CROSS-SECTIONAL ESTIMATION OF ABNORMAL ACCRUALS
USING QUARTERLY AND ANNUAL DATA:
Effectiveness in Detecting Earnings Management
Abstract
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)) which 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. 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. We present a variation of the Jones model which is shown to be well specified for all cash flow levels. Further, using mean squared prediction errors as well as simulation analysis, we show that this 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.
Keywords: Earnings, Management, Abnormal, Discretionary, Accruals, Estimation
1. Introduction
Recently, earnings management around firm-specific events has received considerable attention from researchers. Much of the research in this literature uses discretionary accruals (or, more accura英语论文网 【http://www.51lunwen.org】tely, abnormal accruals) to examine earnings management, where abnormal accruals are defined as actual accruals minus expected accruals. Given the importance of the expectations model in estimating abnormal accruals, it is essential to use the most precise models of expected accruals in tests of earnings management. Several studies have addressed the adequacies and inadequacies of the extant models for estimating discretionary accruals, focusing typically on time-series estimation. For example, Dechow, Sloan and Sweeney (1995) examine a few of the currently available accrual models for misspecification and statistical power and argue that while the models examined are well specified for randomly chosen firms, they are misspecified for firms with extreme cash flows. They also find these models to have low power in detecting earnings management. Guay, Kothari, and Watts (1996) examine the same models as Dechow, et al. (1995), also in a time-series context, and present evidence consistent with their argument that all the models estimate discretionary accruals with considerable imprecision.
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