ESG stock markets and clean energy prices prediction: Insights from advanced machine learning
研究用NGBoost等机器学习方法分析十国ESG股票市场对清洁能源股价的预测能力，发现印度和美国ESG市场预测力强，为投资者和政策制定者提供参考。
In the post-Paris agreement, the clean energy market has grown significantly due to its undeniable environmental sustainability. Therefore, this study aims to predict clean energy stock prices by analyzing ESG stock markets in ten countries using a batter of machine learning (ML) techniques and NGBoost. The analysis integrates Shapley Additive Explanations (SHAP) values to improve interpretability, offering insights into model performance. The dataset spans from January 1, 2014, to September 22, 2023, covering global crises such as the COVID-19 pandemic and the Russia-Ukraine conflict. Results indicate that NGBoost outperforms other models, with a significant correlation between clean energy stock prices and ESG market variables. Notably, ESG markets in India and the USA show strong predictive power for clean energy stocks, while those in Australia and South Africa contribute less. These findings underscore the potential of ML techniques in forecasting clean energy equity trends, providing insights for investors, policymakers, and venture capitalists. The study highlights the importance of considering the degree of market connectivity in portfolio construction, emphasizing a shift from traditional investments to sustainable ones like clean energy. This research adds value to clean energy market analysis by incorporating advanced ML methods and SHAP values, especially during periods of global disruption. These results are important for asset allocation and risk management, supporting investors in transitioning from ordinary to sustainable investments. • Predict clean energy stock prices by analyzing ESG stock markets in ten countries. • Using a batter of machine learning (ML) techniques and NGBoost. • The analysis integrates Shapley Additive Explanations (SHAP) values to improve interpretability. • NGBoost outperforms other models, with a significant correlation between clean energy stock prices and ESG market variables.