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Beyond Green Labels: Assessing Mutual Funds’ ESG Commitments through Large Language Models

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  • Wood, Katherine
  • Pyun, Chaehyun
  • Pham, Hieu

Abstract

This paper investigates whether mutual funds that adopt ESG-related names follow through on the implied increase in ESG commitments. Utilizing Large Language Models (LLMs) to analyze mutual fund prospectuses, we find that, following a name change, funds increase their discussion of ESG and improve their holdings-based ESG scores. While we observe a positive correlation between a fund’s ESG content and its scores, the marginal benefit of additional ESG content diminishes post-name change. Our findings suggest that investors evaluating these funds can use LLMs to gauge a fund’s ESG commitment, especially when traditional ESG metrics are unavailable.

Suggested Citation

  • Wood, Katherine & Pyun, Chaehyun & Pham, Hieu, 2025. "Beyond Green Labels: Assessing Mutual Funds’ ESG Commitments through Large Language Models," Finance Research Letters, Elsevier, vol. 74(C).
    • Handle: RePEc:eee:finlet:v:74:y:2025:i:c:s1544612324017422
    DOI: 10.1016/j.frl.2024.106713
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    References listed on IDEAS

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    Cited by:

    1. Congluo Xu & Yu Miao & Yiling Xiao & Chengmengjia Lin, 2025. "DeepGreen: Effective LLM-Driven Green-washing Monitoring System Designed for Empirical Testing -- Evidence from China," Papers 2504.07733, arXiv.org.

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    More about this item

    Keywords

    Fund Names; ESG; Greenwashing; Large Language Models;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors
    • M14 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - Corporate Culture; Diversity; Social Responsibility

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