Factors Defining Stock Prices' Dynamics in the Russian Stock Market: The Use of Arbitrage Pricing Theory

This paper studies stock pricing of five Russian companies from oil & gas and mining sectors, based on the arbitrage pricing theory. Using multiple regression analysis, the author analyzes statistical influence of oil, gas, and metals’ prices and stock markets of Brazil, China, and the USA on stock prices of chosen five companies. The author hypothesizes that stock prices of companies, initial commodities sellers, have positive linear relation with prices of that commodities. The objective of the study is to create arbitrage pricing models for analyzed companies, demonstrating which factors have linear relation with the stock price. Considering high volatility of the Russian stock market, the analysis is based on daily data, the resulting models are tested with weekly data. The author uses data from 1-year period, which guarantees higher relevance of results, but brings some limitations. For each company, the author presents a set of factors that have statistically significant linear relation with its stock prices. Also, each model represents sensitivity of stock prices to each factor in the model. The analysis was provided for mining companies “GMK Norilsk Nickel”, “Polymetal International plc”, and “Polyus”, and “Gazprom” and “Lukoil” from oil & gas sector. As a result, prices of 5 from 6 analyzed metals have positive linear relation with stock prices of analyzed mining companies, oil prices have positive linear relation with stock prices of “Lukoil”, which produces oil. But at the same time, stock prices of “Gazprom”, which produces gas, does not have positive linear relation with gas prices. Brazilian stock index has positive linear relation with 3 from 5 analyzed companies. For the stocks of 4 companies that have positive linear relation with prices of commodities they produce, the author presents arbitrage pricing models. For the stock prices of “GMK Norilsk Nickel”, “Polymetal International plc”, and “Polyus” are presented pricing models with accuracy of 90%, 91%, and 96% respectively, for “Lukoil” company is presented model with accuracy 59%. This study demonstrates arbitrage pricing models for Russian stocks with accuracy more than 90%. Highly effective tools of stock price forecasting assets attract institutional investors. Furthermore, resulting models may be used to create a diversified investing portfolio. From the scientific side, the results of the study demonstrate which systematic factors have linear relations with stock prices of top Russian companies.