Bankruptcy prediction of small and medium enterprises using a flexible binary generalized extreme value model

Abstract

We introduce a binary regression accounting-based model for bankruptcy prediction of small and medium enterprises (SMEs). The main advantage of the model lies in its predictive performance in identifying defaulted SMEs. Another advantage, which is especially relevant for banks, is that the relationship between the accounting characteristics of SMEs and response is not assumed a priori (eg, linear, quadratic or cubic) and can be determined from the data. The proposed approach uses the quantile function of the generalized extreme value distribution as link function as well as smooth functions of accounting characteristics to flexibly model covariate effects. Therefore, the usual assumptions in scoring models of symmetric link function and linear or pre-specified covariate-response relationships are relaxed. Out-of-sample and out-of-time validation on Italian data shows that our proposal outperforms the commonly used (logistic) scoring model for different default horizons.

Keywords:

• logistic regression
• generalized extreme value distribution
• penalized regression spline
• scoring model
• small and medium enterprises

Acknowledgements

The authors gratefully acknowledge the help of Armando Benincasa and Carlotta Prete at Bureau Van Dijk in providing the data. They also acknowledge comments and suggestions by the anonymous referees that have helped improving the paper.