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Predicting French SME failures: new evidence from machine learning techniques

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  • Christophe Schalck
  • Meryem Yankol-Schalck

Abstract

The aim of this study is to provide new insights into French small and medium-sized enterprises (SME) failure prediction using a unique database of French SMEs over the 2012–2018 period including both financial and nonfinancial variables. We also include text variables related to the type of activity. We compare the predictive performance of three estimation methods: a dynamic Probit model, logistic Lasso regression, and XGBoost algorithm. The results show that the XGBoost algorithm has the highest performance in predicting business failure from a broad dataset. We use SHAP values to interpret the results and identify the main factors of failure. Our analysis shows that both financial and nonfinancial variables are failure factors. Our results confirm the role of financial variables in predicting business failure, while self-employment is the factor that most strongly increases the probability of failure. The size of the SME is also a business failure factor. Our results show that a number of nonfinancial variables, such as localization and economic conditions, are drivers of SME failure. The results also show that certain activities are associated with a prediction of lower failure probability while some activities are associated with a prediction of higher failure.

Suggested Citation

  • Christophe Schalck & Meryem Yankol-Schalck, 2021. "Predicting French SME failures: new evidence from machine learning techniques," Applied Economics, Taylor & Francis Journals, vol. 53(51), pages 5948-5963, November.
    • Handle: RePEc:taf:applec:v:53:y:2021:i:51:p:5948-5963
    DOI: 10.1080/00036846.2021.1934389
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    Cited by:

    1. Dejian Yu & Bo Xiang & Zhuoya Pan, 2024. "Combining text analytics and network path extraction to trace CSR in the social sciences: Intellectual structures and diffusion trajectories," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 31(5), pages 4532-4554, September.
    2. Hoang Hiep Nguyen & Jean-Laurent Viviani & Sami Ben Jabeur, 2023. "Bankruptcy prediction using machine learning and Shapley additive explanations," Post-Print hal-04223161, HAL.
    3. Alonso-Robisco, Andrés & Carbó, José Manuel, 2022. "Can machine learning models save capital for banks? Evidence from a Spanish credit portfolio," International Review of Financial Analysis, Elsevier, vol. 84(C).
    4. Hoang Hiep Nguyen & Jean-Laurent Viviani & Sami Ben Jabeur, 2025. "Bankruptcy prediction using machine learning and Shapley additive explanations," Review of Quantitative Finance and Accounting, Springer, vol. 65(1), pages 107-148, July.
    5. Dina Ait Lahcen, 2023. "Synthetic Reading Of The Different Approaches And Models For Assessing The Risk Of Business Failure [Lecture Synthétique Des Diverses Approches Et Modèles D'Évaluation Du Risque De La Défaillance Des Entreprises]," Post-Print hal-04009420, HAL.
    6. Enrico Schötz, 2025. "Resilience vs. survival: same song, new melody?," Future Business Journal, Springer, vol. 11(1), pages 1-29, December.

    More about this item

    JEL classification:

    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
    • C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies
    • C46 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Specific Distributions

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