Risk Characterization of Firms with ESG Attributes Using a Supervised Machine Learning Method

We examine the risk–return tradeoff of a portfolio of firms that have tangible environmental, social, and governance (ESG) attributes. We introduce a new type of penalized regression using the Mahalanobis distance-based method and show its usefulness using our sample of ESG firms. Our results show that ESG companies are exposed to financial state variables that capture the changes in investment opportunities. However, we find that there is no economically significant difference between the risk-adjusted returns of various ESG-rating-based portfolios and that the risk associated with a poor ESG rating portfolio is not significantly different than that of a good ESG rating portfolio. Although investors require return compensation for holding ESG stocks, the fact that the risk of a poor ESG rating portfolio is comparable to that of a good ESG rating portfolio suggests risk dimensions that go beyond ESG attributes. We further show that the new covariance-adjusted penalized regression improves the out-of-sample cross-sectional predictions of the ESG portfolio’s expected returns. Overall, our approach is pragmatic and based on the ease of an empirical appeal

Non-Pecuniary Risk, ESG Ratings, and Expected Stock Returns

Portfolios incorporating environmental, social, and governance (ESG) criteria present distinct, unobserved risks, the empirical quantification of which has proven challenging. This difficulty stems from sustainable investment strategies being guided by both financial objectives and investors’ non-pecuniary preferences, which fundamentally alter a portfolio’s risk and return characteristics. To address this, we propose a novel methodology that identifies latent, ESG-specific risk factors by applying sparse principal component analysis (SPCA) to two-dimensional portfolio returns. Unlike approaches that rely on subjective judgment, our method extracts risk dimensions inherent to the return data itself. Our analysis reveals that the resulting firm-specific SPCA beta plays a dual role: it explains performance differentials across ESG-rated portfolios and exhibits a statistically significant, negative association with expected individual stock returns. The robust predictive performance of this SPCA-based risk factor confirms its practical utility for analyzing and managing diversification in ESG investing