Prediction of bankruptcy using support vector machines: an application to bank bankruptcy

The purpose of this study was to apply support vector machines (SVMs) to bank bankruptcy analysis using practical steps. Although the prediction of the financial distress of companies is done using several statistical and machine learning techniques, bank classification and bankruptcy prediction sti...

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Bibliographic Details
Published in:Journal of statistical computation and simulation 2013-08, Vol.83 (8), p.1543-1555
Main Author: Erdogan, Birsen Eygi
Format: Article
Language:English
Subjects:
bank classification
Banking
Bankruptcies
Bankruptcy
bankruptcy prediction
Banks
Classification
Early warning systems
Financial ratios
Gaussian
Predictions
Statistical analysis
Studies
Support vector machines
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Summary:The purpose of this study was to apply support vector machines (SVMs) to bank bankruptcy analysis using practical steps. Although the prediction of the financial distress of companies is done using several statistical and machine learning techniques, bank classification and bankruptcy prediction still need to be investigated because few investigations have been conducted in this field of banking. In this study, SVMs were implemented to analyse financial ratios. Data sets from Turkish commercial banks were used. This study shows that SVMs with the Gaussian kernel are capable of extracting useful information from financial data and can be used as part of an early warning system.
ISSN:0094-9655
1563-5163
DOI:10.1080/00949655.2012.666550