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The Default Risk of Firms Examined with Smooth Support Vector Machines

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

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 suitability 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 influence the precision of prediction. Furthermore we showthat oversampling can be employed to gear the tradeoff between error types. Finally, we illustrate graphically how different variants of SSVM can be used jointly to support the decision task of loan officers.

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File URL: http://www.diw.de/documents/publikationen/73/diw_01.c.78291.de/dp757.pdf
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Paper provided by DIW Berlin, German Institute for Economic Research in its series Discussion Papers of DIW Berlin with number 757.

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Length: 30 p.
Date of creation: 2007
Date of revision:
Handle: RePEc:diw:diwwpp:dp757
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  1. Krahnen, Jan Pieter & Weber, Martin, 2001. "Generally accepted rating principles: A primer," Journal of Banking & Finance, Elsevier, vol. 25(1), pages 3-23, January.
  2. Hayne E. Leland and Klaus Bjerre Toft., 1995. "Optimal Capital Structure, Endogenous Bankruptcy, and the Term Structure of Credit Spreads," Research Program in Finance Working Papers RPF-259, University of California at Berkeley.
  3. Martin, Daniel, 1977. "Early warning of bank failure : A logit regression approach," Journal of Banking & Finance, Elsevier, vol. 1(3), pages 249-276, November.
  4. Merton, Robert C., 1973. "On the pricing of corporate debt: the risk structure of interest rates," Working papers 684-73., Massachusetts Institute of Technology (MIT), Sloan School of Management.
  5. Wolfgang Härdle & Rouslan Moro & Dorothea Schäfer, 2007. "Estimating Probabilities of Default With Support Vector Machines," SFB 649 Discussion Papers SFB649DP2007-035, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  6. 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.
  7. Mella-Barral, Pierre & Perraudin, William, 1997. " Strategic Debt Service," Journal of Finance, American Finance Association, vol. 52(2), pages 531-56, June.
  8. 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.
  9. 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.
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