“Hostile” takeovers an investment performance of acquirers and targets
Bibliographic record
Abstract
Mergers and acquisitions (M&A) can be either "hostile" or "friendly" in nature.This study looks at the corresponding long-term investment performance of "hostile" and "friendly" takeovers within the mining sector, pre and post the takeover of targets, with the aim to investigate whether there are statistically significant differences about which the investor community should be aware.36 months of monthly share price performance, pre and post first formal merger/takeover announcement date, are studied, for each acquirer is compared with the bourse mining index to calculate the percentage time the acquirer outperforms the market (mining index).Research of the major mining stock exchanges of the world -New York, Toronto, Australia, London and Johannesburg -reveals that the investment performances of "hostile" acquiring mining companies, pre first formal announcement date, are statistically significantly greater than post first formal announcement date.No statistically significant difference was found pre and post first announcement date for "friendly" acquiring mining companies.Although clear differences in post first formal announcement date investment performance are noted between "hostile" acquirers and "friendly" acquirers, there is no statistically significant difference between the investment performances of "friendly" versus "hostile" acquirers.
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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 | 0.002 |
| 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".