Bankruptcy prediction of small and medium enterprises using a flexible binary generalized extreme value model. Issue 4 (1st April 2016)

• Record Type: Journal Article
• Authors: Calabrese, Raffaella; Marra, Giampiero; Angela Osmetti, Silvia
• Is Part Of: Journal of the Operational Research Society. Volume 67:Issue 4(2016)
• Page Start: 604
• Page End: 615
• Publication Date: 2016-04-01
• Subjects: logistic regression -- generalized extreme value distribution -- penalized regression spline -- scoring model -- small and medium enterprises; Operations research -- Periodicals; 658.4034
• DOI: 10.1057/jors.2015.64
• Languages: English
• ISSNs: 0160-5682

Abstract

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.