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Abstract
Presented are a profile of a sample of earnings manipulators, their distinguishing characteristics, and a suggested model for detecting manipulation. The model's variables are designed to capture either the financial statement distortions that can result from manipulation or preconditions that might prompt companies to engage in such activity. The results suggest a systematic relationship between the probability of manipulation and some financial statement variables. This evidence is consistent with the usefulness of accounting data in detecting manipulation and assessing the reliability of reported earnings. The model identifies approximately half of the companies involved in earnings manipulation prior to public discovery. Because companies that are discovered manipulating earnings see their stocks plummet in value, the model can be a useful screening device for investment professionals. The screening results, however, require determination of whether the distortions in the financial statement numbers result from earnings manipulation or have another structural root.
I benefited from the comments of Vic Bernard, Jack Ciesielski, Linda DeAngelo, Martin Fridson, Cam Harvey, David Hsieh, Charles Lee, Eric Press, Bob Whaley, Mark Zmijewski, and workshop participants at Duke, the University of Maryland, the University of Michigan, and the University of Quebec at Montreal. I am indebted to David Hsieh for his generous econometric advice and the use of his estimation subroutines. I thank Julie Azoulay, Pablo Cisilino, and Melissa McFadden for expert assistance.