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A Bayesian approach for predicting material accounting misstatements

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  • Feng Xu
  • Zinan Zhu

Abstract

In this paper, we develop prediction models of material accounting misstatements in a Bayesian framework. Outputs of the Bayesian approach are probabilistic descriptions for the propensity of conducting an accounting misstatement and for a riskiness comparison across different companies. The models are applied to a comprehensive sample of firms that have been subject to enforcement actions by the Securities and Exchange Commission for allegedly misstating their financial statements between 1982 and 2005. The results suggest that while maintaining a comparable Type-I error, out-of-sample predictions of the Bayesian models improve in terms of sensitivity and the Type-II error. This study provides a useful tool to assess material accounting misstatement risks.

Suggested Citation

  • Feng Xu & Zinan Zhu, 2014. "A Bayesian approach for predicting material accounting misstatements," Asia-Pacific Journal of Accounting & Economics, Taylor & Francis Journals, vol. 21(4), pages 349-367, December.
    • Handle: RePEc:taf:raaexx:v:21:y:2014:i:4:p:349-367
    DOI: 10.1080/16081625.2014.946063
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    References listed on IDEAS

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    1. Patricia M. Dechow & Richard G. Sloan & Amy P. Sweeney, 1996. "Causes and Consequences of Earnings Manipulation: An Analysis of Firms Subject to Enforcement Actions by the SEC," Contemporary Accounting Research, John Wiley & Sons, vol. 13(1), pages 1-36, March.
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    5. Stephen K. Asare & Arnold M. Wright, 2004. "The Effectiveness of Alternative Risk Assessment and Program Planning Tools in a Fraud Setting," Contemporary Accounting Research, John Wiley & Sons, vol. 21(2), pages 325-352, June.
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    Cited by:

    1. Slavko ?odan, 0000. "Can Accrual-based Metrics Indicate Material Accounting Misstatements? Evidence on Audit Adjustments," Proceedings of Economics and Finance Conferences 14416287, International Institute of Social and Economic Sciences.
    2. Papík, Mário & Papíková, Lenka, 2022. "Detecting accounting fraud in companies reporting under US GAAP through data mining," International Journal of Accounting Information Systems, Elsevier, vol. 45(C).

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