Statistical merging of rating models

Abstract

In this paper we introduce and discuss statistical models aimed at predicting default probabilities of Small and Medium Enterprises (SME). Such models are based on two separate sources of information: quantitative balance sheet ratios and qualitative information derived from the opinion mining process on unstructured data. We propose a novel methodology for data fusion in longitudinal and survival duration models using quantitative and qualitative variables separately in the likelihood function and then combining their scores linearly by a weight, to obtain the corresponding probability of default for each SME. With a real financial database at hand, we have compared the results achieved in terms of model performance and predictive capability using single models and our own proposal. Finally, we select the best model in terms of out-of-sample forecasts considering key performance indicators.

Keywords:

• predictive models
• Bayesian merging
• probability of default
• parametric models
• survival analysis
• model selection

Acknowledgements

This work has been supported by MUSING 2006 contract number 027097, 2006 to 2010. The paper is the result of a collaboration between the authors, however it has been written by Silvia Figini.