Bank reliability forecasting using classification trees

Authors

  • Kirillov K.V. Kuban State University, Krasnodar, Российская Федерация
  • Halafyan A.A. Kuban State University, Krasnodar, Российская Федерация

UDC

519.22:336.144.36

Abstract

The authors use the methods of classification analysis to forecast the reliability of banks. The classification trees, which use balance sheet items with the smallest binary correlation as attributes, are made. Special aspects of modelling trees for the group of banks under consideration are described, as well as those of the formation of training and testing samples and selection of criteria for truncating of branches. The model developed was tested on real data. Financial condition of 557 Russian banks was analysed based on the data for 2008, when a significant increase in the number of bankruptcy was recorded.

Keywords:

classification trees, training and testing sample, logical derivation, truncation criterium

Author Infos

Kirill V. Kirillov

аспирант кафедры прикладной математики Кубанского государственного университета

e-mail: k.kirillov@mail.ru

Aleksan A. Halafyan

д-р техн. наук, профессор кафедры прикладной математики Кубанского государственного университета

e-mail: khaliphyan@kubannet.ru

References

  1. Argenti J. Corporate Collapse // England: McGraw-Hill, 1976. 193 p.
  2. Bryant S.M. A case-based reasoning approach to bankruptcy prediction modeling. Intelligent Systems in Accounting // Finance and Management. 1997. Vol. 6. P. 195-214.
  3. Curram S.P., Mingers J. Neural networks, decision trees induction and discriminant analysis: An empirical comparison// Journal of the Operational Research Society. 1994. Vol. 45. No. 4. P. 440-450.
  4. Elhadi M.T., Vamos T. An IR-CBR approach to legal indexing and retrieval in bankruptcy law // In Tenth proceedings in database and expert systems applications. 1999. P. 769-774.
  5. Jo H., Han I., Lee H. Bankruptcy prediction using case-based reasoning, neural networks, and discriminant analysis // Expert Systems with Applications. 1997. Vol. 13. No. 2. P. 97-108.
  6. Park C., Han I. A case-based reasoning with the feature weights derived by analytic hierarchy process for bankruptcy prediction // Expert Systems with Applications. 2000. Vol. 23. No. 3. P. 255-264.
  7. Atiya A.F. Bankruptcy prediction for credit risk using neural networks: A survey and new results // IEEE Transactions on Neural Networks. 2001. Vol. 12. No. 4. P. 929-935.
  8. Baek J., Cho S. Bankruptcy prediction for credit risk using an autoassociative neural network in Korean firms // In IEEE international conference on computational intelligence for financial engineering. 2003. P. 25-29.
  9. Nasir M.L., John R.I., Bennett S.C. Predicting corporate bankruptcy using modular neural networks // In IEEE international conference on computational intelligence for financial engineering. 2000. P. 86-91.
  10. Chen M.-Y. Predicting corporate financial distress based on integration of decision tree classification and logistic regression // Expert Systems with Applications. 2011. Vol. 38. P. 11261-11272.
  11. Pang Su-lin, Gong Ji-zhang C5.0 Classification Algorithm and Application on Individual Credit Evaluation of Banks // Systems Engineering - Theory & Practice. 2009. Vol. 29. Iss. 12. P. 94-102.
  12. Кириллов К.В. Анализ деятельности банка с помощью факторного анализа // Международная молодежная научно-практическая конференция "Математическое моделирование в экономике, страховании и управлении рисками". 05-08 ноября 2013, г. Саратов (принято в печать).
  13. Халафян А.А. STATISTICA 6. Математическая статистика с элементами теории вероятностей: учебник // Москва: Издательство Бином. 2011. 496 с.
  14. Пелипенко Е.Ю., Халафян А.А. Оценка платежеспособности предприятия на основе метода "деревья классификации" // Современные тенденции в экономике и управлении: новый взгляд: сборник материалов III Международной научно-практической конференции: в 2-х частях. Ч. 2. 2010. С. 34-39.
  15. Breiman L., Friedman J.H., Olshen R.A., Stone C.T. Classification and Regression Trees // Wadsworth, Belmont, California, 1984. 368 p.

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Issue

2013. No. 3

Pages

61-66

Submitted

2013-09-11

Published

2013-09-23

How to Cite

Kirillov K.V., Halafyan A.A. Bank reliability forecasting using classification trees. Ecological Bulletin of Research Centers of the Black Sea Economic Cooperation, 2013, no. 3, pp. 61-66. (In Russian)

Copyright (c) 2013 Kirillov K.V., Halafyan A.A.

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This work is licensed under a Creative Commons Attribution 4.0 International License.