Revisiting Islamic banking efficiency using multivariate adaptive regression splines

Abstract Islamic banking is among rapidly-growing components in the worlds financial system. Within its institutions continuous criteria of efficiency facilitate the evaluation of the impact of the reforms and policies on the banks performance. In this paper we employ the Multivariate Adaptive Regression Splines (MARS) method for estimating the efficiency of Islamic banks in developed and developing countries. MARS is a well-known efficient method for the flexible modelling of high-dimensional data. Unlike previous work using a nonparametric technique of such a robustness instead of parametric approaches contributes to the improvement of the various estimates which provides investors with accurate and timely information they can immediately react upon for a better decision-making in turbulent times. On the one hand the results of the experiments show that in the emerging region there is evidence of a strong linkage between Islamic banking efficiency and gross domestic product. On the other hand in the developed region the efficiency is rather based upon Sharia Supervisory Board and board committees. These outcomes confirm previous works showing that governance-related variables have a significant positive effect on Islamic banking efficiency. Furthermore the overall MARS-based predictions reveal that Islamic banks operating in developed countries are relatively more efficient than their counterparts in emerging countries.
Foued Saâdaoui and Monjia Khalfi Foued Saâdaoui: King Abdulaziz University Monjia Khalfi: Université de Monastir
Machine learning ; Islamic banking ; Efficiency ; Governance ; Data-mining ; Subprime crisis ; G3 ; G21 ; C23 (search for similar items in EconPapers)
Machine Learning
Annals of Operations Research 2024 vol. 334 issue 1 No 12 287-315
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2024/03/18 03:31
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