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Analyzing the possibility to apply the arbitrage pricing theory in the Russian stock market

Vol. 12, Iss. 3, SEPTEMBER 2019

Received: 21 November 2018

Received in revised form: 3 December 2018

Accepted: 23 December 2018

Available online: 30 August 2019

Subject Heading: ECONOMIC AND STATISTICAL RESEARCH

JEL Classification: G12

Pages: 269–283

https://doi.org/10.24891/fa.12.3.269

Mikhailusov M.S. Russian State University for the Humanities (RSUH), Moscow, Russian Federation
aventador-mark@yandex.ru

ORCID id: not available

Subject The article considers the fundamental value of financial assets in the Russian stock market using the arbitrage pricing theory.
Objectives The purpose of the study is to identify strong and stable correlation relationships between various financial assets that provide a possibility of arbitrage transactions. The focus is to select shares in the Russian stock market that are best suited for arbitrage operations.
Methods I conducted tests for data stationarity. The obtained results enabled multiple linear regression analysis as required by the arbitrage pricing theory. The regression analysis helped select companies, where the return on equity movement gives the most accurate and complete picture. To test the hypotheses, I used paired correlation coefficients, which determined the existence of linear relationships between the level of arbitrage, free-float and beta coefficients.
Results The obtained regression models show that ordinary shares of Sberbank, Lukoil, Nornickel, Gazprom and MTS are the most suitable financial instruments in the Russian stock market for arbitrage transactions. Furthermore, I confirmed the hypothesis about positive relationships between the volume of free float shares and the level of systematic risk with possibilities for the said transactions.
Conclusions The arbitrage pricing theory proved to be applicable for the Russian stock market, as it tends to consider several predetermined factors and mitigate the nonsystematic risk.

Keywords: arbitrage pricing theory, stock valuation, regression analysis

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