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An innovative feature selection method for support vector machines and its test on the estimation of the credit risk of default

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  • Sariev, Eduard
  • Germano, Guido

Abstract

Support vector machines (SVM) have been extensively used for classification problems in many areas such as gene, text and image recognition. However, SVM have been rarely used to estimate the probability of default (PD) in credit risk. In this paper, we advocate the application of SVM, rather than the popular logistic regression (LR) method, for the estimation of both corporate and retail PD. Our results indicate that most of the time SVM outperforms LR in terms of classification accuracy for the corporate and retail segments. We propose a new wrapper feature selection based on maximizing the distance of the support vectors from the separating hyperplane and apply it to identify the main PD drivers. We used three datasets to test the PD estimation, containing (1) retail obligors from Germany, (2) corporate obligors from Eastern Europe, and (3) corporate obligors from Poland. Total assets, total liabilities, and sales are identified as frequent default drivers for the corporate datasets, whereas current account status and duration of the current account are frequent default drivers for the retail dataset.

Suggested Citation

  • Sariev, Eduard & Germano, Guido, 2018. "An innovative feature selection method for support vector machines and its test on the estimation of the credit risk of default," LSE Research Online Documents on Economics 100211, London School of Economics and Political Science, LSE Library.
  • Handle: RePEc:ehl:lserod:100211
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    File URL: http://eprints.lse.ac.uk/100211/
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    Cited by:

    1. Sariev, Eduard & Germano, Guido, 2020. "Bayesian regularized artificial neural networks for the estimation of the probability of default," LSE Research Online Documents on Economics 101029, London School of Economics and Political Science, LSE Library.

    More about this item

    Keywords

    default risk; logistic regression; support vector machines; ES/ K002309/1;

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General

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