The Accrual Reliability Model, Part 1

Transcription

1 Assessing Accrual Reliability in Periods of Suspected Opportunism Hal White Smeal College of Business Pennsylvania State University 384A Business Building University Park, PA This Draft: January 8, 2007 Abstract The earnings management literature relies heavily on accrual expectation models to make inferences about opportunism. However, many have argued that legitimate changes in accruals are often misclassified as opportunism. This paper proposes an alternative approach to detect opportunism by examining the reliability of the accruals recorded by managers in a suspect period, where reliable accruals translate into the cash flows to which the accruals relate. A key feature of the model is that accrual levels, rather than changes, are examined as they provide a more complete measure of the estimates included in earnings for the period. Simulation-based tests of the accrual reliability model indicate that it is both reasonably powerful and not susceptible to the same performance-related bias as are discretionary accruals. This paper benefits from helpful comments and suggestions from my dissertation committee Karl Muller (chair), Ed Coulson, Andy Leone, and Henock Louis as well as Paul Fischer, Dan Givoly, Karen Hennes, Steve Huddart, Alan Jagolinzer, Michelle Liu, Jim McKeown, Brian Miller, Monica Stefanescu and Amy Sun.

2 1. Introduction Much of the earnings management literature provides evidence of opportunism based on the residuals from accrual expectation models. 1 However, as Kothari, Leone and Wasley (2005, p.164) indicate, Inferences drawn from tests of hypotheses related to incentives for earnings management hinge critically on the researcher s ability to accurately estimate discretionary accruals. That is, shocks to accruals due to normal operations that are not built into the model are potentially misclassified as manipulation. Given the inherent difficulty in accurately modeling accruals, many argue that legitimate accruals are often included in measures of discretionary accruals (see Dechow, Sloan and Sweeney 1995; Guay, Kothari and Watts 1996; McNichols 2000; McNichols 2002; Beneish 2001; Kothari, Leone and Wasley 2005; Liu 2006). As Fields, Lys and Vincent (2001, p. 289) point out, The only convincing conclusion appears to be that relying on existing accruals models may result in serious inference problems. In addition to their susceptibility to current-period performance bias, accrual expectation models potentially induce biases by using an aggregated changes approach to measuring accruals. 2 First, the change in, rather than the level of, an accrual represents only a partial measure of the accrual recorded by managers for the period, as the prior period accrual balance plays a substantial role in determining the measure. Second, each regressor in the expectation model is restricted to a single coefficient to explain the aggregated accrual measure; however, a regressor may be correlated to various degrees with several accrual components. For example, a change in sales (a typical regressor in these models) would certainly affect a large number of accruals since sales is arguably the primitive account (see Dechow, Kothari and Watts 1998). Yet, 1 In this study, I use accrual expectation models to refer to models that develop an expectation of normal accruals where the residual represents abnormal, or discretionary, accruals (e.g., Jones 1991). 2 I use quotes around accruals because this measure actually consists of changes in both accruals and deferrals. As SFAC No.6, paragraph 141, indicates, accruals represent current-period income-related transactions that correspond to future cash receipts (e.g., accounts receivable) and cash payments (e.g., wages payable). In contrast, deferrals represent current-period cash receipts (e.g., unearned/deferred revenue) and cash payments (e.g., prepaid/deferred expenses) that correspond to future income. 1

3 it is not clear that a change in sales should have the same association with each accrual component. Moreover, an aggregated measure obscures the source of the manipulation. The purpose of this paper is to develop an alternative model to detect opportunism that addresses these issues. The intuition behind the model developed in this paper (hereafter accrual reliability model) comes from the fact that, in addition to cash sales and cash expenses, managers incorporate estimates, i.e., accruals, into earnings each period. The accrual reliability model examines the degree to which these reported accruals actually translate into cash flows realizations in the following period. The underlying premise is that reliable accruals articulate directly with the cash flows that they purport to represent. 3 Note that accrual expectation models generally do not examine whether observed accruals actually translate into future cash flows. Given that reliability plays a critical role in determining the usefulness of accruals to stakeholders, I encourage future research to base inferences about opportunism, at least partly, on the reliability of the accruals that managers report. The accrual reliability model estimates cash flows from operations in t+1 as a linear function of current-period working capital accrual components in t along with controls for both current-period cash flows and deferrals in t+1. The accrual components are also interacted with event-period indicator variables to allow for coefficient variation during periods of suspected manipulation. A significant accrual component interaction coefficient indicates a shift in the reliability of the accrual in an event-period. 4 As previously noted, accruals and deferrals differ 3 According to the FASB s Conceptual Framework (SFAC No. 2, paragraph 62), Accounting information is reliable to the extent that users can depend on it to represent the economic conditions or events that it purports to represent. As such, in this paper, accrual reliability is defined as the degree to which the accruals map into the cash flows to which they relate. The cash flow mapping here relates to a direct test of the reliability, or accuracy, of the accrual estimates in a particular suspect period. This differs from the accrual quality notion developed in Dechow and Dichev (2002) corresponding to variances in mapping over time, which the authors relate to persistence. Moreover, Dechow and Dichev (2002) use an aggregated changes specification for measuring accruals, which mitigates the ability to detect both the accrual and the period to which any estimation error occurs. See section for a discussion of Dechow and Dichev (2002). 4 I focus on working capital accruals in this study because prior research suggests working capital accruals capture much of the variation in total accruals (see Dechow and Dichev 2002; Ecker, Francis, Olsson and 2

4 fundamentally in the timing of their articulation with cash flows and earnings. Given the direct effect of accruals on income in the current period, the increased flexibility inherent in estimations not directly paired with cash flows in the same period (as with deferrals), and the prevalence of accrual manipulation as compared to deferral manipulation, 5 I focus solely on accruals in this study. 6 Another distinct feature of the accrual reliability model is its use of current-period cash flow and accrual measures. Current-period cash flows are defined as those cash flows received and/or paid in the current period that relate to current-period income, e.g., cash sales and cash expenses. Accordingly, current-period cash flows do not include cash flows related to accruals and deferrals associated with income in the prior and subsequent periods, respectively. Current-period accruals are defined as accruals recorded by managers in the current period that have a direct effect on current-period income. Assuming that working capital accruals reverse within one year, the balances of the accruals at the end of the current year represent current-period accruals. The intuition behind the measures is similar to that developed in Francis and Smith (2005). They argue that accruals calculated using an accounting-based (i.e., changes) approach are a function of both current- and non-current-period transactions and cause inference problems when examining the Schipper 2005) and managers have greater discretion over current accruals than over long-term accruals (see Healy 1985; Teoh, Welch and Wong 1998; Louis 2004). This is not meant to suggest that long-term accruals are not manipulated. However, to the degree that current and long-term accruals manipulation are undertaken in the same period, tests of opportunism using current period accruals alone should provide similar inferences. The practical advantage of using working capital accruals is that it makes for more tractable theoretical and empirical analyses. 5 See Fraudulent Financial Reporting: : An Analysis of U.S. Public Companies by the Committee of Sponsoring Organizations of the Treadway Commission (COSO) and Dechow, Sloan and Sweeney (1996) for discussions of fraudulent accounting methods related to Accounting and Auditing Enforcement Releases. 6 Note that, at the end of the current period, the cash flows related to both current-period cash flows and deferrals are known to stakeholders. Given that stakeholders are interested in assessing the amount, timing, and uncertainty of prospective cash flows, accruals become quite important as they represent direct estimates of future cash flows. This study is quite different, however, from the cash flow prediction model literature (e.g., Finger 1994; Dechow, Kothari and Watts 1998; Barth, Cram and Nelson 2001; Yoder 2006). These prediction model studies test whether there is an association between earnings (and/or earnings components) and future cash flows to examine the value relevance of earnings. Given the changes specification of the measures as well as the use of cash flows as predictors, it is apparent that the intent of these papers is not to assess the accuracy (i.e., reliability) of the earnings components, but rather their relevance. See SFAC No. 2, paragraphs 60 and 61 for a discussion of relevance and reliability and the difference between the two constructs. 3

5 persistence of accruals and cash flows. I add that the changes approach to calculating accruals can also cause problems for researchers drawing inferences about accrual manipulation, since the changes measure captures only a portion of the accrual estimate made by managers. Hence, focusing on current-period accruals can help mitigate measurement error by eliminating the noncurrent portions of the measures. The accrual reliability model has two additional advantages. First, the model does not develop an accrual expectation using firm characteristics. Instead, it tests the mapping of accruals with their future cash flow realizations. Therefore, exogenous accrual shocks during the current period are of less concern in the analysis because the magnitude of the accrual has little relevance in the test. That is, accrual shifts due to economic conditions, while potentially abnormal, are classified as manipulation only if they do not map into cash flows in the same manner as the nonevent period. 7 Second, the model allows for multiple accrual components to be examined simultaneously. This feature of the model attempts to address the recent call in the literature for more research identifying which specific accruals are being manipulated by managers (Healy and Wahlen 1999; Beneish 2001; McNichols 2002). This also helps mitigate bias from restricting an aggregate accrual measure to a single coefficient. Evidence from specification tests of the accrual reliability model indicate that the test statistics are well specified. Moreover, the evidence suggests that the reliability model does not suffer from the same performance-related bias observed in extant accrual expectation models. I 7 Note that current-period accruals can be less reliable due to shocks in the next period that lead to estimation errors for the current period. For example, receivables at the end of period t might not be collected in period t+1 because of unexpected negative shocks to customer collections in t+1. This would lead to lower articulation between current accruals and future cash flows that is not related to manipulation. However, to be a concern, there would need to be reason that the shocks are present systematically after treatment periods (e.g., unexpectedly poor collection processes, on average, after beating an earnings benchmark). It is important to note that this concern is different from the performance concern related to accrual expectation models, where shocks in the current period might help managers achieve their objectives in the current period, but are misclassified as manipulation. For example, abnormal/unexpected growth in earnings in the current period might be due to new product innovations, which results in earnings beating the consensus analyst forecast, but is related to legitimate shifts in accruals rather than discretion. 4

6 also conduct power tests to illustrate the model s ability to detect manipulation as well as its efficiency in doing so. The power tests show that the accrual reliability model is reasonably powerful compared to the Jones (1991) model. This paper contributes to the earnings management literature by providing an alternative approach to detect earnings manipulation. The contribution of the accrual reliability model lies not only in its alternative reliability scope, but also in its focus on current-period accruals, mitigation of current-period performance bias, and disaggregation of accrual components. Although there are potential limitations, this approach can be used, at a minimum, as a complementary test to substantiate claims of accrual manipulation. More broadly, this model is meant to serve as an initial step toward an alternative future direction for investigating the reliability of accruals. 8 Section 2 provides background, which includes discussion of the current-period measures and related studies. Section 3 develops the model and discusses some of its limitations. Section 4 describes the sample selection and simulation procedures, and section 5 provides a summary and suggestions for future research. 2. Background In this section, I discuss the intuition behind the current-period approach to measuring accruals and cash flows. I provide an example using journal entries that illustrates the difference between this levels measure and the changes measure typically used in the literature. I then draw on this example to provide insight into the way in which accruals, cash flows and earnings relate to 8 Although Richardson, Sloan, Soliman and Tuna (2005) examine accrual reliability, it is in the context of its association with persistence and investor mispricing. As the authors state, they decompose accruals along broad balance sheet categories and use [their] knowledge of the measurement issues underlying each accrual category to make qualitative assessments concerning the relative reliability of each category. These assessments provide the basis for predictions concerning the relative magnitudes of the persistence coefficients. Thus, the reliability of the accrual categories are assumed (rather than directly tested), then the persistence of the accrual categories are tested by regressing future earnings on the accrual categories. Mispricing is then shown for those accruals determined to be less reliable. Note that the accrual categories are aggregated changes measures (e.g., change in working capital accruals, change in non-currrent operating assets). 5

7 one another. I also review the most common accruals models and highlight some of their limitations. 2.1 Current-period accruals Accruals calculated using an accounting-based, or changes, approach are a function of both current- and non-current period transactions. 9 This approach uses the change in non-cash working capital accounts (plus depreciation and amortization expenses, if calculating aggregate accruals). The cash component is then defined as the difference between accruals and income. 10 Francis and Smith (2005) argue that, to assess whether current-period shocks to income persist into next-period income, the two components of income (i.e., accruals and cash flows) need to consist solely of current-period income-generating transactions. The use of accounting-based measures may also be problematic when testing accruals for manipulation. Since prior period accrual balances are included in the calculation of accruals, the accrual measures do not fully reflect the accounting choices (i.e., accrual estimates) made by managers in a particular period. To address this issue, I examine the level of the accruals that impact earnings in the same period, i.e., current-period accruals, using the ending accrual balances. To illustrate the limitations in the accounting-based accrual measure, consider the following summary balance sheet and income statement items (adapted from Francis and Smith 2005): 9 Most accrual earnings management studies use some version of this accounting-based approach. Examples of studies include Healy (1985); Rayburn (1986); Jones (1991); Dechow (1994); Dechow, Sloan and Sweeney (1995); Guay, Kothari and Watts (1996); Sloan (1996); Subramanyam (1996); Beneish (1998); Collins and Hribar (2002); and Kothari, Leone and Wasley (2005). 10 More recently, studies have taken an alternative approach to measuring accruals, which is recommended by Collins and Hribar (2002) to account for merger and acquisition activity, whereby accruals are calculated as the difference between income and cash flow from operations (CFO). Note that this alternative approach is still a changes, rather than a levels, approach for measuring accruals as well as cash flows, since CFO is comprised of not only the cash flows associated with income for the current period, but also the cash flows related to priorperiod income via accruals and next-period income via deferrals. 6

8 Year 1 Year 2 Cash Accounts receivable Retained earnings Income Accounting-based accruals N/A 10 Current-period accruals The year 2 journal entries implied by the balance sheets in years 1 and 2 are as follows: Journal Entries Cash 50 Accounts receivable 50 Accounts receivable 60 Income 60 Cash 25 Income 25 **Collection of year 1 receivables **New credit sales for year 2 **Cash sales in year 2 ($85 - $60) I assume that all receivables are collected over the next year. 11 Therefore, the ending balance of accounts receivable represents the current-period accrual. Also, I use an income account rather than a revenue account for simplicity. Note that there is an important difference between using the journal entries above to identify cash flows and accruals for the current period and using the reduced-form journal entries shown below (which correspond to changes in the balance sheet accounts): 11 The assumption that all receivables are collected is not necessary for the intuition of the current-period approach. The main assumption is simply that receivables reverse, either through collection or write-off, over the next year. This assumption is somewhat definitional in that working capital accounts are classified as current based on their short-term nature, and thereby are meant to reverse in one year. I provide results later in the paper that indicate the vast majority of firms have operating cycles of less than one year, which is consistent with prior research. 7

9 Journal Entries (reduced-form) Accounts receivable 10 Income 10 Cash 75 Income 75 Both sets of journal entries result in the same balance sheet and income statement for year 2; income is $85 in both cases. However, the two components of income are measured quite differently. When using an accounting-based approach to calculate accruals (i.e., as seen in the reduced-form journal entries), the $10 accrual represents only a portion of the actual accrual ($60) that was created by managers in year 2. The accrual amount in year 1 ($50) plays a primary role in determining the accounting-based accrual amount in year 2 even though managers have no discretion over this amount in year 2. A similar issue arises for the measurement of cash flows. Specifically, the $75 cash inflow in year 2 includes $25 that relates to year 2 income-generating transactions and $50 that relates to income-generating transactions in year 1. The $75 represents the CFO measure used in many studies to identify the cash flows for the period and, more importantly, the measure subtracted from earnings to determine the accruals measure. Note that, had the year 1 accounts receivable balance been $70 instead of $50, the accrual measure calculated using a changes approach in year 2 would have indicated negative accruals of $10 ($60 - $70 = -$10) rather than positive accruals of $10 ($60 - $50 = $10). Although the income would still have been $85, the cash flows would have been $95 ($70 + $25 = $95). In comparison, had the accounts receivable balance been $60 in year 1, there would have been no accrual using the changes approach in year 2 ($60 - $60 = 0). Again, the income would have been $85, but the entire amount would have been classified as cash flows that is, $25 in cash sales plus $60 collection of receivables from year 1. In this last example, examining the accrual becomes quite difficult, as it is valued at zero even though the manager recorded $60 in sales through receivable estimates. 8

10 In contrast to the accounting-based approach, a current-period approach to decomposing income focuses explicitly on the level of both the accruals and the cash flows that relate to currentperiod income-generating transactions. That is, year 2 income ($85) consists of $25 in cash flows and $60 in accruals (i.e., the ending accounts receivable balance) regardless of the prior-period accrual balance. The additional $50 collected in cash during year 2 does not relate to income for the year, as it is merely a collection of accounts receivable related to year 1 income. Assuming year 2 is the period of suspected earnings manipulation, the accrual of interest is the $60 accounts receivable balance, not the $10 increase from the prior period receivable balance, because it is the $60 that affects income in year 2. Thus, using an accounting-based, or changes, approach to measure accruals and cash flows in year 2 results in a $50 understatement (overstatement) of the accrual (cash flow) for the current period. This simple illustration indicates that current-period measures of accruals and cash flows can help mitigate measurement error when trying to model accruals for a particular period, since a current-period measure of accruals represents a more accurate measure of the accruals that are recorded by managers during a particular period. 2.2 Related literature Accrual models Earnings management studies have proposed various approaches to detect accrual manipulation. 12 Some studies have used aggregate accruals (e.g., Healy 1985) and/or changes in aggregate accruals (e.g., DeAngelo 1986) to proxy for discretionary accruals while other studies examine a specific accrual component for manipulation (e.g., McNichols and Wilson 1988; Petroni 1992; Wahlen 1994; Guidry, Leone and Rock 1999; Leone and Rock 2002; Marquardt and Wiedman 2004). Most recent earnings management studies, however, use the Jones (1991) model, 12 Several studies have attempted to detect real activities earnings management (e.g., Bartov 1993; Bushee 1998; Roychowdhury 2005; Stefanescu 2006). I do not discuss this literature since the focus of this study is on manipulation related to accruals. 9

11 or some variant thereof, to model accruals. Jones (1991) attempts to separate accruals into a normal component and abnormal component by modeling accruals as a linear function of the change in revenues and property, plant, and equipment (PPE), where the residual proxies for discretionary accruals. The accruals that cannot be explained by a change in sales and/or PPE are classified as manipulation. 13 Jones argues that revenues are included in the model to control for the economic environment of the firm because they are an objective measure of the firms operations before manipulations, but Jones does note that sales are potentially susceptible to manipulation as well. 14 Jones includes gross PPE to control for the portion of total accruals related to nondiscretionary depreciation expense. An implicit assumption of the Jones model is that all changes in sales are nondiscretionary. This seems a strong assumption considering the large amount of anecdotal and empirical evidence related to revenue manipulation. Dechow, Sloan and Sweeney (1995) relax this assumption by subtracting the change in receivables from the change in sales in the event period. However, as the authors point out, this assumes that all changes in credit sales are now discretionary, which seems an equally strong assumption in the other direction. Dechow, Richardson and Tuna (2003) relax these assumptions by adjusting the change in sales measure for the expected increase in credit sales. They also offer two additional modifications of the model. The first is the addition of a lagged total accrual measure, which the authors argue captures the predictable component of accruals (see also Chambers 1999). The second is the 13 It is interesting to note that, in Jones (1991), neither explanatory variable was significant in explaining accruals. Yet, this model has become, as Ball and Shivakumar (2006) term it, the workhorse model for earnings management studies. In fact, as of December 1, 2006, there are 160 citations to Jones (1991) on Thomson ISI s Social Sciences Citation Index ( while Google Scholar ( shows 537 citations. This compares to 123 citations on ISI and 323 on Google Scholar as of November 10, 2005 according to Ball and Shivakumar (2006), which indicates that the Jones model is still working hard today. 14 Kang and Sivaramakrishnan (1995) try to address this endogeneity problem by instrumenting the regressors. They instrument account balances, rather than changes in the account balances, because instruments that are correlated with account balances are relatively easier to find than those correlated with the changes in these accounts. The authors use twice-lagged and thrice-lagged values of the regressors, which include an expense variable (comprised of unmanaged inventory, prepaid expenses, and payable balances) as well as variables for unmanaged receivables and depreciation, but encourage researchers not to limit themselves to these instruments. 10

12 addition of a future sales growth variable, which is meant to capture increases in accruals due to future demand. The notion of future growth is also discussed in McNichols (2000), which shows that discretionary accruals are not only associated with earnings performance, but also with future earnings growth forecasts. McNichols (2000) notes that researchers comparing firms that differ in earnings performance or growth characteristics may mischaracterize accruals related to performance as those related to incentives. In a similar vein, Liu (2006) provides evidence of potential misclassifications of accruals when researchers do not adequately account for the life cycle of the firms under examination. For example, Liu (2006) points out that firms in the growth stage invest more in working capital accruals that result in large positive accruals as compared to firms in the maturity or decline phase. 15 Ball and Shivakumar (2006) explore the role of accrual accounting in the asymmetrically timely recognition of gains and losses, and argue that nonlinear accruals models incorporating the asymmetry offer a substantial specification improvement. Overall, the implication of these issues is the potential misclassification of accruals that are related to legitimate firm performance as manipulation. As Beneish (2001, p.6) indicates, the primary criticism leveled at extant accruals models remains: The models fail to distinguish the accruals that result from managers exercise of discretion from those that result from changes in the firm s economic performance Typical factors that affect accruals include such things as changes in competitive forces, demand shocks from product innovations and/or substitute goods, supply chain conflicts, and borrowing rates. Endogenous factors, such as changes in credit policy or increased bargaining power with suppliers, also play a role in determining the performance of a firm, yet are difficult to accurately model. Any shifts in these factors would certainly affect operations, and thus accruals, but would not necessarily be captured by the extant accrual expectation models. 16 Some studies (e.g., Teoh, Welch and Wong 1998; Kasznik 1999; Kothari, Leone and Wasley 2005) try to address the performance issue by using a matched-pair design where the firms are matched on contemporaneous and/or lagged return on assets. However, the ability of the researcher to find a proper control firm is critical to the effectiveness of the match. Differences in any of the operational factors mentioned in this section can potentially induce undesired bias in the test. Moreover, as Kothari, Leone, and Wasley (2005) point out, Performance matching cannot and does not solve all the problems arising from bad discretionary accrual models or from a researcher s failure to recognize the accrual management incentives that are unique to the research question being addressed. 11

13 In addition to accurately modeling accruals, a potential concern with accrual expectation models is the use of an aggregated measure of accruals. If the separate accrual components differ in their relationship with the explanatory variables, then aggregating accruals into one measure induces even more measurement error. 17 Moreover, the aggregation of the accrual components results in a lack of evidence on the particular accruals managed. To address these issues, Marquardt and Wiedman (2004) disaggregate accruals to identify the specific accruals being managed in three contexts: equity offerings, management buyouts and earnings decline avoidance. They develop separate measures to capture the unexpected component of accounts receivable, inventory, accounts payable, accrued liabilities, depreciation expense, and special items based on changes in functional relationships with other accounts. For example, unexpected accounts receivable (UAR) is determined as: UAR = AR t (AR t-1 *Sales t / Sales t-1 ). Similarly, expected inventory and accounts payable balances are a function of COGS, expected depreciation expense is a function of PPE, and expected special items are assumed to be zero. However, there is no attempt to model other factors that might affect these relationships, which can result in serious biases as previously discussed. Section 3 shows how the accrual reliability model can be used to identify manipulation in a specific accrual, with less concern for this performance-related bias Dechow and Dichev (2002) model Dechow and Dichev (2002) suggest a new approach to assessing accrual and earnings quality, which examines estimation errors related to the mapping of accruals with cash flows. Specifically, Dechow and Dichev (2002) model changes in working capital accruals, follows:! WCt, as 17 Evidence that this indeed is the case appears when Jones (1988) estimates the model separately for each accrual (i.e., accounts receivable, inventory, accounts payable, and depreciation expense). She finds that the coefficient for PPE is significantly negative for depreciation and insignificant for the other accrual variables while the coefficient for the change in sales is significantly positive for receivables and inventory, significantly negative for payables, and insignificant for depreciation. 12

14 # t = 0 + " 1CFOt$ 1 + " 2CFOt + " 3CFOt+ 1 WC " +! t where! WCt is computed as the change in accounts receivable plus the change in inventory minus the change in accounts payable and taxes payable plus the change in other assets (net); and CFO represents cash flow from operations. The model is estimated using firm-specific regressions, where the residual determines the accrual quality the larger the standard deviation of the residuals, the lower the quality of accruals. 18 As Dechow and Dichev (2002) point out, CFO is actually comprised of three components: current-period net cash flows plus net cash flows related to the previous period s income plus net cash flows related to next period s income. Since the entire CFO measure is used for each period, there is considerable measurement error bias in both the coefficient estimates and the residuals, which can cause problems when drawing inferences about accruals quality. 19 In fact, the coefficient estimates for the firm-specific, industry-specific, and pooled regressions range from 0.17 to 0.19 for CFO t-1, to for CFO t, and 0.09 to 0.18 for CFO t+1, which differ substantially from their theoretical values of 1, -1, and 1, respectively. One potential factor contributing to the measurement error relates to operating characteristics, such as operating volatility and risk (see Liu 2006, Liu and Wysocki 2006 and Wysocki 2006). Another potential factor contributing to these significant deviations from theoretical values is the mapping between the change in accruals and the level of cash flow from operations. As discussed in the previous section, the change represents only a portion of the accrual for the period and can take on positive or negative values based on the accrual in the prior period. 18 Since the accrual measure,!wc, is a changes measure, the cash flow mapping actually corresponds to two periods. For example, the cash flow related to the change in receivables portion of!wc corresponds to both CFO t+1 (from the collection of receivables recorded in t) and CFO t (from the collection of receivables recorded in t-1). This holds for each of the other accrual components as well. Deferrals map in a similar fashion, where CFO t-1 and CFO t are the corresponding cash flows of interest. Thus, the use of an aggregated changes specification for measuring accruals mitigates the ability to detect both the accrual and the period to which any estimation errors relates. 19 See Dechow and Dichev (2002), appendix B, for a more thorough explanation of the structure of this bias. 13

15 As a further concern, Wysocki (2006) points out that a manager that uses discretionary accruals to offset shocks to current-period cash flows will mechanically create a negative association between the current-period cash flows and both discretionary accruals and total working capital accruals. As such, the CFO t variable will pick up this correlation and incorrectly assign a higher quality rating than it would have otherwise. The implication is that firms engaging in opportunism through smoothing will be classified as higher accrual quality firms as compared to others that do not engage in this behavior. Wysocki (2006) suggests a modification to the Dechow and Dichev (2002) approach that removes this confounding effect. McNichols (2002) indicates two further concerns related to the Dechow and Dichev (2002) model. First, the use of the standard deviation of the residuals as an assessment of accrual quality induces a mechanical relationship between the level of accruals and the inferred quality. This is due to the variation in the residual reflecting absolute accrual variation rather than variation in the residual relative to the variation in accruals. In other words, given the same magnitude of estimation errors, accruals with higher variances (thus higher standard deviations) will be classified as lower quality even though they may map more fully into cash flows on a relative basis (see also Liu and Wysocki 2006). Second, McNichols (2002) indicates that merger, acquisition and divestiture activity can lead to compromised mapping of accruals with cash flows, as accruals in one period may not articulate well with cash flows in another period. Relatedly, rapidly growing firms can produce cash flows in t+1 that are much larger than anticipated by accruals in t, which induces further measurement error bias as CFO t+1 will contain a relatively smaller portion of cash flows related to the accruals in t. 14

16 2.2.3 McNichols (2002) model McNichols (2002) attempts to link Dechow and Dichev s (2002) accrual quality analysis with the discretionary accruals literature, namely Jones (1991), by offering a potential approach that combines the regressors of both models. She argues that this has the potential to strengthen both approaches, and to calibrate the errors associated with Jones measure of discretionary accruals and [Dechow and Dichev] s measure of earnings quality. Although the results indicate that the hybrid model has more explanatory power than either of the previous models alone, it is not clear to what degree one can infer proper specification of normal accruals from an increase in the R-squared term with total accruals as the dependent variable. For example, the change in sales variable could consist of fictitious sales, which would cause a higher R-squared term for the hybrid model than for the Dechow and Dichev (2002) model. This is because the manipulation would not be picked up solely by the CFO measures. However, the manipulation now becomes part of the expectation of normal accruals once the additional variables are added to the cash flow variables of Dechow and Dichev (2002). In short, any manipulation captured in the regressors will be misclassified as nondiscretionary and potentially increase the explanatory power of the model. McNichols (2002) encourages future research on using the relation between accruals and cash flows to differentiate objective accruals from those that have been manipulated. Moreover, McNichols (2002) echoes the call from Healy and Wahlen (1999) and Beneish (2001) for future research on specific accrual manipulation. In the next section, I attempt to address both of these issues in developing the accrual reliability model. Moreover, I address the measurement error and mapping issues related to Dechow and Dichev (2002) described above by modeling current-period measures of cash flows and accruals, which rids the measures of their non-current portions. 15

17 3. Model Development 3.1 A Model of Cash Flow and Accrual Articulation Cash flows in a particular period generally consist of cash flows related to prior-period income transactions (e.g., collection of accounts receivable and payment of accrued expenses) plus cash flows related to current-period income transactions (e.g., cash sales and expenses) plus cash flows related to next-period income transactions (e.g., payment of prepaid expenses and collection of unearned revenues). Specifically, where CF = CF + CF + CF (1) t t!1 t t+1 t t t t!1 CF t = net cash flows in t related to t-1 income. t CF t = net cash flows in t related to t income (i.e., current-period cash flows). t+1 CF t = net cash flows in t related to t+1 income. To assess the reliability of accruals, I next modify equation (1) to incorporate the effects of accrual accounting, which would include: where t 1 CF, t CF t Accrt! 1 t + 1 t+ 1 t = Def CF, t! = (2) CF (3) CF, t Accr t! 1 = the accrual recorded in t-1 to indicate the cash flows in t that will be collected as a result of income-related transactions in t-1. Def t+ 1 CF, t = the deferral recorded in t to indicate the cash flows in t that will translate into incomerelated transactions in t+1. earnings. Thus, Accruals and deferrals are used to offset timing differences between cash flows and t CF t does not need to be replaced in the model since it relates to period t income under both cash flow and accrual accounting. I replace t!1 CF t and t+1 CF t in equation (1) 16

18 CF, t with Accr t! 1 and Def t+ 1 CF, t, respectively, to model the relationship between cash flows and the accruals that relate to those cash flows. This results in the following model: CF = Accr Def (4) t CF, t t t! 1 + CFt + t+ 1 CF, t Equation (4) indicates that cash flows in period t are comprised of cash flows related to accruals in t-1, current-period cash flows in t, and cash flows related to deferrals that affect income in t+1. This serves as the foundation for the accrual reliability model. 3.2 Empirical Model To empirically operationalize equation (4), I proxy for the accrual and cash flow constructs discussed above; however, for more intuitive notation, I move the model ahead one period to examine accruals in period t. I model next-period cash flows from operations as a function of current-period working capital accruals and controls for current-period cash flows, and next-period deferrals as follows: CFO "! (5) t + 1 = 0 + " 1 ACCRt + " 2CPCFt + " 3Def t t + 1 where CFO t+1 is cash flow from operations (Compustat #308) in year t+1. ACCR t is an aggregate measure of working-capital accruals in year t, calculated as net accounts receivable, AR t, minus the inventory accrual (i.e., the difference between inventory and accounts payable when the accounts payable balance is higher), InvAccr t, minus other current liabilities, CLother t. CPCF t represents the current-period cash flows for year t, calculated as operating income before depreciation (Compustat #13) in year t minus ACCR t plus Def t-1. 20,21 This controls for the cash flows in period t. 22 Def t+1 20 I use Operating Income Before Depreciation as the earnings measure since this measure does not include the effects of special items and long-term accruals/deferrals, such as depreciation and deferred taxes. This earnings measure consists of Sales (net) minus COGS and SGA before depreciation, depletion, and amortization. Also, Def t-1 is added rather than subtracted because it represents expense deferrals. 17

19 represents the current-period deferral components in year t+1, calculated as the ending balance of other current assets (Compustat #68) plus the inventory deferral. 23 Equation (5) is estimated using weighted least squares where all parameters are scaled by lagged total assets. Annual, rather than quarterly, data are used to allow more time for the reversal of the accruals to take place in the following period. 24 I then decompose ACCR t into its three components (AR t, InvAccr t, and CLother t ) to allow for coefficient variation as well as inter-accrual controls. As discussed earlier, this helps to identify the specific accruals that are of lower reliability. The accrual reliability model is as follows: CFO t+ 1 = 0 + " 1 ARt + " 2 InvAccrt + " 3CLothert + " 4CPCFt + " 5Def t+ 1 +! t+ 1 " (6) where AR t and CLother t are the ending balances of net accounts receivable (Compustat #2) and other current liabilities (Compustat #72) in year t, respectively. 25 Inventory and accounts payable are complementary accounts and together can represent either an accrual or a deferral, or neither. Since the action of purchasing inventory, through either cash or credit, does not directly affect 21 Ideally, CPCF t+1 should be used in the analysis. Using the structure in equation (4), CPCF t+1 could be calculated as CFO t+1 ACCR t Def t+1 ; however, any overstatement/understatement in ACCR t would result in an equal understatement/overstatement of CPCF t+1 by construction. Another approach would be to estimate CPCF t+1 as Earnings t+1 ACCR t+1 Def t. This rids the measure of ACCR t in the calculation, but the issue becomes the reversal of the accrual manipulation. That is, assume ACCR t = ACCR t * +! t where ACCR t * is the true accrual and! t represents estimation error. The reversal of! t in t+1 then induces a systematic measurement error (! t ) in Earnings t+1, and therefore in CPCF t+1. This results in both the estimation error under investigation (! t ) and its reversal (! t ) in the same model: CFO t+1 = (ACCR * t +! t ) + (CPCF * t+1 -! t ) + Def t+1. To address this issue, I proxy for CPCF * t+1 using CPCF t, thereby assuming a random walk for current-period cash flows. Although CPCF t also suffers from manipulation reversals (-! t-1 ), it is less of a concern because! t-1 should not be systematically correlated with the manipulation under investigation (! t ) in the model. 22 See Dechow et al. (1995) and Yu (2006) for discussions related to controlling for same-period cash flows when assessing accruals. 23 As in Francis and Smith (2005), I use Compustat s Current Assets Other to proxy for deferrals, since this measure largely contains prepaid expenses, which, according to Compustat, represent advance payments for services or benefits to be received within one operating cycle. 24 An added benefit of using annual data is that, since the financial statements are audited, the accounts are more likely to have been substantiated by management. By comparison, interim statements sometimes use methods, such as the gross profit method of calculating inventory, which can induce additional measurement error. 25 As in Francis and Smith (2005), I use Compustat s Current Liabilities Other to proxy for accruals as this item represents mostly obligations for which payment is deferred (Compustat). However, this account does include unearned revenues, which is more correctly classified as a deferral. To this end, the measure will be biased downward. 18

20 income, examining the inventory level in isolation is of little benefit. Again, the idea is to identify those estimates made by management that directly affect income for the current period. Therefore, to identify the classification, I examine the difference between accounts payable, AP (Compustat #70), and inventory, Inv (Compustat #3). If Inv > AP, then I assume the difference represents inventory purchased with cash in the current period. This difference represents a deferral in that cash was paid in the current period, yet the expense occurs the following year as part of next-period cost of goods sold. If Inv < AP, then I assume this difference, InvAccr t, represents an accrual. That is, inventory has been either sold or written down in the current period (i.e., expensed), and is subsequently paid for in the following period. To test for changes in the reliability of accruals in periods of suspected opportunism, an event-period dummy, EM, is included in the model and interacted with the separate accrual components as shown below: CFO t+ 1 = ( 1 + EM )( 0 + " 1 ARt + " 2 InvAccrt + " 3CLothert ) + " 4CPCFt + " 5Def t+ 1 +! t+ 1 " (7) The coefficients on the accrual component interaction terms, EM*AR, EM*InvAccr and EM*CLother, capture event-period changes in the articulation process between the accrual components and the cash flows that they purport to represent. A negative (positive) interaction coefficient represents a lower (higher) association with next-period cash flows in the event period, which indicates earnings are overstated (understated). For example, a negative coefficient on the accounts receivable interaction term indicates that receivables estimated in the event period are associated with lower than normal cash flow collections, which suggests that accounts receivables, and therefore earnings were overstated. Similarly, if operating expenses were understated to overstate earnings, they would be associated with lower cash flows in the following period as more cash flows are paid out than the accruals indicated. 19

21 Again, current-period accrual levels, rather than accounting-based accrual changes, are used because the intent of the model is to capture the reliability of the working capital accruals recorded in the current period. An implicit assumption in the current-period approach is that working capital accruals turn over within one year. This assumption allows the ending balances of accruals in the current year to serve as the proxy for current-period accruals. This seems reasonable considering that most firms have operating cycles of one year or less (see Barth, Cram and Nelson 2001; Dechow and Dichev 2002; Francis, LaFond, Olsson and Schipper 2005). In fact, Dechow and Dichev (2002) note that all firms in their sample (1,725 firms between 1987 and 1999) have average operating cycles of less than one year. 26 This current-period approach to measuring cash flows also helps mitigate the measurement error described in Dechow and Dichev (2002) by modeling only that component of cash flows that is theoretically specified. 27 There are two potential limitations to the accrual reliability model that relate to revenue recognition practices undertaken by managers to shift sales forward cutoff period adjustments and channel stuffing. Extending the cutoff period allows managers to book, in the current period, sales that actually take place in the next period. The revenue recognition results in higher receivable balances in the current period that translate into cash flows the next period. The accrual reliability model potentially does not detect this form of manipulation. That said, as Mulford and Comiskey (2002, p. 187) indicate, Often, revenue that is recognized in a premature or ficticious manner is not collected. This is due to the uncertainty inherent in incomplete sales transactions. To this end, the accrual reliability model would capture this effect as receivables would be 26 Over 96% of the sample observations in this study also have operating cycles less than one year. Section 4.3 examines the operating cycle statistics for the sample and the effect of operating cycle on model estimation. 27 Some studies (e.g., Dechow and Dichev 2002) include income taxes payable in the analysis of working capital accruals; however, I do not model this account because it is related to tax income, not book income. Alternatively, if income tax expense were examined, the difference between income tax expense and income tax payable would be problematic in that it can be permanent and/or long-term in nature (i.e., deferred tax asset/liability), which makes for unclear mapping expectations. This study focuses on short-term working capital accruals only. 20