Abstract
An approach based on relations between fuzzy numbers to assess the performance of Tunisian banks
Highlights
Tunisian commercial banks continue to spend high portion of their budgets on new technologies and innovation in order to enhance their competitiveness
Fuzzy data envelopment analysis (FDEA) represents an interesting method to deal with this kind of data
Bo et al (2011) proposed a fuzzy super-efficiency slack-based measure Data Envelopment Analysis (DEA) to analyze the performance of 24 commercial banks facing problems on loan and investment parameters with vague characteristics
Summary
Tunisian commercial banks continue to spend high portion of their budgets on new technologies and innovation in order to enhance their competitiveness. Ratio analysis provides relatively insignificant amount of information when we consider the effects of economies of scale, the identification of benchmarking policies, and the estimation of overall performance measures of firms. Another important method used in the evaluation of bank performances is Data Envelopment Analysis (DEA). DEA is a non-parametric method based on linear programming It provides a relative evaluation of technical efficiency of different firms. Fuzzy data envelopment analysis (FDEA) represents an interesting method to deal with this kind of data This method was applied in some studies to measure the efficiency of banks. Bo et al (2011) proposed a fuzzy super-efficiency slack-based measure DEA to analyze the performance of 24 commercial banks facing problems on loan and investment parameters with vague characteristics. Kao and Liu (2004) used FCCR model to predict the performance of 24 commercial banks in Taiwan based on their financial forecasts. Wu et al (2006) used FBCC model to deal with environmental variables in order to assess the efficiency of bank branches from different regions in Canada. Yalcin et al (2009) developed a multi-criteria decision model to evaluate the performances of Turkish banks. Pramodh et al (2008) proposed a measurement technique that combines DEA method and Fuzzy Multi Attribute Decision Making technique to measure the productivity levels of Indian banks. Wang et al (2014) investigated the association between the performance of bank holding companies and their intellectual capital and applied fuzzy multiple objective programming approaches to calculate efficiency scores. Puri and Yadav (2013a) evaluated the fuzzy input mix-efficiency using the α–level based approach for the State Bank of Patiala in the Punjab state of India. Puri and Yadav (2013b) proposed another fuzzy DEA model with undesirable fuzzy outputs to calculate the efficiency scores. Chen et al (2013) applied the Fuzzy Slack-Based Measurement model in the Taiwan banking sector under market risk
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