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Stock price reactions to ESG news: the role of ESG ratings and disagreement

Review of Accounting Studies (2022)Cite this article

Abstract

We investigate whether environmental, social, and governance (ESG) ratings predict future ESG news and the associated market reactions. We find that the consensus rating predicts future news, but its predictive ability diminishes for firms with large disagreement between raters. The relation between news and market reaction is moderated by the consensus rating. In the presence of high disagreement between raters, the relation between news and market reactions weakens, while the rating with the most predictive power predicts future stock returns. Overall, while rating disagreement hinders the incorporation of value-relevant ESG news into prices, ratings predict future news and proxy for market expectations of future news.

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Notes

  1. Financial Times. May 28, 2020. SEC chair warns of risks tied to ESG ratings.

  2. We separate the sample using materiality classifications from the Sustainability Accounting Standards Board (SASB), which is also used by TruValue Labs.

  3. Forbes. The Remarkable Rise of ESG. Jul 11, 2018.

  4. WSJ. ESG Funds Draw SEC Scrutiny. Dec 16, 2019.

  5. We refrain from providing an ex-ante prediction as to which specific rater would have the most predictive ability, because the exact methodology and raw data of different ESG raters are not disclosed (see page 3 of Berg et al. (2020)). We also acknowledge that the predictive ability of ESG ratings with respect to future ESG news is just one dimension of the quality of ESG ratings. However, we view the ability of ratings to predict positivity or negativity of ESG news as a core attribute that investors expect when they use ESG ratings because the ratings reflect the commitments that an organization makes in achieving an ESG outcome (Christensen et al. 2022), which in turn would be reflected in the nature of ESG news.

  6. Our sample uses ESG News scores that have at least five articles, because the algorithm used in TruValue Labs’ sentiment analysis requires at least a few articles to be accurate.

  7. We chose the firm-day specification to exploit the richness in ESG news data that often vary at the daily level. We believe that firm-day specification is advantageous in tying the market reaction to a specific ESG news event (see Section 4.2).

  8. TruValue Labs says it has a separate ESG ratings dataset called TruValue Labs Insights data, and the Pulse dataset used in our paper is an ESG news dataset and not an ESG ratings dataset. However, in order to alleviate the concern that we are not regressing rating on rating, we check whether the latest TruValue Labs Insights data is correlated with the volume of articles that triggers the change in the Pulse data. We find that there is very little correlation between the number of articles in the Pulse data and the Insights ESG ratings data (−0.08). This suggests that TruValue Labs’ ESG rating and news are two very different constructs.

  9. We separate Average ESG Rating into quintiles and deciles to provide additional evidence on the monotonicity of the relation (see Appendix Table 7). Quintile 2 indicates the firms with Average ESG Ratings in the second-lowest quintile, and Quintile 5 indicates the firms with Average ESG Ratings in the highest quintile, during the year. Decile 2 indicates the firms with Average ESG Ratings in the second-lowest decile, and Decile 10 indicates firms with Average ESG Ratings in the highest decile, during the year. In both specifications, firms with the lowest average ESG rating serve as the benchmark. We observe a monotonic increase in the positivity of the news across the portfolio of firms.

  10. For robustness, we also control for ESG disclosure from Bloomberg following Christensen et al. (2022), who found ESG disclosure to be a determinant of ESG ratings disagreement. We find similar results but do not use this as the main specification, because there is a substantial decrease in sample size. Bloomberg data covers a substantially smaller number of firms than in our sample.

  11. We also explore whether market reaction is stronger when there is more investor attention (as proxied by a greater number of news articles written for a particular firm-date). In an untabulated set of results and consistent with the findings in Serafeim and Yoon (2021), we find that the market reaction is stronger when there is more investor attention.

  12. We restrict the long portfolio and short portfolio to have an MSCI ESG Rating higher and lower than 50, respectively, because MSCI ESG Rating is constructed around an average score of 50. So, we use a long (short) portfolio of firms with an MSCI ESG Rating above (below) the mean.

  13. Instead of holding the portfolio for 12 months, we try different time windows (i.e., 24 and 36 months) to examine how long it takes for the disagreement to be resolved. Interestingly, we find that it takes three years for the ratings to be integrated into prices. A potential explanation is that we are still in the early stages of understanding the information content in ESG metrics and that, as a result, such information acquisition and integration happen slowly over time.

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Acknowledgements

We thank Ulrich Atz (discussant), Sadok El Ghoul (discussant), Caroline Flammer, Soohun Kim, Marie Lambert (discussant), Zengquan Li, You-il (Chris) Park (discussant), and seminar participants at Accounting Summer Camp, 4th Annual GRASFI Conference, 19th Annual Corporate Finance Day Conference, International Workshop on Financial System Architecture & Stability, KAIST, Korea Securities Association, Northern Finance Association, Pan Agora Asset Management, Singapore Management University, TruValue Labs ESG Conference, UMASS-EMN Conference, and UN PRI Academic Week 2021 Conference for helpful comments. We are also sincerely grateful to Florian Berg, Julian Koebel, and Roberto Rigobon for sharing their divergence data in the Aggregate Confusion project. This paper received the Crowell Memorial Prize for the best paper on quantitative investing from Pan Agora Asset Management. George Serafeim is the Charles M. Williams Professor of Business Administration at Harvard Business School. Aaron Yoon is an assistant professor at Kellogg School of Management at Northwestern University. Serafeim is grateful for financial support from the Division of Faculty Research and Development at Harvard Business School. We are grateful to TruValue Labs and Sustainalytics for providing access to their ESG data. All errors are our sole responsibility.

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  1. Harvard Business School, Boston, MA, USA

    George Serafeim

  2. Northwestern University, Evanston, IL, USA

    Aaron Yoon

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  1. George Serafeim
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Appendix

Appendix

Table 7 Prediction of news based on the most recent ESG rating using quintiles and deciles
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Table 8 Replication of Tables 3 and 4 using immaterial news
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Table 9 Replication of Table 6B and C using normalized ESG scores
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Cite this article

Serafeim, G., Yoon, A. Stock price reactions to ESG news: the role of ESG ratings and disagreement. Rev Account Stud (2022). https://doi.org/10.1007/s11142-022-09675-3

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  • DOI: https://doi.org/10.1007/s11142-022-09675-3

Keywords

  • Market reaction
  • ESG rating
  • Ratings disagreement
  • ESG news

JEL classification

  • G14
  • M14
  • M41