FORECASTING VOLUMES OF FDI OF COUNTRIES BASED ON INDICATORS OF THEIR INVESTMENT ATTRACTIVENESS

Objective. The objective of the article is the analysis of the state and features of the development of special economic zones in the world.. Methods. The following methods and techniques of cognition are applied in the research process: theoretical generalization and comparison, analysis and synthesis, induction and deduction, grouping, correlation-regression analysis, clustering. Results. It is determined that among the wide list of indicators of investment attractiveness, the following indicators are most often used and are the most authoritative ones: Doing business Index, The Global Competitiveness Index, Global Innovation Index, Fragile States Index, Legatum Prosperity Index, Index of Economic Freedom, as well as credit ratings international rating agencies, including Moody's, Fitch, etc. Based on the analysis of the relationship between indicators of investment attractiveness and the actual volumes of FDI attraction of 101 countries of the world in 2015-2020, it is established that this relationship can be described as direct (Doing business Index, The Global Competitiveness Index, Global Innovation Index , Index of Economic Freedom) or the reverse (Fragile States index, Legatum Prosperity index); weak (Doing Business Index, Index of Economic Freedom, Fragile States Index) or moderate (Global Competitiveness Index, Legatum Prosperity (economy) Index).It is substantiated that despite the fact that the most representative indicators of investment attractiveness, according to the calculated values ​​of the correlation coefficients, are the Global Competitiveness Index and the Global Innovation Index, however, they do not have a significant impact on the actual volumes of FDI attraction of countries (the correlation coefficient varies within 0, 15-0.39), cannot be used as a dominant determinant for forecasting FDI volumes. It is substantiated that for forecasting the volume of FDI, it is advisable to use not one, but a set of indicators of investment attractiveness. It is established that the composite four-factor regression model based on individual regression equations of countries on indicators of investment attractiveness according to their cluster affiliation has the greatest predictive power.