Applying preferred information of the decision maker with production trade-offs in the process of evaluating the performance of banks
Subject Areas : Optimization
1 - عضو هیات علمی دانشگاه آزاد اسلامی واحد شیراز
Keywords: Data envelopment analysis, Banking, Two stage, Preferred information, production trade-offs.,
Abstract :
One of the methods to apply preferred information of the decision maker (DM) in the process of evaluating the efficiency of banks is the method of production trade-offs in data envelopment analysis (DEA). In this paper, we propose a two stage network DEA framework for incorporating value-judgments in the form of production trade-offs to analyze the efficiency of banks. We obtain technical and cost efficiency from banks based on bank manager's opinion. We use the production trade-off method to consider the importance of each of the inputs, intermediate measure and outputs relative to each other to evaluate the performance of commercial banks. We show that by changing the production trade-offs matrix, the technical and cost efficiency scores of banks also change. We propose efficient targets for inefficient banks. At the end, we bring the results of the paper.
An, Q., Chen, H., Wu, J., Liang, L. (2015). Measuring slacks-based efficiency for commercial banks in China by using a two-stage DEA model with undesirable output. Annals of Operations Research, 235(1), 13–35. https://doi.org/10.1007/s10479-015-1987-1
Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European journal of operational research, 2(6), 429-444. https://doi.org/10.1016/0377-2217(78)90138-8
Farrell, M.J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical
Society, Series A (General), 120, 253-290. https://www.jstor.org/stable/2343100
Fang, J., Lau, C.-K. M., Lu, Z., Tan, Y., & Zhang, H. (2019). Bank performance in China: A Perspective from Bank efficiency, risk-taking and market competition. Pacific-Basin Finance Journal, 56, 290–309. https://doi.org/10.1016/j.pacfin.2019.06.011
Fukuyama, H., & Matousek, R. (2011). Efficiency of Turkish banking: Two-stage network system. Variable returns to scale model. Journal of International Financial Markets, Institutions and Money, 21, 75–91. https://doi.org/10.1016/j.intfin.2010.08.004
Fukuyama, H., & Matousek, R. (2017). Modelling bank performance: A network DEA approach. European Journal of Operational Research, 259, 721–732. https://doi.org/ 10.1016/j.ejor.2016.10.044
Fukuyama, H., & Tan, Y. (2022a). Deconstructing three-stage over efficiency into input, output and stability efficiency components with consideration of market power and loan loss provision: An application to Chinese banks. International Journal of Finance and Economics, 27, 953–974. https://doi.org/10.1002/ijfe.2185
Fukuyama, H., & Tan, Y. (2022b). Implementing strategic disposability for performance evaluation: Innovation, stability, profitability and corporate social responsibility in Chinese banking. European Journal of Operational Research, 296, 652–668. https:// doi.org/10.1016/j.ejor.2021.04.022
Fukuyama, H., Tsionas, M., & Tan, Y. (2023). Dynamic network data envelopment analysis with a sequential structure and behavioral-causal analysis: Application to the Chinese banking industry. European Journal of Operational Research, 307, 1360–1373. https://doi.org/10.1016/j.ejor.2021.04.022
Henriques, I. C., Sobreiro, V. A., Kimura, H., & Mariano, E. B. (2020). Two-stage DEA in banks: Terminological controversies and future directions. Expert Systems with Applications, 161 113632. https://doi.org/10.1016/j.eswa.2020.113632.
Gerami, J., Mozaffari, M. R., Wanke, P., & Tan, Y. (2024). Fuzzy cost, revenue efficiency assessment and target setting in fuzzy DEA: a fuzzy directional distance function approach. Journal of Modelling in Management, 19(1), 240-287. https://doi.org/10.1108/JM2-05-2022-0121
Izadikhah, M., Tavana, M., Di Caprio, D., & Santos-Arteaga, F. J. (2018). A novel two-stage DEA production model with freely distributed initial inputs and shared intermediate outputs. Expert Systems with Applications, 99, 213–230. https://doi.org/ 10.1016/j.eswa.2017.11.005
Kao, Ch. (2024). Maximum slacks-based measure of efficiency in network data envelopment analysis: A case of garment manufacturing. Omega, 123, 102989. https://doi.org/10.1016/j.omega.2023.102989
Kraidi, A. A., Daneshvar, S., &, Adesina, K. A. (2024). Weight-restricted approach on constant returns to scale DEA models: Efficiency of internet banking in Turkey. Heliyon, 10, 31008.
Konara, P., Tan, Y., & Johnes, J. (2019). FDI and heterogeneity in bank efficiency: Evidence from emerging markets. Research in International Business and Finance, 49, 100–113. https://doi.org/10.1016/j.ribaf.2019.02.008
Liu, X., Yang, F., & Wu, J. (2020). DEA considering technological heterogeneity and intermediate output target setting: the performance analysis of Chinese commercial banks. Annals of Operations Research, 291, 605–26.
https://doi.org/10.1007/s10479-019-03413-w
Podinovski, V. V. (2004). Production trade-offs and weight restrictions in data envelopment analysis. Journal of the Operational Research Society, 55 (12), 1311–1322. https://doi.org/10.1057/palgrave.jors.2601794.
Podinovski, V. V., & Bouzdine-Chameeva, T. (2013). Weight restrictions and free production in data envelopment analysis. Operations Research, 61 (2), 426–437. https://doi.org/10.1287/opre.1120.1122.
Podinovski, V.V., & Bouzdine-Chameeva, T. (2015). Consistent weight restrictions in data envelopment analysis. European Journal of Operational Research, 244(1), 201–209. https://doi.org/10.1016/j.ejor.2015.01.037.
Podinovski, V.V. (2016). Optimal Weights in DEA Models with Weight Restrictions. European Journal of Operational Research, 254(3), 916-924.
https://doi.org/10.1016/j.ejor.2016.04.035.
Podinovski,V.V., Wu, J., & Argyris, N. (2024). Production trade-offs in models of data envelopment analysis with ratio inputs and outputs: An application to schools in England. European Journal of Operational Research. 313 (1), 359-372. https://doi.org/10.1016/j.ejor.2023.08.019.
Joro, T., & Korhonen, P. (2015). Extension of data envelopment analysis with preference information. New York: International series in operations research & management science, Springer. https://doi.org/10.1007/978-1-4899-7528-7_6
Tan, Y., & Floros, C. (2018). Risk, competition and efficiency in banking: Evidence from China. Global Finance Journal, 35, 223–236. https://doi.org/10.1016/j. gfj.2017.12.001
Tan, Y., Floros, C., & Anchor, J. (2017). The Profitability of Chinese banks: Impacts of risk, competition and efficiency. Review of Accounting and Finance, 16, 86–105. https://doi.org/10.1108/RAF-05-2015-0072
Tone, K. (2002). A strange case of the cost and allocative efficiencies in DEA. Journal of the
Operational Research Society, 53, 1225-1231. https://doi.org/10.1057/palgrave.jors.2601438
Wanke, P., & Barros, C. (2014). Two-stage DEA: An application to major Brazilian banks. Expert Systems with Applications, 41, 2337–2344. https://doi.org/10.1016/j. eswa.2013.09.031
Wanke, P., Barros, C. P., & Emrouznejad, A. (2016). Assessing productive efficiency of banks using integrated Fuzzy-DEA and bootstrapping: A case of Mozambican banks. European Journal of Operational Research, 249, 378–389. https://doi.org/10.1016/j. ejor.2015.10.018
Wanke, P., Barros, C. P., & Emrouznejad, A. (2018). A comparison between stochastic DEA and fuzzy DEA approaches: Revisiting efficiency in Angolan banks. RAIRO-Operations Research, 52, 285–303. https://doi.org/10.1051/ro/2016065
Wang, K., Huang, W., Wu, J., & Liu, Y. N. (2014). Efficiency measures of the Chinese commercial banking system using an additive two-stage DEA. Omega, 44, 5–20. https://doi.org/10.1016/j.omega.2013.09.005
Xu, G., Zhou, Z. (2021). Assessing the efficiency of financial supply chain for Chinese commercial banks: a two-stage AR-DEA model. Industrial Management & Data Systems.
121(4), 894–920. https://doi.org/10.1108/IMDS-01-2020-0022
Yin, P., Chu, J., Wu, J., Ding, J., Yang, M., & Wang, Y. (2020). A DEA-based two-stage network approach for hotel performance analysis: An internal cooperation perspective. Omega, 93, 102035. https://doi.org/10.1016/j.omega.2019.02.004
Zhou, X., Xu, Z., Chai, J., Yao, L., Wang, S., Lev, B. (2019). Efficiency evaluation for banking systems under uncertainty: a multi-period three-stage DEA model. Omega, 85:68–82. https://doi.org/10.1016/j.omega.2018.05.012