The relevance of this study is due to the unprecedented level of sanctions pressure on the Russian Federation. This has led to a need for a fundamental re-evaluation of approaches to assessing the effectiveness of industrial policies aimed at ensuring the country’s technological security. Currently, existing assessment methods suffer from a lack of coherence and insufficient consideration for the specific nature of sanctions restrictions. This significantly reduces the efficacy of monitoring efforts in terms of technological safety. The object of the study is the system of industrial policy of Russia in the field of ensuring technological safety in the context of sanctions pressure. The subject of the study is the criteria for assessing the effectiveness of industrial policy measures to ensure technological safety and methodological approaches to their formation. The purpose of the study is to develop a comprehensive system of criteria for assessing the effectiveness of industrial policy in Russia under sanctions pressure based on improving methodological approaches to determining threshold values and a multi-level structure of technological safety indicators. The methodological basis for this study is the integrated use of a zonal-threshold approach, which includes single-threshold, two-threshold, and multi-threshold assessment options. This approach is based on strategic documents of the Russian Federation and statistical data from Rosstat. The conducted analysis revealed the possibility of using criteria at the macro-, meso- and micro-levels of the economic system. A comprehensive system of new criteria in five strategic areas with the introduction of a specialized “sanction functional criterion” was developed. Scientific novelty lies in the integration of the zonal-threshold approach with a multi-level structure of criteria and the development of fundamentally new tools for monitoring technological security. Practical significance is determined by the possibility of direct implementation of the proposed system in public administration mechanisms.

In the current political conditions, the key attention in the country is paid to the construction of such state budgets in which the influence of external factors on the economy will be minimized. The most important tasks are to ensure the development of the subjects of the Russian Federation, improve the quality of life in Russia, increase the attractiveness of the state both for its citizens and at the international level, and create favorable conditions for the life and work of the population in our country. One of the central mechanisms for solving these tasks is the budget system. It includes federal, regional and local budgets, which are formed according to the principle of solving key socially significant issues in their execution. The benchmark for effectiveness was taken in the Russian Federation in 2004 during the budget reform, the purpose of which was to switch to the use of the program budget. To achieve maximum results, various budgeting tools are used, the main of which today are programs and projects at all levels of government. The purpose of the study is to identify the features of project financing in the construction of program-based budgets at the regional level. Comparison and grouping methods, as well as tabular and graphical methods of data presentation, were used. As an example, the regional budgets of the subjects of the Russian Federation that are part of the Far Eastern Federal District are used: Kamchatsky, Khabarovsky, Primorsky and Zabaykalsky. At the end of the work, conclusions were drawn about the important role and high importance of the project part of program budgeting in solving the main socially and economically significant tasks of the state. Proposals have also been formulated to improve and optimize the formation of regional budgets using tools such as projects to increase the effectiveness of their implementation.

The purpose of the article is to examine the impact of long-term factors affecting human development in emerging market countries, based on the “middle income trap” hypothesis. According to the research question, the radical liberal reforms implemented in the countries of the former Soviet Union, including the Republic of Armenia, in the 1990s have led to the emergence of a middle income trap, which requires large expenditures and new reforms in human capital development to overcome. As a basic methodological approach, the problem of the relationship between human development and the middle income trap has been studied in the context of the dynamics of income differentiation and inequality indicators. According to the results of the study, in the Republic of Armenia, along with the economic growth recorded as a result of liberal reforms and the increase in the human development index, there has been an increase in the level of inequality, while the main factors restraining the latter are the progressive growth of public spending in the education and healthcare sectors. Among the factors that have a decisive impact on long-term human development, the spread of digital technologies, investments in research and development programs, as well as the neutralization of the effects of negative institutional factors, in particular, the reduction of corruption, are of decisive importance. The main findings of the study demonstrate that in the long run, overcoming the “middle income trap” is conditioned not only by increasing costs for education and healthcare sectors and gradual steps towards improving living standards, but also by programs of significant investments in improving the institutional environment.

The paper examines the transformation of the financial market in a modern environment and aims to identify promising areas for its development, taking into account trends in the global financial system and economic challenges. Methods such as scientific abstraction, generalization, analysis, synthesis, and logic are employed in the study. Key trends in the global financial system are identified, including the increasing importance of digital technologies and the shift towards more sustainable and inclusive finance. The Russian financial market is viewed as a unified interconnected space, where synergies can be achieved through the use of public market potential for financial instruments and products, as well as the collaboration of credit institutions, state agencies, and development institutions. The need for increased connectivity between different segments of the market is emphasized, as this can lead to more efficient allocation of resources and better outcomes for all participants. The paper identifies the priority directions for the financial development of financial markets in the current context. It concludes that it is essential to develop the domestic syndicated lending market in order to attract additional investment into the Russian economy. Proposals have been formulated to increase the attractiveness of the stock market for both issuers and investors. The importance of expanding the use of securitization mechanisms in the Russian securities market has also been emphasized. The paper discusses the directions for developing project financing and public-private partnerships. It emphasizes the need for market participants and government agencies to work together to promote the development of the financial market and its various segments. The importance of attracting development institutions, direct government support, and refinancing instruments of the Central Bank of Russia to form sources of long-term investment in the current environment is noted. The novelty of this research lies in developing a methodology for analyzing the contemporary financial market, considering the transformations of the global financial system. The significance of the study lies in providing recommendations for the development of the Russian financial market. Future research could focus on identifying promising areas of securitization market development, considering digitalization, and assessing its potential for creating longterm financing sources in the Russian economy.

The article examines the structure of the growth of the Russian economy in the period 2003–2023. The macroeconomic policy proceeds from linking the instrument to a specific development goal, although in practice the entire set of tools affects the target parameters and economic structure, thereby generating opportunities for its contribution to the rate of economic growth and reduction of inflation. The purpose of the research is to conduct a structural analysis of Russia’s economic growth with the allocation of the distributed impact on the growth rate, inflation and economic structure (by GDP) of the following main macroeconomic policy instruments: the key interest rate, monetization level, exchange rate and budget surplus/deficit. The research methodology is represented by the theory of economic growth, structural analysis, regression models, econometric approach, and statistical data processing. The information base of the study was compiled from Rosstat and the World Bank. The result is a constructed algorithm for structural analysis of growth with an assessment of the distributed impact of policy instruments and an empirical study of the Russian economy, which confirmed the different strengths of the influence of applied policy instruments not only on growth and inflation, but also on the economic structure (raw materials, processing and transaction sectors), as well as the different effects of structural elements on price dynamics and GDP, shaped by the ongoing macroeconomic policy. Such a result in the long term leads to the need to correct the instruments used in terms of the strength and nature of their action, and also allows us to take into account the formulation of structural change tasks together with the macroeconomic policy measures being formed aimed at ensuring the growth rate at relatively low price dynamics.

As environmental, social, and governance (ESG) considerations gain prominence, companies are increasingly integrating ESG factors into their decision-making processes. While extensive research has examined ESG in developed markets, limited studies explore its impact on emerging economies. This study investigates whether ESG scores are positively associated with a firm’s market value and profitability in Indonesia and Malaysia. The study utilizes panel data from Refinitiv Eikon and World Bank covering the period 2010–2022. The sample consists of 421 firm-year observations from non-Shariah-compliant companies in Indonesia and Malaysia. The analysis employs random-effects and fixedeffects panel regressions to assess the relationship between ESG scores and corporate financial performance, measured by Tobin’s Q (market value), Return on Assets (ROA), and Return on Equity (ROE). The results indicate a positive and significant relationship between ESG scores and both market value (coefficient = 3.655) and ROE (coefficient = 0.007), suggesting that strong ESG performance enhances firm valuation and shareholder returns. However, the study finds a negative and significant association between ESG and ROA (coefficient = –0.000024), implying that ESG integration may not consistently improve asset efficiency. These findings highlight the mixed financial effects of ESG adoption in emerging markets. The study underscores the need for greater ESG awareness in Indonesia and Malaysia, particularly in guiding companies toward sustainability-driven financial strategies. As ESG integration continues to shape investment decisions, understanding its nuanced impact on financial performance is critical for stakeholders navigating evolving market expectations.

An integral part of an effective anti-money laundering and counter-terrorism financing (AML/CFT) system is the assessment of these risks, which requires their full understanding by all participants in the national AML/CFT system, prompt response and the right decisions to minimize them. The essence of the problem lies in the need to regulate a new phenomenon associated with the issuance and circulation of digital financial assets (DFA). In countries where digital assets are regulated a lot, modern ways to assess AML/CFT risks involve using technology, stricter monitoring rules, and thorough risk-based methods. Key elements of these approaches include the use of machine learning algorithms to identify anomalies, the creation of rating systems to assess the risk of individual users and transactions, and active cooperation between government agencies, financial institutions and the private sector to share data and better understand the risks. There is increasing attention to the study and analysis of actual transaction flows in blockchain networks, which allows for a better understanding of potential money laundering and terrorist financing channels. Regulators in countries with a high level of control often develop detailed guidelines and recommendations for market participants, which helps standardize approaches to compliance with legal requirements. The purpose of the study is to identify ways to unify methodological approaches to AML/CFT risks using the example of countries with a high level of digital asset regulation. Methods of scientific abstraction, structural-functional analysis, comparison, and deduction were used in this paper. A unified comprehensive approach to assessing the risks of AML/CFT in the context of the issuance and circulation of digital financial assets has been developed. The practical significance of the research results lies in the possibility of using a unified methodological approach to assessing the risks of AML/CFT associated with the issuance and circulation of digital financial assets by government bodies and other participants in the national AML/CFT system.

The relevance of this study lies in the significance of a thorough examination of the implications of the rapid expansion and widespread adoption of modern financial technologies. The purpose of the study is to identify the characteristics of f intech-related risks using multimodal business analytics which is based on machine learning, neural networks and data mining technologies. Hypothesis. The use of methods and tools for multimodal business analytics based on machine learning and neural networks will ensure the further instrumentalization of risk assessment and analysis of fintech, taking into account multifactoriality, polyvariance and interdependence nature of risks. This will fully reflect the complexity of modern financial technologies and their impact on the transformation of financial and economic relations. Research methods. The study was based on multimodal analytics, which involved the construction of cross-analysis risk matrices, highlighting the mutual decreasing and increasing influence on the interests of participants in financial relations. For a comprehensive assessment, key fintech tools were selected —  cryptocurrencies (as an investment instruments and means of payment), digital financial assets and digital financial services, such as digital transfers. The results of the study showed that modern financial technologies play a key role in transforming the financial sector, making it more accessible, efficient, and customer oriented. It has been stated that the introduction of fintech in Russia contributes to financial inclusion by providing access to financial services for those who were previously excluded from the traditional banking system. Interpretation of multimodal analytics materials has demonstrated that the use of cryptocurrencies for investment and settlements in the Russian Federation is subject to high market and regulatory risks. In the digital financial assets market, issuers face problems of insufficient liquidity, and digital financial services demonstrate vulnerabilities in the f ield of data protection and operational reliability. As a result, we can conclude that the use of multimodal analytics tools integrating various data sources and research methods allows for a deeper understanding and effective assessment of the complex risks associated with modern financial technologies. Based on the results of the study, we propose practice oriented recommendations for improving risk management in the Russian financial technology sector for regulators and other parties involved in financial transactions.

The paper is devoted to the construction of models for forecasting the volume of trade between Russia and the BRICS countries under sanctions. Trade between the BRICS countries is the economic foundation of their comprehensive interaction and prosperity, therefore the problem of high-quality forecasting of the volume of this trade under unprecedented Western sanctions against Russia seems to be a relevant task of econometric modeling. The aim of the study is to improve the accuracy of forecasts of Russia’s trade turnover with BRICS partners by ensuring the stability of the forecasting model in the context of sanctions pressure from Western countries and the pandemic. The econometric tool chosen is a system of simultaneous equations describing the foreign trade turnover of each country (other than Russia) using annual levels of macroeconomic factors: the GDP of the BRICS countries, Brent oil prices, the US dollar exchange rate and the pandemic indicator over the time period 2000–2022. In order to take into account structural changes in fast-growing economies such as India and China, two-phase models (switching models) were used to describe their behavioral equations in a system of simultaneous equations. As a test for the significance of structural changes, due to the small sample size after structural changes, the Chow forecast test was used. Taking into account significant structural changes (in the post-pandemic period) within the framework of switching models allowed us to increase the accuracy of the forecast of the volume of trade turnover of the Russian Federation by 2.5 times.

This research is devoted to the analysis of financial crises. We examine different classifications of crises, methods of forecasting, approaches to building systems of early warning indicators. To better understand the potential for predicting f inancial crises, we conduct our own empirical research, comparing logit model and random forest to predict currency crises in developing countries. We also identify the most relevant variables, whose dynamics may signal the currency crisis is approaching. We aim to compare the accuracy of econometric models and machine learning techniques in predicting currency crises in developing countries, and to identify a set of relevant indicators that could be used in a warning system. We use logit regression and random forest models. We compare the predictive power of these models using the ROC curve. The significance of variables in a random forest model is determined by the Shapley values. We found that the random forest model has slightly more accurate predictive power than the logit approach. Both models indicate that oil prices and commercial bank deposits are the most robust predictors of currency crises. The results obtained can be taken into account by economic institutions involved in financial system regulation, as we indicate the variables, which should be primarily taken into account when forecasting currency crises in developing countries.

The rapid growth of the mortgage lending market in recent years has resulted in an increase in the issuance of mortgage bonds, which, on the one hand, represent an object of investment and, on the other hand, a source of funding for the banking sector. In 2023, the total volume of mortgage lending in Russia reached 4 trillion rubles, an increase of 25% compared to the previous year. The experience of financial engineering that led to the mortgage crisis in 2008 in the USA, the high volatility of the Russian stock market and the Bank of Russia rates that reached 21% in 2024 have actualized the study of securitization risks. The purpose of this study is to identify the key factors affecting the price of Russian mortgage bonds. The least squares method was used to create a model based on significant indicators: the ratio of outstanding liabilities to the estimated total liabilities; the proportion of overdue loans, weighted by outstanding debt; the spread between the pooled loan rate, weighted by the volume of outstanding debt, and the weighted average mortgage rate; the spread between the yield on mortgage bonds and the yield on 10-year government bonds. The results obtained led to the conclusion that these factors significantly affect the pricing of MBS and can be utilized by investors and the regulators to more accurately forecast prices for mortgage bonds.

As greenhouse gas emissions are increasing year by year, both developed and developing countries are seeking to incentivize their reduction through emissions trading. Therefore, the price of a carbon unit becomes a driver of change in greenhouse gas emissions. In this regard, understanding how the price of a carbon unit is formed becomes particularly relevant. The object of the study is the combination of factors and conditions of formation of prices for carbon credits as tools for reducing greenhouse gas emissions. The purpose of the study is to identify the key determinants of establishing and changing the price of carbon credits. In the process of writing the article we used both general scientific research methods: analysis, synthesis, generalization and classification of data, and special economic and mathematical methods, including correlation and regression analysis. The article investigated the European, New Zealand and Korean carbon unit trading systems. It was concluded that there are clusters of volatility in their markets of carbon units. The key determinants of carbon price volatility and factors contributing to their growth were identified: the price of crude oil, gas, coal, gasoline; shocks causing recession; the total volume of carbon emission quotas on the market; the volume of free allocated quotas; the number and list of industries covered by the system of trading in quotas. As a result, the stages of forming a price for carbon units for the purpose of reducing greenhouse gas emissions were determined and justified. The results obtained in the course of the study and the recommendations developed are aimed at creating a market for carbon units in Russia and improving its efficiency in comparison with existing practices. The results obtained will be used for further fundamental research and practical developments in the field of greenhouse gas emissions trading.

The study is devoted to identifying the impact of unmanned aerial vehicles (UAVs) on changes in the risk situation and insurance interests in the Russian Federation. The relevance of the topic under consideration is determined by the large number of UAVs at the disposal of the population and organizations, and the high level of freedom in their use, which generates significant risks. The aim of the work is to identify specific changes in the risk landscape and the corresponding insurance needs that arise from the widespread introduction of UAVs. To achieve this goal, the following tasks were formulated and solved: the risks associated with the use of UAVs were classified and the specifics of their manifestation were analyzed; the interrelationships between changes in the risk situation and the development of insurance interests of potential policyholders were identified; ways to increase the effectiveness of insurance protection, primarily against the risks of illegal, sabotage and military use of UAVs, were proposed. When writing the article, analytical and statistical methods were used to assess the quantitative and qualitative indicators of the Russian and international drone markets. The article systematizes the risks of using UAVs by subjects and purposes of use, and provides a comparative analysis of insurance products of Russian insurers. It has been established that the mass use of UAVs generates specific risks. For UAV owners, the risks of Comprehensive Insurance and liability are relevant; for third parties, there is a risk of physical/material damage from the use of UAVs, as well as from military and terrorist attacks. In the face of growing demand, Russian insurers offer products that insure UAV deaths, liability, and cargo, but exclude military and terrorist risks. The results show a need to adapt existing insurance protection mechanisms to meet new insurance requirements. The authors argue for the need to develop military/terrorism risk insurance for the use of UAVs through reinsurance mechanisms, pools, mutual insurance, and government support. The authors emphasize the importance of a more inclusive discussion of the issue, involving all stakeholders.

The subject of study in the paper is the analysis of financial effects associated with the performance of regional airline projects from their launch to maturity, with the goal being the development of financial modeling tools to achieve the most thorough incorporation of such effects in the study context. Financial models as functional aids in optimal route planning for regional airlines have an under-explored potential, making the subject of study especially topical. The research methods utilized by the authors in the study are сash flow-based and accounting indicator-based investment project appraisal methods. These methods rely on integrated (three-statement) nominal financial modeling protocols developed at a monthly frequency and tailored for compliance with the Federal Aviation Guidelines. The resulting model provides and reconciles the derivation of free cash flows on the invested capital (FCFF) and free cash flows to equity (FCFE) under both the direct and indirect methods of cash flow derivation, thereby helping estimate the performance and efficiency of the aviation projects in a comprehensive way. It also incorporates some advanced features, such as accounting for aviation subsidies, provisioning for the overhauls of Airframes and Engines, compliance with the national Tax code and Federal Aviation Guidelines, as well as the treatment of initial Tax Loss Carryforwards. The findings of the model afford a conclusion that financial support measures in the form of existing regional airline subsidies in Russia may just about ensure a minimum acceptable rate of return on capital invested in regional airline projects. The practical significance of the model for regional airlines is in allowing them to support their business planning processes while seeking licenses, flight and subsidy approvals from Aviation Authorities, as well as actually optimize their long-term route maps and schedules with an eye to key financial parameters (e. g. ROE or NPV). In terms of research novelty, the financial model innovates algorithms to endogenize and automate the timing of repair and overhaul flags for the aircraft fleet in the context of investment depreciation and maintenance schedules.

The purpose of this article is to examine how dividend policy and the COVID-19 pandemic impact stock price volatility in the Vietnamese stock market. Panel data regression method was performed on a data set of 402 companies in 9 industries in the period from 2010–2021. The results show that the COVID-19 pandemic in 2020 has played a significant role not only in increasing stock price volatility, but dividend policy as well. The pandemic in 2021 has had an impact on reducing stock price volatility. Moreover, stock price volatility is also affected by the factors related to company characteristics such as the ratio of long-term debt to assets and company size. At the industry level, financial services and pharmaceuticals, and healthcare are the industries with the highest and lowest stock price volatility among the 9 research industries, respectively. Based on the research results, the article offers some implications for interested parties and participating in the Vietnamese stock market.

The subject of the study is factors influencing the formation and use of the tax potential of regions of the Russian Federation. The purpose of the study is to determine the tax potential of the regions of the Russian Federation and identify the factors that determine it. Tax potential is presented as an indicator of the efficiency of the tax system in the region. This factor is critically important for the financial sustainability of both individual regions of the Russian Federation and the state as a whole. Analysis of regional statistical data showed that tax potential varies significantly among the regions of the Russian Federation. Its level is influenced by factors such as the volume of gross regional product (GRP), economic structure, investment levels, demographic indicators, and others. However, the main determinants are economic growth and development of the region, effective tax and social policies, and the dynamics of tax rates. The assessment of tax potential across regions of the Russian Federation revealed its uneven distribution. Economically disadvantaged regions exhibit high tax potential. This is explained by high population density, low levels of financial literacy, a significant volume of shadow economy, and other problems characteristic of regions with weak economic development. To enhance the efficiency of tax revenue use in economically disadvantaged regions, practical recommendations are proposed. These include measures to reduce tax and levy arrears and implement a system for attracting investment. It is expected that these measures will help ensure sustainable development and the realization and use of the high tax potential of economically disadvantaged regions.

Multidimensional assessment of financial inclusion is crucial for understanding both the financial aspect of people’s lives and the state’s overall financial situation. The northern territories play a significant role in modern Russia, and it is important to study their financial inclusion. The aim of this study is to compare financial inclusion in the northern regions of Russia between 2000 and 2022 and identify the main trends and factors influencing financial accessibility in these areas. To achieve this goal, we need to identify and analyze the main factors that affect financial accessibility in the northern regions and create a rating based on these factors. This will help us better understand the current situation and make informed decisions about future policies. The approach proposed in this paper, which is based on a two-stage principal component analysis (PCA), allows us to get rid of subjective processes in the weighing of indicators and form a comprehensive assessment of financial well being. This method involves endogenous assignment of weights and the creation of a composite index. The Kaiser criterion is used to identify the main components. As a result of our study, we have determined that the most significant factors influencing financial well-being are the number of operating credit institutions, their branches, and funds (deposits) held by legal entities and individuals, both in rubles and foreign currency. We have also developed financial accessibility indices that allow us to conduct rating assessments of regions and identify significant changes over time. The results of the study will help us to evaluate the effectiveness of the current policy and provide a basis for developing targeted measures to achieve convergence in financial accessibility in northern Russia.

Mergers and acquisitions (M&A) are used by many companies as a strategy for business expansion. Despite the turbulence of this market in recent years, family-owned companies often act as strategic acquirers, especially in the high-tech sector of the economy. Chinese family-owned companies are active players in this market. The object of the study are mergers and acquisitions performed by Chinese family-owned companies in high-tech sector of the economy from 2018 to 2022. The purpose of the study is the reaction of the Chinese stock market to the announcements on mergers and acquisitions made by high-tech public family-owned companies. The study was conducted by event study and multiple regression analysis methods on a sample of 259 Chinese family companies that had announced mergers and acquisitions in the high-tech sector. It was revealed that the market reacts positively to information about such transactions: 75% of deals in the sample generate positive cumulative abnormal returns, 1.7% higher on average than the return calculated based on the market model. Shareholder value increases as the share of family ownership in the acquiring company increases and decreases as the share decreases. Market reaction is positive when a deal diversifies the core business portfolio. If a chief executive officer does not belong to the owner’s family, the deal is perceived negatively by the market. A positive market reaction is related to the independence of the acquirer’s board of directors, whereas the large size of the board of directors is negatively associated with cumulative abnormal returns. Cross-border deals are negatively related to the market reaction. The results of the study may be useful for the management of Russian companies considering internationalization and investors due to the growing economic ties between Russian and Chinese businesses. They are also of interest to researchers who study mergers and acquisitions in emerging markets.