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Failure prediction models : performance, disagreements, and internal rating systems

  • Janet Mitchell


    (National Bank of Belgium, Financial Stability Department

  • Patrick Van Roy


    (National Bank of Belgium, Financial Stability Department
    Université Libre de Bruxelles)

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

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Paper provided by National Bank of Belgium in its series Working Paper Research with number 123.

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Length: 35 pages
Date of creation: Dec 2007
Date of revision:
Handle: RePEc:nbb:reswpp:200712-18
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  1. Blochlinger, Andreas & Leippold, Markus, 2006. "Economic benefit of powerful credit scoring," Journal of Banking & Finance, Elsevier, vol. 30(3), pages 851-873, March.
  2. 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.
  3. 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.
  4. Hanson, Samuel & Schuermann, Til, 2006. "Confidence intervals for probabilities of default," Journal of Banking & Finance, Elsevier, vol. 30(8), pages 2281-2301, August.
  5. 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.
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