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

Eduard Sariev and Guido Germano

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

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.

Keywords: default risk; logistic regression; support vector machines (search for similar items in EconPapers)
JEL-codes: C10 C13 (search for similar items in EconPapers)
Pages: 1 pages
Date: 2018-11-28
New Economics Papers: this item is included in nep-cmp, nep-ecm, nep-rmg and nep-tra
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Published in Annual Review of Financial Economics, 28, November, 2018, 0(0). ISSN: 1941-1367

Downloads: (external link)
http://eprints.lse.ac.uk/100211/ Open access version. (application/pdf)

Related works:
Journal Article: An innovative feature selection method for support vector machines and its test on the estimation of the credit risk of default (2019)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:100211

Access Statistics for this paper

More papers in LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library LSE Library Portugal Street London, WC2A 2HD, U.K.. Contact information at EDIRC.
Bibliographic data for series maintained by LSERO Manager ().