RT Journal Article
SR Electronic
T1 ESG Fund Usage among Individual Investor Households: A Machine Learning–Based Behavioral Study
JF The Journal of Impact and ESG Investing
FD PMR
SP 28
OP 44
DO 10.3905/jesg.2023.3.3.028
VO 3
IS 3
A1 Thomas J. De Luca
A1 Dhagash Mehta
YR 2023
UL https://pm-research.com/content/3/3/28.abstract
AB Interest in environmental, social, and governance (ESG) investing has increased substantially in recent years. Still, little research has demonstrated how ESG funds are being incorporated into portfolios by individual investor households. In this article, the authors explore a unique dataset containing the demographic and portfolio characteristics of more than five million households and identify those holding an ESG fund. Using an unsupervised machine learning technique called K-prototype clustering, the authors find five unique types of investors who have incorporated ESG mutual funds and/or ETFs into their portfolios. The authors then test to what extent incorporating ESG funds biases traditional portfolio construction decisions and find that ESG households have a strong growth tilt compared with similar non-ESG households. The research serves as a foundation for further study into the motivations behind, and portfolio implications of, implementing any of the identified ESG investment strategies.