Modeling the efficiency of top Arab banks: A DEA–neural network approach |
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| Authors: | Mohamed M. Mostafa |
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| Affiliation: | 1. Faculty of Commerce, Fukuoka University, 8-19-1 Nanakuma, Jonan-ku, Fukuoka 814-0180, Japan;2. Kent Business School, University of Kent, Canterbury CT2 7NZ, England;1. School of Economics, Finance and Accounting, Faculty of Business and Law, Coventry University, Priory Street, Coventry CV1 5FB, United Kingdom;2. Department of Accounting and Finance, Adam Smith Business School, University of Glasgow, University Avenue, West Quadrangle, Gilbert Scott Building, Glasgow G12 8QQ, United Kingdom;3. University of Edinburgh Business School, University of Edinburgh, 29 Buccleuch Place, Edinburgh EH89JS, United Kingdom |
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| Abstract: | This study investigates the efficiency of top Arab banks using two quantitative methodologies: data envelopment analysis and neural networks. The study uses a probabilistic neural network (PNN) and a traditional statistical classification method to model and classify the relative efficiency of top Arab banks. Accuracy indices are used to assess the classification accuracy of the models. Results indicate that the predictive accuracy of NN models is quite similar to that of traditional statistical methods. The study shows that the NN models have a great potential for the classification of banks’ relative efficiency due to their robustness and flexibility of modeling algorithms. From a policy perspective, this study highlights the economic importance of encouraging increased efficiency throughout the banking industry in the Arab world. |
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