Bibliographic record
Abstract
This article aims to describe the contemporary objectives and tactics of activist hedge funds as well as the actions taken by the targeted companies as a result of their intervention. In this research, we explore the consequences of activism over time (impact on operational performance and share price returns) and compare these with a random sample of firms with similar characteristics at the time of intervention; we also analyse the singularities associated with salient sub-groups of targeted firms. The sample used for our research consists of all 259 firms targeted by activist hedge funds in 2010 and 2011. We found evidence that any improvements in operating performance (return on assets, return on equity, Tobin’s Q) result mainly from selling assets, cutting capital expenditures, buying back shares, reduce workforce and other basic financial manoeuvres. Although there is no evidence of deterioration over a 3-year period, the stock’s performance of targeted companies over a 3-year span barely matches the performance of a random sample of companies. We found that the best way for activists to make money for their funds is to get the company sold off or substantial assets spun off. If not sold, the hedge fund episode often results for the targeted firms in change of senior management and board members, stagnation of assets and R&D. This research does not provide any evidence of the superior strategic sagacity of hedge fund managers, but does point to their keen understanding of what moves stock prices in the short term.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".