Financial Performance Analysis of Banks with Topsis and Fuzzy Topsis Approaches

Year 2020, Volume: 33 Issue: 4, 904 - 923, 01.12.2020

Yüksel Akay Ünvan

https://doi.org/10.35378/gujs.730294

Cited By: 17

Abstract

Banks are one of the key components in the world economy's financial activities. With the increasing competitive environment, banks need to use their resources effectively. Effective and efficient operation of banks is one of the important issues of money and financial markets. Effective operation of banks that are at the center of the economy, and measuring and evaluating their performances both determine their positions in the sector and have strategic importance on process management of the country’s economy. Determining which criteria and weights will be determined for performance is very important in the multi-criteria decision making process. Fuzzy techniques have more sensitivity in such evaluations. Fuzzy approaches are preferred as the risk, uncertainty and competition continue to increase in the banking sector. In this study, the criteria that affect the financial performance of banks are determined. Based on relevant criteria, the first seven banks were ranked in total assets according to reports received from The Banks Association of Turkey for 2014-2018 financial year and a performance evaluation was done by using the TOPSIS and Fuzzy TOPSIS methods.

Keywords

Financial Performance, Banks, TOPSIS, Fuzzy TOPSIS

References

• [1] Atkinson, A., Waterhouse, J. H. &Wells, R. B. (1997) “A Stakeholder Approach To Strategic Performance Measurement.” Sloan Management Review, 38: 25-37.
• [2] Tözüm, H. (2002). Performance evaluation of banks. Active: Journal of Banking and Finance, 27, 1–9.
• [3] Li, S., Liu, F., Liu, S., &Whitmore, G. A. (2001). Comparative performance of Chinese commercial banks, findings and policy implications. Reviews of Quantities Finance and Accounting, 16, 149–170.
• [4] Güven, S., &Persentili, E. (1997). A linear programming model for bank balancesheet management. Omega, 25, 449–459.
• [5] Zopounidis, C., Pouliezos, A., &Yannacopoulo, D. (1992). Designing adds for the assessment of company performance and viability. Computer Science in Economics and Management, 5, 41–56.
• [6] Siskos Y., Zopounidis C., & Pouliezos A. (1994). An integrated DSS for financing firms by an industrial development bank in Greece. Decision Support Systems, vol. 12, 151–168.
• [7] Dinçer, H., Gencer, G., Orhan, N. &Şahinbaş, K. (2011) “A Performance Evaluation of the Turkish Banking Sector After the Global Crisis via CAMELS Ratios.” Procedia Social and Behavioral Sciences, 24: 1530-1545.
• [8] Mittal, M. &Dhade, A. (2009) “Awareness and Perception of CAMEL Rating Across Banks: Some Survey Evidence.” ICFAI Journal of Bank Management, 8: 51-63 Parker, C. (2000). Performance measurement. Work Study, 49(2), 63–66.
• [9] Demir, Y., &Astarcıog˘lu, M. (2007). Determining bank performance via financial prediction: An application in ISE. Süleyman Demirel University. Journal of Business Administration and Economics Faculty, 12(1), 273–292.
• [10] Mercan, M., Reisman, A., Yolalan, R., &Emel, A. B. (2003). The effect of scale and mode of ownership on the financial performance of Turkish banking sector: Result of a DEA-based analysis. Socio-Economic Planning Sciences, 37, 185–202.
• [11] Lin, X., &Zhang, Y. (2009). Bank ownership reform and bank performance in China. Journal of Banking&Finance, 33(1), 20–29.
• [12] Bauer, P. W., Berger, A. N., Ferrier, G. D., &Humphrey, D. B. (1998). Consistency conditions for regulatory analysis of financial institutions: A comparison of frontier efficiency methods. Journal of Economic and Business, 50(2), 85–114.
• [13] Parkan, C., &Wu, M. (1999). Measurement of the performance of an investment bank using the operational competitiveness rating procedure. Omega, 27, 210–217.
• [14] Denizer, C. A.; Dinc, M.&Tarimcilar, M. (2000). “Measuring banking efficiency in the pre- and post-liberalization environment : evidence from the Turkish banking system (English)”. Policy, Research working paper; no. WPS 2476. Washington, DC: World Bank.
• [15] Isık, I., Uysal, D., &Meleke, U. (2003). Post-entry performance of de novo banks in Turkey. In 10th Annual conference of the ERF.
• [16] Demirgüç-Kunt, A., &Huizinga, H. (1999). Determinants of commercial bank interest margins and profitability: Some international evidence. The World Bank Economic Review, 13, 379–408.
• [17] Thanassoulis, E., Boussofiane, A., &Dyson, R. G. (1995). A comparison of data envelopment analysis and ratio analysis as tools for performance assessment. Omega, 24, 229–244.
• [18] Lee, H., Kwak, W., &Han, I. (1995). Developing a business performance evaluation system: An analytic hierarchical model. The Engineering Economist, 40, 343–357.
• [19] Suwignjo, P., Bittici, U. S., &Carrie, A. S. (2000). Quantative models for performance measurement system (QMPMS). International Journal of Operation Production Management, 64, 231–241.
• [20] Wang, G., Huang, S., &Dismukes, J. (2004). Product-driven supply chain selection using integrated multi-criteria decision-making methodology. International Journal of Operations and Production Management, 91, 1–15.
• [21] Frei, F. X., &Harker, P. T. (1999). Measuring aggregate process performance using AHP. European Journal of Operational Research, 116, 436–442.
• [22] Yurdakul, M.& Ic, Y.T.(2004). “AHP approach in the credit evaluation of the manufacturing firms in Turkey”. International journal of production economics 88 (3), 269-289.
• [23] Albayrak, E., &Erensal, Y. C. (2005). A study bank selection decision in Turkey using the extended fuzzy AHP method. In 35th International conference on computers and industrial engineering, Istanbul, Turkey.
• [24] Ta, H. P. &Kar, Y. H. (2000) “A Study of Bank Selection Decisions in Singapore Using the Analytical Hierarchy Process.” The International Journal of Bank Marketing, 18: 170-180.
• [25] Che, Z.H., Wang, H.S. &Chuang, C. L. (2010) “A Fuzzy AHP and DEA Approach for Marketing Bank Loan Decisions for Small and Medium Enterprises in Taiwan.” Expert Systems With Applications, 37: 7189-7199.
• [26] Shaverdi, M., Akbari, M. &Tafti, S. F. (2011) “Combining Fuzzy MCDM with BSC Approach in Performance Evaluation of Iranian Private Banking Sector.” Advances in Fuzzy Systems, 2011:1-12.
• [27] Seçme, N.Y., Bayrakdaroğlu, A. ve Kahraman, C. (2009) “Fuzzy performance evaluation in Turkish banking sector using analytic hierarchy process and TOPSIS”, Expert Systems with Applications, 36(9), ss.11699- 11709. [28] Chen, S. J., &Hwang, C. L. (1992). Fuzzy multiple attribute decision making methods and application. Lecture Notes in Economics and Mathematical Systems, vol 375. Springer, Berlin, Heidelberg.
• [29] Hwang, C. L., &Yoon, K. (1981). Multiple attribute decision making: Methods and application. New York: Springer.
• [30] Yurdakul, M., İç, Y.T., (2003), Türk otomotiv firmalarının performans ölçümü ve analizine yönelik TOPSIS yöntemi kullanan bir örnek çalışma, Gazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi, 18(1), p.1-13.
• [31] Eleren, A., Karagül, M . (2008). “1986-2006 Türkiye Ekonomisinin Performans Değerlendirmesi”. Yönetim ve Ekonomi: Celal Bayar Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 15 (1) , 1-14. Retrieved from https://dergipark.org.tr/tr/pub/yonveek/issue/13688/165655.
• [32] Sakthivel, G. Ilangkumaran, M. ve Gaikwad, A. (2015). A hybrid Multi‐Criteria Decision Modeling Approach for the Best Biodiesel Blend Selection Based on ANP‐TOPSIS Analysis, Ain Shams Engineering Journal, 6(1), 239‐256. doi:10.1016/j.asej.2014.08.003.
• [33] Demireli, E. (2010). “TOPSİS Çok Kriterli Karar Verme Sistemi: Türkiye’deki Kamu Bankaları Üzerine Bir Uygulama”. Girişimcilik ve Kalkınma Dergisi, 5:1.
• [34] Alp, S. ve Engin, T., (2011), “Trafik Kazalarının Nedenleri ve Sonuçları Arasındaki İlişkinin, TOPSIS ve AHP Yöntemleri Kullanılarak Analizi ve Değerlendirilmesi”, İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi, 10(19), ss. 65-87.
• [35] Jadidi, O., Hong, T.S., Fırouzı F., Yusuf R.M. and Zulkıflı N., (2008), “TOPSIS and Fuzzy Multi Objective Model Integration for Supplier Selection Problem”, Journal of Achievements in Materials and Manufacturing Engineering, 31(2), pp. 762-769.
• [36] Ustasüleyman Talha; (2009), “Bankacılık Sektöründe Hizmet Kalitesinin Değerlendirilmesi: AHS-TOPSIS Yöntemi”, Bankacılar Dergisi, Sayı 69, ss 33-43.
• [37] Mahmoodzadeh, S. Shahrabi, J. Pariazar, M. ve Zaeri, M.S. (2007). Project Selection by Using Fuzzy AHP and TOPSIS Technique, International Journal of Social, Education, Economics and Management Engineering, 1(6), 301‐306.
• [38] Korkmaz, M. (2012). Orman İşletmelerinde İktisadilik Düzeyinin TOPSIS Yöntemi ile Analizi. SDÜ Orman Fakültesi Dergisi / SDU Faculty of Forestry Journals, 14‐20.
• [39] Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 338–353.
• [40] Vatansever, K. (2013). Use of Fuzzy TOPSIS Method in Supplier Selection Decision and an Application. Anadolu University Journal of Social Sciences, 13(3), 155-168.
• [41] Lee, H. S. (2005). A fuzzy multi-criteria decision making model for the selection of the distribution center. Lecture notes in artificial intelligence, 3612, 1290–1299.
• [42] Chang, D. Y. (1996). Applications of the extent analysis method on fuzzy AHP. European Journal of Operational Research, 95(3), 649–655.
• [43] Chen, C. T. (2000). Extensions to the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets and Systems, 114, 1–9.
• [44] Ünal, Ö. F. (2011). Analitik Hiyerarşi Prosesi ve Personel Seçimi Alanında Uygulamaları, Journal of Alanya Faculty of Business / Alanya İşletme Fakültesi Dergisi, 3(2): 1-20.
• [45] Jahanshahloo,G.R.,Hosseinzadeh,F., Izadikhah, M. (2006), “Extension of the TOPSIS Method for Decision Making Problems with Fuzzy Data, Applied Mathematics and Computation, 181,1544-1551.
• [46] Chen, Chen-Tung, Ching-Torng, Hwang, Sue-Fn (2006). “A Fuzzy Approach for Supplier Evaluation and Selection in Supply Chain Management”, International Journal Of Production Economics, S. 102, s. 289–301.