Subject. This article examines the particularities of the green bond issuance in various countries, as well as the issues of forecasting the impact of macroeconomic and socio-economic indicators on the development of the green finance market. Objectives. The article aims to determine the factors influencing the green bonds issuance through building regression models. Methods. For the study, we used the methods of comparative analysis, synthesis, regression analysis, decision tree, and machine learning algorithms, namely random forest and gradient boosting. Results. The article defines the fundamental links between the indicators of the size of the economy and the green bond issuance, assesses the quality of the constructed models under the accepted limitations of the study, and describes the degree of influence of the selected factors on the green bond market. Conclusions and Relevance. The selected market is characterized by linear patterns, which are highly likely to be predicted by regression models. Logical connections are less accurate in describing the existing green bond market. The results of the study can be used by investors and financial analysts to make decisions in the field of green finance.
Keywords: ESG, green bonds, regression analysis, machine learning
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