Determining the Difference in Predictive Capabilities of ESG Raw Scores versus ESG Aggregated Scores on Annual Company Stock Returns And Volatility

Abstract

Investors are increasingly incorporating Environmental, Social, and Governance (ESG) ratings into their investment strategies to evaluate and manage potential risks and sustainability of companies. ESG ratings typically follow a hierarchical structure, where raw data points are progressively aggregated, leading to individual E, S, G scores and ultimately aggregating in a broad, consolidated ESG score. While many studies have investigated the relationship between stock performance and individual or overall ESG scores, few have used raw ESG data into their analyses. Therefore, this paper aims to explore the difference in predictive capabilities of ESG raw scores versus aggregated scores on annual company stock returns and volatility. Our findings reveal a trend where the predictive power is strongest at the raw data level, and it gradually weakens through successive stages of aggregation, with the overall scores exhibiting the weakest predictive capability. This result highlights the effectiveness of raw ESG data in capturing the complex dynamics between ESG factors and financial performance, making it the superior choice in further study.