Bibliographic record
Abstract
A growing number of investors want to integrate information about company sustainability into their investment decision processes to avoid risk, satisfy an asset owner’s needs, or find a new alpha-generating factor. Few new users of environment, social, and governance (ESG) data understand how ESG ratings behave over time. We use the CSRHub data set to show that ESG ratings regress strongly toward the mean. These ratings include both data from most commercial ESG ratings firms and another 640 sources. The observed regression persists within the ratings data across nine years, for a sample set of more than 8,000 companies. Newly-rated companies show even more reversion than “seasoned” companies. It is rare that a company maintains an especially high or low ESG rating. Investors and company managers should both realize that ESG ratings are likely to change toward the mean and that this pattern does not necessarily mean that a good company is getting worse or a bad one is getting better. TOPIC:ESG investing Key Findings • ESG ratings exhibit behavior that may make them difficult to use in an investment process. • ESG-based investment strategies that seek to “buy the best and sell the worst” may not perform as well as might be expected. • Both investors and corporate managers should adjust their understanding of the significance of ESG ratings and their expectations about how they change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".