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Rating Companies with Support Vector Machines

Author

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

Abstract

The goal of this work is to introduce one of the most successful among recently developed statistical techniques - the support vector machine (SVM) - to the field of corporate bankruptcy analysis. The main emphasis is done on implementing SVMs for analysing predictors in the form of financial ratios. A method is proposed of adapting SVMs to default probability estimation. A survey of practically and commercially applied methods is given. This work proves that support vector machines are capable of extracting useful information from financial data although extensive data sets are required in order to fully utilise their classification power.

Suggested Citation

  • Wolfgang K. Härdle & Rouslan A. Moro & Dorothea Schäfer, 2004. "Rating Companies with Support Vector Machines," Discussion Papers of DIW Berlin 416, DIW Berlin, German Institute for Economic Research.
    • Handle: RePEc:diw:diwwpp:dp416
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    References listed on IDEAS

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    3. Dimitras, A. I. & Slowinski, R. & Susmaga, R. & Zopounidis, C., 1999. "Business failure prediction using rough sets," European Journal of Operational Research, Elsevier, vol. 114(2), pages 263-280, April.
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    5. Kar Yan Tam & Melody Y. Kiang, 1992. "Managerial Applications of Neural Networks: The Case of Bank Failure Predictions," Management Science, INFORMS, vol. 38(7), pages 926-947, July.
    6. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    7. Altman, Edward I. & Haldeman, Robert G. & Narayanan, P., 1977. "ZETATM analysis A new model to identify bankruptcy risk of corporations," Journal of Banking & Finance, Elsevier, vol. 1(1), pages 29-54, June.
    8. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
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    Cited by:

    1. Błażej Prusak, 2018. "Review of Research into Enterprise Bankruptcy Prediction in Selected Central and Eastern European Countries," IJFS, MDPI, vol. 6(3), pages 1-28, June.
    2. Prusak Błażej, 2019. "Corporate Bankruptcy Prediction in Poland Against the Background of Foreign Experience," Financial Internet Quarterly (formerly e-Finanse), Sciendo, vol. 15(1), pages 10-19, March.
    3. Laura Auria & Rouslan A. Moro, 2008. "Support Vector Machines (SVM) as a Technique for Solvency Analysis," Discussion Papers of DIW Berlin 811, DIW Berlin, German Institute for Economic Research.

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    More about this item

    Keywords

    Support vector machines; Company rating; Default probability estimation;
    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
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

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