Chapter 1

This chapter presents the reviews of the most significant theoretical researches, which determine Russian banks classification.

Nowadays in Russia there are about 1000 banks, and the 200 largest of them concentrate the majority of resources. Traditionally, an analysis focuses on the largest banks, which operate as universal banks. However, for theoretical aspects and empirical calculations, it is important to take all banks into consideration. Moreover, an appropriate classification allows choosing the groups of banks, which have similar characteristics.

There is no generally accepted methodology for classifying banks. All researches can be classified into 4 categories. Firstly, it is classification by statistical indicators. Secondly, it is an analytical classification. Thirdly, it is classification on the basis of factor analysis. And finally, there are classifications that do not use previous methods, in other words, alternative methods of classification.

The first method is classification by statistical indicators. This criterion should be essential and uniquely determined. Most researches consider the size of bank, namely, the amount of assets, as such a criterion. As an example, Ovcharov [2009], Klepach et al. [2005], use this method of classification. The result of such analysis is 3 groups of banks: small, medium-sized and large banks. This method of classification is based on determination the amount of banks with assets, which is more or less than the set point. Another method is dividing banks, ranked in descending order of assets, into relevant groups [Moiseev, Sukhov, 2010]. Also, there are examples of using other criteria. The Bank of Russia differentiates banks according to the amount of capital adequacy ratio, the ratio of using short-term liabilities to generate long-term assets and others indicators of the bank’s performance.

The method of classification according to the amount of assets can be explained in details in researches by Solnchev and Chromov [2003], Styrin [2005]. Researches use not only the amount of assets as a statistical indicator, but also the location of the head office, the structure of ownership. The most complex classification in this category is the classification by the Bank of Russia [2012]. The result of this analysis is 5 groups of banks: state-owned banks, foreign-owned banks, large private banks, medium-sized and small Moscow banks, medium-sized and small regional banks.

The second method is an analytical classification. Akindinova et al. [2003] separates banks by belonging to business groups. There are 3 groups: financial types, industrial types, and the combination of financial and industrial types. Another classification is based on the brand of industry and the type of the owner. The result is distinction of state banks, foreign banks, lending banks and the banks focused on the resource sector. Mamonov [2009] analyzed the growth dynamic and the risk policy in banks, and selected 4 groups: state-owned banks, private Moscow banks, bank subsidiaries of foreign owners and private regional banks. And Gnezditskaia [2003] separates pocket banks, universal banks, retail banks, state-owned banks, and foreign-owned banks.

The third classification is based on factor analysis. The concept of this approach is to get a small set of variables from a large set of variables and to create indexes with variables that measure similar items. The purpose of factor analysis is to choose relevant indicators of bank performance. A lot of researchers use this method. The most complex classification in this category is the classification by Ivanter [2005]. The result is 9 types of clusters: banks oriented to clients, lending banks, clearing bank, captive bank, foreign-owned banks focused on the resource sector, banks oriented to international trade, universal banks.

Finally, there are alternative methods of classification. The example is the research by Aleskerov et al. [2006]. This work classified banks on the basis of the CAMEL model. The CAMEL model is a method of evaluating the health of credit unions. The rating is based on five elements of a credit union's operations: Capital, Asset quality, Management, Earnings, asset Liability management. Such an analysis allows taking into account and reflecting all significant financial and operational factors, examiners assess in their evaluation of a credit union's performance.

Another example is the research by Bobyshev [2001]. The peculiarity of this method is fuzzy K-Means clustering. This is a statistical method that discovers soft clusters where a particular bank can belong to more than one cluster with certain probability.

The method of another classification is the research by Matovnikov [2013]. The author studies banks on the basis of the proportion of retail and corporate components in the structure of a bank. There are 3 groups subject to the ratio of credits to individuals in assets, and 3 groups subject to the ratio of deposits of individuals in liabilities. The result of this classification is 9 groups of banks:

• universal banks, like Sberbank, Rosbank, Uralsib, that have the same ratios of operations with individuals;

• corporate banks like UniCredit, Promsvyazbank, VTB, that have the high ratio of operations with corporate clients;

• retail banks like VTB24, Renaissance Capital, HomeCredit and Finance, that have the high ratio of operations with individuals;

• monolines (focus on operating in one specific financial area: loans to individuals by financial markets and nonresidents), like Rusfinance bank, GE Money bank, DeltaCredit, that have the high ratio of credits to individuals and the low ratio of deposits of individuals;

• banks, that credit legal entities by deposits, like SMP bank, Minbank, that have the high ratio of credits to individuals and the low ratio of deposits of individuals;

• other banks, that use mixed strategy of lending