Question 1

Financial statements are prepared for a range of different business entities.

1. Identify the different entities financial statements can be prepared for.

2. Identify the different financial statements and explain the type of information contained within each of the financial statements.

3. Explain the purpose of accounting information and how this purpose is linked to the needs of stakeholders involved with different entities.

Question 2

Accounting information is guided by various principles, assumptions and qualitative characteristics.

1. Describe the process of generating accounting information.

2. Identify and describe the assumptions, qualitative characteristics and framework which guide the preparation of accounting information.

3. Explain why accountants have flexibility in accounting choices.

4. Explain how the assumptions and qualitative characteristics of accounting guide the choice of the following accounting methods.

a. Revenue recognition

b. Accounting for bad debts

Question 3

Accountants have the ability to choose different ways to report accounting information, and this choice can have an impact on the reported financial performance and position of an entity.

1. Identify and describe in detail the different methods available when making reporting decisions for:

a. Revenue recognition

b. Accounting for uncollectible receivables (bad debts)

2. Explain the advantages and disadvantages of each method you have just described in part one of this question.

3. Explain why accountants are able to choose different ways to report accounting information. In your answer you may like to discuss the advantages and disadvantages of being able to make different accounting choices with brief reference to the article Gowthorpe and Amat (2005), Creative Accounting: Some Ethical Issues of Macro- and Micro-Manipulation.

4. Explain how the choice of these accounting methods can be used by managers for earnings management. In your answer you may like to refer to how the assumptions and qualitative characteristics of accounting reduce earnings management.

Question 4

Predicting future cash flows is important to determine the viability and value of a company.

1. Describe the different components of the cash flow statement and the information it provides

to users.

Question 5

Using the Barth, Cram, Nelson (2001) to answer the following questions:

1. Explain the relation between the following accruals and future cash flow as suggested by Barth, Cram and Nelson (2001).

a. Accounts receivable

b. Accounts payable

c. Inventory

d. Depreciation and amortization

2. Identify accrual components in which the paper examines the effect on future cash flows. Briefly discuss the findings regarding the effect of changes in accrual components on future cash flows.

Accruals and the prediction of future cash flows Mary E Barth; Donald P Cram; Karen K Nelson The Accounting Review; Jan 2001; 76, 1; ABI/INFORM Global pg. THE ACCOUNTING REVIEW Vol. 76, No.1 January 2001 pp. 27~58 Accruals and the Prediction of Future Cash Flows Mary E. Barth Stanford University Donald P. Cram California State University, Fullerton Karen K. Nelson Stanford University ABSTRACT: Building on the Dechow et al. (1998) model of the accrual process, this study investigates the role of accruals in predicting future cash flows. The model shows that each accrual component reflects different information relating to future cash flows; aggregate earnings masks this information. As predicted, disaggregating accruals into major components-change in accounts receivable, change in accounts payable, change in inventory, depreciation, amortization, and other accruals-significantly enhances predictive ability. Each accrual component, including depreciation and amortization, is significant with the predicted sign in predicting future cash flows, incremental to current cash flow. The cash flow and accrual components of current earnings have substantially more predictive ability for future cash flows than several lags of aggregate earnings. The inferences are robust to alternative specifications, including controlling for operating cash cycle and industry membership. Key Words: Accruals, Cash flow, Earnings, Cash flow prediction. Data Availability: The data used in this study are from the public sources identified in the text. The authors appreciate comments by Sudipta Basu, Bill Beaver, Gerald Feltham, S. P. Kothari, Stephen Ryan, two anonymous reviewers, Ken Gaver (the Associate Editor), and workshop participants at the American Accounting Association 1999 Annual Meeting, the Ninth Annual Conference on Financial Economics and Accounting, Stanford University Accounting Summer Camp, University of British Columbia, California State University, Fullerton, Baruch College-CUNY, The George Washington University, Massachusetts Institute of Technology, and Rutgers University. The support of the Massachusetts Institute of Technology Sloan School of Management, and the Stanford University Graduate School of Business Faculty Trust and Financial Research Initiative is gratefully acknowledged. Submitted November 1999 Accepted August 2000 27 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 28 The Accounting Review, January 2001 I. INTRODUCTION his study investigates the role of accruals in predicting future cash flows. A firm's ability to generate cash flow affects the values of its securities. For this reason, the Financial Accounting Standards Board (FASB) indicates that a primary objective of financial reporting is to provide information to help investors, creditors, and others assess the amount and timing of prospective cash flows (FASB 1978, ~37-39). Moreover, the FASB asserts that information about earnings and its components is generally more predictive of future cash flows than current cash flow (FASB 1978, ~44). Several prior studies test the relative abilities of aggregate earnings and cash flow to predict future cash flows, but do not examine how the components of earnings affect its ability to predict future cash flows. We build on the model of Dechow et al. (1998) (hereafter DKW) to develop predictions about the role of accruals in predicting future cash flows. As predicted, we find that dis aggregating earnings into cash flow and the major components of accruals significantly enhances earnings' predictive ability. Thus, this study extends our understanding of the temporal relations among accruals, cash flows, and earnings, and provides evidence consistent with the FASB's assertion that knowledge of the components of earnings is important for predicting future cash flows. Our extended analysis of the DKW model, which focuses on predicting cash flow next period, reveals that the various accrual components of earnings capture different information not only about delayed cash flows related to past transactions, but also about expected future cash flows related to management's expected future operating and investing activity. Aggregate earnings, and thus aggregate accruals, masks this information by weighting the components equally. Thus, we predict that disaggregating earnings into cash flow and the components of accruals enhances earnings' predictive ability relative to aggregate earnings. A key insight from our extended analysis of the model is that current cash flow and the major components of current period accruals reflect the same information about cash flow next period as do multiple lags of aggregate earnings. The empirical tests focus on annual amounts and dis aggregate accruals into six major components: change in accounts receivable, change in inventory, change in accounts payable, depreciation, amortization, and other accruals. I Based on the weights on the accrual components implied by the model, we predict that each accrual component has a different relation with future cash flows, and that increases in accounts receivable and inventory and decreases in accounts payable are associated with higher future cash flows. We also predict that depreciation of fixed assets and amortization of intangible assets are associated with higher future cash flows. We find that the relation between cash flow next year and current cash flow and each component of accruals is significant and has a sign consistent with predictions. Moreover, the relations differ significantly from each other, in absolute value. Thus, as predicted, we find that aggregate earnings masks information relevant for predicting future cash flows. We also find that long-term accruals, i.e., depreciation and amortization, have significant predictive ability for future cash flows. This evidence is inconsistent with suggestions in the financial press that depreciation and amortization do not predict future firm performance (e.g., MacDonald 1999a, 1999b), calling into question analysts' recent focus on "cash earnings," which excludes these accruals. The model leads to the prediction that dis aggregated current earnings has the same predictive ability for next period cash flow as do current and two lags of aggregate earnings. T 1 The tenn "'accruals" refers to adjustments from operating cash flow to earnings. e.g., change in accounts receivable. rather than adjustments from cash to net worth, e.g., accounts receivable. which we refer to as balance sheet accruals. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Barth, Cram, and Nelson-Accruals and the Prediction of Future Cash Flows 29 However, our empirical analysis reveals that disaggregated current earnings has significantly more predictive ability than current and up to six years of lagged aggregate earnings. This evidence suggests that the model's simplifying assumptions about the accrual process understate the predictive ability of the accrual components relative to aggregate earnings. We investigate whether disaggregated earnings' significantly greater predictive ability is attributable to disaggregating cash flow and aggregate accruals or to disaggregating accruals. The findings reveal that disaggregating earnings into cash flow and aggregate accruals significantly increases predictive ability relative to aggregate earnings, but that dis aggregating accruals into its major components further significantly increases predictive ability. Our inferences regarding the superior predictive ability of disaggregated earnings are robust to a variety of sensitivity checks, including predicting cash flows several years in the future, controlling for firms' operating cash cycles and industry membership, and using share prices, returns, and discounted future cash flows as proxies for expected future cash flows. The remainder of the paper is organized as follows. Section II relates this study to extant research. Section III develops the model and empirical predictions. Section IV specifies the estimation equations. Section V describes the sample and presents the primary findings. Section VI presents results from additional analyses. Section VII summarizes and concludes. II. RELATION TO PRIOR RESEARCH The role of accruals in predicting future cash flows is a fundamental question underlying financial reporting. Prior research directly addressing this question typically uses small samples limited by long time-series requirements; the results of this research are mixed. Bowen et al. (1986) does not find that aggregate earnings provides better predictions of future cash flows than past cash flow. In contrast, Greenberg et al. (1986) concludes that aggregate earnings has more predictive ability than cash flow, and Lorek and Willinger (1996), focusing on quarterly rather than annual amounts, finds that accruals have predictive ability incremental to cash flow. Finger (1994) finds that cash flow is marginally superior to aggregate earnings for short prediction horizons, but earnings and cash flow perform equally well for longer horizons. Using a larger sample, Burgstahler et al. (1998) finds that cash flow has more predictive ability than aggregate earnings. 2 Our analysis of the model reveals that neither current aggregate earnings nor current cash flow is an unbiased predictor of future cash flows, and that the bias in each is a function of accruals. Thus, one possible explanation for the mixed results of prior research is that the relative magnitudes of the biases depend on sample composition, which is manifest in the small sample studies. Similar to Burgstahler et al. (1998), for our large sample of firms we find that current cash flow has more predictive ability for future cash flows than current aggregate earnings. However, we leave to future research clear resolution of the mixed findings of prior research. Our contribution to this research is showing that limiting the inquiry to aggregate earnings masks the ability of accrual components to predict future cash flows incremental to current cash flow. DKW models cash flow and the accrual process related to accounts receivable, accounts payable, and inventory to derive the prediction that current earnings is the best predictor of future cash flows. DKW reports that firm-specific variation in cash flow forecast errors based on aggregate earnings is significantly lower than that based on cash flow. DKW also reports that in firm-specific regressions of future cash flows on aggregate current earnings 2 Several of these studies also predict cash flow more than one period ahead; we do so in Section VI. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 30 The Accounting Review, January 2001 and cash flow, both have incremental explanatory power. DKW does not explore the model's implications for the predictive ability of earnings components, including the components of accruals. We extend the analysis of the DKW model to show that earnings' superiority for predicting future cash flows stems from disaggregating earnings into cash flow and the components of accruals. The differences across accrual components are not evident from DKW's empirical analysis because DKW implicitly permits the aggregate earnings coefficient to differ firm by firm. Our analysis of the model and empirical results also extend DKW by showing that several past aggregate earnings have explanatory power for predicting future cash flows, incremental to aggregate current earnings, and that disaggregated current earnings has significantly more predictive ability than several lags of aggregate earnings. DKW's analysis also indicates that the predictive ability of aggregate earnings relative to cash flow varies with firms' operating cash cycles. We show that even after partitioning on operating cash cycle, following DKW, aggregate earnings masks significant information relevant to predicting future cash flows. Finally, we show that long-term accruals aid in predicting future cash flows; DKW focuses on working capital accruals. This study also relates to research comparing the predictive abilities of earnings and cash flow using share prices as an implicit or explicit proxy for expected future cash flows. For example, Ball and Brown (1968), Beaver and Dukes (1972), and Dechow (1994) find that returns are more highly associated with aggregate earnings than with cash flow. Several other studies (e.g., Rayburn 1986; Wilson 1986, 1987; Bowen et al. 1987; Ali 1994; Cheng et al. 1996; Pfeiffer et al. 1998) document that aggregate earnings and cash flow are incrementally informative for returns. Some prior research also finds that components of earnings, including accruals and accrual components, have different pricing multiples, as predicted by differences in the components' persistence (e.g., Lipe 1986; Barth et al. 1990; Barth et al. 1992; Barth et al. 1999, 2000). We contribute to this research by showing that cash flow and the major accrual components of earnings have different multiples when predicting future cash flows, as predicted by a model of the accrual process. We focus on the implications of cash flow and the accrual components of earnings for firms' expected future cash flows rather than for firms' share prices for two primary reasons. First, cash flow prediction is fundamental to assessing firm value as reflected in share prices. Thus, cash flow is a primitive valuation construct. Our use of realized future cash flows as a proxy for expected future cash flows assumes rational expectations, as does prior accounting research in a variety of contexts (e.g., McNichols and Wilson 1988; Penman and Sougiannis 1998; Aboody et al. 1999). Second, prior research provides evidence that share prices fail to reflect accurately the differential persistence of accruals and cash flow (e.g., Sloan 1996; Barth and Hutton 2000; DeFond and Park 2000; Xie 2000). This evidence calls into question the use of share prices as a proxy for expected future cash flows when examining the predictive ability of accruals and cash flow. Nonetheless, to facilitate comparison with prior research, we estimate price- and returns-based specifications with no change in inferences regarding the predictive ability of disaggregated earnings. III. MODEL DKW models operating cash flows and the accrual process to generate predictions for the relative abilities of earnings and cash flow to predict future cash flows. Because our objective is similar to DKW, we expand the analysis of the DKW model to obtain our predictions. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Barth, Cram, and Nelson-Accruals and the Prediction of Future Cash Flows 31 Model Assumptions The model assumes earnings, EARN, equals a constant proportion of sales, S, and sales follows a random walk: EARN, = 1TS, and S, S'_I + " = (1) where 0, E t[6.INV t+l ] equals 0 only in the rare case when St = St-I' i.e., lOt = O. Aggregate Earnings as a Predictor of Future Cash Flow Next period cash flow, CFt+I' equals cash inflows from sales, adjusted for uncollected amounts reflected in the change in accounts receivable, minus outflows from purchases, adjusted for unpaid amounts reflected in the change in accounts payable. That is: (5) Equations (1) through (3) show that equation (5) can be expressed in terms of SHI and three sales shocks, HI' lOt' and H' In particular, DKW shows that: CFt+1 = 1TS t+1 - [a + (l - 1Thl - [3(1 - 1T)]t+l (6) + 'Yl(l - 1T)[[3 + 'Yil - [3)]6.'+1 + [3'Yl'Yil - 1T)6.,. Because earnings equals cash flow plus accruals, i.e., EARN'+I = CF'+1 + 6.AR,+1 + 6.INV,+1 - 6.AP'+1 in the model, and EARN'+1 = 1TS'+I' the terms in equation (6) are accruals. 6 As DKW explains, the second term in equation (6) reflects the permanent change in working capital accruals (accounts receivable plus inventory minus accounts payable) that results from the current sales shock. This term equals the change in working capital accruals only if the entire target inventory adjustment occurs and is paid for in period t + 1. DKW refers to the multiple on '+1 in the second term as the firm's operating cash cycle, 0, expressed as a fraction of a year. The third and fourth terms in equation (6) reflect the one- and two-year effects on cash flow of cash payments related to the new levels of cost of goods sold and inventory arising from the sales shocks. DKW ignores the second line of equation (6) to obtain current earnings as the best predictor of next period cash flow, and all future cash flows. 7 However, the second line of equation (6) does not equal 0 in expectation at time t. Specifically, E,[6.'+I] = -, and E,[6.,] = , - ,-1' where lOt and H are the time t and t - I realizations of the random variable , which only equal 0 by chance. As we show below, including these terms reveals that expected next period cash flow does not equal current earnings, i.e., EARN, is not an unbiased predictor of CF t +I' and, more importantly, facilitates insights into the incremental role of accruals in predicting future cash flows. Equation (6) can be used to express expected next period cash flow as a function of current and two lags of earnings. In particular, from equation (6): 6 7 Because PH = (I - TI) st-l + aINV,+" equation (6) can be expressed as CF'+I = TI S'+I - aAR'+1 - aINV,+1 + a AP,+ I' which is earnings minus accruals. From equation (6), ignoring the second line, E,[CF,+tl = Et[TIS,+1 - S ,+1] = Et[TIS,] = EARN,. which also suggests that TIS, is an optimal forecast of next period cash flow. The second line of equation (6) does not affect the predicted sign of the correlation between cash flow and earnings, or accruals, which is a primary focus of DKW. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Barth, Cram, and Nelson-Accruals and the Prediction of Future Cash Flows 33 From equation 0), ( = 1T- I (EARN( - EARN(_I) and (-1 = 1T- I (EARN'_I - EARN(_2)' Thus, current and two lags of earnings provide information about the sales shocks relevant to expected next period cash flow. 8 Substituting EARN variables for the terms in equation (7) and rearranging yields equation (8): E([CF(+I] 'YI(l - 1T)1T- I[13 + 'Yil - 13) - 13'Y2])EARN( + 'YI(1 - 1T)1T- I[13 + 'Yil - 13) - 213'Y2]EARN(_1 + 'YI(1 - 1T)1T- I 13'Y2EARN(_2' = (1 - (8) Equation (8) shows that expected next period cash flow equals current earnings, adjusted for the one- and two-year effects of inventory changes and associated payments. For example, if the two prior years' sales changes, i.e., ( and (-1' are positive, then EARN( overstates expected cash flow in period t + I because EARN( omits the future cash flow effects of payments related to delayed inventory increases. In this case, cash flow in period t + 1 will be less than earnings in period t because of payments related to (1) the period t + 1 inventory increase arising from the period t sales increase, (2) the period t accounts payable for the period t inventory increase arising from the period t sales increase, and (3) the period t accounts payable for the period t inventory increase arising from the period t - 1 sales increase. Cash Flow and Components of Accruals as Predictors of Future Cash Flow The analysis thus far focuses on using aggregate earnings to predict next period cash flow. However, our objective is to understand the relation between earnings and its components, and future cash flows. We next use the model to derive this relation. Key to understanding this relation is the observation that equation (5) can also be used to express expected next period cash flow in terms of the components of current earnings. Specifically, using equation (5) to obtain expressions for CF( and E([CF(+I] shows that next period cash flow is expected to differ from current period cash flow because the firm collects the amount of the change in receivables, pays the amount of the change in accounts payable, and pays an amount associated with the change in expected purchases next period. Thus, CF( is not an unbiased predictor of CF(+ I: E([CF(+I] = CF( + ~AR, - ~AP( - (1 - 13)(E([P(+I] - PI) = CF( + ~AR( - ~AP( - (1 - 13)(E([~INV(+l] - (9) ~INV(). The second line of equation (9) follows from the first by noting that purchases equals cost of goods sold plus the change in inventory and recalling that expected cost of goods sold in period t + 1, E([(l - 1T)S(+l]' equals (1 - 1T)S(. To state equation (9) in terms of the components of current earnings, recall from equations (2) and (4) that the sales shock, (' affects current period change in receivables and expected change in inventory next period. Thus, the expected change in inventory can be stated in terms of current change in receivables. 8 Because EARN, = 7TS" equation (7) can be equivalently stated in terms of current and two lags of sales. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 34 The Accounting Review, January 2001 Expressing Et[~INVt+l] in terms of ~ARt and collecting terms yields an expression of expected next period cash flow in terms of the components of current earnings: Thus, under the assumptions of the model, expected cash flow can be expressed as a function of either (I) current and two lags of aggregate earnings, as in equation (8), or (2) current earnings disaggregated into cash flow and the components of accruals, as in equation (10). In other words, the model predicts that equations (8) and (10) have equal predictive ability. In equation (10), the first part of the multiple on ~AR, i.e., 1, reflects the expected collection next period of the current period change in receivables. The second part of the multiple, i.e., -(1 - I3httil - 1T)a- 1, reflects the expected payment next period of the expected change in inventory, because the expected change in inventory next period, like the change in accounts receivable, depends on the current period sales shock. The multiple on ~ INV reflects the payment deferred to next period of the current period change in inventory. The multiple on ~ AP reflects the expected change in cash next period associated with the current change in accounts payable. Equation (10) shows that accruals reflect information about expected future cash flows relating to management's expected future purchasing activity, as well as relating to collections and payments associated with current period transactions, i.e., collecting accounts receivable and paying accounts payable. Thus, the predictive ability of accruals for future cash flows is not limited to the "mechanical" delayed cash flow effects of past transactions. 9 Predictions The model provides the basis for the predictions we test in Section V. First, equation (8) leads us to predict that current and two lags of aggregate earnings are significant in predicting next period cash flow. Equation (8) includes three earnings variables because the assumed target inventory policy is a two-period phenomenon. However, asset investment policies and related cash payment policies likely differ across short-term assets, such as inventory, and long-term assets, such as property, plant, and equipment. If the policy were longer lived, as one might expect for long-term assets, then equation (8) would include additional lags of earnings. Because firms invest in long-term assets, we expect that earnings lags greater than two also are significant in predicting future cash flows. 10 9 10 Our predictions assume that managers choose accounting policies to portray accurately the firm's economic situation. To the extent managers choose accounting policies opportunistically, we expect earnings to be a relatively poor predictor of future cash flows, thereby biasing against finding evidence consistent with our predictions. Also, it is possible that components of cash flow could enhance the predictive ability of aggregate cash flow for future cash flows, just as components of earnings enhance the predictive ability of aggregate earnings. However, such components are available only for the relatively few firms using the direct method under Statement of Financial Accounting Standards No. 95. Moreover, because accruals inherently incorporate information about future cash flows, whereas past cash flow does not, it would not be surprising to find that accruals enhance the predictive ability of disaggregated cash flow. This can be seen by noting that equation (8) includes one more EARN term than the sales shock affecting cash flow next period. In the modeled case of a two-period investment policy, the sales shock from period t - 1 affects period t + 1 cash flow. Thus, EARNt _ 2 is included in equation (8). In the case of a six-period investment policy, the sales shock from period t - 5 affects period t + 1 cash flow. Thus, EARN, .. o would be included in equation (8). An equation analogous to equation (8) using current and lagged cash flows as predictor variables would include an infinite number of lags of cash flow. Even if the only accrual relates to accounts receivable, if the series of changes in cash flows is invertible, i.e., a + k 2: T=O 4>H EARN j. H +. U j I' (11) where i and t denote firm and year, and k ranges from 0 to 6. Based on equation (8), we predict that

1-2 are significantly different from 0. 11 For the reasons set forth I1 Although intuition suggests the earnings coefficients should be positive and decline with longer lags. this is not predictable from equation (8). Thus, we do not predict the signs or relative magnitudes of the coefficient estimates. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 36 The Accounting Review, January 2001 in Section III, we estimate equation (11) using up to six lags of EARN and expect that at least some t-k are significantly different from 0 for k > 2. We use up to six lags of earnings because this results in the same number of explanatory variables in equation (11) as in the accrual components equation (12), ensuring that any difference in explanatory power is not attributable solely to the number of explanatory variables. The second set of tests dis aggregates earnings into its major components: CF j,I+1 + CFCFj.I + ARIlARj,I + I IlINV j,t + AP11 APj,I + DDEPRj,I + AMAMORTj,t + oOTHER;,t + Uj,t, = (12) where DEPR is depreciation expense, AMORT is amortization expense, and OTHER is the aggregate of other accruals, i.e., OTHER == EARN - (CF + IlAR + MNV - IlAP - DEPR - AMORT).12 As explained in Section III, we predict that equation (12) has the same predictive ability as equation (11) estimated using current and two lags of EARN. We also predict that the coefficients on the accrual components in equation (12) differ from each other and from that on cash flow. That is, the components of accruals add to cash flow in predicting future cash flows. We also predict that cP> AR' 1' D' and AM are positive, and AP is negative. We have no prediction for 0' Tests of predictions comparing the explanatory power of equations (11) and (12) are based on a Vuong (1989) Z-statistic. Tests of predictions relating to differences in coefficients in equation (12) are based on F-tests of coefficient equality. Results associated with other tests of coefficient constraints provide additional insights into the predictive ability of earnings components and facilitate comparing the predictive abilities of cash flow and aggregate earnings, as in prior research. In particular, constraining the coefficients on cash flow and the accrual components to be equal with signs implicit in EARN is equivalent to including only aggregate earnings in equation (12). Constraining the coefficients on the accrual components to equal 0 is equivalent to including only cash flow in equation (12). Similarly, constraining the coefficient on cash flow to equal 0 is equivalent to including only the accrual components in equation (12). v. DATA AND EMPIRICAL RESULTS Data and Descriptive Statistics Data are from the 1997 Compustat annual industrial and research files. The sample spans 1987-1996 because the analyses require at least one year of future cash flows and use cash from operations reported under Statement of Financial Accounting Standards No. 95 (SFAS No. 95).13 EARN is income before extraordinary items and discontinued operations, and CF is net cash flow from operating activities, adjusted for the accrual portion of 12 13 The accruals disaggregation follows the model in Section III. An alternative disaggregation follows the line items typically reported in firms' income statements. i.e., sales, cost of goods sold (CGS), selling, general, and administrative expenses (SGA), DEPR, AMORT, and OTHER. The two alternatives are closely related in that change in receivables typically relates to sales, change in inventory relates to CGS, and change in accounts payable relates to CGS and SGA. Not surprisingly. our inferences are unaffected by using this alternative disaggregation. SFAS No. 95 requires firms to present a statement of cash flows for fiscal years ending after July 15, 1988 (FASB 1987). Our sample begins in 1987 because some firms early-adopted SFAS No. 95. We predict cash flows for 1988-1997, depending on the length of the prediction horizon and the number of lagged predictors in the equation. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Barth, Cram, and Nelson-Accruals and the Prediction of Future Cash Flows 37 extraordinary items and discontinued operations. The accrual components are from the statement of cash flows, when available, and calculated from balance sheet data otherwise. Following Sloan (1996), all variables are deflated by average total assets.14 The sample excludes financial services firms (SIC codes 6000-6999) because the model is not developed to reflect their activities. It also excludes observations with sales less than $10 million, share price less than $1, EARN or CF in the extreme upper and lower 1 percent of their respective distributions, and studentized residuals greater than 3 in absolute value. 15 Because we seek to compare the findings from equation (11) for k = 2 and equation (12), sample firms in the primary analyses must have data sufficient for estimating these two specifications. These criteria result in a primary sample of 10,164 firm-year observations. Table 1 presents descriptive statistics for the variables used in the estimation equations. Panel A reports distributional statistics and Panel B reports Pearson and Spearman correlations. Consistent with prior research, e.g., Sloan (1996), Panel A reveals that the means and medians of EARN and CF are positive and those of aggregate accruals, ACCRUALS = EARN - CF, are negative. The negative mean and median for ACCRUALS reflect the fact that aggregate accruals includes depreciation and amortization, but acquisition of depreciable and amortizable assets is an investing, not operating, activity under SFAS No. 95. Also consistent with Sloan (1996), current accruals, i.e., ~AR, ~INV, and ~AP, are smaller in magnitude and more variable than DEPR, a long-term accrual. Finally, less than onehalf of the sample firms report amortization expense, as evidenced by a median AMORT of 0.00. 16 Panel B of Table 1 reveals that, as expected, EARN is significantly positively correlated with CF and ACCRUALS, and CF and ACCRUALS are significantly negatively correlated.17 With the exception of AMORT, the accrual components are individually significantly correlated with EARN and CF, and generally are also correlated with each other. Untabulated statistics indicate that EARN, ACCRUALS, and CF are significantly autocorrelated. The persistence of CF, 0.47, is substantially greater than that of ACCRUALS, 0.26, consistent with prior research such as Sloan (1996) and Barth et al. (1999). The persistence of EARN, 0.66, exceeds those of ACCRUALS and CF. 18 Results: Aggregate Earnings Table 2 presents regression summary statistics from estimating equation (11), which tests the predictive ability of current and past aggregate earnings for next period cash flow. The results reveal that current earnings, EARN!, is significant in predicting one-year-ahead 14 15 16 17 18 Our variable definitions follow DKW. except that DKW calculates cash from operations from balance sheet and income statement amounts. Also, DKW deflates by the number of shares outstanding. Section VI reports that our inferences are unaffected by the source of cash from operations or the choice of deflator. Our inferences are unaffected by eliminating observations based on any of the stated criteria. If amortization expense is missing. then AMORT equals total depreciation and amortization minus depreciation and minus depletion; if depletion is missing and the firm is not in an industry for which depletion is common, i.e., SIC codes 1000-1499, 2900-2999, and 4600-4699, then we set depletion equal to O. Depletion is included in OTHER. Throughout. the term "significant" refers to statistical significance at less than the 0.05 level. The statistics in Table I differ somewhat from those in some prior studies, e.g., Dechow (1994), Sloan (1996), DKW, and Barth et al. (1999), but are consistent with those in others, e.g., Burgstahler et al. (1998). For example, we report that the standard deviations of EARN, CF, and ACCRUALS are similar, whereas Dechow (1994) and DKW find that earnings is less variable than cash flow. Un tabulated statistics reveal that the differences are primarily attributable to the definition of accruals, sample selection, and deflators. Despite these differences in descriptive statistics, Section VI reports that our inferences are robust to these choices. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. V-l 00 TABLE 1 Descriptive Statistics on Earnings, Cash from Operations, and Accruals Sample of 10,164 Firm-Year Observations, 1987-1996 Panel A: Distributional Statistics Variable Mean Median Std. Dev. EARN CF ACCRUALS 0.04 0.08 -0.04 0.01 0.01 0.01 0.05 0.01 -0.Ql 0.04 0.09 -0.04 0.01 0.01 0.01 0.04 0.00 -0.01 0.08 0.08 0.08 0.05 0.05 0.04 0.03 0.02 0.05 ~AR ~INV ~AP DEPR AMORT OTHER Panel B: Pearson (Spearman) Correlations Above (Below) the Diagonal Variable EARN CF ACCRUALS ~AR ~INV ~AP DEPR AMORT OTHER EARN CF 0.48* 0.51 * 0.29* 0.27* 0.25* 0.16* -0.02* -0.Ql 0.09* -0.58* -0.l3* -0.21* 0.13* 0.32* 0.01 -0.26* ACCRUALS LlAR MNV LlAP DEPR AMORT OTHER 0.44* -0.58* 0.26* -0.19* 0.44* 0.24* -0.30* 0.53* 0.19* 0.12* 0.11 * -0.Ql 0.43* 0.37* -0.05* 0.26* -0.32* -0.03* -0.06* -0.01 -0.04* -0.01 -0.04* 0.01 -0.01 -0.01 -0.49* 0.27* -0.22* 0.48* -0.16* -0.01 0.02* 0.01 -0.01 0.40* 0.47* -0.02* -0.41 * -0.03* 0.41 * 0.21* 0.40* -0.04* 0.03* -0.08* 0.30* -0.05* -0.02* 0.01 0.01* -0.03 0.02* -0.12* -0.04* -0.04* -l ::r ." > n n 0 ~ g. ;:l (Jq 10 ." --l ::r ('I) Tests comparing explanatory power: Current and One Lag Current and Two Lags p-value Adj. R2 Adj. R2 (') (') 0 c g :r (Jq ::l ('I) <: .r p c .. see table i for variable definitions p-values are based on vuong z-statistic comparing the explanatory power of unrestricted specification with year t cash flow and six accrual components to following specifications k="0" cf accruals only earn n barth cram nelson-accruals prediction future flows current have significantly more predictive ability than up four years however significance difference between two declines inclusion lagged variables untabulated findings reveal that is insignificant in a including lags recall from tables aggregate earnings less disaggregated into accruals. superior relative multiple derives disaggregating as well vi. additional analyses operating cycle model section iii single firm whereas estimation equations cross-sectional. thus differences parameters across firms could affect our inferences. dkw shows depends o. this because first line equation realized cft reflected where investigate whether incremental robust controlling we repeat after partitioning o-quartiles dkw.21 panel presents descriptive statistics full sample distribution comparable dkw. quartiles substantially reduces variation especially three portfolios. b regression summary estimating by o-quartile. consistent those indicating results not solely attributable particular each o-quartile portfolios all component coefficients predicted signs differ exception amort quartile finally tests various coefficient restrictions industry membership types mix likely vary also estimate industries identified et al. findings. reports industry. surprisingly varies industries. substantial within reveals perfectly correlated. eight interquartile range exceeds maximum a. estimated it abilities adjusted r s separate-industry largely art_i f3="(APt" apt-i st e averaged number which data available gl g inv ii average days accounts receivable inventory minus payable. interprets fraction multiplying permits interpretation terms days. firm-specific regressions requires at least five data. reproduced permission copyright owner. further reproduction prohibited without permission. formed compustat> + 1>CF CF;,/ + 1>ARLlARi,t + 1>I Ll1NV;" + 1>Ap LlAPi,t + 1>JJEPRi,t + 1>A~MORT;" + 1>uOTHER;" + ui,t Panel A: Descriptive Statistics on Operating Cash Cycle. 8 Mean Median Std. Dev. Interquartile Range n Full Sample Quartile I Quartile 2 Quartile 3 Quartile 4 0.22 0.20 0.15 0.17 2,723 0.06 0.07 0.04 0.06 680 0.16 0,15 0.02 0.04 681 0.24 0,24 0.03 0.04 681 0.41 0.37 0.13 0.12 681 Panel B: Regression Summary Statistics Quartile 1 Quartile 2 Quartile 3 Quartile 4 Variable Prediction Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic Intercept CFt ? 0.02 0.72 0.57 0.40 -0.60 0.25 0.14 0.15 7.86 46.25 17.21 12.44 -18.50 7.83 1.45 6.42 0.02 0.58 0.40 0.36 -0.57 0.35 0.43 0.11 7.43 33.84 14.61 11.03 -15.90 7.89 2.64 3.99 0.02 0.54 0.31 0.31 -0.46 0.36 0.38 0.11 6.65 30.44 11.67 10.36 -12.24 7.13 1.84 4.59 0.02 0.41 0.26 0.17 -0.40 0.35 0.41 0.09 5.30 20.89 9.60 6.41 -9.58 4.74 4.94 3.43 ~ARt ~INVt ~APt DEPRt AMORT, OTHER. Adj. R2 n + + + + + ? 0.59 2,304 0.39 2,422