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A probabilistic neural network approach for modelling and classifying efficiency of GCC banks

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  • Mohamed M. Mostafa

Abstract

Understanding the efficiency levels is crucial for understanding the competitive structure of a market and/or segments of a market. This study uses two artificial Neural Networks (NN) and a traditional statistical classification method to model and classify the relative efficiency of top Gulf Cooperation Council (GCC) 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 modelling algorithms. The implications of these results for potential efficiency programmes are discussed.

Suggested Citation

  • Mohamed M. Mostafa, 2009. "A probabilistic neural network approach for modelling and classifying efficiency of GCC banks," International Journal of Business Performance Management, Inderscience Enterprises Ltd, vol. 11(3), pages 236-258.
    • Handle: RePEc:ids:ijbpma:v:11:y:2009:i:3:p:236-258
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    Cited by:

    1. Kwon, He-Boong, 2017. "Exploring the predictive potential of artificial neural networks in conjunction with DEA in railroad performance modeling," International Journal of Production Economics, Elsevier, vol. 183(PA), pages 159-170.
    2. Samoilenko, Sergey & Osei-Bryson, Kweku-Muata, 2013. "Using Data Envelopment Analysis (DEA) for monitoring efficiency-based performance of productivity-driven organizations: Design and implementation of a decision support system," Omega, Elsevier, vol. 41(1), pages 131-142.
    3. Samoilenko, Sergey & Osei-Bryson, Kweku-Muata, 2010. "Determining sources of relative inefficiency in heterogeneous samples: Methodology using Cluster Analysis, DEA and Neural Networks," European Journal of Operational Research, Elsevier, vol. 206(2), pages 479-487, October.

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