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Financial distress prediction: The case of French small and medium-sized firms


  • Mselmi, Nada
  • Lahiani, Amine
  • Hamza, Taher


Financial distress prediction is a central issue in empirical finance that has drawn a lot of research interests in the literature. This paper aims to predict the financial distress of French small and medium firms using Logit model, Artificial Neural Networks, Support Vector Machine techniques, Partial Least Squares, and a hybrid model integrating Support Vector Machine with Partial Least Squares. Empirical results indicate that for one year prior to financial distress, Support Vector Machine is the best classifier with an overall accuracy of 88.57%. Meanwhile, in the case of two years prior to financial distress, the hybrid model outperforms Support Vector Machine, Logit model, Partial Least Squares, andArtificial Neural Networks with an overall accuracy of 94.28%. Distressed firms are found to be smaller, more leveraged and with lower repayment capacity. Moreover, they have lower liquidity, profitability, and solvency ratios. Besides the academic research contribution, our findings can be useful for managers, investors, and creditors. With respect to managers, our findings provide them with early warnings signals of performance deterioration in order to take corrective actions and reduce the financial distress risk. For investors, understanding the main factors leading to financial distress allows them to avoid investing in risky firms. Creditors should correctly evaluate the firm financial situation and be vigilant to signs of impending financial distress to avoid capital loss and costs related to counterpart risk.

Suggested Citation

  • Mselmi, Nada & Lahiani, Amine & Hamza, Taher, 2017. "Financial distress prediction: The case of French small and medium-sized firms," International Review of Financial Analysis, Elsevier, vol. 50(C), pages 67-80.
  • Handle: RePEc:eee:finana:v:50:y:2017:i:c:p:67-80
    DOI: 10.1016/j.irfa.2017.02.004

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    References listed on IDEAS

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    Cited by:

    1. Mselmi, Nada & Hamza, Taher & Lahiani, Amine & Shahbaz, Muhammad, 2019. "Pricing corporate financial distress: Empirical evidence from the French stock market," Journal of International Money and Finance, Elsevier, vol. 96(C), pages 13-27.
    2. Khoja, Layla & Chipulu, Maxwell & Jayasekera, Ranadeva, 2019. "Analysis of financial distress cross countries: Using macroeconomic, industrial indicators and accounting data," International Review of Financial Analysis, Elsevier, vol. 66(C).
    3. Mauro Paoloni & Massimiliano Celli, 2018. "Crisi delle PMI e strumenti di warning. Un test di verifica nel settore manifatturiero," MANAGEMENT CONTROL, FrancoAngeli Editore, vol. 2018(2), pages 85-106.
    4. Fernández-Gámez, Manuel Ángel & Soria, Juan Antonio Campos & Santos, José António C. & Alaminos, David, 2020. "European country heterogeneity in financial distress prediction: An empirical analysis with macroeconomic and regulatory factors," Economic Modelling, Elsevier, vol. 88(C), pages 398-407.

    More about this item


    Financial distress prediction; Logit model; Artificial neural networks; Support vector machine; Partial least squares; Hybrid model;

    JEL classification:

    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics


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