Self-Organizing Atlas of Russian Banks

  • S. A. Shumsky
  • A. V. Yarovoy
Part of the Springer Finance book series (FINANCE)


The banking system in Russia is in deep crisis. Since 1994 the number of working banks has constantly decreased. Financial analysts predict even more drastic decreases in the future. In this chapter Serge Shumsky and A.V. Yarovoy present an analysis of newly available data on the emerging Russian banking system. They develop a methodology that involves the use of principal component analysis and unsupervised artificial neural networks in order to extract useful information from this newly published data. They discuss the qualitative meaning of different approaches and pay special attention to estimating the limitations of their results. This chapter demonstrates the value of unsupervised artificial neural networks and self-organizing maps in particular, as a tool for financial analysis of banking institutions.


Principal Component Analysis Total Asset Financial Analyst Total Dispersion Nonlinear Principal Component Analysis 
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Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • S. A. Shumsky
  • A. V. Yarovoy

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