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Bankruptcy Prediction Using Machine Learning Techniques

Author

Listed:
  • Shekar Shetty

    (College of Business Administration, Lamar University, Beaumont, TX 77705, USA)

  • Mohamed Musa

    (Department of Mathematics & Natural Science, College of Arts & Sciences, Gulf University for Science & Technology, Mishref 32093, Kuwait)

  • Xavier Brédart

    (Warocqué School of Business and Economics, University of Mons, 7000 Mons, Belgium)

Abstract

In this study, we apply several advanced machine learning techniques including extreme gradient boosting (XGBoost), support vector machine (SVM), and a deep neural network to predict bankruptcy using easily obtainable financial data of 3728 Belgian Small and Medium Enterprises (SME’s) during the period 2002–2012. Using the above-mentioned machine learning techniques, we predict bankruptcies with a global accuracy of 82–83% using only three easily obtainable financial ratios: the return on assets, the current ratio, and the solvency ratio. While the prediction accuracy is similar to several previous models in the literature, our model is very simple to implement and represents an accurate and user-friendly tool to discriminate between bankrupt and non-bankrupt firms.

Suggested Citation

  • Shekar Shetty & Mohamed Musa & Xavier Brédart, 2022. "Bankruptcy Prediction Using Machine Learning Techniques," JRFM, MDPI, vol. 15(1), pages 1-10, January.
    • Handle: RePEc:gam:jjrfmx:v:15:y:2022:i:1:p:35-:d:723511
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    References listed on IDEAS

    as
    1. Altman, Edward I. & Haldeman, Robert G. & Narayanan, P., 1977. "ZETATM analysis A new model to identify bankruptcy risk of corporations," Journal of Banking & Finance, Elsevier, vol. 1(1), pages 29-54, June.
    2. Ciampi, Francesco, 2015. "Corporate governance characteristics and default prediction modeling for small enterprises. An empirical analysis of Italian firms," Journal of Business Research, Elsevier, vol. 68(5), pages 1012-1025.
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    Cited by:

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    2. Alexey Litvinenko, 2023. "A Comparative Analysis of Altman's Z-Score and T. Jury's Cash-Based Credit Risk Models with The Application to The Production Company and The Data for The Years 2016-2022," Journal of Accounting and Management Information Systems, Faculty of Accounting and Management Information Systems, The Bucharest University of Economic Studies, vol. 22(3), pages 518-553, September.
    3. Lily Davies & Mark Kattenberg & Benedikt Vogt, 2023. "Predicting Firm Exits with Machine Learning: Implications for Selection into COVID-19 Support and Productivity Growth," CPB Discussion Paper 444, CPB Netherlands Bureau for Economic Policy Analysis.

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