Abstract
This paper contributes to the empirical literature on Islamic
finance by doing comparison of Islamic and conventional banks in Pakistan
over the period 2005-2014. We apply both non-parametric and parametric
classification methods (neural network, linear discriminant analysis, and
logistic regression) to investigate whether financial ratios can be used to
distinguish between Islamic and conventional banks. Univariate analysis
reveals that Islamic banks are less profitable, better capitalized, more
liquid, and have low level of credit risk as compared to their conventional
counterparts. We also find that Islamic banks have more operating leverage
in comparison to conventional banks. The results from classification
techniques show that the two types of banks may be distinguished in terms
of insolvency risk, credit risk, efficiency, and operating leverage, but not
in terms of liquidity and profitability. More interestingly, we find that the
financial crisis has a negative effect on the profitability of both Islamic and
conventional banks. Lastly, the results show that the neural model obtained
higher classification accuracies as compared to other models used in the
study
Abdul Rashid, Mamoona Riaz, Atiq-uz-Zaffar. (2018) Are Islamic Banks Really Different from Conventional Banks? An Investigation using Classification Techniques, Journal of Islamic Business and Management, Volume 8, Issue 1.
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