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Financial solvency of China, the USA, and Russia: Modern methods of evaluation

Vol. 24, Iss. 11, NOVEMBER 2018

Received: 18 July 2018

Received in revised form: 6 August 2018

Accepted: 20 August 2018

Available online: 29 November 2018

Subject Heading: Financial system

JEL Classification: E62, F30, G35, O19

Pages: 2425–2441

Smirnov V.V. I.N. Ulianov Chuvash State University, Cheboksary, Russian Federation

Mulendeeva A.V. I.N. Ulianov Chuvash State University, Cheboksary, Russian Federation

ORCID id: not available

Subject The study addresses financial soundness of China, the USA, and Russia, and analyzes its determinants.
Objectives The aim is define the role of principal factors in ensuring the financial solvency of the said countries.
Methods The study employs methods of graphical, factor, and cluster analysis, using the linear least-squares approximation, hierarchical clustering and K-means analysis, a neural network with a hierarchy of importance determination. The World Bank Group is a source of the being estimated statistical indicators.
Results The paper unveils the overall need for broad money supply growth of the said countries. We reveal differences in achieving a high level of financial solvency by China (gross (domestic) savings), the USA (growth rates of broad money supply, non-bank loans and inflation), and Russia (growth of gross savings). At the same time, the financial system of China generates gross savings for value added growth, of the USA – increases the growth rate of broad money supply owing to the U.S. dollar potential, Russia – increases gross savings by means of replenishing the Russian Reserve Fund with United States dollars.
Conclusions The hierarchy of importance of financial solvency indicators of China (from broad money growth to gross domestic savings), Russia (from non-bank loans to gross fixed capital formation), the USA (from gross accumulation to the bank capital ratio) enabled to identify optimal directions of cross-country financial flows.

Keywords: hierarchy, cluster analysis, neural network, agglomeration, factor analysis


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