Failure prediction models : performance, disagreements, and internal rating systems
We address a number of comparative issues relating to the performance of failure prediction models for small, private firms. We use two models provided by vendors, a model developed by the National Bank of Belgium, and the Altman Z-score model to investigate model power, the extent of disagreement between models in the ranking of firms, and the design of internal rating systems. We also examine the potential gains from combining the output of multiple models. We find that the power of all four models in predicting bankruptcies is very good at the one-year horizon, even though not all of the models were developed using bankruptcy data and the models use different statistical methodologies. Disagreements in firm rankings are nevertheless significant across models, and model choice will have an impact on loan pricing and origination decisions. We find that it is possible to realize important gains from combining models with similar power. In addition, we show that it can also be beneficial to combine a weaker model with a stronger one if disagreements across models with respect to failing firms are high enough. Finally, the number of classes in an internal rating system appears to be more important than the distribution of borrowers across classes
|Date of creation:||Dec 2007|
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- Ooghe, H. & Spaenjers, C. & Pieter vandermoere, 2005.
"Business failure prediction: simple-intuitive models versus statistical models,"
Vlerick Leuven Gent Management School Working Paper Series
2005-22, Vlerick Leuven Gent Management School.
- H. Ooghe & C. Spaenjers & P. Vandermoere, 2005. "Business failure prediction: simple-intuitive models versus statistical models," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/338, Ghent University, Faculty of Economics and Business Administration.
- Stein, Roger M., 2005. "The relationship between default prediction and lending profits: Integrating ROC analysis and loan pricing," Journal of Banking & Finance, Elsevier, vol. 29(5), pages 1213-1236, May.
- S. Balcaen & H. Ooghe, 2004. "35 years of studies on business failure: an overview of the classical statistical methodologiesand their related problems," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 04/248, Ghent University, Faculty of Economics and Business Administration.
- Blochlinger, Andreas & Leippold, Markus, 2006. "Economic benefit of powerful credit scoring," Journal of Banking & Finance, Elsevier, vol. 30(3), pages 851-873, March.
- Jankowitsch, Rainer & Pichler, Stefan & Schwaiger, Walter S.A., 2007. "Modelling the economic value of credit rating systems," Journal of Banking & Finance, Elsevier, vol. 31(1), pages 181-198, January.
- Stephen Satchel & Wei Xia, 2006. "Analytic Models of the ROC Curve: Applications to Credit Rating Model Validation," Research Paper Series 181, Quantitative Finance Research Centre, University of Technology, Sydney.
- Hanson, Samuel & Schuermann, Til, 2006. "Confidence intervals for probabilities of default," Journal of Banking & Finance, Elsevier, vol. 30(8), pages 2281-2301, August.
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