RT Journal Article SR Electronic T1 Assessing the Impact of Sustainability on Fund Flows: An Excess Information Approach and US-Based Case Study JF The Journal of Impact and ESG Investing FD PMR SP 137 OP 147 DO 10.3905/jesg.2022.1.057 VO 3 IS 2 A1 Alik Sokolov A1 Jonathan Mostovoy A1 Eduard Losing A1 Marco Ceccarelli A1 Qingyang (Emma) Zhang A1 Yichi Zhang A1 Kurtis Laurion A1 Baker Jackson A1 Luis Seco YR 2022 UL https://pm-research.com/content/3/2/137.abstract AB Motivated by studies of the interplay between environmental, social, and governance (ESG) factors and fund flows, the authors demonstrate an alternative approach for analyzing the impact of sustainability on fund flows and present a case study applying their approach to US-domiciled equity mutual funds. In particular, they use supervised machine learning techniques to isolate excess information available within ESG drivers. This approach is chosen instead of commonly used regression approaches to control for multicollinearity and address the known correlation of sustainability to other factors. With respect to the case study undertaken, the authors find that when controlling for nonlinear drivers and interactions between input factors (past fund flows, past returns and variances, fees and other fund-level variables), sustainability provides little excess information for predicting fund flows. This result is seen through a comparison of two predictive models with a robust benchmark that uses only features unrelated to ESG/sustainability and has only marginally worse predictive power compared to a model built using all available data.