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这里所指的是指预付款被记录的时间,详细指往返现金流的类型以及是否应用权责发生制。权责发生制在时间t反映了实际的现金流的这将发生在时间t + 1预期。因为一个错误的估计,当前收益等于实际的现金流实现在t + 1加上一个误差项,反映了收益预期和实现之间的区别。最后收益现金集合和支付在时间t抵消了权责发生制之前的时期,和实际现金流等于相应的收集或支付t加上一个误差项等于最后一期的期望之间的差异和这一时期的现金流，因此也可以说明每个时期有两个独立的错误条件。 Here the subscript refers to the period the accrual is recorded and the superscript refers to the type of the corresponding cash flow and whether it is an opening or a closing accrual. Here the opening accrual at time t reflects the expectation of the actual cash flow that will occur at time t+1. Since there is an error of estimation, the current accrual is equal to the actual cash flow realized at t+1 plus an error term that reflects the difference between the accrual expectation and realization. The closing accrual for cash collections and payments at time t offsets the opening accrual from the previous period, and is equal to the corresponding actual cash flow collected or paid in t plus an error term equal to the difference between last period’s expectation and this period’s cash flow realization. Thus, each period there are two independent error terms. There is an estimation error for accruals made this period whose actual value will be determined next period (εt+1POST), and there is the realized error that is determined from knowing actual cash flow realization today (εtPOST). Note that the model and the discussions so far present the notion of estimation error as only referring to situations where the cash flows occur after the opening accrual. However, the notion of estimation error is more general in nature, and applies with similar logic for many cases where the relevant cash flows occur at the same time as the opening accrual. For example, consider a different inventory example where inventory is bought for $1,000, written down to $700 in the same period, and sold next period. The net result is a cash outflow of $1,000 and a net positive accrual of $700 in the first period, and a negative accrual of $700 in the second period. The difference of $300 is very much like an estimation error, only here it is the cash outflow that is the poor predictor of the accrual benefit of inventory. For the sake of parsimony, we do not explicitly include in. http://www.ukassignment.org/jndzydx/ 我们的模型的估计错误情况下,现金流发生的同时可以有权责发生制。然而我们感兴趣的读者参考附录1,它提供了一个更全面的讨论和可能的收益估计错误的例子。对于我们的目的,底线是估计错误存在,即使相关的现金流发生的同时打开权责发生制,这些估计错误输入模型一样应收及应付款项的错误,并导致相同的结论。这里所指的继续模型我们可以得到一个表达式为收益等于现金流和收益。 Our model the estimation errors for cases where the cash flows occur at the same time as the opening accrual. However, we refer the interested reader to Appendix 1, which provides a more comprehensive discussion and examples of possible accrual estimation errors. For our purposes, the bottom line is that estimation errors exist even when the relevant cash flows occur at the same time as the opening accrual, these estimation errors would enter the model the same way as the errors for receivables and payables, and lead to the same conclusions. Continuing with the model, we derive an expression for earnings as equal to cash flows plus accruals. Cash flows for period t is equal to expression (1), and total accruals is equal to the sum of opening and closing accruals for both cash advances and cash collections. This allows us to express earnings as: Et = CFt + Accrualst Et = (CFtPRE + CFtCUR + CFtPOST) + (AtPRE-O + AtPRE-C +AtPOST-O + AtPOST-C) Et = (CFtPRE + CFtCUR + CFtPOST) + (- CFtPRE + CFt-1PRE + CFt+1POST + εt+1POST - CFtPOST - εtPOST) Canceling out terms yields: Et = CFt-1PRE + CFtCUR + CFt+1POST + εt+1POST - εtPOST (3) (2). determination of accrual quality. In other words, the regression approach to determining accrual quality is based on ex post identification. However, many applications require ex ante identification of accrual quality (e.g., abnormal return strategies based on quality of earnings), and economic fundamentals are the natural bridge between ex post and ex ante specifications. Based on existing theory, results, and economic intuition, we expect that: • Accrual quality is decreasing in the magnitude of total accruals. Everything else equal, more accruals indicate more estimation and errors of estimation, and therefore lower quality of accruals. Note that this conjecture could potentially provide an explanation for why Sloan (1996) finds that high level of accruals signals low persistence of earnings. • Accrual quality is decreasing in the length of the operating cycle. Longer operating cycles indicate more uncertainty, more estimation and errors of estimation, and therefore lower quality of accruals. • Accrual quality is decreasing in the standard deviation of sales and the standard deviation of cash flow from operations. High standard deviation in sales indicates high uncertainty in the operating environment, and therefore large use of approximations and estimation, with corresponding large errors of estimation and low accrual quality. The standard deviation of cash flow from operations captures a similar idea, only this is a more specific measure of pre-accrual volatility. For example, a firm can have low volatility of sales but large volatility of cash flows from operations because of high fixed costs or because of poor cash management practices.#p#分页标题#e# There are special cases like the insurance industry, where firms provide specific and comprehensive information about their accrual estimates, the revisions in accruals, and the subsequent cash flow。 Accrual quality is decreasing in the standard deviation of earnings. Volatility of sales and cash flows captures volatility in the underlying operations. However, volatility in operations can be highly predictable, and lead to few estimation errors (e.g., seasonality in sales). We posit that accruals smooth the predictable volatility of cash flows, so we expect that more volatile earnings are indicative of unpredictable cash flows and large estimation errors in accruals. Since volatility of earnings captures information about both the volatility and unpredictability of cash flows, we expect a strong negative relation between accrual quality and the standard deviation of earnings. • Accrual quality is increasing in firm size. We expect that large firms。 have more stable and predictable operations, and therefore less estimation errors. In other words, firm size likely proxies for several of the fundamentals discussed above. In addition, large firms are likely to be more diversified and various portfolio effects across divisions and business activities reduce the relative effect of estimation errors. Sample selection, descriptive statistics, and calibration tests Table 1 summarizes our sample selection. Our sample is obtained from the Compustat annual industrial and research files over the years 1987 to 1999. We limit our attention to this period because we want precise measures of operating cash flows and related accruals. Collins and Hribar (2000) document that the popular balance-sheet approach to deriving cash flow from operations leads to noisy and biased estimates. Therefore, we use cash flow from operations using data from the Statement of Cash Flows reported under the Statement of Financial Accounting Standards No. 95 (SFAS realizations. We do not pursue this venue because our interest is in more generalizable results. Further, we restrict ourselves to firms that have non-missing data for assets, earnings, cash flow from operations, changes in accounts receivable and changes in inventory. We require firms to have accounts receivable and inventory since we want to include firms where working capital accruals are important. Truncating the most extreme one percent of cash from operations, earnings, and changes in working capital and a requirement to have at least one year of past and future cash flows and earnings yields a sample of 30,317 firm-years. In addition, we require firms to have at least eight years of data because some of our main regressions are run on a firm-specific basis. This restriction further reduces our sample to 15,234 firm-year observations for 1,725 firms. For some of our industry-level analyses, the data restrictions are less stringent, yielding a sample of 27,204 firm-year observations over 136 three-digit SIC code industries. We obtain cash flow from operations (CFO) (Compustat item 308) directly from the statement of cash flows. The change in working capital (∆WC) is computed as: ∆WC = ∆AR + ∆Inventory - ∆AP - ∆TP + ∆Other Assets (net) Where AR is accounts receivable, AP is accounts payable, and TP is taxes payable. Specifically, ∆WC is calculated from Compustat Statement of cash flows items as: ∆WC = - (item 302 + item 303 + item 304 + item 305 + item 307). From the preceding discussion it is clear that we are interested in earnings after short-term accruals but before long-term accruals (Earn). Using the statement of cash flows items allows us to compute a precise measure of this variable as:4。 This Standard required the presentation of the statement of cash flows for fiscal years ending after July 15, 1988. However, some firms adopted this standard early in 1987. 4 An alternative and equivalent way to define and derive Earn is: Earn = Income before extraordinary items + Depreciation + Extraordinary items + Deferred taxes + Equity in net loss + Gain from sale of property, plant and equipment + Funds from Operations (other). The specific Compustat items for this alternative derivation of Earn are: Earn = CFO + ∆WC Following Sloan (1996), all variables are scaled by average total assets. We also calculate the length of the operating cycle as: OC = (Sales/365)/(Average AR) + (Cost of goods sold/365)/(Average Inventory) Where Sales is Compustat item 12, cost of goods sold is item 41, AR is item 2, and Inventory is item 3. Descriptive statistics and correlations between the variables of our sample are provided in Table 2. In addition to the other variables described above, we include Earnings before extraordinary items (Prof) to provide comparability with other studies that use this definition of income. An examination of the descriptive statistics in Panel A of Table 2 reveals that they are in line with those of other studies using similar variables and time period (e.g., Barth, Cram, and Nelson 2001). Our sample consists of sizable firms with average total assets of $2,436 million and a median of $240 million. The fact that Earn is greater than CFO implies that short-term accruals are mostly positive. This is not surprising, given that most firms are growing, and therefore continuously increasing their stocks of net working capital. In contrast, long-term accruals are negative (Prof is lower than both Earn and CFO), which is expected because the main component of longterm accruals is depreciation, always a negative accrual. The correlations in Panels B and C illustrate the relations between the variables of our sample, and provide comparability with existing research. These simple relations also provide a check for how well our model captures important properties of our sample. An examination of the results reveals that the empirical correlations are in general.#p#分页标题#e# Agreement with existing findings and the predictions of the model. Table 2 presents both Pearson and Spearman correlations but since the results are similar, our discussion focuses on Pearson correlations only. Following the order specified earlier, first we document the expected strong positive contemporaneous correlation between Earn and CFO (0.73), a lower positive correlation between contemporaneous Earn and ∆WC (0.33), and a negative contemporaneous correlation between CFO and ∆WC (-0.41). Second, we find that present earnings and changes in working capital anticipate future cash flows from operations, consistent with Barth, Cram, and Nelson (2001). Note that the predicted positive relation between present accruals and future cash flows is not present in the simple correlation between ∆WCt and CFOt+1 (-0.01 and statistically insignificant). The reason is that ∆WCt is strongly negatively correlated to CFOt, and CFOt is positively correlated to CFOt+1 (0.56), which counteracts the expected positive relation between ∆WCt and CFOt+1. In Panel C, we report the partial correlation between ∆WCt and CFOt+1, controlling for CFOt. As expected, the partial correlation is large, positive, and highly significant (0.29). Third, as predicted by the model, we find that present changes in working capital are positively related to past cash flows, which implies that accruals defer to the present the recognition of some past cash flows. Note that in this case the simple correlation between ∆WCt and CFOt-1 in Panel B is again close to zero but the partial correlation controlling for CFOt in Panel C is large, positive, and significant (0.31). Summarizing, our descriptive statistics and correlation results are in line with predictions and existing results, indicating that our simple model captures reasonably well some of the key features of accrual accounting. Results 4.1 An empirical measure of accrual quality Table 3 presents regressions of current working capital accruals on past, present and future cash flows from operations. Our main specification is firm-level regressions because our theory is defined and most naturally applied on a firm-level basis. However, we also present industry-specific and pooled results because our firm-specific time series is short, and we are concerned about noisy estimation at the firm level. An examination of the firm-specific specification in Panel A of Table 3 reveals that the results are consistent with the theory and the univariate results in Table 2. As predicted, current changes in working capital are negatively related to current cash flow from operations, and positively related to past and future cash flow from operations. The mean coefficient of current cash flows is -0.62, while the mean coefficient on past cash flows is 0.17, and the mean coefficient on future cash flows is 0.09. Based on the cross-sectional distribution of the firm-specific coefficients, all of these means are highly statistically significant, with t-statistics ranging in absolute value from 10 to 57. The results for medians and lower and upper quartiles also suggest that coefficient means are fair summaries of the cross-sectional distributions of the coefficients, and are not overly affected by extreme outcomes. Adjusted R2s are on the magnitude of 50 percent, which indicates that this specification provides reasonable explanatory power for current changes in working capital. Results for industry-specific and pooled regressions in Panels B and C are consistent with the firm-specific results. The coefficients on current CFO are negative and large, with a mean of –0.51 for industry-specific regressions, and a coefficient of。 For the pooled regression. The coefficients on past and future cash flows are also comparable in magnitude with the firm-specific results, on the magnitude of 0.15 to 0.19, and are reliably positive. However, the adjusted R2s are somewhat lower, with an average of 0.34 for the industry specification and 0.29 for the pooled regression, most likely because the implications of our firm-specific model are less descriptive in crosssectional specifications. Summarizing, a comparison of the firm-specific, industry, and pooled results reveals consistency across these specifications. Based on better theoretical grounding and better empirical fit, we proceed further with the firm-level specification. In addition to the three specifications in Table 3, we perform a variety of sensitivity tests to assess the robustness of our results. Recall that perhaps the major concern about the regression specification in Table 3 is that the total cash flow variables we use are noisy estimates of the theoretically prescribed cash flow variables (theoretically, the cash flow variables should include only cash flows related to accruals). The noise in the independent variables potentially leads to biased estimates of the coefficients of the cash flow variables, and more importantly for the task at hand, to biased estimates of the residuals. Our sensitivity checks rely on the observation that total cash flow are likely to be a good proxy for the theoretical cash flow variable when the firm is in a steady state. The reason is that when the firm is in a steady state, the cash flows related to accruals are likely to be a fairly constant proportion of total cash flows. We implement this observation on two dimensions. First, we rerun the regressions in Table 3 controlling for the effect of sales growth. We control for sales growth by either including a growth term in the regression or by running the regression only on low-growth firms, where low-growth is defined as percentage sales growth。 Between –5 and 5 percent. The tenor of the results in Table 3 remains the same for both of these specifications (actual results not included). In addition, controlling for growth does not appreciably affect the relations between accrual quality, economic fundamentals, and persistence of earnings (discussed in more detail later). Second, we investigate for the effect of cash flow volatility on accrual quality. If cash flow volatility is low, we expect a relatively stable relation between total cash flows and cash flows related to accruals. Similar to sales growth, we control for cash flow volatility by either including a cash flow volatility term in the regressions or by running the regression on low-volatility firms only. After running both of these specifications, we find that the results remain qualitatively unchanged.5 As a final robustness check, we investigate the effect of special items on the results. The potential problem with special items is that they often contain long-term accruals, which can possibly affect both our measure of short-term accruals and our measure of cash flow from operations (recall that we assume that cash flow from operations is only related to short-term accruals). For example, sometimes Compustat includes restructuring charges in "other assets and liabilities" (Data item 307), contaminating our measure of short-term accruals. In addition, a portion of the Compustat cash flows from operations can be related to restructuring charges, contaminating our measure of cash flows related to short-term accruals. #p#分页标题#e# Note that an explicit control for cash flow volatility introduces a “throwing the baby with the bathwater” effect because, as discussed earlier, we have reasons to believe that accrual quality is theoretically negatively related to cash flow volatility. In other words, the results after controlling for cash flow volatility are probably more correctly interpreted to indicate that the quality of accruals concept has construct validity beyond that captured by cash flow volatility. However, that does not imply that one should control for cash flow volatility in deriving accrual quality. As discussed earlier, theoretically, high cash flow volatility causes low accrual quality because of large forecast errors in volatile environments, and one does not want to exclude the effect of this causal variable from the empirical construct. For this reason. |
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