Machine-Learning about ESG Preferences: Evidence from Fund Flows

60 Pages Posted: 29 Jul 2023 Last revised: 30 Jan 2024

See all articles by George O. Aragon

George O. Aragon

Arizona State University (ASU) - Finance Department

Shuaiyu Chen

Purdue University - Mitchell E. Daniels, Jr. School of Business

Date Written: August 12, 2024

Abstract

We construct Environmental, Social, and Governance (ESG) scores for U.S. active equity mutual funds based on over 500 underlying metrics covering a wide range of ESG issues and rating agencies. We use a revealed preference approach, combined with machine learning methods, to identify key issues driving fund flows, including waste and pollution (E), product responsibility (S), and business ethics (G). We also generate ESG-driven flows as proxies for a fund's ESG performance. ESG-outperforming funds subsequently attract greater flows but yield lower benchmark-adjusted returns. Investors pay $17 million per year more for a top ESG fund.

Keywords: ESG, Fund flow, Value-added, Machine learning JEL Codes: G10, G23

JEL Classification: G10, G23

Suggested Citation

Aragon, George O. and Chen, Shuaiyu, Machine-Learning about ESG Preferences: Evidence from Fund Flows (August 12, 2024). Available at SSRN: https://ssrn.com/abstract=4522156 or http://dx.doi.org/10.2139/ssrn.4522156

George O. Aragon

Arizona State University (ASU) - Finance Department ( email )

W. P. Carey School of Business
PO Box 873906
Tempe, AZ 85287-3906
United States

Shuaiyu Chen (Contact Author)

Purdue University - Mitchell E. Daniels, Jr. School of Business ( email )

1310 Krannert Building
West Lafayette, IN 47907-1310
United States
5853198838 (Phone)
47906-1744 (Fax)

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