FINANCIAL PERFORMANCE OF TURKISH BANKS IN THE COVID-19 ERA: A CLUSTER ANALYSIS

Year 2021, Volume: 8 Issue: 4, 184 - 189, 31.12.2021

Melike Betul Tavsanlı Tarkan Hamlacı

https://doi.org/10.17261/Pressacademia.2021.1474

Abstract

Purpose- Stability of the financial system and the performance of its most important constituents, namely the banks, are crucial for the wellbeing of an economy. Turkey is one of the biggest Emerging Market economies making its banking sector a good case for analysing the bank
performance in the era surrounding the Covid-19 Pandemic. This paper aims to map Turkish banking sector in terms of its players’ financial
strength and identify the attributes of the banks that present weaknesses in the period around Covid-19 Pandemic.
Methodology- A hierarchical cluster analysis with Ward’s method and squared Euclidean distance measure is conducted to divide the Turkish
banking sector into groups which display maximum between cluster variance and minimum within-cluster variance based on 14 attributes
both derived from CAMEL ratios and categorical characteristics. The analysis repeated with non-hierarchical and two-steps clustering to
identify the most relevant characteristics in distinguishing the banks. A subsequent ANOVA test is also applied looking at any statistically
significant differences among the clusters in regard to bank credit ratings. 32 banks are included in the study which are headquartered in
Turkey and regularly publish independently audited annual financial reports.
Findings- Turkish banking sector can be divided into three groups in terms of their financial strength: the large local banks with strong capital
levels, the large banks owned by foreigners and the small local banks with limited lending capabilities. The results of ANOVA test shows that
there is a significant main association of a bank’s cluster with its potency, F (2,29) = 16.106, P=0.000. The tests reveal that 2 clusters that
make up the three-fourths of Turkish banking sector have underperformed.
Conclusion- The analysis provides an ease for understanding the Turkish banking sector’s structure by grouping the banks into certain
categories. Such grouping enables the reader to grasp which attributes are important in evaluating the strength of the players, as well as the
overall banking sector. It is found that there is room for improvement for a significant three-fourth portion of the sector. It is also shown
that the key attribute which is going to play a central role in this improvement is capitalization.

Keywords

Turkish banks, Turkish banking sector, Cluster Analysis, bank performance in Turkey, Turkish Banking in Covid19 Era

References

• Allen N. Berger (1995). The Relationship between Capital and Earnings in Banking Journal of Money, Credit and Banking, 27(2): 432-456.
• Athanasoglou, P. P., Brissimis, S. N., & Delis, M. D. (2008). Bank-specific, industry-specific and macroeconomic determinants of bank profitability. Journal of International Financial Markets, Institutions and Money, 18(2): 121-136
• BDDK. (2021, May 13). Monthly Banking Sector Data. BDDK. https://www.bddk.org.tr/BultenAylik/
• Bourke, P. (1989). Concentration and other determinants of bank profitability in Europe, North America and Australia. Journal of Banking and Finance, 13(1): 65-79.
• Boyacıoğlu, M. A., Kara, Y., & Baykan, Ö. K. (2009). Predicting bank financial failures using neural networks, support vector machines and multivariate statistical methods: A comparative analysis in the sample of savings deposit insurance fund (SDIF) transferred banks in Turkey. Expert Systems with Applications, 36(2): 3355-3366.
• Buckley, A. (2004). Multinational Finance. London: FT: Prentice Hall.
• Diamond, D. W. (1984). Financial Intermediation and Delegated Monitoring. Review of Economic Studies, 51(3): 393-414.
• Goddard, J., Molyneux, P., & Wilson, J. O. S. (2004). The profitability of European banks: A cross-sectional and dynamic panel analysis. Manchester School, 72(3): 363-381.
• Gorton, G., & Pennacchi, G. (1990). Financial Intermediation and Liquidity Creation. The Journal of Finance, 45(1): 19-71.
• Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2018). Multivariate Data Analysis. Andover, Hampshire: Cengage Learning, EMEA.
• Heffernan, Shelagh A. and Fu, Xiaoqing, The Determinants of Bank Performance in China (August 22, 2008). Available at SSRN: https://ssrn.com/abstract=1247713 or http://dx.doi.org/10.2139/ssrn.1247713
• Karaatlı, M., & Yıldız, E. (2021). Mevduat bankaların finansal yapılarının kümeleme analizi ile incelenmesi. Business & Management Studies: An International Journal, 9(1): 1-17.
• Molyneux, P., & Seth, R. (1998). Foreign banks, profits and commercial credit extension in the United States. Applied Financial Economics, 8(5): 533-539.
• Molyneux, P., & Thornton, J. (1992). Determinants of European bank profitability: A note. Journal of Banking and Finance, 16(6): 1173- 1178.
• Oral, C. & Akkaya C. G., (2015). Profitability Analysis Of Banks By Using Clustering Method: An Application On Turkish Banking
• Sector, Proceedings of International Academic Conferences, 2503730, International Institute of Social and Economic Sciences.
• Pasiouras, F., & Kosmidou, K. (2007). Factors influencing the profitability of domestic and foreign commercial banks in the European Union. Research in International Business and Finance, 21(2): 222-237.
• Pereira, V. M. M., & Bonito Filipe, J. A. C. (2018). Quality of board members’ training and bank financial performance: Evidence from
• Portugal. International Journal of Economics and Business Administration, 6(3): 47-79.
• Perry, P. (1992). Do banks gain or lose from inflation? Journal of Retail Banking, 14(2): 25-30.
• Petersen, M. A., & Rajan, R. G. (1994). The Benefits of Lending Relationships: Evidence from Small Business Data. The Journal of Finance, 49(1): 3-37.
• Polatoglu, V. N., & Ekin, S. (2001). An empirical investigation of the Turkish consumers’ acceptance of Internet banking services. International Journal of Bank Marketing, 19(4): 156-165.
• Sevinç, V. (2015). "A Classification Of The Banks In Turkey With Bayesian Cluster Analysis Based On Mixture Models," Eurasian Eononometrics, Statistics and Emprical Economics Journal, 2(2): 16-24.
• Vong, A. P. I., & Chan, H. S. (2009). Determinants of Bank Profitability in Macao. Macau Monetary Research Bulletin, 12(6): 93-113.
• Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 301(58): 236- 244.
• Yayar, R., & Karaca, S. S. (2014). Efficiency Analysis in Turkish Banking Sector. Niğde Üniversitesi İİBF Dergisi, 7(2): 1-15.