Ihor Rekunenko

The paper investigates discretionary budget expenditure and determines its role in the system of regulation of country’s socio-economic development. In a very difficult political and socio-economic situation, Ukraine faces an urgent need to finda balance between the amount of functions performed by the state and the level of their financial support. The analysis of the State Budget of Ukraine expenditure according to the functional classification in 2014–2017 has been carried out. In particular, the discretionary budget expenditures (on state functions, economic activity, defense budget expenditures, budget expenditure on public order, security and judiciary; environmental protection, housing and utilities) are carefully analyzed. The purpose of the article is to study trends in financing discretionary budget expenditure and determine their impact on the socio-economic development of a country. Discretionary budget expenditures are the study object. It is determined that socio-economic development of a country requires government to apply progressive forms, methods and principles of expenditure management between the budget system levels. This need is due to the objective necessity to achieve sustainable development of economy and population welfare. The main problems that reduce budget discretionary expenditure effectiveness in the current conditions are investigated and the main directions to improve their financing are offered. The obtained results indicate the need to revise the funding of discretionary budget expenditures depending on the state policy priorities.

The development of the corporate securities market and the effective use of tools for its regulation cannot be achieved without models and methods of economic and mathematical modeling. The aim is to analyze and systematize the structural and temporal characteristics of the corporate securities market in Ukraine by applying economic and mathematical modeling methods. In the paper, linear interpolation is used to assess the temporal characteristics of corporate securities under market uncertainty. Descriptive and simulation modeling methods are also applied to carry out a formal description of the process of evaluating the structural characteristics of securities. The result of the study involves developing a descriptive model to analyze the structural and temporal characteristics of the Ukrainian corporate securities market. The approbation of the proposed model makes it possible to draw the following conclusions. First, Perspektiva Stock Exchange, Ukrainian Exchange and PFTS – the First Stock Trading System, are the most important trading platforms. They are determined by the monthly bidding dynamics and can belong to the same group – active players in the corporate securities market of Ukraine. Second, in terms of endogenous priorities, the development of the corporate securities market is mostly influenced by inflation rates (consumer price index), economic development indicators (key branches production index) and interest rates on alternative financial instruments (new deposit interest rates of deposit-taking corporations). Third, the rate of corporate securities issue and the native currency rate do not significantly affect the corporate securities market development, in particular, the former is characterized by a slight negative impact, and the latter – by a slight positive impact on the price dynamics.

Financial security of a country is an integral part of its economic security and the basis of national security. The paper aims to assess and forecast the level of Ukraine’s financial security using two methodological approaches (the existing one and the authors’ elaboration) to choose the best alternative. The first one is based on the Methodology of the Ministry of Economy of Ukraine. The alternative one has been developed as a multiplicative model of non-linear convolution of relevant direct and indirect impact indicators, considering the opportunity and risk, which is based on a combination of a power function and the Harrington method. A database of input indicators was formed with further differentiation according to their impact on Ukraine’s financial security. The research results demonstrated that during 2013–2019 Ukraine’s financial security integrated index was cyclical and constantly changing. A comparison of the existing methodology and the developed model demonstrated a certain discrepancy between the obtained results. It was substantiated that the proposed multiplicative non-linear convolution model for assessing and forecasting the state’s financial security is more relevant, includes current indicators sorted by their direct and indirect impact, and adjusts them according to the risk of impact on overall security in the country.

Artificial intelligence in public administration is critical for the modernization of the public sector and adaptation to the challenges of modern society. The paper analyzes studies dedicated to the impact of artificial intelligence on the efficiency, innovation, and transparency of management processes in the public sector using meta- and bibliometric analysis. The goal is to identify the main areas and keywords that highlight theoretical and practical aspects of artificial intelligence in public administration. In total, 879 scientific articles were analyzed, of which 598 works are devoted to artificial intelligence. Dynamic time analysis revealed a significant surge in scientific interest in artificial intelligence in public administration: from 2010 to 2019, 135 publications were devoted to this issue, and from 2020 to 2024, 421. Bibliographic maps of keywords and publication maps showed the main thematic areas of research on artificial intelligence: the application of AI in public administration and the public sector, decision-making in public administration, data management and digital technologies in public administration, and the strategic use of AI to forecast socio-economic trends.
The obtained data became the basis for expanding the scientific and practical potential of using artificial intelligence in public administration. The main areas of future research will concern the regulation of ethical issues to ensure the trust of citizens, the development of a regulatory framework and standards, increasing the efficiency of public services, the integration of artificial intelligence into strategic planning, and the use of artificial intelligence to achieve sustainable development goals.

Acknowledgment
The analysis was carried out within the framework of the implementation of the perspective plan for the development of the scientific area “Social Sciences” of Sumy State University, number d/r 0121U112685.

A significant aspect of financial regulation provides for risk mitigation, transparency improvement, and maintaining economic stability, making financial control systems more efficient. This article analyzes the interaction of financial regulation strength with financial control efficiency in five economies, such as the USA, the UK, Germany, Poland, and China, from 2020 to 2023. An econometric model is utilized and the World Bank Financial Regulatory Index is incorporated as the core independent variable, along with financial infrastructure, efficiency of risk modeling, GDP growth, inflation, and financial leverage; all variables are used to understand their effect on financial control mechanisms. It is confirmed that the stronger financial control efficiency of the USA, the UK and Germany is associated with their stronger scoring by financial regulation (the countries with higher scores of financial regulations are better enforced and have more appropriate risk management strategies). On the other hand, Poland and China have problems in terms of regulatory enforcement which translates into lower effectiveness of financial control. The results also show that inflation and financial leverage decrease the efficiency of financial control, and financial infrastructure and risk modeling are positively related to financial control efficiency. The study emphasizes the exigency of regulating financial oversight in emerging markets, strict enforcement policies, and embracing technological advancements that supplement the area. A future research agenda needs to broaden the scope to other economies and qualitative assessments of regulatory effectiveness.

The correlation between macroeconomic dynamics and the inflation rate is the subject of many economic studies. The principles of monetary policy are developed in classical economics studies, which are based on the theories of Keynes, Phillips, Campbell, etc. However, classic approaches require practical validation, especially with regard to modern economic trends in times of crisis and emerging economies. Therefore, the purpose of the paper is to investigate and summarize the impact of inflation targeting and other key monetary policy instruments on fundamental economic indicators in Ukraine during periods of stability and crises. An empirical analysis is based on official statistics from Ukraine for 2011–2019. This study uses econometric methods (multivariate regression and simultaneous equation model), which are applied for the general and transmission impact of inflation on the estimation of economic growth. The results prove that inflation does not affect (less than 0.46 linear correlation) fundamental economic indicators during periods of real GDP growth and a quarterly CPI level of less than 2%. On the other hand, there are significant simultaneous regressions (more than 0.8 coefficients of determination) between unemployed, spending on real final consumption, hryvnia exchange rate and monetary policy instruments (discount rate, international reserves, amount of government bonds, M3 monetary aggregate) for periods when the quarterly CPI (consumer price index) is more than 2%. Therefore, the traditional monetary policy implications are discussed for emerging economies.

The study reviews the application of machine learning tools in financial investment portfolio management, focusing on cluster analysis for asset allocation, diversification, and risk optimization. The paper aims to explore the use of clustering analysis to broaden the concept of portfolio diversification beyond traditional volatility metrics. An open dataset from Yahoo Finance includes a ten-year historical period (2014–2024) of 130 actively traded securities from international stock markets used. Dataset selection prioritizes top liquidity and trading activity. Python analytical tools were employed to clean, process, and analyze the data. The methodology combines classical Markowitz optimization with clustering analysis techniques, highlighting variance-return trade-offs. Various asset characteristics, including annualized return, standard deviation, Sharpe ratio, correlation with indices, skewness, and kurtosis, were incorporated into the clustering models to reveal hidden patterns and groupings among financial assets. Results show that while clustering enhances insights into asset diversity, classical approaches remain historically superior in optimizing risk-adjusted returns. This study concludes that clustering complements, rather than replaces, classical methods by broadening the understanding of diversification and addressing many diversity factors, such as metrics of the technical, graphical, and fundamental analysis. The paper also introduces the diversity rate based on clustering, which measures the variance balance by all features within and between clusters, providing a broader perspective on diversification beyond traditional metrics. Future research should investigate dynamic clustering techniques, integrate fundamental economic indicators, and develop adaptive models for effective portfolio management in evolving financial markets.