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The Default Risk of Firms Examined with Smooth Support Vector Machines

Author

Listed:
  • Wolfgang Härdle
  • Yuh-Jye Lee
  • Dorothea Schäfer
  • Yi-Ren Yeh

Abstract

In the era of Basel II a powerful tool for bankruptcy prognosis is vital for banks. The tool must be precise but also easily adaptable to the bank's objections regarding the relation of false acceptances (Type I error) and false rejections (Type II error). We explore the suitabil- ity of Smooth Support Vector Machines (SSVM), and investigate how important factors such as selection of appropriate accounting ratios (predictors), length of training period and structure of the training sample in°uence the precision of prediction. Furthermore we show that oversampling can be employed to gear the tradeo® between error types. Finally, we illustrate graphically how di®erent variants of SSVM can be used jointly to support the decision task of loan o±cers.

Suggested Citation

  • Wolfgang Härdle & Yuh-Jye Lee & Dorothea Schäfer & Yi-Ren Yeh, 2008. "The Default Risk of Firms Examined with Smooth Support Vector Machines," SFB 649 Discussion Papers SFB649DP2008-005, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  • Handle: RePEc:hum:wpaper:sfb649dp2008-005
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    References listed on IDEAS

    as
    1. Shiyi Chen & Wolfgang Härdle & Rouslan Moro, 2006. "Estimation of Default Probabilities with Support Vector Machines," SFB 649 Discussion Papers SFB649DP2006-077, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    2. Mella-Barral, Pierre & Perraudin, William, 1997. " Strategic Debt Service," Journal of Finance, American Finance Association, vol. 52(2), pages 531-556, June.
    3. Altman, Edward I. & Marco, Giancarlo & Varetto, Franco, 1994. "Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience)," Journal of Banking & Finance, Elsevier, vol. 18(3), pages 505-529, May.
    4. 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.
    5. Martin, Daniel, 1977. "Early warning of bank failure : A logit regression approach," Journal of Banking & Finance, Elsevier, vol. 1(3), pages 249-276, November.
    6. Leland, Hayne E & Toft, Klaus Bjerre, 1996. " Optimal Capital Structure, Endogenous Bankruptcy, and the Term Structure of Credit Spreads," Journal of Finance, American Finance Association, vol. 51(3), pages 987-1019, July.
    7. repec:bla:joares:v:18:y:1980:i:1:p:109-131 is not listed on IDEAS
    8. Krahnen, Jan Pieter & Weber, Martin, 2001. "Generally accepted rating principles: A primer," Journal of Banking & Finance, Elsevier, vol. 25(1), pages 3-23, January.
    9. Härdle, Wolfgang Karl & Moro, Rouslan A. & Schäfer, Dorothea, 2007. "Estimating probabilities of default with support vector machines," Discussion Paper Series 2: Banking and Financial Studies 2007,18, Deutsche Bundesbank.
    10. Huang, Chien-Ming & Lee, Yuh-Jye & Lin, Dennis K.J. & Huang, Su-Yun, 2007. "Model selection for support vector machines via uniform design," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 335-346, September.
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    Cited by:

    1. Jan-Henning Trustorff & Paul Konrad & Jens Leker, 2011. "Credit risk prediction using support vector machines," Review of Quantitative Finance and Accounting, Springer, vol. 36(4), pages 565-581, May.

    More about this item

    Keywords

    Insolvency Prognosis; SVMs; Statistical Learning Theory; Non-parametric Classification models; local time-homogeneity;

    JEL classification:

    • G30 - Financial Economics - - Corporate Finance and Governance - - - General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

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