Bibliographic record
Abstract
We estimate how an acquiring firm’s risk changes depending on whether the market initially judges the acquisition to be neutral, strongly negative, or strongly positive for the shareholders of the acquiring firm. We found that for an average neutral acquisition, the annualized standard deviation of an acquiring firm’s total return declines by 5%. In contrast, acquisitions judged negatively by the market result in a 5% increase in total risk, while acquisitions judged positively by the market feature a 30-basis-point increase in total risk. We found the median acquisition to be value creating, not value destructive. Value destruction tends to be concentrated among large firms and to be associated with extreme negative outliers. Acquiring firms with longholder CEOs are more prone to undertake acquisitions and more prone to take on risk, but are less prone to engage in value-destructive acquisitions than acquiring firms with non-longholder CEOs. In this respect, acquiring firms with non-longholder CEOs are more apt to undertake risky bad acquisitions, especially when their prior returns lie above the industry average. In addition, acquiring firms with non-longholder CEOs are less prone to take on good acquisitions that are high in risk. As a general matter, firms with longholder CEOs are less risk sensitive to changes in prior returns than firms with non-longholder CEOs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".