How to Overcome Challenges in ESG Data

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Published on February 12, 2020

| 6 min read

Vassilios Papathanakos, PhD, Distinguished Researcher


Naughty and Nice

Separating ‘naughty’ from ‘nice’ in a verified list is no longer a seasonal activity limited to residents of the Arctic Circle. Investors and managers have been steadily increasing their utilization of ESG ratings of companies, but confusion and uncertainty has been increasing even faster. There’s little consensus on the appropriate source of data, which issues are material, how to assign weights to them, and how to combine all these pieces into a coherent approach.

Some of these challenges are intrinsic to the nature of ESG considerations; they are here to stay. Whether investors employ ESG because of their own values, or because ESG represents an important investment dimension (e.g., an alpha opportunity, or an active risk) due to the values of other investors, challenges will often arise from questions of principle. Even within a homogeneous society, people will disagree on moral questions. Further, many of the values of a single person are often self-contradictory, and must be reconciled on a case-by-case basis. Philosophers have been working for a long time towards a comprehensive and compelling moral system, and there’s no end in sight.

Other challenges to developing a universal ESG approach are temporary, and are mostly related to data availability. Despite the plethora of vendors offering data and ratings for a large number of companies and for a wide variety of issues, most of them sprouted not more than a few years ago, and they don’t cover a significant percentage of public companies before their inception. What’s worse, the ESG packages are often internally inconsistent in their underlying data or the employed ratings methodology. This inconsistency is sometimes because the vendors have access to more resources (data feeds, larger teams), sometimes because they improve their data treatment (error correction, statistical normalization), and sometimes simply because they respond to evolving market demands.

Still, there are practical and useful approaches to overcoming the above obstacles, which are applications of the same principle: focus on the commonalities.

The Forest and the Trees

The big picture matters more than the individual issues. It is tempting to try and unearth the specific metric that best highlights where a company stands with respect to E, S, or G. However, the more idiosyncratic the data point, the less it facilitates comparison with other companies. Similarly, the more specialized the measurement, the less likely it can be reliably computed over time, especially when trying to extend the data to the further past. In general, we observe that an important issue will fall in one of two categories:

  1. It is recurrent in many other companies, so that even coarse-grained analyses are materially affected by it.
  2. It dominates for a specific company, but in a highly idiosyncratic manner.

What this means is that one can revert to the big picture without losing any statistically meaningful information from the specific. That’s why the use of the three so-called pillars (E, S, and G) is so widespread: they reflect a reasonable balance between the high-level and the stock-specific (for those of the physics persuasion, E, S, and G are the thermodynamic variables emerging from the microscopic interactions).

That’s not to say that no information is lost by disregarding the specific issue; only that one cannot make statistically supported statements about the potential alpha opportunity, or the inherent risk, as there are no comparable data.

Isn’t this inherently limiting the ESG potential of quantitative methodologies, compared to a case-by-case, issue-by-issue fundamental approach? Simply put, no.

This is because quantitative methodologies are best employed in a portfolio setting, where a large number of holdings diversify the investment risk due to various considerations, including ESG. As long as the systematic component of the ESG risk is properly managed, the idiosyncratic component will be diluted and become statistically negligible.

Fossil Hunting

The fact that the big picture is what really matters at the practical level for a quantitative strategy opens the way to reconstructing ESG ratings for companies long before genuine ESG ratings data become available. This works in two steps:

  1. Look at companies over the recent past, where their ESG ratings are available, correlate these ratings with other characteristics that are publically accessible, such as their industry and their domicile, and derive statistical rules.
  2. For companies in the past, analyze the publically accessible data to recover a reconstruction of what the inaccessible ESG ratings would likely have been.

This is analogous to how paleontologists analyze fossils. They start by studying modern animals, and deriving reliable rules (e.g., the marks that tendons leave on the bones, the relation between the speed of an animal and their stride length, etc.). Then they use these findings to extrapolate from the fossils what the corresponding animals would look and behave like.

This approach is limited to reconstructing systematic relations; idiosyncratic issues are elusive (just like it’s hard to guess what color a dinosaur may actually have been). Thankfully, these issues can generally be dealt with in a portfolio setting as described above.

Beyond Data

Understanding and getting the most out of ESG ratings data is critical, but it’s still just one factor out of many to consider when evaluating an ESG strategy. Check out our comprehensive implementation guide, “What to Look for on the Road to ESG”, for a deeper look.

What to Look for on the Road to ESG ESG investing guide for decision-making. Download Paper


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