Journals / Uluslararası İktisadi ve İdari İncelemeler Dergisi / 2019 / Sayı: 24

ON DIMENSION REDUCTIONS IN DATA ENVELOPMENT ANALYSIS WITH AN ILLUSTRATIVE APPLICATION TO TURKISH BANKING SECTOR PERFORMANCE

Pages
259–268
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

We also found that our main model which uses four input and four output factors yields similar results with the investigated dimension reduction technique approaches.

📌 3 Key Takeaways

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Objective & Hypothesis:In this study we tested the Turkish Banking Sector Performance development and the usage of various methodologies.
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Key Findings & Data:For this purpose we used several data envelopment analysis(DEA) performance measurement systematics from 2002.12 to 2017.06.
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Conclusion & Impact:We also found that our main model which uses four input and four output factors yields similar results with the investigated dimension reduction technique approaches.
🔬Design:Modeling & Simulation
📍Scope:2002 Verileri
⚙️Variables:Performans · DEA · PCA

Abstract

In this study we tested the Turkish Banking Sector Performance development and the usage of various methodologies. For this purpose we used several data envelopment analysis(DEA) performance measurement systematics from 2002.12 to 2017.06. Proposed DEA models include dimension reduction techniques. The first one is principal component analysis which is abbreviated as PCA and the second one is correlation based DEA model construction. We investigated performance change over time and found that banks in Turkish Banking Sector should take steps now for maintaining their improved performances. Policies implemented after the 2001 crisis had positive effects during the 2008 crisis period in the Turkish banking sector, but gains should be sustained. We also found that our main model which uses four input and four output factors yields similar results with the investigated dimension reduction technique approaches.

Özet

Bu çalışmada Türk Bankacılık Sektörü Performansı gelişimini ve çeşitli metodolojilerin kullanımını test ettik. Bu amaçla 2002.12 ile 2017.06 arasında çeşitli veri zarflama analizi (DEA) performans ölçüm sistematiği kullandık. Önerilen DEA modelleri, boyut azaltma tekniklerini içermektedir. Birincisi, PCA olarak kısaltılmış olan temel bileşen analizi ve ikincisi ise korelasyon bazlı DEA model kurulmasıdır. Zaman içerisinde performans değişimini inceledik ve Türk Bankacılık Sektöründeki bankaların, artan performanslarını sürdürmek için adımlar atmaları gerektiğini tespit ettik. 2001 krizinden sonra uygulanan politikalar, 2008 kriz döneminde Türk bankacılık sektöründe olumlu etkiler yaratmış, ancak kazanımlar sürdürülebilmelidir. Dört girdi ve dört çıktı faktörü kullanan ana modelimizin, araştırılan boyut azaltma tekniği yaklaşımları ile benzer sonuçlar verdiğini de bulduk.

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Cite This Article

DAVER, G., KÜÇÜKKOCAOĞLU, G. (2019). ON DIMENSION REDUCTIONS IN DATA ENVELOPMENT ANALYSIS WITH AN ILLUSTRATIVE APPLICATION TO TURKISH BANKING SECTOR PERFORMANCE. Uluslararası İktisadi ve İdari İncelemeler Dergisi, Sayı: 24, 259–268. https://ajindex.com/articles/322542

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References (4)

  1. Adler, Nicole, and Ekaterina Yazhemsky. 2010. “Improving Discrimination in Data Envelopment Analysis: PCA-DEA or Variable Reduction.” European Journal of Operational Research 202 (1). Elsevier B.V.: 273–84. doi:10.1016/j.ejor.2009.03.050. Andrejić, Milan, Nebojša Bojović, and Milorad Kilibarda. 2013. “Benchmarking Distribution Centres Using Principal Component Analysis and Data Envelopment Analysis: A Case Study of Serbia.” Expert Systems with Applications 40 (10): 3926–33. doi:10.1016/j.eswa.2012.12.085. BDDK/Strateji Geliştirme Daire Başkanlığı. 2010. “Bankacılıkta Yapısal Gelişmeler.” Berger, Allen N., and Loretta J. Mester. 1997. “Inside the Black Box: What Explains Differences in the Efficiencies of Financial Institutions?” Journal of Banking & Finance 21 (7): 895–947. doi:10.1016/S0378-4266(97)00010-1.
  2. Chen, Jihong, Zheng Wan, Fangwei Zhang, Nam-kyu Park, Xinhua He, and Weiyong Yin. 2015. “Operational Efficiency Evaluation of Iron Ore Logistics at the Ports of Bohai Bay in China.” Mathematical Problems in Engineering 2016: 13. doi:http://dx.doi.org/10.1155/2016/9604819.
  3. Cook, Wade D., and Larry M. Seiford. 2009. “Data Envelopment Analysis (DEA) - Thirty Years On.” European Journal of Operational Research 192 (1): 1–17. doi:10.1016/j.ejor.2008.01.032. Cooper, W.W., Angela T. Kingyens, and Joseph C. Paradi. 2014. “Two-Stage Financial Risk Tolerance Assessment Using Data Envelopment Analysis.” European Journal of Operational Research 233 (1). Elsevier B.V.: 273–80. doi:10.1016/j.ejor.2013.08.030. Daver, Gizay, and Güray Küçükkocaoğlu. 2016. “Turkish Banking Sector Performance Analyses.” In 5th Global Business and Finance Research Conference, edited by Mr. Md. Mahbubul Hoque Bhuiyan. 31 Blake Street, Berwick 3806, Melbourne, Victoria, Australia: World Business Institute Australia. Duygun Fethi, Meryem, and Fotios Pasiouras. 2010. “Assessing Bank Efficiency And Performance With Operational Research And Artificial Intelligence Techniques: A Survey.” European Journal of Operational Research 204 (2). Elsevier B.V.: 189–98. doi:10.1016/j.ejor.2009.08.003. Eskelinen, Juha. 2017. “Comparison of Variable Selection Techniques for Data Envelopment Analysis in a Retail Bank.” European Journal of Operational Research 259 (2). Elsevier B.V.: 778–88. doi:10.1016/j.ejor.2016.11.009. Fried, Harold O., C. A. Knox Lovell, and Shelton S. Schmidt. 2008. The Measurement of Productive Efficiency and Productivity Change. Oxford University Press.
  4. Oxford University Press, Inc. d