Evaluating Banks Performance Using Key Financial Indicators – A Quantitative Modelling of Russian Banks

Shebalkov, Mikhail, Sharma, Satish and Yukhanaev, Andrey (2016) Evaluating Banks Performance Using Key Financial Indicators – A Quantitative Modelling of Russian Banks. The Journal of Developing Areas, 50 (1). pp. 425-453. ISSN 0022-037X

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Official URL: http://dx.doi.org/10.1353/jda.2016.0015

Abstract

Since the financial crisis of 2008, risk based performance management has been one of the important indicator to determine the financial health of banks and financial institutions. This study relates to the problem within the Russian Banking sector for regulators to determine and reduce risks at the marco-level and assessing performance of banks at the micro-level. The objectives are: to analyse a range of performance indicators and to structure the Russian banking sector. To explore the structure of Russian Banking sector in terms of performance over the period 2000-2010, we took a sample of 1279 banks and the financial data which was in the HTML format was extracted through PHP programming. With the help of trend analysis, the period 2000-2010 was divided into four sub periods: the period of stabilization (2002-2004), substantial development (2004-2007), financial crisis (2007-2009) and moderate development (2009-2010). Multivariate analysis were applied to classify the sample banks in these sub periods which provides evidence that despite the changes in the stage of development of the economy, the Russian Banking sector can be described with quantitative modeling. Naturally, the structural changes are affected by the described economic cycles, but these changes do not affect the determination capabilities of the model. In the period 2002-2004, nine types of banks are found. There are some prosperous as well as weak banks. During the period 2004-2007, banks had a chance to increase their profits; the banking sector became more differentiated – 12 clusters are singled out. There is no doubt that the financial crisis also affected the banking industry; there were still 12 clusters in 2007-2009, but the majority were concentrated into a single cluster with low performance indicators. Finally, the Russian banking sector started its development in the period 2009-2010, uniting some bank clusters, 10 groups are found. The results indicated that through mathematical modelling, Russian banks could be rated as “rating groups” based on their performance which might be of particular interest to bank’s managers, investors, credit analysts and bank regulators. Moreover, it could be emphasized that the changes in structure are not significant, as certain groups of banks can be found at any period of time. These groups or clusters can be referred to certain “rating groups” (from the banks with the best results to those with low results) and compared to international ratings.

Item Type: Article
Additional Information: Copyright © 2016 John Hopkins University. This article first appeared in The Journal of Developing Areas 50:1 2016, 425-453. Reprinted with permission by Johns Hopkins University Press.
Subjects: N100 Business studies
Department: Faculties > Business and Law > Newcastle Business School > Business and Management
Depositing User: Becky Skoyles
Date Deposited: 07 Nov 2014 14:16
Last Modified: 21 Jun 2017 17:06
URI: http://nrl.northumbria.ac.uk/id/eprint/18008

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