Non-Governmental Organization (NGO) Tweets: Do Shareholders Care?
Bibliographic record
Abstract
We study how messages on Twitter by large non-governmental organizations (NGOs), targeting companies from the S&P500, affect these companies’ stock prices. With a sample of 1,611 tweets between 2009 and 2017 by 18 large NGOs, we observe significant changes in the stock prices of the targeted firms. More specifically, NGO tweets stating a positive message about the environmental, social, or governance (ESG). Actions of the firm have a positive effect on stock prices, while negative tweets have a negative effect. Nevertheless, we find that the presence of institutional owners hampers this effect: firms with high institutional ownership value positive tweets more negatively, and negative tweets more positively. These results support the idea that shareholders react significantly to NGO tweets but they react differently depending on their time horizon: for shareholders who have a more short-term horizon, typically institutional owners, the reaction diverges societal expectations about how firms should contribute to society.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".