Investors' Perceptions of Activism via Voting: Evidence from Contentious Shareholder Meetings*
Bibliographic record
Abstract
ABSTRACT Motivated by the increasing influence of shareholder votes on corporate policies, we examine investors' perceptions of activism via voting. To identify instances of activism via voting, we focus on annual meetings with at least one ballot item where a substantial fraction of shareholders is expected to vote against management's voting recommendation, indicating an increase in their monitoring activity. We define such meetings as “contentious.” Using a sample of almost 28,000 meetings between 2003 and 2012, we examine stock returns over the period between the proxy filing and the annual meeting. This period captures when investors learn about the contentious nature of the upcoming meeting and form expectations about its likely impact on firms' policies. We find that abnormal stock returns prior to contentious meetings are significantly positive and higher than those prior to noncontentious meetings. These higher abnormal returns increase with the contentiousness of the meeting; are more pronounced in firms with poor past performance, which are more likely to respond to shareholder pressure; and persist after controlling for firm‐specific news and proxies for risk factors. Our results are consistent with investors' expecting activism via voting to have a positive impact on firm value, on average, and cast doubts on regulatory attempts to restrict the use of shareholder votes.
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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 | 0.003 | 0.025 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".