Bankaların Risk Alma Davranışını Etkileyen Faktörler: Türk Bankacılık Sektöründen Kanıtlar

Yıl 2025, Cilt: 10 Sayı: 2, 636 - 654, 30.06.2025

Erdal Demir , Yüksel Aydın

https://doi.org/10.30784/epfad.1645226

Öz

Bu araştırma, 2010-2023 dönemi için Türkiye'deki ticari bankaların risk almasını etkileyen faktörleri ampirik olarak analiz etmektedir. Geliştirilen panel veri regresyon modellerinde banka risk alma ölçütü olarak ters Z skoru kullanılırken, bağımsız değişken olarak çeşitli banka düzeyi ve makro düzey değişkenler kullanılmıştır. Bu makalede geliştirilen modeller, tüm bankaları içeren ana örneklem ve oluşturulan alt örneklemler için ayrı ayrı tahmin edilmiştir. Sabit etkili regresyonlardan elde edilen sonuçlara göre, banka büyüklüğü, banka sermayesi, banka mevduatı ve net faiz marjı değişkenleri ana örneklem açısından banka risk alma düzeyini azaltma eğilimindedir. Ancak likidite riski, kredi riski, enflasyon oranı, ekonomik büyüme ve COVID-19 pandemi krizi banka risk alma düzeyini artırma eğilimindedir. Halka açık, halka açık olmayan, yerli ve yabancı bankalardan oluşan alt örneklemlerden elde edilen bulgular, banka büyüklüğü, banka sermayesi ve net faiz marjının banka risk alma düzeyini azaltma eğiliminde olduğunu göstermektedir ki bu da ana örneklemden elde edilen bulguları desteklemektedir. Son olarak, bu makalenin sonuçları, bankaların risk alma davranışlarının kontrol edilmesi, bankacılık sektöründe istikrarın sağlanması ve sürdürülebilir bir bankacılık sektörünün oluşturulması açısından banka yönetimi, düzenleyici mekanizmalar ve politika yapıcılar için önemli çıkarımlara sahiptir.

Anahtar Kelimeler

Banka Risk Alma , Ticari Bankalar , Türk Bankacılık Sistemi , Panel Veri Analizi

The Factors Influencing Banks' Risk-Taking Behavior: Evidence from The Turkish Banking Industry

Öz

This research employs an empirical approach to analyse the factors affecting the risk-taking of commercial banks in Turkey for the period 2010-2023. The inverse Z score was utilised as a measure of bank risk-taking in the developed panel data regression models, while various bank-level and macro-level variables were employed as independent variables. The developed models in this article are estimated separately for the main sample, which includes all banks, and for the sub-samples that have been formed. According to the results based on fixed effects regressions, bank size, bank capital, bank deposit and net interest margin variables tend to reduce the level of bank risk taking in terms of the main sample. However, liquidity risk, credit risk, inflation rate, economic growth and the COVID-19 pandemic crisis tend to increase the level of bank risk taking. Findings from subsamples of listed, non-listed, domestic and foreign banks indicate that bank size, bank capital and net interest margin tend to reduce the level of bank risk taking, which supports the findings from the main sample. Finally, the results of this article have important implications for bank management, regulatory mechanisms and policy makers in terms of controlling the risk-taking behavior of banks, ensuring stability in the banking sector and building a sustainable banking sector.

Anahtar Kelimeler

Bank Risk Taking , Commercial Banks , Turkish Banking System , Panel Data Analysis

Kaynakça

• Adhikari, B.K. and Agrawal, A. (2016). Does local religiosity matter for bank risk-taking? Journal of Corporate Finance, 38, 272-293. https://doi.org/10.1016/j.jcorpfin.2016.01.009
• Ahamed, F. (2021). Determinants of liquidity risk in the commercial banks in Bangladesh. European Journal of Business and Management Research, 6(1), 164-169. https://doi.org/10.24018/ejbmr.2021.6.1.729
• Akbar, S., Kharabsheh, B., Poletti-Hughes, J. and Shah, S.Z.A. (2017). Board structure and corporate risk taking in the UK financial sector. International Review of Financial Analysis, 50, 101-110. https://doi.org/10.1016/j.irfa.2017.02.001
• Albaity, M., Mallek, R.S. and Noman, A.H.M. (2019). Competition and bank stability in the MENA region: The moderating effect of Islamic versus conventional banks. Emerging Markets Review, 38, 310-325. https://doi.org/10.1016/j.ememar.2019.01.003
• Ashraf, B.N. (2017). Political institutions and bank risk-taking behavior. Journal of Financial Stability, 29, 13-35. https://doi.org/10.1016/j.jfs.2017.01.004
• Baselga-Pascual, L., Trujillo-Ponce, A. and Cardone-Riportella, C. (2015). Factors influencing bank risk in Europe: Evidence from the financial crisis. The North American Journal of Economics and Finance, 34, 138-166. https://doi.org/10.1016/j.najef.2015.08.004
• Chaibi, H. and Ftiti, Z. (2015). Credit risk determinants: Evidence from a cross-country study. Research in International Business and Finance, 33, 1-16. https://doi.org/10.1016/j.ribaf.2014.06.001
• Chen, F.W., Feng, Y. and Wang, W. (2018). Impacts of financial inclusion on non-performing loans of commercial banks: Evidence from China. Sustainability, 10(9), 3084. https://doi.org/10.3390/su10093084
• Danisman, G.O. and Demirel, P. (2019). Bank risk-taking in developed countries: The influence of market power and bank regulations. Journal of International Financial Markets, Institutions and Money, 59, 202-217. https://doi.org/10.1016/j.intfin.2018.12.007
• Diaconu, R.I. and Oanea, D.C. (2014). The main determinants of bank's stability. Evidence from Romanian banking sector. Procedia Economics and Finance, 16, 329-335. https://doi.org/10.1016/S2212-5671(14)00810-7
• Dias, R. (2020). Capital regulation and bank risk‐taking–new global evidence. Accounting & Finance, 61(1), 847-884. https://doi.org/10.1016/S2212-5671(14)00810-7
• Driscoll, J. and Kraay, A.C. (1998). Consistent covariance matrix estimation with spatially dependent data. Review of Economics and Statistics, 80, 541-560. https://doi.org/10.1162/003465398557825
• Erdinç, D. and Gurov, A. (2016). The effect of regulatory and risk management advancement on non-performing loans in European banking, 2000–2011. International Advances in Economic Research, 22, 249-262. https://doi.org/10.1007/s11294-016-9591-y
• Ghenimi, A., Chaibi, H. and Omri, M.A.B. (2020). Liquidity risk determinants: Islamic vs conventional banks. International Journal of Law and Management, 63(1), 65-95. https://doi.org/10.1108/IJLMA-03-2018-0060
• Işık, Ö. and Belke, M. (2017). Likidite riskinin belirleyicileri̇: Borsa İstanbul’da kote mevduat bankalarından kanıtlar. Ekonomi Politika ve Finans Araştırmaları Dergisi, 2(2), 113-126. https://doi.org/10.30784/epfad.354458
• Isik, O. and Bolat, S. (2016). Determinants of non-performing loans of deposit banks in Turkey. Journal of Business Economics and Finance, 5(4), 341-350. https://doi.org/10.17261/Pressacademia.2017.356
• Işık, Ö., Shabir, M., Demir, G., Puska, A. and Pamucar, D. (2025). A hybrid framework for assessing Pakistani commercial bank performance using multi-criteria decision-making. Financial Innovation, 11(1), 38. https://doi.org/10.1186/s40854-024-00728-x
• Martínez-Malvar, M. and Baselga-Pascual, L. (2020). Bank risk determinants in Latin America. Risks, 8(3), 94. https://doi.org/10.3390/risks8030094
• Mercan, M. (2021). Determinant factors influence bank risk-taking: Evidence from commercial bank of Georgia. Globalization and Business, 6(11), 59-65. https://doi.org/10.35945/gb.2021.11.007
• Mohamad, A. and Jenkins, H. (2020). Corruption and banks’ non-performing loans: Empirical evidence from MENA countries. Macroeconomics and Finance in Emerging Market Economies, 14(3), 308-321. https://doi.org/10.1080/17520843.2020.1842478
• Nur, T. (2022). Risk alma eğilimini etkileyen içsel faktörler: BİST Banka endeksi üzerine panel eşbütünleşme ve nedensellik analizi. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 52, 363-378. https://doi.org/10.30794/pausbed.1076301
• Pesaran, M.H. (2007). A simple panel unit root test in the presence of cross‐section dependence. Journal of Applied Econometrics, 22(2), 265-312. https://doi.org/10.1002/jae.951
• Pesaran, M.H. (2015). Testing weak cross-sectional dependence in large panels. Econometric Reviews, 34(6-10), 1089-1117. https://doi.org/10.1080/07474938.2014.956623
• Pesaran, M.H. (2021). General diagnostic tests for cross-sectional dependence in panels. Empirical Economics, 60(1), 13-50. https://doi.org/10.1007/s00181-020-01875-7