How Simple ESG Tilts Affect Your Portfolio

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Published on March 23, 2021

| 7 min read

Vassilios Papathanakos, PhD, Distinguished Researcher


In previous blogs, we’ve identified some of the data issues associated with ESG investing and offered some ways to address them. Here, we present some simple hypothetical portfolios that will help demonstrate many of the observations made above regarding data challenges and analyzing the available data to identify stable characteristics. In this case, we find that:

  • Even a naïve pillar filtering can boost overall scores.
  • The pillars are largely independent of each other.
  • Most of the risk is due to systematic factors.

Data and Methodology

We look at the MSCI ESG ratings, widely employed by many investors, partly because it is one of the most reliable, comprehensive, and extensive datasets available. Even so, the data only start in 2007 and really only reach a high degree of coverage by 2013 (see Figure 1). The plateau in coverage setting in then suggests that there was a change in the implementation of the overall model.

Fig_1_Percentage of Stocks in MSCI

While MSCI’s ESG analysis does include a deeper, more granular assessment of each of the ESG-related ‘issues’, we will limit ourselves to a focus on the higher-level scores for the three pillars (E, S, G) and the composite ESG score. All four scores take values in the range of 0-10, with 10 being the best.

In addition, we look at the MSCI Carbon-Intensity data, which start in 2009, and reach a high degree of coverage by 2015 (see Figure 2). Carbon Intensity (CI) represents carbon emissions normalized by a measure of the size of the company, so that large companies are not penalized just for being large. As in Figure 1, the plateau in 2015 suggests a change in the implementation at that time.

Fig_2_Carbon Intensity Package

The Carbon Intensity data do not represent a grade but normalized carbon emissions, so they have no upper bound; in fact, the range of values spans many orders of magnitude, with some stocks being extremely high outliers in the universe.

On a monthly basis for each of the 5 metrics (the composite ESG rating, the 3 individual ‘E’, ‘S’ and ‘G’ pillars, and the Carbon Intensity), we construct hypothetical portfolios containing the ‘best’ rated half of the stocks in the MSCI All Country World Index (ACWI) for each measure, weighted by capitalization.

Ratings Boost

As you might expect, keeping only the top half of the index stocks by ESG score boosts the ESG rating of the resulting portfolio (by 1-2 units relative to the index on the rating scale of 0-10), but also boosts all the three pillar scores and suppresses CI, albeit by differing amounts and with varying consistency (see Figure 3).

Fig_3 Boost in the Scores by Composite ESG

It is worth pointing out that the sharp change in the benchmark-weighted G score in 2015, which cannot be explained by intrinsic market changes, is due to a model change, demonstrating again the self-consistency issues we mentioned earlier.

Keeping the top half of the index stocks by ‘E’ score boosts the ‘E’ rating of the portfolio relative to the index by about 1 unit, but it also boosts the composite ESG score and suppresses CI (see Figure 4). However, it doesn’t consistently boost the ‘S’ or ‘G’ scores.

Fig_4 E Score

Keeping the top half by ‘S’ score boosts the ‘S’ rating of the portfolio relative to the index by about 1 unit on average, and it also boosts the composite ESG score (see Figure 5). It doesn’t consistently affect the ‘E’ or ‘G’ scores, or CI.

Fig_5 S Score

Keeping the top half by ‘G’ score boosts the ‘G’ rating of the portfolio by about 1 unit relative to the index, and it also boosts the composite ESG score (see Figure 6). However it doesn’t consistently boost the ‘E’ or ‘S’ scores or suppress CI.

Fig_6 G Score

Keeping the bottom half of stocks by CI suppresses the CI of the portfolio by 80-90% relative to the index and boosts the ‘E’ score (see Figure 7), albeit not consistently. It doesn’t significantly affect the composite ESG, ‘S’ or ‘G’ scores.

Fig_7 Carbon Intensity

Unsurprisingly, concentrating the portfolio in stocks of a particular type boosts the rating used for the sorting. However, it is important that, in all cases of even this simple approach of keeping the top half of the stocks, it results in a stable boost (a relatively consistent difference from the total benchmark rating over time).

This analysis also demonstrates that the individual ESG pillars are quite independent of each other e.g., a stock with a high ‘E’ score doesn’t necessarily exhibit a high ‘S’ score. This is particularly the case in the recent past, which is partially a reflection of the continued evolution of the MSCI ratings model, and its increased coverage.

A deeper look at the characteristics associated with these simple portfolios exhibiting ESG composite and pillar tilts reveals the persistency and dominance of some systematic factors. This is further evidence that a portfolio-level approach can encapsulate most of the important insights, and that stock-specific information, while meaningful, is not as vital for ESG integration. In particular, expressing the stable characteristics of the ESG ratings in non-ESG terms allows for a reliable extrapolation over a more extensive history than is available through the MSCI database.

Seek Problems and Solutions

Like the financial data investors are used to processing, we’ve shown some ways that ESG data is independent and the ways it behaves similarly. For more information on the specific shortcomings commonly associated with available ESG data – and some potential solutions – check out our latest paper, Overcoming ESG Data Challenges.

Overcoming ESG Data Challenges Explore the issues – and potential solutions – with ESG data. Read Paper


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The hypothetical portfolios shown are for illustrative purposes only and do not represent any particular investment.
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