The Impact of Divestment Announcements on the Share Price of Fossil Fuel Stocks
Bibliographic record
Abstract
Several prominent institutional investors concerned about climate change have announced their intention or have divested from fossil fuel shares, to limit their exposure to the industry. The act of fossil fuel divestment may directly depress share prices or stigmatize the industry’s reputation, resulting in lower share value. While there has been considerable research conducted on the performance of the fossil fuel industry, there is not yet any empirical evidence that divestment announcements influence share prices. Adopting an event study methodology, this study measures abnormal deviations in stock prices of the top 200 global oil, gas, and coal companies by proven reserves, on days of prominent divestment announcements. Events are analyzed independently and in aggregate. The results make several notable contributions. While many events experienced short-term negative abnormal returns around the event day, the effects of events were more pronounced over longer event windows following the New York Climate March, suggesting a shift in investor perception. The results also find that divestment announcements related to campaigns, pledges, and endorsements all have a significant effect over the short-term event window. Finally, the results control for the general underperformance of the industry over the estimation window, attesting that the price change is caused by divestment announcements. Several robustness tests using alternate expected returns models and statistical tests were conducted to ensure the accuracy of the result. Overall, this study finds that divestment announcements decrease the share price of the fossil fuel companies, and thus, we conclude that ‘divestors’ can influence the share price of their target companies. Theoretically, the result adds new knowledge regarding the efficacy of the efficient market hypothesis in relation to divestment.
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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 | 0.011 |
| 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 | 0.001 |
| Research integrity | 0.000 | 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".