Are Cross Border Acquisitions More Profitable, or Do They Make Profit More Persistent, than Domestic Acquisitions? UK Evidence
Bibliographic record
Abstract
Cross border acquisitions were relatively more popular than domestic acquisitions in the UK and many other countries during late 1990s and the beginning of this century (Martynova and Renneboog, 2008, among others). Apart from attributing it to the wave of globalisation that occurred at the time, hardly any other reason has been given for this phenomenon in the literature. In this paper, we check whether cross border acquisitions were more profitable than domestic acquisitions to bidders, or whether cross border acquisitions made the profitability of bidders to be more persistent than domestic acquisitions, during the period referred to above. Evidence observed from a sample of 199 cross border, and 174 domestic, acquisitions made by firms in the UK during 1996-2003 shows that the cross border acquisitions were significantly less profitable, and that they did not make the profitability of the bidders significantly more persistent, than the domestic acquisitions. These indications are similar to those of the US evidence reported by Moeller and Schlingemann (2005) and raise questions about why cross border acquisitions were relatively more popular than domestic acquisitions during the period referred to above.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".