Comparing the Bank Failure Prediction Performance of Neural Networks and Support Vector Machines: The Turkish Case

Fatih Ecer

Economic Research-Ekonomska Istraživanja, 2013, vol. 26, issue 3, 81-98

Abstract: Experience from the banking crises during the past two decades suggest that advanced prediction models are needed for helping prevent bank failures. This paper compares the ability of artificial neural networks and support vector machines in predicting bank failures. Although artificial neural networks have widely been applied complex problems in business, the literature utilizing support vector machines is relatively narrow and their capability for predicting bank failures is not very familiar. In this paper, these two intelligent techniques are applied to a dataset of Turkish commercial banks. Empirical findings show that although the prediction performance of the two models can be considered as satisfactory, neural networks show slightly better predictive ability than support vector machines. In addition, different types of error from each model also indicate that neural network models are better predictors.

Date: 2013
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://hdl.handle.net/10.1080/1331677X.2013.11517623 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:reroxx:v:26:y:2013:i:3:p:81-98

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/rero20

DOI: 10.1080/1331677X.2013.11517623

Access Statistics for this article

Economic Research-Ekonomska Istraživanja is currently edited by Marinko Skare

More articles in Economic Research-Ekonomska Istraživanja from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().