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

Abstract

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.

Keywords:

• bankruptcy prediction
• bank classification
• financial ratios
• support vector machines

Acknowledgements

This research was supported as a D-Type project by Marmara University Scientific Research Projects Committee (BAPKO).

Notes

SDIF is managed by the BRSA, which is a legal entity with administrative and financial autonomy.