特殊項目的存在可能導致我們衡量短期收益的估計錯誤。我們提供兩個測試調查結果。首先,我們復制表的結果在面板與小公司沒有特殊項目(特殊項目不到百分之五的收入和百分之一的資產)。第二,我們排除“其他資產和負債(307年數據)從我們的營運資本收益和現金流和復制所有的測試。再一次結果不變。總之我們可以總結良好的經驗作為所有系數的預測標志。 Presence of special items could lead to bias in our measure of short-term accrual estimation errors. We provide two tests to investigate the sensitivity of our results with respect to special items. First, we replicate the results in panel A of table 3 for firms with little of no special items (special items that are less than five percent of earnings and one percent of assets). The tenor of the results is unchanged. Second, we exclude "other assets and liabilities (data 307) from our measure of working capital accruals and cash flows and replicate all tests in the paper. Again, the tenor of the results is unchanged. Summarizing, our regression specifications have good empirical fit, and all coefficients have the predicted sign. In addition, we find that the tenor of our results remains the same across firms-specific, industry, and economy-wide specifications and for sensitivity analyses accounting for growth, cash flow volatility, and special items. http://www.nzbtu.tw/xxlzy/ 這個經驗衡量應計質量可以用于各種各樣的項目。例如,它可以用于基于市場的股票價格和收益之間的關系的測試。另一個應用程序是我們衡量應計質量適應設計替代收益管理的測試。 This empirical measure of accrual quality can be utilized for a variety of purposes. For example, it can be used in market-based tests of the relation between stock prices and earnings. Another application is to adapt our measure of accrual quality to devise alternative tests of earnings management. Such alternative tests can be potentially fruitful because existing tests of earnings management typically use empirical constructs like “discretionary。 we present the main results without a control for cash flow volatility. A similar argument applies for sales growth as well. 6 An alternative firm-year specific measure of accrual quality is absolute value of the residual for that year. As one would expect, these two measures are closely related, and the tenor of the results is similar for this alternative specification. However, we find that the standard deviation of the residuals has moderately stronger relations to economic fundamentals and earnings persistence. Since these tests are essentially part of a identification process, we proceed with the more powerful empirical specification. Accruals”, which can be problematic (e.g., Dechow, Sloan, and Sweeney 1995). Note that accruals manipulated by management in an opportunistic manner often behave empirically just like accruals that are the result of unavoidable errors in estimation. For example, from the point of view of the accounting system, booking a fictitious receivable and not collecting it looks similar to booking a regular receivable and not collecting it. 7 For the purposes of this paper, we concentrate on two empirical applications. As discussed earlier, first we explore the relation between our measure of accrual quality and economic fundamentals. This investigation expands our understanding of the economic underpinnings of accrual quality, and suggests possible approaches for an ex ante identification of accrual quality. Table 4 provides the results about the hypothesized relations between accrual quality and selected economic fundamentals. Panel A of table 4 provides descriptive statistics where the variables are first averaged on a firm basis. Note that the distribution of the variable Average operating cycle (mean of 141 days, standard deviation of 62 days) indicates that the vast majority of the firms in our sample have an operating cycle of less than one year. Thus, our assumption that most accruals reverse within one year seems reasonable for this sample. Panel B of table 4 presents the Pearson correlations between our measure of accrual quality (standard deviation of the residuals or sresid) and economic fundamentals. All of our predictions are supported by the results, with large coefficients and high statistical significance. For the purposes of this paper, the difference between unavoidable errors in estimation and opportunistic accruals is largely irrelevant – both translate into low quality accruals and earnings. However, if the purpose is to derive a powerful measure of opportunistic accruals, a possible approach is to start with our measure of low-quality accruals and adjust it for the effect of unavoidable errors of estimation. For example, one can control for the effect of unavoidable errors of estimation by controlling for their economic determinants (as discussed earlier in this study). Average level of working capital accruals (correlation of 0.69), length of the operating cycle (0.28), standard deviations of sales (0.34), cash flow from operations (0.60), and earnings (0.82), and is decreasing in firm size (-0.55). The magnitude of these correlations suggests that quality of accruals is strongly related to economic fundamentals. In particular, note that standard deviation of earnings alone explains about 67 percent of the variation in accrual quality (the simple correlation between these two variables, squared). As discussed earlier, standard deviation of earnings probably has the largest explanatory power because it captures both the volatility and the unpredictability of cash flows from operations. Panel C of table 4 presents the results from combining the explanatory power of these fundamentals in multiple regressions. We start with a regression of accrual quality on the four variables with relatively low individual correlations, average operating cycle, standard deviation of sales and cash flows, and size. The results show a good fit, where all variables have the predicted sign, highly significant t-statistics, and an adjusted R2 of 0.44. However, adding the two remaining variables, mean level of accruals and standard deviation of earnings, leads to dramatic changes. The adjusted R2 nearly doubles to 0.79, while all initial variables either lose significance (operating cycle and standard deviation of sales), switch signs (standard deviation of cash flows), or have a substantially lower coefficient and t-statistic (firm size). The overall impression from the second multiple regression is that mean level of accruals and standard deviation of earnings nearly subsume the explanatory power of the rest of the variables. The last multiple regression in Panel C confirms this impression. Explanatory power of the expanded regression, with adjusted R2 dropping only 5 percent, from 0.79 to 0.74. Considering more closely the two remaining variables, standard deviation of earnings is incrementally more important than level of accruals. Having in mind the simple correlations in Panel B, note that adding level of accruals to standard deviation of earnings adds an incremental R2 of only 7 percent, while adding standard deviation of earnings to level of accruals adds an incremental R2 of 26 percent. Summarizing, our measure of accrual quality has a strong relation to economic fundamentals, and even fairly simple specifications yield high explanatory power. These results suggest that one can use firm fundamentals to derive ex ante proxies for our measure of accrual quality. The relation of accrual quality to earnings persistence Our second area of empirical inquiry examines the relation between accrual quality and persistence of earnings. Intuitively, most practitioners and academics think of low-quality earnings as having low persistence. The theory in this paper provides a natural and specific link between our measure of accrual and earnings quality and earnings persistence. Firms with low quality accruals have a greater proportion of accruals that do not map into cash flow realizations. Thus, for such firms the accrual portion of current earnings is a poor predictor of future earnings, which implies lower persistence of earnings. The results on the empirical relation between accrual quality and earnings persistence are presented in Panel A of Table 5. We report portfolio results to maintain comparability with earlier studies (e.g., Sloan 1996 and Barth, Cram, and Nelson 2001). And to examine for potential non-linearities in the quality-persistence relation. In unreported tests, the results have the same tenor in a regression specification. In Panel A, firms are first sorted on the standard deviation of the firm-specific residuals (sresid) from Table 3 into quintile portfolios. Within each portfolio, we run a regression of future earnings on present earnings, and report the slope coefficient (called Persistence) and the adjusted R2. An examination of Panel A reveals a strong negative relation between standard deviation of the residuals and persistence. The slope coefficient Persistence declines from 0.94 to 0.55 between quintiles 1 and 5. The decline in Persistence is monotonic but the relation is fairly flat in the middle and steeper in the tails. Since these are univariate regressions, the decline in adjusted R2 from 0.83 to 0.28 closely follows that of the slope coefficient, displaying the same pattern of fairly flat middle and steeper tails. Thus, we find strong empirical evidence confirming the hypothesized positive relation between accrual quality and earnings persistence.8 Panel A also reports mean level of working capital accruals for each sresid portfolio. Similar to the results in Table 4, the portfolio results in Panel A reveal a strong positive relation between standard deviation of the residuals and level of accruals. This relation is important because Sloan (1996) shows that level of accruals is negatively related to earnings persistence. Thus, at this point it is difficult to distinguish empirically between the effect of accrual quality and level of accruals on earnings persistence. From the definition of our measure of accrual quality, it is clear that it has some “look-ahead” bias in explaining earnings persistence. The reason is that good accrual quality in our paper means good relation between current accruals and future cash flows. Since both accruals and cash flows are part of earnings, one would expect that high accrual quality would be correlated with high persistence by construction. This concern is certainly valid. However, it does not seem to be overly important empirically. An alternative specification, which uses residuals from regressing accruals on only past and present cash flows produces almost the same portfolio results. This robustness in results is probably explained by the relative stability of accrual quality over time. Since the results are similar, we present the original specification to preserve continuity with the rest of the paper. However, as mentioned earlier, there are a priori reasons to believe that our measure of accrual quality and level of accruals are really two sides of the same coin, while both proxy for the unobservable “true” accrual quality. Thus, regarding their relation to earnings persistence, the real question is only partly about which of these measures is “better” on some conceptual grounds. To a large extent, the real issue is which of these two measures performs better empirically, in this case in explaining earnings persistence. As an aside, the interrelations between accrual quality, level of accruals, and earnings persistence are also intriguing because they suggest a reconciliation of the findings of Dechow (1994) and Sloan (1996). Although a full and specific reconciliation is beyond the scope of this study, it seems valuable to lay out the basic idea because the findings of these two studies concern basic properties of the accrual process, while they seem somewhat contradictory on the surface. As mentioned before, Dechow (1994) finds that accruals make earnings a better measure of firm performance than cash flows. However, Sloan (1996) finds that the accrual portion is less persistent than the cash flow portion of earnings, which suggests that firms with high levels of accruals have low quality of earnings, and questions the beneficial role of accruals. Our reconciliation of these two studies is based on the observation that in a wellfunctioning system of accruals that involves estimation errors, a high level of accruals signifies both earnings which are a great improvement over underlying cash flows, and low-quality earnings. The reason is that a well-functioning system of accruals creates the most accruals when the underlying cash flows have the most timing and mismatching problems. Thus, a high level of accruals signifies a great improvement over the underlying cash flows. However, the first-order benefit of accruals comes at the cost of. Incurring estimation errors, and there will be a positive correlation between levels of accruals and magnitude of estimation errors. Thus, everything else equal, large accruals signify low quality of earnings, and less persistent earnings. In other words, the results in Dechow (1994) and Sloan (1996) are reconciled by the fact that level of accruals proxies for both the first-order benefit of accruals and for the correlated second-order cost of the accrual process.9 Future research could provide more specific evidence about the validity of this reconciliation. Returning to our main line of inquiry, we pursue two approaches to distinguish between the explanatory power of sresid and level of accruals with respect to earnings persistence. The first approach is provided in Panel B of table 5. We start with a simple portfolio ranking on the absolute magnitude of accruals.10 An examination of Panel B confirms the expected negative relation between the level of accruals and earnings persistence. The slope coefficient declines from 0.81 to 0.55, and the adjusted R2 from 0.57 to 0.33 between quintiles 1 and 5. However, the relative declines in slope coefficient and R2 across extreme quintiles are less pronounced in Panel B as compared to those in Panel A. The relative decline in Persistence is 0.39 (0.55 for adjusted R2) for the accrual quality quintiles vs. 0.26 (0.24 for adjusted R2) for the level of accrual quintiles. To explain the same point in a somewhat different way, consider an efficient accrual system, where accruals are recorded until the marginal benefit of accruals (resolving cash flow timing problems) equals the marginal cost of accruals (incurring errors of estimation). Optimizing over the level of accruals will result in the highest accruals for firms, which have the largest problems in their underlying cash flows. Thus, firms with the highest accruals will have the greatest benefit from accruals but also the greatest cost, and the lowest quality of earnings. 10 Note that our measure of accruals includes only changes in working capital, while the measure of accruals in Sloan (1996) includes both changes in working capital and depreciation. However, most of the variation in Sloan’s measure of accruals is due to variation in accounts receivable and inventory (Sloan 1996, page 297), which implies that for our purposes these two measures of level of accruals are reasonably similar. Non-monotonic, and it is essentially flat for low to medium-level accruals. The overall impression from Panel B is that level of accruals is a signal of low earnings persistence only for high accrual realizations. Summarizing, portfolio results reveals that the relation between accrual quality and earnings persistence is stronger than the relation between level of accruals and persistence, especially for firms with high accrual quality. Our second approach to distinguish between the explanatory power of sresid and level of accruals with respect to earnings persistence is provided in Table 6. In Table 6 we offer portfolio results of the relation between sresid or the level of accruals to earnings persistence, holding the other variable constant. To illustrate our procedure, consider the portfolio results for sresid in Panel A of Table 6. To hold the level of accruals constant, first we sort the sample into decile portfolios based on level of accruals. Within each level of accruals decile, we further sort the observations into five portfolios based on standard deviation of the residuals (subportfolios 1 to 5). Then, we pool all ten subportfolios 1 together into one portfolio, all subportfolios 2 together into another portfolio, and so on. At the end, we have five portfolios, which have substantial variation of sresid but nearly the same level of accruals. An inspection of Panel A in Table 6 reveals that this procedure performs empirically quite well. The two-pass portfolio construction controls for the level of accruals nearly perfectly, with mean ?WC ranging only from 0.047 to 0.051 across portfolios 1 to 5. Note also that, despite the strong positive correlation between sresid and level of accruals at the firm level, the portfolios in Panel A exhibit a large variation in sresid. In fact, the conditional range between mean sresid of portfolios 1 and 5 in Table 6 of 0.55 is only marginally lower than the corresponding unconditional range in Table 5. Of 0.63. This result indicates that the variation in sresid independent of level of accruals is substantial and empirically important. Further, a consideration of the regression results reveals that the conditional variation in sresid is strongly related to earnings persistence. The slope coefficient Persistence, varies from 0.91 in portfolio 1 to 0.60 in portfolio 5, and adjusted R2 varies from 0.78 in portfolio 1 to 0.33 in portfolio 5. Remarkably, the variation of coefficients and R2s across portfolios in the conditional specification of Table 6 is only marginally lower than that of the unconditional specification in Table 5 (differences in Persistence of 0.31 vs. 0.39, and differences in R2s of 0.45 vs. 0.54). In other words, the main finding in Panel A of Table 6 is that most of the strength of the relation between accrual quality (sresid) and earnings persistence is preserved after controlling for level of accruals. Panel B in Table 6 contains the results for the relation between level of accruals and earnings persistence after controlling for accrual quality. Portfolio derivation method in Panel B is similar to that of Panel A. First, we rank observations into 10 portfolios based on sresid, then within each decile portfolio we rank observations into 5 subportfolios on level of accruals, and finally we pool all subportfolios with the same rank together. At the end, we have five portfolios which preserve most of the variation in level of accruals, while holding sresid nearly constant across portfolios. An examination of the portfolio results reveals a slight decline in the variation of the slope coefficient Persistence across portfolios, with a difference between portfolios 1 and 5 of 0.22 in Table 6 vs. 0.26 in Table 5. However, there is a substantial erosion in the range of R2, difference of 0.09 in Table 6 vs. 0.24 in Table 5. In addition, the differences in slope. Coefficients and R2 for Panel B in Table 6 are substantially smaller than their counterparts for sresid in Panel A. Summarizing, the combined evidence of Tables 5 and 6 reveals that our measure of quality of accruals captures variation in earnings persistence distinct from that due to the level of accruals. If anything, both the unconditional and the conditional specifications suggest that the relation between accrual quality and earnings persistence is stronger than that between level of accruals and persistence. We offer this evidence with two caveats. First, level of accruals is a measure that is simple, easy to use, and requires no extensive identification and prediction process. Thus, the comparison presented here is somewhat biased against the performance of level of accruals, especially for settings that require ex ante identification and prediction of quality of earnings measures (e.g., real-time trading strategies). Second, future research along the lines of this study could identify other empirical measures of accrual quality, focusing on those that capture earnings persistence better than the proxy used here. Conclusion This study suggests a new approach to assessing accrual and earnings quality. The key insight behind this approach is that accruals are temporary adjustments that resolve timing and mismatching problems in the underlying cash flows at the cost of making assumptions and estimates. High precision of estimation in the accrual process implies a good match between current accruals and past, present, or future cash flow realizations. However, imprecise or erroneous estimates are essentially noise in accruals. Reducing the beneficial role of accruals. Following this insight, we define accrual quality as the extent to which accruals map into cash flow realizations. We also investigate the implications of this theory in the empirical domain. Our empirical measure of accrual quality is equal to the standard deviation of the residuals from firm-specific regressions of working capital accruals on last-year, current, and oneyear-ahead cash flow from operations. We explore the characteristics and the role of this measure in two specific contexts. First, we document that accrual quality is strongly related to economic fundamentals. This analysis expands our understanding of the economic underpinnings of accrual quality, and suggests that fundamentals provide an alternative way to derive measures of accrual quality. Second, we find a strong negative relation between accrual quality and earnings persistence. This relation is robust to controlling for the level of accruals effect of Sloan (1996), and suggests that there are important practical benefits from identifying and measuring accrual quality. The evidence in this paper also opens new possibilities for research. For example, it would be interesting to investigate whether the stock market understands and fully impounds the implications of accrual quality for earnings persistence. Another possibility is to examine whether the approach of this paper can be adapted to provide alternative measures of earnings management. A still different and much less structured possibility is to think more carefully about how to use this approach to accrual quality to enhance the rules for financial reporting. For example, one could argue that investors interested in earnings quality would welcome disclosure about what part of accruals is correcting timing discrepancies and what part is correcting estimation errors or other noise. Reduce the likelihood of earnings management. In this respect, our ideas about improving financial reporting are in the same spirit as Lundholm (1999), which argues that restating of old statements after observing ex post realizations creates better ex ante incentives for recording proper accruals. References: Barth, Mary E., Donald P. Cram, and Karen K. Nelson, 2001, Accruals and the prediction of future cash flows, The Accounting Review 76 No.1 (January), 27-58. Collins, Daniel W., and Paul Hribar, 2000, Errors in estimating accruals: Implications for empirical research, Working paper, University of Iowa. Dechow, Patricia M., 1994, Accounting earnings and cash flows as measures of firm performance: The role of accounting accruals, Journal of Accounting and Economics, Volume 18, No. 1 (July), 3-42. Dechow, Patricia M., Richard G. Sloan, and Amy P. Sweeney, 1995, Detecting earnings management; The Accounting Review 70 (2), 193-225. Dechow, Patricia M., S.P. Kothari, and Ross L. Watts, 1998, The relation between earnings and cash flows, Journal of Accounting and Economics 25, 133-168. Financial Accounting Standards Board (FASB), Statement of Accounting Concepts Number 1, Objectives of Financial Reporting by Business Enterprises, November 1978. Finger, Catherine A., 1994, The ability of earnings to predict future earnings and cash flow, Journal of Accounting Research, Vol. 32, 210-223. Lundholm, Russel J., 1999, Reporting on the past: A new approach to improving accounting today, Accounting Horizons 13 (4), 315-322. Palepu, Krishna G., Paul M. Healy, and Victor L. Bernard, 2000, Business Analysis and Valuation, South-Western College Publishing, Cincinnati, Ohio. Sloan, Richard G., 1996, Do stock prices fully reflect information in accruals and cash flows about future earnings?, The Accounting Review 71 (July): 289-315. The following table outlines the recording conventions when cash is received or paid before or after it is recognized in earnings: Cash flow occurs at t-1 (before it is recognized in earnings) Case (1) Recognized in earnings at t as revenue Cash Inflow: Advance Accrual created and reversed: Liability e.g., Deferred Revenue Case (2) Recognized in earnings at t as an expense Cash Outflow: Prepayment Accrual created and reversed: Asset e.g., Inventory, Prepaid Rent Cash flow occurs at t+1 (after it is recognized in earnings) Case (3) Cash Inflow: Collection Accrual created and reversed: Asset e.g., Accounts Receivable Case (4) Cash Outflow: Payment Accrual created and reversed: Liability e.g., Warranty Liability. Below we provide an example of each case, show the link between the accrual and the cash flow, and how an estimation error is corrected. We also include the notation introduced in section 2 to link the appendix with the paper. |