To Catch a Thief: Can Forensic Accounting Help Predict Stock Returns?

36 Pages Posted: 2 Aug 2011 Last revised: 12 May 2014

See all articles by Messod Daniel Beneish

Messod Daniel Beneish

Indiana University - Kelley School of Business - Department of Accounting

Craig Nichols

Syracuse University

Charles M.C. Lee

Stanford University - Graduate School of Business

Date Written: July 27, 2011

Abstract

An earnings manipulation detection model based on forensic accounting principles (Beneish 1999) has substantial out-of-sample ability to predict cross-sectional returns. We show that the model correctly identified, ahead of time, 12 of the 17 highest profile fraud cases in the period 1998-2002. Moreover, the probability of manipulation estimated from this model (PROBM) consistently predicts returns over 1993-2007, even after controlling for size, book-to-market, momentum, accruals and the level of open short interest. Separating high PROBM from low PROBM firms within each of these characteristic deciles greatly improves long/short hedge returns. Further analyses show that PROBM also helps predict future earnings because of its ability to anticipate the persistence of current years’ reported accruals. Overall, our findings offer significant empirical support for the investment approach advocated by forensic accountants.

Keywords: forensic accounting, stock return prediction, earnings manipulation

JEL Classification: M41, G11, G14

Suggested Citation

Beneish, Messod Daniel and Nichols, Craig and Lee, Charles M.C., To Catch a Thief: Can Forensic Accounting Help Predict Stock Returns? (July 27, 2011). Available at SSRN: https://ssrn.com/abstract=1903593 or http://dx.doi.org/10.2139/ssrn.1903593

Messod Daniel Beneish (Contact Author)

Indiana University - Kelley School of Business - Department of Accounting ( email )

1309 E. 10th Street
Bloomington, IN 47405
United States
812-855-2628 (Phone)
812-855-4985 (Fax)

Craig Nichols

Syracuse University ( email )

900 S. Crouse Avenue
Syracuse, NY 13244-2130
United States

Charles M.C. Lee

Stanford University - Graduate School of Business ( email )

Stanford Graduate School of Business
655 Knight Way
Stanford, CA 94305-5015
United States
650-721-1295 (Phone)

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