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Record W6983376267

Market Reactions to Dual-class Share Creation and Unification: Evidence from Canada

2024· other· en· W6983376267 on OpenAlexaboutno aff

Bibliographic record

VenueBrock University Digital Repository (Brock University) · 2024
Typeother
Languageen
FieldEnvironmental Science
TopicEcology, Conservation, and Geographical Studies
Canadian institutionsnot available
Fundersnot available
KeywordsUnificationAbnormal returnMarket liquidityEvent studyMarket shareCarry (investment)Event (particle physics)Efficient-market hypothesisOrder (exchange)
DOInot available

Abstract

fetched live from OpenAlex

This study examines the short-term market reactions to the creation of dual-class shares and the unification 
\nof dual-class shares in Canada from 1980 to2022. Specifically, we analyze abnormal returns, trading 
\nvolume, liquidity, and institutional ownership changes surrounding these two events. We use the event 
\nstudy methodology to conduct abnormal return analysis on three event days: The Announcement Day (AD), 
\nthe Approval Day (ApD), and the Effective Day (ED). For our creation sample, we observe the market 
\nreaction on all three days and abnormal returns of more than 5% associated with the Announcement Day. 
\nFor our unification sample, we find a significant abnormal gain of nearly 4% on the Announcement Day. 
\nWe also observe that trading volume activity increases significantly around both the creation and unification 
\nof dual-class shares. Consistent with previous literature, we find that stocks that adopt the dual-class share 
\nstructure experience a significant decline in liquidity. Cross-sectional regression analysis suggests that 
\npositive abnormal returns of dual-class share creation can be explained by the firm’s arrangement of 
\nfinancing or prospect of financing, whereas positive abnormal returns of unification are associated with 
\nchanges in institutional ownership. Overall, our analyses suggest strong market reactions and changes in 
\nimportant aspects, such as liquidity of firms, to both events.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.481
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.181
Teacher spread0.171 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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