Bibliographic record
Abstract
We analyze the impact of MSCI ESG rating score changes on stock returns for U.S.-listed firms. Consistent with ESG’s importance for long-term value, we find that stock prices adjust over a prolonged period of time. Specifically, we find that it takes the market multiple months to reflect changes in numerical ratings. Using holding periods of six months, decreases in ratings are followed by annualized negative abnormal returns of approximately 3 %. Our results are not driven by significant firm-level ESG news events. We find evidence that part of the effect is driven by relatively salient aspects of ESG. In line with this, we find that only E rating changes are important for six-month returns while S and G changes do not have a discernible impact. We consider two mechanisms through which ESG rating changes could impact stock returns. We find that institutional investors changing their holdings around rating changes is the primary mechanism that drives our results, with sustainable index revisions having a secondary effect. Our results suggest that ESG rating changes are relevant for capital markets. • MSCI ESG rating changes impact U.S. stock returns over multiple months. • E rating changes affect six-month abnormal returns; S and G have no clear impact. • Rating downgrades lead to annualized negative abnormal returns of 3 %. • Institutional investors drive the impact of ESG rating downgrades.
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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".