Market Reactions to Dual-class Share Creation and Unification: Evidence from Canada
Bibliographic record
Abstract
This study examines the short-term market reactions to the creation of dual-class shares and the unification of dual-class shares in Canada from 1980 to2022. Specifically, we analyze abnormal returns, trading volume, liquidity, and institutional ownership changes surrounding these two events. We use the event study methodology to conduct abnormal return analysis on three event days: The Announcement Day (AD), the Approval Day (ApD), and the Effective Day (ED). For our creation sample, we observe the market reaction on all three days and abnormal returns of more than 5% associated with the Announcement Day. For our unification sample, we find a significant abnormal gain of nearly 4% on the Announcement Day. We also observe that trading volume activity increases significantly around both the creation and unification of dual-class shares. Consistent with previous literature, we find that stocks that adopt the dual-class share structure experience a significant decline in liquidity. Cross-sectional regression analysis suggests that positive abnormal returns of dual-class share creation can be explained by the firm’s arrangement of financing or prospect of financing, whereas positive abnormal returns of unification are associated with changes in institutional ownership. Overall, our analyses suggest strong market reactions and changes in important aspects, such as liquidity of firms, to both events.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".