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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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    1. Glennon, Dennis & Nigro, Peter, 2005. "Measuring the Default Risk of Small Business Loans: A Survival Analysis Approach," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 37(5), pages 923-947, October.
    2. João Fernandes, 2005. "Corporate Credit Risk Modeling: Quantitative Rating System And Probability Of Default Estimation," Finance 0505013, University Library of Munich, Germany.
    3. Engelmann, Bernd & Hayden, Evelyn & Tasche, Dirk, 2003. "Measuring the Discriminative Power of Rating Systems," Discussion Paper Series 2: Banking and Financial Studies 2003,01, Deutsche Bundesbank.
    4. Chen, Andrew H. & Ju, Nengjiu & Mazumdar, Sumon C. & Verma, Avinash, 2006. "Correlated Default Risks and Bank Regulations," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 38(2), pages 375-398, March.
    5. Merton, Robert C, 1974. "On the Pricing of Corporate Debt: The Risk Structure of Interest Rates," Journal of Finance, American Finance Association, vol. 29(2), pages 449-470, May.
    6. Karatzoglou, Alexandros & Meyer, David & Hornik, Kurt, 2006. "Support Vector Machines in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 15(i09).
    7. de Leeuw, Jan & Hornik, Kurt & Mair, Patrick, 2009. "Isotone Optimization in R: Pool-Adjacent-Violators Algorithm (PAVA) and Active Set Methods," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 32(i05).
    8. Martens, David & Baesens, Bart & Van Gestel, Tony & Vanthienen, Jan, 2007. "Comprehensible credit scoring models using rule extraction from support vector machines," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1466-1476, December.
    9. Duan, Jin-Chuan & Sun, Jie & Wang, Tao, 2012. "Multiperiod corporate default prediction—A forward intensity approach," Journal of Econometrics, Elsevier, vol. 170(1), pages 191-209.
    10. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    11. Martin, Daniel, 1977. "Early warning of bank failure : A logit regression approach," Journal of Banking & Finance, Elsevier, vol. 1(3), pages 249-276, November.
    12. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    13. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    14. Mark J Manning, 2004. "Exploring the relationship between credit spreads and default probabilities," Bank of England working papers 225, Bank of England.
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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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