MODELING TACTICAL INNOVATIVE TOOLS OF ECONOMIC DEVELOPMENT IN THE CONTEXT OF FINANCIALIZATION OF THE ECONOMY

Purpose: The financialization of the economy is pivotal in contemporary economic development, facilitating investment attraction, resource allocation efficiency, and innovation stimulation. However, it can also lead to financial instability and income inequality, necessitating careful regulation. Financialization ensures access to financial resources essential for economic growth. Effective management of this process requires the application of relevant tactical tools of economic development, which can be generalized into external environmental influence factors. Given the relevance of numerous external and internal drivers in economic activity and management practice, the formation of tactical innovative tools for economic development demands thorough research and modeling within the context of financialization. Design/methodology/approach: To achieve the article's objectives, economic and mathematical modeling was conducted using a multifactor regression model, with national income as the dependent variable. The independent variables were selected based on components such as economic policy activities, project activity, investments in educational and scientific activities, foreign trade, and the social sphere. The modeling process utilizes data from the European Commission, aggregated across EU countries. The number of observations is restricted to the statistical summaries available for the EU-27 countries. Findings: The regression parameters were estimated using the least squares method. The model’s quality was evaluated using criteria such as R-square (0.985) and adjusted R-square (0.980). The absence of multicollinearity among the independent variables was confirmed (Variance Inflation Factor (VIF) test), and the Durbin-Watson (2.025) test was employed to assess the autocorrelation of residuals. The results of the Durbin-Watson test indicate the absence of autocorrelation among the independent variables. At a significance level of a = 0.05, and with tabulated parametric values ( dv = 0.98; du = 1.89 ), calculated using degrees of freedom V1, V2, no autocorrelation was detected. To determine the model’s suitability for forecasting purposes, its adequacy was thoroughly assessed (Fisher’s Exact Test). Originality/value: The modeling results revealed a close relationship between the level of financialization and the studied indicators, ranked by decreasing influence as follows: x1 (gross accumulation of fixed capital);x6 (income distribution inequality); x5 (state budget allocations or expenditures on research and development); x2 (investments by institutional sectors); x3 (investments in environmental protection); x4 (total expenditures on education). Conversely, x7 (foreign trade balance) negatively impacts financialization. The model was evaluated based on quality, adequacy, and multicollinearity indicators. The developed model is suitable for forecasting the level of financialization, with the obtained levels of influence of independent variables on target indicators being crucial for strategic planning and tactical management within enterprises. Practical implications: The model can be utilized by government bodies for forecasting financialization levels in Ukraine and EU countries, aiding in strategic planning and tactical management at micro, meso, and macro levels.