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Credit risk prediction using support vector machines

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  • Jan-Henning Trustorff
  • Paul Konrad
  • Jens Leker

Abstract

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Suggested Citation

  • 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.
    • Handle: RePEc:kap:rqfnac:v:36:y:2011:i:4:p:565-581
    DOI: 10.1007/s11156-010-0190-3
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. B Baesens & T Van Gestel & S Viaene & M Stepanova & J Suykens & J Vanthienen, 2003. "Benchmarking state-of-the-art classification algorithms for credit scoring," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(6), pages 627-635, June.
    6. Pavel Cizek & Wolfgang Karl Härdle & Rafal Weron, 2005. "Statistical Tools for Finance and Insurance," HSC Books, Hugo Steinhaus Center, Wroclaw University of Science and Technology, number hsbook0501, December.
    7. Lili Sun, 2007. "A re-evaluation of auditors’ opinions versus statistical models in bankruptcy prediction," Review of Quantitative Finance and Accounting, Springer, vol. 28(1), pages 55-78, January.
    8. Chen, Liang-Hsuan & Chiou, Tai-Wei, 1999. "A fuzzy credit-rating approach for commercial loans: a Taiwan case," Omega, Elsevier, vol. 27(4), pages 407-419, August.
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    Citations

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    Cited by:

    1. Ahmed, Shamima & Alshater, Muneer M. & Ammari, Anis El & Hammami, Helmi, 2022. "Artificial intelligence and machine learning in finance: A bibliometric review," Research in International Business and Finance, Elsevier, vol. 61(C).
    2. Roy Cerqueti & Francesca Pampurini & Annagiulia Pezzola & Anna Grazia Quaranta, 2022. "Dangerous liasons and hot customers for banks," Review of Quantitative Finance and Accounting, Springer, vol. 59(1), pages 65-89, July.
    3. Peter Grundke & Kamil Pliszka & Michael Tuchscherer, 2020. "Model and estimation risk in credit risk stress tests," Review of Quantitative Finance and Accounting, Springer, vol. 55(1), pages 163-199, July.
    4. Natalia Nehrebecka, 2021. "Internal Credit Risk Models and Digital Transformation: What to Prepare for? An Application to Poland," European Research Studies Journal, European Research Studies Journal, vol. 0(Special 3), pages 719-736.
    5. Marco Locurcio & Francesco Tajani & Pierluigi Morano & Debora Anelli & Benedetto Manganelli, 2021. "Credit Risk Management of Property Investments through Multi-Criteria Indicators," Risks, MDPI, vol. 9(6), pages 1-23, June.
    6. Martin Kukuk & Michael Rönnberg, 2013. "Corporate credit default models: a mixed logit approach," Review of Quantitative Finance and Accounting, Springer, vol. 40(3), pages 467-483, April.
    7. Gunter Löffler, 2013. "Can rating agencies look through the cycle?," Review of Quantitative Finance and Accounting, Springer, vol. 40(4), pages 623-646, May.
    8. 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.
    9. Vasilios Giannopoulos & Eleftherios Aggelopoulos, 2019. "Predicting SME loan delinquencies during recession using accounting data and SME characteristics: The case of Greece," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 26(2), pages 71-82, April.
    10. 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.
    11. Hyeongjun Kim & Hoon Cho & Doojin Ryu, 2022. "Corporate Bankruptcy Prediction Using Machine Learning Methodologies with a Focus on Sequential Data," Computational Economics, Springer;Society for Computational Economics, vol. 59(3), pages 1231-1249, March.
    12. Jairaj Gupta & Andros Gregoriou & Jerome Healy, 2015. "Forecasting bankruptcy for SMEs using hazard function: To what extent does size matter?," Review of Quantitative Finance and Accounting, Springer, vol. 45(4), pages 845-869, November.

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    More about this item

    Keywords

    Support vector machines; Credit risk prediction; Default classification; Estimation of probabilities of default; Training sample size; Accounting data; C14; G33;
    All these keywords.

    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

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