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Classification of Turkish Commercial Banks Under Fuzzy c-Means Clustering

Year 2013, Volume: 7 Issue: 2, 13 - 36, 01.12.2013

Abstract

As the major actors of credit system, banks have a great importance not just for financial system but also for the whole of economy. Thus, financial soundness of banks, affected by many financial risks, should be monitored closely. This study focuses on classification of the deposit and participation banks of Turkey regarding their soundness. Financial Stability Indicators FSIs are used to attain this goal. Research method is mainly based on fuzzy cmeans clustering method which relies on fuzzy logic. The results show that the participation banks are grouped together in the same cluster. Also, Denizbank A.Ş., Finansbank A.Ş., Yapı ve Kredi Bankası A.Ş. and Türk Ekonomi Bankası A.Ş., having similar characteristics regarding ownership and scope of financial services, are found to be grouped together in all periods under consideration. Moreover, it has been seen that size is not the most decisive factor for classification purposes

References

  • 1. Alam, P., Booth, D., Lee, K., & Thordarson, T. (2000). The use of fuzzy clustering algorithm and self-organizing neural networks for identifying potentially failing banks: an experimental study. Expert Systems with Applications, 185-199.
  • 2. Babuska, R. (2009). Fuzzy and neural control disc: Course lecture notes. Delft, the Netherlands: Delft University of Technology.
  • 3. Bellman, R., Kalaba, R., & Zadeh, L. (1996). Fuzzy sets, fuzzy logic, and fuzzy systems. Abstraction and pattern classification, 44-50.
  • 4. Bezdek, J. C. (1981). Pattern Recognition with Fuzzy Objective Function Algorithms. Norwell, MA. USA: Kluwer Academic Publishers.
  • 5. Bezdek, J. C., Ehrlich, R., & Full, W. (1984). FCM: The fuzzy c-means clustering algorithm. Computers & Geosciences 10(2-3), 191-203.
  • 6. Chen, L.-H., & Chiou, T.-W. (1999). A Fuzzy Credit-rating Approach for Commercial Loans: a Taiwan Case. Omega, Int. J. Mgmt. Sci., 407-419.
  • 7. Connor, G., Flavin, T., & O'Kelly, B. (2012). The U.S. and Irish credit crises: Their distinctive differences and common features. Journal of International Money anc Finance, 60-79.
  • 8. Doğan, B. (2008). CLUSTER ANALYSIS AS A BANKING SUPERVISION TOOL: AN APPLICATION FOR TURKISH BANKING SECTOR. İstanbul: Kadir Has University.
  • 9. Dunn, J. C. (1973). A fuzzy relative of the isodata process and its use in detecting compact well-separated clusters. Mathematical and Computer Modelling, 32–57.
  • 10. Gökgöz, İ. H. (2012). Stochastic credit default swap pricing.
  • 11. International Monetary Fund. (2006). Financial Soundeness Indicators. Washington, D.C.: International Monetary Fund Publication Services .
  • 12. Krishnapuram, R. (1993). A possibilistic approach to clustering. IEEE Transactions on Fuzzy Systems, 98-110.
  • 13. OeNB, & FMA. (November 2004). Guidelines on credit risk management: Rating models and validation. Vienna, Austria: OeNB Printing Office.
  • 14. Pedrycz, W. (2005). Knowledge-Based Clustering: From Data to Information Granules. New Jersey: John Wiley & Sons. Inc.
  • 15. Ruspini, E. H. (1969). A new approach to clustering. Information and Control, 22-32.
  • 16. Servigny, A. D., & Renault, O. (2004). Measuring and Managing Credit Risk. New York: The McGraw-Hill Companies. Inc.
  • 17. Tufan, E., & Hamarat, B. (2003). Clustering of Financial Ratios of the Quoted Companies Through Fuzzy Logic Method. Journal of Naval Science and Engineering, 123-140.
  • 18. Yang, M. S. (1993). A survey of fuzzy clustering. Mathematical and Computer Modelling 1S(11), 1-16.
  • 19. Zadeh, L. A. (1965). Fuzzy sets. Information and Control Vol 8, 338-353.

Türk Bankacılık Sistemindeki Bankaların c-Ortalamalı Bulanık Kümeleme Analizi ile Sınıflandırılması

Year 2013, Volume: 7 Issue: 2, 13 - 36, 01.12.2013

Abstract

Kredi sisteminin ana aktörlerinden olan bankalar sadece finans sistemi için değil tüm ekonomi için büyük önem taşır. Bu sebeple, birçok riskle karşı karşıya olan bankaların sağlamlıklarının yakından izlenmesi gerekir. Bu çalışmada, Türk mevduat ve katılım bankalarının finansal sağlamlıklarına göre sınıflandırılması amaçlanmıştır. Bu amaç için Finansal Sağlamlık Göstergeleri FSIs kullanılmıştır. Çalışma ana yöntem olarak c-ortalamalı bulanık kümeleme analizine dayanmaktadır. Çalışma sonucunda katılım bankalarının birlikte gruplandığı görülmüştür. Bunun yanında, sahiplik ve faaliyet gösterilen alan açısından benzer özellikler taşıyan Denizbank A.Ş., Finansbank A.Ş., Yapı ve Kredi Bankası A.Ş. ile Türk Ekonomi Bankası A.Ş.’nin araştırmaya konu olan tüm dönemlerde aynı grup altında gruplanmıştır. Ayrıca, finansal büyüklüğün gruplandırmada en belirleyici gösterge olmadığı sonucuna ulaşılmıştır

References

  • 1. Alam, P., Booth, D., Lee, K., & Thordarson, T. (2000). The use of fuzzy clustering algorithm and self-organizing neural networks for identifying potentially failing banks: an experimental study. Expert Systems with Applications, 185-199.
  • 2. Babuska, R. (2009). Fuzzy and neural control disc: Course lecture notes. Delft, the Netherlands: Delft University of Technology.
  • 3. Bellman, R., Kalaba, R., & Zadeh, L. (1996). Fuzzy sets, fuzzy logic, and fuzzy systems. Abstraction and pattern classification, 44-50.
  • 4. Bezdek, J. C. (1981). Pattern Recognition with Fuzzy Objective Function Algorithms. Norwell, MA. USA: Kluwer Academic Publishers.
  • 5. Bezdek, J. C., Ehrlich, R., & Full, W. (1984). FCM: The fuzzy c-means clustering algorithm. Computers & Geosciences 10(2-3), 191-203.
  • 6. Chen, L.-H., & Chiou, T.-W. (1999). A Fuzzy Credit-rating Approach for Commercial Loans: a Taiwan Case. Omega, Int. J. Mgmt. Sci., 407-419.
  • 7. Connor, G., Flavin, T., & O'Kelly, B. (2012). The U.S. and Irish credit crises: Their distinctive differences and common features. Journal of International Money anc Finance, 60-79.
  • 8. Doğan, B. (2008). CLUSTER ANALYSIS AS A BANKING SUPERVISION TOOL: AN APPLICATION FOR TURKISH BANKING SECTOR. İstanbul: Kadir Has University.
  • 9. Dunn, J. C. (1973). A fuzzy relative of the isodata process and its use in detecting compact well-separated clusters. Mathematical and Computer Modelling, 32–57.
  • 10. Gökgöz, İ. H. (2012). Stochastic credit default swap pricing.
  • 11. International Monetary Fund. (2006). Financial Soundeness Indicators. Washington, D.C.: International Monetary Fund Publication Services .
  • 12. Krishnapuram, R. (1993). A possibilistic approach to clustering. IEEE Transactions on Fuzzy Systems, 98-110.
  • 13. OeNB, & FMA. (November 2004). Guidelines on credit risk management: Rating models and validation. Vienna, Austria: OeNB Printing Office.
  • 14. Pedrycz, W. (2005). Knowledge-Based Clustering: From Data to Information Granules. New Jersey: John Wiley & Sons. Inc.
  • 15. Ruspini, E. H. (1969). A new approach to clustering. Information and Control, 22-32.
  • 16. Servigny, A. D., & Renault, O. (2004). Measuring and Managing Credit Risk. New York: The McGraw-Hill Companies. Inc.
  • 17. Tufan, E., & Hamarat, B. (2003). Clustering of Financial Ratios of the Quoted Companies Through Fuzzy Logic Method. Journal of Naval Science and Engineering, 123-140.
  • 18. Yang, M. S. (1993). A survey of fuzzy clustering. Mathematical and Computer Modelling 1S(11), 1-16.
  • 19. Zadeh, L. A. (1965). Fuzzy sets. Information and Control Vol 8, 338-353.
There are 19 citations in total.

Details

Primary Language English
Journal Section Research Article
Authors

İsmail Hakkı Gökgöz This is me

Fatih Altınel This is me

F.pınar Yetkin Gökgöz This is me

İlker Koç This is me

Publication Date December 1, 2013
Published in Issue Year 2013 Volume: 7 Issue: 2

Cite

APA Gökgöz, İ. H., Altınel, F., Gökgöz, F. Y., Koç, İ. (2013). Classification of Turkish Commercial Banks Under Fuzzy c-Means Clustering. BDDK Bankacılık Ve Finansal Piyasalar Dergisi, 7(2), 13-36.