In this paper, we propose to evaluate the scores of productive efficiency of 20 Tunisian commercial banks throughout the period 1990-2009. The local banking landscape was marked, during the studied period, by significant changes following the adoption, by the Tunisian government, of various measures of financial liberalization. In order to study the levels of efficiency cost realized by the Tunisian commercial banks, we propose a parametric method, named the Stochastic Frontier Approach “SFA”, on the one hand, and the determination of the variables explaining the level of inefficiency (of efficiency) on the other hand. Our results make it possible to conclude that the banks which obtained the best scores of efficiency are the BT (99,5%), the BH (98,5%) followed the (94,9%) and de ATB (94,5%)
This article aims to address current gaps in the literature on banking efficiency using the data envelopment analysis (DEA) model. The study assesses the technical efficiency of the Turkish banking sector in the period spanning from 1990 to 2010 in-line with DEA model. In comparison with the DEA model, the CCR model (developed by Charnes, Cooper & Rhodes, European Journal of Operational Research, 1978, 2(6): 429–444) does not need model assumptions on input/output orientation. It also avoids the dilemma about the choice of input/output indicators. The comparison analysis of this study reveals that the CCR model yields more significant efficiency results than the BCC model (developed by Banker, Charnes & Cooper, Management Science, 1984, 30(9): 1078–1092). This study provides important contribution to the current empirical research on banking efficiency. The empirical results of the study reveal a steady increase in global efficiency of the Turkish banking sector over the past two decades. The future research could focus on the behavioural aspects of bank efficiency (e.g., managerial practices). The study also offers managerial implications.
The deterioration of bank profitability poses a threat not only to the interests of consumers and internal staff members but also affects investors who may equally suffer from significant financial losses. It is important to establish an effective system which assists investors in their investment choices. In prior literature, traditional models have been developed, but achieved short‐term performances such as logistic regression and discriminant analysis. This paper applies a partial least squares discriminant analysis (PLS‐DA) to distinguish between conventional and Islamic banks in the Middle East and North Africa (MENA) region based on the financial information for the period 2005–2011. This method can successfully identify the non‐linearity and correlations between financial indicators. The results demonstrate superior performance of the proposed method. On one hand, our model can select all financial ratios to distinguish between banks and at the same time identify the most important variables in the distinction process. On the other hand, the proposed model has high levels in terms of accuracy and stability.
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