NALYSIS of earnings management often focuses on management' s use of discretionary accruals.1 Such research requires a model that estimates the discretionary component(s) of reported income. Existing models range from simple models in which discretionary accruals are measured as total accruals, to more sophisticated models that attempt to separate total accruals into discretionary and nondiscretionary components. There is, however, no systematic evidence bearing on the relative performance of these alternative models at detecting earnings management.
We evaluate the relative performance of the competing models by comparing the specifica-tion and power of commonly used test statistics. The specification of the test statistics is evaluated by examining the frequency with which they generate type I errors. Type I errors arise when the null hypothesis that earnings are not systematically managed in response to the stimulus identified by the researcher is rejected when the null is true. We generate type I errors for both a random sample of firm-years and for samples of firm-years with extreme financial performance. We focus on samples with extreme financial performance because the stimuli investigated in previous research are frequently correlated with financial performance Thus, our findings shed light on the specification of test statistics in cases where the stimulus identified by the researcher does not cause earnings to be managed, but is correlated with firm performance.
The power of the test statistics is evaluated by examining the frequency with which they generate type II errors. Type II errors arise when the null hypothesis that earnings are not systematically managed in response to the stimulus identified by the researcher is not rejected when it is false. We generate type II errors in two ways. First, we measure rejection frequencies for samples of firm-years in which we have artificially added a fixed and known amount of accruals to each firm-year. These simulations are similar to those performed by Brown and Warner (1980, 1985) in evaluating alternative models for detecting abnormal stock price performance. However, our simulations differ in several respects. In particular, we must make explicit assumptions concerning the components of accruals that are managed and the timing of the accrual reversals. To the extent that our assumptions are not representative of the circum¬stances of actual earnings management, our results lack external validity. To circumvent this problem, we generate type II errors for a second set of firms, for which we have strong priors that earnings have been managed.2 This sample consists of firms that have been targeted by the Securities and Exchange Commission (SEC) for allegedly overstating annual earnings. The external validity of these results rests on the assumption that the SEC has correctly identified firm-years in which earnings have been managed. This assumption seems reasonable, since the SEC (1992) indicates that out of the large number of cases that are brought to its attention, it only pursues cases involving the most significant and blatant incidences of earnings manipulation.
The empirical analysis generates the following major insights. First, all of the models appear well specified when applied to a random sample of firm-years. Second, the models all generate tests of low power for earnings management of economically plausible magnitudes (e.g., one to five percent of total assets). Third, all models reject the null hypothesis of no earnings