Bibliographic record
Abstract
Abstract This paper examines the merger announcements of Canadian companies between 1994 and 2000 during an exceptional merger boom. The results show that both the target companies and the acquirer companies obtain significant positive abnormal returns during this time period. Companies that acquire private targets with stock have positive returns; however, acquirers of private firms have significantly higher risk compared with those that acquire public targets, despite nonsignificant differences in returns. Acquirers pay significantly less to acquire private firms than public firms, especially with stock. Overall, the findings suggest there is support for a liquidity discount for private firms, and the market is efficient in valuing firms in asymmetric conditions. Résumé Dans cet article, nous examinons les annonces de fusions des compagnies canadiennes entre 1994 et 2000, période de grand boom de fusion. Les résultats montrent qu'au cours de cette période, les compagnies cibles et les compagnies acquéreuses obtiennent des rendements anormaux positifs. Les entreprises qui achètent des cibles privées avec des actions ont des rendements positifs; cependant, ces entreprises ont des risques considérablement plus élevés par rapport aux entreprises qui achètent des cibles publiques nonobstant des différences négligeables dans les rendements. Par ailleurs, les acquéreurs paient nettement moins pour acheter les entreprises privées que pour acheter les entreprises publiques, en particulier celles qui ont des actions. Dans l'ensemble, les résultats de l'étude révèlent qu'il est nécessaire d'escompter la liquidité pour les entreprises privées et que le marché permet de valoriser les entreprises dans les conditions asymétriques.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.004 | 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".