Estimating Efficiency of Bank Branches by Dynamic Network Data Envelopment Analysis and Artificial Neural Network

Advances in Mathematical Finance and Applications

مقاله 8، دوره 5، شماره 3، مهر 2020، صفحه 377-390   

نوع مقاله: Research Paper

شناسه دیجیتال (DOI): 10.22034/amfa.2019.1585957.1192

نویسندگان

Javad Niknafs؛ Mohammad Ali Keramati^* ؛ Jalal Haghighatmonfared

Department of Industrial Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran

چکیده

Network data envelopment analysis models assess efficiency of decision-making unit and its sections using historical data but fail to measure efficiency of its units and their internal stages in the future. In this paper we aim to measure efficiency of stages of bank branches and obtain efficiency trend of stages during the time, then to estimate their efficiency in the future therefore we can be aware of stages inefficiency before occurrence and prevent them. First, a two-stage structure including deposit collection and loan giving was designed for bank branches using literature review and comments of experts. Human forces and fixed assets were considered as input variables of the first stage, deposit as mediator variable, delayed claims as interim variable, and loan amount as output variable of the second stage. Then, a dynamic network data envelopment analysis model was formulated and stages efficiency were obtained for 16 consecutive periods. Therefore, efficiency trend of stages was obtained during the time. In the following, efficiency of various stages of branches were estimated using artificial neural network and some recommendations are provided according to obtained amounts in order to prevent inefficiency before occurrence.

کلیدواژه‌ها

Network data envelopment analysis؛ Dynamic network data envelopment analysis؛ Artificial Neural Network؛ Efficiency estimating؛ Bank

مراجع

[1] Fukuyama, H., Weber, W. L., A directional slacks-based measure of technical inefficiency, Socio-Economic Planning Sciences, 2009, 43(4), P. 274–287. Doi: 10.1016/j.seps.2008.12.001

 

[2] Fukuyama, H., Weber, W. L., Estimating indirect allocative inefficiency and productivity change, Journal of the Operational Research Society, 2009, 60(11), P. 1594–1608. Doi: 10.1057/jors.2009.62

 

[3] Liu, S. T., Slacks-based efficiency measures for predicting bank performance, Expert Systems with Applications, 2009, 36(2), P. 2813–2818. Doi: 10.1016/j.jeconbus.2015.07.002

 

[4] Charnes, A., Cooper, W. W., and Rhodes, E., Measuring the efficienc of decision making units, European Journal of Operational Research, 1978, 2, P. 429-444. Doi: 10.1016/0377-2217(78)90138-8

 

[5] Kao, C., Hwang, S. N., Efficiency measurement for network systems: IT impact on firm performance, Decision Support Systems, 2010, 48, P. 437-446. Doi: 10.1016/j.dss.2009.06.002

 

[6] Lin, T. Y., Chiu, S. H., Using independent component analysis and network DEA to improve bank performance evaluation, Economic Modelling, 2013, 32, P. 608-616. Doi: 10.1016/j.econmod.2013.03.003

 

[7] Fare, R., Grosskopf, S., Network DEA. Socio-Economic Planning Sciences, 2000, 34, P. 35-49. Doi:10.1016/38-0121(99)00012-9

 

[8] Kao, C., Efficiency decomposition for general multi-stage systems in data envelopment analysis, European Journal of Operational Research, 2014, 232, P. 117- 124. Doi: 10.1016/j.ejor.2013.07.012.

 

[9] Soleymani-Damaneh, R., Momeni, M, Mostafaee, A. and Soleymani Mal Khalifeh, M., Presenting Dynamic NDEA model for Evaluation Efficiency of Banks, Journal of Industrial Management Perspective, 2017, 25, P. 67-89. (In Persian)

 

[10] Kao, C., Liu, S. T., Multi-period efficiency measurement in data envelopment analysis: The case of Taiwanese commercial banks. Omega, 2014, 47, P. 90-98. Doi: 10.1016/j.omega.2013.09.001

 

[11] Avkiran, N.K., An illustration of dynamic network DEA in commercial banking including robustness tests, Omega, 2015, 55, P. 141-150. Doi: 10.1016/j.omega.2014.07.002

 

[12] Fukuyama, H., Weber, W.L., Measuring Japanese bank performance: A dynamic network DEA approach, Journal of Productivity Analysis, 2015, 44(3), P. 249-264. Doi: 10.1007/s11123-014-0403-1

 

[13] Fukuyama, H., Weber, W.L., Japanese bank productivity, 2007- 2012: A dynamic network approach. Pacific Economic Review, Wiley Blackwell, 2016, 22(4), P. 649-676. Doi: 10.1111/1468-0106.12199

 

[14] Ibiwoye, A., Ajibola, E., Sogunro, A.B., Artificial neural network model for predicting insurance insolvency, International Journal of Management and Business Research, 2012, 2(1), P. 59-68.

Doi:10.1109/NABIC.2009.5393413

 

[15] Luo, X. M., Evaluating the profitability and marketability efficiency of large banks: An application of data envelopment analysis, Journal of Business Research, 2003, 56(8), P. 627-635.

Doi:10.1016/S0148-2963(01)00293-4

 

[16] Jahanshahloo, G. R., Hadi-Vencheh, A., Foroughi, A. A., Kazemi-Matin, R., Inputs/ outputs estimation in DEA when some factors are undesirable, Applied Mathematics and Computations, 2004, 156(1), P. 19–32. Doi:10.1016/S0096-3003(03)00814-2

 

[17] Cook, W. D., Zhu, J., Bi, G., and Yang, F., Network DEA: additive efficiency decomposition, European Journal of Operational Research, 2010, 207(2), P. 11-22. Doi: 10.1016/j.ejor.2010.05.006

 

[18] Chen, Y., Cook, W. D., Li, N., and Zhu, J., Additive efficiency decomposition in two-stage DEA, European Journal of Operational Research, 2009, 196(3), P. 1170-1176. Doi: 10.1016/j.ejor.2008.05.011

 

[19] Du, J. A., Liang, L. A., Chen, Y., Cook, W. D., and Zhu, J., A bargaining game model for measuring performance of two-stage network structures, European Journal of Operational Research, 2011, 210(2), P. 390-397. Doi: 10.1016/j.ejor.2010.08.025

 

[20] Chen, C., Yan, H., Network DEA model for supply chain performance evaluation, European Journal of Operational Research, 2011, 213(1), P. 147–55. Doi: 10.1016/j.ejor.2011.03.010

 

[21] Färe, R., Grosskopf, S., Productivity and intermediate products: A frontier approach, Economics letters, 1996, 50(1), P. 65-70. Doi: 10.1016/0165-1765(95)00729-6

 

[22] Kao, C., Hwang, S.N., Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan, European Journal of Operational Research, 2008, 185(1), P. 418-429. Doi: 10.1016/j.ejor.2006.11.041

 

[23] Kao, C., Efficiency decomposition in network data envelopment analysis: A relational model. European Journal of Operational Research, 2009, 192(3), P. 949–962. Doi: 10.1016/j.ejor.2007.10.008

 

[24] Zhou, Z., Sun, L., Yang, W., Liu, W., and Ma, C., A bargaining game model for efficiency decomposition in the centralized model of two-stage systems, Computers and Industrial Engineering, 2013, 64(1), P. 103-108. Doi: 10.1016/j.cie.2012.09.014

 

[25] Kao, C., Network data envelopment analysis: A review, European journal of operational research, 2014, 239(1), P. 1-16. Doi: 10.1016/j.ejor.2014.02.039

 

[26] Tone, T., Tsutsui, M., Dynamic DEA with network structure: A slacks-based measure approach, Omega, 2014, 42, P. 124-131. Doi: 10.1016/j.omega.2013.04.002

 

[27] Soltanzadeh, E., Omrani, H., Dynamic network data envelopment analysis model with fuzzy inputs and outputs: An application for Iranian Airlines, Applied Soft Computing, 2018, 63, P. 268-288.

Doi: 10.1016/j.asoc.2017.11.031

 

[28] Khushalani, J., Ozcan, Y. A., Are hospitals producing quality care efficiently? An analysis using Dynamic Network Data Envelopment Analysis (DEA), Socio-Economic Planning Sciences, 2017, 60, P. 15-23.

Doi: 10.1016/j.seps.2017.01.009

 

[29] Zha, Y., Liang, N., Wu, M., Bian, Y., Efficiency evaluation of banks in china: A dynamic two-stage slacks-based measure approach, Omega. 2016, 60, P. 60-72. Doi: 10.1016/j.omega.2014.12.008

 

[30] Moreno, P. Lozano, S., Super SBI Dynamic Network DEA approach to measuring efficiency in the provision of public services, International Transactions in Operational Research, 2016, P. 1-21.

Doi:10.1111/itor.12257

 

[31] Wu, Y., Ting, I., Lu, W., Nourani, M., Kweh, Q.,The impact of earnings management on the performance of ASEAN banks, Economic Modelling, 2016, 53, P. 156-165. Doi: 10.1016/j.econmod.2015.11.023

 

[32] Izadikhah, M., Improving the banks shareholder long-term values by using data envelopment analysis model, Advances in Mathematical Finance and Applications. 2018, 3(2), P. 27-41.

Doi: 10.22034/amfa.2018.540829

 

[33] NabaviChashmi, S. A., Mansourian R., Azizi A., The Effect of Internal and External Factors on Outstanding Claims of Banks (Case Study of Listed Banks on the Tehran Stock Exchange), Advances in Mathematical Finance and Applications, 2016, 1(2), P. 43-56. Doi: 10.22034/amfa.2016.527815

 

[34] Fukuyama, H., Weber, W. L., A slacks-based inefficiency measure for a two-stage system with bad outputs, Omega, 2010, 38(5), P. 239–410. Doi: 10.1016/j.omega.2009.10.006

 

[35] Holod, D., Lewis, H. F., Resolving the deposit dilemma: A new DEA bank efficiency mode, Journal of Banking and Finance, 2011, 35, P. 2801-2810. Doi: 10.5267/j.ac.2018.09.001

 

[36] Akther, S., Fukuyama, H., Weber, W.L., Estimating two-stage network slacks-based inefficiency: An application to Bangladesh banking, Omega, 2013, 41(1), P. 88-96.4. Doi: 10.1016/j.omega.2011.02.009

 

[37] Fukuyama, H., Matousek, R., Modelling Bank Performance: A Network DEA Approach, European Journal of Operational Research, 2017, 259(2), P. 721-732. Doi: 10.1016/j.ejor.2016.10.044

 

[38] Akbari, S., Keramati, M., Keramati, A., Designing A Mixed System of Network DEA for Evaluating the Effi-ciency of Branches of Commercial Banks in Iran, Advances in Mathematical Finance and Applications, 2019, 4(1), P. 1-3. DOI:10.22034/AMFA.2019.582260.1165

 

[39] Kao, C., Efficiency measurement for parallel production systems, European Journal of Operational Research, 2009, 196, P. 1107-1112. Doi: 10.1016/j.ejor.2008.04.020

 

[40] McCulloch, W., and Pitts, W., A logical calculus of the ideas immanent in nervous activity, The bulletin of mathematical biophysics. 1943, 5(4), P. 115-133. Doi:10.1007/BF02459570

 

[41] Rumelhart, D.E., Hinton, G.E., and Williams, R.J., Learning representations by back propagating errors, Nature 1986, 323(6088), P. 533-536. Doi:10.1038/323533a0

 

[42] Zhang, G.P., Neural networks for classification: a survey, IEEE Trans. Syst. Man. Cybern. C. Appl. Rev. 2000, 30 (4), P. 451-462. Doi:10.1109/5326.897072

 

[43] Zhang, W.J., and Wei, W., Spatial succession modeling of biological communities: a multi-model approach, Environ. Monit. Assess. 2009, 158(1-4), P. 213-230. Doi:10.1007/s10661-008-0574-1

 

[44] Lu, P., and Rosenbaum, M.S., Artificial neural network and grey system for the prediction of slope stability, Nat. Hazards, 2003, 30(3), P. 383-398. Doi: 10.1023/B: NHAZ.0000007168.00673.27

 

[45] Krakovsky, R., Forgac, R., Neural network approach to multidimensional data classification via lustering, In: IEEE. 9th International Symposium on Intelligent Systems and Informatics, P. 169-174.

Doi:10.1109/SISY.2011.6034316

 

[46] Melchiorre, C., Matteucci, M., Azzoni, A., and Zanchi, A., Artificial neural networks and cluster analysis in landslide susceptibility zonation, Geomorpho 2008, 94(3), P. 379-400. Doi:10.1016/j.geomorph.2006.10.035

 

[47] Izadikhah, M., Saen, RF., Ahmadi, K., How to assess sustainability of suppliers in the presence of dual-role factor and volume discounts? A data envelopment analysis approach, Asia-Pacific Journal of Operational Research, 2017, 34 (03), 1740016, Doi: 10.1142/S0217595917400164

 

[48] Emrouznejad, A., Shale, E., A combined neural network and DEA for measuring efficiency of large scale datasets, Comput. Ind. Eng, 2009, 56 (1), P. 249e254. Doi:10.1016/j.cie.2008.05.012

 

[49] Desheng, W.U., Yang, Z., Liang, L., Using DEA-neural network approach to evaluate branch efficiency of a large Canadian bank, Expert Syst. Appl. 2006, 31(1), P. 108-115. Doi: 10.1016/j.eswa.2005.09.034

 

[50] Chun Tsai, M., Ping Lin, S., Chan Cheng, C., Ping Lin, Y., The consumer loan default predicting model-An application of DEA-DA and neural network, Expert System. Appl. 2009, 36(4), P. 11682-11690.

Doi: 10.1016/j.eswa.2009.03.009

 

[51] Wacker, J. G., A definition of theory: research guidelines for different theory-building research methods in operations management, Journal of Operations Management, 1998, 16(4), P. 361-385.

Doi: 10.1016/S0272-6963(98)00019-9