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Türkiye’de Faaliyet Gösteren Mevduat Bankaların Finansal Performansının Entropi destekli EDAS Yöntemi ile Değerlendirilmesi

Year 2023, Volume: 8 Issue: 1, 239 - 255, 30.06.2023

Sevgi Sumerli Sarıgül Pınar Avcı Esra Yaşar

Abstract

Bu çalışmanın amacı, 2012-2021 döneminde Türkiye ekonomisinde yer alan 18 mevduat bankasının finansal performansını ÇKKV yöntemleri ile analiz etmektir. Çalışmada 18 mevduat bankası, 10 finansal oran ile değerlendirilmiştir. ÇKKV yöntemlerinden Entropi yöntemiyle kriter ağırlıkları belirlenmiş ve EDAS yöntemi ile ilgili bankaların finansal performans sıralaması elde edilmektedir. Bulgulara göre, en önemli performans kriterleri 2021 yılında Duran Varlıklar/Toplam Varlıklar finansal oranı olduğu saptanmaktadır. En düşük performans kriteri ise 2021 yılında Faiz Gelirleri/Toplam Varlıklar olduğu saptanmaktadır. EDAS metodunun bulgularında ise, 2012-2021 döneminde en yüksek finansal performansa sahip olan bankanın Deutsche Bank olduğu ve en düşük ise Denizbank olduğu belirlenmiştir.

Keywords

Finansal performans Entropi yöntemi EDAS yöntemi Mevduat bankalar

References

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  • Akgül, Y. (2019). Çok kriterli karar verme yöntemleriyle Türk bankacilik sisteminin 2010-2018 yillari arasindaki performansinin analizi. Finans Ekonomi ve Sosyal Araştırmalar Dergisi, 4(4), 567-582.
  • Altay Topçu, B. & Oralhan, B. (2017). Türkiye ve OECD Ülkeleri’nin Temel Makroekonomik Göstergeler Açısından Çok Kriterli Karar Verme Yöntemleri ile Karşılaştırılması. International Journal of Academic Value Studies (Javstudies), 3(14), 260-277.
  • Aras, G., Tezcan, N., Furtuna, O.K. & Kazak, E.H. (2017). Corporate sustainability measurement based on entropy weight and TOPSIS: A Turkish banking case study. Meditari Accountancy Research, 25, 391-413. https://doi.org/10.1108/MEDAR-11-2016-0100
  • Avkiran, N. K. (2011). Association of DEA super-efficiency estimates with financial ratios: Investigating the case for Chinese banks. Omega, 39(3), 323-334.
  • Aydın, Y. (2020). A hybrid multicriteria decision making (MCDM) model consisting of SD & COPRAS methods in performance evaluation of foreign deposit Banks. Journal of Economics, Business & Political Studies, VII (2), 160-176.
  • Bandyopadhyay, S. (2021). Comparison among multi-criteria decision analysis techniques: A novel method. Progress in Artificial Intelligence, 10(2), 195-216.
  • Bansal, R., Singh, A., Kumar, S. & Gupta, R. (2018). Evaluating factors of profitability for Indian banking sector: a panel regression. Asian Journal of Accounting Research, 3(2), 236-254.
  • Bayyurt, N. (2013). Ownership effect on bank’s performance: Multi criteria decision making approaches on foreign and domestic Turkish Banks. Procedia - Social and Behavioral Sciences, 99, 919-928.
  • Chodha, V., Dubey, R., Kumar, R., Singh, S., & Kaur, S. (2022). Selection of Industrial Arc Welding Robot with TOPSIS and Entropy MCDM Techniques. Materials Today: Proceedings, 50, 709-715.
  • Çakır, S., & Perçin, S. (2013). AB ülkeleri’nde bütünleşik entropi ağırlık-topsıs yöntemiyle ar-ge performansının ölçülmesi. Uludağ Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 32(1), 77-95.
  • Daly, S. & Frikha, M. (2017). Determinants of bank Performance: Comparative Study Between Conventional and Islamic Banking in Bahrain. J Knowl Econ, 8, 471-488.
  • Dash, M. (2017). A model for bank performance measurement integrating multivariate factor structure with multi-criteria PROMETHEE methodology. Asian Journal of Finance & Accounting, 9(1), 310-332.
  • Dhanalakshmi, C. S., Madhu, P., Karthick, A., Mathew, M., & Vignesh Kumar, R. (2020). A Comprehensive MCDM-Based Approach Using TOPSIS and EDAS as an Auxiliary Tool for Pyrolysis Material Selection and its Application. Biomass conversion and biorefinery, 1-16.
  • Dogan, M. (2015). Comparison of financial performance of participation banks in Turkey. Journal of Economics Finance and Accounting, 2(4).
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  • Hidayat, S. E., Sakti, M. R. P. & Al-Balushi, R. A. A. (2021). Risk, efficiency and financial performance in the GCC banking industry: Islamic versus conventional banks. Journal of Islamic Accounting and Business Research, 12(4), 564-592.
  • Hussain, S. A. I., & Mandal, U. K. (2016). Entropy Based MCDM Approach for Selection of Material. In National Level Conference on Engineering Problems and Application of Mathematics. 1-6.
  • Ic, Y. T., Celik, B., Kavak, S., & Baki, B. (2021). Development of a multi-criteria decision-making model for comparing the performance of Turkish commercial banks. Journal of Advances in Management Research, 18(2), 250-272.
  • Karaatlı, M. (2016). Entropi-Gri ilişkisel analiz yöntemleri ile bütünleşik bir yaklaşim: Turizm sektöründe uygulama. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 21(1), 63-77.
  • Keshavarz Ghorabaee, M., Zavadskas, E. K., Olfat, L., & Turskis, Z. (2015). Multi-criteria inventory classification using a new method of evaluation based on distance from average solution (EDAS). Informatica, 26(3), 435-451.
  • Laha, S., & Biswas, S. (2019). A hybrid unsupervised learning and multi-criteria decision making approach for performance evaluation of Indian banks. Accounting, 5(4), 169-184.
  • Malik, M.S., Awais, M. & Khursheed, A. (2016). Impact of liquidity on profitability: A comprehensive case of Pakistan’s private banking sector. International Journal of Economics and Finance, 8(3), 69-74.
  • 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
  • No, R.K.G., Niroomand, S., Didehkhani, H., & Mahmoodirad, A. (2021). Modified interval EDAS approach for the multi-criteria ranking problem in banking sector of Iran. Journal of Ambient Intelligence and Humanized Computing, 12, 8129-8148. https://doi.org/10.1007/s12652-020-02550-6
  • Oralhan, B. & Büyüktürk, M. A. (2019). Avrupa Birliği Ülkeleri ve Türkiye’nin İnovasyon Performansının Çok Kriterli Karar Verme Yöntemleriyle Kıyaslanması. Avrupa Bilim ve Teknoloji Dergisi, 16, 471-484.
  • Oralhan, B. & Özsoy, C. (2019). İşletme Performansının Çok Kriterli Karar Verme Yöntemleri ile Değerlendirilmesi. H. Hale Künüçen, X. Quliyeva, Y.(Eds.), Seçgin, Sosyal, Beşeri ve İdari Bilimlerde Yenilikçi Yaklaşımlar, Ekin Yayınevi.
  • Ozcalici, M. & Bumin, M. (2020). An integrated multi-criteria decision making model with Self-Organizing Maps for the assessment of the performance of publicly traded banks in Borsa Istanbul. Applied Soft Computing, 90. https://doi.org/10.1016/j.asoc.2020.106166
  • Öksüzkaya, M., & Atan, M. (2023). 2016–2021 döneminde Türkiye’de kalkınma ve yatırım bankalarının finansal etkinliği: CRITIC yöntemi ve MABAC yöntemi ile bir uygulama. Bankacılık ve Finansal Araştırmalar Dergisi, 10(1), 14-32.
  • Özcan, M. (2021), Türk bankacılık sektörünün finansal performans göstergeleri: BIST mali sektörü üzerine bir araştırma. Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi, 35(2), 389-406. https://doi.org/10.16951/atauniiibd.689612
  • Radulescu, M., Fedajev, A. & Nikolic, D. (2017). Ranking of EU national banking systems using multi-criteria analysis in the light of Brexit. Acta Oeconomica, 67(4), 473-509.
  • Reig-Mullor, J. & Brotons-Martinez, J.M. (2021). The evaluation performance for commercial banks by intuitionistic fuzzy numbers: the case of Spain. Soft Computing, 25, 9061-9075. https://doi.org/10.1007/s00500-021-05847-6
  • Sakinc, S.O. (2016). Comparison of Turkish State Banks' performances via multi-criteria performance measurement method. International Journal of Scientific Research & Management Studies, 4(1), 4857-4871.
  • Sama, H.R., Kosuri, S.V.K., & Kalvakolanu, S. (2022). Evaluating and ranking the Indian private sector banks-A multi-criteria decision-making approach. Journal of Public Affairs, 22(2). https://doi.org/10.1002/pa.2419
  • Sezal, L. (2023) Examination of non-interest incomes and bank performance relationship in the Türkiye banking sector. Journal of Economics and Administrative Sciences, 24(2): 186-194. https://doi.org/10.37880/cumuiibf.1196021
  • Sufian, F. (2009). Determinants of bank profitability in a developing economy: Empirical evidence from the China banking sector. Journal of Asia-Pacific Business, 10(4), 281-307. https://doi.org/10.1080/10599230903340205
  • Torkayesh, A.E., Deveci, M., Karagoz, S. & Antucheviciene, J. (2023). A state-of-the-art survey of evaluation based on distance from average solution (EDAS): Developments and applications. Expert Systems with Applications, 221, https://doi.org/10.1016/j.eswa.2023.119724
  • Tüysüz, F. & Yıldız, N. (2020). A novel multi-criteria analysis model for the performance evaluation of bank regions: an application to Turkish agricultural banking. Soft Computing, 24, 5289-5311. https://doi.org/10.1007/s00500-019-04279-7
  • Ulutaş, A. (2019). Entropi Tabanli Edas Yöntemi ile Lojistik Firmalarinin Performans Analizi. Uluslararası İktisadi ve İdari İncelemeler Dergisi, (23), 53-66.
  • Ünal, E.A. (2019). Özel sermayeli ticari bankalarının finansal performansının SD ve WASPAS yöntemleri ile ölçümü. Ekonomi, Politika & Finans Araştırmaları Dergisi, 4(3), 384-400.
  • Unvan, Y.A. (2020). Financial performance analysis of banks with Topsis and Fuzzy Topsis approaches. Gazi University Journal of Science, 33(4), 904-923.
  • Wang, C. N., Le, T. Q., Chang, K. H., & Dang, T. T. (2022). Measuring Road Transport Sustainability Using MCDM-Based Entropy Objective Weighting Method. Symmetry, 14(5), 1033, 1-19. https://doi.org/10.3390/sym14051033
  • Wanke, P., Azad, M.D.A.K. & Barros, C.P. (2016). Predicting efficiency in Malaysian Islamic banks: A two-stage TOPSIS and neural networks approach. Research in International Business and Finance, 36, 485-498. https://doi.org/10.1016/j.ribaf.2015.10.002
  • 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.2009.01.005
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Evaluation of the Financial Performance of Deposit Banks Operating in Turkey by Entropy-supported EDAS Method

Year 2023, Volume: 8 Issue: 1, 239 - 255, 30.06.2023

Sevgi Sumerli Sarıgül Pınar Avcı Esra Yaşar

Abstract

The aim of this study is to analyze the financial performance of 18 deposit banks in the Turkish economy in the period of 2012-2021 with MCDM methods. In the study, 18 deposit banks were evaluated with 10 financial ratios. Criterion weights are determined by the Entropy method from the MCDM methods and the financial performance ranking of the banks related to the EDAS method is obtained. According to the findings, the most important performance criteria are the Fixed Assets/Total Assets financial ratio in 2021. The lowest performance criterion is Interest Income/Total Assets in 2021. It was also determined that the bank with the highest financial performance in the period of 2012-2021 was Deutsche Bank and the lowest performance was Denizbank.

Keywords

Financial performance Entropy method EDAS method Deposits banks.

References

  • Akçakanat, Ö., Eren, H., Aksoy, E. & Ömürbek, V. (2017). Bankacilik sektöründe ENTROPI ve WASPAS yöntemleri ile performans değerlendirmesi. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 22(2), 285-300.
  • Akgül, Y. (2019). Çok kriterli karar verme yöntemleriyle Türk bankacilik sisteminin 2010-2018 yillari arasindaki performansinin analizi. Finans Ekonomi ve Sosyal Araştırmalar Dergisi, 4(4), 567-582.
  • Altay Topçu, B. & Oralhan, B. (2017). Türkiye ve OECD Ülkeleri’nin Temel Makroekonomik Göstergeler Açısından Çok Kriterli Karar Verme Yöntemleri ile Karşılaştırılması. International Journal of Academic Value Studies (Javstudies), 3(14), 260-277.
  • Aras, G., Tezcan, N., Furtuna, O.K. & Kazak, E.H. (2017). Corporate sustainability measurement based on entropy weight and TOPSIS: A Turkish banking case study. Meditari Accountancy Research, 25, 391-413. https://doi.org/10.1108/MEDAR-11-2016-0100
  • Avkiran, N. K. (2011). Association of DEA super-efficiency estimates with financial ratios: Investigating the case for Chinese banks. Omega, 39(3), 323-334.
  • Aydın, Y. (2020). A hybrid multicriteria decision making (MCDM) model consisting of SD & COPRAS methods in performance evaluation of foreign deposit Banks. Journal of Economics, Business & Political Studies, VII (2), 160-176.
  • Bandyopadhyay, S. (2021). Comparison among multi-criteria decision analysis techniques: A novel method. Progress in Artificial Intelligence, 10(2), 195-216.
  • Bansal, R., Singh, A., Kumar, S. & Gupta, R. (2018). Evaluating factors of profitability for Indian banking sector: a panel regression. Asian Journal of Accounting Research, 3(2), 236-254.
  • Bayyurt, N. (2013). Ownership effect on bank’s performance: Multi criteria decision making approaches on foreign and domestic Turkish Banks. Procedia - Social and Behavioral Sciences, 99, 919-928.
  • Chodha, V., Dubey, R., Kumar, R., Singh, S., & Kaur, S. (2022). Selection of Industrial Arc Welding Robot with TOPSIS and Entropy MCDM Techniques. Materials Today: Proceedings, 50, 709-715.
  • Çakır, S., & Perçin, S. (2013). AB ülkeleri’nde bütünleşik entropi ağırlık-topsıs yöntemiyle ar-ge performansının ölçülmesi. Uludağ Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 32(1), 77-95.
  • Daly, S. & Frikha, M. (2017). Determinants of bank Performance: Comparative Study Between Conventional and Islamic Banking in Bahrain. J Knowl Econ, 8, 471-488.
  • Dash, M. (2017). A model for bank performance measurement integrating multivariate factor structure with multi-criteria PROMETHEE methodology. Asian Journal of Finance & Accounting, 9(1), 310-332.
  • Dhanalakshmi, C. S., Madhu, P., Karthick, A., Mathew, M., & Vignesh Kumar, R. (2020). A Comprehensive MCDM-Based Approach Using TOPSIS and EDAS as an Auxiliary Tool for Pyrolysis Material Selection and its Application. Biomass conversion and biorefinery, 1-16.
  • Dogan, M. (2015). Comparison of financial performance of participation banks in Turkey. Journal of Economics Finance and Accounting, 2(4).
  • Ecer, F. & Pamucar, D. (2022). A novel LOPCOW‐DOBI multi‐criteria sustainability performance assessment methodology: An application in developing country banking sector. Omega, 112.
  • Elmas, B., & Yetim, A. (2021). Katılım bankalarının finansal performanslarının TOPSIS yöntemi ile uluslararası boyutta değerlendirilmesi. International Journal of Islamic Economics and Finance Studies, 7(3), 230-263.
  • Ertuğrul, İ. & Karakaşoğlu, N. (2009). Performance evaluation of Turkish cement firms with fuzzy analytic hierarchy process and TOPSIS methods. Expert Systems with Applications, 36(1), 702-715.
  • Genç, T. & Masca, M. (2013). TOPSIS ve PROMETHEE yöntemleri ile elde edilen üstünlük sıralamalarının bir uygulama üzerinden karşılaştırılması. Afyon Kocatepe Üniversitesi İİBF Dergisi, XV(II), 539-567.
  • Gupta, S., Mathew, M., Gupta, S. & Dawar, V. (2021). Benchmarking the private sector banks in India using MCDM approach. Journal of Public Affairs, 21(2). https://doi.org/10.1002/pa.2409
  • Hidayat, S. E., Sakti, M. R. P. & Al-Balushi, R. A. A. (2021). Risk, efficiency and financial performance in the GCC banking industry: Islamic versus conventional banks. Journal of Islamic Accounting and Business Research, 12(4), 564-592.
  • Hussain, S. A. I., & Mandal, U. K. (2016). Entropy Based MCDM Approach for Selection of Material. In National Level Conference on Engineering Problems and Application of Mathematics. 1-6.
  • Ic, Y. T., Celik, B., Kavak, S., & Baki, B. (2021). Development of a multi-criteria decision-making model for comparing the performance of Turkish commercial banks. Journal of Advances in Management Research, 18(2), 250-272.
  • Karaatlı, M. (2016). Entropi-Gri ilişkisel analiz yöntemleri ile bütünleşik bir yaklaşim: Turizm sektöründe uygulama. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 21(1), 63-77.
  • Keshavarz Ghorabaee, M., Zavadskas, E. K., Olfat, L., & Turskis, Z. (2015). Multi-criteria inventory classification using a new method of evaluation based on distance from average solution (EDAS). Informatica, 26(3), 435-451.
  • Laha, S., & Biswas, S. (2019). A hybrid unsupervised learning and multi-criteria decision making approach for performance evaluation of Indian banks. Accounting, 5(4), 169-184.
  • Malik, M.S., Awais, M. & Khursheed, A. (2016). Impact of liquidity on profitability: A comprehensive case of Pakistan’s private banking sector. International Journal of Economics and Finance, 8(3), 69-74.
  • 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
  • No, R.K.G., Niroomand, S., Didehkhani, H., & Mahmoodirad, A. (2021). Modified interval EDAS approach for the multi-criteria ranking problem in banking sector of Iran. Journal of Ambient Intelligence and Humanized Computing, 12, 8129-8148. https://doi.org/10.1007/s12652-020-02550-6
  • Oralhan, B. & Büyüktürk, M. A. (2019). Avrupa Birliği Ülkeleri ve Türkiye’nin İnovasyon Performansının Çok Kriterli Karar Verme Yöntemleriyle Kıyaslanması. Avrupa Bilim ve Teknoloji Dergisi, 16, 471-484.
  • Oralhan, B. & Özsoy, C. (2019). İşletme Performansının Çok Kriterli Karar Verme Yöntemleri ile Değerlendirilmesi. H. Hale Künüçen, X. Quliyeva, Y.(Eds.), Seçgin, Sosyal, Beşeri ve İdari Bilimlerde Yenilikçi Yaklaşımlar, Ekin Yayınevi.
  • Ozcalici, M. & Bumin, M. (2020). An integrated multi-criteria decision making model with Self-Organizing Maps for the assessment of the performance of publicly traded banks in Borsa Istanbul. Applied Soft Computing, 90. https://doi.org/10.1016/j.asoc.2020.106166
  • Öksüzkaya, M., & Atan, M. (2023). 2016–2021 döneminde Türkiye’de kalkınma ve yatırım bankalarının finansal etkinliği: CRITIC yöntemi ve MABAC yöntemi ile bir uygulama. Bankacılık ve Finansal Araştırmalar Dergisi, 10(1), 14-32.
  • Özcan, M. (2021), Türk bankacılık sektörünün finansal performans göstergeleri: BIST mali sektörü üzerine bir araştırma. Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi, 35(2), 389-406. https://doi.org/10.16951/atauniiibd.689612
  • Radulescu, M., Fedajev, A. & Nikolic, D. (2017). Ranking of EU national banking systems using multi-criteria analysis in the light of Brexit. Acta Oeconomica, 67(4), 473-509.
  • Reig-Mullor, J. & Brotons-Martinez, J.M. (2021). The evaluation performance for commercial banks by intuitionistic fuzzy numbers: the case of Spain. Soft Computing, 25, 9061-9075. https://doi.org/10.1007/s00500-021-05847-6
  • Sakinc, S.O. (2016). Comparison of Turkish State Banks' performances via multi-criteria performance measurement method. International Journal of Scientific Research & Management Studies, 4(1), 4857-4871.
  • Sama, H.R., Kosuri, S.V.K., & Kalvakolanu, S. (2022). Evaluating and ranking the Indian private sector banks-A multi-criteria decision-making approach. Journal of Public Affairs, 22(2). https://doi.org/10.1002/pa.2419
  • Sezal, L. (2023) Examination of non-interest incomes and bank performance relationship in the Türkiye banking sector. Journal of Economics and Administrative Sciences, 24(2): 186-194. https://doi.org/10.37880/cumuiibf.1196021
  • Sufian, F. (2009). Determinants of bank profitability in a developing economy: Empirical evidence from the China banking sector. Journal of Asia-Pacific Business, 10(4), 281-307. https://doi.org/10.1080/10599230903340205
  • Torkayesh, A.E., Deveci, M., Karagoz, S. & Antucheviciene, J. (2023). A state-of-the-art survey of evaluation based on distance from average solution (EDAS): Developments and applications. Expert Systems with Applications, 221, https://doi.org/10.1016/j.eswa.2023.119724
  • Tüysüz, F. & Yıldız, N. (2020). A novel multi-criteria analysis model for the performance evaluation of bank regions: an application to Turkish agricultural banking. Soft Computing, 24, 5289-5311. https://doi.org/10.1007/s00500-019-04279-7
  • Ulutaş, A. (2019). Entropi Tabanli Edas Yöntemi ile Lojistik Firmalarinin Performans Analizi. Uluslararası İktisadi ve İdari İncelemeler Dergisi, (23), 53-66.
  • Ünal, E.A. (2019). Özel sermayeli ticari bankalarının finansal performansının SD ve WASPAS yöntemleri ile ölçümü. Ekonomi, Politika & Finans Araştırmaları Dergisi, 4(3), 384-400.
  • Unvan, Y.A. (2020). Financial performance analysis of banks with Topsis and Fuzzy Topsis approaches. Gazi University Journal of Science, 33(4), 904-923.
  • Wang, C. N., Le, T. Q., Chang, K. H., & Dang, T. T. (2022). Measuring Road Transport Sustainability Using MCDM-Based Entropy Objective Weighting Method. Symmetry, 14(5), 1033, 1-19. https://doi.org/10.3390/sym14051033
  • Wanke, P., Azad, M.D.A.K. & Barros, C.P. (2016). Predicting efficiency in Malaysian Islamic banks: A two-stage TOPSIS and neural networks approach. Research in International Business and Finance, 36, 485-498. https://doi.org/10.1016/j.ribaf.2015.10.002
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There are 53 citations in total.

Details

Primary Language English
Subjects Finance
Journal Section Research Article
Authors

Sevgi Sumerli Sarıgül 0000-0002-3820-6288

Pınar Avcı 0000-0001-9480-8016

Esra Yaşar 0000-0002-0313-9126

Early Pub Date June 21, 2023
Publication Date June 30, 2023
Published in Issue Year 2023 Volume: 8 Issue: 1

Cite

APA Sumerli Sarıgül, S., Avcı, P., & Yaşar, E. (2023). Evaluation of the Financial Performance of Deposit Banks Operating in Turkey by Entropy-supported EDAS Method. JOEEP: Journal of Emerging Economies and Policy, 8(1), 239-255.
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