Credit risk prediction using support vector machines
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Bibliographic InfoArticle provided by Springer in its journal Review of Quantitative Finance and Accounting.
Volume (Year): 36 (2011)
Issue (Month): 4 (May)
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Web page: http://springerlink.metapress.com/link.asp?id=102990
Support vector machines; Credit risk prediction; Default classification; Estimation of probabilities of default; Training sample size; Accounting data; C14; G33;
Find related papers by JEL classification:
- 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
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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.
- 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.
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- Pavel Cizek & Wolfgang Karl Härdle & Rafal Weron, 2005. "Statistical Tools for Finance and Insurance," HSC Books, Hugo Steinhaus Center, Wroclaw University of Technology, number hsbook0501.
- Wosnitza, Jan Henrik & Leker, Jens, 2014. "Why credit risk markets are predestined for exhibiting log-periodic power law structures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 393(C), pages 427-449.
- Wosnitza, Jan Henrik & Denz, Cornelia, 2013. "Liquidity crisis detection: An application of log-periodic power law structures to default prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(17), pages 3666-3681.
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