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The Impact of Machine Learning Derived Green Bonds Sentiment on Performance of Green Bond Portfolio

Author

Listed:
  • Janda, Karel
  • Rozsahegyi, Marketa
  • Quang Van Tran
  • Zhang, Binyi

Abstract

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.

Suggested Citation

  • Janda, Karel & Rozsahegyi, Marketa & Quang Van Tran & Zhang, Binyi, 2025. "The Impact of Machine Learning Derived Green Bonds Sentiment on Performance of Green Bond Portfolio," EconStor Preprints 335550, ZBW - Leibniz Information Centre for Economics.
    • Handle: RePEc:zbw:esprep:335550
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    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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