Portfolio management of Russian companies’ stocks in case of changes in the key rate of the Bank of Russia

   Aim. The work aimed to assess the impact of changes in the key rate of the Bank of Russia on the Russian stock market to develop an effective strategy for managing a portfolio of Russian companies’ stocks.

   Objectives. The work seeks to create a machine learning model that can predict short-term dynamics of index values in case of changes in the key rate; determine the optimal structure of the investment portfolio taking into account the results obtained; formulate the basics of a strategy for managing a portfolio of stocks under conditions of key rate volatility.

   Methods. The study employed correlation and regression analysis, gradient boosting (XGBoost), SHAP analysis to interpret the results of machine learning models and assess the direction of dependencies, as well as a method for assessing the impulse response of index quotes to changes in the key rate.

   Results. The work revealed statistically significant effects of the impact of changes in the key rate on quotes of industry indices of the Moscow Exchange in the short term (one to three days). The gradient boosting models constructed demonstrate high predictive ability for most of the indices analyzed. A method for forming an optimal stock portfolio taking into account the predicted change in the key rate was developed.

   Conclusions. The work established that the reaction of stocks to changes in the key rate varies significantly depending on the economy sector. The greatest sensitivity is demonstrated by stocks of companies in the financial sector and companies in the electric power industry, while telecommunication companies and chemistry and petrochemistry-related companies are the least susceptible to the impact of rate changes. The results obtained can be used to form investment portfolios taking into account the expected changes in the monetary policy of the Bank of Russia and minimize risks from the key rate fluctuations.

1. Key rate of the Bank of Russia. Bank of Russia. URL: https://www.cbr.ru/hd_base/keyrate/ (accessed on 18. 03. 2025). (In Russ.).

2. Moscow Exchange sector indices of total gross return. Moscow Exchange. URL: https://www.moex.com/ru/index/MOEXOG (accessed on 18. 03. 2025). (In Russ.).

3. Blanchard O.J. Output, the stock market, and interest rates. The American Economic Review. 1981;71(1):132-143. URL: https://www.researchgate.net/publication/4723102_Output_The_Stock_Market_and_Interest_Rates (accessed on 18. 03. 2025).

4. Jensen G.R., Johnson R.R. Discount rate changes and security returns in the U.S., 1962-1991. Journal of Banking & Finance. 1995;19(1):79-95. DOI: 10.1016/0378-4266(94)00048-8

5. Bernanke B.S., Kuttner K.N. What explains the stock market’s reaction to Federal Reserve policy? The Journal of Finance. 2005;60(3):1221-1257. DOI: 10.1111/j.1540-6261.2005.00760.x

6. Fedorova E.A., Pankratov K.A. Influence of macroeconomic factors on the Russian stock market. Studies on Russian Economic Development. 2010;21(2):165-168. DOI: 10.1134/S1075700710020061 (In Russ.: Problemy prognozirovaniya. 2010;(2):78-83.).

7. Alexiou C., Tyagi A. Gauging the effectiveness of sector rotation strategies: Evidence from the USA and Europe. Journal of Asset Management. 2020;21(3):239-260. DOI: 10.1057/s41260-020-00161-6

8. Hau H., Lai S. Asset allocation and monetary policy: Evidence from the eurozone. Journal of Financial Economics. 2016;120(2):309-329. DOI: 10.1016/j.jfineco.2016.01.014

9. Elyasiani E., Mansur I. Sensitivity of the bank stock returns distribution to changes in the level and volatility of interest rate: A GARCH-M model. Journal of Banking & Finance. 1998;22(5):535-563. DOI: 10.1016/S0378-4266(98)00003-X

10. Gnabo J.-Y., Soudant J. Monetary policy and portfolio rebalancing: Evidence from European equity mutual funds. Journal of Financial Stability. 2022:63:101059. DOI: 10.1016/j.jfs.2022.101059

11. Guo H., Hung C.-H.D., Kontonikas A. The Fed and the stock market: A tale of sentiment states. Journal of International Money and Finance. 2022;128:102707. DOI: 10.1016/j.jimonfin.2022.102707

12. Kumar D., Sarangi P. K., Verma R. A systematic review of stock market prediction using machine learning and statistical techniques. Materials Today: Proceedings. 2022;49(Pt.8):3187-3191. DOI: 10.1016/j.matpr.2020.11.399

13. Lyukevich I.N., Dolgov A.M. Application of machine learning in pricing innovations. In: Shirokova S.V., ed. Fundamental and applied research in management, economics and trade. Proc. All-Russ. sci.-pract. and educ.-methodol. conf. (St. Petersburg, May 15-19, 2023). In 8 pts. Pt. 1. St. Petersburg: Polytech-Press; 2023:114-122. (In Russ.).