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Testing the Effectiveness of Altman and Beneish Models in Detecting Financial Fraud and Financial Manipulation: Case Study Kuwaiti Stock

Author

Listed:
  • Raif M. Akra
  • Jamil K. Chaya

Abstract

This study is an adoption of two probabilistic financial analysis methods, Altman and Beneish Models that have proven effective in early detection of possible financial distress and profit manipulation respectively. Motivated by the effectiveness of the models, this paper applies the methodology on the Kuwaiti Stock Market excluding banking and insurance companies. Results demonstrated that Altman has less predictive power in the context of industrial and real estate companies while Beneish has a strong predictive power to uncover possible manipulation in earnings or fraudulent reporting in the tested companies as confirmed with an ex-post review of the companies and news sources. We recommend a recalibration of the Altman model according to industry in addition to recommending that financial analysts and interested parties use both models.

Suggested Citation

  • Raif M. Akra & Jamil K. Chaya, 2021. "Testing the Effectiveness of Altman and Beneish Models in Detecting Financial Fraud and Financial Manipulation: Case Study Kuwaiti Stock," International Journal of Business and Management, Canadian Center of Science and Education, vol. 15(10), pages 1-70, July.
    • Handle: RePEc:ibn:ijbmjn:v:15:y:2021:i:10:p:70
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    References listed on IDEAS

    as
    1. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    2. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
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    More about this item

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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