Investor Sentiment Impact on Green Exchange Traded Funds

This work expands the application of sentiment finance into the underexplored domain of ESG fixed-income instruments. It contributes to the growing literature on sustainable finance by empirically investigating the influence of news sentiment on the return and volatility dynamics of green bond ETFs. By combining NLP modelling to gauge the sentiment from financial news with vector autoregression (VAR) and GARCH models, the study provides a comprehensive framework to test both return predictability and volatility effects. The findings show that while sentiment has limited predictive power over returns, it significantly amplifies volatility, highlighting its relevance for risk management rather than return forecasting. Additionally, the thesis confirms the presence of volatility clustering in green bond ETFs, aligning them with known stylized facts of financial markets.