Evaluation of Bank Branches with Financial Indicators Using Data Envelopment Analysis

Subject Areas : Financial engineering

somayeh Razipour-GhalehJough ¹ , ّFarhad Hosseinzadeh Lotfi ² , Mohsen Rostamy-Maslkhalifeh ³ , Hamid Sharafi ⁴

1 - Department of Mathematics, Science and Research Branch, Islamic Azad University, Tehran, Iran.
2 - Department of Mathematics, Science and Research Branch, Islamic Azad University, Tehran, Iran.
3 - Department of Mathematics, Science and Research Branch, Islamic Azad University,Tehran,Iran.
4 - Department of Mathematics, Science and Research Branch, Islamic Azad University, Tehran, Iran.

Received: 2020-01-26 Accepted : 2020-05-22 Published : 2020-12-21

Keywords: Sensitivity analysis, Data envelopment analysis, Benchmarking, Ratio data,

Abstract :

Banks play an important role in the economic development of any country as the main part of the financial system. Calculating efficiency and finding the strengths and weaknesses of branches has a significant impact on increasing the efficiency of banks. Data envelopment analysis is one of the performance evaluation techniques that is able to introduce benchmarks for inefficient decision-making units in addition to estimating relative efficiency. In this paper, using a data envelopment analysis, a model for evaluating efficiency, studying sensitivity analysis of 18 branches of one of the commercial banks of Iran with financial ratios is presented. For this purpose, a model is proposed for output estimating with ratio data. By changing the input values the output values can be estimated. The amount of changes required in the outputs to maintain the efficiency as well as maintaining the rank is possible by using the proposed model with ratio data.

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8) Charnes, A., Cooper, WW., Rhodes, E. (1978). Measuring the efficiency of decision making units, European Journal of Operational Research, 2, 429-444.
9) Despic.O, Despic.M, Paradi. J. C. (2007). DEA-R: ratio-based comparative efficiency model, its mathematical relation to DEA and its use in applications. J Prod Anal 28, 33–44.
10) Fethi, M. D., & Pasiouras, F. (2010). Assessing bank efficiency and performance with operational research and artificial intelligence techniques: A survey. European Journal of Operational Research, 204, 189–198.
11) Gumus, Y., Celikkol, H., (2011). Data envelopment analysis: An augmented method for analysis of firm performance. International Research Journal of Finance and Economics, Euro Journal Publishing.
12) Halkos, G.E., Salamouris, D.S. (2004). Efficiency measurement of Greek commercial banks with the use of financial ratios: a data envelopment analysis. Management accounting research, 15, 201-224.
13) Kaffash, S. & Marra. (2017). Data envelopment analysis in financial services: a citations network analysis of banks, insurance companies and money market funds. Annals of Operations Research, 253(1), 307-344.
14) Kamyab, P., Mozaffari, M.R., Gerami, J. and Wankei, P.F. (2020), Two-stage incentives system for commercial banks based on centralized resource allocation model in DEA-R. International Journal of Productivity and Performance Management. https://doi.org/10.1108/IJPPM-11-2018-0396.
15) Mozaffari .M.R, Gerami .J, Jablonsky .J. (2012). Relationship between DEA models without explicit inputs and DEA-R models. CEJOR, 22, 1–12.
16) Mozaffari, M.R., Kamyab, P., Jablonsky, J., Gerami, J. (2014). Cost and revenue efficiency in DEA-R models, Computers & Industrial Engineering. 78, 188-194
17) Ouenniche.J., Carrales. S., (2018). Assessing efficiency profiles of UK commercial banks: a DEA analysis with regression-based feedback. Annals of Operations Research, 266: (1-2) 551–587.
18) Paradi, J.C., Sherman, H.D., Tam F.K., (2018). Data Envelopment Analysis in the Financial Services Industry: A Guide for Practitioners and Analysts Working in Operations Research Using DEA. International Series in Operations Research & Management Science. Springer.
19) Wei, C.K., Chen, L.C., Li, R.K., Tsai, C.H. (2011). A study of developing an input oriented ratio-based comparative efficiency model. Expert Systems with Applications. 38, 2473-2477