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Dynamic Analysis of the Behavioural Patterns of the Largest Commercial Banks in the Russian Federation

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Author Info

  • Fuad Aleskerov

    ()

  • V. Belousova

    ()

  • M. Serdyuk

    ()

  • V. Solodkov

    ()

Abstract

This paper presents a pattern behavio ral analysis of 100 largest Russian commercial banks by total assets during an eight- year period: from the first quarter of 1999 to the second quarter of 2007. Bank performance indicators are analyzed. Structural similarities in the development of the banks are examined. A cluster analysis is applied to determine banks with a similar structure of operations. This analysis allows to estimate how the structure of the Russian banking system has been changing over time. In particular, it allows to identify prevailing patterns in the behavior of Russian commercial banks and to analyze the stability of their position in a particular pattern.

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File URL: http://www.icer.it/docs/wp2008/ICERwp12-08.pdf
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Bibliographic Info

Paper provided by ICER - International Centre for Economic Research in its series ICER Working Papers - Applied Mathematics Series with number 12-2008.

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Length: 18 pages
Date of creation: Jun 2008
Date of revision:
Handle: RePEc:icr:wpmath:12-2008

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Related research

Keywords: Bank; dynamic pattern analysis; cluster analysis;

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References

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  1. James B. Thomson, 1991. "Predicting bank failures in the 1980s," Economic Review, Federal Reserve Bank of Cleveland, issue Q I, pages 9-20.
  2. Cole, Rebel A. & Gunther, Jeffery W., 1995. "A CAMEL rating's shelf life," MPRA Paper 24693, University Library of Munich, Germany, revised 01 Nov 2008.
  3. Alexis Derviz & Jiří Podpiera, 2008. "Predicting Bank CAMELS and S&P Ratings: The Case of the Czech Republic," Emerging Markets Finance and Trade, M.E. Sharpe, Inc., vol. 44(1), pages 117-130, January.
  4. Otchere, Isaac & Chan, Janus, 2003. "Intra-industry effects of bank privatization: A clinical analysis of the privatization of the Commonwealth Bank of Australia," Journal of Banking & Finance, Elsevier, vol. 27(5), pages 949-975, May.
  5. Fuad Aleskerov & C. Emre Alper, 2000. "A Clustering Approach to Some Monetary Facts: A Long-Run Analysis of Cross-Country Data," The Japanese Economic Review, Japanese Economic Association, vol. 51(4), pages 555-567, December.
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Cited by:
  1. Aysan, Ahmet Faruk & Ertek, Gurdal & Ozturk, Secil, 2009. "Assessing the adverse effects of interbank funds on bank efficiency through using semiparametric and nonparametric methods," MPRA Paper 38113, University Library of Munich, Germany.

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