The Default Risk of Firms Examined with Smooth Support Vector Machines
AbstractIn 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.
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Bibliographic InfoPaper provided by Sonderforschungsbereich 649, Humboldt University, Berlin, Germany in its series SFB 649 Discussion Papers with number SFB649DP2008-005.
Length: 32 pages
Date of creation: Jan 2008
Date of revision:
Insolvency Prognosis; SVMs; Statistical Learning Theory; Non-parametric Classification models; local time-homogeneity;
Other versions of this item:
- Wolfgang Härdle & Yuh-Jye Lee & Dorothea Schäfer & Yi-Ren Yeh, 2007. "The Default Risk of Firms Examined with Smooth Support Vector Machines," Discussion Papers of DIW Berlin 757, DIW Berlin, German Institute for Economic Research.
- 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
This paper has been announced in the following NEP Reports:
- NEP-ALL-2008-04-29 (All new papers)
- NEP-BAN-2008-04-29 (Banking)
- NEP-RMG-2008-04-29 (Risk Management)
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- 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.
- 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 449-70, May.
- 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.
- Krahnen, Jan Pieter & Weber, Martin, 2001. "Generally accepted rating principles: A primer," Journal of Banking & Finance, Elsevier, vol. 25(1), pages 3-23, January.
- 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.
- 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, Research Centre.
- Pierre Mella-Barral & William R M Perraudin, 1993.
"Strategic Debt Service,"
CEPR Financial Markets Paper
0039, European Science Foundation Network in Financial Markets, c/o C.E.P.R, 77 Bastwick Street, London EC1V 3PZ.
- 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.
- 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.
- 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.
- 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.
- Martin, Daniel, 1977. "Early warning of bank failure : A logit regression approach," Journal of Banking & Finance, Elsevier, vol. 1(3), pages 249-276, November.
- 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.
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