بانک های اسلامی در مقابل بانک های متعارف در کشورهای شورای همکاری خلیج: مطالعه مقایسه ای با استفاده از تکنیک های طبقه بندی

This paper contributes to the empirical literature on Islamic finance by investigating the feature of Islamic and conventional banks in Gulf Cooperation Council (GCC) countries over the period 2003–2010. We use parametric and non-parametric classification models (Linear discriminant analysis, Logistic regression, Tree of classification and Neural network) to examine whether financial ratios can be used to distinguish between Islamic and conventional banks. Univariate results show that Islamic banks are, on average, more profitable, more liquid, better capitalized, and have lower credit risk than conventional banks. We also find that Islamic banks are, on average, less involved in off-balance sheet activities and have more operating leverage than their conventional peers. Results from classification models show that the two types of banks may be differentiated in terms of credit and insolvency risk, operating leverage and off-balance sheet activities, but not in terms of profitability and liquidity. More interestingly, we find that the recent global financial crisis has a negative impact on the profitability for both Islamic and conventional banks, but time shifted. Finally, results show that Logit regression obtained slightly higher classification accuracies than other models.