BibTex RIS Kaynak Göster

Classification of Domestic and Foreign Commercial Banks In Turkey Based on Financial Efficiency: A Comparison of Decision Tree, Logistic Regression and Discriminant Analysis Models

Yıl 2009, Cilt: 14 Sayı: 2, 113 - 139, 01.06.2009

  Yrd.Doç.Dr.Ali Sait Albayrak

Öz

Anahtar Kelimeler

-

Kaynakça

  • 1. Avkiran, N. (1997). Performance of Foreign Banks in Australia. The Australian Banker, 6 (111), 222–224.
  • 2. Biggs, D., Ville, B. D., and Suen, E. (1991). A Method of Choosing Multiway Partitions for Classification and Decision Trees. Journal of Applied Statistics, 18, 49-62.
  • 3. Brieman, L., Friedman, J. H., Olshen, R. A., and Stone, C. J. (1984). Classification and Regression Trees, Brooks/Cole Advanced Books and Software, California.
  • 4. Buch, C. M., and Golder, S. (2001). Foreign Versus Domestic Banks in Germany and the US: A Tale of Two Markets?, Journal of Multinational Financial Management, 11, 341–61.
  • 5. Camdeviren, H. A., Yazici, A. C., Akkus, Z., Bugdayci, R., and Sungur, M. A. (2007). Comparison of Logistic Regression Model and Classification Tree: An Application to Postpartum Depression Data. Expert Systems With Applications, 32, 987–994.
  • 6. Claessens, S., Demirguc-Kunt, A., and Huizinga, H. (2001). How Does Foreign Entry Affect Domestic Banking Markets? Journal of Banking and Finance, 25, 891–911.
  • 7. Cooper, J. A., Saracci, R., and Cole, P. (1979). British Journal of Cancer, 39, 87–89.
  • 8. De Young, R., and Noll, D. E. (1996). Foreign-Owned Banks in The United States: Earning Market Share or Buying It? Journal of Money Credit and Banking, 4 (28), 622–636.
  • 9. Demirguc-Kunt, A., and Huizinga, H. (1999). Determinants of Commercial Bank Interest Margins and Profitability: Some International Evidence. The World Bank Economic Review, 2 (13), 379–408.
  • 10. Dobson, A. J. (1990). An Introduction to Generalized Linear Model, Chapman and Hall, New York.
  • 11. Fabrigar, L. R., Wegener, D. T., Maccallum, R. C., and Strahan, E. J. (1999). Evaluating the Use of Exploratory Factor Analysis in Psychological Research. Psychological Research, 4 (3), 272-299.
  • 12. Fadel, H. (1977). The Predictive Power of Financial Ratios in The British Construction Industry. Journal of Business Finance andaccounting, 4 (3), 339-352.
  • 13. Golin, J. (2001). The Bank Credit Analysis Handbook: A Guide for Analysts, Bankers and Investor Wiley, New York.
  • 14. Hair, J. F., Anderson, R. E., Tatham, R. L., and C.Black, W. (1998). Maltivariate Data Analysis, Prentice Hall, New Jersey.
  • 15. Harrison, W. B., and Wood, D. R. (1989). The Development of a Bank Classification Scheme Through Discriminant Analysis. Atlantic Economic Journal, XVII (1), 35-42.
  • 16. Hasan, I., and Lozano-Vivas, A. (1998). Foreign Banks, Production Technology, and Efficiency: Spanish Experience, Paper Presented at The Georgia Productivity Workshop III, Athens, Georgia.
  • 17. Horrigan, J. O. (1968). A Short History of Financial Ratio Analysis, The Accounting Review, 284–94.
  • 18. Hosmer, D. W., and Lemeshow, S. (1989). Applied Logistic Regression, Jonh Wiley, New York.
  • 19. Johnson, R., and Wichern, D. W. (2002). Applied Multivariate Statistical Analysis (Fifth Edition), Prentice-Hall, New Jersey.
  • 20. Kass, G. (1980). An Exploratory Technique for Investigating Large Quantities of Categorical Data, Applied Statistics, 2 (29), 119-127.
  • 21. Kitsantas, P., Moore, T. W., and Sly, D. F. (2007). Using Classification Trees to Profile Adolescent Smoking Behaviors, Addictive Behaviors, 32, 9–23.
  • 22. Kitsantasa, P., Hollanderb, M., and Li, L. M. (2007). Assessing the Stability of Classification Trees Using Florida Birth Data, Journal of Statistical Planning and Inference, 137, 3917-3929.
  • 23. Kosmidou, K., Pasiouras, F., Zopounidis, C., and Doumpos, M. (2006). A Multivariate Analysis of the Financial Charachteristics of Foreign and Domestic Banks in the UK. Omega (The International Journal of Management Science), 34, 189-195.
  • 24. Lachenbruch, P. A. (1967). An Almost Unbiased Method of Obtaining Confidence Intervals for the Probability of Misclassification in Discriminant Analysis, Psychological Bulleti, 99, 422-431.
  • 25. Lee, T.S., Chiu, C.C., Chou, Y.C., and Lu, C.J. (2006). Mining the Customer Credit Using Classification and Regression Tree and Multivariate Adabtive Regression Splines, Computational Statistics and Data Analysis, 50, 1113-1130.
  • 26. Lim, T. S., Loh, W. Y., and Shih, Y. S. (2000). A Comparison of Prediction Accuracy, Complexity, and Training Time of Thirty-Three Old and New Classification Algorithms, Machine Learning, 3 (40), 203- 228.
  • 27. Loh, W. Y., and Shih, Y. S. (1997). Split Selection Methods for Classification Trees, Statistica Sinica, 7, 815-840.
  • 28. Sathye, M. X. (2001). Efficiency in Australian Banking: An Empirical Investigation, Journal of Banking and Finance, 25, 613-630.
  • 29. Seth, R. (1992). Profitability of Foreign Banks in The US, Federal Reserve Bank of New York, Research Paper, No: 9225.
  • 30. Sharma, S. (1996). Applied Multivariate Techniques, John Wiley and Sons, New York.
  • 31. SPSS Inc. (2004). SPSS Classification Trees 13.0,SPSS Inc., Chichago.
  • 32. SPSS Inc. (2002). Answertree 3.1 User's Guide, SPSS Inc., Chichago.
  • 33. SPSS Inc. (2006). Clementine 10.1 Algorithms Guide,: SPSS Inc., Chicago.
  • 34. Tabachnick, B., and Fidell, L. S. (1996). Using Multivariate Statistics, Harper Collins, New York.
  • 35. Walter, I., and Gray, H. P. (1983). Protectionism, and International Banking, Sectoral Efficiency, Competitive Structure and National Policy, Journal of Banking and Finance, 7, 597–609.
  • 36. West, D. (2000). Neural Network Credit Scoring Models, Computer Operation Research, 27, 1131–1152.
  • 37. Worth, A. P., and Cronin, M. T. (2003). The Use of Discriminant Analysis, Logistic Regression and Classification Tree Analysis in The Development of Classification Models for Human Health Effects, Journal of Molecular Structure (Theochem), 622, 97–111.+

TÜRKİYE’DE YERLİ VE YABANCI TİCARET BANKALARININ FİNANSAL ETKİNLİĞE GÖRE SINIFLANDIRILMASI: KARAR AĞACI, LOJİSTİK REGRESYON VE DİSKRİMİNANT ANALİZİ MODELLERİNİN BİR KARŞILAŞTIRMASI

Yıl 2009, Cilt: 14 Sayı: 2, 113 - 139, 01.06.2009

  Yrd.Doç.Dr.Ali Sait Albayrak

Öz

Bu çalışmada yerli ve yabancı olarak önceden grup üyeliği belirlenmiş bankaların sınıflandırmasında yaygın olarak kullanılan veri madenciliği tekniklerinden diskriminant, lojistik regresyon ve karar ağacı modelleri karşılaştırılmaktadır. Üç sınıflandırma tekniği, bankalarla ilgili seçilmiş likidite, gelir-gider, karlılık ve faaliyet oranları kullanılarak karşılaştırılmaktadır. Araştırmanın sonuçları, bankaların sınıflandırmasında karar ağacı modelinin geleneksel diskriminant ve lojistik regresyon modellerine üstünlük sağlayarak alternatif etkili bir sınıflandırma tekniği olarak kullanılabileceğini göstermektedir.

Anahtar Kelimeler

Lojistik Regresyon ve Diskriminant Analizi Veri Madenciliği CRT ve Banka Performansı

Kaynakça

  • 1. Avkiran, N. (1997). Performance of Foreign Banks in Australia. The Australian Banker, 6 (111), 222–224.
  • 2. Biggs, D., Ville, B. D., and Suen, E. (1991). A Method of Choosing Multiway Partitions for Classification and Decision Trees. Journal of Applied Statistics, 18, 49-62.
  • 3. Brieman, L., Friedman, J. H., Olshen, R. A., and Stone, C. J. (1984). Classification and Regression Trees, Brooks/Cole Advanced Books and Software, California.
  • 4. Buch, C. M., and Golder, S. (2001). Foreign Versus Domestic Banks in Germany and the US: A Tale of Two Markets?, Journal of Multinational Financial Management, 11, 341–61.
  • 5. Camdeviren, H. A., Yazici, A. C., Akkus, Z., Bugdayci, R., and Sungur, M. A. (2007). Comparison of Logistic Regression Model and Classification Tree: An Application to Postpartum Depression Data. Expert Systems With Applications, 32, 987–994.
  • 6. Claessens, S., Demirguc-Kunt, A., and Huizinga, H. (2001). How Does Foreign Entry Affect Domestic Banking Markets? Journal of Banking and Finance, 25, 891–911.
  • 7. Cooper, J. A., Saracci, R., and Cole, P. (1979). British Journal of Cancer, 39, 87–89.
  • 8. De Young, R., and Noll, D. E. (1996). Foreign-Owned Banks in The United States: Earning Market Share or Buying It? Journal of Money Credit and Banking, 4 (28), 622–636.
  • 9. Demirguc-Kunt, A., and Huizinga, H. (1999). Determinants of Commercial Bank Interest Margins and Profitability: Some International Evidence. The World Bank Economic Review, 2 (13), 379–408.
  • 10. Dobson, A. J. (1990). An Introduction to Generalized Linear Model, Chapman and Hall, New York.
  • 11. Fabrigar, L. R., Wegener, D. T., Maccallum, R. C., and Strahan, E. J. (1999). Evaluating the Use of Exploratory Factor Analysis in Psychological Research. Psychological Research, 4 (3), 272-299.
  • 12. Fadel, H. (1977). The Predictive Power of Financial Ratios in The British Construction Industry. Journal of Business Finance andaccounting, 4 (3), 339-352.
  • 13. Golin, J. (2001). The Bank Credit Analysis Handbook: A Guide for Analysts, Bankers and Investor Wiley, New York.
  • 14. Hair, J. F., Anderson, R. E., Tatham, R. L., and C.Black, W. (1998). Maltivariate Data Analysis, Prentice Hall, New Jersey.
  • 15. Harrison, W. B., and Wood, D. R. (1989). The Development of a Bank Classification Scheme Through Discriminant Analysis. Atlantic Economic Journal, XVII (1), 35-42.
  • 16. Hasan, I., and Lozano-Vivas, A. (1998). Foreign Banks, Production Technology, and Efficiency: Spanish Experience, Paper Presented at The Georgia Productivity Workshop III, Athens, Georgia.
  • 17. Horrigan, J. O. (1968). A Short History of Financial Ratio Analysis, The Accounting Review, 284–94.
  • 18. Hosmer, D. W., and Lemeshow, S. (1989). Applied Logistic Regression, Jonh Wiley, New York.
  • 19. Johnson, R., and Wichern, D. W. (2002). Applied Multivariate Statistical Analysis (Fifth Edition), Prentice-Hall, New Jersey.
  • 20. Kass, G. (1980). An Exploratory Technique for Investigating Large Quantities of Categorical Data, Applied Statistics, 2 (29), 119-127.
  • 21. Kitsantas, P., Moore, T. W., and Sly, D. F. (2007). Using Classification Trees to Profile Adolescent Smoking Behaviors, Addictive Behaviors, 32, 9–23.
  • 22. Kitsantasa, P., Hollanderb, M., and Li, L. M. (2007). Assessing the Stability of Classification Trees Using Florida Birth Data, Journal of Statistical Planning and Inference, 137, 3917-3929.
  • 23. Kosmidou, K., Pasiouras, F., Zopounidis, C., and Doumpos, M. (2006). A Multivariate Analysis of the Financial Charachteristics of Foreign and Domestic Banks in the UK. Omega (The International Journal of Management Science), 34, 189-195.
  • 24. Lachenbruch, P. A. (1967). An Almost Unbiased Method of Obtaining Confidence Intervals for the Probability of Misclassification in Discriminant Analysis, Psychological Bulleti, 99, 422-431.
  • 25. Lee, T.S., Chiu, C.C., Chou, Y.C., and Lu, C.J. (2006). Mining the Customer Credit Using Classification and Regression Tree and Multivariate Adabtive Regression Splines, Computational Statistics and Data Analysis, 50, 1113-1130.
  • 26. Lim, T. S., Loh, W. Y., and Shih, Y. S. (2000). A Comparison of Prediction Accuracy, Complexity, and Training Time of Thirty-Three Old and New Classification Algorithms, Machine Learning, 3 (40), 203- 228.
  • 27. Loh, W. Y., and Shih, Y. S. (1997). Split Selection Methods for Classification Trees, Statistica Sinica, 7, 815-840.
  • 28. Sathye, M. X. (2001). Efficiency in Australian Banking: An Empirical Investigation, Journal of Banking and Finance, 25, 613-630.
  • 29. Seth, R. (1992). Profitability of Foreign Banks in The US, Federal Reserve Bank of New York, Research Paper, No: 9225.
  • 30. Sharma, S. (1996). Applied Multivariate Techniques, John Wiley and Sons, New York.
  • 31. SPSS Inc. (2004). SPSS Classification Trees 13.0,SPSS Inc., Chichago.
  • 32. SPSS Inc. (2002). Answertree 3.1 User's Guide, SPSS Inc., Chichago.
  • 33. SPSS Inc. (2006). Clementine 10.1 Algorithms Guide,: SPSS Inc., Chicago.
  • 34. Tabachnick, B., and Fidell, L. S. (1996). Using Multivariate Statistics, Harper Collins, New York.
  • 35. Walter, I., and Gray, H. P. (1983). Protectionism, and International Banking, Sectoral Efficiency, Competitive Structure and National Policy, Journal of Banking and Finance, 7, 597–609.
  • 36. West, D. (2000). Neural Network Credit Scoring Models, Computer Operation Research, 27, 1131–1152.
  • 37. Worth, A. P., and Cronin, M. T. (2003). The Use of Discriminant Analysis, Logistic Regression and Classification Tree Analysis in The Development of Classification Models for Human Health Effects, Journal of Molecular Structure (Theochem), 622, 97–111.+
Toplam 37 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm Makaleler
Yazarlar

  Yrd.Doç.Dr.Ali Sait Albayrak Bu kişi benim

Yayımlanma Tarihi 1 Haziran 2009
Yayımlandığı Sayı Yıl 2009 Cilt: 14 Sayı: 2

Kaynak Göster

APA Albayrak, .Y.S. (2009). TÜRKİYE’DE YERLİ VE YABANCI TİCARET BANKALARININ FİNANSAL ETKİNLİĞE GÖRE SINIFLANDIRILMASI: KARAR AĞACI, LOJİSTİK REGRESYON VE DİSKRİMİNANT ANALİZİ MODELLERİNİN BİR KARŞILAŞTIRMASI. Süleyman Demirel Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi, 14(2), 113-139.