Marra, G;
Calabrese, R;
Osmetti, SA;
(2016)
Bankruptcy prediction of small and medium enterprises using a flexible binary generalized extreme value model.
Journal of the Operational Research Society
, 67
(4)
pp. 604-615.
10.1057/jors.2015.64.
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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 covariateresponse 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.
Type: | Article |
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Title: | Bankruptcy prediction of small and medium enterprises using a flexible binary generalized extreme value model |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1057/jors.2015.64 |
Publisher version: | http://dx.doi.org/10.1057/jors.2015.64 |
Language: | English |
Additional information: | This is a post-peer-review, pre-copyedit version of an article published in the Journal of the Operational Research Society. The definitive publisher-authenticated version, Marra, G; Calabrese, R; Osmetti, SA; (2016) Bankruptcy prediction of small and medium enterprises using a flexible binary generalized extreme value model. Journal of the Operational Research Society, 67 (4) pp. 604-615, 10.1057/jors.2015.64, is available online at: http://dx.doi.org/10.1057/jors.2015.64. |
Keywords: | logistic regression; generalized extreme value distribution; penalized regression spline; scoring model; small and medium enterprises |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
URI: | https://discovery.ucl.ac.uk/id/eprint/1476056 |
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