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TÜRK TİCARİ BANKACILIK SEKTÖRÜNDE KREDİ RİSKİNİN BELİRLEYİCİLERİ TEMELİNDE BANKA SIRALAMASI

Year 2017, Volume: 4 Issue: 1, 1 - 20, 16.01.2017

Abstract

Çalışmada
ticari bankaların kredi risklerinin belirleyicileri analiz edilmiş ve ardından
anlamlı bulunan değişkenlerden hareketle örneklem döneminde ele alınan bankalar
riskliliklerine göre sıralanmıştır. Yapılan deneysel analizler 2004-2014
döneminde Türk Bankacılık Sektöründe faaliyet gösteren 27 ticari bankayı kapsamaktadır.
Kredi riskinin belirleyicileri, uluslararası deneysel çalışmaların bulguları
doğrultusunda modellenmiştir. Bu bağlamda bankaya özgü ve makro ekonomik
değişkenleri kapsayan dinamik bir panel veri modelleri tahmin edilmiştir.
Ardından anlamlı katsayı değerleri veren değişkenler ile TOPSIS analizi
uygulanmıştır. Dinamik modellerin tahmininden elde edilen anlamlı katsayılar,
TOPSIS analizinde değişken ağırlıkları olarak dikkate alınmıştır. Bulgular hem
bankaya özgü hem de makro ekonomik değişkenlerin kredi riski üzerinde güçlü
etkileri olduğunu göstermektedir. Özellikle ekonomik faaliyet hacmindeki
daralma ve makro istikrarsızlıkların kredi riskini arttırdığı belirlenmiştir.
TOPSIS analizleri ise, 2007 sonrası dönemde kredi riski sıralamasının değiştiğini,
bu tarihten itibaren kamu bankaların ilk üç sırayı almasına karşın, önemli özel
sermayeli milli bankaların ilk onun dışına çıktığını göstermiştir.

References

  • Ali, A., ve Daly, K. 2010. Macroeconomic Determinants of Credit Risk: Recent Evidence from a Cross Country Study, International Review of Financial Analysis, 19: 165-171.
  • Arellano, M. ve Bond, S. 1991. Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Unemployment Equations, Review of Economic Studies, 58: 277-297.
  • Arellano, M. ve Bover, O. 1995. Another Look at the Instrumental Variable Estimation of Error-Components Models, Journal of Econometrics, 68(1): 29-51.
  • Blundell, R. ve Bond, S. 1998. Initial Conditions and Moment Restrictions in Dynamic Panel Data Models, Journal of Econometrics, 87(1): 115-143.
  • Ayanoğlu, Y. ve Ertürk, B. 2007. Modern Kredi Riski Yönetiminde Derecelendirmenin Yeri ve İMKB’de Kayıtlı Şirketler Üzerine Bir Uygulama, Gazi Üniversitesi İ.İ.B.F. Dergisi, 9(2): 75-90.
  • Budak, H. ve Erpolat, S. 2012. Kredi Riski Tahmininde Yapay Sinir Ağları ve Lojistik Regresyon Analizi Karşılaştırması, Online Academic Journal of Information Technology, 3(9): 23-30.
  • Castro, V. 2013. Macroeconomic Determinants of the Credit Risk in the Banking System: The Case of the GIPSI, Economic Modeling, 672-683.
  • Chaibi, H. ve Ftiti, Z. 2015. Credit Risk Determinants: Evidence from A Cross-country Study, Research in International Business and Finance, 33: 1-16.
  • Hwang, C.L. ve Yoon, K. 1981. Multiple Attribute Decision Making. In: Lecture Notes in Economics and Mathematical Systems 186. Springer-Verlag, Berlin.
  • Jakubik, P. ve Schmieder, C. 2008. Stress Testing Credit Risk: Comparison of the Czech Republic and Germany, BIS Financial Stability Institute Paper, September.
  • Kavcıoğlu, Ş. 2011. Ticari Bankacılıkta Kredi Riskinin ve Kredi Riski Ölçüm Modellerinin Değerlendirilmesi, Finansal Araştırmalar ve Çalışmalar Dergisi, 3(5): 11-19.
  • Koçyiğit, S. Ç. ve Demir, A. 2014. Türk Bankacılık Sektöründe Kredi Riski ve Yönetimine İlişkin Bir Uygulama: Türkiye Garanti Bankası Örneği, Journal of Business Research Turk, 6(3): 222-246.
  • Louzis, D.P., Vouldis, A.T., ve Metaxas, V.L. 2012. Macroeconomic and Bank-spesific Determinants of Non-performing Loans in Greece: A Comparative Study of Mortgage, Business and Consumer Loan Portfolios, Journal of Banking and Finance, 36: 1012-1027.
  • Manab, N.A., Theng, N.Y. ve Md-Rus, R. 2015. The Determinants of Credit Risk in Malaysia, Procedia – Social and Behavioral Science, 172: 301-308.
  • Mileris, R. 2012. Macroeconomic Determinants of Loan Portfolio Credit Risk in Banks, Engineering Economics, 23(5): 496-504.
  • Misman, F.N., Bhatti, I., Lou, W., Samsudin, S., ve Abd Rahman, N. H. 2015. Islamic Banks Credit Risk: A Panel Study, Procedia – Economics and Finance, 31: 75-82.
  • Monjezi, M., Amiri,H., Farrokhi, A. ve Goshtasbi, K. 2010. Prediction of Rock Fragmentation due to Blasting in Sarcheshmeh Copper Mine Using Artificial Neural Networks, Geotechnique and Geology Engineering, 28(4): 423–430
  • Nkusu, M. 2011. Nonperforming Loans and Macrofinancial Vulnerabilities in Advanced Economies, IMF Working Paper, No. WP/11/161, July.
  • Oktar, S. ve Yüksel, S. 2015. 1998 Yılında Rusya’da Yaşanan Bankacılık Krizi ve Öncü Göstergeleri, M.Ü. İ.İ.B.F. Dergisi, 37(2): 327-340.
  • Olson, D.L. 2004. Comparison of Weights in Topsis Models, Mathematical and Computer Modelling, 40(7-8): 721-727.
  • Opricovic, S. ve Tzeng, G.H. 2004. Compromise Solution by MCDM Methods: A Comparative Analysis of VIKOR and TOPSIS, European Journal of Operational Research, 156(2): 445-455.
  • Rao, R.V. 2008. Evaluation of Environmentally Conscious Manufacturing Programs using Multiple Attribute Decision-Making Methods, Proceedings of the Institution of Mechanical Engineers - Part B - Engineering Manufacture, 222 (3): 441-451.
  • Roodman, D. 2006. How to Do xtabond2: An Introduction to Difference and System GMM in Stata, Center for Global Development Working Paper, No:103, December.
  • Roodman, D. 2008. A Note on the Theme of Too Many Instruments, Center for Global Development Working Paper, No: 125, May.
  • Tunay, K.B. 2012. Banka Kredi Portföylerinin Yönetiminde Ödememe Riski Analizi: Kalman Filtresine Dayanan Alternatif Bir Yöntem Önerisi, Finansal Araştırmalar ve Çalışmalar Dergisi, 3(6): 56-63.
  • Tunay, K.B. 2015. Kredi Portföylerinde Sektörel Yoğunlaşma ve Risk İlişkisi: Türk Ticari Bankacılık Sektörü Üzerine Bir Analiz, BDDK Bankacılık ve Finansal Piyasalar Dergisi, 9(1): 127-147.
  • Vogiazas, S.D. ve Nikolaidou, E. 2011. Investigating the Determinants of Nonperfoming Loans in the Romanian Banking System: An Empirical Study with Reference to Greek Crisis, Economics Research International, 2011: 1-13.
  • Yurdakul, F. 2014. Macroeconomic Modeling of Credit Risk for Banks, Procedia – Social and Behavioral Science, 109: 784-793.
  • Yüksel, S. 2016. Bankaların Takipteki Krediler Oranını Belirleyen Faktörler: Türkiye İçin Bir Model Önerisi. Bankacılar Dergisi, 98: 41-56.
  • Zhu, N., Wang, B., and Wu, Y. 2015. Productivity, Efficiency, and Non-performing Loans in the Chinese Banking Industry, The Social Science Journal, 52: 468-480.

BANK RANKING BASED ON CREDIT RISK DETERMINANTS IN TURKISH BANKING SECTOR

Year 2017, Volume: 4 Issue: 1, 1 - 20, 16.01.2017

Abstract

In the study, the
determinants of the credit risk of commercial banks were analyzed and then the
banks
ranked,
according to risks taken into consideration during the sample period, based on
the variables found to be significant. The empirical analyzes included 27
commercial banks operating in the Turkish Banking Sector for the period
2004-2014. The determinants of credit risk are modeled in the frame of findings
of international experimental studies. In this context, dynamic panel data
models are estimated by using bank-specific and macroeconomic variables. Then,
TOPSIS analysis was applied on the variables giving meaningful coefficient
values. Significant coefficients obtained from the prediction of dynamic models
are considered as variable weights in the TOPSIS analysis. Findings show that
both bank-specific and macroeconomic variables have strong effects on credit
risk. In particular, it has been determined that the contraction in the volume
of economic activity and macro instability have increased the credit risk. The
TOPSIS analysis shows that the ranking of credit risk in the post-2007 period
has changed, and since then public banks have taken the first three orders, but
the major private banks have gone out of the market first.

References

  • Ali, A., ve Daly, K. 2010. Macroeconomic Determinants of Credit Risk: Recent Evidence from a Cross Country Study, International Review of Financial Analysis, 19: 165-171.
  • Arellano, M. ve Bond, S. 1991. Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Unemployment Equations, Review of Economic Studies, 58: 277-297.
  • Arellano, M. ve Bover, O. 1995. Another Look at the Instrumental Variable Estimation of Error-Components Models, Journal of Econometrics, 68(1): 29-51.
  • Blundell, R. ve Bond, S. 1998. Initial Conditions and Moment Restrictions in Dynamic Panel Data Models, Journal of Econometrics, 87(1): 115-143.
  • Ayanoğlu, Y. ve Ertürk, B. 2007. Modern Kredi Riski Yönetiminde Derecelendirmenin Yeri ve İMKB’de Kayıtlı Şirketler Üzerine Bir Uygulama, Gazi Üniversitesi İ.İ.B.F. Dergisi, 9(2): 75-90.
  • Budak, H. ve Erpolat, S. 2012. Kredi Riski Tahmininde Yapay Sinir Ağları ve Lojistik Regresyon Analizi Karşılaştırması, Online Academic Journal of Information Technology, 3(9): 23-30.
  • Castro, V. 2013. Macroeconomic Determinants of the Credit Risk in the Banking System: The Case of the GIPSI, Economic Modeling, 672-683.
  • Chaibi, H. ve Ftiti, Z. 2015. Credit Risk Determinants: Evidence from A Cross-country Study, Research in International Business and Finance, 33: 1-16.
  • Hwang, C.L. ve Yoon, K. 1981. Multiple Attribute Decision Making. In: Lecture Notes in Economics and Mathematical Systems 186. Springer-Verlag, Berlin.
  • Jakubik, P. ve Schmieder, C. 2008. Stress Testing Credit Risk: Comparison of the Czech Republic and Germany, BIS Financial Stability Institute Paper, September.
  • Kavcıoğlu, Ş. 2011. Ticari Bankacılıkta Kredi Riskinin ve Kredi Riski Ölçüm Modellerinin Değerlendirilmesi, Finansal Araştırmalar ve Çalışmalar Dergisi, 3(5): 11-19.
  • Koçyiğit, S. Ç. ve Demir, A. 2014. Türk Bankacılık Sektöründe Kredi Riski ve Yönetimine İlişkin Bir Uygulama: Türkiye Garanti Bankası Örneği, Journal of Business Research Turk, 6(3): 222-246.
  • Louzis, D.P., Vouldis, A.T., ve Metaxas, V.L. 2012. Macroeconomic and Bank-spesific Determinants of Non-performing Loans in Greece: A Comparative Study of Mortgage, Business and Consumer Loan Portfolios, Journal of Banking and Finance, 36: 1012-1027.
  • Manab, N.A., Theng, N.Y. ve Md-Rus, R. 2015. The Determinants of Credit Risk in Malaysia, Procedia – Social and Behavioral Science, 172: 301-308.
  • Mileris, R. 2012. Macroeconomic Determinants of Loan Portfolio Credit Risk in Banks, Engineering Economics, 23(5): 496-504.
  • Misman, F.N., Bhatti, I., Lou, W., Samsudin, S., ve Abd Rahman, N. H. 2015. Islamic Banks Credit Risk: A Panel Study, Procedia – Economics and Finance, 31: 75-82.
  • Monjezi, M., Amiri,H., Farrokhi, A. ve Goshtasbi, K. 2010. Prediction of Rock Fragmentation due to Blasting in Sarcheshmeh Copper Mine Using Artificial Neural Networks, Geotechnique and Geology Engineering, 28(4): 423–430
  • Nkusu, M. 2011. Nonperforming Loans and Macrofinancial Vulnerabilities in Advanced Economies, IMF Working Paper, No. WP/11/161, July.
  • Oktar, S. ve Yüksel, S. 2015. 1998 Yılında Rusya’da Yaşanan Bankacılık Krizi ve Öncü Göstergeleri, M.Ü. İ.İ.B.F. Dergisi, 37(2): 327-340.
  • Olson, D.L. 2004. Comparison of Weights in Topsis Models, Mathematical and Computer Modelling, 40(7-8): 721-727.
  • Opricovic, S. ve Tzeng, G.H. 2004. Compromise Solution by MCDM Methods: A Comparative Analysis of VIKOR and TOPSIS, European Journal of Operational Research, 156(2): 445-455.
  • Rao, R.V. 2008. Evaluation of Environmentally Conscious Manufacturing Programs using Multiple Attribute Decision-Making Methods, Proceedings of the Institution of Mechanical Engineers - Part B - Engineering Manufacture, 222 (3): 441-451.
  • Roodman, D. 2006. How to Do xtabond2: An Introduction to Difference and System GMM in Stata, Center for Global Development Working Paper, No:103, December.
  • Roodman, D. 2008. A Note on the Theme of Too Many Instruments, Center for Global Development Working Paper, No: 125, May.
  • Tunay, K.B. 2012. Banka Kredi Portföylerinin Yönetiminde Ödememe Riski Analizi: Kalman Filtresine Dayanan Alternatif Bir Yöntem Önerisi, Finansal Araştırmalar ve Çalışmalar Dergisi, 3(6): 56-63.
  • Tunay, K.B. 2015. Kredi Portföylerinde Sektörel Yoğunlaşma ve Risk İlişkisi: Türk Ticari Bankacılık Sektörü Üzerine Bir Analiz, BDDK Bankacılık ve Finansal Piyasalar Dergisi, 9(1): 127-147.
  • Vogiazas, S.D. ve Nikolaidou, E. 2011. Investigating the Determinants of Nonperfoming Loans in the Romanian Banking System: An Empirical Study with Reference to Greek Crisis, Economics Research International, 2011: 1-13.
  • Yurdakul, F. 2014. Macroeconomic Modeling of Credit Risk for Banks, Procedia – Social and Behavioral Science, 109: 784-793.
  • Yüksel, S. 2016. Bankaların Takipteki Krediler Oranını Belirleyen Faktörler: Türkiye İçin Bir Model Önerisi. Bankacılar Dergisi, 98: 41-56.
  • Zhu, N., Wang, B., and Wu, Y. 2015. Productivity, Efficiency, and Non-performing Loans in the Chinese Banking Industry, The Social Science Journal, 52: 468-480.
There are 30 citations in total.

Details

Journal Section Makaleler
Authors

İlyas Akhisar This is me

K. Batu Tunay

Publication Date January 16, 2017
Published in Issue Year 2017 Volume: 4 Issue: 1

Cite

APA Akhisar, İ., & Tunay, K. B. (2017). BANK RANKING BASED ON CREDIT RISK DETERMINANTS IN TURKISH BANKING SECTOR. Journal of Banking and Financial Research, 4(1), 1-20.