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Does the bankrupt cheat? Impact of accounting manipulations on the effectiveness of a bankruptcy prediction

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  • Przemysław Mućko
  • Adam Adamczyk

Abstract

The aim of this article is to answer the question whether the unreliability of the Altman bankruptcy prediction model may be caused by manipulations in financial statements. Our study was carried out on a group of 369 bankrupt Polish companies, with the research period covering the years 2011–2020. In the study, we divided the companies into two groups: those correctly classified by Altman’s model as at risk of bankruptcy, and companies for which the model did not indicate a significant bankruptcy risk. Using a logit model, we tested whether the probability of companies being correctly classified as failed depends on the risk of a manipulation of financial statements. We use Benford’s law to measure the risk of a manipulation of financial statements. We also repeated our study using panel data models. Our analyses show that the manipulation of financial statements is not the cause of the inaccurate predictions of the Altman model. On the contrary, the results of the analyses indicate that manipulations occurs for companies with a lower Z-score and therefore a worse financial situation. This means that a deterioration in the quality of financial statements can be a signal of an increasing probability of bankruptcy.

Suggested Citation

  • Przemysław Mućko & Adam Adamczyk, 2023. "Does the bankrupt cheat? Impact of accounting manipulations on the effectiveness of a bankruptcy prediction," PLOS ONE, Public Library of Science, vol. 18(1), pages 1-13, January.
    • Handle: RePEc:plo:pone00:0280384
    DOI: 10.1371/journal.pone.0280384
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    References listed on IDEAS

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    1. Dan Amiram & Zahn Bozanic & James D. Cox & Quentin Dupont & Jonathan M. Karpoff & Richard Sloan, 2018. "Financial reporting fraud and other forms of misconduct: a multidisciplinary review of the literature," Review of Accounting Studies, Springer, vol. 23(2), pages 732-783, June.
    2. Jackson, Richard H.G. & Wood, Anthony, 2013. "The performance of insolvency prediction and credit risk models in the UK: A comparative study," The British Accounting Review, Elsevier, vol. 45(3), pages 183-202.
    3. Tam Cho, Wendy K. & Gaines, Brian J., 2007. "Breaking the (Benford) Law: Statistical Fraud Detection in Campaign Finance," The American Statistician, American Statistical Association, vol. 61, pages 218-223, August.
    4. Beber, Bernd & Scacco, Alexandra, 2012. "What the Numbers Say: A Digit-Based Test for Election Fraud," Political Analysis, Cambridge University Press, vol. 20(2), pages 211-234, April.
    5. Balcaen, Sofie & Ooghe, Hubert, 2006. "35 years of studies on business failure: an overview of the classic statistical methodologies and their related problems," The British Accounting Review, Elsevier, vol. 38(1), pages 63-93.
    6. 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.
    7. 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.
    8. Magali Costa & Inês Lisboa & Ana Gameiro, 2022. "Is the Financial Report Quality Important in the Default Prediction? SME Portuguese Construction Sector Evidence," Risks, MDPI, vol. 10(5), pages 1-24, May.
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    Cited by:

    1. Adeel Qaiser & Alia Ahmed, 2026. "Engineering accounting manipulation: assessing the role of management controller, corporate governance, and accountability with accounting capacity as a strategic moderator," Quality & Quantity: International Journal of Methodology, Springer, vol. 60(2), pages 4247-4274, April.

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