Macroeconomic Indicators Research Articles

Dynamic volatility linkages among crude oil, exchange rate, interest rate, gold and equity markets: an empirical Asian perspective

PurposeThe study aims to investigate the volatility linkages among macroeconomic indicators such as crude oil, exchange rate, interest rate, gold and equity markets in the three major Asian economies: India, China and Japan.Design/methodology/approachTo investigate these volatility linkages, a time-varying parameter vector autoregression-based connectedness approach was used.FindingsThe study found that each country’s exchange rate is significantly influenced by spillovers from other variables. Gold and interest rates act as the primary sources of volatility spillovers. In India and China, gold serves as the main transmitter of this volatility, while in Japan, it is the interest rate that plays this role. Among the three equity markets examined, only Japan’s equity market acts as a net receiver of volatility spillovers.Practical implicationsInvestors should actively manage their investment strategies in light of market volatility, particularly during uncertain times. The research indicates that market participants need to monitor the fluctuations in gold and interest rates, as these two factors are the primary sources of volatility.Originality/valueGlobal economic uncertainty, geopolitical tensions and rapid shifts in investor sentiment have underscored the critical need to understand the volatility linkages among benchmark indicators. There is a lack of studies focusing on examining the volatility linkages in the Asian context. Hence, the present study contributes to addressing this overlooked research gap.

ANALYZING THE RESILIENCE OF GLOBAL STOCK MARKETS DURING POST-PANDEMIC RECOVERY

This research explores emerging trends in the global stock market after the COVID-19 pandemic, emphasizing economic recovery, investor behavior, market volatility, and regulatory influences. The pandemic resulted in significant financial turmoil, leading to both market crashes and subsequent rapid recoveries. This study investigates how markets have adapted to post-pandemic realities, analyzing the role of fiscal stimulus, monetary policies, and technological innovations in stabilizing and shaping stock market trends. A mixed-method research design is used, incorporating both qualitative and quantitative approaches. Macroeconomic indicators, financial statements, and stock indices—including S&P 500, Nasdaq, FTSE 100, NIFTY 50, and Nikkei 225—are analyzed to evaluate performance trends. Predictive models such as GARCH and ARIMA assess market volatility, while sentiment analysis using NLP examines investor behavior. Regression analysis identifies relationships between stock performance and key economic indicators like GDP growth, inflation, and interest rates. The study primarily relies on historical stock market data, which may not fully capture future uncertainties. Sentiment analysis, while insightful, may be influenced by biases in data interpretation. Additionally, predictive models such as ARIMA and GARCH estimate future trends but cannot account for unforeseen economic disruptions. The implications suggest a need for adaptive investment strategies, improved financial regulations, and enhanced risk assessment frameworks to strengthen market resilience in the face of uncertainty. Keywords: Stock Market Trends, Post-Pandemic Economy, Investment Behavior, Market Volatility, Behavioral Finance, Financial Technology, ESG Investments

Artificial Intelligence and Monetary Policy: Enhancing Central Bank Decision-Making through AI-Driven Text Analysis

Artificial Intelligence (AI) is revolutionizing the field of economics and finance, offering data-driven techniques for improving the analysis of macroeconomic policies. Among these, the integration of AI-based natural language processing (NLP) tools with monetary policy analysis is an emerging frontier. Central banks around the world, particularly the European Central Bank (ECB), rely heavily on public communication to shape market expectations and manage economic stability. However, the interpretation of these communications has traditionally been subjective and inconsistent. This research explores how AI, through machine learning-powered text analysis, can significantly improve the forecasting and interpretation of central bank policy decisions. Using real-world ECB statements as a dataset, the study applies NLP models to classify policy sentiment into expansionary, restrictive, or neutral categories. Findings indicate that AI-based analysis can uncover subtle linguistic cues in policy texts, enhance predictive models when combined with macroeconomic indicators, and ultimately improve decision-making for policymakers, investors, and economists. This paper highlights the transformative role AI is playing in modern monetary policy frameworks and offers a roadmap for its future integration into central banking systems.

COMPARATIVE ANALYSIS OF AI- DRIVEN AND TRADITIONAL FINANCIAL CREDIT RISK MODEL IN REAL ESTATE SUPPLY CHAINS

Abstract: The assessment of credit risk in the real estate supply chain is an essential part of financial risk management that influences investment decisions, financial stability, and the health of the overall real estate segment. Traditional financial credit risk models have long been used for the assessment of borrower credibility and potential default prediction with historical financial data, credit score, and some various financial ratios, while other methods could complement this approach. Although these conventional approaches have some merit, they frequently fail in capturing real-time market fluctuations, new emerging risks, and complex interdependencies that build creditworthiness. The introduction of artificial intelligence (AI) and machine-learning technologies has planted the seeds of change in the credit risk analysis horizon. AI-based models have given way to advanced analytical techniques that use big data, predictive analytics, and real-time insights to assess risk dynamically and more accurately. This particular paper gives a thorough comparison between the AI-driven and the traditional financial credit risk models alongside their methodologies and performance on prediction, adaptability, and limitation. Credit risk assessment is AI-driven because it utilizes machine learning algorithms to process both structured and unstructured data of large sizes to identify so-called hidden behaviours that conventional models are not able to detect. Real-time market conditions as well as transaction behaviours and macroeconomic indicators are incorporated in AI risk models to improve accuracy and timeliness of risk evaluation. Such models also help financial institutions, lenders, and investors of the real estate sector in decision-making, thus reducing possible financial losses and improving total risk management strategies. On the contrary, traditional models remain relevant since they are regulatory-compliant, transparent, and rely on well-documented financial indicators. They might be slower in reacting to changing market conditions, yet they maintain an aspect of interpretability that is usually absent in AI models. The regulatory authorities and financial institutions are sceptical of the black box of AI models within which lies the accountability, ethical considerations, and potential biases woven into machine-learning algorithms. Data privacy issues and regulatory frameworks concerning AI adoption in financial risk assessment remain reverse challenges that require immediate attention. By systematically comparing AI techniques with the classic credit risk models, the study delineates some of the parameters of distinction, including accuracy, scalability, cost-effectiveness, and applicability in the real world for the real estate sector. Two comparison tables depict the efficiency and application of the two approaches, along with usefulness in contrasting their efficacy. The results, though, suggest that AI-based credit risk models possess superior predictive accuracy, adaptability, and risk mitigation when weighed against traditional methods; yet, those features need to be balanced against regulatory oversight and ethical viewpoints to allow for successful implementation. Ultimately, the aforementioned study shows that innovation and regulatory compliance should be seen as two sides of the same coin for credit risk evaluation. The application of AI for the financial risk evaluation process reconstructively resembles giving an identity to the rehabilitation of the entire real estate supply chain by making decision-making more proactive and also helping in mitigating defaults. However, the transition phase from conventional models to AI-driven models needs a holistic understanding of both these approaches, along with their relative pros and cons. With an active evolution of AI technologies, future works may focus on developing transparent, non-biased, and interpretable AI systems that comply with available industry regulations and ethical principles, so that their adoption in real estate credit risk management can be considered responsible. Keywords: Risk of Credit, Supply Chain in the Real Estate sector, Financial Stability, Conventional Templates of Credit, Models for Credit Powered by AI, Machine Learning, Big Data Analytics, Predictive Analytics, Risk Evaluation Recurrently, Default Risk Mitigation, Decision making in Investments, Credit Scoring, Financial Ratios, Risk Management Strategies, Efficiency of Models, Ethics in AI, Regulatory Compliance.

Dynamic relationship between rising public debt and economic stability: an assessment of Nigeria economy

The consistent increase in public debt of Nigerian economy has raised concerns regarding its impact on economic stability. This paper examines the dynamic relationship between Nigeria's rising public debt and its economic stability by analyzing empirical data and existing literature. The study emphasizes on the key macroeconomic indicators affected by debt accumulation, such as inflation, GDP growth, investment, and exchange rates. The findings suggest that while public debt can drive short-term economic growth, excessive borrowing without sustainable fiscal policies can undermine long-term economic stability.

A causality investigation into stock prices and macroeconomic indicators in the Indian stock market

Background of the study The systematic impact of macroeconomic variables on stock market returns makes it crucial to comprehend the link between macroeconomic variables and the stock market. Stock prices are closely linked to macroeconomic indicators, a crucial aspect for investors, policymakers, and researchers in emerging markets like India, influencing investment decisions and policy formulation. Methods The autoregressive distributed lag (ARDL) model was used in this study to examine the causal links between specific macroeconomic factors and Indian stock prices from April 2009 to March 2023. Results The outcomes of the research suggest that macroeconomic variables exert influence on the Indian stock market, across the short and long term. Moreover, the results of the paired Granger causality test suggest that the domestic macroeconomic variables possess predictive significance for stock prices in the Indian stock market. Conclusion The study reveals that macroeconomic variables significantly impact the Indian stock market, highlighting the need for investors and portfolio managers to monitor these conditions to optimize returns and mitigate risks. The Reserve Bank of India should maintain an optimal money supply to prevent inflation and exchange rate fluctuations, while bolstering the export sector and facilitating imports through initiatives like Atma-nirbhar Bharat Abhiyan and Make in India. Policies focusing on productivity, infrastructure, and a favourable business environment are also crucial. Therefore, it is crucial for investors and portfolio managers to consistently analyse the current macroeconomic conditions in order to maximize their profits and minimize risks. This research has extensive significance for comprehending the intricate connections between the stock market and macroeconomic issues.

Measuring Shortages since 1900

This paper introduces a monthly shortage index spanning 1900 to the present, constructed from 25 million newspaper articles. The index captures shortages across industry, labor, food, and energy, and spikes during economic crises and wars. We validate the index and show that it provides information beyond traditional macroeconomic indicators. Using predictive regressions, we find that shortages are associated with persistently high inflation and lower economic activity. A structural VAR model reveals that, compared to a traditional supply shock, surprise movements in shortages produce less inflation relative to their GDP impact, suggesting that shortages are associated with constraints on price adjustment that limit inflation but magnify the decline in real activity. We also show that post-pandemic shortages and inflation were primarily driven by supply forces, with demand factors playing a less important role.

INVESTIGATING MULTICOLLINEARITY BETWEEN COUNTRY’S LEVEL OF DIGITAL COMPETITIVENESS AND INFLUENCING VARIABLES

The purpose of scientific research is to identify and argue the connection between the level of digital competitiveness of countries and variable factors, and to propose solutions for its improvement. The object of the research is the level of digital competitiveness of countries in 2023 and the variables that affect it (GDP per capita, population in the country, Digital Quality of Life Index). The subject of the study is the digital capabilities and innovative solutions of countries to strengthen their competitive position in the world in the context of globalisation. Methodology. The study is based on the method of multicollinearity according to the Farrar-Glauber algorithm, which makes it possible to understand the dependence of the level of digital competitiveness on three variable factors (GDP per capita, the number of people in the country and the Digital Quality of Life Index). The method of generalisation made it possible, on the basis of a multicollinear study, to provide recommendations for strengthening the country's digital competitiveness in the international arena, taking into account the potential of human resources, the degree of technological progress and the level of economic development. Results. The research revealed an insignificant relationship between the level of a country's digital competitiveness and GDP per capita. However, it was found that the more economically strong the state, the faster and larger the implementation of digital technologies. It has been posited that there exists a negligible relationship between a nation's digital competitiveness and its population size. Nevertheless, it is evident that as a nation's population increases, there is a concomitant rise in the number of individuals engaged in the production and implementation of innovative solutions and digital technologies. The multicollinearity study demonstrated that there is no multicollinear relationship between the level of the country's digital competitiveness and variable factors. However, it was determined that a country can acquire competitive advantages under the condition of contributing to the increase of the economic well-being of the nation and its accessibility to digital goods and services. Practical implications. The value of the publication is determined by the breadth of the author's recommendations for enhancing the Digital Quality of Life Index of the population, which, in the long term, will ensure the country's competitive position in the digital era and contribute to sustainable economic development. Value/Originality. The contribution of the article to the scientific value consists in the study of multicollinearity using the Farrar-Glauber algorithm to assess the impact on the level of digital competitiveness of such variable factors as GDP per capita (a macroeconomic indicator that indicates the well-being of the nation), the number of inhabitants of the country (an indicator that determines the intellectual potential of the country) and the Digital Quality of Life Index (characterises the accessibility and penetration of digital technologies in the life of society).

Unlocking Economic Growth: Insights from Macroeconomic Indicators and Stock Markets Development in Key OIC Countries

Indonesia, Malaysia, Bangladesh, Qatar, Turkey and Kuwait show obvious differences in economic structure, level of development and implemented economic policies. The aim of this study is to analyze the impact of macroeconomic indicators and stock market developments on economic growth in OIC member countries in the period 2018-2022. Panel data is used to determine the impact of market capitalization and turnover ratio as indicators of stock market development, along with inflation and foreign direct investment (FDI) as indicators of macroeconomics, on economic growth.The results show that market capitalization has a negative effect on economic growth, while the turnover rate has a positive effect. On the other hand, inflation contributes positively to economic growth, while FDI has no effect on economic growth. Based on these findings, this study suggests that the governments of OIC countries should focus more on proper management of stock markets and macroeconomic variables to support economic growth.

The Nexus Between Defense Expenditures, Geopolitical Risk, Political Stability and Macroeconomic Indicators: Evidence from Türkiye

Defense expenditures are an item that exists cyclically throughout the world in every period and covers most of the public expenditures of countries. The effect of defense expenditures on inflation, employment, balance of trade balance, gross domestic product, geopolitical risk index, and political stability index within the scope of Türkiye has been examined. The findings, while the causality relationship from defense spending to balance of trade, geopolitical risk index, and political stability index is determined and there is a positive relationship from defense spending to foreign trade balance and a negative relationship to geopolitical risk index, there is a cointegration towards gross domestic product. Empirical findings support the military Keynesian approach in the long and short term, indicating that the increase in defense expenditures in Türkiye increases exports and the balance of trade. Increasing R&D activities and implementing policies that encourage investment in higher-density defense industry products will increase positive externalities in the balance of trade. Long-term, the rise in defense spending allows one to draw the conclusion that the country tends to lower geopolitical risks by boosting confidence in its political, economic, and security realities and lowering the threat of terrorism.

Rating the Impact of Risks in Banking on Performance: Utilizing the Adaptive Neural Network-Based Fuzzy Inference System (ANFIS)

This study aims to rate the impact of the three major risks (credit, capital adequacy, and liquidity) on three financial performance measures (return on equity (ROE), earnings per share (EPS), and price-earnings ratio (PER)). This study stands out as one of the few in its field, and the only one focusing on banks in the Middle East and Africa, to employ the adaptive neural network-based fuzzy inference system (ANFIS) that combines neural networks and fuzzy logic systems. The significance of this study lies in its comprehensive coverage of major risks and performance variables and its application of highly technical, sophisticated, and precise AI techniques (ANFIS). The main findings indicate that credit risk, as measured by the non-performing loans (NPL) has significant impact on both ROE and EPS. Liquidity risk comes second in importance for ROE and EPS, with the loan-deposit ratio (LDR) being the dominant component. In contrast, liquidity risk is the most significant determinant of PER, followed by capital adequacy. Our results also show that CAR, LDR, and NPL are the most significant risk components of capital adequacy, liquidity, and credit risks, respectively. The study contributes to business knowledge by applying the ANFIS technique as an accurate predictor of risk rating. Future research will explore the relationship between risks and macroeconomic indicators and differences among countries.

DYNAMIC LINKAGES BETWEEN TURKISH ISLAMIC STOCK MARKET AND GLOBAL MACROECONOMIC RISK FACTORS: EVIDENCE FROM DCC-GARCH MODEL

Over the past two decades, Islamic finance has gained increasing prominence, with Islamic equities emerging as particularly attractive to investors. This study aims to investigate the volatility transmission between the Turkish Islamic stock market and selected global macroeconomic risk factors, specifically the US Dollar Index, the CBOE Gold Volatility Index, the CBOE Crude Oil Volatility Index, and the CBOE Volatility Index. We use the DCC-GARCH model with the daily data set from April 11, 2013, to April 25, 2024 to examine the dynamic connectiveness between the indexes. The results of the study show that there is a negative interaction between macroeconomic risk factors and the Turkish Islamic stock market. There is a volatility transmission from all macroeconomic risk factors to the Turkish Islamic stock market in the long-term investment period, but there is a volatility transmission only from the US dollar index to the Turkish Islamic stock market in the short-term investment period. Investors view the Turkish Islamic stock market as a safe haven, less susceptible to macroeconomic risk indicators, and less integrated with the international financial system in the short term. According to the findings of the DCC-GARCH model, investments in the Turkish-Islamic equity market should be viewed as riskier over the long term due to the transmission of volatility between selected macroeconomic risk factors and the Turkish-Islamic equity market. This study provides valuable insights for investors and portfolio managers seeking to enhance their portfolio management strategies.

Key challenges to the development of modern Spain:political and economic aspects

The article analyzes the main political and economic challenges to the left-wing PSOE-Sumar coalition government formed in 2023. The paper characterizes the positions of the ruling bloc and assesses the influence the Catalan and Basque nationalist parties have on the decision-making process in the Spanish Parliament. In particular, the risks for the central authorities after the adoption of the amnesty law for Catalan separatists in 2024 are highlighted. Additionally, foreign policy factors influencing the development of Spain are revealed. Special attention is paid to the group of socio-economic challenges on the agenda of the Kingdom. The article gives an overview of the main macroeconomic indicators of the country in 2023, underscores problem areas and presents possible directions for improving the economic policy. The research also compares the legal basis of the Spanish and Ukrainian territorial structure in terms of interaction between the center and regions. The author emphasizes the limited capacity of the Ukrainian regions to protect their interests in the political process of the country. The paper concludes with an assessment of the medium-term prospects for Pedro Sanchez’s coalition government.

미국 경기국면의 Markov Switching 추정과 한국 경제에 대한 전염 효과 분석

This study aims to empirically analyze the spillover effects of U.S. business cycle regime transitions on Korea’s real economy and financial markets. Using a Markov Switching Vector Autoregression (MS-VAR) model, U.S. economic regimes—expansion and recession—are identified endogenously, and the resulting regime-switching probabilities are introduced as exogenous variables into Markov Switching Regression and Structural VAR (SVAR) models. These models are used to evaluate the dynamic responses of key Korean macroeconomic indicators, including industrial production, consumer prices, unemployment, the policy interest rate, and KOSPI returns. Based on monthly data from 2000 to 2023, the empirical results show that Korea’s unemployment, industrial production, and stock market returns exhibit strong sensitivity to U.S. regime shifts, whereas interest rates and consumer prices show relatively weaker transmission. Notably, the KOSPI displays excessive and leading reactions to U.S. financial shocks, while unemployment responds with structural lags. The SVAR analysis further confirms a sequential transmission path from real activity to financial markets and monetary policy, with regime-dependent dynamic effects. This study contributes by empirically identifying nonlinear regime shifts and spillover structures between the U.S. and Korean economies, offering implications for macroeconomic forecasting and crisis response. However, limitations remain in addressing endogeneity and fixed identification ordering. Future research could expand the analysis using GVAR models and incorporate financial instability indices to better capture global systemic risks.

Stock Market Development Determinants in Africa: A Review of Literature

The development of stock markets is crucial for economic growth, capital formation, and financial stability, particularly in Africa, where economies are often characterised by volatility and underdevelopment. Results of this paper are drawn from a systematic literature review which synthesizes findings from twenty-nine newly published research papers published in peer-reviewed journals between 01 January 2020 and 31 December 2024, obtained freely on Google Scholar. The main objective of this paper is to identify the determinants of stock market development across different African countries. From the twenty-nine reviewed papers, it can be noted that the key determinants include macroeconomic indicators such as economic growth, inflation, interest rates, trade openness, and foreign investment. The review of literature highlighted the mixed effects of these factors, indicating that their influence can vary significantly across countries and regions. Stakeholders, such as policymakers, investors, and financial institutions, have shown increasing interest in understanding these dynamics due to the potential for stock markets to enhance capital mobilization and economic resilience. The review of literature also identified contested areas in the literature, suggesting that while some determinants are widely acknowledged, others remain debated. Future research directions include exploring the impact of technological advancements, regulatory frameworks, and socio-political factors. This paper underscores the need for a nuanced understanding of stock market dynamics to inform effective policy interventions aimed at fostering sustainable economic growth in Africa.

Empirical Dynamics in Econometrics: Analyzing Behavioral Patterns, Predictive Modeling, and Policy Implications in Economic Data

This paper aims to contribute to the uses of time series econometrics, combining them with some contemporary machine learning techniques for data understanding to enhance the analysis of behaviour patterns, the improvement of the forecasting capability of the models, and the facilitation of policy assessment. The quantitative analysis works through econometric models including ARIMA, VAR, and TVP-SVAR for the selected macroeconomic indicators like GDP, Inflation rate; and machine learning models including LSTM, Random Forest and Gradient Boosting. The data collected was retrieved from different global databases such as the International Monetary Fund, World Bank, and Google Trends with various data points spanning for about 25-30 years. The performance comparison shows that the applied machine learning models, most of all LSTM, are categorically more accurate than the traditional models when forecasting under non linearity and time varying environments. Further, Policy Exercise with the help of TVP-SVAR imply that the fiscal policy is most effective for economic growth compared with a reduction in the interest rate and subsidies. Among variables, interest rates and unemployment have shown the greatest influence in the Random Forest model of GDP. According to the forecasts and simulation of various scenarios proposed by the author, only an integrated policy can bring the highest GDP growth rate. Despite the designs gaining higher performance in the models, the relevant drawbacks in accuracy and interpretability are still challenging, leading to the creation of more hybrids that balance between the two factors. In conclusion, this work highlights the significance of empirical dynamics in the study of increasingly complex economic behavior and contributes to establishing rigorous, flexible, and policy-oriented econometric models.

Empirical Dynamics in Econometrics: Analyzing Behavioral Patterns, Predictive Modeling, and Policy Implications in Economic Data

This paper aims to contribute to the uses of time series econometrics, combining them with some contemporary machine learning techniques for data understanding to enhance the analysis of behaviour patterns, the improvement of the forecasting capability of the models, and the facilitation of policy assessment. The quantitative analysis works through econometric models including ARIMA, VAR, and TVP-SVAR for the selected macroeconomic indicators like GDP, Inflation rate; and machine learning models including LSTM, Random Forest and Gradient Boosting. The data collected was retrieved from different global databases such as the International Monetary Fund, World Bank, and Google Trends with various data points spanning for about 25-30 years. The performance comparison shows that the applied machine learning models, most of all LSTM, are categorically more accurate than the traditional models when forecasting under non linearity and time varying environments. Further, Policy Exercise with the help of TVP-SVAR imply that the fiscal policy is most effective for economic growth compared with a reduction in the interest rate and subsidies. Among variables, interest rates and unemployment have shown the greatest influence in the Random Forest model of GDP. According to the forecasts and simulation of various scenarios proposed by the author, only an integrated policy can bring the highest GDP growth rate. Despite the designs gaining higher performance in the models, the relevant drawbacks in accuracy and interpretability are still challenging, leading to the creation of more hybrids that balance between the two factors. In conclusion, this work highlights the significance of empirical dynamics in the study of increasingly complex economic behavior and contributes to establishing rigorous, flexible, and policy-oriented econometric models.

Life Insurance Penetration and Its Economic Impact: A Financial Analysis

Abstract Life insurance plays a crucial role in financial stability, risk management, and economic growth by providing individuals and families with financial security and fostering long-term investments. Despite its importance, life insurance penetration varies significantly across countries due to differences in economic development, regulatory frameworks, financial literacy, and consumer behavior. This study examines the extent of life insurance penetration across various regions, its correlation with economic growth, and the key factors influencing penetration levels. The research employs empirical analysis using financial data from multiple countries to provide insights and policy recommendations for enhancing life insurance adoption and using empirical analysis based on financial data from multiple countries, the study applies statistical techniques such as correlation and regression analysis to assess the relationship between life insurance penetration and macroeconomic indicators. The findings highlight the major determinants of insurance adoption, including per capita income, Gross Fixed Capital Formation, and National Savings. Based on these insights, the research provides policy recommendations aimed at enhancing life insurance adoption, improving financial awareness, and strengthening regulatory frameworks to maximize its economic benefits.

Human Capital, Fixed Capital Formation and Economic Growth: An Empirical Analysis of Endogenous Growth Drivers in Uganda

This research aimed to look at the effect of key endogenous variables such as GNI, Human Capital, FDI, Inflation, and GCF, which have impacted the economic growth in Uganda. This study adopted a time-series research design. Data from the study were obtained through secondary sources, including government publications, international financial databases, and reports obtained from UBOS. The econometric methods the study adopted included the ADF test for stationarity, the VIF test for multicollinearity, and the ARDL model in testing both the short-run and long-run relationships. The study found that in the long run, human capital, GCF, and inflation significantly contributed to economic growth at favorable rates, while FDI negatively influenced economic growth. In the short run, GNI, Human Capital, FDI, and Capital are significant determinants of economic growth, with an adverse short-run effect for GNI and FDI. The results emphasize that capital formation and human capital development are important for sustainable economic growth in Uganda. The study concludes that different aspects of fashion affect Uganda's economic performance, indicating the need to stabilize major macroeconomic indicators to achieve long-term growth, focusing on human capital, capital formation, and managing inflation.

ФІНАНСОВІ ІНСТРУМЕНТИ ЯК ДЕТЕРМІНАНТИ СТІЙКОСТІ ІНВЕСТИЦІЙНОЇ ДІЯЛЬНОСТІ ПІДПРИЄМСТВ МАЛОГО ТА СЕРЕДНЬОГО БІЗНЕСУ В КОНТЕКСТІ МАКРОЕКОНОМІЧНОЇ ВОЛАТИЛЬНОСТІ

Introduction. In the current conditions of macroeconomic instability, small and medium-sized businesses face increased risks of investment activities. Fluctuations in exchange rates, changes in the discount rate and a decrease in the level of liquidity in the financial sector complicate the attraction and effective use of investment resources. Therefore, it is relevant to study financial instruments as key determinants of the stability of investment activities that contribute to the adaptation of enterprises to a volatile environment. Purpose. The purpose of the study is to determine the role of financial instruments in ensuring the stability of investment activities of small and medium-sized businesses in conditions of macroeconomic volatility, to determine the mechanisms of their adaptation to an unstable environment and to substantiate risk minimization strategies. Methods. The methodological basis of the study is a systematic approach, which involves the use of specialized analysis methods to assess the impact of financial instruments on the investment stability of enterprises. Content analysis was used to identify the dynamics of macroeconomic indicators that affect the financial mechanisms of small and medium-sized businesses. Abstract and comparative methods, as well as the method of logical generalization, were used to formulate conclusions regarding the role of financial instruments in stabilizing the investment activities of enterprises in conditions of macroeconomic volatility. Results. The article considers the impact of macroeconomic volatility on the investment activities of small and medium-sized businesses. The mechanisms for using financial instruments that provide for hedging currency risks and insurance of investments to ensure the long-term financial stability of business entities are analyzed. The impact of monetary policy on the availability of financial resources for small businesses is studied. It is proven that the integration of modern financial technologies contributes to increasing the investment stability of enterprises. The effectiveness of combined approaches to financial risk management in an unstable economic environment is assessed. Conclusions. The dynamic transformation of the economic environment necessitates a multi-vector analysis of investment risks, which results in the need to integrate multi-level financial forecasting algorithms and use complex capital management models. This approach requires the use of adaptive financial mechanisms that combine strategic diversification of capital sources and optimization of risk management. At the same time, the financial stability of small and medium-sized businesses is directly determined by the level of effectiveness of hedging currency and interest rate risks, which, in turn, depends on the depth of integration of financial technologies and the level of availability of insurance mechanisms.