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

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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

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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

GLOBAL EXPERIENCE IN IMPLEMENTING CENTRAL BANKS DIGITAL CURRENCIES FOR RETAIL PAYMENTS IN EMERGING MARKETS AND DEVELOPING COUNTRIES

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Abstract

The article is devoted to the definition of current trends in the development of retail payments, the study of the motives for the introduction of digital currencies, the architecture and design features of retail CBDC systems in emerging markets and developing countries (EMDCs), as well as possible risks. Proposals have been formulated to improve the digital ruble project. As a result of the study, conclusions are drawn about the global trend of more and more expanding use of non-cash payment instruments and the growing competition between private payment service providers and the government. The key motives for the introduction of retail CBDCs are: expanding financial accessibility, improving the security of payments, offering digital money of universal use, etc. Having analyzed the experience of implementing rCBDC systems in developing economies we conclude that these countries use a two-tier architecture, hybrid infrastructure and different types of digital wallets. The main difficulties facing the adoption of rCBDCs in EMDCs include risks of cyber security, bank disintermediation, low adoption, etc. For widespread the adoption of rCBDCs in EMDCs, including in Russia, it is necessary to: eliminate fees for CBDC payments; expand storage limits and transaction amounts; support anonymous low-denomination payments; encourage the use of digital currency in government payments, etc.

Keywords: non-cash payments, central bank digital currencies (CBDCs), retail CBDCs, CBDC systems, digital wallets, disintermediation of banks, financial inclusion, monetary regulation, digital rouble.

JEL: E42, E50, E58

EDN: XGGDXK

DOI: https://doi.org/10.52180/2073-6487_ 2024_5_130_171

References

1. Andryushin A., Kochergin D.A. Stablecoins as a New Form of Digital Money: Issue, Circulation, Regulation and Risk Management // Voprosy Ekonomiki. 2022. No. 6. Pp. 42–68. EDN: MFFYVV. DOI: 10.32609/0042-8736-2022-6-42-68. (In Russ.).
2. Kochergin D.A. Central Banks Digital Currencies: World Experience // World Eсonomy and International Relations. 2021. Vol. 65. No. 5. Pp. 68–77. EDN: CUETEZ. DOI: 10.20542/0131-2227-2021-65-5-68-77. (In Russ.).
3. Kochergin D.A. Central Bank Digital Currencies: Experience of Introducing a Digital Yuan and Development of a Digital Ruble Conception // Russian Journal of Economics and Law. 2022. Vol. 16. No. 1. Pp. 51–78. EDN: BHOKOZ. DOI: 10.21202/2782-2923.2022.1.51-78. (In Russ.).
4. Kochergin D.A. Modern Models of Systems of Central Bank Digital Currency // Petersburg University Journal of Economic Studies. 2021b. Vol. 37. No. 2. Pp. 205–240. EDN: XPJNDD. DOI: 10.21638/spbu05.2021.202. (In Russ.).
5. Alfonso V., Kamin S., Zampolli F. Central Bank Digital Currencies (CBDCs) in Latin America and the Caribbean. Bank for International Settlements BIS Representative Office for the Americas Monetary and Economic Department // BIS Working Papers. 2022. No. 989.
6. Arauz A., Garratt R., Ramos D. Dinero Electrónico: The Rise and Fall of Ecuador’s Central Bank Digital Currency // Latin American Journal of Central Banking. 2021, June. Vol. 2. Iss. 2.
7. Auer R., Banka, Boakye-Adjei N. et al. Central Bank Digital Currencies: A New Toll in the Financial Inclusion Toolkit? / World Bank Group. Bank for International Settlements // FSI Insights on Policy Implementation. 2022. No. 41.
8. Auer R., Boar C., Cornelli G. et al. CBDCs Beyond Borders: Results from a Survey of Central Banks // Monetary and Economic Department. BIS Working Papers. 2021. No. 116.
9. Auer R., Monnet C., Shin H. Distributed Ledgers and the Governance of Money // BIS Working Papers. 2021. No. 924.
10. Bolzani, J. B. Leading the Way in Payments: How Central Banks Are Using Innovation To Promote Financial Inclusion and Reshape Competition // Journal of Law & Commerce. 2022. Vol. 41. No. 1. Pp. 103–73.
11. Di Iorio, A. D. Kosse A. and Szemere R. Tap, Click and Pay: How Digital Payments Seize the Day // Committee on Payments and Market Infrastructures, CPMI Brief. 2024, February. No. 3. Available at: https://www.bis.org/statistics/payment_stats/commentary2402.pdf (accessed: 10.05.2024).
12. Di Iorio, A., Rocco, G. Easier Said Than Done: Why Italians Pay in Cash While Preferring Cashless // Bank of Italy Occasional Paper Series. 2022, November. No. 731.
13. Fernandez-Villaverde J., Sanches D., Schilling L., Uhlig H. Central Bank Digital Currency: Central Banking for All? // Federal Reserve Bank of Philadelphia, Working Papers. 2020. No. 2019.
14. Glowka, M, Kosse A, Szemere R. Digital Payments Make Gains but Cash Remains // CPMI Briefs. 2023, January. No. 1. Available at: https://www.bis.org/statistics/payment_stats/commentary2301.pdf (accessed: 10.05.2024).
15. Khiaonarong T., Humphrey D. Falling Use of Cash and Population Age Structure // Journal of Financial Market Infrastructures. 2022b. Vol. 10. No. 2. Pp. 47–64.
16. Khiaonarong T., Humphrey D. Instant Payments: Regulatory Innovation and Payment Substitution Across Countries // IMF Working Papers. 2022a, November. No. 228.
17. Khiaonarong T., Humphrey D. Measurement and Use of Cash by Half the World’s Population // IMF Working Papers. 2023, March. No. 62.
18. Kosse, A. and Mattei, I. Gaining Momentum – Results of the 2021 BIS Survey on Central Bank Digital Currencies // Monetary and Economic Department, May 2022, BIS Papers. 2022. No. 125.
19. Kosse, A. and Mattei, I. Making Headway – Results of the 2022 BIS Survey on Central Bank Digital Currencies and Crypto // Monetary and Economic Department, BIS Papers. 2023, July. No. 136.
20. Mersch Y. An ECB Digital Currency – a Flight of Fancy? // Speech by, Member of the Executive Board of the ECB and Vice-Chair of the Supervisory Board of the ECB at the Consensus 2020 Virtual Conference. 2020, 11 May. Available at: https://www.ecb.europa.eu/press/key/date/2020/html/ecb.sp200511~01209cb324.en.html (accessed: 10.05.2024).
21. Prasad, E. The Future of Money. How the Digital Revolution Is Transforming Currencies and Finance. The Belknap Press of Harvard University 2021.
22. Ree J. Nigeria’s eNaira, One Year After // IMF Working Paper. 2023, May. No. 2023/104. Available at: https://www.imf.org/-/media/Files/Publications/WP/2023/English/wpiea2023104-print-pdf.ashx (accessed: 10.05.2024).
23. Sarmiento A. Seven lessons from the e-Peso pilot plan: The possibility of a Central Bank Digital Currency // Latin American Journal of Central Banking. 2022. Vol. 3. Iss. 2.
24. Shy O. Cash Is Alive: How Economists Explain Holding and Use of Cash // Journal of Economic Literature. 2023. Vol. 61. No. 4. Pp. 1465–1520.
25. Wright A., Shavonne C. McKenzie, Lance R. Bodie, Carlisa L. Belle. Financial Inclusion and Central Bank Digital Currency in The Bahamas // Research Department Central Bank of The Bahamas. 2022, July 26.

Manuscript submission date: 27.06.2024 

For citation:

Kochergin D.A. Global experience in implementing central banks digital currencies for retail payments in emerging markets and developing countries // Vestnik Instituta Ekonomiki Rossiyskoy Akademii Nauk. 2024. № 5. Pp. 130-171. (In Russ.).  https://doi.org/10.52180/2073-6487_ 2024_5_130_171 EDN: XGGDXK

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

Daria A. Dinets

Dr. Sci. (Econ.), Associate Professor, Head of the Department of the Finance and Credit, Peoples’ Friendship University of Russia named after Patrice Lumumba;

Leading Researcher, Institute for Research of Socio-Economic Transformations and Financial Policy, Financial University under the Government of the Russian Federation, Moscow, Russia

ORCID: 0000-0001-8734-8998

CONTRADICTIONS BETWEEN MONETARY AND FISCAL POLICIES: ANALYSIS OF THE CURRENT SITUATION AND POSSIBILITIES OF REDUCING THE LEVEL OF INTERGENERATIONAL DEBT

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The article proposes the author’s approach to grouping countries according to the level of use of resources inherited from past generations, as well as resources “borrowed” from future generations. The sample of countries was grouped based on the increase in production capacity compared to the level of past generations, which characterized the degree of use of those achievements that were formed by past generations, or formed their own reproduction circuit based on the achievements of engineering trends and future technologies. Further, these countries were grouped on the basis of “predatory” use of resources to the detriment of future generations. At the junction of two grouping characteristics, the author’s grouping of countries according to the level of intergenerational debt was formed. An analysis of the influence of monetary and fiscal policies on the economies of the countries of each group was carried out. Conclusions are drawn about the possibility of reducing the level of intergenerational debt through policy coordination without the use of automatic rules or recommendations of the Washington Consensus. For each group of countries, their own characteristic features of the fiscal and monetary policies combination have been identified, which have a stronger impact on the dynamics of intergenerational debt.

Keywords: monetary policy, fiscal policy, resource consumption, productivity, growth, intergenerational debt.

JEL: H10, H60

EDN: UATXCM

DOI: https://doi.org/10.52180/2073-6487_ 2024_5_172_210

References

1. Conceptual models of integration of monetary and budgetary policies: monograph / collective of authors edited by V.A. Slepov. Moscow: Rusains, 2024.
2. Barro R.J. Economic growth / R.J. Barro, H. Sala-i-Martin; trans. from English. Moscow: BINOM. Knowledge Laboratory, 2017.
3. Denisenko M., Kozlov V. Intergenerational accounts and demographic dividend in Russia // Demographic Review. 2018. Vol. 5. No. 4. Pp. 6–35. DOI: 10.17323/demreview.v5i4.8661.
4. Acemoglu D. Introduction to the theory of modern economic growth: in 2 books. Book 1 = Introduction to Modern Economic Growth (2009). Moscow: Delo Publishing House, RANEPA, 2018.
5. Easterly W. In Search of Growth. Adventures and Misadventures of Economists in the Tropics / William Easterly, 2006.
6. Aghion F., Williamson D. Economic Growth, Inequality, and Globalization: Theory, History, and Political Practice. Moscow: Delo Publishing House, RANEPA, 2015.
7. Stiglitz J. People, Power, and Profits: Progressive Capitalism in the Age of Mass Discontent / Joseph Stiglitz, translated from English. Moscow: Alpina-Publisher, 2020.
8. Graeber D. Debt: The First 5000 Years of History. M.: Ad Marginem Press: Garage Museum of Contemporary Art, 2021.
9. Budgetary, tax and monetary instruments for achieving financial stability and ensuring economic growth: monograph / coll. authors; edited by M.A. Abramova. M.: KNORUS, 2023.
10. Suetin A.A. The crisis is behind us, emerging markets are ahead // Financial analytics. Problems and solutions. 2010. No. 2 (26). Pр. 12–21.
11. Nadtochey Yu.I. Models of defense cooperation on the northern and eastern flank of the EU (the experience of Norden and the Visegrad Group) // Comparative politics. 2018. No. 2. Pр. 26–40. DOI: 10.18611/2221-3279-2018-9-2-26-40.
12. Lewis M. Boomerang. How to Transform from a Developed Country into a Third World Country. Moscow: Alpina-Publisher, 2019.
13. Grigoriev O. The Age of Growth. Lectures on Neoeconomics. The Rise and Decline of the World Economic System. Moscow: Karera-Press, 2016.
14. Acemoglu D., Robinson D.A. Why Some Countries Are Rich and Others Poor. Moscow: AST Publishing House, 2023.
15. Friedman T. The Flat World. A Brief History of the 21st Century. Moscow: AST: KHRANITEL, 2006.
16. Polezharova L.V. Tax systems and economic growth in China and India // Economy. Taxes. Law. 2023. No. 16 (5). Pр. 114–124. DOI: 10.26794/1999-849x-2023-16-5-114-124.

Manuscript submission date: 11.09.2024 

For citation:

Dinets D.A. Contradictions between Monetary and Fiscal Policies: Analysis of the Current Situation and Possibilities of Reducing the Level of Intergenerational Debt // Vestnik Instituta Ekonomiki Rossiyskoy Akademii Nauk. 2024. № 5. Pp. 172-210. (In Russ.).  https://doi.org/10.52180/2073-6487_ 2024_5_172_210 EDN: UATXCM

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The Bulletin of the Institute of Economics of the Russian Academy of Sciences № 4/2024.  Finance.

Anastasia M. Matevosova

student of the Faculty of Economics of Moscow State University, Senior Laboratory Assistant at the Center for International Macroeconomics Research and Foreign Economic Relations, Institute of Economics of the Russian Academy of Sciences, Moscow, Russia

ORCID: 0009-0004-7490-5248

HIGH-FREQUENCY MODELING OF THE IMPACT OF SANCTIONS ON INFLATION EXPECTATIONS OF THE RUSSIAN POPULATION

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Abstract

This article examines the impact of sanctions concerns on the inflation expectations of the Russian population during the period of large-scale sanctions in 2022–2023. Using the methods of text processing of data from posts and comments on the social network, indicators of inflation expectations and sanctions concern were built with a weekly frequency and then used in econometric modeling. By evaluating autoregressive models of the integrated moving average with generalized autoregressive conditional heteroscedasticity in residuals and exogenous regressors (ARIMA-X-GARCH-X), an attempt has been made to model both the value and volatility of inflation expectations. As a result, it was revealed that increased sanctions concern, other things being equal, leads to an increase in inflation expectations, but does not affect the uncertainty of the population regarding inflation expectations. Despite the detection of weak structural changes, the degree of impact of sanctions concern on the inflation expectations of the Russian population is relatively stable in the period from March 2022 to December 2023.

Keywords: inflation expectations, sanctions, indicator, AutoRegressive Integrated Moving Average (ARIMA), Generalized AutoRegressive Conditional Heteroskedasticity (GARCH).

JEL: C22, C55, C58, C82, D84, E31, F51

EDN: VYHHCE

DOI: https://doi.org/10.52180/2073-6487_ 2024_4_139_158

References

1. The Annual report of the Bank of Russia for 2023. The Central Bank of the Russian Federation, 2023. (In Russ.).
2. Golovnin M.Y. Monetary policy in Russia: reaction to new external challenges // Bulletin of the Institute of Economics of the Russian Academy of Sciences. 2023. P. 1. С. 7–20. EDN: BOEIAH. DOI: 10.52180/2073-6487_2023_1_7_20. (In Russ.).
3. Goloshchapova I.O. Development of a methodology for constructing high-frequency indicators of economic expectations of the population based on big data (on the example of inflation expectations) // Autoref. diss. candidate of Economic Sciences. Moscow, 2018. (In Russ.).
4. Goloshchapova , Andreev M. Measuring inflation expectations of the Russian population with the help of machine learning // Voprosy Ekonomiki. 2017. (6). Р. 71–93. DOI: 10.32609/0042-8736-2017-6-71-93 (In Russ.).
5. Matevosova A.M. Russians’ inflation expectations under sanctions: big data research // Bulletin of the Institute of Economics of the Russian Academy of Sciences. 2023. № 5. 181–200. EDN: ZBJKRC. DOI: 10.52180/2073-6487_2023_5_181_200. (In Russ.).
6. Medvedev I.D., Solntsev O.G. Monetary Policy of the Bank of Russia in the Conditions of Structural Transformation of the Economy // Scientific works: Institute of Economic Forecasting of the Russian Academy of Sciences. 2023. № 2. P. 6–28. DOI: 10.47711/2076-3182-2023-2-6-28. (In Russ.).
7. The main directions of the unified state monetary policy for 2023 and the period 2024 and 2025. The Central Bank of the Russian Federation, 2022. (In Russ.).
8. Russia 2035: Toward a New Quality of National Economy. Scientific report / ed. corresponding member of the Russian Academy of Sciences A.A. Shirov. Scientific Report. Institute of Economic Forecasting RAS. M.: Artique Print, 2024. DOI: 10.47711/sr1-2024. (In Russ.).
9. Smorodinskaya N.V., Katukov D.D. Russia under sanctions: limits of adaptation // Bulletin of the Institute of Economics of the Russian Academy of Sciences. 2022. № 6. С. 52–67. DOI: 10.52180/2073-6487_2022_6_52_67. (In Russ.).
10. Ushkalova D.I. Russia’s foreign trade: preliminary results of the second year of counteraction to sanctions pressure // Bulletin of the Institute of Economics of the Russian Academy of Sciences. 2023. № 6. P. 43–60. EDN: DVZHYT. DOI: 10.52180/2073-6487_2023_6_43_60. (In Russ.).
11. Generalized autoregressive conditional heteroskedasticity // Journal of Econometrics. 1986. Vol. 31. Pp. 307–327.
12. R. Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation // Econometrica. 1982. Vol. 50. №. 4. Pp. 987–1007.

Manuscript submission date: 22.07.2024 

For citation:

Matevosova A.M. High-Frequency Modeling of the impact of sanctions on inflation expectations of the Russian population // Vestnik Instituta Ekonomiki Rossiyskoy Akademii Nauk. 2024. № 4. Pp. 139-158. (In Russ.).  https://doi.org/10.52180/2073-6487_ 2024_4_139_158 EDN: VYHHCE

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