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Estimation of default probabilities with Support Vector Machines

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  • Chen, Shiyi
  • Härdle, Wolfgang Karl
  • Moro, Rouslan A.

Abstract

Predicting default probabilities is important for firms and banks to operate successfully and to estimate their specific risks. There are many reasons to use nonlinear techniques for predicting bankruptcy from financial ratios. Here we propose the so called Support Vector Machine (SVM) to estimate default probabilities of German firms. Our analysis is based on the Creditreform database. The results reveal that the most important eight predictors related to bankruptcy for these German firms belong to the ratios of activity, profitability, liquidity, leverage and the percentage of incremental inventories. Based on the performance measures, the SVMtool can predict a firms default risk and identify the insolvent firm more accurately than the benchmark logit model. The sensitivity investigation and a corresponding visualization tool reveal that the classifying ability of SVM appears to be superior over a wide range of the SVM parameters. Based on the nonparametric Nadaraya-Watson estimator, the expected returns predicted by the SVM for regression have a significant positive linear relationship with the risk scores obtained for classification. This evidence is stronger than empirical results for the CAPM based on a linear regression and confirms that higher risks need to be compensated by higher potential returns.

Suggested Citation

  • Chen, Shiyi & Härdle, Wolfgang Karl & Moro, Rouslan A., 2006. "Estimation of default probabilities with Support Vector Machines," SFB 649 Discussion Papers 2006-077, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    • Handle: RePEc:zbw:sfb649:sfb649dp2006-077
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    Cited by:

    1. Selçuk BAYRACI & Orkun SUSUZ, 2019. "A Deep Neural Network (DNN) based classification model in application to loan default prediction," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania / Editura Economica, vol. 0(4(621), W), pages 75-84, Winter.
    2. repec:diw:diwwpp:dp757 is not listed on IDEAS
    3. repec:hum:wpaper:sfb649dp2008-005 is not listed on IDEAS
    4. 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.

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    More about this item

    Keywords

    Support Vector Machine; Bankruptcy; Default Probabilities Prediction; Expected Profitability; CAPM;
    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
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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