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A genetic algorithm approach to detecting temporal patterns indicative of financial statement fraud

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  • Bethany Hoogs
  • Thomas Kiehl
  • Christina Lacomb
  • Deniz Senturk

Abstract

This study presents a genetic algorithm approach to detecting financial statement fraud. The study uses a sample comprising a target class of 51 companies accused by the Securities and Exchange Commission of improperly recognizing revenue and a peer class of 339 companies matched on industry and size (revenue). Variables include 76 comparative metrics, based on specific financial metrics and ratios that capture company performance in the context of historical and industry performance, and nine company characteristics. Time‐based patterns detected by the genetic algorithm accurately classify 63% of the target class companies and 95% of the peer class companies. Copyright © 2007 John Wiley & Sons, Ltd.

Suggested Citation

  • Bethany Hoogs & Thomas Kiehl & Christina Lacomb & Deniz Senturk, 2007. "A genetic algorithm approach to detecting temporal patterns indicative of financial statement fraud," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 15(1‐2), pages 41-56, January.
    • Handle: RePEc:wly:isacfm:v:15:y:2007:i:1-2:p:41-56
    DOI: 10.1002/isaf.284
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    References listed on IDEAS

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    1. Thomas A. Lee & Robert W. Ingram & Thomas P. Howard, 1999. "The Difference between Earnings and Operating Cash Flow as an Indicator of Financial Reporting Fraud," Contemporary Accounting Research, John Wiley & Sons, vol. 16(4), pages 749-786, December.
    2. Zmijewski, Me, 1984. "Methodological Issues Related To The Estimation Of Financial Distress Prediction Models," Journal of Accounting Research, Wiley Blackwell, vol. 22, pages 59-82.
    3. K. Peasnell & P. Pope & S. Young, 2000. "Detecting earnings management using cross-sectional abnormal accruals models," Accounting and Business Research, Taylor & Francis Journals, vol. 30(4), pages 313-326.
    4. Karpoff, Jonathan M & Lott, John R, Jr, 1993. "The Reputational Penalty Firms Bear from Committing Criminal Fraud," Journal of Law and Economics, University of Chicago Press, vol. 36(2), pages 757-802, October.
    5. Kurt M. Fanning & Kenneth O. Cogger, 1998. "Neural network detection of management fraud using published financial data," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 7(1), pages 21-41, March.
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    Cited by:

    1. Gianni Filograsso & Giacomo Tollo, 2023. "Adaptive evolutionary algorithms for portfolio selection problems," Computational Management Science, Springer, vol. 20(1), pages 1-38, December.
    2. Adrian Gepp & Kuldeep Kumar & Sukanto Bhattacharya, 2021. "Lifting the numbers game: identifying key input variables and a best‐performing model to detect financial statement fraud," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 61(3), pages 4601-4638, September.
    3. Joanna Wyrobek & Lukasz Poplawski & Marcin Surowka, 2020. "Identification of a Fraudulent Organizational Culture in Enterprises Listed in Warsaw Stock Exchange," European Research Studies Journal, European Research Studies Journal, vol. 0(Special 2), pages 622-637.
    4. Elias Zavitsanos & Dimitris Mavroeidis & Konstantinos Bougiatiotis & Eirini Spyropoulou & Lefteris Loukas & Georgios Paliouras, 2023. "Financial misstatement detection: a realistic evaluation," Papers 2305.17457, arXiv.org.

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