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The Wealth Effects of Reducing Private Placement Resale Restrictions

2010· article· en· W2121140934 on OpenAlexaffabout
Elizabeth Maynes, J. Ari Pandes

Bibliographic record

VenueEuropean Financial Management · 2010
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsUniversity of CalgaryYork University
Fundersnot available
KeywordsIssuerPrivate placementBusinessPrivate equityInformation asymmetryMonetary economicsBiddingEquity (law)Market liquidityPrivate information retrievalCommissionFinanceLegislatureEconomicsInvestment banking

Abstract

fetched live from OpenAlex

Abstract Recently, the US Securities and Exchange Commission reduced resale restrictions on Rule 144 private placements from 12 months to 6 months with the intention of lowering the cost of equity capital for issuing firms. In Canada, similar regulatory changes were adopted several years ago, providing a unique opportunity to test the wealth effects of reducing private placement resale restrictions. We find that shortening resale restrictions reduces the liquidity portion of offer price discounts, and thus lowers the cost of equity capital for issuing firms, but has no significant effect on announcement‐period abnormal returns after controlling for issuer type. However, there is a fundamental shift in the types of firms making private placements of common stock after the legislation‐induced easing of resale restrictions. Specifically, we find that smaller firms and firms with greater information asymmetry are less likely to issue privately placed common stock after the legislative change, suggesting that the easing of resale restrictions reduces the costly signal that helps to overcome the Myers and Majluf (1984) underinvestment problem.

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)0.002
metaresearch head score (Gemma)0.015
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 score0.007
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.192
Teacher spread0.185 · 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

Citations26
Published2010
Admission routes2
Has abstractyes

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