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

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  • Prodosh Eugene Simlai

    (Nistler College of Business and Public Administration, University of North Dakota, 3025 University Avenue, Grand Forks, ND 58202, USA)

Abstract

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.

Suggested Citation

  • Prodosh Eugene Simlai, 2024. "Risk Characterization of Firms with ESG Attributes Using a Supervised Machine Learning Method," JRFM, MDPI, vol. 17(5), pages 1-9, May.
    • Handle: RePEc:gam:jjrfmx:v:17:y:2024:i:5:p:211-:d:1397625
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    References listed on IDEAS

    as
    1. Christina E. Bannier & Yannik Bofinger & Björn Rock, 2023. "The risk-return tradeoff: are sustainable investors compensated adequately?," Journal of Asset Management, Palgrave Macmillan, vol. 24(3), pages 165-172, May.
    2. Matthew J. Kotchen & James H. Stock & Catherine D. Wolfram, 2019. "Introduction to "Environmental and Energy Policy and the Economy"," NBER Chapters, in: Environmental and Energy Policy and the Economy, volume 1, pages 3-7, National Bureau of Economic Research, Inc.
    3. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    4. Safiullah, Md & Alam, Md Samsul & Islam, Md Shahidul, 2022. "Do all institutional investors care about corporate carbon emissions?," Energy Economics, Elsevier, vol. 115(C).
    Full references (including those not matched with items on IDEAS)

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