Navigating Uncertainty in ESG Investing
Abstract
The widespread confusion among investors regarding Environmental, Social, and Governance (ESG) rankings assigned by rating agencies has underscored a critical issue in sustainable investing. To address this uncertainty, our research has devised methods that not only recognize this ambiguity but also offer tailored investment strategies for different investor profiles. By developing ESG ensemble strategies and integrating ESG scores into a Reinforcement Learning (RL) model, we aim to optimize portfolios that cater to both financial returns and ESG-focused outcomes. Additionally, by proposing the Double-Mean-Variance model, we classify three types of investors based on their risk preferences. We also introduce ESG-adjusted Capital Asset Pricing Models (CAPMs) to assess the performance of these optimized portfolios. Ultimately, our comprehensive approach provides investors with tools to navigate the inherent ambiguities of ESG ratings, facilitating more informed investment decisions.
Keywords
Cite
@article{arxiv.2310.02163,
title = {Navigating Uncertainty in ESG Investing},
author = {Jiayue Zhang and Ken Seng Tan and Tony S. Wirjanto and Lysa Porth},
journal= {arXiv preprint arXiv:2310.02163},
year = {2025}
}
Comments
36 pages, 2 figures, presented at Fields - Institute's Mathematics for Climate Change (MfCC) Network & Waterloo Institute for Complexity and Innovation (WICI): Math for Complex Climate Challenges Workshop, Waterloo, Canada; 26th International Congress on Insurance: Mathematics and Economics, Edinburgh, UK; and the 58th Actuarial Research Conference (ARC), Des Moines, Iowa, USA