Forecasting of stock quotations of PJSC «Lukoil» on the basis of correlation and regression analysis
Abstract
The scientific article concerning the prediction of the situation on the Russian stock market is relevant because it reveals the prospects of the analyzed oil company PJSC «Lukoil». The purpose of this scientific work is to construct an equation of multiple linear regression with independent variables in the form of Brent crude oil prices, USD/RUB currency pair, M2 monetary aggregate in Russia, affecting the stock quotes of PJSC «Lukoil». This equation is the basis of economic and mathematical modeling of the future value of securities of PJSC «Lukoil». In the process of work, general and special scientific methods were used: analysis, synthesis, monographic, statistical. The main research method is correlation and regression analysis. As a result, it was found that the prices of Brent crude oil, the USD/RUB currency pair, and the M2 monetary aggregate in Russia tend to increase and have a direct impact on the shares of PJSC «Lukoil», which indicates that the shares of the Russian oil company represented are undervalued. The key provisions of this research may be useful to investors considering the Russian stock market for investing funds. Based on the proposed scientific study, it is possible to build investment strategies for buying securities.
About the Author
L. I. Tenkovskaya
Public Joint Stock Company «Moscow Exchange MICEX-RTS»
Russian Federation
Russian Federation
L. I. Tenkovskaya, candidate of economic sciences, Associate Professor, Stock Market Analyst,
Moscow.
References
1. Барышева Е.Н., Никишов В.Н., Круглов Е.В. Выбор основных показателей финансового рынка для прогнозирования ценовой динамики // Вестник Нижегородского университета им. Н.И. Лобачевского. – 2013. № 1–3. – С. 154–160.
2. Безсмертная Е.Р. Поведение частных инвесторов на фондовом и срочном рынках в 2022–2023 годах: возможная реакция на изменения денежно-кредитной политики // Экономика. Налоги. Право. – 2022. – №15(1). – С. 63-71. DOI: 10.26794/1999849x-2022-15-1-63-71.
3. Жабунин А.Ю., Иевлева Н.В., Чередниченко И.А., Соколова А.В. Исследование применения метода DCF для определения целевых цен акций российских публичных корпораций // Российское предпринимательство. – 2016. – Т. 17. – № 24. – С. 3643–3656
4. Зубов Я.О. Влияние денежно-кредитной политики Банка России на развитие экономики страны // В сборнике: Экономика и бизнес: цифровая трансформация и перспективы развития. Материалы международной научно-практической конференции, в 2 т. – М:. – 2022. – С. 100–113.
5. Кондратьева Т.Н., Гуртовая Е.А., Алексанян Л.Г. Анализ основных характеристик статистической выборки на примере акций ПАО «Лукойл» // Интернет-журнал «НАУКОВЕДЕНИЕ». – 2017. – Т. 9, № 5.
6. Ломакин Н.И., Дженнифер О.Ч., Езангина И.А., Шевченко С.А., Бескоровайная Н.Н. Прогнозирование прибыли ПАО «НК «Роснефть» с помощью системы искусственного интеллекта в условиях цифровой экономики // Фундаментальные исследования. – 2020. – № 5. – С. 117–124. DOI: 10.17513/fr.42757.
7. Нуреев Р.М., Бусыгин Е.Г. Крупнейшие публичные нефтяные компании: влияние внешних и внутренних факторов на капитализацию // Финансы: теория и практика. – 2019. – №23 (5). – С. 87–100. https://doi.org/10.26794/2587-5671-2019-23-5-87-100
8. Окулов В.Л. Исследование эффективности российского рынка акций: реакция рынка на публикацию прогнозов аналитиков // Вестник Санкт-Петербургского университета. Менеджмент. – 2010. – № 3. С. 3–22.
9. Павлова О.Ю. Банк Англии под руководством Марка Карни (2013–2020 гг.): в поисках новой стабильности // Российский экономический интернет-журнал. – 2022. – №2. – С. 1–12.
10. Anarfi D., Nerudová D. Profit-shifting activities in the mining sector: evidence from the Czech Republic // European Journal of Business Science and Technology. 2017. – № 3 (1). P. 5–12. DOI: 10.11118/ejobsat.v3i1.72.
11. Avdasheva S.B., Golovanova S.V. Concerted practice enforcement in Russia: How judicial review shapes the standards of evidence and number of enforcement targets // Russian Journal of Economics. – 2020. – № 6 (3). – P. 239–257. https://doi.org/10.32609/j.ruje.63.51277
12. Celebi K., Hönig M. The impact of macroeconomic factors on the German stock market: evidence for the crisis, pre- and post-crisis periods // International Journal of Financial Studies. – 2019. – №7 (2), 18. https://doi.org/10.3390/ijfs7020018.
13. Chen D.Y., Huang B. Financial crises and stock market indices: Markov switching approach // Global Business and Finance Review. – 2012. –№17 (2). –P. 101–120.
14. Dabrowski M. Thirty years of economic transition in the former Soviet Union: Macroeconomic dimension // Russian Journal of Economics. – 2022. – № 8(2). – P. 95–121. https://doi.org/10.32609/j.ruje.8.90947.
15. Gurvich E., Prilepskiy I. The impact of financial sanctions on the Russian economy // Russian Journal of Economics. – 2015. – № 1(4). – P. 359–385. https://doi.org/10.1016/j.ruje.2016.02.002.
16. Jebran K., Chen S., Saeed G. et al. Dynamics of oil price shocks and stock market behavior in Pakistan: evidence from the 2007 financial crisis period // Financial Innovation. – 2017. – №3, 2. https://doi.org/10.1186/s40854-017-0052-2.
17. Jiang Y., Tian G., Mo B. Spillover and quantile linkage between oil price shocks and stock returns: new evidence from G7 countries // Financial Innovation. – 2020. – № 6, 42. https://doi.org/10.1186/s40854-020-00208-y.
18. Laopodis N.T. Exchange rate and stock market interactions: evidence from an emerging economy // Global Business and Finance Review. – 2002. – №7 (1). – P. 49–60.
19. Metel’skaya V.V. Correlation-and-regression analysis of the influence of macroeconomic factors on capital structure of Russian corporations under crisis conditions // Journal of Innovation and Entrepreneurship. – 2021. – № 10, 20. https://doi.org/10.1186/s13731-021-00160-w.
20. Mironov V. Russian devaluation in 2014–2015: Falling into the abyss or a window of opportunity? // Russian Journal of Economics. – 2015. – №1 (3). – P. 217-239. https://doi.org/10.1016/j.ruje.2015.12.005.
21. Nisar T.M., Yeung M. Twitter as a tool for forecasting stock market movements: a short-window event study // The Journal of Finance and Data Science. – 2018. – №4. – P. 101–119. https://doi.org/10.1016/j.jfds.2017.11.002
22. Nivorozhkin E., Castagneto-Gissey G. Russian stock market in the aftermath of the Ukrainian crisis // Russian Journal of Economics. – 2016. – №2 (1). – P. 23–40. http://dx.doi.org/10.1016/j.ruje.2016.04.002.
23. Pavlata J., Strejček P., Albrecht P., Širůček M. The empirical linkage between oil prices and the stock returns of oil companies // European Journal of Business Science and Technology. – 2021. – № 7 (2). –. 186–197. DOI: 10.11118/ejobsat.2021.016.
24. Wehrle N.P.B. The cost of renewable electricity and energy storage in Germany // European Journal of Business Science and Technology. – 2022. – № 8 (1). – P. 19–41. DOI: 10.11118/ejobsat.2022.005.
Review
For citations:
Tenkovskaya L.I. Forecasting of stock quotations of PJSC «Lukoil» on the basis of correlation and regression analysis. Regional Economic Journal. 2023;(1):54-68. (In Russ.)
Views:
16
Email this article 