Trading ESG vs. Trading E, S, and G Separately: An Exploratory Research

Environment, Social, and Governance (ESG) criteria become a relevant factor in the investment universe. We develop an AI-based algorithm that uses public data, mainly Web-based information, to assign E, S, and G ratings to companies. Using our scoring procedure, we construct portfolios, comprising 50 firms each from the SnP 500 index, 50 firms with the highest scores and 50 with the lowest scores for 4 scoring categories: ESG, E, S, and G, for the years 2018-2021. We find that, except in 2021, high-ESG score portfolios consistently outperform low-ESG score portfolios. In particular, we observe that the shares of high G-score companies outperform low G-score portfolios, with the largest difference in performance between high and low-score portfolios. The data support the hypothesis that indicators of good corporate governance can identify better performing firms. We also note the outperformance of high S-rated portfolios in 2018–2020. We find that the E-portfolios behave differently from the S and G portfolios. Due to data constraints, we view this paper as exploratory only, and further research is due to validate our findings.