In our previous blog, we covered what makes for useful ESG data and how the available data set is lacking in significant ways, especially in comparison to financial data. With such formidable challenges, what are investors to do?
Considering that no approach can single-handedly and fully address these issues, investors are well advised to seek a variety of complementary approaches among their managers. That said, there are some practices that work well on their own, and even better in combination.
No Black Boxes
Managers can benefit greatly from undertaking deep dives into third-party ESG data. This will allow them to understand first-hand the limitations of the data and help prevent unpleasant surprises after the data have already been integrated into their process. For the same reason, it is important to evaluate thoroughly the exposures and risks inherent in ESG tilts and compensate for them through appropriate constraints or risk controls.
Early approaches to ESG integration tended to rely heavily on negative screens in order to exclude offending companies from the investable universe. While this can be effective, it may be more constructive to concentrate on the outcome of the investment methodology, and express ESG goals in portfolio terms.
For example, including a stock with an exceptionally low ESG score in the portfolio may seem counterintuitive in a positive ESG-tilt strategy. Nevertheless, there are scenarios where such an inclusion, in turn, allows for a greater overall tilt towards highly rated stocks, such that the ultimate portfolio-weighted rating as a whole ends up being higher than it otherwise would have been. The surprisingly high frequency of such scenarios requires managers to keep their eyes on the big picture.
Moreover, this portfolio-centric approach directly helps address the issues of subjectivity and timeliness plaguing ESG data: if individual stocks’ ratings matter less, then uncertainties, errors and inconsistencies will often cancel out “in the wash,” assuming the broad outlines of the ESG ratings have been properly considered.
Look for Stability
Proper consideration includes analyzing the ESG ratings for stable characteristics. For example, does a particular sector have a predictably low rating, or does incorporating a heavy E, S, or G tilt likely result in persistent exposure to a specific common risk factor?
Identifying stable characteristics in this manner allows for much more thorough and extensive backtesting of the integration of ESG considerations into an investment process, as the available ESG data can be extrapolated to a longer history (but not a wider investable universe) than is available from the ESG data providers themselves.
Also, stability allows for building consensus across different ESG ratings models: even though the ESG scores from different vendors for the same companies exhibit low correlations, stable ESG characteristics are much more portable. For example, tilting a portfolio in a way that directly targets a boost in the ESG profile measured by the MSCI ESG ratings, through identification and use of these stable characteristics, will also tend to boost the ESG profile of the portfolio as measured by Sustainalytics. This is not necessarily the case if a manager uses only the stock-specific ratings instead.
Explore the Data
We’ve identified some of the issues plaguing ESG data, and offered some potential treatments. In our paper, Overcoming ESG Data Challenges, we perform a simple sorting experiment in order to demonstrate many of our observations above, as well as analyze the available data to identify some stable characteristics.
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