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Random Survival Forests Models for SME Credit Risk Measurement

Author

Listed:
  • Dean Fantazzini

    (University of Pavia)

  • Silvia Figini

    (University of Pavia)

Abstract

This paper extends the existing literature on empirical research in the field of credit risk default for Small Medium Enterprizes (SMEs). We propose a non-parametric approach based on Random Survival Forests (RSF) and we compare its performance with a standard logit model. To the authors’ knowledge, no studies in the area of credit risk default for SMEs have used a variety of statistical methodologies to test the reliability of their predictions and to compare their performance against one another. As for the in-sample results, we find that our non-parametric model performs much better that the classical logit model. As for the out-of-sample performances, the evidence is just the opposite, and the logit performs better than the RSF model. We explain this evidence by showing how error in the estimates of default probabilities can affect classification error when the estimates are used in a classification rule.

Suggested Citation

  • Dean Fantazzini & Silvia Figini, 2009. "Random Survival Forests Models for SME Credit Risk Measurement," Methodology and Computing in Applied Probability, Springer, vol. 11(1), pages 29-45, March.
    • Handle: RePEc:spr:metcap:v:11:y:2009:i:1:d:10.1007_s11009-008-9078-2
    DOI: 10.1007/s11009-008-9078-2
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    References listed on IDEAS

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