Credit risk prediction using support vector machines

Credit risk prediction using support vector machines 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Review of Quantitative Finance and Accounting Springer Journals

Credit risk prediction using support vector machines

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Publisher
Springer US
Copyright
Copyright © 2010 by Springer Science+Business Media, LLC
Subject
Finance; Corporate Finance; Accounting/Auditing; Econometrics; Operation Research/Decision Theory
ISSN
0924-865X
eISSN
1573-7179
D.O.I.
10.1007/s11156-010-0190-3
Publisher site
See Article on Publisher Site

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