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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Notes
- 1.
Regression results available on request.
- 2.
Our results, available on request, show that if a cutoff point of 0.02 is fixed, a Type II Error of 0.5 is obtained with the 2003 model (84 % of bankruptcy cases correctly predicted). However, as at this cutoff point we also wrongly classify as unsound 16 % of healthy firms, we prefer to accept a small increase in Type I Error in order to reach a better classification for the group of healthy firms. Thus, a cutoff point of 0.04 seems to be a reasonable compromise (74 % of bankruptcy cases correctly predicted and 90 % of sound firms correctly classified).
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Baussola, M., Bartoloni, E., Corbellini, A. (2014). Business Failure Prediction in Manufacturing: A Robust Bayesian Approach to Discriminant Scoring. In: Carpita, M., Brentari, E., Qannari, E. (eds) Advances in Latent Variables. Studies in Theoretical and Applied Statistics(). Springer, Cham. https://doi.org/10.1007/10104_2014_8
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DOI: https://doi.org/10.1007/10104_2014_8
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