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

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  • Laura Auria
  • Rouslan A. Moro
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    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.

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    File URL: http://www.diw.de/documents/publikationen/73/diw_01.c.88369.de/dp811.pdf
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    Bibliographic Info

    Paper provided by DIW Berlin, German Institute for Economic Research in its series Discussion Papers of DIW Berlin with number 811.

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    Length: 16 p.
    Date of creation: 2008
    Date of revision:
    Handle: RePEc:diw:diwwpp:dp811

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    Keywords: Company rating; bankruptcy analysis; support vector machines;

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    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, Research Centre.
    2. 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.
    3. 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.
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