Bibliographic record
Abstract
Abstract Research Question/Issue This study examines activism campaigns where multiple activists simultaneously target the same firm—which we term clustered shareholder activism. Despite the growing influence of shareholder activism on corporate governance, the clustered activism phenomenon has previously only been addressed indirectly, anecdotally, or with limited data. We consider cost sharing motives for clustered activism and whether the phenomenon exerts a positive or negative impact on the performance of the target firm. Research Findings/Insights Using a large dataset of shareholder activism events at U.S. firms, we find that clustered activism campaigns are more common at larger firms and among geographically proximate activists, which is consistent with our prediction that activists cluster to reduce the costs associated with activism campaigns. Furthermore, we find that clustered activism produces elevated profitability and abnormal returns, which is consistent with our prediction that activists cluster to address principal–agency costs. Theoretical/Academic Implications Our study provides some of the first theoretical and empirical evidence on the clustered activism phenomenon. We contribute to the understanding of the role of shareholder activism by considering their effect on principal–agency and principal–principal problems. Our results also contribute to the literature that examines factors relating to the success of shareholder activism by documenting the effect of clustered activism on activism costs and target firm performance. Practitioner/Policy Implications Our study adds to the debate among practitioners and regulators on the merits (or lack thereof) of clustered activism. Our findings suggest that a regulatory approach that encourages clustered activism can benefit shareholders. Video Abstract https://onlinelibrary.wiley.com/page/journal/14678683/homepage/videoabstracts.html youtube https://www.youtube.com/watch?time_continue=1&v=0_D-6Pw9sYo
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".