Detecting Earnings Management: A New Approach

  1. PATRICIA M. DECHOW1,
  2. AMY P. HUTTON2,
  3. JUNG HOON KIM3,
  4. RICHARD G. SLOAN1

Article first published online: 17 APR 2012

DOI: 10.1111/j.1475-679X.2012.00449.x

Issue

Journal of Accounting Research

Journal of Accounting Research

Volume 50, Issue 2, pages 275–334, May 2012

Additional Information

How to Cite

DECHOW, P. M., HUTTON, A. P., KIM, J. H. and SLOAN, R. G. (2012), Detecting Earnings Management: A New Approach. Journal of Accounting Research, 50: 275–334. doi: 10.1111/j.1475-679X.2012.00449.x

Author Information

  1. 1

    The Haas School of Business, University of California, Berkeley

  2. 2

    Carroll School of Management, Boston College

  3. 3

    School of Accounting, Florida International University. We are grateful for the comments of the referee, Phil Berger (the Editor), Frank Ecker, Jennifer Francis, Joseph Gerakos (the discussant), Maureen McNichols, Per Olsson, Katherine Schipper and workshop participants at the 2011 Journal of Accounting Research Conference, the 2011 American Accounting Association Annual Meetings, the University of Arizona, the University of California Los Angeles, Brigham Young University, the University of Houston, the University of Texas at Austin, and the University of Washington.

Publication History

  1. Issue published online: 17 APR 2012
  2. Article first published online: 17 APR 2012
  3. Accepted manuscript online: 6 FEB 2012 04:10PM EST
  4. Received 22 December 2010; accepted 25 December 2011

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ABSTRACT 

This paper provides a new approach to test for accrual-based earnings management. Our approach exploits the inherent property of accrual accounting that any accrual-based earnings management in one period must reverse in another period. If the researcher has priors concerning the timing of the reversal, incorporating these priors can significantly improve the power and specification of tests for earnings management. Our results indicate that tests incorporating reversals increase test power by around 40% and provide a robust solution for mitigating model misspecification arising from correlated omitted variables.

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