Bibliographic record
Abstract
This study examines the relationships between the management control strength of target and acquiring firms, and takeover premiums. It uses a scorecard system, which aggregates the scores of twelve variables reflecting corporate governance quality and ownership structure characteristics, to define management control strength. Using descriptive statistics and regression analysis, a sample of eighty-one North American publicly traded companies that were in involved in M&A transactions between 2010 and 2013 is examined. Acquirers were found to have paid significantly higher premiums when at least half of the directors sitting on target boards held multiple directorships. Additionally, when at least half of the directors sitting on acquirers’ boards held multiple directorships, acquirers paid significantly lower premiums. The study also found that when an acquirer’s management control is strong and a target’s management control is weak, the size of the premium is significantly lower than the sample average. This could be explained by the acquirer’s greater ability to negotiate deal premiums when its management control is strong and by a lower perceived value of the target firm when target management control is weak. When a target management control is strong and acquirer management control is weak, and when both target and acquirer’s management control is either strong or weak, the premiums paid are not significantly different from the sample mean. These results provide the first step towards developing an investment screening tool for companies involved in M&A transactions.
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 | 0.001 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".