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Predicting Bankruptcy with Support Vector Machines

Author

Listed:
  • Wolfgang Härdle
  • Rouslan A. Moro
  • Dorothea Schäfer

Abstract

The purpose of this work is to introduce one of the most promising among recently developed statistical techniques – the support vector machine (SVM) – to corporate bankruptcy analysis. An SVM is implemented for analysing such predictors as financial ratios. A method of adapting it to default probability estimation is proposed. A survey of practically applied methods is given. This work shows that support vector machines are capable of extracting useful information from financial data, although extensive data sets are required in order to fully utilize their classification power.

Suggested Citation

  • Wolfgang Härdle & Rouslan A. Moro & Dorothea Schäfer, 2005. "Predicting Bankruptcy with Support Vector Machines," SFB 649 Discussion Papers SFB649DP2005-009, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    • Handle: RePEc:hum:wpaper:sfb649dp2005-009
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    File URL: http://sfb649.wiwi.hu-berlin.de/papers/pdf/SFB649DP2005-009.pdf
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    References listed on IDEAS

    as
    1. Wiginton, John C., 1980. "A Note on the Comparison of Logit and Discriminant Models of Consumer Credit Behavior," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 15(3), pages 757-770, September.
    2. Zmijewski, Me, 1984. "Methodological Issues Related To The Estimation Of Financial Distress Prediction Models," Journal of Accounting Research, Wiley Blackwell, vol. 22, pages 59-82.
    3. Wilcox, Jw, 1971. "Simple Theory Of Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 9(2), pages 389-345.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Llano Monelos Pablo De & Piñeiro Sánchez Carlos & Rodríguez López Manuel, 2014. "DEA as a business failure prediction tool. Application to the case of galician SMEs," Contaduría y Administración, Accounting and Management, vol. 59(2), pages 65-96, abril-jun.
    2. Yu, Lean & Yao, Xiao & Zhang, Xiaoming & Yin, Hang & Liu, Jia, 2020. "A novel dual-weighted fuzzy proximal support vector machine with application to credit risk analysis," International Review of Financial Analysis, Elsevier, vol. 71(C).
    3. Shiyi Chen & Kiho Jeong & Wolfgang K. Härdle, 2008. "Recurrent Support Vector Regression for a Nonlinear ARMA Model with Applications to Forecasting Financial Returns," SFB 649 Discussion Papers SFB649DP2008-051, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    4. Shiyi Chen & Kiho Jeong & Wolfgang Härdle, 2015. "Recurrent support vector regression for a non-linear ARMA model with applications to forecasting financial returns," Computational Statistics, Springer, vol. 30(3), pages 821-843, September.
    5. Neuberg Richard & Hannah Lauren, 2017. "Loan pricing under estimation risk," Statistics & Risk Modeling, De Gruyter, vol. 34(1-2), pages 69-87, June.
    6. Detlef Seese & Christof Weinhardt & Frank Schlottmann (ed.), 2008. "Handbook on Information Technology in Finance," International Handbooks on Information Systems, Springer, number 978-3-540-49487-4, November.
    7. Wei Li & Wolfgang Karl Hardle & Stefan Lessmann, 2022. "A Data-driven Case-based Reasoning in Bankruptcy Prediction," Papers 2211.00921, arXiv.org.
    8. Rafal Weron, 2006. "Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach," HSC Books, Hugo Steinhaus Center, Wroclaw University of Technology, number hsbook0601.
    9. Jurij Weinblat, 2018. "Forecasting European high-growth Firms - A Random Forest Approach," Journal of Industry, Competition and Trade, Springer, vol. 18(3), pages 253-294, September.

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    More about this item

    Keywords

    support vector machine; classification method; statistical learning theory; electric load prediction; optical character recognition; predicting bankruptcy; risk classification;
    All these keywords.

    JEL classification:

    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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