) extends Burgstahler and Dichev

(1997) by testing whether firms with small profits use discretionary accruals to avoid reporting a

loss. They find similar magnitudes of discretionary accruals for small loss and small profit

firms, and they conclude that if firms overstate earnings to report profits, their accrual model is

not powerful enough to detect it.

Dechow, Richardson, and Tuna (2003) also examines the relative importance of

reporting profits, earnings increases, and positive earnings forecast errors. They show that

although the discontinuities in the annual earnings and earnings change distributions have

decreased over time, the discontinuity in the analyst forecast error distribution has increased.

These results suggest that meeting analysts’ consensus forecasts is becoming the more important

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benchmark.2 Several studies have attempted to find evidence that firms manage earnings to meet

consensus forecasts, with mixed results (Burgstahler and Eames, 2002; Matsumoto, 2002;

Phillips, Pincus, and Rego, 2003; and Dhaliwal, Gleason, and Mills, 2004). For example,

Burgstahler and Eames (2002) finds that firms with small positive forecast errors have higher

abnormal accruals using the Jones (1991) model, whereas Phillips, Pincus, and Rego (2003),

using the modified Jones model, does not.3

Firms can manage earnings to meet benchmarks using revenues, expenses, or both.

Studying components of earnings can provide insights into how firms manage earnings.

However, most studies rely on measures of discretion in aggregate earnings. Three exceptions

are Plummer and Mest (2001), Marquardt and Wiedman (2004), and Roychowdhury (2004).4

Plummer and Mest (2001) studies the discretion over earnings components using distributional

tests similar to those of Burgstahler and Dichev (1997). They find evidence that suggests firms

manage earnings upward to meet earnings forecasts by overstating revenues and understating

operating expenses but not by understating depreciation or non-operating expenses. However,

they do not test whether discretionary revenues explain the discontinuity they find in the revenue

forecast error distribution. Marquardt and Wiedman (2004) estimates the unexpected portions of

several accrual components, including receivables, to determine which components of earnings

firms manipulate. They find evidence that firms with small earnings increases understate special

2 Graham, Harvey, and Rajgopal (2005) finds that capital market incentives dominate CFOs’ reasons for managing

earnings; CFOs think meeting benchmarks leads to credibility in the market and higher stock prices. Although it is

possible that investors are able to completely “unravel” the discretionary portion of reported financial results at least

in some cases, the survey results suggest that managers perceive a benefit for using discretion to meet benchmarks,

and this perception leads them to do so. 3 Managers can also meet revenue forecasts by guiding analysts to lower their forecasts prior to the revenue

announcement. Because of the difficulty of measuring managerial guidance, I do not control for its impact on firms’

ability to meet forecasts. To the extent firms guide analysts, finding earnings and revenue management is more

difficult. However, Matsumoto (2002) finds growth firms manage earnings upward but not forecasts downward. 4 Other studies that examine discretion over particular earnings components to meet earnings benchmarks have been

conducted in the banking industry (Beatty, Ke, and Petroni, 2002) and the property-casualty insurance industry

(Beaver, McNichols, and Nelson, 2003). Phillips, Pincus, and Rego (2003) and Dhaliwal, Gleason, and Mills (2004)

examine the manipulation of income tax expense to meet earnings benchmarks.

8

items but do not overstate revenues. They also find evidence that firms use discretion in

revenues to increase (decrease) earnings before equity issuances (management buyouts).

Roychowdhury (2004) finds evidence that firms offer sales discounts to avoid reporting losses,

but does not find evidence for discretionary revenues.

Of the earnings management choices available, revenues is one of the most costly.

Marquardt and Wiedman (2004) discusses the costs associated with earnings management and

conclude that the manipulation of recurring items, especially revenues, results in the most severe

costs through an increased probability of detection and more negative pricing consequences if

detected. Beneish (1999) finds a positive association between overstated revenues and the

probability a firm will be targeted by an SEC enforcement action. Furthermore, although the fact

that revenues are the most common type of financial statement restatement (Turner, Dietrich,

Anderson, and Bailey, 2001) could mean that revenue manipulation is commonly attempted, it

also could mean that revenue manipulation is more likely to be detected. In addition to

increasing the probability of detection, revenue manipulation increases the stock price

consequences if the manipulation is detected. Wu (2002) finds that restatements of revenues are

associated with significantly more negative stock returns than other types of restatements, and

Palmrose, Richardson, and Scholz (2004) finds that the likelihood of litigation after a restatement

increases when revenues are involved.

It is possible that the greater costs associated with earnings management using revenues

would discourage firms from engaging in this form of reporting manipulation. However, growth

firms and firms with high gross margins potentially realize greater benefits than other firms from

overstating revenues, which makes these firms more likely to bear the higher costs associated

with discretionary revenues.

9

The competitive strategy literature predicts that firms pursuing growth through revenue

increases are different from those pursuing growth through expense reductions from cost cutting

or productivity gains (Porter, 1985). Revenue growth indicates growth in product demand rather

than merely cost control and is more sustainable. Thus, for growing firms, it is especially

important to report earnings growth and earnings surprises that are driven by revenues.

Consistent with this idea, Ertimur, Livnat, and Martikainen (2003) finds that investors value

revenue surprises more highly than expense surprises, especially for growth firms.5 Firms with

high gross margins also have incentives to manage earnings using revenues. For these firms,

each dollar of discretionary revenue has a greater impact on earnings.

These arguments lead to the following hypotheses (stated in alternative form).

H1: Discretionary revenues are positive for firms with small positive earnings forecast

errors

H1a: Among firms with small positive earnings forecast errors, growth firms have higher

discretionary revenues

H1b: Among firms with small positive earnings forecast errors, firms with high gross

margins have higher discretionary revenues

2.2 Revenue Management

Many studies address firms’ incentives to meet earnings targets. However, because

earnings components differ in persistence, they can be differentially informative about firm

performance (Lipe, 1986). Consequently, the source of the earnings surprise can be important.

Because revenue increases are more sustainable than expense decreases, revenue surprises are a

5 Evidence from the financial press corroborates this emphasis on revenues. One Wall Street Journal article notes,

“Only earnings generated by revenue improvements are getting investors excited.” (Zuckerman, 2000).

10

better indicator of future growth than expense surprises (Ghosh, Gu, and Jain, 2005). Ertimur,

Livnat, and Martikainen (2003) finds that among growth firms that just meet earnings forecasts,

those that miss revenue forecasts have significantly negative announcement-period abnormal

returns, whereas those that meet revenue forecasts have significantly positive abnormal returns.

For value firms, those that meet earnings forecasts have significantly positive abnormal returns

regardless of whether the revenue forecast is met. Thus, growth firms have incentives to meet

revenue forecasts, in addition to just meeting earnings forecasts.

Because growth firms have incentives to meet revenue forecasts, it is likely that they

manage revenue to meet revenue forecasts. Magrath and Weld (2002) argues that the pressure to

meet revenue forecasts is particularly intense (as compared to pressure to meet earnings

forecasts) and may be the primary catalyst leading to “questionable, improper, or fraudulent

revenue-recognition practices.”

In response to this pressure on firms with respect to revenues, regulators have focused on

revenue accounting for several years. In 2005, the Financial Accounting Standards Advisory

Council’s annual survey listed revenue recognition as the number one concern for the Financial

Accounting Standards Board for the fourth consecutive year. Arthur Levitt, the former chairman

of the Securities and Exchange Commission, argues that revenue recognition is one of the

principal concerns with financial reporting (Levitt, 1998).

Consistent with firms facing pressure to meet revenue targets, several sources suggest

that revenue manipulation is relatively common. Nelson, Elliott, and Tarpley (2002) surveys

auditors and find that revenue manipulation is one of the most commonly attempted forms of

discretion by client firms. According to a report by PricewaterhouseCoopers (2001), in the year

2000, approximately 66% of all accounting litigation cases allege some sort of revenue

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recognition violations. Dechow and Schrand (2004, page 42) documents that over 70% of SEC

Accounting and Auditing Enforcement Releases involve overstated revenue, and Turner,

Dietrich, Anderson, and Bailey (2001) finds that revenue recognition restatements are the most

frequent. Despite this evidence, and the many studies on earnings management, there is little

empirical academic research on discretion over revenues.

Additional evidence of revenue management to meet revenue targets is provided by

Plummer and Mest (2001), which finds more firms than expected reporting small positive

revenue forecast errors.6 I test whether growth firms overstate revenues to meet this target.

H2: Discretionary revenues are positive for growth firms with small positive revenue

forecast errors

3. Research Design

3.1 Identification of Firms Suspected to Have Used Discretionary Revenues to Meet Forecasts

My hypotheses require measures for high growth, high gross margin, small positive

earnings and revenue forecast errors, and discretionary revenues. I define growth firms as firms

in the highest quartile of revenue growth each industry and year, measured as revenues in year t-

1 divided by revenues in year t-2. I measure gross margin as the difference between annual sales

and cost of sales, divided by sales (all in year t-1). High gross margin firms are those in the

highest quartile of the gross margin distribution each industry and year.

I define small positive revenue forecast errors as revenue realizations that are greater than

the consensus forecast by less than 1% of beginning-of-year market value of equity. Similarly,

small positive earnings forecast errors are earnings realizations that are greater than the

6 Plummer and Mest (2001) examines the revenue forecast error distribution to determine whether firms overstate

revenues to meet earnings forecasts. However, their finding more than expected firms with small positive revenue

forecast errors could also indicate revenue management to meet revenue forecasts.

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consensus forecast by less than 0.3% of beginning-of-year market value of equity. The choice of

0.3% reflects approximately the median gross margin for sample firms (0.3). That is, on average

discretionary revenues of 1% of market value produce discretionary earnings of 0.3%. Because

the scaled earnings forecast error distribution is less disperse than the scaled revenue forecast

error distribution, this choice also serves to produce similar proportions of small positive

earnings and revenue forecast errors.7 To provide additional confidence in the results, I compare

estimates of discretion by firms with small positive forecast errors to estimates of discretion by

firms with small negative forecast errors. I define small negative revenue (earnings) forecast

errors are revenue (earnings) realizations that miss the consensus forecast by less than 1% (0.3%)

of beginning-of-year market value of equity.

3.2 Estimates of Discretionary Revenues

The research design also requires estimates of discretionary revenues. Discretion in

revenues can take a number of forms. Some involve the manipulation of real activities (e.g.,

sales discounts, relaxed credit requirements, channel stuffing, and bill and hold sales), and others

do not (e.g., sales recognized before recognition criteria are met, fictitious revenues, and revenue

deferrals). In this paper, I model premature revenue recognition and its effect on the relation

between revenues and accounts receivable. Premature revenue recognition includes channel

stuffing and bill and hold sales, if customers do not pay cash for the inventory, and sales

recognized before recognition criteria are met. It also includes fictitious sales recorded on

account.

7 I find similar results when scaling by average total assets or defining small earnings forecast errors as those greater

than the consensus forecast by less than either 0.5% or 1% of market value of equity.

13

I focus on premature revenue recognition because evidence suggests it is a common form

of revenue management.8 For example, Levitt (1998) argues that premature revenue recognition

is one of five fundamental problems with financial reporting, and Feroz, Park, and Pastena

(1991) finds that more than half of SEC enforcement actions issued between 1982 and 1989

involved overstatements of accounts receivable resulting from premature revenue recognition. In

addition, other forms of revenue manipulation, such as sales discounts, could be profit-

maximizing business decisions and not merely attempts to meet a performance benchmark.

The revenue model is as follows. Reported, or managed, sales (S) equals

nondiscretionary sales (SUM

) plus discretionary sales (δ RM).

9

RM

it

UM

itit SS δ+=

By assumption, there are no cash collections of discretionary sales during the current

period; these managed sales increase reported ending accounts receivable (AR) and reported sales

by the same amount. Thus, discretionary receivables equals the discretionary portion of sales (δ

RM).

10 Ending accounts receivable equals the portion of current nondiscretionary sales that were

not collected in cash (c × SUM

) plus all discretionary sales. I assume that current accruals are

resolved within one year and receivables relating to sales in prior years are no longer

collectible.11

RM

it

UM

itit ScAR δ+×=

8 In a future version of the paper, I plan to include measures of additional types of discretionary revenues.

9 If revenues are continually managed, δ RM

could be interpreted as net revenue management (i.e., revenue

management net of reversals from the prior period). 10

Because I estimate this model using net accounts receivable, the estimated discretion also includes any discretion

in the allowance for doubtful accounts. 11

I address the impact of this assumption in untabulated tests by adding additional lags of sales to the model.

Results are similar.

14

Because nondiscretionary sales are not observable, I express ending receivables in terms

of reported sales.

RM

ititit cScAR δ×−+×= )1(

The receivables accrual is the change in ending balances of receivables. It is a function

of the change in reported sales and the change in discretion in sales.

RM

ititit cScAR δ∆)1(∆∆ ×−+×=

I estimate the following equation, which I refer to as the annual equation because it is

based on the change in annual revenues. The estimate of a firm’s discretionary revenues is the

residual from this equation:12

).(∆∆ ARAnnSβαAR ititit ε+×+=

The advantage of modeling receivables as opposed to accruals is that they are directly

related to sales. However, this may not be true for other current accruals (Kang and

Sivaramakrishnan, 1995). Accounts payable relates to purchases, and following the model of

Dechow, Kothari, and Watts (1998), inventory relates to forecasted sales for the next period, not

current actual sales. Forecasted sales equals current sales if sales follows a random walk, but this

is not true for growth firms. The relation between other accruals and change in sales is not clear.

Because sales alone does not explain payables, inventory, and other accruals, accrual models

based on sales alone produce noisy estimates of discretion. The estimates are also biased for

growth firms if the noise is correlated with growth.

The coefficient β in Eq. (Ann AR) is an estimate of the portion of sales that are not

collected in cash by the end of the year, and the error represents scaled revenue management

12

My measure of premature revenue recognition is not independent of other forms of revenue management. For

example, relaxed credit requirements increases receivables relative to sales and will be detected by the model.

Channel stuffing where cash is received increases sales relative to receivables and will bias against finding

discretionary revenues with my revenue model.

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(scaled by 1 – c).13

Because discretionary revenues are in reported revenues, the amount of

revenue management estimated by the annual revenue model will be understated (footnote 31 of

Jones, 1991).14

The modified Jones model (Dechow, Sloan, and Sweeney, 1995) conditions on

the change in cash sales rather than total sales, which avoids systematically understating the

amount of earnings management. However, this approach introduces another problem: credit

sales are treated as discretionary. Firms with a higher (lower) than average portion of

nondiscretionary sales that are credit will have discretionary accruals that are greater (less) than

zero. I condition on total sales because this understates estimated earnings management, which

biases against finding in favor of my alternative hypotheses. I report for comparison results

using the change in cash sales, which I refer to as the modified equation.

One limitation of accrual models is that they, by conditioning on annual sales, treat sales

made early in the year the same as sales made late in the year. Current accruals are generally

resolved within one year. Thus, sales made late in the year are more likely to be receivable at

year end. Therefore, I also estimate a version of the annual model allowing the estimated portion

of sales that are uncollected at year end to vary in the fourth quarter. I refer to this as the interim

equation because it incorporates interim revenue data.

).(∆∆∆ 21 ARInt4Sβ1_3SβαAR itititit ε+×+×+=

In Eq. (Int. AR), S1_3 is sales in the first three quarters, and S4 is sales in the fourth

quarter. Even though Eq. (Int. AR) incorporates quarterly sales, I estimate discretion on an

13

The coefficient on sales is also affected by economic events such as a change in credit policy or factoring accounts

receivable (McNichols, 2000). Regarding factoring, Sopranzetti (1998) searches a database of over 4,000 publicly

traded firms from 1972-1993 and finds only 269 reports of factored accounts receivable by 98 firms. Nearly half of

the firms (47) were from the Textile and Apparel industries, and no other industry had more than 7 firms. To control

for the potential effects of factored receivables, I repeat my tests after excluding firms from the Textile and Apparel

industries and find similar results. 14

To mitigate the bias that arises because discretionary revenues are in the explanatory variable and the error, I

exclude firms suspected of manipulation (i.e., firms with small positive forecast errors) when estimating the models.

16

annual level. Any revenue management in early quarters that reverses by year end will not be

captured.

To the extent discretionary revenues are not offset by corresponding expenses, accrual

models should detect them. As a benchmark for the revenue models, I estimate discretionary

accruals using the term-adjusted modified Jones model (Teoh, Wong, and Rao, 1998), which is

similar to the modified Jones model (Dechow, Sloan, and Sweeney, 1995) but excludes

depreciation expense and property, plant, and equipment. The following equations for accruals

correspond to the annual and interim equations for receivables:

).(∆∆

).(∆

21 ACInt4Sβ1_3SβαAC

ACAnnSβαAC

itititit

ititit

ε

ε

+×+×+=

+×+=

where AC represents current accruals. Estimates from the modified accrual model are calculated

using the estimated coefficients from the annual accrual model (Dechow, Sloan, and Sweeney,

1995). The Appendix summarizes the revenue and accrual models I use in this paper. Following

Kothari, Leone, and Wasley (2005), I estimate nondiscretionary accruals with scaled and

unscaled intercepts (by assets), to control for scale differences among firms (Barth and Kallapur,

1996).

Finding positive discretionary accruals may not be sufficient to conclude earnings

management. McNichols (2000) finds that discretionary accrual estimates are biased for high

growth and highly profitable firms. Kothari, Leone, and Wasley (2005), following Teoh, Welch,

and Wong (1998) and Kasznik (1999), suggests performance-matched discretionary accrual

estimates to remedy this concern. That is, rather than relying on a firm’s raw discretionary

accrual estimate, they subtract the discretionary accrual estimate of a firm in the same industry

with smallest absolute difference in return on assets in the current year. Their results suggest

that performance-matched discretionary accrual measures enhance the reliability of inferences

17

from earnings management research. I report results of performance-matched estimates for

comparison. To provide additional confidence in the results, I compare discretionary revenues

and discretionary accruals of firms just above the forecast with those of firms just below the

forecast.

4. Sample Description and Variable Measurement

4.1 Sample and Variable Measurement

I perform the analysis using annual performance targets and discretion in annual

revenues.15

The sample includes firms on the Compustat annual file with available data between

1988 and 2003. My sample period begins in 1988 because prior to that date cash flow from

operations disclosed under Statement of Financial Accounting Standards No. 95 (FASB, 1987) is

unavailable. I exclude firms in regulated industries (financial, insurance, and utilities) because

their incentives to manage earnings and revenues likely differ from those of other firms.

I measure the change in receivables directly from the cash flow statement, and I calculate

accruals as earnings before extraordinary items plus depreciation and amortization less cash flow

from operations. I collect annual (quarterly) sales from the Compustat annual (quarterly) file.

Sales of the first three quarters is the difference between annual sales and fourth-quarter sales.

All sales and accrual variables are deflated by average total assets. Earnings growth is the

change in income before extraordinary items, deflated by average total assets. Industries are as

defined in Barth, Beaver, Hand, and Landsman (2005). I obtain analysts’ consensus earnings

and revenue forecasts and their realizations from I/B/E/S unadjusted summary file. Earnings and

15

In a future version of the paper, I plan to conduct similar tests based on quarterly benchmarks.

18

revenue forecasts are the last consensus (median) forecast prior to the earnings announcement.16

I winsorize at 2% gross margin, earnings growth, earnings and revenue forecast errors, and each

model input variable by industry and year.

4.2 Descriptive Statistics

Table 1 presents distributional statistics. Panel A indicates that mean (median) accruals

are –1% (0%) of average assets. Many prior studies document slightly lower mean accruals.

However, unlike those studies, I do not include depreciation the accrual measure. The mean and

median change in receivables is 1% of average assets. Panel A also indicates that the mean

(median) change in sales is 10% (8%) of average assets. On average, the sales change is

approximately evenly distributed across quarters. The median change in sales of the first three

quarters is 5% of average assets (approximately 2% per quarter), and the median change in

fourth-quarter sales is 2% of average assets.

Panel B of Table 1 presents correlations. Because the Pearson and Spearman correlations

are similar, I focus on the Pearson correlations. All correlations are significantly different from

zero, except the Spearman correlation between change in annual sales and accruals other than

receivables.17

Change in receivables is positively correlated with accruals (0.41) largely by

construction because change in receivables is typically a large component of current accruals.

However, change in receivables is more highly correlated with change in sales than are total

accruals. The correlation between annual sales change and change in receivables is 0.47

compared to the 0.26 correlation between annual sales change and accruals. Additionally,

change in receivables is more highly correlated with change in fourth-quarter sales than with the

16

I/B/E/S began tracking revenue forecasts in 1996, and the proportion of firms with revenue forecasts has increased

each year since then. By 2003, 94% of I/B/E/S firms had a revenue forecast (Ertimur and Stubben, 2005). 17

I use the term significance to denote statistical significance at less than the 0.05 level, based on a one-sided test

when I have signed predictions and a two-sided test otherwise.

19

change in sales of the first three quarters (0.51 versus 0.38). Also, the change in annual

receivables is more highly correlated with the change in fourth-quarter sales than it is with the

change in annual sales (0.51 versus 0.47). Taken together, these correlations suggest estimates

from models of receivables are less noisy than estimates from accrual models, and that using

quarterly data to disaggregate annual change in sales might lead to better specified discretionary

accrual models. However, I base my inferences on multivariate tests presented in the next

section.

4.3 Estimation of the Models

Table 2 presents results from the estimation of the annual and interim equations. Panel A

presents results of pooled estimates of the annual equations with year and industry fixed effects.

The accrual model and the revenue model produce similar coefficients (0.10 and 0.09 in the

pooled estimation), but the t-statistic and adjusted r-squared are higher in the revenue model

(120.42 and 0.25 versus 56.83 and 0.11), consistent with the higher correlation between change

in receivables and change in sales shown in panel B of Table 1. Untabulated results reveal that

the coefficient in the revenue model is positive (significantly positive) in 285 (274) out of 285

industry-year regressions, compared to 272 (222) for the accrual model.

Panel A also presents results of a variation of the annual equation with accruals other

than receivables as the dependent variable. The coefficient on change in sales is zero. This

finding indicates that the change in receivables drives much of the correlation between accruals

and change in sales. As expected, the relation between other accruals and sales change is weaker

20

than that of the receivables accrual and sales change, which leads to more noisy estimates of

discretion for accrual models.18

Panel B presents results from estimations of the interim equations. In the revenue model,

the coefficient on change in fourth-quarter sales (0.26) is significantly higher—over six times

higher—than that of the change in sales of the first three quarters (0.04), although both are

significantly positive. The corresponding fourth-quarter accrual model coefficient is also

significantly higher than that of the first three quarters (0.19 versus 0.07). Also, when allowing

for a separate coefficient on fourth-quarter sales, the adjusted r-squared of the revenue model

increases from 0.25 to 0.30, and the adjusted r-squared of the accrual model remains at 0.11.

Panel B also presents an estimation of the interim equation with accruals other than

receivables as the dependent variable. The coefficient on change in sales of the first three

quarters is significantly positive, but the coefficient on change in fourth-quarter sales is

significantly negative. Untabulated results reveal that this negative coefficient is largely

attributable to the payables accrual, which is positively correlated with the change in sales, but

subtracted in the calculation of accruals. Similar to panel A, the explanatory power of the model

for accruals other than receivables is low.

5. Evaluation of Discretionary Revenue Estimates

Before testing the hypotheses, I use two approaches to evaluate estimates of discretion

from the various models. In the first approach, I simulate manipulation of revenues and

expenses and then assess the ability of the models to detect it. In the second approach, I rely on

actual earnings and revenue manipulation in a sample of firms that are known to have misstated

18

Finding little or no relation between aggregate accruals other than receivables and change in sales does not imply

that there is no relation between change in sales and individual accrual components. However, it does support

modeling specific accruals, such as receivables, rather than aggregate accruals.

21

their financial results. This approach assesses the ability of the models to detect revenue and

expense manipulation in a sample of firms that were investigated by the Securities and Exchange

Commission (SEC) and subsequently restated their annual financial results.

5.1 Detection of Simulated Revenue Manipulation

5.1.1 Simulation Procedure

I evaluate the specification and power of the revenue and accrual models using simulated

revenue and expense manipulation. Such simulations have been used by Dechow, Sloan, and

Sweeney (1995) and Kothari, Leone, and Wasley (KLW, 2005), among others, to test the power

and specification of discretionary accrual models in the presence of extreme performance.

By comparing estimates of discretionary revenues and expenses against a known quantity

of manipulation, I am able to obtain evidence of the bias, specification, and power of competing

models. I measure the bias of each model as the difference between the mean estimate of

discretion and the amount of manipulation I induce. If the model is unbiased, then the difference

will equal zero. I evaluate the specification of the models by computing how often tests reject

the null hypothesis of no manipulation for samples in which I induce no manipulation. Finally, I

evaluate the power of the models by computing how often tests detect manipulation when I

induce it.

I perform this simulation on subsamples of firms known to produce biased estimates of

discretion—i.e., subsamples with high growth (McNichols, 2000). I follow the approach

employed by KLW, with three exceptions I describe below. The procedure is as follows. In

each industry and year, I sort observations into quartiles of earnings growth and then repeat the

following steps 250 times on firms in the highest quartile:

(1) Draw a random sample of 100 firm-year observations without replacement.

22

(2) Simulate revenue manipulation by adding 2% (of average total assets) to the change

in sales, the change in fourth-quarter sales, and the receivables accrual, and 2% times

the gross margin to current accruals of these 100 firm-years; or simulate expense

manipulation by adding 2% to current accruals.

(3) Estimate the models using observations from all earnings growth quartiles, excluding

the 100 sample firm-years.

(4) Use each model’s coefficient estimates to calculate estimates of discretion for the 100

sample firm-years.

(5) Calculate the mean estimate of discretion from each model, and test whether the mean

is significantly greater than zero.

The statistics from the 250 samples form the basis of the tests. I report the mean and

standard error of the 250 estimates of discretion, as well as the percent of the 250 times that the

model rejects the null hypothesis of no manipulation. A rejection rate of 5% is expected when

manipulation is not introduced, and the 95% confidence interval for the rejection rate of 5%

ranges from 2% to 8% (KLW). If the actual rejection rate is below 2% or above 8%, the test is

misspecified. When manipulation is introduced, however, the rejection rate should be 100%.

My procedure differs from that of KLW in three ways. First, I simulate combinations of

revenue and expense manipulation to evaluate the models under different forms of earnings

management. Second, I calculate accruals using items from the statement of cash flows. Hribar

and Collins (2002) finds that the error in the balance sheet approach of estimating accruals is

correlated with firms’ economic characteristics. As KLW note, this error not only reduces the

models’ power to detect earnings management, but also has the potential to generate incorrect

23

inferences about earnings management. Finally, I winsorize model variables before, rather than

after, estimating the models. This ensures that each models’ mean estimate of discretion is zero.

5.1.2 Simulation Results

Table 3, panel A, presents descriptive statistics from the simulation. The table presents

estimates of discretionary accruals and discretionary revenues from the annual, modified, and

interim equations and four combinations of induced manipulation: no manipulation, revenue

manipulation of 2% of assets, expense manipulation of 2%, and both revenue and expense

manipulation of 2%.

Table 3, panel A, reveals that each of the six equations produces a positive estimate of

discretion for growth firms with zero induced manipulation, which indicates a positive bias for

growth firms. However, the bias is smaller for the revenue models than for the accrual models.

The annual, modified, and interim accrual model estimates are 1.70, 1.92, and 1.63 percent of

assets; revenue model estimates are 0.41, 0.66, and 0.25 percent of assets. The larger estimates

for the accrual models are consistent with accruals other than receivables not being explained by

the change in sales alone, and the factors omitted from the models being correlated with growth.

For example, it is likely that growth firms invest in inventory beyond what would be predicted

by the change in current sales alone.

The results in Table 3, panel A, indicate that the modified equation produces the most

biased estimate of discretionary revenues for both the accrual and the revenue models. For the

revenue models, the bias from the modified equation (0.66) is larger than that of the annual

(0.41) or interim (0.25) equation. This finding is consistent with growth firms having large

increases in receivables, which are treated as discretionary in the modified equations.

24

The results in Table 3, panel A, indicate that the interim equations produce the least

biased estimate of discretionary revenues for both the accrual and the revenue models. For the

revenue models, the bias from the interim equation (0.25) is less than that of the annual equation

(0.41). This finding is consistent with growth firms having a greater portion of annual revenues

in the fourth quarter, which is controlled for in the interim equation.

Table 3, panel A, also presents standard errors across models. A model that produces

estimates with lower standard errors is more likely to detect revenue manipulation when it

occurs. The standard errors from the revenue models are less than half those of the accrual

models for each of the annual, modified, and interim equations. The standard errors of accrual

models are 1.06, 1.08, and 1.07 percent of assets, and those of the revenue models are 0.49, 0.54,

and 0.47 percent of assets. Also, for both the accrual models and the revenue models, the

modified equation produces estimates with the largest standard error, which confirms the lower

explanatory power of the change in cash from sales that is used in these equations.

Table 3, panel A, presents evidence on the bias of the competing models when I induce

revenue and expense manipulation. When revenue manipulation is induced, the bias of the

annual and interim equations decreases whereas that of the modified equations remains the same.

When revenue manipulation of 2% of assets is induced, the bias of the annual revenue equation

decreases from 0.41 to 0.23 percent of assets; revenue manipulation is estimated at 2.23% when

only 2% is induced. This decrease in the bias is a result of the annual equation treating a portion

of the manipulated revenue as nondiscretionary. The modified equation, however, is biased for a

different reason. It treats non-manipulated credit sales as discretionary, leading to a bias that is

larger than that of the annual equation (0.66 versus 0.41 percent of assets without revenue

manipulation and 0.66 versus 0.23 percent of assets with revenue manipulation of 2%).

25

By construction, all the expense manipulation is incorporated in the discretionary accrual

estimates, and none is incorporated in the discretionary revenue estimates. Thus, the success of

the revenue model in detecting earnings management depends on how much of the discretion

involves revenues.

Table 3, panel B, reports results on the specification and power of the models under the

null hypothesis of no discretion.19

Evidence on the specification of the models for growth firms

is presented in the first column of panel B. Each of the three accrual models over-rejects the null

hypothesis of no manipulation. Rejection rates for the annual, modified, and interim equations

are 40.0%, 44.8%, and 38.0%. In general, the revenue models are better specified than the

accrual models. Rejection rates for the annual, modified, and interim equations are 11.2%,

20.8%, and 8.0%. These findings indicate that only the interim revenue model produces well-

specified tests of revenue manipulation. All other models significantly over-reject the null

hypothesis of no manipulation.

With revenue manipulation of 2% of assets, the rejection rates for the revenue models

exceed their accrual model counterparts, indicating that the revenue models are more powerful

than the accrual models at detecting revenue manipulation. Rejection rates for the annual,

modified, and interim accrual models are 57.6%, 69.2%, and 48.8%, and rejection rates for the

annual, modified, and interim revenue models are 100.00%, 100.00%, and 94.8%. Thus, despite

the general tendency of accrual models to over-reject the null hypothesis, the revenue models

reject more often in the presence of revenue manipulation.

19

This analysis assumes zero discretionary revenues/accruals on average for growth firms. This does not, however,

assume no manipulation. Because models of discretion are estimated in cross section, estimated manipulation is

relative to the industry-year average. Therefore, the assumption of this analysis is that growth firms, on average, do

not manipulate more than other firms in the same industry and year. To the extent this is not true, I overstate the

bias and misspecification of the models.

26

When expense manipulation is added, the accrual models detect it most of the time.

Rejection rates for the annual, modified, and interim accrual models are 92.8%, 94.4%, and

91.2%. Finally, when both revenue and expense manipulation are added, each of the models

detects manipulation more than 90% of the time. However, the high rejection rates of the accrual

models are in part attributable to their general tendency to over-reject for growth firms.

Table 3, panel B, also tabulates rejection rates for performance-matched estimates from

the six models. For growth firms with no revenue manipulation, the performance-matched

estimates produce lower rejection rates for each of the models. However, the rejection rate for

each of the accrual models is significantly higher than 5% (18.0%, 20.4%, and 17.2% for the

annual, modified, and interim equations), indicating that each is misspecified, even after

performance matching. The rejection rates of the revenue models decrease from 11.2%, 20.8%,

and 8.0% to 8.8%, 10.0%, and 4.8%. This indicates that after performance matching, the interim

revenue model is well specified and the other models over-reject.

Table 3, panel B, reveals that performance-matched estimates from each model are less

powerful than their non-performance-matched counterparts. With revenue manipulation of 2%,

rejection rates decrease from 57.6%, 69.2%, and 48.8% to 35.2%, 43.6%, and 26.0% for the

annual, modified, and interim accrual models. Rejection rates for the revenue models decrease

from 100.0%, 100.0%, and 94.8% to 90.8%, 92.4%, and 76.0%. For the best specified model,

the interim revenue model, performance matching has a small effect on specification and reduces

power. Although in theory both the revenue and accrual models should detect revenue

management, the interim revenue model is the only model that is well specified for growth firms,

and one of the most powerful.

27

The accrual models detect expense manipulation much of the time. Rejection rates for

the annual, modified, and interim accrual models are 72.8%, 73.6%, and 71.6%. Finally, when

both revenue and expense manipulation are added, the power of the accrual models increases

slightly (over that of expense manipulation alone), whereas that of the revenue models remains

unchanged (compared to that of revenue manipulation alone).

The findings indicate that performance matching improves the specification of the

accrual models more so than the revenue models. Performance matching improves the

specification of the accrual models by controlling for factors omitted from the models—for

example, changes in purchases to explain the payables accrual and changes in expected sales to

explain the inventory accrual. The interim revenue model is more complete. Thus, it is less

important to control for omitted variables, thereby reducing the power of the model. The results

from Table 3, panel B, indicate that for growth firms, performance matching generally improves

the specification of the models, but only the interim revenue model is well specified with or

without performance matching. Furthermore, performance matching reduces the power each of

the models tested.

5.2 Detection of Actual Revenue Manipulation

5.2.1 SEC/Restatement Sample Procedure

The second procedure I use to evaluate revenue and accrual models assesses their ability

to detect revenue and expense manipulation in a sample of firms that are known to have

misstated their financial results. The known manipulators are a sample of 68 firms that were

investigated by the SEC for accounting irregularities between 1997 and 2003 and then

subsequently restated their annual financial results. I study the intersection of SEC enforcement

actions and restatements in order to identify a sample of firms that are more likely to have

28

manipulated their financial results. When studying enforcement actions, it is possible that the

allegations are not true. With restatements, it is possible the restatement reflects a minor

correction that does not represent intentional manipulation by managers. A combined sample

provides a more reliable benchmark against which to validate the models.

I divide sample firms into two groups: those that manipulated revenues, and those that

manipulated expenses but not revenues.20

For each sample firm, I group observations into four

time periods: the manipulation period, the year before the manipulation, the year after the

manipulation, and all other years. I assume that, on average, sample firms overstate revenues

and earnings during the manipulation period.21

I also assume that no manipulation took place the

year before the manipulation period.

I make the following predictions. For the sample firms that manipulated revenues, if the

models are correctly specified, mean discretionary revenue and accrual estimates should not

differ from zero during the year before the manipulation. If the models are powerful, mean

discretionary accrual and revenue estimates should be significantly positive during the revenue

manipulation period.

For the sample firms that manipulated only expenses, I predict that the accrual models, if

correctly specified, will not detect discretionary accruals before the manipulation. If the accrual

models are powerful, they will detect positive discretionary accruals during the manipulation. If

the revenue model is correctly specified, I predict discretionary revenues to be insignificantly

different from zero before, during, and after the manipulation period.

Based on the findings in the previous section, I use the interim revenue model to measure

discretionary revenues because it is the most correctly specified of the three revenue models. I

20

Expenses include “Reserves/Accruals” and “Inventory”, as categorized by Huron Consulting Group. 21

Consistent with an overstatement on average, Dechow, Sloan, and Sweeney (1996) finds positive discretionary

accruals during the manipulation period by firms subject to SEC enforcement actions.

29

use performance-matched estimates from the modified annual accrual model to measure

discretionary accruals. This model serves as a benchmark because it is the most commonly used

of the accrual models. Although it is not as well specified as the annual and interim accrual

models, it is generally the most powerful. Based on the simulation analysis, the interim revenue

model is the only model correctly specified for high growth firms, and prior research documents

that firms targeted by SEC enforcement actions tend to be growth firms (Beneish, 1999).

5.2.2 SEC/Restatement Sample Results

Table 4, panel A, displays the distribution of sample firms through event time. Revenues

were manipulated over 42 firm-years, and expenses were manipulated over 24 firm-years,

consistent with revenue manipulation being one of the most common forms of earnings

management.

Panel B provides evidence consistent with both models being well specified for the entire

sample. Neither model detects discretion for the year before the manipulation (t = –0.28 for the

accrual model and t = –0.62 for the revenue model). Assuming the sample firms overstated

earnings and revenues during the manipulation period, only the revenue model is powerful

enough to detect this discretion. The mean discretionary accrual estimate is not significant

(0.45%, t = 0.36) and the discretionary revenue estimate is significantly positive (1.34%, t =

2.26).

Panel C presents results for the sample of firms that manipulated revenues. Both models

appear to be well specified; mean estimates are not significantly different from zero for the year

before the manipulation (t = 0.63 for the accrual model and t = –1.06 for the revenue model).

However, only the revenue model is powerful enough to detect revenue manipulation during

30

event year 0. The accrual model detects revenue manipulation of 2.26% (t = 1.35), and the

revenue model detects manipulation of 1.78% (t = 2.19).

Panel D presents results on the detection of expense manipulation. Again, both models

appear to be well specified. Mean estimates are not significantly different from zero for the year

before the manipulation (t = 0.28 for the accrual model and t = 0.05 for the revenue model). As

expected, the revenue model does not detect discretion during the manipulation period (t = 0.87).

However, the accrual model does not either (t = 0.67). In sum, the revenue model detects

discretion by firms that manipulated revenues, but the accrual model is unable to detect the

manipulation of revenues or expenses. These findings suggest that the revenue model is superior

for detecting revenue manipulation, and also earnings manipulation in general for this sample of

firms.

6. Primary Results

Tables 5 and 6 present mean estimates of discretionary revenues and accruals for firms

just meeting earnings and revenue forecasts. For the same reasons as with Table 4, I use the

interim revenue model to measure discretionary revenues and performance-matched estimates

from the modified accrual model to measure discretionary accruals. I also report performance-

matched revenue model estimates to provide further assurance that the detected discretion is not

attributable to variables omitted from the revenue model. Because firms with analyst forecasts

have different characteristics than those without forecasts, I require that the matched firm also be

covered by analysts when performance matching in Tables 5 and 6.22

22

Inferences are not sensitive to this choice.

31

6.1 Earnings Management Using Revenues

Table 5 provides evidence on the use of discretionary revenues to meet earnings

benchmarks. Panel A presents descriptive statistics. Thirty-seven percent of the sample of firms

with earnings forecasts report earnings above the consensus forecast by less than 0.3% of equity

market value. The mean (median) revenue growth is 20% (10%), and the mean (median) gross

margin is 36% (35%) of sales.

Panel B presents results for the pooled sample. Consistent with H1, the interim revenue

model detects discretionary revenues (0.32%, t = 9.91), and this estimate is significantly higher

than that of firms that just missed the earnings forecast. The modified accrual model detects

discretionary accruals (1.14%, t = 17.08), but the estimate is not significantly higher than that of

firms that just missed the target. After performance matching, it is no longer significantly

positive, which is consistent with Phillips, Pincus, and Rego (2003).

Panel C presents results for growth firms. The interim revenue model detects

discretionary revenues for growth firms (0.59%, t = 8.55), and this estimate is significantly

higher than that of growth firms that just missed the earnings forecast. Consistent with H1a, it is

also significantly higher than that of non-growth firms that just met the earnings forecast. The

modified accrual model detects discretionary accruals (0.96%, t = 6.55), but the estimate is not

significantly higher than that of firms that just missed the forecast. After performance matching

the estimate, it is no longer significantly positive. The performance-matched estimate from the

revenue model for growth firms is significantly positive (0.23%, t = 2.53).

Panel D presents results for firms with high gross margins. The interim revenue model

detects discretionary revenues for firms with high gross margins (0.65%, t = 11.82; 0.22%, t =

2.81 after performance matching), and this estimate is significantly higher than that of high gross

32

margin firms that just missed the earnings forecast. Consistent with H1b, it is also significantly

higher than that of other firms that just met the earnings forecast. The modified accrual model

detects discretionary accruals (1.11%, t = 8.92). Although this estimate is significantly higher

than that of firms that just missed the forecast, it is no longer significantly positive after

performance matching.

6.2 Revenue Management

Table 6 presents evidence on the use of discretionary revenues to meet revenue

benchmarks. Panel A presents descriptive statistics. Twenty-nine percent of the sample of firms

with revenue forecasts report revenues above the consensus forecast by less than 1.0% of equity

market value. The mean (median) revenue growth is 23% (12%).

Panel B presents results for the pooled sample. The interim revenue model detects

discretionary revenues (0.20%, t = 3.59), but this estimate is not significantly higher than that of

firms that just missed the revenue forecast. The modified accrual model does not detect

discretionary accruals (0.08%, t = 0.47), and the estimate is not significantly higher than that of

firms that just missed the earnings forecast. Furthermore, neither the discretionary revenue

estimate nor the discretionary accrual estimate is significantly positive after performance

matching.

Panel C presents results for growth firms. Consistent with H2, the interim revenue model

detects discretionary revenues for growth firms (0.42%, t = 3.81), but not after performance

matching (0.21%, t = 1.35). Although this estimate is significantly higher than that of non-

growth firms that just met the revenue forecast, it is not significantly higher than that of growth

firms that just missed the revenue forecast. The modified accrual model does not detect

33

discretionary accruals (–0.72%, t = –2.14), and the estimate is not significantly higher than that

of firms that just missed the target.

7. Conclusion

This paper studies whether firms use discretion in revenues to meet earnings and revenue

forecasts. One advantage to studying revenues rather than aggregate earnings is that it can

provide insights into how firms manage earnings. I find that firms prematurely recognize

revenues to meet earnings forecasts. I also find that firms with greater incentives to use

discretion in revenues as opposed to expenses (i.e., growth firms and firms with high gross

margins) do so to a greater extent than other firms.

Another advantage to studying revenues is that a revenue model can provide more

precise estimates of discretion than existing discretionary accrual models. I use simulated

revenue and expense manipulation to show that the revenue model is better specified than

accrual models for growth firms. It is also more powerful than accrual models at detecting

revenue manipulation or an even combination of revenue and expense manipulation. This latter

finding is supported further using a sample of firms that were targeted by SEC enforcement

actions and subsequently restated their financial results. The accrual model, performance

matched to correct for misspecification, does not detect revenue or expense manipulation. The

revenue model detects discretion in both the full sample of SEC firms and the sub-sample of

firms that manipulated revenues.

The revenue model can also be used to test for revenue management. I find limited

evidence that firms in general and especially growth firms manage revenues to meet revenue

34

forecasts. This is the first study I am aware of that examines whether firms use discretionary

revenues to meet revenue forecasts.

The revenue model in this paper is relevant for the estimation of discretionary accruals.

In developing the revenue model, I suggest solutions for two limitations of commonly used

accrual models. First, I suggest modeling nondiscretionary accruals as a function of the change

in reported revenues rather than the change in cash revenues, in order to avoid biased estimates

of discretion for growth firms. Second, I suggest separating the change in fourth-quarter

revenues from annual revenues when modeling discretionary accruals. This helps to avoid

biased estimates of discretion for growth firms with relatively high fourth-quarter revenues.

One limitation of this study is that it measures only one (albeit common) form of

discretionary revenues. Firms have a number of alternatives available to manage revenues, and I

plan to include additional measures of discretionary revenues in a future version of the paper.

35

Appendix: Summary of Revenue and Accrual Models

Annual Revenue Model (Ann. AR)

Estimated Discretion = itit SAR ∆ˆˆ ×−−∆ βα

Using estimated coefficients from: ititit SAR εβα +×+= ∆∆

Modified Revenue Model (Mod. AR)

Estimated Discretion = )∆∆(ˆˆ∆ ititit ARSAR −×−− βα

Using estimated coefficients from: ititit SAR εβα +×+= ∆∆

Interim Revenue Model (Int. AR)

Estimated Discretion = ititit SSAR 4∆ˆ3_1∆ˆˆ21 ×−×−−∆ ββα

Using estimated coefficients from: itititit SSAR εββα +×+×+= 4∆3_1∆∆ 21

Annual Accrual Model (Ann. AC, Term-adjusted Jones model)

Estimated Discretion = itit SAC ∆ˆˆ ×−− βα

Using estimated coefficients from: ititit SAC εβα +×+= ∆

Modified Accrual Model (Mod. AC, Term-adjusted modified Jones model)

Estimated Discretion = )∆∆(ˆˆititit ARSAC −×−− βα

Using estimated coefficients from: ititit SAC εβα +×+= ∆

Interim Accrual Model (Int. AC)

Estimated Discretion = ititit SSAC 4∆ˆ3_1∆ˆˆ21 ×−×−− ββα

Using estimated coefficients from: itititit SSAC εββα +×+×+= 4∆3_1∆ 21

where

AR = End of fiscal year accounts receivable

AC = Annual current accruals (excluding depreciation), = earnings before extraordinary items

+ depreciation – cash from operations

S = Annual revenues

S1_3 = Revenues of the first three quarters

S4 = Revenues of the fourth quarter

∆ = Denotes annual change

36

References

Barth, M.; W. Beaver; J. Hand; and W. Landsman. 2005. Accruals, accounting-based valuation

models, and the prediction of equity values. Journal of Accounting, Auditing & Finance

20: 311-345.

Barth, M; and S. Kallapur. 1996. The effects of cross-sectional scale differences on regression

results in empirical accounting research. Contemporary Accounting Research 13: 527-

567.

Beatty, W.; B. Ke; and K. Petroni. 2002. Differential earnings management to avoid earnings

declines and losses across publicly and privately-held banks. The Accounting Review 77:

547-570.

Beaver, W.; M. McNichols; and K. Nelson. 2003. Management of the loss reserve accrual and

the distribution of earnings in the property-casualty insurance industry. Journal of

Accounting and Economics 35: 347-376.

Beneish, M. 1999. The Detection of Earnings Manipulation. Financial Analysts Journal

(Sep/Oct): 24-36.

Burgstahler, D.; and I. Dichev. 1997. Earnings management to avoid earnings decreases and

losses. Journal of Accounting and Economics 24: 99-126.

Burgstahler, D.; and M. Eames. 2002. Management of earnings and analyst forecasts to achieve

zero and small positive earnings surprises. Working paper, University of Washington.

Dechow, P.; S. P. Kothari; and R. Watts. 1998. The relation between earnings and cash flow.

Journal of Accounting and Economics 25 (May): 131-168.

Dechow, P.; S. Richardson; and I. Tuna. 2003. Why are earnings kinky? An examination of the

earnings management explanation. Review of Accounting Studies 8: 355-384.

Dechow, P., and C. Schrand. 2004. Earnings quality. The Research Foundation of CFA Institute.

Charlottesville, Virginia.

Dechow, P.; R. Sloan; and A. Sweeney. 1995. Detecting earnings management. The Accounting

Review 70: 193-225.

Dechow, P.; R. Sloan; and A. Sweeney. 1996. Causes and consequences of earnings

manipulation: An analysis of firms subject to enforcement actions by the SEC.

Contemporary Accounting Research 13:1-36.

Dhaliwal, D.; C. Gleason; and L. Mills. 2004. Last change earnings management: Using the tax

expense to achieve earnings targets. Contemporary Accounting Research 21: 431-459.

37

Ertimur, Y.; J. Livnat; and M. Martikainen. 2003. Differential market reactions to revenue and

expense surprises. Review of Accounting Studies 8, 185-211.

Ertimur, Y., and S. Stubben. 2005. Analysts’ incentives to issue revenue and cash flow forecasts.

Working paper, Stanford University.

Feroz, E.; K. Park; and V. Pastena. 1991. The financial and market effects of the SEC’s

accounting and auditing enforcement releases. Journal of Accounting Research 29

(Supplement):107-142.

Financial Accounting Standards Board. 1987. Statement of Financial Accounting Standards No.

95: Statement of Cash Flows (FASB, Stamford, CT).

Ghosh, A.; Z. Gu; and P. Jain. 2005. Sustained revenue and earnings growth, earnings quality,

and earnings response coefficients. Review of Accounting Studies 10:33-57.

Graham, J.; C. Harvey; and S. Rajgopal. 2005. The economic implications of corporate financial

reporting. Journal of Accounting and Economics 40: 3-73.

Guay, W.; S. P. Kothari; and R. Watts. 1996. A market-based evaluation of discretionary accrual

models. Journal of Accounting Research 34 (Supplement): 83-105.

Hribar, P., and D. Collins. 2002. Errors in estimating accruals: Implications for empirical

research. Journal of Accounting Research 40: 105-135.

Jones, J. 1991. Earnings management during import relief investigations. Journal of Accounting

Research 29: 193-228.

Kang, S., and K. Sivaramakrishnan. 1995. Issues in testing earnings management and an

instrumental variable approach. Journal of Accounting Research 33: 353-367.

Kasznik, R. 1999. On the association between voluntary disclosure and earnings management.

Journal of Accounting Research 37: 57-81.

Kothari, S. P.; A. Leone; and C. Wasley. 2005. Performance matched discretionary accrual

measures. Journal of Accounting and Economics 39: 163-197.

Levitt, A. 1998. The numbers game. Speech delivered at the NYU Center for Law and Business,

New York, NY, September 28.

Lipe, R. 1986. The information contained in the components of earnings. Journal of Accounting

Research (Supplement): 37-64.

Magrath, L; and L. Weld. 2002. Abusive earnings management and early warning signs. The

CPA Journal (August).

38

Marquardt, C.; and C. Wiedman. 2004. How are earnings managed? An examination of specific

accruals. Contemporary Accounting Research 21: 461-491.

Matsumoto, D. 2002. Management’s incentives to avoid earnings surprises. The Accounting

Reveiew 77: 483-514.

McNichols, M. 2000. Research design issues in earnings management studies. Journal of

Accounting and Public Policy 19: 313-345.

Nelson, M.; J. Elliott; and R. Tarpley. 2002. Evidence from auditors about managers’ and

auditors’ earnings-management decisions. The Accounting Review 77 (Supplement): 175-

202.

Palmrose, Z.; V. Richardson; and S. Scholz. 2004. Determinants of market reactions to

restatement announcements. Journal of Accounting and Economics 37:1-32.

Phillips, J.; M. Pincus; and S. Rego. 2003. Earnings Management: New Evidence Based on

Deferred Tax Expense. The Accounting Review 78: 491-521.

Plummer, E., and D. Mest. 2001. Evidence on the management of earnings components. Journal

of Accounting, Auditing & Finance 16: 301-323.

Porter, M. 1985. Competitive Advantage: Creating and Sustaining Superior Performance, New

York: The Free Press.

PricewaterhouseCoopers. 2001. Revenue is revenue, right? Under SAB No. 101, the answer is

not always apparent. Deal Flash 11.

Roychowdhury, S. 2004. Management of earnings through the manipulation of real activities that

affect cash flow from operations. Working paper, Massachusetts Institute of Technology.

Sopranzetti, B. 1998. The economics of factoring accounts receivable. Journal of Economics and

Business 50: 339-359.

Teoh, S.; I. Welch; and T. Wong. 1998. Earnings management and the long-run

underperformance of seasoned equity offerings. Journal of Financial Economics 50: 63-

100.

Teoh., S.; T. Wong; and G. Rao. 1998. Are earnings during initial public offerings opportunistic?

Review of accounting Studies 3: 175-208.

Thomas, J., and X. J. Zhang. 2000. Identifying unexpected accruals: a comparison of current

approaches. Journal of Accounting and Public Policy 19: 347-376.

Turner, L.; J. R. Dietrich; K. Anderson; and A. Bailey. 2001. Accounting restatements. Working

paper, Colorado State University.

39

Wu, M. 2002. Earnings restatements: A capital market perspective. Working paper, New York

University.

Zuckerman, G. 2000. Abreast of the market. Wall Street Journal. New York, N.Y.: Sep 25, 2000.

40

Table 1: Sample descriptive statistics (N = 56,403)

Panel A: Distributional statistics

Variable Mean Std. Dev. Q1 Median Q3

AC –0.01 0.11 –0.04 0.00 0.04

∆AR 0.01 0.05 –0.01 0.01 0.03

AC – ∆AR –0.02 0.10 –0.05 –0.01 0.02

∆S

0.09 0.26 –0.03 0.07 0.20

∆S1_3

0.07 0.21 –0.02 0.05 0.16

∆S4

0.03 0.08 –0.01 0.02 0.06

Panel B: Pearson (above) Spearman (below) correlation matrix

AC ∆AR AC – ∆AR ∆S ∆S1_3

∆S4

AC 0.41 0.87 0.26 0.23 0.24

∆AR 0.44 –0.09 0.47 0.38 0.51

AC – ∆AR 0.77 –0.12 0.03 0.05 –0.01

∆S

0.27 0.49 –0.01 0.96 0.71

∆S1_3

0.25 0.40 0.02 0.95 0.48

∆S4

0.25 0.53 –0.06 0.72 0.51

AC = Annual current accruals (excluding depreciation), = earnings before extraordinary items

+ depreciation – cash from operations

AR = End of fiscal year accounts receivable

S = Annual revenues

S1_3 = Revenues of the first three quarters

S4 = Revenues of the fourth quarter

∆ = Denotes annual change

Variables are deflated by average total assets. All correlations in panel B are significantly

different from zero (p < 0.05), except the Spearman correlation between ∆S and AC – ∆AR (p =

–0.14).

41

Table 2: Estimation of Discretionary Accrual and Revenue Models (N = 56,403)

Panel A: Estimation of Annual Models (Ann. AC and Ann. AR)

ititit ∆SVarDep εβα +×+=..

β

Dep. Var. Estimate t-statistic Adj. R2

AC 0.10 56.83 0.11

∆AR 0.09 120.42 0.25

AC – ∆AR

0.00 2.25 0.04

Panel B: Estimation of Interim Models (Int. AC and Int. AR)

itititit SSVarDep εββα +×+×+= 4∆1_3∆.. 21

β1 β2

Dep. Var. Estimate t-statistic Estimate t-statistic Adj. R2

AC 0.07 28.79 0.19 33.39 0.11

∆AR 0.04 41.82 0.26 102.97 0.30

AC – ∆AR

0.02 11.05 –0.07 –12.26 0.04

Variables are defined in Table 1. Each panel presents the results of pooled estimations of the

models, with an intercept scaled by average total assets and separate industry and year unscaled

intercepts. The scaled and separate intercepts are not tabulated.

42

Table 3: Specification and Power of Discretionary Accrual and Revenue Models for

Earnings Growth Firms

Panel A: Descriptive Statistics

%Manip:

(Rev,Exp) (0,0) (2,0) (0,2) (2,2)

Model Mean Mean Mean Mean S.E.

Ann. AC 1.70 2.29 3.70 4.29 1.06

Mod. AC 1.92 2.67 3.92 4.67 1.08

Int. AC 1.63 2.03 3.63 4.03 1.07

Ann. AR 0.41 2.23 0.41 2.23 0.49

Mod. AR 0.66 2.66 0.66 2.66 0.54

Int. AR 0.25 1.71 0.25 1.71 0.47

Panel B: Rejection Rates (Ha: Discretion > 0)

Performance-matched Estimates

%Manip:

(Rev,Exp) (0,0) (2,0) (0,2) (2,2) (0,0) (2,0) (0,2) (2,2)

Model Rate Rate Rate Rate Rate Rate Rate Rate

Ann. AC 40.0 57.6 92.8 96.0 18.0 35.2 72.8 89.2

Mod. AC 44.8 69.2 94.4 99.2 20.4 43.6 73.6 90.4

Int. AC 38.0 48.8 91.2 93.6 17.2 26.0 71.6 82.8

Ann. AR 11.2 100.0 11.2 100.0 8.8 90.8 8.8 90.8

Mod. AR 20.8 100.0 20.8 100.0 10.0 92.4 10.0 92.4

Int. AR 8.0 94.8 8.0 94.8 4.8 76.0 4.8 76.0

Earnings growth firms are those in the highest quartile of change in earnings before

extraordinary items deflated by average total assets, where quartiles are determined by industry

and year. “%Manip” is the percent of revenue or expense manipulation induced in each of 250

random samples of 100 firm-years before estimation of the models—either 0% or 2% of average

assets. Sample firm-years are excluded from estimation of the models. “Mean” is the mean of

the mean discretionary revenue/accrual estimates from 250 samples of 100 firms, “S.E.” is the

standard deviation of the 250 sample means, and “Rate” is the percent of the 250 sample means

that were significantly greater than zero (α = 0.05). The accrual and revenue models are

described in the appendix. Performance-matched estimates are calculated for each sample firm

by subtracting the discretionary revenue/accrual estimate of the firm from the same industry and

year with the closest return on assets.

43

Table 4: Detection of Revenue and Expense Manipulation by Firms Subject to SEC

Enforcement Actions and Financial Statement Restatements

Panel A: Distribution of Sample Firms through Event Time

Event Year REV = 0 REV = 1

–1 13 18

0 37 42

1 18 27

Total 68 87

Panel B: Estimated Discretionary Accruals/Revenues by Event Year – All Firms

Modified Accrual Model (PM) Interim Revenue Model

Event Year Pred. Mean t-statistic Pred. Mean t-statistic

–1 0 –0.48 –0.28 0 –0.51 –0.62

0 + 0.45 0.36 + 1.34 2.26

1 –0.94 –0.44 –0.94 –1.23

Panel C: Estimated Discretionary Accruals/Revenues by Event Year – Revenue Manipulators

Modified Accrual Model (PM)

Interim Revenue Model

Event Year Pred. Mean t-statistic Pred. Mean t-statistic

–1 0 1.08 0.63 0 –1.17 –1.06

0 + 2.26 1.35 + 1.78 2.19

1 0.87 0.28 –1.81 –1.75

Panel D: Estimated Discretionary Accruals/Revenues by Event Year – Expense Manipulators

Modified Accrual Model (PM)

Interim Revenue Model

Event Year Pred. Mean t-statistic Pred. Mean t-statistic

–1 0 1.00 0.28 0 0.10 0.05

0 + 1.56 0.67 0 1.11 0.87

1 –4.78 –1.42 0 0.83 0.58

Sample firms are drawn from the intersection of the set of firms subject to SEC enforcement

actions between 1997 and 2003 and a database of financial restatements maintained by Huron,

Inc. REV indicates whether the sample firm restated revenues, and EXP indicates whether the

44

sample firm restated expenses (reserves, accruals, or inventory), but not revenues. Event years

include –1, the year preceding the first year of the manipulation, 0, one or more years during the

manipulation, and 1, the first year after the manipulation. Panels B through D present mean

estimates of discretion using the modified accrual model and the interim revenue model, which

are described in the appendix. Estimation of the models is carried out by industry-year groups,

excluding the sample firms from the estimation. “Mean” represents the mean regression residual

across all sample firm observations in the event year, and “t-statistic” is calculated as the mean

estimate divided by the standard error of the mean estimate. Performance-matched (PM)

estimates are calculated for each sample firm by subtracting the discretionary accrual estimate of

the firm from the same industry and year with the closest return on assets.

45

Table 5: Mean Estimates of Discretion for Firms Just Meeting Earnings Targets

Panel A: Distributional Statistics (N = 31,886)

Variable Mean Std. Dev. Q1 Median Q3

FE –0.01 0.04 –0.00 0.00 0.00

FE_SMP 0.37

GROWTH

1.20 0.38 1.01 1.11 1.28

GM

0.36 0.25 0.23 0.35 0.51

Panel B: Firms with Small Positive Earnings Forecast Errors – Entire Sample

All Firms (n = 11,882)

Model Mean t-statistic

Int. AR 0.32* 9.91

Int. AR (PM) 0.02 0.35

Mod. AC 1.14 17.08

Mod. AC (PM) −0.41 −4.81

Panel C: Firms with Small Positive Earnings Forecast Errors – Growth Firms

Growth Firms

(n=3,441)

Other Firms

(n=8,381) Difference

Model Mean t-statistic Mean t-statistic t-statistic

Int. AR 0.59* 8.55 0.20* 5.83 5.06

Int. AR (PM) 0.23 2.53 –0.07 –1.42 2.90

Mod. AC 0.96 6.55 1.22 16.77 –1.59

Mod. AC (PM) –0.25 –1.41 −0.48 –4.96 1.14

46

Table 5 (continued): Mean Estimates of Discretion for Firms Just Meeting Earnings

Targets

Panel D: Firms with Small Positive Earnings Forecast Errors –Firms with High Gross Margins

High Gross Margin

Firms (n=3,763)

Other Firms

(n=8,059) Difference

Model Mean t-statistic Mean t-statistic t-statistic

Int. AR 0.65* 11.82 0.16* 4.12 7.28

Int. AR (PM) 0.22 2.81 –0.08 –1.46 3.14

Mod. AC 1.11* 8.92 1.15 14.65 –0.27

Mod. AC (PM) −0.67 −4.20 −0.30 −2.88 –1.94

FE is the difference between realized annual earnings and the last consensus (median) earnings

forecast before the earnings announcement, scaled by beginning-of-year market value of equity,

FE_SMP is an indicator variable that equals one if FE is between 0% and 0.3%, SG equals sales

in year t-1 divided by sales in year t-2, and GM is gross margin in year t-1, which equals sales

minus cost of goods sold, divided by sales. Growth (high gross margin) firms are those with SG

(GM) in the highest quartile of firms in the same industry and year. Discretionary revenue and

accrual estimates are expressed as a percent of average total assets. The discretionary accrual

and revenue models are described in the appendix. Performance-matched (PM) estimates are

calculated for each sample firm by subtracting the discretionary accrual estimate of the firm from

the same industry and year with the smallest absolute difference in return on assets. The final

column in panels C and D presents results from a two-sample t-test for differences between

growth (high gross margin) firms and other firms. * indicates that the estimate of discretion is

significantly higher than that of firms that just missed the forecast (FE greater than −0.03% but

less than 0%).

47

Table 6: Mean Estimates of Discretion for Firms Just Meeting Revenue Forecasts

Panel A: Distributional Statistics (N = 10,435)

Variable Mean Std. Dev. Q1 Median Q3

RFE –0.01 0.11 –0.01 0.00 0.01

RFE_SMP 0.29

SG

1.23 0.44 0.99 1.12 1.34

Panel B: Firms with Small Positive Revenue Forecast Errors – Entire Sample

All Firms (n = 3,023)

Model Mean t-statistic

Int. AR 0.20 3.59

Int. AR (PM) –0.03 –0.41

Mod. AC 0.08 0.47

Mod. AC (PM) −1.05 −5.14

Panel C: Firms with Small Positive Revenue Forecast Errors – Growth Firms

Growth Firms

(n=978)

Other Firms

(n=2,045) Difference

Model Mean t-statistic Mean t-statistic t-statistic

Int. AR 0.42 3.81 0.10 1.52 2.49

Int. AR (PM) 0.21 1.35 –0.15 –1.53 1.96

Mod. AC –0.72 –2.14 0.46 2.38 –3.04

Mod. AC (PM) –1.04 –2.75 −1.05 −4.38 0.02

RFE is the difference between realized annual revenues and the last consensus (median) revenue

forecast before the earnings announcement, scaled by beginning-of-year market value of equity,

RFE_SMP is an indicator variable that equals one if RFE is between 0% and 1%, SG equals sales

in year t-1 divided by sales in year t-2. Growth firms are those with SG in the highest quartile of

firms in the same industry and year. Discretionary revenue and accrual estimates are expressed

as a percent of average total assets. The discretionary accrual and revenue models are described

in the appendix. Performance-matched (PM) estimates are calculated for each sample firm by

subtracting the discretionary accrual estimate of the firm from the same industry and year with

the smallest absolute difference in return on assets. The final column in panel C presents results

from a two-sample t-test for differences between growth firms and other firms. * indicates that

the estimate of discretion is significantly higher than that of firms that just missed the forecast

(RFE greater than −1% but less than 0%).