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Record W3121340383 · doi:10.17578/4-1/2-2

The Relationship Between Overallotment Options, Underwriting Fees and Price Stabilization For Canadian IPOs

2000· article· en· W3121340383 on OpenAlexafffundabout
Richard Chung, Lawrence Kryzanowski, Ian Rakita

Bibliographic record

VenueMultinational Finance Journal · 2000
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCapital Investment and Risk Analysis
Canadian institutionsSocial Sciences and Humanities Research CouncilConcordia University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsUnderwritingInitial public offeringSecondary marketBusinessOrder (exchange)FinanceMonetary economicsEconomics

Abstract

fetched live from OpenAlex

The overallotment option (OAO) gives underwriters the right to acquire additional shares from the issuing firm at the offer price (less underwriting fees) in order to meet any excess demand for an issue. Thus, underwriters can use overallotment options to stabilize market prices post-issue by increasing the supply of shares for oversold issues. Unlike IPOs in the U.S., the Canadian evidence finds that OAOs are included less frequently, that underwriting fees are positively associated with OAO inclusion, and that the OAO appears to play a minor role in market price stabilization, which is itself less detectable and appears to be limited to the very early stages of secondary market trading. These results suggest that the role of the OAO differs markedly for IPOs in Canadian versus U.S. markets

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.107
Threshold uncertainty score0.215

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.055
GPT teacher head0.259
Teacher spread0.203 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations21
Published2000
Admission routes3
Has abstractyes

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