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Support Vector Machines (SVM) as a Technique for Solvency Analysis

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  • Laura Auria
  • Rouslan A. Moro

Abstract

This paper introduces a statistical technique, Support Vector Machines (SVM), which is considered by the Deutsche Bundesbank as an alternative for company rating. A special attention is paid to the features of the SVM which provide a higher accuracy of company classification into solvent and insolvent. The advantages and disadvantages of the method are discussed. The comparison of the SVM with more traditional approaches such as logistic regression (Logit) and discriminant analysis (DA) is made on the Deutsche Bundesbank data of annual income statements and balance sheets of German companies. The out-of-sample accuracy tests confirm that the SVM outperforms both DA and Logit on bootstrapped samples.

Suggested Citation

  • 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.
  • Handle: RePEc:diw:diwwpp:dp811
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    File URL: http://www.diw.de/documents/publikationen/73/diw_01.c.88369.de/dp811.pdf
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    References listed on IDEAS

    as
    1. Engelmann, Bernd & Hayden, Evelyn & Tasche, Dirk, 2003. "Measuring the Discriminative Power of Rating Systems," Discussion Paper Series 2: Banking and Financial Studies 2003,01, Deutsche Bundesbank.
    2. 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.
    3. Wolfgang K. Härdle & Rouslan A. Moro & Dorothea Schäfer, 2004. "Support Vector Machines: eine neue Methode zum Rating von Unternehmen," DIW Wochenbericht, DIW Berlin, German Institute for Economic Research, vol. 71(49), pages 759-765.
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    Cited by:

    1. Kyuhan Lee & Jinsoo Park & Iljoo Kim & Youngseok Choi, 0. "Predicting movie success with machine learning techniques: ways to improve accuracy," Information Systems Frontiers, Springer, vol. 0, pages 1-12.

    More about this item

    Keywords

    Company rating; bankruptcy analysis; support vector machines;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: 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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