Predicting Bankruptcy with Support Vector Machines

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Predicting Bankruptcy with Support Vector Machines

by Wolfgang Härdle of Humboldt-Universität zu Berlin,
Rouslan A. Moro of Humboldt-Universität zu Berlin and German Institute for Economic Research, and
Dorothea Schäfer of the German Institute for Economic Research

June 2005

Abstract: The purpose of this work is to introduce one of the most promising among recently developed statistical techniques – the support vector machine (SVM) – to corporate bankruptcy analysis. An SVM is implemented for analysing such predictors as financial ratios. A method of adapting it to default probability estimation is proposed. A survey of practically applied methods is given. This work shows that support vector machines are capable of extracting useful information from financial data, although extensive data sets are required in order to fully utilize their classification power.

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