The Role of Accruals in Asymmetrically Timely Gain and Loss Recognition

Transcription

1 The Role of Accruals in Asymmetrically Timely Gain and Loss Recognition by Ray Ball * and Lakshmanan Shivakumar ** * Graduate School of Business University of Chicago 5807 S. Woodlawn Ave Chicago, IL Tel. (773) ray.ball@gsb.uchicago.edu ** London Business School Regent s Park London NW1 4SA United Kingdom Tel. (44) lshivakumar@london.edu First version: 23 August 2004 This version: 24 April 2005 Acknowledgments We are grateful for comments from Mark Bradshaw, Richard Frankel, Sudipta Basu, anonymous referee and seminar participants at Emory University, Harvard Business School and London Business School. Ball gratefully acknowledges financial support from the University of Chicago, Graduate School of Business. 1

2 The Role of Accruals in Asymmetrically Timely Gain and Loss Recognition Abstract We investigate the role of accrual accounting in the asymmetrically timely recognition of unrealized gains and losses (i.e., prior to the actual realization of those losses in cash). This role of accrual accounting has not been directly recognized in the literature. We show that non-linear accruals s are a substantial specification improvement, explaining up to three times the amount of variation in accruals as conventional linear specifications such as Jones (1991). Conversely, we conclude that conventional linear accruals s, by omitting the role of accruals in asymmetrically timely loss recognition, offer a comparatively poor specification of the accounting accrual process. We also conclude that linear specifications of the relation between earnings and future cash flows, ignoring the implications of asymmetrically timely loss recognition (conditional conservatism), substantially understate the ability of current earnings to predict future cash flows. These findings have important implications for our understanding of accrual accounting, and for researchers using estimates of discretionary accruals, earnings management and earnings quality from misspecified linear accruals s. 2

3 The Role of Accruals in Asymmetrically Timely Gain and Loss Recognition 1. Introduction An important role of accrual accounting is to align the timing of revenue and expense recognition under the matching rule (Dechow 1994; Dechow, Kothari and Watts 1998). By adding accruals to operating cash flow, accountants produce an earnings variable that is less noisy than operating cash flow, because accruals ameliorate the noise in operating cash flow that arises from exogenous or manipulative variation in working capital items such as inventory, prepayments, accounts receivable and accounts payable. Earnings also is a less noisy variable than the sum of operating and investing cash flows, because depreciation accounting smoothes the volatility in investment outlays. We investigate another role of accrual accounting, the recognition of unrealized gains and losses. By definition, timely gains and loss recognition must occur around the time of revisions in expectations of future cash flows. This normally will be prior to the actual realization of those losses in cash, so timely recognition generally requires accounting accruals. This role of accrual accounting has important implications for the interpretation of accruals. For example, we argue below that timely gain and loss recognition increases the usefulness of financial reporting, but that it also increases the volatility of accruals (and of earnings as well as analysts earnings forecast errors), which the literature generally has taken to indicate lower reporting quality. Furthermore, because the accounting recognition of gains and losses is asymmetric, in that losses generally are recognized in a more timely fashion than gains, the relation between accruals and cash flows cannot be linear (Basu 1997, Ball and 1

4 Shivakumar 2005). This observation challenges the linear specification that is common to the standard accruals s, including the workhorse Jones (1991). The resulting misspecification of accruals s, and thus of -dependent measures such as discretionary accruals and earnings quality, has not been directly recognized in the literature. 1 The objective of this study thus is to further explore the role of accruals in timely gain and loss recognition. There are several reasons for doing so. First, the research provides new insight into the function of accounting accruals, which occupy a central position in financial reporting. It is accruals that distinguish accounting from mere counting of cash. Accruals give accounting earnings its prime role in valuation, contracting and performance measurement. Accrual accounting also is required to produce all balance sheet variables other than cash. In general terms, accrual accounting exists from a costly-contracting perspective because it improves the contractingefficiency of financial statement information (Ball 1989). However, only recently (beginning with Dechow 1994) have researchers begun to investigate specifically how accruals function. We therefore explore more fully the hypothesis in Basu (1997) and Ball and Shivakumar (2005), that an important role of accrual accounting is the timely recognition of unrealized gains and losses. Second, this study furthers our understanding of the role of accruals in conditional conservatism, defined as the asymmetry between gain and loss recognition timeliness. 2 1 Evidence of non-linearity in the relation between accruals and cash flows is published in Basu (1997), and replicated for an international sample in Ball, Kothari and Robin (2000), in that cash flow and earnings variables exhibit different incremental slopes when regressed on negative stock returns. The implication is that accruals are a piecewise linear function of stock returns. However, this evidence does not indicate the extent to which linear accruals s are misspecified. 2 Basu (1997) describes the asymmetry as conservatism. Ball, Kothari and Robin (2000) describe it as income statement conservatism as distinct from balance sheet conservatism, a distinction drawn more 2

5 We propose that loss accruals in particular are an important determinant of earnings quality, improving the usefulness of financial statements generally, and more specifically in the contexts of corporate governance, management compensation and debt contracting. Third, accruals have attracted significant attention from researchers studying earnings management (e.g., Healy 1986, Jones 1991, Dechow and Dichev 2002) and earnings quality (e.g., Burgstahler, Hail and Leuz 2004). Estimates of earnings management and earnings quality in these studies rely on accruals s, particularly the developed by Jones (1991). Other accruals s include the Dechow, Kothari and Watts (1998) and the Dechow and Dichev (2002), which was developed specifically for use in measuring earnings quality. This literature invariably specifies linear s. We extend these studies by incorporating the role of accruals in timely gain and loss recognition, and by specifying the non-linearity implied by timelier loss recognition (conditional conservatism). We show that non-linear accruals s are a substantial specification improvement, explaining up to three times the amount of variation in accruals as equivalent linear specifications. We conclude that conventional linear accruals s, by omitting the role of accruals in asymmetrically timely loss recognition, offer a misspecification of the accounting accrual process. Fourth, inferences drawn from studies of earnings management and earnings quality often hinge critically on the proper specification for accruals, as these studies use -dependent estimates of abnormal or discretionary accruals to measure earnings management or earnings quality. These studies have not incorporated the role of sharply by Ball and Shivakumar (2005) as conditional conservatism, to emphasize the correlation with economic losses and thus to differentiate it from unconditional conservatism (reporting low book values, independent of economic gains and losses). They argue that conditional conservatism can be contractingefficient, but unconditional conservatism cannot. Beaver and Ryan (2005) employ the same terminology. 3

6 accruals in conditional conservatism, and their linear specification could lead to systematic biases. In the symmetric Dechow (1994) noise reduction view of accruals, high quality accrual accounting reduces the variance of earnings, conditional on the variance of cash flows. Timely loss recognition has the opposite effect, increasing the variance of earnings conditional on the variance of periodic cash flows, by including capitalized losses in earnings. By increasing the volatility of accruals, and of earnings relative to cash flows, timely loss recognition could be mistaken for poor earnings quality (e.g., Leuz, Nanda, and Wysocki 2003, Burgstahler, Hail and Leuz 2004 and Graham, Harvey and Rajgopal 2005), whereas we would argue that timely recognition of losses through accounting accruals actually improves reporting quality (see Basu 1997, Ball, Kothari and Robin 2000 and Ball and Shivakumar 2005). Fifth, much has been made of the alleged declining value relevance of earnings. However, Basu (1997) documents a substantial increase over three decades in the sensitivity of earnings to economic losses. We therefore are interested in providing evidence on misspecification effects arising from ignoring the Basu loss recognition asymmetry. We conclude that linear specifications of the relation between earnings and future cash flows, ignoring the implications of asymmetrically timely loss recognition (conditional conservatism), substantially understate the ability of current earnings to predict future cash flows, as far as three years ahead. We study accruals and cash flow data obtained from U.S. firms cash flow statements over The data confirm that a major role of accruals is to recognize gains and losses in a timely fashion, particularly losses. Accruals therefore play a crucial role in conditional conservatism, or the asymmetry between gain and loss recognition 4

7 timeliness. Because accruals are a piecewise linear function of current period operating cash flows, standard linear accruals s (Jones 1991, Dechow and Dichev 2002) are misspecified. We show that a piecewise linear specification of the standard s causes a two or threefold increase in their explanatory power. Finally, we show that the accruals asymmetry has increased in recent decades, consistent with Basu (1997) and Givoly and Hayn (2000). The following section outlines our hypothesis that a major role of accruals lies in timely gain and loss recognition, particularly loss recognition. Section three describes the sample and the variable definitions we use, section four outlines the results, and section five presents our conclusions. 2. Role of Accruals in Timely Gain and Loss Recognition In general terms, the economic role of accrual accounting is to improve the contracting-efficiency of financial reporting (Ball 1989). An important insight into how this is accomplished is gained in a literature commencing with Dechow (1994), where the role of accruals in essence is to mitigate the noise in cash flows that arises from exogenous or manipulative variation in firms working capital and investment decisions. Removing noise from earnings presumably creates a more efficient contracting variable. Further, accruals affect balance sheet variables at the same time as affecting earnings, and presumably increase the contracting-efficiency of those variables as well. While Dechow (1994) focuses on earnings, working capital accruals also remove noise from the balance sheet items relating to working capital, which are used by short term creditors. An 5

8 important property of accruals as led in this literature is that they are symmetric. For example, accruals respond to both increases and decreases in inventory levels. A second economic role of accrual accounting is timely recognition of gains and losses, particularly losses. Timely gain and loss accruals improve the timeliness of earnings, and presumably increase its efficiency in debt and compensation contracting. Simultaneously, gain and loss accruals improve the efficiency of contracting on balance sheet variables, by more quickly revising the book values of assets and liabilities. The remainder of this section describes and contrasts the two roles of accrual accounting Noise Reduction Role of Accruals Working Capital Accruals. The role of accruals proposed in Dechow (1994), Guay, Kothari and Watts (1996) and Dechow, Kothari and Watts (1998) can be described as mitigation of operating cash flow noise that arises from exogenous or manipulative variation in firms working capital levels. Compared with accounting income, operating cash flow is noisy because it incorporates period-to-period variation in working capital assets such as inventory, prepayments, and accounts receivable, and in working capital liabilities such as unearned revenue, warranty provisions and accounts payable. This noise makes operating cash flow a less efficient contracting variable than accounting earnings, which incorporates noise-reducing accrual accounting adjustments. Consider a firm that consumes a service (e.g., rent) near the end of its fiscal year, but departs from its historical accounts payable payment policy and delays paying the account until the following year. The delay could be exogenous (e.g., due to accounts 3 We do not the role of accruals in creating more efficient contracting variables in the context of growth and decline. For example, firms experiencing growth in sales levels typically experience declines in operating cash flow, other things equal, due to consequential increases in working capital requirements 6

9 being received late in the mail, a computer glitch, or a sick accounts payable clerk). Alternatively, the delay could be manipulative (e.g., managers attempting to put a gloss on current-year performance measures by timing the cash payments). The delay in payment increases the firm s year-end cash balance and hence its current-year operating cash flow. The cash flow effect is only transitory, because the payment merely is delayed by one period. When payment is made in the following year, that year s operating cash flow is reduced commensurably. The delay in payment causes a transitory increase in accounts payable (a working capital liability) and thereby adds transitory noise to operating cash flow, which reverses over time. Accrual accounting attempts to purge this transitory noise from accounting income by expensing the cost of the service when it is used in generating revenue, rather than when it is paid for. Consequently, accounting earnings is a less noisy variable than cash flow from operations. Noise in a financial statement variable adds risk to the payoffs to all parties contracting on it. Working capital accruals are costly to produce (e.g., it is costly to count inventory and to estimate bad debt allowances on receivables), but subject to cost considerations they make accounting earnings a more contracting-efficient variable than cash flow from operations. Testable implications of the noise-reduction role of working capital accruals are that, other things equal (notably, long term gain and loss accruals): accruals and cash flows from operations are contemporaneously negatively correlated; cash flows are more negatively serially correlated than earnings; cash flows are more volatile than earnings; and earnings are more highly correlated with stock returns (Dechow 1994). such as inventories and receivables. Accrual accounting adjusts cash flows for such effects. See our discussion below of deflating the Jones (1991) intercepts by total assets. 7

10 Depreciation and amortization. Accruals play a similar role in relation to longercycle assets and liabilities. Accrued depreciation and amortization is a source of permanent, not transitory, difference between earnings and cash flow from operations, because the latter does not have investments outlays deducted from it. The closest cash equivalent to earnings is the sum of operating and investing cash flows and, here too, accrual accounting seeks to reduce both exogenous and potentially manipulative noise in cash flow. Earnings is less noisy than the sum of operating and investing cash flows because depreciation and amortization accounting smoothes the volatility in investment outlays and takes timing discretion away from managers. For example, if a class of durable assets is depreciated under the straight line method over L years with no salvage, depreciation charged against earnings is the simple average of the last L years investment outlays. Accelerated depreciation is a weighted average, giving more weight to the more recent investments. Accrued depreciation and amortization thus is a weighted moving average function of current-period and lagged expenditures which reduces exogenous noise in annual investment (e.g., due to the lumpiness of durables expenditures, or exogenous variation in investment opportunities) as well as the potential for managers to manipulate periodic earnings through the timing of investment outlays. Here too, we note that scheduled depreciation and amortization accruals are symmetric with respect to increases and decreases in investment outlays. 2.2 Gain and Loss Recognition Role of Accruals Ball and Shivakumar (2005) and Kothari, Leone and Wasley (2005) discuss a second major function for accounting accruals, timely recognition of economic gains and losses. The economic gain or loss during a period can be thought of as the current-period 8

11 cash flow plus or minus any upward or downward revision in the present value of expected future cash flows. Timely recognition of gains and losses must be accomplished at least in part through accounting accruals, because it is based in part on revisions of cash flow expectations made prior to their actual realization. Examples of timely recognition involving working capital assets and liabilities include gains and losses on trading securities, inventory write-downs due to factors such as spoilage, obsolescence or declines in market value, receivables revaluations, and provisions for operating costs arising from adverse events in the current period. Examples of timely recognition involving long term assets and liabilities include restructuring charges arising from attending to failed strategies or excessive headcounts, goodwill impairment charges arising from negative-npv acquisitions, and asset impairment charges arising from negative-npv investments in long term assets. Timely gain and loss accruals directly improve the timeliness of accounting earnings, and thereby (subject to cost considerations, discussed below) increase its efficiency in debt and compensation contracting. Timely recognition simultaneously improves the effectiveness of contracting on the basis of balance sheet variables. Consider a long term loan agreement that requires lender approval of any new borrowing, new investment or payment of dividends in the event the borrower violates specified financial-statement ratios. The intent of such covenants is to transfer some decision rights to lenders conditional on adverse outcomes. The specified ratios might be based on income statement variables, such as the ratio of Earnings Before Interest, Taxes, Depreciation and Amortization (EBITDA) to Interest commitments, or in terms of balance-sheet variables, such as the ratio of Long Term Debt to Net Tangible Assets. 9

12 Timely recognition of gains and losses revises these ratios conditional on the outcome for the borrower s economic income. In contrast, untimely recognition revises the ratios with a delay, the limit being the time it takes for all the reduced future cash flows to be realized, and thus makes the ratios less effective. Subject to cost, untimely recognition of gains and losses reduces the efficiency of debt contracts. In contrast to noise-reducing operating accruals, gain and loss accruals are a source of positive correlation between accruals and current-period operating cash flow. This is due to revisions in the current-period cash flow from a durable asset being positively correlated with current-period revisions in its expected future cash flows. For example, a plant with decreased current-period cash flow due to becoming uncompetitive most likely faces a downward revision in its expected future cash flows as well. Timely recognition of the impaired future cash flows requires an income-decreasing accrual. The positive correlation between gain and loss accruals and current-period cash flow is illustrated by a durable asset that, at the beginning of period t, is an L-period annuity of expected future cash flows, CF. 4 At the end of period t, information causes a revision of ΔCF t in the amount of the annuity (retrospective to the beginning of t). Other things equal, the current-period cash flow changes by ΔCFt, the cash effect in year t of the asset s change in value. The year-end present value of future cash flows from the asset changes by F(L-1, r) times ΔCF t, where F(L-1,r) is the annuity factor for (L-1) periods and an interest rate r. To the extent the asset s value is booked on the balance sheet (e.g., is not a growth option) and the change in its present value triggers an accrued gain or loss, there is an accrued component of accounting income in year t that is 4 The example is from Ball, Robin and Wu (2003). 10

13 correlated with ΔCFt. The new information therefore causes a positive correlation between accruals and cash flows. Some of the new information about cash flows relates to unbooked items such as growth options and synergies, which affect neither bookable gains or losses nor current-period cash flows. Some affect one but not the other. The hypothesized positive (though not perfect) correlation between current-period cash flows and accrued gains or losses is based on the expectation that some information affects both current period cash flows and bookable gains or losses arising from revisions in expected future cash flows. A major testable implication of the gain and loss recognition role of accruals thus is that other things equal (notably, exogenous working capital changes) accruals are positively correlated with current-period operating cash flows. Other testable implications of the gain and loss recognition role of accruals are that, other things equal (notably, noise-reducing working capital accruals): earnings changes are more negatively serially correlated than cash flows, because they incorporate transitory accrued losses; earnings are more volatile than cash flows; and earnings are more highly correlated with stock returns (Basu 1997). Several of these predictions are the opposite of those arising from the Dechow (1994) noise reduction role of accruals, even though both roles serve to increase financial reporting quality. For example: timely gain and loss recognition induces positive correlation between accruals and current-period operating cash flow, but noise mitigation induces negative; and one increases earnings volatile relative to cash flows, but the other decreases it. The problem for researchers interested in discriminating between the two roles of accruals thus is that we observe the net effect of two offsetting 11

14 processes. Fortunately, a discriminating test is made feasible by the asymmetric nature of one process but not the other, a topic to which we now turn. 2.3 Asymmetry in Loss and Gain Accruals While increased timeliness generally leads to increased contracting-efficiency of financial statement variables, there appears to be a notable exception: timely gain recognition. As first reported in Basu (1997), financial reporting exhibits pronounced conditional conservatism, defined as substantively timelier recognition of losses than of gains. Alternatively stated, accountants adopt a substantively lower verification standard for recognizing decreases in expected future cash flows than they do for recognizing increases (Basu 1997, Watts 2003). In this definition of conservatism, book value of equity and (assuming clean surplus) accounting income are not biased unconditionally, but are biased conditionally, as a function of contemporaneous economic income. From a costly-contracting perspective, unconditional conservatism is inefficient (Ball 2004, Ball and Shivakumar 2005). However, there are several reasons why conditional conservatism (higher loss recognition timeliness) would be contractingefficient as compared to symmetrically timely recognition of both gains and losses. One reason is cost. Accrual accounting is a costly activity generally, but managers expectations of increases in future cash flows (i.e., unrealized gains) are especially costly for accountants to independently verify, perhaps prohibitively so. Verification costs induce additional litigation costs, because timely gain recognition is based on estimates of future cash flow increases that can subsequently turn out to be incorrect. 5 Another 5 Expected litigation costs are not symmetric, with lower expected cost from timely loss recognition than from timely gain recognition (e.g., Skinner 1997; Brown, Hillegeist and Lo 2004). Ball, Kothari and Robin (2000) argue that asymmetric expected litigation costs are costs of the common-law mechanism of 12

15 reason is demand. Loan agreements transfer decision rights to lenders asymmetrically, conditional on adverse but not favourable outcomes, generating lower demand for timely gain recognition than for timely loss recognition. 6 Managers have a greater incentive to disclose unrealized economic gains than unrealized losses (they can realize gains by selling), so users would demand an offsetting asymmetry in the financial statements. Managers have more incentive to disclose economic gains to potential lenders when negotiating debt pricing, so lenders are less likely to demand timely gain recognition in the financial statements. In corporate governance, there is a greater agency problem with managers undertaking or continuing to operate negative-npv investments, acquisitions and strategies than with positive-npv equivalents, tilting demand toward timely loss recognition in contracting with managers. In sum, the benefits of timely loss recognition are more likely to exceed the costs than is the case with timely gain recognition, and the economics of contracting on the basis of financial statement variables helps explain the observed asymmetry in gain and loss accounting. 2.4 Piecewise Linear Accruals Models We hypothesize that conditional conservatism introduces an asymmetry in the relation between accruals and cash flows. Economic losses are more likely to be recognized on a timely basis, as accrued (i.e., non-cash) charges against income, whereas economic gains are more likely to await recognition until realized in cash. This asymmetry holds for both working capital accruals (e.g., the lower-of-cost-or-market rule for inventories requires income-decreasing but not income-increasing accruals) and enforcing the (implicit or explicit) contract between firms and financial statement users that financial reporting is conditionally conservative (recognizes losses in a timely fashion). 6 Performance pricing (Beatty and Weber 2002), under which interest rates vary inversely with accounting performance measures, would create an element of symmetric demand. 13

16 longer cycle accruals (e.g., impairing but not revaluing property, plant and equipment, or goodwill). 7 It implies that the positive correlation between cash flows and accruals arising from the timely recognition role of accruals, discussed in section 2.2 above, is greater in periods with economic losses than in periods with economic gains. In turn, this implies that accruals s that are linear in cash flows are misspecified, and that the correct specification most likely is piecewise linear. No such asymmetry is predicted by the noise reduction role of accruals. There is some evidence of non-linearity in the prior literature. In Basu (1997), cash flow and earnings variables exhibit different incremental slopes when regressed on negative stock returns. A similar result is in Ball, Kothari and Robin (2000) for an international sample. The implication is that accruals are a piecewise linear function of stock returns, which proxy for economic gains and losses. However, this implication does not in itself indicate the extent to which linear accruals s such as the Jones and Dechow-Dichev s are misspecified. DeAngelo, DeAngelo and Skinner (1994) and Butler, Leone and Willenborg (2004) show that financially distressed firms have extremely negative abnormal accruals. Butler et al. attribute this to "liquidity enhancing transactions (such as factoring receivables)" and DeAngelo et al. attribute it to earnings management. However, it also is consistent with timely loss recognition, which is more likely to occur in distressed firms. Dechow, Sloan and Sweeney (1995) and Kothari, Leone and Wasley (2005) find that accrual s are misspecified for firms with extreme performance, which in part could be due to timely loss recognition in the 7 The accruals function in Dechow (1994), Guay, Kothari and Watts (1996) and Dechow, Kothari and Watts (1998) is symmetric with respect to income-increasing and income-decreasing accruals. However, there is no reason to confine the Basu (1997) asymmetry to long term assets and liability accounting. 14

17 extremely poor-performing firms. Kothari, Leone and Wasley (2005) discuss the role of timely loss recognition in accruals, but do not estimate non-linear accruals s. To test the hypothesized asymmetry in the relation between accruals and currentperiod cash flows, we estimate versions of a piecewise-linear relation that takes the following generic form: ACC t = β 0 + β 1 *X t + β 2 *VAR t + β 3 * DVAR t + β 4 *DVAR t *VAR t + ν t (1) where ACC t is accruals in year t, X t is the set of independent variables that prior studies have used to explain accruals, VAR t is a proxy for gain or loss and DVAR t is a (0,1) dummy variable that takes the value 1 if VAR t implies a loss occurs in year t. 8 The above piecewise-linear framework accommodates both roles of accruals: mitigation of noise in cash flow and asymmetric recognition of unrealized gains and losses. We consider three s used in prior studies to explain accruals: Cash flow (CF) : ACC t = α 0 + α 1 CF t + ε t (2.1) Dechow-Dichev (DD) : ACC t = α 0 + α 1 CF t + α 2 CF t-1 + α 3 CF t+1 + ε t (2.2) Jones : ACC t = α 0 + α 1 ΔREV t + α 2 GPPE t + ε t (2.3) where ΔREV t is change in total revenue and GPPE t is the undepreciated acquisition cost of property, plant and equipment. These s are estimated first in their linear form, replicating the results of prior studies. The s then are re-estimated in a piecewise-linear form, using different proxies for the existence of gains or losses in the current year. Our predictions are: 1. β 1 < 0 in the CF, where contemporaneous cash from operations is the sole explanatory variable (i.e., X t = CF t ). This prediction assumes the 8 We do not include VAR t as a separate variable in the regressions if it induces perfect correlation with X t. 15

18 negative correlation due to the noise reduction role of accruals (Dechow 1994, Dechow, Kothari and Watts 1998) exceeds the positive correlation we hypothesize due to the timely gain recognition role. We also expect a negative slope in the DD. 2. β 4 > 0 in all accruals s. We predict a positive incremental coefficient on VAR t in years when the loss-proxy dummy equals one, because in those years there is more likely to be timely recognition than in years when the proxy indicates gains. 3. β 1 increases in magnitude in the piecewise linear specification (1), relative to the conventional linear specification. 4. The adjusted r-squared of the piecewise linear specification (1) exceeds its equivalent in the conventional linear specification. We offer no predictions for β 2 and β 3, due to correlation between the accrual s independent variables and our proxies for economic gains and losses. 3. Sample and Definition of Variables The data are obtained from the CRSP and annual Compustat files. 9 Accruals and cash flow data are obtained from cash flow statements, and are not estimated indirectly from balance sheet data (Hribar and Collins 2002). This restricts the sample to the post period, which ends in We exclude financial firms from our sample and also exclude firm-years in which an acquisition occurred. We Winsorize the data by excluding the 1% extreme observations in each tail of the distribution of each variable for each year. 9 Dechow, Kothari and Watts have a discussion on the advantages of using annual data. 16

19 Regressions are estimated from pooled data using either the entire sample or separately for each 3-digit SIC industry as in Dechow and Dichev (2002). For industryspecific regressions, t-statistics are obtained from the cross-sectional distribution of industry-specific coefficients. Each industry regression requires a minimum of 30 observations. After imposing the above data restrictions and requiring firms to have data on accruals and cash flows, our sample consists of firm-years for the pooled regressions, and 197 three-digit SIC industries for the industry-specific regressions. We do not estimate regressions separately for each firm because each has at most 17 observations, from 1987 to 2003, and very few of the observations are for periods of economic losses. As a result, estimates of conditional conservatism from firm-specific time-series regressions are noisy and unreliable (Givoly, Hayn and Natarajan 2004). Variable definitions are as follows: ACC t : Accruals in year t, the dependent variable in all regressions, scaled by average total assets (Average of Compustat item 6). Accruals are defined as earnings taken from the cash flow statement (Compustat item 123) minus cash flow from operations, also taken from the cash flow statement (Compustat item 308). CF t : Cash flow from operations in year t, taken from the cash flow statement (Compustat item 308), scaled by average total assets. DCF t : Dummy variable = 1 iff CF t < 0. ΔCF t : Change in cash from operations in year t, scaled by average assets. DΔCF t : Dummy variable = 1 iff ΔCF t < 0. REV t : Net revenue (sales) in year t (Compustat item 12) 17

20 ΔREV t : Change in revenue in year t, REV t - REV t-1, scaled by average total assets. GPPE t : Gross Property, Plant and Equipment (Compustat item 7), scaled by average total assets. We employ four proxies VAR for fiscal-year gains and losses (and hence for the loss-year dummy variable DVAR). Three of these are non-market measures: Gain/loss Proxy VAR t Level of cash flows Change in cash flows Industryadjusted cash flows Loss Proxy DVAR t *VAR t Variable definitions DCF t *CF t CF t : Cash flow from operations DCF t = 1 if CF t < 0, 0 otherwise DΔCF t *ΔCF t ΔCF t : Change in cash flow from operations DΔCF t = 1 if ΔCF t < 0, 0 otherwise DIND t * INDADJ_CF t INDADJ_CF t = (CF t MEDIAN_CF t ) MEDIAN_CF t : Median cash flow from operations in three-digit SIC industry DIND t = 1 if INDADJ_CF t < 0, 0 otherwise We also consider a proxy based on stock market returns, as in Basu (1997): Gain/loss Proxy VAR t Abnormal returns Loss Proxy DVAR t *VAR t Variable definitions DABNRET t * ABNRET t ABNRET t = (RET t MKTRET t ) RET t = Stock return in fiscal year t MKTRET t = CRSP equally-weighted market return in the fiscal year t DABNRET t = 1 if ABNRET t < 0, 0 otherwise Each proxy has potential strengths and weaknesses. Market returns normally have the advantage of incorporating more information than financial statement-based book variables. However, revisions to market value also incorporate information about unbooked items, such as growth options and synergies, which cannot generate bookable current-period gains or losses. Market returns therefore constitute a 18

21 potentially noisy measure of the economic losses that trigger accounting accruals, even when they are adjusted for market-wide effects in order to control for exogenous (to financial reporting) shifts in expected returns. However, unbooked assets such as growth options and synergies are less likely to generate current-period cash flows, let alone changes in cash flows. Of the three non-market proxies, the change in cash flow DΔCF*ΔCF seems more likely than the level of cash flow DCF*CF to be correlated with revisions in the levels of future cash flows, but it has the disadvantage that cash flow cannot conform to a random walk process (Dechow 1994, Dechow, Kothari and Watts 1998) and hence cash flow changes do not capture the new information in that variable. To the extent that the industry median constitutes a valid expectation for individual firms, DIND*INDADJ_CF could be a superior proxy for economic gains and losses that trigger accrued book gains and losses, but it ignores gains and losses from industry-wide shocks to current and future cash flows. Because each proxy has potential strengths and weaknesses, we explore them all individually and in combination. 4. Results We begin by replicating prior results using three linear accruals s: a simple cash flow, the Dechow-Dichev (2002), and the popular Jones (1991). We then report the improvement in each of these s when the loss recognition role of accruals is incorporated. This is accomplished by allowing accruals to be a piecewise linear function of the s independent variables, using a variety of proxies for the existence of current-year losses. We conduct a variety of robustness checks, investigate non-linearity in individual accruals components such as inventories and receivables, 19

22 report that accruals non-linearities have increased over time, and show that the non-linear accruals specification increases the ability of accruals to predict future cash flows. 4.1 Linear accruals s Table 1 replicates s (2.1) through (2.3) in linear form, as in prior studies, with no allowance for asymmetric loss recognition. Current-year accruals are the dependent variable in all specifications. Regression slopes on current-year cash flows are negative, and slopes on prior and following year cash flows are positive, consistent with the noise reduction role of accruals. The pooled regressions exhibit lower adjusted r- squareds than the industry-specific regression, consistent with variation across industries in parameters. These results generally are consistent with prior studies. 4.2 Incorporating loss recognition via piecewise linear accruals s Table 2 provides evidence on the degree of collinearity among the four proxies for economic gain/loss. Panel A provides the matrix of Pearson correlation coefficients among the proxies. Coefficients above the diagonal are for pooled data and coefficients below the diagonal are averages of the coefficients for individual industries. Panel B provides equivalent correlations among the four loss dummy proxies. In both panels, all are positively correlated, with the market-based proxy exhibiting the least correlation with others. Because each variable has strengths and weaknesses as a proxy for bookable economic gains and losses that can trigger accruals, in the analysis below we explore them in different combinations. Panels A through C of Table 3 incorporate an asymmetric, piecewise-linear allowance for the loss recognition role of accruals, using the three non-market based proxies for economic losses. We require at least 5 loss observations in each industry 20

23 regression. The loss recognition role of accruals predicts positive coefficients on the dummy variables that proxy for economic loss when they are interacted with current-year cash flow. 10 This prediction is borne out in all specifications, i.e., for all accruals s and all proxies, and for both the pooled and industry-specific regressions (i.e., a total of eighteen out of eighteen specifications). The incremental loss coefficient is quite consistent across specifications, ranging from 0.45 to 0.58 in Panel A, 0.11 to 0.38 in B, and 0.23 to 0.48 in C. The coefficient always is statistically significant. Thus there is consistent evidence of the role of accounting accruals in the timely recognition of economic losses. Compared with the linear specifications in Table 1, the adjusted r-squareds for the piecewise linear specifications in Table 3 increase substantially. The most prominent example is the Jones estimated from industry data, where the increase is from 12% under a conventional linear specification to approximately 30% under the piecewise linear specification with proxies for gains and losses. The Dechow-Dichev exhibits the least improvement, which is not surprising because the incorporates future cash flow as an explanatory variable and hence captures some of the gain/loss recognition role of accruals. Overall, the increase in explanatory power is consistent with loss recognition being an important role of accounting accruals. In the accruals regressions, the coefficients on current-period cash flows CF t generally are higher in magnitude in the piecewise linear specification than in the conventional linear specification. For the linear CF, the coefficient is 0.27 (Table 1), compared with 0.45 for the equivalent piecewise linear using current-period 10 We do not make predictions for the coefficients on the dummy variable by itself (that is, we focus on the dummy slope but not the dummy intercept). Arguments in Beaver, Nelson and McNichols (2003) imply 21

24 cash flow as the gain/loss proxy (Table 3, Panel A). By failing to discriminate between the noise mitigation role of accruals (which predicts a symmetric, negative correlation between cash flows and accruals) and the timely loss recognition role (which predicts an offsetting positive correlation, conditional on losses), the linear specification underestimates the extent of both. The linear specification also provides misleading estimates of earnings quality. 11 Table 4 incorporates loss recognition using a piecewise-linear market based proxy for economic losses, as in Basu (1997). RET t is annual return measured over the fiscal year. MKTRET t is the CRSP equally-weighted market return measured over the same period as RET t. The proxy for economic gain or loss is the market-adjusted return, ABNRET t ( = RET t - MKTRET t ). The coefficient on economic loss (DABNRET t *ABNRET t ) is positive and significant, as predicted. The coefficient varies between 0.11 and 0.13 in the full-sample regression and between 0.07 and 0.10 in the industry-specific regressions. Consistent with conditional conservatism, only the negative abnormal market returns contain significant information about book accruals: the coefficient on ABNRET t (for positive abnormal returns) has inconsistent signs across the regressions and tends to be statistically insignificant. In the Jones, the negative coefficient on ABNRET t potentially reflects the negative correlation between accruals and operating cash flow, which is correlated with ABNRET t but is not controlled for in this. Compared with the linear specification in Table 1, the adjusted r-squareds are substantially higher, that, if anything, the coefficient should be positive. 11 Ironically, Dechow and Dichev (2002) interpret greater negative correlation as an indicator of higher earnings quality (due to noise reduction), but Leuz, Nanda and Wysocki (2003) interpret it as an indicator 22

25 consistent with improved specification. For instance, for the cash flow in industry-specific regressions, the adjusted r-squared increases from 13.9% to 20.8% when the timely loss recognition role of accruals is specified - an increase of approximately one-third. In addition, the coefficients on current-period cash flows CF t generally increases in magnitude under the piecewise linear specification, as is the case in Table 3 for book-based proxies: for the CF, the coefficient increases in magnitude from 0.27 (Table 1) to 0.35 for the equivalent piecewise linear using abnormal market return as the gain/loss proxy (Table 4). The market-based proxy confirms the conclusions reached from using book-based proxies, namely that asymmetrically timely loss recognition is a major role of accruals and that conventional linear accruals s are substantially misspecified. In general, the specification gains using the market-based proxy are similar to but smaller than those from using the individual financial statement based proxies in Table 3. This indicates that, taken by itself, the market-based proxy is inferior to book-based proxies for the purpose of identifying bookable accrued gains and losses. This is not surprising, since unbooked assets such as growth options and synergies are more likely to generate market returns than current-period cash flows, let alone changes in cash flows. Since all the proxies are noisy measures of economic losses that are potentially bookable by accrual accounting, both the market and non-market based proxies could provide incremental information. We therefore re-estimate the accruals s incorporating both. Panels A through C of Table 5 report results for each of the nonmarket proxies when combined with the market proxy. The eighteen r-squareds (for all of lower quality (they view all variance reduction as manipulative smoothing). While we prefer the former interpretation, we note that a linear specification understates the effect whose interpretation is in dispute. 23

26 combinations of loss proxies, accruals s and pooling methods) exceed their Table 3 and Table 4 counterparts, consistent with both market and non-market proxies containing incremental information about accruals. In the industry-specific regressions, the r-squared rises to 32% - 34 % for the Jones and Dechow-Dichev accruals s. These represent an increase of over 25% on the corresponding r-squareds in Table 1. Further, almost all eighteen coefficients on the book-based loss proxies (DCF t *CF t, DΔCF t *ΔCF t and DIND t * IND_CF t ) as well as all the eighteen coefficients on DABNRET t *ABNRET t are positive, as predicted. These coefficients capture the incremental sensitivity of accruals to underlying fundamentals in loss years. Finally, the coefficients on current-period cash flows CF t generally increase in magnitude even more when the piecewise linear specification uses both proxies: for the CF, the coefficient increases in magnitude from (Table 1), (Table 3) and (Table 4) to (Table 5). Panel D of Table 5 takes this analysis even further. It reports regression estimates of accruals s that incorporate conditional conservatism using all four non-marketbased and market-based proxies together. Due to multicollinearity, we caution against placing too much emphasis on the coefficients for individual variables. We do note that this table reports higher adjusted RSQs for all accruals s and estimation methods than in any of the panels in tables 3-5. The piecewise linear Jones, when fitted to industry data using all four proxies, obtains an RSQ of 42%. This compares with only 12% for a conventional linear Jones (Table 1), 29-30% when using individual nonmarket gain/loss proxies alone (Table 3), 14% when using the market-based proxy alone (Table 4) and 33-35% when using individual non-market gain/loss proxies in conjunction with the market proxy (earlier panels of Table 5). We conclude that all proxies add some 24

27 information about bookable economic losses, that better proxies for gains and losses strengthen the result that timely loss recognition is an important role of accounting accruals, and that conventional linear accruals s are comparatively poor specifications of the accounting accrual process. 4.3 Further Robustness Tests The results reported in the previous subsection indicate that the loss-recognition non-linearity evident in accounting accruals is robust with respect to a variety of proxies, and combinations of proxies, for gains and losses. In this subsection we show that the results also are robust with respect to a variety of sample and specification changes. Constant Sample Results. The results in Tables 1 to 5 are not directly comparable because their samples vary with data availability. To check whether this affects our inferences, we replicate the earlier regressions using a constant sample. For comparability, we restrict the full-sample regressions in this table to include only the firms that are included in the industry-specific regressions. The constant sample consists of observations in pooled regressions that span digit SIC codes. Results are reported in Table 6. Since the results are qualitatively similar to those in Tables 1 to 5, Table 6 reports only the adjusted r-squares across the different s. The adjusted r- squareds from s incorporating asymmetric loss recognition (rows 2 to 4) exhibit substantial increases relative to conventional linear accrual s (row 1), particularly in the pooled regressions. Even in the industry-specific regressions, the Jones and CF s, which use only contemporaneously available data to explain accruals, exhibit increases in adjusted r-squareds from approximately 13% to 24-36%. 25

28 Working Capital Accruals. The dependent variable in our regressions is total accruals. However, the Dechow and Dichev (2002) was developed to explain working capital accruals, and their empirical results are based on working capital accruals. As McNichols (2002) points out, their may offer a noisy specification for total accruals. While our hypothesis of non-linearity in accruals due to timely loss recognition applies to working capital accruals as well as longer-cycle accruals (due, for example, to losses from inventory write-downs, receivables revaluations and accrued expense provisions), the distinction makes it difficult to compare our results directly with Dechow and Dichev (2002). Hence for comparison we re-estimate our earlier regressions using working capital accruals as the dependent variable, defined as ΔAccounts receivable + ΔInventory - ΔAccounts payable - ΔTax payable + ΔOther assets, net. The results are presented in Panel A of Table 7. We reports results from industryspecific regressions only; regressions based on pooled data yield similar conclusions. In this replication, the average coefficients on contemporaneous, lagged and one-year-ahead cash flows are 0.45, 0.18 and 0.14 respectively. These are comparable with -0.51, 0.19 and 0.15 in Dechow and Dichev (2002, Table 3). Moreover, the average adjusted r- squareds for our replication is 32%, comparable to the average of 34% reported by Dechow and Dichev (2002). When this is extended to incorporate the lossrecognition non-linearity, the coefficient on contemporaneous cash flows increases in magnitude to as much as 0.57 and the adjusted r-squareds increase to as much as 42%, a proportional increase of over 30% relative to the linear. Thus, when we confine the dependent variable to include only working capital accruals, we are able to closely 26