Assessing Bank Soundness with Classification Techniques

Omega - The International Journal of Management Science, Vol. 38, pp. 345-357, 2010 (revised version)

University of Bath School of Management Working Paper Series 2009.04

36 Pages Posted: 6 Mar 2009 Last revised: 28 Sep 2012

See all articles by Christos Ioannidis

Christos Ioannidis

University of Bath-Department of Economics

Fotios Pasiouras

GSCM-Montpellier Business School

C. Zopounidis

Technical University of Crete (TUC) - Department of Production Engineering and Management

Date Written: March 5, 2009

Abstract

The recent crisis highlighted, once again, the importance of early warning models to assess the soundness of individual banks. In the present study, we use six quantitative techniques originating from various disciplines to classify banks in three groups. The first group includes very strong and strong banks; the second one includes adequate banks, while the third group includes banks with weaknesses or serious problems. We compare models developed with financial variables only, with models that incorporate additional information in relation to the regulatory environment, institutional development, and macroeconomic conditions. The accuracy of classification of the models that include only financial variables is rather poor. We observe a substantial improvement in accuracy when we consider the country-level variables, with five out of the six models achieving out-of-sample classification accuracy above 70% on average. The models developed with multi-criteria decision aid and artificial neural networks achieve the highest accuracies. We also explore the development of stacked models that combine the predictions of the individual models at a higher level. While the stacked models outperform the corresponding individual models in most cases, we found no evidence that the best stacked model can outperform the best individual model.

Keywords: Bank; Classification, Integration, Soundness

JEL Classification: G21, C63, G33

Suggested Citation

Ioannidis, Christos and Pasiouras, Fotios and Zopounidis, Constantin, Assessing Bank Soundness with Classification Techniques (March 5, 2009). Omega - The International Journal of Management Science, Vol. 38, pp. 345-357, 2010 (revised version), University of Bath School of Management Working Paper Series 2009.04, Available at SSRN: https://ssrn.com/abstract=1354199

Christos Ioannidis

University of Bath-Department of Economics ( email )

Claverton Down
Bath, BA2 7AY
United Kingdom

Fotios Pasiouras (Contact Author)

GSCM-Montpellier Business School ( email )

2300, Avenue des Moulins
Montpellier, 34185
France

Constantin Zopounidis

Technical University of Crete (TUC) - Department of Production Engineering and Management ( email )

University Campus
Chania
Crete, 73100
Greece
+30 28210 37236 (Phone)
+30 28210 69410, 37236 (Fax)

Do you have negative results from your research you’d like to share?

Paper statistics

Downloads
258
Abstract Views
1,447
Rank
215,727
PlumX Metrics