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The Bulletin of the Institute of Economics of the Russian Academy of Sciences № 6/2025. Finance.

 

Boris D. Klyukin

Postgraduate Student at Lomonosov Moscow State; Chief Analyst, Industrial Development Fund (Federal State Autonomous Institution “Russian Fund for Technological Development”), Moscow, Russia

ORCID: 0009-0000-8038-6035

 

CLUSTER ANALYSIS OF FINANCIAL SYSTEM INDICATORS TO DETERMINE THRESHOLD VALUES OF THEIR CONTRIBUTION TO ECONOMIC GROWTH

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The paper estimates the impact and threshold values of financial system indicators on the economic growth rates in 34 countries. Bisecting K-Means allows for making reasonable assumptions about their optimal combinations and levels. The cluster analysis identified seven latent groups, each differing in their financial system indicators. Calculating the weighted average growth rate for each group allowed for a comprehensive analysis of the relationship between financial factors and economic growth. An analysis of stock market turnover revealed that its growth is associated with accelerated economic growth, although the direction of this influence remains to be determined. The author also managed to roughly determine the lower limit of the turnover indicator (~30%), below which there is no positive impact on the economy. An analysis of the relative size of domestic loans to the private sector found arguments in favor of the existence of an optimal range of bank loans (75–125% of GDP) and thus a threshold, which is consistent with the findings of other authors. The analysis also suggested the existence of a lower limit on loan volume at 25%. The results of the study can be used in developing and substantiating target indicators for national economic policy.

Keywords: cluster analysis, stock market turnover, relative credit size, threshold values, economic growth.

JEL: F63, O57, P52

EDN: GPARPU

DOI: https://doi.org/10.52180/2073-6487_2025_6_192_215

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Manuscript submission date: 29.05.2025

Manuscript acceptance date: 18.11.2025

 

For citation:

Klyukin B.D. Cluster analysis of financial system indicators to determine threshold values of their contribution to economic growth // Vestnik Instituta Ekonomiki Rossiyskoy Akademii Nauk. 2025. № 6. Pp. 192-215. (In Russ.). https://doi.org/10.52180/2073-6487_2025_6_192_215 EDN: GPARPU

  Creative Commons 4.0

The Bulletin of the Institute of Economics of the Russian Academy of Sciences № 6/2025. Finance.

 

Alexander E. Segal

Head of Corporate Finance Practice in Equity Capital Markets, PJSC Sberbank, Moscow, Russia

ORCID: 009-0004-1534-9376

 

Anton V. Malkov

Cand. Sci. (Econ.), Managing Director for Capital Markets, JSC T-Bank, Moscow, Russia

 

Andrey V. Osadchev

Manager of the Capital Markets Department, JSC T-Bank, Moscow, Russia

 

THE ANALYSIS OF THE RELATIONSHIP BETWEEN RETAIL INVESTOR SENTIMENT AND INITIAL IPO RETURNS IN RUSSIA’S “NEW REALITY” (SINCE 2022)

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Against the backdrop of the transformation of the Russian stock market after 2022, the relevance of studying the factors influencing the pricing of shares during their initial public offerings (IPOs) has increased. This article presents the results of the first stage of the re- search – an analysis of the relationships between the initial returns of IPOs on the Russian stock market during the period 2022–2024 and retail market sentiment, retail demand in monetary terms and the number of investors. The correlation analysis revealed a statistically significant positive relationship between the level of sentiment and first-day returns, while retail demand in monetary terms and the number of investors showed no significant relationship with IPO returns. The study also identified a persistent behavioral pattern: retail investors act as net buyers (their total purchases exceed their total sales) on the first trading day. The findings open up prospects for further modeling of causal relationships between sentiment, retail demand, the number of investors and initial IPO returns on ex- tended datasets.

Keywords: IPO, underpricing, sentiment, retail investors, retail demand, behavioral finance, Russian stock market, investor behavior.

JEL: G14, G11, G32

EDN: IKYUDG

DOI: https://doi.org/10.52180/2073-6487_2025_6_170_191

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Manuscript submission date: 17.07.2025

Manuscript acceptance date: 18.11.2025

 

For citation:

Segal A.E., Malkov A.V., Osadchev A.V. The Analysis of the relationship between retail investor sentiment and initial IPO returns in Russia’s “new reality” (since 2022) // Vestnik Instituta Ekonomiki Rossiyskoy Akademii Nauk. 2025. № 6. Pp. 170-191. (In Russ.). https://doi.org/10.52180/2073-6487_2025_6_170_191 EDN: IKYUDG

  Creative Commons 4.0

The Bulletin of the Institute of Economics of the Russian Academy of Sciences № 5/2025. Finance.

Nikolai G. Sinyavsky

Dr. Sci. (Econ.), Associate Professor, Leading Researcher, Institute of Economic Policy and Economic Security Problems, Faculty of Economics and Business, Financial University under the Government of the Russian Federation, Moscow, Russia

ORCID: 0000-0003-1034-6489

 

SYSTEMIC RISK FACTORS IN THE IMPLEMENTATION OF DIGITAL TECHNOLOGIES TO ANTI-MONEY LAUNDERING: IDENTIFICATION, RANKING AND REGULATORY MEASURES

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Digital products have the potential to make anti-money laundering and counter-terrorist financing operations less costly, more efficient and significantly faster, ensure high-quality compliance with the Financial Action Task Force Standards and improve cross-border cooperation. This enables financial institutions to provide services to a greater number of economic entities. Therefore, the global anti-money laundering system is widely introducing innovative digital technologies to provide prompt and reliable information about economic entities and their operations by significantly increasing the volume of processed data. However, their use is associated with regulatory or operational systemic risks. The purpose of the study is to identify risk factors and measures to regulate them for systemic risks, as well as to systematize and rank them by importance. Such ordering provides grounds for the appropriate distribution of resources used to influence risk factors. The level of risks, risk factors and measures for their regulation are assessed on the basis of a risk-oriented approach using surveys of authoritative organizations, research by specialists and regulatory documents.

Keywords: anti-money laundering, Financial Action Task Force on Money Laundering (FATF), digitalization, risks.

JEL: O33, O38, L73

EDN: LTKMAC

DOI: https://doi.org/10.52180/2073-6487_2025_5_167_187

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Manuscript submission date: 19.05.2025

Manuscript acceptance date: 13.10.2025

 

For citation:

Sinyavsky N.G. Systemic risk factors in the implementation of digital technologies to anti-money laundering: identification, ranking and regulatory measures // Vestnik Instituta Ekonomiki Rossiyskoy Akademii Nauk. 2025. № 5. Pp. 167-187. (In Russ.). https://doi.org/10.52180/2073-6487_2025_5_167_187 EDN: LTKMAC

  Creative Commons 4.0

The Bulletin of the Institute of Economics of the Russian Academy of Sciences № 6/2025. Finance.

 

Dmitry A. Kochergin

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

ORCID 0000-0002-7046-1967

 

MAIN TRENDS IN THE USE OF ARTIFICIAL INTELLIGENCE IN THE FINANCIAL SECTOR

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This article is devoted to researching the current applications of artificial intelligence in the financial sector. The study examines the basic concepts and elements of artificial intelligence technology, identifies the main areas of its application in the financial sector, and reveals new opportunities and risks associated with the introduction of artificial intelligence. The study found that the introduction of artificial intelligence allows for the automation of business processes, the optimization of resource and time use, the execution of routine processes, and the solution of complex tasks through big data analysis and pattern recognition. The main areas of application of artificial intelligence in the financial sector are: payments, financial intermediation, insurance, and asset management. In these areas, the use of artificial intelligence im- proves the efficiency of financial services by reducing the costs of internal transaction processing, regulatory compliance, fraud detection, and customer service. At the same time, the use of AI generates new sources of cyber risks and exacerbates problems of bias and discrimination in financial decision-making, which contributes to an increase in legal and operational risks.

Keywords: artificial intelligence, machine learning, neural networks, large language models, generative artificial intelligence, artificial intelligence agents, general artificial intelligence, application of artificial intelligence in payments, lending, insurance and asset management, risks of artificial intelligence implementation.

JEL: C63, G21, G22, O33

EDN: EQJOKW

DOI: https://doi.org/10.52180/2073-6487_2025_6_147_169

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Manuscript submission date: 01.09.2025

Manuscript acceptance date: 18.11.2025

 

For citation:

Kochergin D.A. Main trends in the use of artificial intelligence in the financial sector // Vestnik Instituta Ekonomiki Rossiyskoy Akademii Nauk. 2025. № 6 Pp. 147-169. (In Russ.). https://doi.org/10.52180/2073-6487_2025_6_147_169 EDN: EQJOKW

  Creative Commons 4.0

The Bulletin of the Institute of Economics of the Russian Academy of Sciences № 4/2025. Finance.

Alexander A. Rubinstein

Cand. Sci. (Econ.), Senior Researcher, Institute of Economics of the RAS, Moscow, Russia

 

ON THE QUESTION OF KEY RATE REDUCTION

Размер файла125-149 Размер файла  1.07 MB Размер файла Full text

Since October 2024, the key rate in the Russian Federation exceeds the profitability of a significant number of economic sectors, resulting in such risks as difficulty in lending, the possibility of sliding into recession, and the emergence of a monetary overhang due to individual deposit growth. Using the example of a similar situation in the USA in 1980–1982 with the help of the model of the shifting mode of reproduction (SMR model) the importance of additional commodity supply is shown, which was provided by a strong rise in import supplies. An alternative series of precautionary measures damping the negative effects of a key rate cut is also proposed. On possible options, approximate calculations of the consequences were made with the help of the SMR model.

Keywords: inflation, key rate, recession, fixed capital investment, shifting mode of reproduction model (SMR model).

JEL: C02, E22, E42

EDN: CQJRQH

DOI: https://doi.org/10.52180/2073-6487_2025_4_125_149

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Manuscript submission date: 25.05.2025

Manuscript acceptance date: 07.08.2025

 

For citation:

Rubinstein A.A. On the question of key rate reduction// Vestnik Instituta Ekonomiki Rossiyskoy Akademii Nauk. 2025. № 4. Pp. 125-149. (In Russ.). https://doi.org/10.52180/2073-6487_2025_4_125_149 EDN: CQJRQH

  Creative Commons 4.0

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