This paper provides a methodological analysis of credit risk in manufacturing firms. By using a representative sample of both healthy and bankrupted firms during the period 2003–2009 we provide an in-depth comparison of the standard discriminant approach for bankruptcy prediction based on a logistic regression model and a Robust Bayesian Approach. We conclude that the use of a robust GLM regression methodology enables us to provide a more accurate separation between sound and unsound firms thus suggesting that this methodological framework may be used to achieve a more reliable measure of firms credit worthiness.
Baussola, M. L., Bartoloni, E., Corbellini, A., Business failure prediction in manufacturing: A robust bayesian approach to discriminant scoring, in Carpita, M. E. B. A. E. M. Q. (ed.), Advances in Latent Variables, Springer International Publishing, Basilea 2015: 277- 285. 10.1007/10104_2014_8 [http://hdl.handle.net/10807/163930]
Business failure prediction in manufacturing: A robust bayesian approach to discriminant scoring
Baussola, Maurizio LuigiPrimo
;Corbellini, Aldo
Ultimo
2015
Abstract
This paper provides a methodological analysis of credit risk in manufacturing firms. By using a representative sample of both healthy and bankrupted firms during the period 2003–2009 we provide an in-depth comparison of the standard discriminant approach for bankruptcy prediction based on a logistic regression model and a Robust Bayesian Approach. We conclude that the use of a robust GLM regression methodology enables us to provide a more accurate separation between sound and unsound firms thus suggesting that this methodological framework may be used to achieve a more reliable measure of firms credit worthiness.
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