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Record W1903061678 · doi:10.1177/0266242610383790

Entrepreneurial equity financing and securities regulation: An empirical analysis

2010· article· en· W1903061678 on OpenAlexafffundabout
Cécile Carpentier, Jean‐Marc Suret

Bibliographic record

VenueInternational Small Business Journal Researching Entrepreneurship · 2010
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsCenter for Interuniversity Research and Analysis on OrganizationsUniversité Laval
FundersSocial Sciences and Humanities Research Council of CanadaAutorité des Marchés Financiers
KeywordsProspectusInitial public offeringListing (finance)BusinessRevenueEquity (law)Stock marketInformation asymmetryPublic offeringFinanceMonetary economicsEconomics

Abstract

fetched live from OpenAlex

To protect investors, securities regulation generally restrains entrepreneurial ventures from entering the stock market. Scholars and regulators contend that strong rules and requirements for listing are essential to prevent the market from failing. However, these constraints can also unduly impede the growth of new ventures. We use the Canadian case to examine the effects of the relaxation of the regulatory constraints. Unlike in other countries, firms in Canada can list at a very early stage, without revenues, with a minimal size and even without writing a prospectus using the reverse merger technique. This provides a unique opportunity to examine entrepreneurial ventures listed on a public market. The quality of firms, their post-listing operating performance and strategy, and their fate largely support the opinion that strong listing requirements are essential to prevent the emergence of a lemon market. Investors involved in this market obtain very poor returns. This indicates that they are neither able to set correct prices in this market nor deal with the high level of information asymmetry therein. The reluctance of most regulators to relax the requirements for small business finance can therefore, be justified.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.065
GPT teacher head0.330
Teacher spread0.265 · 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 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

Citations6
Published2010
Admission routes3
Has abstractyes

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