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Record W4391566115 · doi:10.3390/jrfm17020061

Offer Price and Post-IPO Ownership Structure

2024· article· en· W4391566115 on OpenAlexvenueno aff
Martin Abrahamson

Bibliographic record

VenueJournal of risk and financial management · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsnot available
FundersUppsala UniversitetKungl. Vitterhets Historie och Antikvitets Akademien
KeywordsInitial public offeringBusinessFinancial systemMonetary economicsAccountingEconomics

Abstract

fetched live from OpenAlex

In an initial public offering (IPO) the firm can set the offer price of its shares, based on the valuation of the firm, by changing the number of shares. This study uses stock ownership records and hand-collected IPO data to analyze the offer prices, the underpricing of IPO shares (measured as the initial return, IR) and the relationship with the post-IPO ownership structure. Specifically, the paper focuses on individual IPO investors. The results show that for the lowest priced IPOs the IR is significantly higher priced IPOs. Furthermore, for the low-priced IPOs, there is a negative relationship between offer price and breadth of ownership. This implies that stocks with a low price can attract more investors than stocks with higher offer prices. However, for high-priced IPOs the relationship is positive, suggesting that also the IPOs with highest price attract more investors. Overall, this study shows that the offer price of an IPO firm may have a moderate effect on its post-IPO ownership structure.

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)0.006
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 score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.007
GPT teacher head0.186
Teacher spread0.180 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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