Predicting Efficiency in Islamic Banks: An Integrated Multicriteria Decision Making (MCDM) Approach

Journal of International Financial Markets, Institutions & Money (2016)

32 Pages Posted: 1 Nov 2018

See all articles by Peter Wanke

Peter Wanke

Universidade Federal do Rio de Janeiro (UFRJ) - Alberto Luiz Coimbra Institute for Graduate Studies and Research in Engineering (COPPE)

Abul Kalam Azad

University of Malaya (UM) - Department of Applied Statistics

Carlos Pestana Barros

Technical University of Lisbon - Instituto Superior de Economia e Gestao

M. Kabir Hassan

University of New Orleans - College of Business Administration - Department of Economics and Finance

Date Written: October 9, 2018

Abstract

This paper presents an efficiency assessment of 114 Islamic banks from 24 countries using the “Technique for Order Preference by Similarity to the Ideal Solution” (TOPSIS). TOPSIS is a multicriteria decision making technique similar to Data Envelopment Analysis (DEA), which ranks a finite set of units based on the minimization of distance from an ideal point and the maximization of distance from an anti-ideal point. In this research, TOPSIS is used first in a two-stage approach to assess the relative efficiency of Islamic banks using the most frequent indicators adopted by the literature. Then, in the second stage, neural networks are combined with TOPSIS results as part of an attempt to produce a model for banking performance with effective predictive ability. The results reveal that variables related to both country origin and cost structure have a prominent impact on efficiency. Findings also indicate that the Islamic banking market would benefit from a higher level of competition between institutions.

Keywords: Islamic banks, TOPSIS, two-stage, neural networks, efficiency

Suggested Citation

Wanke, Peter and Azad, Abul Kalam and Barros, Carlos Pestana and Hassan, M. Kabir, Predicting Efficiency in Islamic Banks: An Integrated Multicriteria Decision Making (MCDM) Approach (October 9, 2018). Journal of International Financial Markets, Institutions & Money (2016), Available at SSRN: https://ssrn.com/abstract=3263351

Peter Wanke

Universidade Federal do Rio de Janeiro (UFRJ) - Alberto Luiz Coimbra Institute for Graduate Studies and Research in Engineering (COPPE) ( email )

Rua 36 n. 355
Rio de Janeiro, 21949-900
Brazil

HOME PAGE: http://www.coppead.ufrj.br/en/

Abul Kalam Azad

University of Malaya (UM) - Department of Applied Statistics ( email )

Malaysia

Carlos Pestana Barros

Technical University of Lisbon - Instituto Superior de Economia e Gestao ( email )

R. Miguel Lupi, 20
Lisbon, 1200
Portugal

M. Kabir Hassan (Contact Author)

University of New Orleans - College of Business Administration - Department of Economics and Finance ( email )

2000 Lakeshore Drive
New Orleans, LA 70148
United States

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