Machine-learning about ESG preferences: Evidence from fund flows

Journal of Financial Economics · 2026 · Volume 185

DOI: 10.1016/j.jfineco.2026.104361

Abstract

We construct Environmental, Social, and Governance (ESG) scores for U.S. active equity mutual funds using roughly 500 metrics across ESG issues and rating agencies. Under a revealed-preference framework, we use machine learning to identify issues driving fund flows and generate ESG-driven flows as proxies for ESG performance. We estimate expected benchmark-adjusted returns using ex-post realized returns and ex-ante measures. Better-ESG funds attract larger subsequent flows and face fewer redemptions after poor performance, yet have lower expected returns, indicating flows reflect investor ESG preferences rather than high-return beliefs. Investors effectively pay $48 million more annually for a top-ESG fund.

Keywords

ESG, Fund flow, Machine learning, Value-added

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