Affiliation:
1. The University of North Carolina at Chapel Hill.
Abstract
ABSTRACT: This study examines the ability of revenue and accrual models to detect simulated and actual earnings management. The results indicate that revenue models are less biased, better specified, and more powerful than commonly used accrual models. Using a simulation procedure, I find that revenue models are more likely than accrual models to detect a combination of revenue and expense manipulation. Using a sample of firms subject to SEC enforcement actions for a mix of revenue- and expense-related misstatements, I find that, although revenue models detect manipulation, accrual models do not. These findings provide support for using measures of discretionary revenues to study earnings management.
Publisher
American Accounting Association
Subject
Economics and Econometrics,Finance,Accounting
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