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Review of Quantitative Finance and Accounting

01.05.2011 | Original Research

Credit risk prediction using support vector machines

verfasst von: Jan-Henning Trustorff, Paul Markus Konrad, Jens Leker

Erschienen in: Review of Quantitative Finance and Accounting | Ausgabe 4/2011

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Abstract

The main purpose of this paper is to examine the relative performance between least-squares support vector machines and logistic regression models for default classification and default probability estimation. The financial ratios from a data set of more than 78,000 financial statements from 2000 to 2006 are used as default indicators. The main focus of this paper is on the influence of small training samples and high variance of the financial input data and the classification performance measured by the area under the receiver operating characteristic. The resolution and the reliability of the predicted default probabilities are evaluated by decompositions of the Brier score. It is shown that support vector machines significantly outperform logistic regression models, particularly under the condition of small training samples and high variance of the input data.

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Regarding company size, three classes of sales volume are distinguished: small (<EUR 8 million), medium (EUR 8–16 million) and large (>EUR 16 million). These sales classes refer to §267 HGB (German Accounting Standard).

A company-unique data sample includes N data points x _k of the companies k = 1, …, N.

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Titel: Credit risk prediction using support vector machines
verfasst von: Jan-Henning Trustorff
Paul Markus Konrad
Jens Leker
Publikationsdatum: 01.05.2011
Verlag: Springer US
Erschienen in: Review of Quantitative Finance and Accounting / Ausgabe 4/2011
Print ISSN: 0924-865X
Elektronische ISSN: 1573-7179
DOI: https://doi.org/10.1007/s11156-010-0190-3