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Company rating with support vector machines

Author

Listed:
  • Moro Russ A.

    (Department of Economics and Finance, Brunel University London, UxbridgeUB8 3PH, United Kingdom)

  • Härdle Wolfgang K.

    (Center for Applied Statistics and Economics, Humboldt-Universität zu Berlin, Spandauer Str. 1, 10178Berlin, Germany)

  • Schäfer Dorothea

    (German Institute for Economic Research, Mohrenstr. 58, 10117Berlin, Germany)

Abstract

This paper proposes a rating methodology that is based on a non-linear classification method, a support vector machine, and a non-parametric isotonic regression for mapping rating scores into probabilities of default. We also propose a four data set model validation and training procedure that is more appropriate for credit rating data commonly characterised with cyclicality and panel features. Tests on representative data covering fifteen years of quarterly accounts and default events for 10,000 US listed companies confirm superiority of non-linear PD estimation. Our methodology demonstrates the ability to identify companies of diverse credit quality from Aaa to Caa–C.

Suggested Citation

  • Moro Russ A. & Härdle Wolfgang K. & Schäfer Dorothea, 2017. "Company rating with support vector machines," Statistics & Risk Modeling, De Gruyter, vol. 34(1-2), pages 55-67, June.
    • Handle: RePEc:bpj:strimo:v:34:y:2017:i:1-2:p:55-67:n:1
    DOI: 10.1515/strm-2012-1141
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    References listed on IDEAS

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    Cited by:

    1. Wosnitza, Jan Henrik, 2022. "Calibration alternatives to logistic regression and their potential for transferring the dispersion of discriminatory power into uncertainties of probabilities of default," Discussion Papers 04/2022, Deutsche Bundesbank.

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