TÜRK BANKACILIK SEKTÖRÜNÜN PERFORMANS DEĞERLENDİRMESİNDE ENTROPİK AĞIRLIKLARLA VERİ ZARFLAMA ANALİZİ

Veri zarflama analizi firmaların performansını değerlendirmede sıklıkla kullanılan araçlardan birisidir. Özellikle günümüz rekabet şartlarında işletmelerin gerek dış gerekse iç faktörlere yakından maruz kalması ve bunun sonucunda girdi ve çıktıların operasyonel süreçlere göreli etkinliklerinin ölçülmesi için kullanılan bir yöntemdir. Türkiye’de bankacılık sektörü incelendiğinde bankalar arasındaki rekabetin düzeyi bankaların kaynak kullanımını en etkin şekilde kullanması gerektiğini göstermiştir. Bu yüzden bankaların finansal performansları açısından takip etmesi gereken etkinlik sınırlamalarının belirlenmesi bazı istatistiksel araçlarla daha kolay ve etkin olmaktadır. Bu çalışmanın amacı, entropik ağırlıkları içeren veri zarflama analizi ile Türkiye’de faaliyet gösteren tüm mevduat bankalarının performanslarının incelenmesidir.

Anahtar Kelimeler:

Veri Zarflama Analizi, Bankacılık Sektörü, Bankacılık Performansı, Entropik Ağırlıklar

PERFORMANCE EVALUATION OF TURKISH BANKING SECTOR WITH DATA ENVELOPMENT ANALYSIS USING ENTROPIC WEIGHTS

Data envelopment analysis is one of the tools that has been used frequently on evaluating the performance of the firms. Particularly in today's competitive conditions, since the firms have been facing many external and/or internal factors closely, data envelopment anaysis (DEA) method is used for measuring inputs and outputs on the relative efficiency of operational processes. When the banking sector is analysed in Republic of Turkey, the degree of competition among the banks has shown that it is necessary for them to use their resources more efficiently. Hence, in terms of financial performance of banks, defining the efficiency limitations would be easier and more efficient with some statistical tools. The purpose of this study is to analyze the performance of all deposit banks in Turkey by using data envelopment anaysis with entropic weights.

Keywords:

Data Envelopment Analysis, Banking Sector, Banking Performance, Entropic Weights,

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Andersen, P. and Petersen, N. C. (1993). A procedure for ranking efficient units in data envelopment analysis, Management science, 39(10), 1261-1264.
Asmilda, M. and Zhub, M. (2016). Controlling for the use of extreme weights in bank efficiency assessments during the financial crisis, European Journal of Operational Research, 251(3), 999-1015.
Atan, M. and Karpat, G. (2005). Bankacılıkta Etkinlik Ve Sermaye Yapısının Bankaların Etkinligine Etkisi, VII.Ulusal Ekonometri ve İstatistik Sempozyumu, 27-28 Mayıs, İnönü Üniversitesi, Malatya.
Avkıran, N.K. (2015). An illustration of dynamic network DEA in commercial banking ncluding robustness tests, Omega, 55, 141-150.
Ayadi, R., Naceur, S.B., Casu, B. and Quinn, B. (2015). Does Basel Compliance Matter for Bank Performance? Journal of Financial Stability, 23, 15-32.
Banker, R.D. Charnes, A. and Cooper, W.W. (1984). Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis, Management Science, 30(9), 1078-1092.
Barra, C., Destefanis, S. and Lavadera, G.L. (2016). Risk and Regulation: A Difference-in-Differences Analysis for Italian Local Banks, Finance Research Letters, 17, 25-32.
Belanès, A., Ftiti, Z. and Regaïeg, R. (2015). What can we learn about Islamic banks efficiency under the subprime crisis? Evidence from GCC Region, Pacific-Basin Finance Journal, 33, 81-92.
Bod’a, M. and Zimková, E. (2015). How non-radiality matters – Pareto-Koopmans technical efficiency in production of branches of a Slovak commercial bank, Procedia Economics and Finance, 30, 100-110.
Chan, S-G., Koh, E. H.Y., Zainir, F. and Yong, C-C. (2015). Market structure, institutional framework and bank efficiency in ASEAN 5. Journal of Economics and Business, 82, 84-112.
Cooper, W.W., Seiford, L.M., and Zhu, J. (2004). Data envelopment analysis, In Handbook on data envelopment analysis, Springer US, 1-39.
Costa, M.A., Lopes, A.L.M. and Matos, G.B.B.P. (2015). Statistical evaluation of Data Envelopment Analysis versus COLS CobbeDouglas benchmarking models for the 2011 Brazilian tariff revision. Socio-Economic Planning Sciences, 49, 47-60.
Dotoli, M. Epicoco, N., Falagario, M. and Sciancalepore, F. (2015). A Cross-Efficiency Fuzzy Data Envelopment Analysis Technique For Performance Evaluation of Decision Making Units Under Uncertainty. Computers and Industrial Engineering, 79, 103-114.
Du, K. and Sim, N., (2016). Mergers, acquisitions, and bank efficiency: Cross-country evidence from emerging markets. Research in International Business and Finance, 36, 499-510.
Erol, İ. (2004), Toplam Kalite Yönetimi ve Tam Zamanında Üretim Yaklaşımlarının Satınalma İşlevi ile İlişkilendirilmesi, Bütünsel Bir Yaklaşım Önerisi ve Örnek Olay Analizi. Endüstri Mühendisliği Dergisi, 15(4), 2-18.
Erol, İ. and Ferrell Jr., W.G. (2009). Integrated approach for reorganizing purchasing: Theory and a case analysis on a Turkish company. Computers and Industrial Engineering, 56, 1192-1204.
Hajiagha, S.H.R., Hashemi, S.S., Mahdiraji, H.A. and Azaddel, J. (2015). Multi-period data envelopment analysis based on Chebyshev inequality bounds. Expert Systems with Applications, 42, 7759-7767.
Herrera-Restrepo, O., Triantisa, K., Seaver, W.L., Paradi, J.C. and Zhu, H. (2016). Bank branch operational performance: A robust multivariate and clustering approach. Expert Systems with Applications, 50, 107-119.
Huang, C., Dai, C. and Guo, M. (2015). A hybrid approach using two-level DEA for financial failure prediction and integrated SE-DEA and GCA for indicators selection, Applied Mathematics and Computation, 251, 431–441.
Ji, Y. B. and Lee, C. (2010). Data envelopment analysis. The Stata Journal, 10(2), 267-280.
Kaur, S. and Gupta, P.K. (2015). Productive efficiency mapping of the Indian banking system using data envelopment analysis. Procedia Economics and Finance, 25, 227-238.
Kwon, H-B. and Lee, J. (2015). Two-stage production modeling of large U.S. banks: A DEA-neural network approach. Expert Systems with Applications, 42, 6758-6766.
Kyritsis, C., Rekleitis, P. and Trivelas, P. (2015). Simulation for the stability and DEA risk analysis of Greek banks within a prolonged duration of the debt crisis. Procedia Economics and Finance, 33, 376-387.
Molinero, C.M. and Woracker, D. (1996). Data Envelopment Analysis. OR Insight, 9(4), 22-28.
Moradi-Motlagh, A. and Babacan, A. (2015). The impact of the global financial crisis on the efficiency of Australian banks. Economic Modelling, 46, 397-406.
Ohsato, S. and Takahashi, M. (2015). Management Efficiency in Japanese Regional Banks: A Network DEA. Procedia - Social and Behavioral Sciences, 172, 511-518.
Puri, J. and Yadav, S.P. (2015). Intuitionistic fuzzy data envelopment analysis: An application to the banking sector in India. Expert Systems with Applications, 42, 4982-4998.
Řepková, I. (2015). Banking Efficiency Determinants in the Czech Banking Sector. Procedia Economics and Finance, 23, 191-196.
Sherman, H.D. and Zhu, J. (2006). Service Productivity Management: Improving Service Performance using Data Envelopment Analysis (DEA). US: Springer. 1st ed.
Stoica, O., Mehdian, S. and Sargua, A. (2015). The impact of internet banking on the performance of Romanian banks: DEA and PCA approach. Procedia Economics and Finance, 20, 610-622.
Tsolas, I. E. and Charles, V. (2015). Incorporating risk into bank efficiency: A satisficing DEA approach to assess the Greek banking crisis. Expert Systems with Applications, 42, 3491-3500.
Türkiye Bankalar Birliği (2017). https://www.tbb.org.tr/en/banks-and-banking-sector-information/statistical-reports/20 (27 March 2017)
Türkiye Bankalar Birliği (2017). https://www.tbb.org.tr/tr/bankacilik/ banka-ve-sektor-bilgileri/veri-sorgulama-sistemi/mali-tablolar/71 (27 March 2017)
Ulaş, E. and Keskin, B. (2015). Performance evaluation and ranking of Turkish banking sector. Procedia Economics and Finance, 25, 297-307.
Wanke, P., Barros, C.P. and 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.
Wei, Q. (2001). Data envelopment analysis, Chinese Science Bulletin, 46(16), 1321-1332.
Widiarto, I. and Emrouznejad, A. (2015). Social and financial efficiency of Islamic microfinance institutions: A Data Envelopment Analysis application. Socio-Economic Planning Sciences, 50, 1-17.
Wijesiri, M. and Meoli, M. (2015). Productivity change of microfinance institutions in Kenya: A bootstrap Malmquist approach. Journal of Retailing and Consumer Services, 25, 115-121.
Wijesiri, M., Viganò, L. and Meoli, M. (2015). Efficiency of microfinance institutions in Sri Lanka: a two-stage double bootstrap DEA approach. Economic Modelling, 47, 74–83.
Wild, J. (2016). Efficiency and risk convergence of Eurozone financial markets. Research in International Business and Finance, 36, 196-211.
Wu, Y-C., Ting, I., Wei, K., Lu, W-M., Nourani, M. and Kweh, Q. L. (2016). The impact of earnings management on the performance of ASEAN banks. Economic Modelling, 53, 156-165.
Yıldırım, Bahattin Fatih, Önder, Emrah (Ed.). Veri Zarflama Analizi (Bölüm Yazarı: Savaş, Filiz) (2015). İşletmeciler, Mühendisler ve Yöneticiler için Operasyonel, Yönetsel ve Stratejik Problemlerin Çözümünde Çok Kriterli Karar Verme Teknikleri. Dora Yayınevi. Bursa.
Yılmaz, A. and Güneş, N. (2015). Efficiency Comparison of Participation and Conventional Banking Sectors in Turkey between 2007-2013. Procedia - Social and Behavioral Sciences, 195, 383-392.
Yılmaz, C., Özdil, T. and Akdoğan, G. (2002). Seçilmiş İşletmelerin Toplam Etkinliklerinin Veri Zarflama Yöntemi İle Ölçülmesi, Manas Üniversitesi Sosyal Bilimler Dergisi, Kırgızistan Türkiye Manas Üniversitesi Yayınları: 20, Süreli Yayınlar Dizisi: 6, Sayı 4, Bişkek, 174-183.