Do Firms Use Discretionary Revenues to Meet Earnings and Revenue Targets?

Transcription

1 Do Firms Use Discretionary Revenues to Meet Earnings and Revenue Targets? Stephen R. Stubben* Graduate School of Business Stanford Universy 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 s effect on the relation between revenues and accounts receivable. Using this model, I find that firms wh earnings just above analysts consensus forecasts report posive discretionary revenues. Firms wh greater incentives to use discretion in revenues as opposed to expenses (i.e., growth firms and firms wh high gross margins) do so to a greater extent than other firms. I find limed evidence that growth firms use discretionary revenues to meet revenue forecasts. * I would like to thank my dissertation commtee 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 Universy and the 2005 Accounting Research Symposium at Brigham Young Universy. I thank Huron Consulting Group for providing data on restatements and SEC enforcement actions.

2 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 wh high gross margins, may manage earnings using revenues because the potential benefs are greater. Growth firms reap greater benefs from managing earnings using revenues because investors value revenues of growth firms significantly more highly than expenses (Ertimur, Livnat, and Martikainen, 2003). Firms wh high gross margins reap greater benefs 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; is the largest earnings component for most firms, and 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 Auding 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 eher revenue management or earnings management using revenues. 1

3 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 cricized 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), 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. A substantial amount of academic research has addressed firms capal market incentives to meet earnings targets. However, for growth firms 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, is likely that growth firms have incentives to manage revenues to meet revenue forecasts, in addion to meeting earnings forecasts. Consistent wh this idea, Plummer and Mest (2001) finds a discontinuy in the revenue forecast error distribution, wh more than expected small posive revenue forecast errors. However, I am not aware of any 2

4 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 wh 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 posive discretionary revenues. Among firms wh small posive earnings forecast errors, I also test whether growth firms have higher discretionary revenues than non-growth firms, and whether firms wh 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 posive discretionary revenues. These tests require an estimate of discretion in revenues. I model a common form of revenue manipulation premature revenue recognion and s effect on the relation between revenues and accounts receivable. In this paper, prematurely recognized revenues are sales recognized before GAAP creria 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 wh 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 wh 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, is less 3

5 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 termadjusted modified Jones model of aggregate accruals (Teoh, Wong, and Rao, 1998). Prior research finds that the Jones (1991) model and s variants are misspecified for firms wh extreme performance (e.g., Dechow, Sloan, and Sweeney, 1995; McNichols, 2000). Because I study growth firms, 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 Securies and Exchange Commission and subsequent restatements, I find that only the revenue model detects discretionary revenues for firms that admted to revenue manipulation. 4

6 Regarding using revenues to meet earnings targets, I find the following. Discretionary revenues are significantly posive 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 wh high gross margins than for other firms wh small posive earnings forecast errors. Thus, firms wh greater benefs from managing earnings using revenues rather than expenses are willing to bear the greater costs associated wh this type of earnings management. The performancematched 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 posive 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 wh small posive revenue forecast errors are not significantly higher than those for growth firms wh small negative revenue forecast errors, which casts doubt on a revenue management interpretation. As wh 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 does not detect discretionary expenses, detects earnings management (via revenues) to meet earnings forecasts where the benchmark accrual model does not. 5

7 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 wh zero or small earnings and increases in earnings in crosssectional distributions. Dechow, Richardson, and Tuna (2003) extends Burgstahler and Dichev (1997) by testing whether firms wh small profs use discretionary accruals to avoid reporting a loss. They find similar magnudes of discretionary accruals for small loss and small prof firms, and they conclude that if firms overstate earnings to report profs, their accrual model is not powerful enough to detect. Dechow, Richardson, and Tuna (2003) also examines the relative importance of reporting profs, earnings increases, and posive earnings forecast errors. They show that although the discontinuies in the annual earnings and earnings change distributions have decreased over time, the discontinuy in the analyst forecast error distribution has increased. These results suggest that meeting analysts consensus forecasts is becoming the more important 6

8 benchmark. 2 Several studies have attempted to find evidence that firms manage earnings to meet consensus forecasts, wh 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 wh small posive 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 discontinuy 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 wh small earnings increases understate special 2 Graham, Harvey, and Rajgopal (2005) finds that capal market incentives dominate CFOs reasons for managing earnings; CFOs think meeting benchmarks leads to credibily in the market and higher stock prices. Although 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 benef 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 s impact on firms abily 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. 7

9 ems but do not overstate revenues. They also find evidence that firms use discretion in revenues to increase (decrease) earnings before equy 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 wh earnings management and conclude that the manipulation of recurring ems, especially revenues, results in the most severe costs through an increased probabily of detection and more negative pricing consequences if detected. Beneish (1999) finds a posive association between overstated revenues and the probabily 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, also could mean that revenue manipulation is more likely to be detected. In addion to increasing the probabily of detection, revenue manipulation increases the stock price consequences if the manipulation is detected. Wu (2002) finds that restatements of revenues are associated wh significantly more negative stock returns than other types of restatements, and Palmrose, Richardson, and Scholz (2004) finds that the likelihood of ligation after a restatement increases when revenues are involved. It is possible that the greater costs associated wh earnings management using revenues would discourage firms from engaging in this form of reporting manipulation. However, growth firms and firms wh high gross margins potentially realize greater benefs than other firms from overstating revenues, which makes these firms more likely to bear the higher costs associated wh discretionary revenues. 8

10 The competive strategy lerature predicts that firms pursuing growth through revenue increases are different from those pursuing growth through expense reductions from cost cutting or productivy gains (Porter, 1985). Revenue growth indicates growth in product demand rather than merely cost control and is more sustainable. Thus, for growing firms, is especially important to report earnings growth and earnings surprises that are driven by revenues. Consistent wh this idea, Ertimur, Livnat, and Martikainen (2003) finds that investors value revenue surprises more highly than expense surprises, especially for growth firms. 5 Firms wh 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 posive for firms wh small posive earnings forecast errors H1a: Among firms wh small posive earnings forecast errors, growth firms have higher discretionary revenues H1b: Among firms wh small posive earnings forecast errors, firms wh 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 exced. (Zuckerman, 2000). 9

11 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 posive abnormal returns. For value firms, those that meet earnings forecasts have significantly posive abnormal returns regardless of whether the revenue forecast is met. Thus, growth firms have incentives to meet revenue forecasts, in addion to just meeting earnings forecasts. Because growth firms have incentives to meet revenue forecasts, 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-recognion practices. In response to this pressure on firms wh 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 recognion as the number one concern for the Financial Accounting Standards Board for the fourth consecutive year. Arthur Levt, the former chairman of the Securies and Exchange Commission, argues that revenue recognion is one of the principal concerns wh financial reporting (Levt, 1998). Consistent wh firms facing pressure to meet revenue targets, several sources suggest that revenue manipulation is relatively common. Nelson, Elliott, and Tarpley (2002) surveys audors 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 ligation cases allege some sort of revenue 10

12 recognion violations. Dechow and Schrand (2004, page 42) documents that over 70% of SEC Accounting and Auding Enforcement Releases involve overstated revenue, and Turner, Dietrich, Anderson, and Bailey (2001) finds that revenue recognion restatements are the most frequent. Despe this evidence, and the many studies on earnings management, there is ltle empirical academic research on discretion over revenues. Addional evidence of revenue management to meet revenue targets is provided by Plummer and Mest (2001), which finds more firms than expected reporting small posive revenue forecast errors. 6 I test whether growth firms overstate revenues to meet this target. H2: Discretionary revenues are posive for growth firms wh small posive 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 posive 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 posive revenue forecast errors as revenue realizations that are greater than the consensus forecast by less than 1% of beginning-of-year market value of equy. Similarly, small posive 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 wh small posive revenue forecast errors could also indicate revenue management to meet revenue forecasts. 11

13 consensus forecast by less than 0.3% of beginning-of-year market value of equy. 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 posive earnings and revenue forecast errors. 7 To provide addional confidence in the results, I compare estimates of discretion by firms wh small posive forecast errors to estimates of discretion by firms wh 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 equy. 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 activies (e.g., sales discounts, relaxed cred requirements, channel stuffing, and bill and hold sales), and others do not (e.g., sales recognized before recognion creria are met, fictious revenues, and revenue deferrals). In this paper, I model premature revenue recognion and s effect on the relation between revenues and accounts receivable. Premature revenue recognion includes channel stuffing and bill and hold sales, if customers do not pay cash for the inventory, and sales recognized before recognion creria are met. It also includes fictious 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 eher 0.5% or 1% of market value of equy. 12

14 I focus on premature revenue recognion because evidence suggests is a common form of revenue management. 8 For example, Levt (1998) argues that premature revenue recognion is one of five fundamental problems wh 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 recognion. In addion, other forms of revenue manipulation, such as sales discounts, could be profmaximizing 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 (S UM ) plus discretionary sales (δ RM ). 9 S = UM S + δ RM 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 S UM ) plus all discretionary sales. I assume that current accruals are resolved whin one year and receivables relating to sales in prior years are no longer collectible. 11 AR = c UM S + δ RM 8 In a future version of the paper, I plan to include measures of addional 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 addional lags of sales to the model. Results are similar. 13

15 Because nondiscretionary sales are not observable, I express ending receivables in terms of reported sales. AR = c S + ( 1 c ) δ RM 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. AR = c S + (1 c ) δ RM I estimate the following equation, which I refer to as the annual equation because is based on the change in annual revenues. The estimate of a firm s discretionary revenues is the residual from this equation: 12 AR = α + β S + ε ( Ann. AR) 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 wh 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 recognion is not independent of other forms of revenue management. For example, relaxed cred 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 wh my revenue model. 14

16 (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) condions 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: cred sales are treated as discretionary. Firms wh a higher (lower) than average portion of nondiscretionary sales that are cred will have discretionary accruals that are greater (less) than zero. I condion 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 limation of accrual models is that they, by condioning on annual sales, treat sales made early in the year the same as sales made late in the year. Current accruals are generally resolved whin 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 incorporates interim revenue data. = α + β1 S1_3 + β S4 + ε ( Int. AR) AR 2 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 cred policy or factoring accounts receivable (McNichols, 2000). Regarding factoring, Sopranzetti (1998) searches a database of over 4,000 publicly traded firms from 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 migate the bias that arises because discretionary revenues are in the explanatory variable and the error, I exclude firms suspected of manipulation (i.e., firms wh small posive forecast errors) when estimating the models. 15

17 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: AC AC = α + β S + ε ( Ann. AC) = α + β1 S1_3 + β2 S4 + ε ( Int. AC) 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 wh scaled and unscaled intercepts (by assets), to control for scale differences among firms (Barth and Kallapur, 1996). Finding posive discretionary accruals may not be sufficient to conclude earnings management. McNichols (2000) finds that discretionary accrual estimates are biased for high growth and highly profable 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 wh smallest absolute difference in return on assets in the current year. Their results suggest that performance-matched discretionary accrual measures enhance the reliabily of inferences 16

18 from earnings management research. I report results of performance-matched estimates for comparison. To provide addional confidence in the results, I compare discretionary revenues and discretionary accruals of firms just above the forecast wh 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 wh available data between 1988 and 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 utilies) 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 ems 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 ems, 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. 17

19 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 posively correlated wh 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 wh 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. Addionally, change in receivables is more highly correlated wh change in fourth-quarter sales than wh the 16 I/B/E/S began tracking revenue forecasts in 1996, and the proportion of firms wh 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. 18

20 change in sales of the first three quarters (0.51 versus 0.38). Also, the change in annual receivables is more highly correlated wh the change in fourth-quarter sales than is wh 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 wh 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 ( and 0.25 versus and 0.11), consistent wh 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 posive (significantly posive) 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 wh 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 19

21 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 posive. 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 Panel B also presents an estimation of the interim equation wh accruals other than receivables as the dependent variable. The coefficient on change in sales of the first three quarters is significantly posive, 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 posively correlated wh 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 abily of the models to detect. 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 ltle 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, does support modeling specific accruals, such as receivables, rather than aggregate accruals. 20

22 their financial results. This approach assesses the abily of the models to detect revenue and expense manipulation in a sample of firms that were investigated by the Securies and Exchange Commission (SEC) and subsequently restated their annual financial results. 5.1 Detection of Simulated Revenue Manipulation 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 quanty 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. I perform this simulation on subsamples of firms known to produce biased estimates of discretion i.e., subsamples wh high growth (McNichols, 2000). I follow the approach employed by KLW, wh 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 whout replacement. 21

23 (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 ems from the statement of cash flows. Hribar and Collins (2002) finds that the error in the balance sheet approach of estimating accruals is correlated wh 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 22

24 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 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 posive estimate of discretion for growth firms wh zero induced manipulation, which indicates a posive 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 wh accruals other than receivables not being explained by the change in sales alone, and the factors omted from the models being correlated wh growth. For example, 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 wh growth firms having large increases in receivables, which are treated as discretionary in the modified equations. 23

25 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 wh 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 wh lower standard errors is more likely to detect revenue manipulation when 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 wh 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 cred sales as discretionary, leading to a bias that is larger than that of the annual equation (0.66 versus 0.41 percent of assets whout revenue manipulation and 0.66 versus 0.23 percent of assets wh revenue manipulation of 2%). 24

26 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 wellspecified tests of revenue manipulation. All other models significantly over-reject the null hypothesis of no manipulation. Wh 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 %, %, and 94.8%. Thus, despe 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. 25