Detecting earnings management using cross-sectional abnormal accruals models

This paper examines specification and power issues in relation to three models used to estimate abnormal accruals. In contrast to the majority of prior work evaluating models estimated in time-series, we examine the performance of cross-sectionally estimated models. In addition to testing the standard-Jones (Jones, 1991) and modified-Jones (Dechow et al., 1995) models, we also develop and test a new specification, labelled the ‘margin model’. Consistent with prior US research employing time-series specifications of the two Jones models, our findings suggest that each of the three cross-sectional models are well specified when applied to a random sample of firm-years. However, the margin model appears to generate relatively better specified estimates of abnormal accruals when cash flow performance is extreme. Analysis of the models' ability to detect artificially induced earnings management indicates that all three procedures are capable of generating relatively powerful tests for economically plausible levels of accruals management (e.g., less than 10% of lagged total assets). Regarding their relative performance, the standard-Jones and modified-Jones models are found to be more powerful for revenue and bad debt manipulations. In contrast, the margin appears to be more powerful at detecting non-bad debt expense manipulations.