Does a Change in the ESG Ratings Influence Firms’ Market Value? Evidence from an Event Study
Bibliographic record
Abstract
In recent years, the field of “ESG finance” has seen rapid growth, resulting in the emergence and expansion of ESG ratings and rating agencies. This study investigates how financial investors react to updates in ESG ratings provided by two prominent ESG rating agencies, namely MSCI and Refinitiv. The main objective is to determine whether any positive or negative changes in a company’s sustainability ratings directly impact its market value. The Event Study methodology was used for this investigation, which analyses the Cumulated Average Abnormal Returns (CAARs) of economic events to assess their influence on corporate valuations. We analysed over 840 rating updates (events) using a sample of 75 companies across various industries, all listed on major stock exchanges. Our findings indicate that shifts in sustainability ratings, as evaluated by the two rating agencies, do not significantly impact companies’ market capitalisation. Furthermore, these outcomes remain consistent over time, suggesting that financial markets are not assigning increasing significance to ESG ratings. We offer potential explanations for these findings, which are discussed in light of the existing literature on the subject.
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 | 0.006 | 0.044 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".