The effects of global climate change and increasing environmental awareness have led to an increase in the significance of climate projects and, accordingly, climate finance and green bonds. Despite the increasing significance, the fact that the price or index value forecasting studies on green bonds are extremely scarce
has been the main motivation of this study. The aim of the paper is to forecast the corporate green bond index value with the Artificial Neural Network model and to determine the predictor by addressing the conceptual framework of green bonds. For this purpose, the Multi-Layer Feedback Artificial Neural Network (MLF-ANN) model, in which S&P 500 bond index values are determined as input and S&P green bond index values as output, is designed. To determine whether the conventional bond index values are the predictor of the corporate green bond index, the S&P 500 bond index were used as the sole input of the forecasting model. The findings show that S&P green bond index values are forecasted with 1.13% Mean
Absolute Percentage Error (MAPE) and 98.93% Regression Determination Coefficient (R2). The results of the research provide data to maximize profits and/or minimize risk for green bond investors and market makers, while providing insight into the effectiveness of green bonds in financing climate projects for policy makers. This paper is the first study in the literature in terms of proving the effectiveness of the MLFANN model in forecasting corporate green bond index value and revealing that conventional bond index is the predictor of the model.
