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An econometric analysis of factors affecting the stock price of Russian companies

Vol. 25, Iss. 4, APRIL 2019

Received: 12 February 2019

Received in revised form: 26 February 2019

Accepted: 12 March 2019

Available online: 26 April 2019

Subject Heading: Securities market

JEL Classification: C38, G11

Pages: 912–924

https://doi.org/10.24891/fc.25.4.912

Malkina M.Yu. National Research Lobachevsky State University of Nizhny Novgorod (UNN), Nizhny Novgorod, Russian Federation
mmuri@yandex.ru

https://orcid.org/0000-0002-3152-3934

Yakovleva E.K. Nizhny Novgorod Institute of Management of Russian Presidential Academy of National Economy and Public Administration (RANEPA), Nizhny Novgorod, Russian Federation
yakov-ekaterina@mail.ru

ORCID id: not available

Subject This paper considers the factors affecting the dynamics of stock market value of the Russian share market.
Objectives The paper aims to identify factors determining the stock prices of Russian emitters and to assess the degree of their influence. We are to build a regression model for shares of the Acron Company as well as examine the resulting model for compliance with quality criteria and explain the obtained results.
Methods We conduct a correlation-regression analysis with the elimination of the seasonal component and bringing the time series to a stationary state. The work uses the least squares method with robust standard errors applying the Cochrane–Orcutt autoregressive transformation, and the Prais–Winsten amendment.
Results The research identifies nine industry and macroeconomic factors affecting the market value of the shares of the company under consideration. We built three alternative models that met all the quality criteria.
Conclusions and Relevance The study reveals the most significant factors for Acron's shares prices: the RTS index, the dollar exchange rate, the volume of commercial transportation of goods by transport etc. The constructed regression equations enable to predict the future stock price of the considered companies.

Keywords: stock market value, correlation-regression analysis, least squares method, autoregressive transformation, Cochrane–Orcutt model

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