In our new working paper “ESG Confusion and Stock Returns: Tackling the Problem of Noise”, we take a first step towards solving the problem of measurement noise in ESG ratings.

If you have worked with ESG ratings, you most likely heard something like: “How can we trust ESG ratings if the correlations between ESG raters are so low compared to credit risk ratings?”. Let me explain in the following post why comparing the two makes little sense.

Let’s speak about the regulation of ESG rating agencies. When talking about it, I often encounter a mix up between regulation of firm level disclosure and ESG ratings. But these are, of course, two separate issues.

In our new paper “Is History Repreating Itself?: The (Un)Predictable Past of ESG Ratings” we show that Refinitiv rewrites data points on a weekly basis. Some changes are related to changes in the underlying raw data, while others are just accidental (?) and apparently momentarily deletions of raw data.

In our working paper “Aggregate Confusion: The Divergence of ESG Ratings” we disentangle the disagreement between ESG raters in three different sources: scope, weights and measurement. Scope relates to what you measure (do you include the issue Bribery?), weights to how important an issue is (do you give more weight to CO2e Emissions than Bribery?) and measurement to how you measure an issue. We find that scope and measurement divergence are the main drivers, while weights divergence is less important.