TY - JOUR
T1 - Bankruptcy prediction of small and medium enterprises using a flexible binary generalized extreme value model
AU - Osmetti, Silvia Angela
AU - Calabrese, Raffaella
AU - Marra, Giampiero
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
KW - generalized extreme value distribution
KW - logistic regression
KW - penalized regression spline
KW - scoring model
KW - small and medium enterprise
KW - generalized extreme value distribution
KW - logistic regression
KW - penalized regression spline
KW - scoring model
KW - small and medium enterprise
UR - http://hdl.handle.net/10807/75545
U2 - 10.1057/jors.2015.64
DO - 10.1057/jors.2015.64
M3 - Article
VL - 67
SP - 604
EP - 615
JO - Journal of the Operational Research Society
JF - Journal of the Operational Research Society
SN - 0160-5682
ER -