The Benefits of Banking Mega‐Mergers: Event Study Evidence from the 1998 Failed Mega‐Merger Attempts in Canada
Bibliographic record
Abstract
Abstract We investigate the main benefit(s) of specific banking mega‐mergers, and whether or not we can infer the benefit(s) from event study evidence of stock market reactions to the mega‐mergers. In addressing these questions, we examine the market's reactions to three announcements surrounding the 1998 failed mega‐merger attempts in the Canadian banking industry. From our analysis, we conclude that market power—not scale, scope, or X‐eficiency economies, or access to government safety net subsidies—was the primary benefit ascribed by Canadian shareholders to the merger proposals. To the extent that the market's perception of merger benefits is an accurate indicator of the merging partners' motives, we also conclude that an analysis of shareholder reactions to a merger announcement—as undertaken here—is a productive avenue for regulators attempting to discern a particular merger's main motivations. Résumé Dans cette étude, nous examinons les avantages principaux de certaines méga‐fusions bancaires. Nous nous demandons s'il est possible d'inférer ces avantages à partir de l'étude des réactions du marché boursier aux méga‐fusions. Notre étude s'appuie sur les réactions du marché enregistrées à la suite de trois annonces, en 1998, de tentatives de méga‐fusions avortées dans l'industrie bancaire canadienne. Notre analyse débouche sur la conclusion que le pouvoir du marché—et non son échelle, son étendue, l'efficacité de son économie (économies en efficacité X) ou l'accès au filet de sécurité des subventions gouvernementales—est le principal avantage que les actionnaires canadiens évoquent pour justifier les propositions de fusion. La perception des avantages de la fusion étant un indicateur clair des motivations des partenaires de la fusion, nous pensons qu'une analyse des réactions des actionnaires à l'annonce d'une fusion est une piste pertinente dans la détermination des principales raisons qui la sous‐tendent.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".