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Credit risk prediction using support vector machines

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  • Jan-Henning Trustorff

    ()

  • Paul Konrad
  • Jens Leker
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    Abstract

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    File URL: http://hdl.handle.net/10.1007/s11156-010-0190-3
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    Bibliographic Info

    Article provided by Springer in its journal Review of Quantitative Finance and Accounting.

    Volume (Year): 36 (2011)
    Issue (Month): 4 (May)
    Pages: 565-581

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    Handle: RePEc:kap:rqfnac:v:36:y:2011:i:4:p:565-581

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    Web page: http://springerlink.metapress.com/link.asp?id=102990

    Related research

    Keywords: Support vector machines; Credit risk prediction; Default classification; Estimation of probabilities of default; Training sample size; Accounting data; C14; G33;

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    References

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    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.:
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    1. Varetto, Franco, 1998. "Genetic algorithms applications in the analysis of insolvency risk," Journal of Banking & Finance, Elsevier, vol. 22(10-11), pages 1421-1439, October.
    2. 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.
    3. 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.
    4. Carling, Kenneth & Jacobson, Tor & Linde, Jesper & Roszbach, Kasper, 2007. "Corporate credit risk modeling and the macroeconomy," Journal of Banking & Finance, Elsevier, vol. 31(3), pages 845-868, March.
    5. Giovanni Butera & Robert Faff, 2006. "An integrated multi-model credit rating system for private firms," Review of Quantitative Finance and Accounting, Springer, vol. 27(3), pages 311-340, November.
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    Cited by:
    1. 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.
    2. 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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