Enhancing Green Finance through Graph Neural Network Algorithms: An Analysis of Market Trends and Investment Opportunities

Abstract

Due to the profound effects of climate change, green finance has become increasingly relevant in the global financial arena, playing a crucial role in fostering sustainable environmental growth. The graphical neural network algorithm, a sophisticated machine learning tool, offers high predictive accuracy and robustness, making it invaluable for advancing green financial markets. This experiment seeks to evaluate the effectiveness of graphical neural networks in green finance by analyzing their performance on the Green bonds dataset. The findings reveal that the algorithm predicted the issuance of Green bonds with an impressive 98.7% accuracy. Specifically, the amounts issued in 2014-2016 were $10 million, $50 million, and $75 million, while the predictions for 2017-2019 were $12 million, $55 million, and $90 million, respectively. These results highlight the crucial role of graphical neural network algorithms as potent tools for analysis and forecasting in green finance, opening up new avenues for merging environmental sustainability with the financial sector.