The Bulletin of the Institute of Economics of the Russian Academy of Sciences № 2/2025.  Finance.

Konstantin D. Plachinda

Master’s student at the Faculty of Economics, Lomonosov Moscow State University, Expert at the Banking Book Interest Rate Risk Management Department, PJSC “Credit Bank of Moscow”, Moscow, Russia

 

THE IMPACT OF CHANGES IN OVERNIGHT INTEREST RATES ON THE MOSCOW EXCHANGE INDUSTRY INDICES

Размер файла120-143 Размер файла  1018.54 KB Размер файла Full text

The study evaluates the impact of changes in the overnight RUONIA rate on the dynamics of the Moscow Exchange industry total return indices. The analysis included index quotations, calculation of implied in interest rate swaps RUONIA rates for T+1, and the development of predictive models using machine learning, followed by their backtesting. The results show that the models correctly predict the direction of index changes in more than 50% of cases. A connection between changes in the implied RUONIA rate and index dynamics was identified. This confirms that the RUONIA rate and its forecast can be used to improve the prediction of index price movements, optimizing portfolio theory and investor strategies.

Keywords: monetary policy, monetary policy, key rate, stocks, stock market, sectoral indices, investments.

JEL: G11, G15, G17, G18

EDN: OKWWLJ

DOI: https://doi.org/10.52180/2073-6487_2025_2_120_143

References

  1. Kartaev F.S., Kozlova N.S. Econometric assessment of the impact of monetary policy on the dynamics of the Russian stock market // Moscow University Bulletin. Series 6. Economics. 2016. No. 1. Pp. 22-43. EDN: VQSZLD. DOI: 10.38050/01300105201612
  2. Kudinova M.M. Transformation of state monetary policy during the global pandemic // Finance: Theory and Practice. 2022. Vol. 26. No. 1. Pp. 41-54. EDN: ULXFMJ. DOI: 10.26794/2587-5671-2022-26-1-41-54
  3. Blanchard O.J. Output, the stock market, and interest rates // The American Economic Review. 1981. Vol. 71. No. 1. Pp. 132-143.
  4. Brewer III.E. et al. Interest rate risk and equity values of life insurance companies: A GARCH–M model // Journal of Risk and Insurance. 2007. Vol. 74. No. 2. Pp. 401-423. DOI: 10.1111/j.1539-6975.2007.00218.x
  5. Cao G. Time-varying effects of changes in the interest rate and the RMB exchange rate on the stock market of China: Evidence from the long-memory TVP-VAR model // Emerging Markets Finance and Trade. 2012. Vol. 48. No. sup2. Pp. 230-248. DOI: 10.2753/ree1540-496x48s213
  6. Conover C.M. et al. Is Fed policy still relevant for investors? // Financial Analysts Journal. 2005. Vol. 61. No. 1. Pp. 70-79. DOI: 10.2469/faj.v61.n1.2685
  7. Conover C.M., Jensen G.R., Johnson R.R. Monetary conditions and international investing // Financial Analysts Journal. 1999. Vol. 55. No. 4. Pp. 38-48. DOI: 10.2469/faj.v55.n4.2283
  8. Chen S.S. Does monetary policy have asymmetric effects on stock returns? // Journal of money, credit and banking. 2007. Vol. 39. No. 2-3. Pp. 667-688. DOI: 10.1111/j.0022-2879.2007.00040.x
  9. Durham J.B. More on monetary policy and stock price returns // Financial Analysts Journal. 2005. Vol. 61. No. 4. Pp. 83-90. DOI: 10.2469/faj.v61.n4.2745
  10. Ehrmann M., Fratzscher M. Taking stock: Monetary policy transmission to equity markets // Journal of Money, Credit and Banking. 2004. Pp. 719-737.
  11. 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. Vol. 22. No. 5. Pp. 535-563. DOI: 10.1016/S0378-4266(98)00003-X
  12. English W.B., Van den Heuvel S.J., Zakrajšek E. Interest rate risk and bank equity valuations // Journal of Monetary Economics. 2018. Vol. 98. Pp. 80-97. DOI: 10.1016/j.jmoneco.2018.04.010
  13. 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. Vol. 128. 102707. DOI: 10.1016/j.jimonfin.2022.102707
  14. Hajilee M., Nasser O.M.A. The impact of interest rate volatility on stock market development: Evidence from emerging markets // The Journal of Developing Areas. 2017. Vol. 51. No. 2. Pp. 301-313. DOI: 10.1353/jda.2017.0046
  15. Jensen G.R., Johnson R.R. Discount rate changes and security returns in the US, 1962-1991 // Journal of Banking & Finance. 1995. Vol. 19. No. 1. Pp. 79-95. DOI: 10.1016/0378-4266(94)00048-8
  16. Jensen G.R., Mercer J.M. New evidence on optimal asset allocation // Financial Review. 2003. Vol. 38. No. 3. Pp. 435-454. DOI: 10.1111/1540-6288.00054
  17. Jensen G.R., Mercer J.M. Security markets and the information content of monetary policy turning points // The Quarterly Review of Economics and Finance. 2006. Vol. 46. 4. Pp. 477-494. DOI: 10.1016/j.qref.2004.08.002
  18. Litzenberger R.H., Tuttle D.L. Interest Rate Changes and the Required Rate of Return on Risk Assets // Southern Economic Journal. 1970. Pp. 45-50. DOI: 10.2307/1056235
  19. Patelis A.D. Stock return predictability and the role of monetary policy // The Journal of Finance. 1997. Vol. 52. No. 5. Pp. 1951-1972. DOI: 10.1111/j.1540-6261.1997.tb02747.x
  20. Thorbecke W. On stock market returns and monetary policy // The Journal of Finance. 1997. Vol. 52. No. 2. Pp. 635-654. DOI: 10.1111/j.1540-6261.1997.tb04816.x
  21. Tobin J. A general equilibrium approach to monetary theory // Journal of money, credit and banking. 1969. Vol. 1. No. 1. Pp. 15-29. DOI: 10.2307/1991374

Manuscript submission date: 08.01.2025

Manuscript acceptance date: 16.04.2025

 

For citation:

Plachinda K.D. The Impact of changes in overnight interest rates on the Moscow Exchange industry indices // Vestnik Instituta Ekonomiki Rossiyskoy Akademii Nauk. 2025. № 2. Pp. 120-143. (In Russ.). https://doi.org/10.52180/2073-6487_2025_2_120_143  EDN: OKWWLJ

  Creative Commons 4.0

The Bulletin of the Institute of Economics of the Russian Academy of Sciences № 2/2025.  Finance.

Andrey V. Polbin

Cand. Sci. (Econ.), Head of the Laboratory for Mathematical Modeling of Economic Processes, Gaidar Institute for Economic Policy; Leading Researcher, Financial University under the Government of the Russian Federation, Moscow, Russia

ORCID: 0000-0003-4683-8194

 

Margarita A. Kropocheva

Researcher, Laboratory for Mathematical Modeling of Economic Processes, Gaidar Institute for Economic Policy, Moscow, Russia

ORCID: 0000-0001-5069-7094

 

THE IMPACT OF DOWNWARD NOMINAL WAGE RIGIDITY ON FISCAL AND MONETARY POLICY IN RUSSIA

Размер файла93-119  Размер файла  905.94 KB Размер файла Full text

This paper evaluates the impact of downward nominal wage rigidity (DNWR) on fiscal and monetary policy for Russian economy. We obtain a piecewise linear approximation of the solution for DSGE model, which makes it possible to take into account the constraint on wage dynamics. The results indicate greater efficiency of fiscal policy during a recession compared to an economic expansion. In addition, the dependence of the multipliers value on the nature of the shock affecting the economy is noted. The efficiency of monetary policy, on the contrary, is lower in the presence of DNWR. The results of the study allow us to conclude that DNWR plays a significant role as one of the factors weakening the impact of monetary policy and causing the asymmetry of government spending multipliers. The results of the study can be useful in planning fiscal and monetary policy, as well as in constructing DSGE models that capture more complex dynamics of economic indicators.

Keywords: dynamic stochastic general equilibrium models, DSGE, government spending multiplier, downward nominal wage rigidity, DNWR.

JEL: C68, E63

EDN: LTTGAI

DOI: https://doi.org/10.52180/2073-6487_2025_2_93_119

References

  1. Ivashchenko S. Long-term growth sources for sectors of Russian economy // Journal of the New Economic Association. 2020. Vol. 48. No. 4. Pp. 86–112. EDN: SJEEYV. (In Russ.).
  2. Lomonosov D. Shocks of Business Activity and Specific Shocks to Oil Market in DSGE Model of Russian Economy and Their Influence Under Different Monetary Policy Regimes // Russian Journal of Money and Finance. 2023. Vol. 82. No. 4. Pp. 44–79. EDN: SPAGIW.
  3. Shulgin A.G. How much monetary policy rules do we need to estimate DSGE model for Russia? // Applied Econometrics. 2014. Vol. 36. No. 4. Pp. 3–31. EDN: TEOKMF. (In Russ.).
  4. Andreyev M. Adding a fiscal rule into a DSGE model: How much does it change the forecasts // Bank of Russia working paper series. 2020. No. 64.
  5. Eliseev A. Short-term estimation of Russia’s GDP using a DSGE model with mixed data frequency and a panel of non-modeled variables. Bank of Russia Economic Research Report Series. 2025. No. 145. (In Russ.).
  6. Ivashchenko S. Dynamic Stochastic General Equilibrium Model with Multiple Trends and Structural Breaks // Russian Journal of Money and Finance. 2022. Vol. 81. No. 1. Pp. 46–72. EDN: UXTYTY.
  7. Kreptsev D., Seleznev S. Forecasting for the Russian Economy Using Small-Scale DSGE Models // Russian Journal of Money and Finance. 2018. Vol. 77. No. 2. Pp. 51–67. DOI: 10.31477/rjmf.201802.51
  8. Andreyev M.Y. Effectiveness of the stabilization fiscal rule for resource-rich countries // Voprosy Ekonomiki. 2022. No. 12. Pp. 72-97. (In Russ.) DOI: 10.32609/0042-8736-2022-12-72-97.
  9. Andreev M. Yu. Deep Consumer Habits and Fiscal Policy Shocks // Studies on Russian Economic Development. 2025. Vol. 36. No. 1. Pp. 53–65. DOI: 10.1134/S1075700724700527.
  10. Votinov A. I., Elkina M. A. Estimation of fiscal stimulus efficiency in Russian economy: Simple DSGE model with government sector // Financial Journal. 2018. No. 5 (43). 83-96. DOI: 10.31107/2075-1990-2018-5-83-96. (In Russ.).
  11. Ivashchenko S. Do We Need Тaylor-type Rules in DSGE? Bank of Russia working paper series. 2025. No. 144.
  12. Vikharev P., Novak A., Shulgin A. Inequality and monetary policy in a model with three groups of households // Bank of Russia working paper series. 2023. No. 113. (In Russ.).
  13. Shulgin A. Optimization of Simple Monetary Policy Rules on the Base of Estimated DSGE-model // Journal of the New Economic Association. 2015. Vol. 26. No. 2. Pp. 64–98. EDN: UBFBBV. (In Russ.).
  14. Novak A., Shulgin A. Monetary policy in an economy with regional heterogeneity: approaches based on aggregated and regional information // Bank of Russia working paper series. 2020. (In Russ.).
  15. Serkov L. A. Inter-Regional Inflation Differential as a Consequence of Heterogeneity of the Russian Economic Space // Economy of regions. 2020. Vol. 16. No. 1. Pp. 325–339. DOI: 10.17059/2020-1-24. (In Russ.).
  16. Dubrovskaya J., Shults D., Kozonogova E. Constructing a region DSGE model with institutional features of territorial development // Computation. 2022. Vol. 10. No. 7. 105. DOI: 10.3390/computation10070105.
  17. Larin A. Downward Nominal Wage Rigidity: Unions’ Merit or Firms’ Foresight? // Higher School of Economics Research Paper No. WP BRP. 2014. Vol. 86. DOI: 10.2139/ssrn.2542516.
  18. Benigno P., Antonio Ricci L. The inflation-output trade-off with downward wage rigidities // American Economic Review. 2011. Vol. 101. No. 4. Pp. 1436–1466. DOI: 10.1257/aer.101.4.1436.
  19. Amano R., Gnocchi S. Downward nominal wage rigidity meets the zero lower bound // Journal of Money, Credit and Banking. 2023. Vol. 55. No. 4. Pp. 859-887. DOI: 10.34989/swp-2017-16.
  20. Polbin A., Sinelnikov-Murylev S. Developing and impulse response matching estimation of the DSGE model for the Russian economy // Applied Econometrics. 2024. Vol. 73. No. 1. 5–34. (In Russ.). DOI: 10.22394/1993-7601-2024-73-5-34.
  21. Dickens W. T. et al. How wages change: micro evidence from the International Wage Flexibility Project // Journal of Economic Perspectives. 2007. Vol. 21. No. 2. Pp. 195–214. DOI: 10.1257/jep.21.2.195.
  22. Babecký J. et al. Downward nominal and real wage rigidity: Survey evidence from European firms // Scandinavian Journal of Economics. 2010. Vol. 112. No. 4. 884–910. DOI: 10.1111/j.1467-9442.2010.01624.x.
  23. Gorodnichenko Y., Mendoza E. G., Tesar L. L. The Finnish great depression: From Russia with love // American Economic Review. 2012. Vol. 102. No. 4. Pp. 1619–1643. DOI: 10.1257/aer.102.4.1619.
  24. Abbritti M., Fahr S. Downward wage rigidity and business cycle asymmetries // Journal of Monetary Economics. 2013. Vol. 60. No. 7. Pp. 871-886. DOI: 10.1016/j.jmoneco.2013.08.001.
  25. Auerbach A. J., Gorodnichenko Y. Measuring the output responses to fiscal policy // American Economic Journal: Economic Policy. 2012. Vol. 4. No. 2. Pp. 1–27. DOI: 10.1257/pol.4.2.1.
  26. Fazzari S. M., Morley J., Panovska I. State-dependent effects of fiscal policy // Studies in Nonlinear Dynamics & Econometrics. 2015. Vol. 19. No. 3. Pp. 285–315. DOI: 10.1515/snde-2014-0022.
  27. Shen W., Yang S. C. S. Downward nominal wage rigidity and state-dependent government spending multipliers // Journal of Monetary Economics. 2018. Vol. 98. Pp. 11-26. DOI: 10.1016/j.jmoneco.2018.04.006.
  28. Jo Y. J., Zubairy S. State-Dependent Government Spending Multipliers: Downward Nominal Wage Rigidity and Sources of Business Cycle Fluctuations // American Economic Journal: Macroeconomics. 2025. Vol. 17. No. 1. Pp. 379–413. DOI: 10.1257/mac.20220156.
  29. Canzoneri M. et al. Fiscal multipliers in recessions // The Economic Journal. 2016. Vol. 126. No. 590. Pp. 75–108. DOI: 10.1111/ecoj.12304.
  30. Schmitt-Grohé S., Uribe M. Downward nominal wage rigidity and the case for temporary inflation in the eurozone // Journal of Economic Perspectives. 2013. Vol. 27. 3. Pp. 193–212. DOI: 10.1257/jep.27.3.193.
  31. Fernández-Villaverde J. et al. Nonlinear adventures at the zero lower bound // Journal of Economic Dynamics and Control. 2015. Vol. 57. Pp. 182–204. DOI: 10.1016/j.jedc.2015.05.014.
  32. Kekre R., Lenel M. Exchange rates, natural rates, and the price of risk // University of Chicago, Becker Friedman Institute for Economics Working Paper. 2024. No. 2024–114. DOI: 10.2139/ssrn.4957831.
  33. Polyakova O.V. Effectiveness of Fiscal Policy in Different Economi c Conditions // Economic Development of Russia. 2023. 30 (10). Pp. 45–52. EDN: BIRNWO. (In Russ.).

Manuscript submission date: 18.02.2025

Manuscript acceptance date: 16.04.2025

 

For citation:

Polbin A.V., Kropocheva M.A. The Impact of downward nominal wage rigidity on fiscal and monetary policy in Russia// Vestnik Instituta Ekonomiki Rossiyskoy Akademii Nauk. 2025. № 2. Pp. 93-119. (In Russ.). https://doi.org/10.52180/2073-6487_2025_2_93_119 EDN: LTTGAI

  Creative Commons 4.0

The Bulletin of the Institute of Economics of the Russian Academy of Sciences № 5/2024.  Finance.

Dmitry A. Kochergin

Dr. Sci. (Econ.), Assistant Professor, Chief Researcher, Institute of Economics of the Russian Academy of Sciences, Moscow, Russia

ORCID: 0000-0002-7046-1967

 

Sergey A. Andryushin

Dr. Sci. (Econ.), Professor, Chief Researcher, Institute of Economics of the Russian Academy of Sciences, Moscow, Russia

ORCID: 0000-0003-2620-8515

 

PROSPECTS FOR INTERNATIONAL SETTLEMENTS IN CENTRAL BANK DIGITAL CURRENCIES ON A PLATFORM BASIS IN FOREIGN COUNTRIES AND RUSSIA

Размер файла156-185  Размер файла  1.09 MB Размер файла Full text

The article investigates the main problems, state and development of the system of modern cross-border settlements in the global economy; shows the classification of multilateral platforms of central bank digital currencies through the prism of currency agreements used in cross-border payments; considers models of interoperability of central bank digital currencies and options for access of payment service providers; analyzes international projects of both wholesale and retail multicurrency systems of central bank digital currencies; proposes the author’s version of the Eurasian project of a multicurrency system of central banks, which can increase the efficiency of cross-border payments of the Russian Federation and countries that are its main trading partners.

Keywords: currency agreements, central bank digital currencies (CBDCs), mCBDC arrangements, multilateral digital currency platforms, interoperability models, payment service providers, cross-border payments.

JEL: E4, G2

EDN: LQCJRN

DOI: https://doi.org/10.52180/2073-6487_2024_6_156_185

References

  1. Kochergin D.A. Modern models of systems of central bank digital currency. St. Petersburg University Journal of Economic Studies. 2021. Vol. 37. No. 2. Рp. 205–240. DOI: 10.21638/spbu05.2021.202. EDN: XPJNDD. (In Russ.).
  2. Kochergin D.A. Central banks digital Currencies for Cross-border Payments: Interoperability Models and Implementation Possibilities. Finance: Theory and Practice. 2024. Vol. 28. No. 2. Рp. 82–100. DOI: 10.26794/2587-5671-2024-28-2-82-100. EDN: AUCSSF. (In Russ.).
  3. An Analysis of Trends in Cost of Remittance Services. Remittance Prices Worldwide Quarterly. 2022. Iss. 43. World Bank, 2022. September.
  4. Annual Review 2022. SWIFT, 2023. https://www.swift.com/swift-resource/252040/download (дата обращения: 10.12.2023).
  5. Arner D., Buckley R., Lammer T., Zetzsche D., Gazi S. Building Regional Payment Areas: The Single Rule Book Approach // BIS Working Papers. 2022, May. No. 1016.
  6. Auer R., Haene P., Holden H. Multi-CBDC Arrangements and the Future of Cross-border Payments // BIS Papers. 2021, March. No. 115.
  7. Bech M., Hancock J. Innovations in Payments // BIS Quarterly Review. 2020, March.
  8. Bindseil U., Pantelopoulos G. Towards the Holy Grail of Cross-border Payments // ECB. Working Paper Series. 2022, August. No. 2693.
  9. Boar C., Claessens S., Kosse A., Leckow R., Rice T. Interoperability Between Payment Systems Across Borders // BIS Bulletin. 2021, December. No. 49.
  10. Carstens A. Digital Currencies and the Future of the Monetary System // BIS speech. 2021, 27 January.
  11. Carstens A. Innovation and the Future of the Monetary System // BIS speech. 2023, 22 February.
  12. Central Bank Digital Currencies for Cross-Border Payments. Report to the G20. CPMI, BISIH, IMF, WB, 2021, July.
  13. Chaboud A., Rime D., Sushko V. The Foreign Exchange Market // BIS Working Papers. 2023, April. No. 1094.
  14. Correspondent Banking Trends Persisted in 2020, Even as Payment Landscape Changed, New Data Show. Press release. CPMI. 2021, December 13.
  15. Cross-border payment trends. Statistics Report on Different Strategies Undertaken to Develop Cross-border Payments in the Future. Statista, 2023. https://www.statista.com/study/135368/trends-in-cross-border-payments-worldwide (дата обращения: 10.12.2023).
  16. Cross-border Payments. Bank of England, 2023.
  17. Cross-Border Retail Payments. BIS. Committee on Payments and Market Infrastructures, 2018, February.
  18. Enhancing Cross-Border Payments: Building Blocks of a Global Roadmap. Stage 2 Report to the G20. CPMI. Bank for International Settlements, 2020, July.
  19. Exploring Multilateral Platforms for Cross-border Payments. BIS. 2023, January.
  20. Future of Payments 2022: Turning the Cross-Border Payments Roadmap into Reality. DMI, 2022.
  21. Interlinking Payment Systems and the Role of Application Programming Interfaces: A Framework for Cross-Border Payments. Report to the G20. CPMI, 2022, July.
  22. Inthanon-LionRock to mBridge: Building a Multi CBDC Platform for International Payments. BISIH, HKMA, BoT, DCIoPBoC, BoUAE. 2021, September.
  23. Kosse A., Mattei I. Gaining momentum – Results of the 2021 BIS Survey on Central Bank Digital Currencies. BIS Papers. 2022, May. No. 125.
  24. Lessons Learnt on CBDCs. Report submitted to the G20 Finance Ministers and Central Bank Governors. BISIH. 2023, July.
  25. Monthly Reporting and Statistics on Renminbi (RMB) progress Towards Becoming an International Currency. RMB Tracker. SWIFT. 2023, November. https://www.swift.com/swift-resource/252178/download (дата обращения: 10.12.2023).
  26. Options for Access to and Interoperability of CBDCs for Cross-border Payments. Report to the G20. CPMI, BISIH, IMF, WB, 2022, July.
  27. Project Dunbar. International Settlements Using Multi-CBDCs. BIS Innovation Hub, 2022, March.
  28. Project Icebreaker: Breaking New Paths in Cross-border Retail CBDC Payments. BISIH, BoI, NB, SR. 2023, March.
  29. Project Jura: Cross-Border Settlement Using Wholesale CBDC. BoF, BIS, SNB, 2021, December.
  30. Project Mariana. Cross-border Exchange of Wholesale CBDCs Using Automated Market-makers. BISIH, BoF, SNB, MAS, 2023, September.
  31. Project mBridge Update. Experimenting With a Multi-CBDC Platform for Crossborder Payments. BIS Innovation Hub, 2023, October.
  32. Project mBridge: Connecting Economies through CBDC. BISIH, HKMA, BoT, DCIoPBoC, BoUAE, 2022, October.
  33. Renzetti M., Dinacci F., Börestam A. Cross-Currency Settlement of Instant Payments in a Multi-Currency Clearing and Settlement Mechanism // CPMI Conference Proceedings from «Pushing the frontiers of payments: towards faster, cheaper, more transparent and more inclusive cross-border payments». 2021, 18–19 March.
  34. Rice T., von Peter G., Boar C. On the Global Retreat of Correspondent Banks // BIS Quarterly Review. 2020. Pp. 37–52.
  35. SWIFT in Figures. SWIFT. 2022. https://www.swift.com/swift-resource/251971/download (дата обращения: 10.12.2023).
  36. The Future Monetary System. BIS Annual Economic Report 2022. 2022, June. Pp. 75–115.
  37. The Role of Central Bank Money in Payment System. CPSS. Bank of International Settlement, 2003, August.
  38. Using CBDCs Across Borders: Lessons from Practical Experiments. BIS Innovation Hub, 2022, June.

 Manuscript submission date: 02.10.2024

 

For citation:

Kochergin D.A., Andryushin S.A. Prospects for international settlements in central bank digital currencies on a platform basis in foreign countries and Russia// Vestnik Instituta Ekonomiki Rossiyskoy Akademii Nauk. 2024. № 6. Pp. 156-185. (In Russ.).  https://doi.org/10.52180/2073-6487_2024_6_156_185  EDN: LQCJRN

  Creative Commons 4.0

The Bulletin of the Institute of Economics of the Russian Academy of Sciences № 1/2025.  Finance.

Alina A. Loktionova

Employee of the Department of Finance and Credit, Faculty of Economics, Lomonosov Moscow State University, Moscow, Russia

ORCID: 0009-0007-5421-1929

 

Ashot G. Mirzoyan

Employee of the Department of Innovation Economics, Faculty of Economics, Lomonosov Moscow State University, Moscow, Russia

ORCID: 0009-0005-9275-0099

 

POLITICAL NEWS’ IMPACT ON RUSSIAN COMPANIES’ STOCK PRICES

Размер файла152-166  Размер файла  370.21 KB Размер файла Full text

This study explores the impact of political news on the stock prices of Russian companies over the period 1 September 2021 –– 31 August 2023. The sample includes 200 companies, for each of which the industry of activity and the region of location of the headquarters were determined. Political news data were sourced from 50 Telegram channels, and 20 thematic topics were created using the Latent Dirichlet Allocation (LDA) model. The research tests hypotheses of the impact of political news on stock returns throughout the entire period in both regional and industry contexts. The results show that integrating political news improves return forecasts for 117 companies, with effects consistent across industries and regions. The study highlights the political news topics that had the most significant impact on Russian companies during the analyzed period.

Keywords: political news, textual analysis, Russian stock market, stock return forecasting, industry analysis, regional analysis.

JEL: C32, C53, G17

EDN: RHVODJ

DOI: https://doi.org/10.52180/2073-6487_2025_1_152_166

References

  1. Buklemishev O.V. «Structural Transformation» of the Russian Economy and Economic Policy. Studies on Russian Economic Development. 2023. №4. Рp. 456–463 DOI: 10.47711/0868-6351-199-42-53. (In Russ.).
  2. Peresetsky A.A. What determines the behavior of the Russian stock market // MRPA Paper. 2011. No. 41508. https://mpra.ub.uni-muenchen.de/41508/
  3. Baker S.R., Bloom N., Davis S.J. Measuring Economic Policy Uncertainty // SSRN Electronic Journal, 2013. DOI: 10.2139/ssrn.2198490.
  4. Baker M., Wurgler J. Behavioral corporate finance: An updated survey // Handbook of the Economics of Finance. 2013. Vol. 2. Elsevier. Рp. 357–424. DOI: 10.1016/B978-0-44453594-8.00005-7.
  5. Merton R.C. A simple model of capital market equilibrium with incomplete information // J. Financ. 1987. 42. Pp. 483–510. DOI: 10.1111/j.1540-6261.1987.tb04565.x.
  6. Tetlock P. Giving Content to Investor Sentiment: The Role of Media in the Stock Market // The Journal of Finance. 2007. Vol. 12 (3). Рp. 1139–1168. DOI: 10.1111/j.15406261.2007.01232.x.
  7. Al-Maadid A., Caporale G.M., Spagnolo F., Spagnolo N. The impact of business and political news on the GCC stock markets // Research in International Business and Finance. 2020. Vol. 52. DOI: 10.1016/j.ribaf.2019.101102.
  8. Volodin S.N., Zueva E.S. The impact of news on price and volumes of pharmacompanies // Moscow University Economics Bulletin. 2020. Vol. 6. №5. Рp. 217–238. DOI: 10.38050/013001052020510. (In Russ.).
  9. Fedorova E.A., Rogov O.Yu., Klochnikov V.Yu. The Impact of News on the MICEX Oil and Gas Index: Text Analysis // Moscow University Economics Bulletin. 2018. Vol. 6. No. 4. Pp. 79–97. DOI: 10.38050/01300105201845. (In Russ.).
  10. Jacobs B. W., Singhal V. R. Shareholder value effects of the Volkswagen emissions scandal on the automotive ecosystem // Production and Operations Management. 2020. Vol. 29 (10). Рp. 2230–2251. DOI: 10.1111/poms.13228.
  11. Kumar R., Bhatia P., Gupta D. The impact of the COVID-19 outbreak on the Indian stock market – A sectoral analysis // Investment Management and Financial Innovations. 2021. Vol. 18. No. 3. Рp. 334–346. DOI: 10.21511/imfi.18(3).2021.28.
  12. Snowberg E., Wolfers J., Zitzewitz E. Partisan impact on the economy: Evidence from prediction markets and close elections // The Quarterly Journal of Economics. 2007. Vol. 122 (2). Рp. 807–829. http://users.nber.org/~jwolfers/Papers/Snowberg-WolfersZitzewitz%20-%20Close%20Elections.pdf.
  13. Girardi D., Bowles S. Institution shocks and economic outcomes: Allende’s election, Pinochet’s coup and the Santiago stock market // Journal of Development Economics. 2018. Vol. 134. Рp. 16–27. DOI: 10.1016/j.jdeveco.2018.04.0.
  14. Pastor L., Veronesi, P. Political uncertainty and risk premia // J. Financ. Econ. 2013. Vol. 110 (3). Рp. 520–545. DOI: 10.1016/j.jfineco.2013.08.007.
  15. Fedorova E.A., Musienko S.O., Fedorov F. Yu. Development of Russian political uncertainty index (RPUI): textual analysis. Economics of Contemporary Russia. 2019. No. 2 (85). Pp. 52–64. DOI: 10.33293/1609-1442-2019-2(85)-52-64. (In Russ.).
  16. Alesina A., Tabellini G. External debt, capital flight and political risk. J. Int. Econ. 1989. 27 (3–4), Pp. 199–220. DOI: 10.1016/0022-1996(89)90052-4.
  17. Shanaev S., Ghimire B. Is all politics local? Regional political risk in Russia and the panel of stock returns // Journal of Behavioral and Experimental Finance, 2019. DOI: 10.1016/j.jbef.2018.11.002.
  18. Amin M.H., Mohamed E.K.A., Elragal A. CSR disclosure on Twitter: Evidence from the UK // International Journal of Accounting Information Systems. 2021. Vol. 40. DOI: 10.1016/j.accinf.2021.100500.
  19. Fedorova E.A., Pyltsin I.V., Kovalchuk U.A., Drogovoz P.A. News and social media of Russian company: influence on Russian stock market // Journal of the New Economic Association. 2022. No. 1. Рp. 32–52. DOI: 10.31737/2221-2264-2022-53-1-2. (In Russ.).
  20. Choi I., Kim W.C. Detecting and Analyzing Politically-Themed Stocks Using Text Mining Techniques and Transfer Entropy—Focus on the Republic of Korea’s Case // Entropy. 2021. Vol. 23 (6). Р. 734. DOI: 10.3390/e23060734.
  21. Atri H., Kouki S., Gallali M. The impact of COVID-19 news, panic and media coverage on the oil and gold prices: an ARDL approach // Resources Policy. 2021. Vol. 72. DOI: 10.1016/j.resourpol.2021.102061.
  22. Loktionova A.A., Lavrinenko P.A., Mirzoyan A.G., Loktionova O.A. The impact of political news about Russia on the prices of Russian companies’ shares: Comparative analysis of Russian and foreign media. Economic and Social Changes: Facts, Trends, Forecast. 2024. 17 (5). Pp. 114–132. DOI: 10.15838/esc.2024.5.95.6. (In Russ.).
  23. Bollen J., Mao H., Zeng X. Twitter mood predicts the stock market // J. Comput. Sci. 2011. Vol. 2. No. 1. Pp. 1–8. DOI: 10.1016/j.jocs.2010.12.007.

Manuscript submission date: 12.01.2025

 

For citation:

Loktionova A.A., Mirzoyan A.G. Political news’ impact on Russian companies’ stock prices // Vestnik Instituta Ekonomiki Rossiyskoy Akademii Nauk. 2025. № 1. Pp. 152-166. (In Russ.). https://doi.org/10.52180/2073-6487_2025_1_152_166 EDN: RHVODJ

  Creative Commons 4.0

The Bulletin of the Institute of Economics of the Russian Academy of Sciences № 5/2024.  Finance.

Georgiy A. Borisenko

Postgraduate student, Faculty of Economics, Lomonosov Moscow State University, Moscow, Russia

ORCID: 0009-0000-8430-7744

 

NEURAL NETWORKS TO FORECAST STOCK PRICES BASED ON NEWS DATA

Размер файла211-232  Размер файла  594.08 KB  Размер файла Full text

Abstract

This work is devoted to forecasting the movement of the stock prices of large Russian companies represented in the Moscow Exchange Index, based on news data. Transformer neural networks as well as classical machine learning are used as forecast models. Large Russian news sources and Telegram channels on economics and finance concerns are used as news data. The problem is solved in two settings: classification into 2 classes (the share price will be higher/lower than the current one) and classification into 3 classes (the share price will be higher/approximately at the same level/lower than the current one). As a result of the study, it was found that classical machine learning methods cope better with this task in the general case, but neural networks also show good quality for large companies.

Keywords: stock price, news, neural networks.

JEL: С63, G14

EDN: KJVYKJ

DOI: https://doi.org/10.52180/2073-6487_ 2024_5_211_232

References

  1. Kuznetsov R.S., Tumarova T.G. Forecasting stock prices of PJSC Gazprom using LSTM neural networks // Bulletin of the Institute of Economics of the Russian Academy of Sciences. 2023. No. 3. Pp. 84–98. DOI: 10.52180/2073-6487_2023_3_84_98. (In Russ.).
  2. Biau G., Scornet E. A random forest guided tour // Test. 2016. Vol. 25. Pp. 197–227. DOI: 1007/s11749-016-0481-7.
  3. De Fortuny E.J. et al. Evaluating and understanding text-based stock price prediction models // Information Processing & Management. 2014. Vol. 50. No. 2. Pp. 426–441. DOI: 10.1016/j.ipm.2013.12.002.
  4. Fama E.F. Efficient capital markets // Journal of finance. 1970. Vol. 25. No. 2. Рp. 383–417. DOI: 10.2307/2325486.
  5. Gidofalvi G., Elkan C. Using news articles to predict stock price movements // Department of computer science and engineering, university of California. San Diego. 2001. Vol. 17. DOI: 10.1109/IJCNN.2018.8489208.
  6. Li Y., Pan Y. A novel ensemble deep learning model for stock prediction based on stock prices and news // International Journal of Data Science and Analytics. 2022. Pp. 1–11. DOI: 10.1007/s41060-021-00279-9.
  7. Liu J. et al. Transformer-based capsule network for stock movement prediction // Proceedings of the First Workshop on Financial Technology and Natural Language Processing. 2019. Pp. 66–73. DOI: 10.1016/j.eswa.2022.117239.
  8. Mittal A., Goel A. Stock prediction using twitter sentiment analysis // Standford University, CS229. 2012. Vol. 15. P. 2352.
  9. Natekin A., Knoll A. Gradient boosting machines, a tutorial // Frontiers in neurorobotics. 2013. Vol. 7. P. 21. DOI: 10.3389/fnbot.2013.00021.
  10. Sekioka S., Hatano R., Nishiyama H. Market prediction using machine learning based on social media specific features // Artificial Life and Robotics. 2023. Vol. 28. No. 2. 410–417. DOI: 10.1007/s10015-023-00857-z.
  11. Vaswani A. et al. Attention is all you need // Advances in neural information processing systems. 2017. Vol. 30. DOI: 10.48550/arXiv.1706.03762.
  12. Volodin S.N., Kuranov G.M., Yakubov A.P. Impact of Political News: Evidence from Russia // Scientific Annals of Economics and Business. 2017. Vol. 64. No. 3. Pp. 271–287. DOI: 10.1515/saeb-2017-0018.
  13. Xu Y., Cohen S. B. Stock movement prediction from tweets and historical prices // Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Vol. 1: Long Papers). 2018. Pp. 1970–1979. DOI: 10.18653/v1/P18-1183.
  14. Zhang J., Ye L., Lai Y. Stock price prediction using CNN-BiLSTM-Attention model // Mathematics. 2023. Vol. 11. No. 9. P. 1985. DOI: 10.3390/math11091985.

Manuscript submission date: 12.09.2024

 

For citation:

Borisenko G.A. Neural networks to forecast stock prices based on news data // Vestnik Instituta Ekonomiki Rossiyskoy Akademii Nauk. 2024. № 5. Pp. 211-232. (In Russ.).  https://doi.org/10.52180/2073-6487_ 2024_5_211_232 EDN: KJVYKJ

  Creative Commons 4.0

© Vestnik Instituta Ekonomiki Rossiyskoy Akademii Nauk, 2021 - 2024

32, Nakhimovskiy Prospekt, Moscow, Russia 117218, Institute of Economics of the Russian Academy of Sciences.

Phone.: +7 (499) 724-13-91, E-mail: vestnik-ieran@inbox.ru