Home  Journals  Financial Analytics: Science and Experience  3(361) - 2022 July-September

An analysis of growth drivers of prices for the Russian companies' shares

Received: 18 September 2018

Received in revised form: 9 October 2018

Accepted: 30 October 2018

Available online: 30 August 2022

Subject Heading: MATHEMATICAL ANALYSIS AND MODELING IN ECONOMICS

JEL Classification: C38, G11

Pages: 332–353

https://doi.org/10.24891/re.17.1.183

Subject. The article discusses the impact of various factors on a growth in prices for the Russian companies' stocks.
Objectives. The research identifies factors that influence prices for the Russian companies' stocks and evaluates their significance. We also specify the regression model of growth in prices for stocks, illustrating the case of PAO Uralkali, evaluate its parameters and test the quality of the model. The article also provides my interpretation of the findings.
Methods. The methodological framework is based on graphic, correlation-regression analysis. Building the regression, we applied the least squares method with the correction of standard errors for robustness. To eliminate the residual autocorrelation, we conducted the Cochrane–Orcutt procedure and Prais–Winsten estimation.
Results. We identified a set of related factors that influence the price for the analyzable company's stocks. Using various methods, we build four alternative models that feature the production of mineral fertilizers as a common factor triggering a growth in prices for shares of respective companies. The highest quality was found in the model based on the autoregressive modification with the Prais–Winsten estimation.
Conclusions. Volume of commercial cargo shipments, export of goods and services were found to have the greatest effect on prices for stocks of an analyzable issuer, in addition to the production of mineral fertilizers. The findings can be used to forecast foreign exchange rates of a certain company.

Keywords: stock prices, factor, correlation analysis, regression analysis, least squares method, autoregressive transformation

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