Correlation regression based forecast of Gazprom PJSC stock quotes

Authors

DOI:

https://doi.org/10.17072/1994-9960-2023-1-25-52

Abstract

Introduction. A scientific research examining the concern of Gazprom PJSC (a leading Russian energy company) stock quote forecasts is relevant because it could determine investment prospects in the stock market of the Russian Federation. The key idea of this work is to find the most profitable and investable Russian company and outline the factors among many others that affect its stock quotes.

Purpose. The author attempts to construct multiple linear regression equations reflecting the impact of the economic factors (the prices of the American natural gas, the USD/RUB currency pair, the M2 monetary aggregate in Russia) on the Gazprom PJSC stock quotes. These multiple linear regression equations are used to develop economic and mathematical models with the projected values of the factors.

Materials and Methods. The paper refers to the general and special scientific methods – analysis, synthesis, a monographic method, and statistical methods – graphs, tables, trend spotting, correlation and regression analysis. The choice of independent variables in the correlation and regression analysis was driven by several factors: global natural gas prices determine the financial performance of the Russian energy companies; the revenue of the Russian exporters has long been dependent on the USD/RUB currency pair; security quotes rose at the stock markets due to the monetary policy of central banks which strive to build up the monetary supply.

Results. The study developed the equations of multiple linear regressions for the selected four periods with different economic scenarios. This proves the relevancy of the issue under analysis. These equations reflect the dependence of the Gazprom PJSC stock quotes from the prices of the American natural gas, the USD/RUB currency pair, and the money supply in Russia. They could give rise to the economic and mathematical models with the projected values of the analyzed factors and their possible correlations.

Conclusion. The economic factors in question could have a positive impact on the Gazprom PJSC stock quotes against a weak Russian ruble and the national energy companies refocusing their attention on other markets. This information could be of use for the economic entities to boost their investment performance.

Keywords: natural gas prices, USD/RUB currency pair, M2 monetary aggregate in Russia, Gazprom PJSC stock quotes, trends, correlation and regression analysis, forecast, investments, stock market, Russian economy

For citation

Tenkovskaya L. I. Correlation regression based forecast of Gazprom PJSC stock quotes. Perm University Herald. Economy, 2023, vol. 18, no. 1, pp. 25–52. DOI 10.17072/1994-9960-2023-1-25-52

  1. Shuranova А. А., Petrunin Yu. Yu. The 2021–2022 energy crisis in relations between Russian and European Union. Public Administration E-Journal, 2022, no. 90, pp. 74–89. (In Russian). http://doi.org/10.24412/2070-1381-2022-90-74-89
  2. Borovsky Yu. V., Shishkina О. V. The priorities of EU energy policy. Contemporary Europe, 2021, no. 3, pp. 117–127. (In Russian). http://doi.org/10.15211/soveurope32021117127
  3. Maslennikov А. О. World and regional natural gas markets after COVID- World Economy and International Relations, 2020, vol. 64, no. 10, pp. 74–83. (In Russian). http://doi.org/10.20542/0131-2227-2020-64-10-74-83
  4. Safronchuk M. V., Sokolova M. А. Pricing methods in the global natural gas market. Economics and Management: Problems, Solutions, 2019, vol. 6, no. 2, pp. 100–105. (In Russian).
  5. Ershova Е. V. Liquefied natural gas pricing as a globalization factor in natural gas world trade. Baikal Research Journal, 2016, vol. 7, no. 4, pp. 118–126. (In Russian). http://doi.org/10.17150/2411-6262.2016.7(4).18
  6. Kondratov D. I. Challenges for Russia in the global natural gas market. Economics, Taxis and Law, 2022, vol. 15, no. 1, pp. 35–44. (In Russian). http://doi.org/10.26794/1999-849X-2022-15-1-35-44
  7. Aksyutin О. Е., Ishkov А. G., Romanov К. V., Grachev V. А. Role of natural gas in the implementation of goals of sustainable development. Gas Industry, 2018, no. 7, pp. 102–114. (In Russian).
  8. Noskov V. А. Global natural gas market: Macro-regional features and the role of Russia. Vestnik of Samara State University of Economics, 2020, no. 9 (191), pp. 9–16. (In Russian). http://doi.org/10.46554/1993-0453-2020-9-191-9-16
  9. Ulchenko М. V. Strategic forecasting applying to development of raw-material based industrial complexes in the Artic. Sever i rynok: formirovanie ekonomicheskogo poryadka, 2020, no. 1 (67), pp. 79–91. (In Russian). http://doi.org/10.37614/2220-802X.1.2020.67.007
  10. Shumilin O. V. Istoricheskie vremennye ryady ofitsial'nogo kursa dollara SShA po otnosheniyu k rossiiskomu rublyu (1990–2019). Ekonomicheskaya bezopasnost' lichnosti, obshchestva, gosudarstva: problemy i puti obespecheniya: materialy ezhegodnoi vseros. nauch.-prakt. konf. Saint Petersburg, 2021, pp. 238–242. (In Russian).
  11. Tretyakova G. V., Kazachenko А. А., Lyong N. L. V. Trends and prospects of national currencies in international settlements with the participation of Russia and China. Tomsk State University Journal of Economics, 2022, no. 58, pp. 190–207. (In Russian). http://doi.org/10.17223/19988648/58/12
  12. Polivach А. P. Renminbi and rouble in the Central Asia economies. Russia and New States of Eurasia, 2020, no. 4 (49), pp. 87–102. (In Russian). http://doi.org/10.20542/2073-4786-2020-4-87-102
  13. Minakr P. А., Izotov D. А. World money in time and space: A blow to the dollar or a blow by the dollar? Spatial Economics, 2022, vol. 18, no. 1, pp. 7–33. (In Russian). http://doi.org/10.14530/se.2022.1.007-033
  14. Fedotov D. Yu. Devaluation of the national currency and the economic security of the state. Finance and Credit, 2021, vol. 27, no. 11 (815), pp. 2442–2464. (In Russian). http://doi.org/10.24891/fc.27.11.2442
  15. Boldanova Е. V. Dependence on external factors of oil and gas companies financial state. Azimuth of Scientific Research: Economics and Administration, 2021, vol. 10, no. 3 (36), pp. 75–78. (In Russian). http://doi.org/10.26140/anie-2021-1003-0016
  16. Kazantsev S. V. On some negative result of anti-Russian sanctions and the ways to parry them. ETAP: Economic Theory, Analysis, and Practice, 2020, no. 2, pp. 65–73. (In Russian). http://doi.org/10.24411/2071-6435-2020-10013
  17. Golubeva T. V., Stadnik Ya. V. Rossiya i sanktsii. Aktual'nye problemy obshchestva, ekonomiki i prava v kontekste global'nykh vyzovov: materialy XII Mezhdunar. nauch.-prakt. konf. Мoscow, 2022, pp. 223–226. (In Russian).
  18. Konduktorov А. S. Replacing the US dollar with the Russian ruble in payments for Russian exports: A financial and legal mechanism for implementation and implications for the monetary policy. Lex Russica, 2022, vol. 75, no. 2, pp. 24–33. (In Russian). http://doi.org/10.17803/1729-5920.2022.183.2.024-033
  19. Manushin D. V. Global economy of sanctions, sanctions, countersactions and the new global currency. Russian Journal of Economics and Law, 2022, vol. 16, no. 2, pp. 345–369. (In Russian). http://doi.org/10.21202/2782-2923.2022.2.345-369
  20. Afanas’eva О. N. Monetary policy: Money supply and inflation in various countries. Economics of Sustainable Development, 2022, no. 1 (49), pp. 112–117. (In Russian). http://doi.org/10.37124/20799136_2022_1_49_112
  21. Matres J. O. G., Le T. V. The Impact of Money Supply on the Economy: A Panel Study on Selected Countries. Journal of Economic Science Research, 2021, vol. 4, iss. 4, pp. 48–53. https://doi.org/10.30564/jesr.v4i4.3782
  22. Chaitip P., Chokethaworn K., Chaiboonsri Ch., Khounkhalax M. Money supply influencing on economic growth-wide phenomena of AEC Open Region. Procedia Economics and Finance, 2015, vol. 24, pp. 108–115. https://doi.org/10.1016/S2212-5671(15)00626-7
  23. Zulkhibri M. Causal link between money, output and prices in Malaysia: An empirical reexamination. Applied Econometrics and International Development, 2007, vol. 7, no. 1. 10 p. Available at: https://ssrn.com/abstract=1249962 (accessed 12.10.2022).
  24. Palamalai S., Mariappan K., Devakumar C. On the temporal causal relationship between macroeconomic variables: Empirical evidence from India. SAGE Open, 2014, vol. 4, no. 1, pp. 1–14. https://doi.org/10.1177/2158244014525419
  25. Mundell R. A. Capital mobility and stabilization policy under fixed and flexible exchange rates. Canadian Journal of Economics, 1963, vol. 29, pp. 475–485. https://doi.org/10.2307/139336
  26. Herwartz H., Reimers H.-E. Long-run links among money, prices and output: Worldwide evidence. German Economic Review, 2006, vol. 7, pp. 65–86. https://doi.org/10.1111/j.1468-0475.2006.00147.x
  27. Glaz’ev S. Yu., Goridko N. P., Nizhegorodtsev R. M. The critics of Irving Fisher’s formula and some illusions about contemporary monetary policy. Economic and Mathematical Methods, 2016, vol. 52, no. 4, pp. 3–23. (In Russian).
  28. Delyagin M. G. Krakh optimisticheskikh illyuzii i otpravnoi punkt ekonomicheskogo ozdorovleniya. Russian Economic Journal, 2014, no. 1, pp. 19–23. (In Russian).
  29. Delyagin M. G. Rossiiskaya inflyatsiya: postanovka diagnoza: vystuplenie na kruglom stole «Ekonomicheskii rost Rossii». Nauchnye trudy Vol'nogo ekonomicheskogo obshchestva Rossii, 2009, vol. 111, pp. 32–36. (In Russian).
  30. Tenkovskaya L. I. The essence of optimism of the economists and investors with regard to the economy of Russia. Journal of Economic Science Research, 2019, vol. 53, iss. 4, pp. 1–9. https://doi.org/10.30564/jesr.v2i4.1080
  31. 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, vol. 4, iss. 2, pp. 101–119. https://doi.org/10.1016/j.jfds.2017.11.002
  32. Nivorozhkin E., Castagneto-Gissey G. Russian stock market in the aftermath of the Ukrainian crisis. Russian Journal of Economics, 2016, no. 2 (1), pp. 23–40. http://dx.doi.org/10.1016/j.ruje.2016.04.002
  33. Laopodis N. T. Exchange rate and stock market interactions: Evidence from an emerging economy. Global Business and Finance Review, 2002, vol. 7, no. 1, pp. 49–60.
  34. Chen D. Y., Huang B. Financial crises and stock market indices: Markov switching approach. Global Business and Finance Review, 2012, vol. 17, no. 2, pp. 101–120.
  35. Huerta D., Perez-Liston D. The impact of hurricanes on investor sentiment and stock market returns. Global Business and Finance Review, 2011, vol. 16, no. 2, pp. 136–149.
  36. 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, vol. 7, no. 2, article 18. https://doi.org/10.3390/ijfs7020018
  37. Nenu E. A., Vintilă G., Gherghina Ş. C. The impact of capital structure on risk and firm performance: Empirical evidence for the Bucharest stock exchange listed companies. International Journal of Financial Studies, 2018, vol. 6, no. 2, article 41. https://doi.org/10.3390/ijfs6020041
  38. 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, no. 10, article 20. https://doi.org/10.1186/s13731-021-00160-w
  39. Grigoryev L. M., Medzhidova D. D. Global energy trilemma. Russian Journal of Economics, 2020, vol. 6, no. 4, pp. 437–462. https://doi.org/10.32609/j.ruje.6.58683
  40. Mazur I. K. Using the Sharp model in the modern conditions of the Russian stock market. Teoriya i praktika sovremennoi nauki, Moscow, 2022, pp. 254–260. (In Russian).
  41. Votchel L. M., Ivashina N. S., Nenova E. A., Chabanenko A. V. Model prediction of corporate securities yield. Korporativnaya ekonomika, 2018, no. 2 (14), pp. 18–24. (In Russian).
  42. Alzheev A. V., Kochkarov R. A. Comparative analysis of ARIMA and LSTM predictive models: Evidence from Russian stocks. Finance: Theory and Practice, 2020, vol. 24, no. 1, pp. 14–23. (In Russian). https://doi.org/10.26794/2587-5671-2020-24-1-14-23
  43. Puchkov E. V., Belyavsky G. I. Local trends for time series pre-preparation in forecasting problems. Software and Systems, 2018, no. 4, pp. 751–756. (In Russian). https://doi.org/10.15827/0236-235X.751-756
  44. Lomakin N. I., Dzhennifer O. Ch., Chernaya E. G., Sycheva A. V., Kabina V. V. Perseptron AI-system for forecasting financial results of the oil industry of the Russian Federation. Fundamental Research, 2019, no. 12-1, pp. 98–103. (In Russian). https://doi.org/10.17513/fr.42629
  45. Lesik I. A. Forecasting stock price growth using feedforward neural networks. Software and Systems, 2015, no. 2, pp. 70–74. (In Russian). https://doi.org/10.15827/0236-235X.110.070-074
  46. Zolotova T. V., Volkova D. A. Intelligent data processing methods for the atypical values correction of stock quotes. Statistics and Economics, 2022, vol. 19, no. 2, pp. 4–13. (In Russian). http://dx.doi.org/10.21686/2500-3925-2022-2-4-13
Show full text

Information about the Author

  • Lyudmila I. Tenkovskaya, PJSC «Moscow Exchange MICEX-RTS»

    Candidate of Economic Sciences, Associate Professor, Stock Market Analyst

Downloads

Published

2023-04-03

Issue

Vol. 18 No. 1 (2023)

Section

Economic-Mathematical Modeling

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.