Assessing the Performance of Different Bank Types Based on Total and Systematic Risk

Yıl 2025, Sayı: 103, 246 - 262

İstemi Çömlekçi

https://doi.org/10.17753/sosekev.1672175

Öz

The study aims to determine the performance of different types of banks by considering total risk and systematic risk factors using the entropy-based Topsis method. In line with this objective, decision-making criteria were calculated using six different performance evaluation models: Sharpe Ratio, M2 Performance Measure, and Sortino Ratio for total risk-based models, as well as Treynor Ratio, T2 Performance Measure, and Jensen's Alpha for systematic risk-based models. The weights of the criteria determined based on total and systematic risk were calculated using the Entropy method, and the ranking of performances was conducted using the Topsis method. The results of the Entropy method indicated that Jensen's Alpha was the most influential criterion affecting bank performance, while T2 value was the least influential. According to the Topsis results, the highest performance among the bank types was observed in the following order: Participation Banks, Foreign Banks, Private Banks, and Public Banks.

Anahtar Kelimeler

Bank performance, Total risk, Systematic risk, Entropy method, Topsis method

TOPLAM RİSKE VE SİSTEMATİK RİSKE GÖRE BANKA TÜRLERİNİN PERFORMANSLARININ DEĞERLENDİRİLMESİ

Öz

Çalışmada toplam risk ve sistematik risk faktörlerini dikkate alarak farklı banka türlerinin performanslarını entropi temelli Topsis yöntemi ile belirlenmesi amaçlanmıştır. Bu amaç doğrultusunda toplam riske göre performans ölçüm modellerinden Sharpe oranı, M2 performans ölçütü, Sortino Oranı ve sistematik riske göre performans ölçüm modellerinden Treynor Oranı, T2 performans ölçütü, Jensen Alfa Değeri olmak üzere toplam 6 farklı model kullanılarak karar verme kriterleri hesaplanmıştır. Toplam riske ve sistematik riske göre belirlenen kriterlerin ağırlıkları Entropi yöntemi ile hesaplanmış, daha sonra performanslarının sıralanması ise Topsis yöntemi ile gerçekleştirilmiştir. Entropi yöntemi ile kriter ağırlık değerleri hesaplanması sonucunda, banka türlerinin performansına etki eden en önemli kriterin Jensen Alfa Değeri olduğu, en az etkili kriterin ise T2 değeri olduğu sonucuna ulaşılmıştır. Topsis yöntemi sonucunda banka türlerine göre en yüksek performans sırasıyla Katılım Bankaları, Yabancı sermayeli bankalar, Özel sermayeli bankalar ve kamu bankaları şeklinde gerçekleşmiştir.

Anahtar Kelimeler

Banka performansı, Toplam risk, Sistematik risk, Entropi yöntemi, Topsis yöntemi

Kaynakça

• Abdel-Basset, M., Ding, W., Mohamed, R., & Metawa, N. (2020). An integrated plithogenic MCDM approach for financial performance evaluation of manufacturing industries. Risk Management, 22(3), 192–218. https://doi.org/10.1057/s41283-020-00061-4
• Ashraf, M. M., & Rehman, Z. U. (2011). The performance analysis of Islamic and conventional banks: The Pakistan’s perspective. Journal of Money, Investment and Banking, 22, 99–113.
• Bakır, M., & Atalık, Ö. (2018). Entropi ve Aras yöntemleriyle havayolu işletmelerinde hizmet kalitesinin değerlendirilmesi. İşletme Araştırmaları Dergisi, 10(1), 617–638. https://doi.org/10.20491/isarder.2018.410
• Banu, A. R., & Santhiyavalli, G. A. (2019). TOPSIS approach to evaluate the financial performance of scheduled commercial banks in India. IOSR International Journal of Business and Management, 21(1), 24–33.
• Berger, A. N., & Humphrey, D. B. (1997). Efficiency of financial institutions: International survey and directions for future research. European Journal of Operational Research, 98(2), 175–212. https://doi.org/10.1016/S0377-2217(96)00342-6
• Bessis, J. (2011). Risk management in banking (3rd ed.). John Wiley & Sons.
• Bulgurcu, B. K. (2012). Application of TOPSIS technique for financial performance evaluation of technology firms in Istanbul stock exchange market. Procedia - Social and Behavioral Sciences, 62, 1033–1040. https://doi.org/10.1016/j.sbspro.2012.09.176
• Bulut, E., & Şimşek, A. İ. (2024). Evaluation of financial performance of BIST participation banks: CAMELS and multi-criteria decision making (MCDM) approach. Alanya Akademik Bakış, 8(3), 923–940.
• Chen, P. (2019). Effects of normalization on the entropy-based TOPSIS method. Expert Systems with Applications, 136, 33–41. https://doi.org/10.1016/j.eswa.2019.06.035
• Chitnis, A., & Vaidya, O. S. (2016). Efficiency ranking method using DEA and TOPSIS (ERM-DT): Case of an Indian bank. Benchmarking: An International Journal, 23(1), 165–182. https://doi.org/10.1108/BIJ-09-2013-0093
• Christopherson, J. A., Cariño, D. R., & Ferson, W. E. (2009). Portfolio performance measurement and benchmarking. McGraw-Hill.
• Coşkun, B., Öncü, M. A., Çömlekçi, İ., & Hiçyılmaz, E. (2021). COVID-19’un banka finansal performanslarına etkisinin Entropi ve Waspas yöntemiyle analizi. Uluslararası İşletme, Ekonomi ve Yönetim Perspektifleri Dergisi (IJBEMP), 5(2), 810–828. https://doi.org/10.29228/ijbemp.54686
• Erdoğan, B. (2023). Financial performance analysis of development and investment banks: TOPSIS method. Dicle Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 34, 1–15. https://doi.org/10.15182/diclesosbed.1264349
• Gülençer, S. (2020). Türkiye’deki mevduat bankalarının TOPSIS ve VIKOR yöntemleriyle analizi. Kırklareli Üniversitesi Sosyal Bilimler Meslek Yüksekokulu Dergisi, 1(1), 1–22.
• Hazzi, O. A., & Kilani, M. I. A. (2013). The financial performance analysis of Islamic and traditional banks: Evidence from Malaysia. European Journal of Economics, Finance and Administrative Sciences, 57, 133–144.
• Hemmati, M., Dalghandi, S. A., & Nazari, H. (2013). Measuring relative performance of banking industry using a DEA and TOPSIS. Management Science Letters, 3(2), 499–504. https://doi.org/10.5267/j.msl.2012.12.025
• Hwang, C. L., & Yoon, K. (1981). Methods for multiple attribute decision making. In Multiple attribute decision making (Lecture Notes in Economics and Mathematical Systems, Vol. 186). Springer. https://doi.org/10.1007/978-3-642-48318-9_3
• Jayachitra, T. A., & Geetha, K. T. (2019). Profitability evaluation of selected public sector banks using TOPSIS. Journal of Xi’an University of Architecture & Technology, 9, 1177–1187.
• Jensen, M. C. (1968). The performance of mutual funds in the period 1945–1964. The Journal of Finance, 23(2), 389–416. https://doi.org/10.2307/2325404
• Kakakhel, S. J., Raheem, F., & Tariq, M. (2013). A study of performance comparison between conventional and Islamic banking in Pakistan. Abasyn Journal of Social Sciences, 6(2), 91–105.
• Karakaş, A., & Öztel, A. (2020). BIST’de yer alan turizm işletmelerinin finansal performanslarının Entropi tabanlı TOPSIS yöntemi ile belirlenmesi: Bir Python uygulaması. Dicle Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 10(20), 543–562.
• Mandic, K., Delibasic, B., Knezevic, S., & Benkovic, S. (2014). Analysis of the financial parameters of Serbian banks through the application of the fuzzy AHP and TOPSIS methods. Economic Modelling, 43, 30–37. https://doi.org/10.1016/j.econmod.2014.07.036
• Marhfor, A. (2016). Portfolio performance measurement: Review of literature and avenues of future research. American Journal of Industrial and Business Management, 6(4), 432–438. https://doi.org/10.4236/ajibm.2016.64039
• Moghimi, R., & Anvari, A. (2014). An integrated fuzzy MCDM approach and analysis to evaluate the financial performance of Iranian cement companies. The International Journal of Advanced Manufacturing Technology, 71(1), 685–698. https://doi.org/10.1007/s00170-013-5370-6
• Oral, N., & Engin, O. (2024). Ticari kredilerde Bulanık-AHP ve TOPSIS yardımıyla risk analizi. Harran Üniversitesi Mühendislik Dergisi, 9(1), 22–38. https://doi.org/10.46578/humder.1419372
• Ova, A. (2021). Analyzing financial performance of Turkish deposit banks using TOPSIS method. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 45, 1–13. https://doi.org/10.30794/pausbed.796587
• Pineda, P. J. G., Liou, J. J., Hsu, C. C., & Chuang, Y. C. (2018). An integrated MCDM model for improving airline operational and financial performance. Journal of Air Transport Management, 68, 103–117. https://doi.org/10.1016/j.jairtraman.2017.06.003
• Rabbani, A., Zamani, M., Yazdani-Chamzini, A., & Zavadskas, E. K. (2014). Proposing a new integrated model based on sustainability balanced scorecard (SBSC) and MCDM approaches by using linguistic variables for the performance evaluation of oil producing companies. Expert Systems with Applications, 41(16), 7316–7327. https://doi.org/10.1016/j.eswa.2014.05.023
• Safiullah, M. (2010). Superiority of conventional banks & Islamic banks of Bangladesh: A comparative study. International Journal of Economics and Finance, 2(3), 199–207.
• Samad, A. (2004). Performance of interest-free Islamic banks vis-à-vis interest-based conventional banks of Bahrain. IIUM Journal of Economics and Management, 12(2), 1–15. https://doi.org/10.31436/ijema.v12i2.99
• Seçme, N. Y., Bayrakdaroğlu, A., & Kahraman, C. (2009). Fuzzy performance evaluation in Turkish banking sector using analytic hierarchy process and TOPSIS. Expert Systems with Applications, 36(9), 11699–11709. https://doi.org/10.1016/j.eswa.2009.03.013
• Sharpe, W. F. (1966). Mutual fund performance. The Journal of Business, 39(1), 119–138.
• Şendurur, U., & Temelli, F. (2018). Türkiye’de faaliyet gösteren geleneksel bankalar ve katılım bankalarının sürdürülebilirlik açısından karşılaştırılması. Muhasebe Bilim Dünyası Dergisi, 20(2), 330–346. https://doi.org/10.31460/mbdd.344785
• Tavana, M., Khalili-Damghani, K., & Rahmatian, R. (2015). A hybrid fuzzy MCDM method for measuring the performance of publicly held pharmaceutical companies. Annals of Operations Research, 226, 589–621. https://doi.org/10.1007/s10479-014-1738-8
• Türkiye Bankalar Birliği. “Bankalarımız Kitabı 2020”. 339, 1-328. Mayıs 2021, Web: https://www.tbb.org.tr/istatistiki-raporlar/2020-yillik-bankalarimiz-kitabi adresinden 02 Mart 2025’de alınmıştır.
• Teker, S., Teker, D., & Kent, O. (2008). Yatırım fonlarının risk odaklı performans değerlemesi. Doğuş Üniversitesi Dergisi, 9(1), 89–105.
• Treynor, J. (1965). How to rate management of investment funds. Harvard Business Review, 43(1), 63–75.
• Ünvan, Y. A. (2020). Financial performance analysis of banks with TOPSIS and fuzzy TOPSIS approaches. Gazi University Journal of Science, 33(4), 904–923. https://doi.org/10.35378/gujs.730294
• Vásquez, J. A., Escobar, J. W., & Manotas, D. F. (2021). AHP–TOPSIS methodology for stock portfolio investments. Risks, 10(1-4), 1-20. https://doi.org/10.3390/risks10010004
• Wang, Y. J., & Lee, H. S. (2007). Generalizing TOPSIS for fuzzy multiple-criteria group decision-making. Computers & Mathematics with Applications, 53(11), 1762–1772. https://doi.org/10.1016/j.camwa.2006.08.037
• Wanke, P., Hassan, M. K., & Gavião, L. O. (2017). Islamic banking and performance in the ASEAN banking industry: A TOPSIS approach with probabilistic weights. International Journal of Business & Society, 18, 139–162. https://doi.org/10.33736/ijbs.1322.2017
• Weerathunga, P. R., Xiaofang, C., Samarathunga, W. H. M. S., & Kulathunga, K. M. M. C. B. (2020). Application of entropy based TOPSIS in analysis of sustainability performance of Sri Lanka hotels. RISUS Journal on Innovation and Sustainability, 11(3), 100–108. https://doi.org/10.23925/2179-3565.2020v11i3p100-108
• Wu, H. Y., Tzeng, G. H., & Chen, Y. H. (2009). A fuzzy MCDM approach for evaluating banking performance based on balanced scorecard. Expert Systems with Applications, 36(6), 10135–10147. https://doi.org/10.1016/j.eswa.2009.02.042
• Wu, J., Sun, J., Liang, L., & Zha, Y. (2011). Determination of weights for ultimate cross efficiency using Shannon entropy. Expert Systems with Applications, 38(5), 5162–5165. https://doi.org/10.1016/j.eswa.2010.11.090
• Yetiz, F. (2021). TOPSIS yöntemi ile Türk katılım bankalarının performans analizi ve bankacılıkta risk yönetim politikalarının önemi. Journal of Empirical Economics and Social Sciences, 3(1), 121–138. https://doi.org/10.46959/jeess.899919
• Zavadskas, E. K., Turskis, Z., & Kildienė, S. (2014). State of art surveys of overviews on MCDM/MADM methods. Technological and Economic Development of Economy, 20(1), 165–179. https://doi.org/10.3846/20294913.2014.892037