Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1998 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Shumsky, S.A., Yarovoy, A.V. (1998). Self-Organizing Atlas of Russian Banks. In: Deboeck, G., Kohonen, T. (eds) Visual Explorations in Finance. Springer Finance. Springer, London. https://doi.org/10.1007/978-1-4471-3913-3_5
Download citation
DOI: https://doi.org/10.1007/978-1-4471-3913-3_5
Publisher Name: Springer, London
Print ISBN: 978-1-84996-999-4
Online ISBN: 978-1-4471-3913-3
eBook Packages: Springer Book Archive