Cross-sectional estimation of abnormal accruals using quarterly and annual data: effectiveness in detecting event-specific earnings management

This paper addresses certain methodological issues that arise in estimating abnormal (or discretionary) accruals for detection of event-specific earnings management. Unlike prior studies (e.g., Dechow, Sloan, and Sweeney, 1995; Guay, Kothari, and Watts, 1996) that rely primarily on time-series models, we focus on the specification of cross-sectional models of expected accruals using quarterly as well as annual data. Perhaps more importantly, we present a variation of the Jones model that is shown to be well specified for all cash flow levels. We show that the cross-sectional Jones model yields systematically positive (negative) estimates of abnormal accruals for firms whose cash flows are below (above) their industry median. Using mean squared prediction errors as well as simulation analysis, we show that our model is more powerful than the cross-sectional Jones model in detecting earnings management. In addition, we examine differences in the power of current accrual models in detecting earnings management across audited and unaudited quarters.