Corporate governance and the returns to acquiring firms' shareholders: an international comparison
Bibliographic record
Abstract
Abstract We examine the effects of mergers on the returns to acquiring companies' shareholders for a large sample of companies from both Anglo‐Saxon and non‐Anglo‐Saxon countries over the 1980s and 1990s. With the important exception of Japan, we find similar patterns of returns across both types of countries. For a sample of 9733 acquiring companies the mean percentage gain over a short window of 21 days is 0.6%. This picture changes dramatically as the market has more time to evaluate the mergers and/or the acquiring firms. After three years, acquirers' shareholders in the United States and continental Europe lost on average 19% of their market value compared to a portfolio of non‐merging firms in their size deciles and their two‐digit industry, in Canada, Australia and New Zealand roughly 16%, and in the four Scandinavian countries almost 15%. Further analysis indicates that some mergers are consistent with the hypothesis that mergers generate synergies, but that a majority of mergers in Continental Europe are explained by the managerial discretion and/or hubris hypothesis. Our findings also suggest that corporate governance institutions in the United States and the other Anglo‐Saxon countries lead to better investment performance than in continental Europe, when one confines one's attention to mergers. Copyright © 2007 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".