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Artificial Intelligence Techniques for Bankruptcy Prediction of Tunisian Companies: An Application of Machine Learning and Deep Learning-Based Models

Author

Listed:
  • Manel Hamdi

    (International Finance Group Tunisia Lab, Faculty of Management and Economic Sciences of Tunis, University of Tunis El Manar, Tunis 2092, Tunisia)

  • Sami Mestiri

    (Applied Economics and Simulation, Faculty of Management and Economic Sciences of Mahdia, University of Monastir, Rue Ibn Sina Hiboun, Mahdia 5111, Tunisia)

  • Adnène Arbi

    (Laboratory of Engineering Mathematics (LR01ES13), Tunisia Polytechnic School, University of Carthage, Tunis 2078, Tunisia
    Department of Advanced Sciences and Technologies, National School of Advanced Sciences and Technologies of Borj Cedria, University of Carthage, Hammam-Chott 1164, Tunisia)

Abstract

The present paper aims to compare the predictive performance of five models namely the Linear Discriminant Analysis (LDA), Logistic Regression (LR), Decision Trees (DT), Support Vector Machine (SVM) and Random Forest (RF) to forecast the bankruptcy of Tunisian companies. A Deep Neural Network (DNN) model is also applied to conduct a prediction performance comparison with other statistical and machine learning algorithms. The data used for this empirical investigation covers 25 financial ratios for a large sample of 732 Tunisian companies from 2011–2017. To interpret the prediction results, three performance measures have been employed; the accuracy percentage, the F1 score, and the Area Under Curve (AUC). In conclusion, DNN shows higher accuracy in predicting bankruptcy compared to other conventional models, whereas the random forest performs better than other machine learning and statistical methods.

Suggested Citation

  • Manel Hamdi & Sami Mestiri & Adnène Arbi, 2024. "Artificial Intelligence Techniques for Bankruptcy Prediction of Tunisian Companies: An Application of Machine Learning and Deep Learning-Based Models," JRFM, MDPI, vol. 17(4), pages 1-14, March.
    • Handle: RePEc:gam:jjrfmx:v:17:y:2024:i:4:p:132-:d:1361973
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    References listed on IDEAS

    as
    1. Manel Hamdi & Sami Mestiri, 2014. "Bankruptcy prediction for Tunisian firms : An application of semi-parametric logistic regression and neural networks approach," Economics Bulletin, AccessEcon, vol. 34(1), pages 133-143.
    2. Peter Martey Addo & Dominique Guegan & Bertrand Hassani, 2018. "Credit Risk Analysis using Machine and Deep learning models," Working Papers 2018:08, Department of Economics, University of Venice "Ca' Foscari".
    3. 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.
    4. 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.
    5. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 18(1), pages 109-131.
    6. Peter Martey Addo & Dominique Guegan & Bertrand Hassani, 2018. "Credit Risk Analysis Using Machine and Deep Learning Models," Risks, MDPI, vol. 6(2), pages 1-20, April.
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    Cited by:

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    2. Hu, Wendi & Shao, Chujian & Zhang, Wenyu, 2025. "Predicting U.S. bank failures and stress testing with machine learning algorithms," Finance Research Letters, Elsevier, vol. 75(C).
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    4. Bernard Kokczynski & Dorota Witkowska & Blazej Socha, 2024. "Predicting Bankruptcy: Insights from Polish Non-Public Companies (2019–2022)," European Research Studies Journal, European Research Studies Journal, vol. 0(Special A), pages 252-264.
    5. Rakshith Bhandary & Bidyut Kumar Ghosh, 2025. "Credit Card Default Prediction: An Empirical Analysis on Predictive Performance Using Statistical and Machine Learning Methods," JRFM, MDPI, vol. 18(1), pages 1-20, January.

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    JEL classification:

    • F1 - International Economics - - Trade

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