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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

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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

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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

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 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

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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

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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

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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

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