Building a Rating of Russian Regions According to their Level of Financial Market Development

Abstract

The use of indicators characterizing the regional level of its functioning is relevant for monitoring the development of the financial market. The purpose of the article is to develop an approach to build a rating of Russian regions according their level of the financial market development. The authors’ methodology includes integral indicators calculating, which characterize the development of the financial market in terms of the territorial entities using the method of principal components. As a result, we have built a composite index, and on its basis a rating of Russian regions is compiled. The components of the composite index are subindexes, calculated using the method of principal components for each of the five market sectors: banking, insurance, microfinance, non-state pension funds and stock market. On the one hand, this approach allows to aggregate heterogeneous initial indicators of financial market sectors. On the other hand, it helps making a comparative analysis of regions in intersectoral terms. The research is based on the statistical data of Rosstat, the Bank of Russia and the Federal Tax Service for 2020–2022. The authors conclude that the constructed rating allows to track changes in the positions of the constituent entities of regions on the financial market and to detail the development areas of its specific sectors. For example, in 2022, the rating leaders were Moscow (1st place), Tyumen (2nd place) and Novosibirsk Oblasts (3rd place). In general, over 2020–2022, the Kamchatka Kray (banking sector), Chukotka Autonomous Okrug (banking sector), Kostroma Oblast (banking and stock sectors) significantly improved their position in the rating. Growth zones for the Tver Oblast are the insurance, microfinance and stock sectors, for the Bryansk and Volgograd Oblasts the insurance and microfinance sectors. The approach proposed by the authors allows expanding the composition of the index components, which characterizes a high potential of its application in solving the problems of the financial market assessment.