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Predicting Bankruptcy with Support Vector Machines

Wolfgang Härdle (), Rouslan Moro and Dorothea Schäfer

SFB 649 Discussion Papers from Humboldt University, Collaborative Research Center 649

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.

Keywords: support vector machine; classification method; statistical learning theory; electric load prediction; optical character recognition; predicting bankruptcy; risk classification (search for similar items in EconPapers)
JEL-codes: C40 G10 (search for similar items in EconPapers)
Pages: 25 pages
Date: 2005-03
New Economics Papers: this item is included in nep-ecm, nep-fin, nep-ict and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:hum:wpaper:sfb649dp2005-009

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