The Impact of Machine Learning Derived Green Bonds Sentiment on Performance of Green Bond Portfolio - Karel Janda; Marketa Rozsahegyi; Quang Van Tran; Binyi Zhang (ZBW - Leibniz Information Centre for Economics, 2025)
This paper investigates the role of investor sentiment in the pricing and volatility dynamics of green bond exchange-traded funds (ETFs). For a construction of green sentiment one original and two already existing natural language processing models are used and evaluated. The VAR model found no significant impact of green sentiment on ETF returns. The GARCH (1,1) estimation strongly supported the presence of volatility clustering and time-varying volatility in green bond ETF returns, validating the use of conditional heteroskedasticity models. Regressing the conditional volatility on sentiment scores revealed a significant negative relationship – higher sentiment is associated with lower volatility. This finding implies that positive green sentiment contributes to market stability and may reduce perceived risk, reinforcing the importance of investor psychology in green financial markets.
Keywords: C45; C55; G11; G17; 330; Machine learning; NLP model; ESG; Exchange Traded Funds