Predicting French SME failures: new evidence from machine learning techniques

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.

KEYWORDS:

• SME
• failure prediction
• machine learning
• xgboost
• shap values

JEL CLASSIFICATION:

• G33
• C41
• C46

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

¹ http://www.rfi.fr/en/france/20200330-france-boosts-financial-aid-for-small-businesses-hit-by-covid-19-bankruptcy-economy-coronavirus

² This category of enterprise is defined by the Application Decree (n°2008-1354) of Article 51 of the Law on Modernization of the Economy, and by Article 2 of the annexe to Recommendation 2003/361/EC.