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

Raffaella Calabrese, Giampiero Marra, Silvia Angela Osmetti

Research output: Contribution to journalArticlepeer-review

24 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)604-615
Number of pages12
JournalJournal of the Operational Research Society
Volume67
DOIs
Publication statusPublished - 2016

Keywords

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

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