Using Natural Language Processing to Identify Sentiment of Green Investors

Karel Janda, Marketa Rozsahegyi, Quang Van Tran and Binyi Zhang

EconStor Preprints from ZBW - Leibniz Information Centre for Economics

Abstract: This paper investigates the role of investor sentiment in the pricing and volatility dynamics of green bond exchange-traded funds (ETFs). The paper combines verbal description with a literature review, and it does not engage in actual data-based research analysis. While the literature on sentiment finance and ESG investing has expanded rapidly, empirical evidence focusing on fixed-income ESG instruments remains limited. We address this gap by employing modern natural language processing (NLP) techniques to construct sentiment indicators derived from news coverage and sustainability-related textual information. These indicators may be used to examine their impact on returns and volatility of selected green bond ETFs. By combining behavioural finance insights with state-of-the-art NLP methods, the paper contributes to sustainable finance research and highlights the informational role of textual data in green financial markets.

Keywords: NLP model; ESG; Exchange Traded Funds (search for similar items in EconPapers)
JEL-codes: C45 C55 G11 G17 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:esprep:335572

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