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Record W2905641933

Entrepreneurs and Junior Markets: An Assessment

2018· preprint· en· W2905641933 on OpenAlexaboutno aff
Cécile Carpentier, Jean‐Marc Suret

Bibliographic record

VenueRePEc: Research Papers in Economics · 2018
Typepreprint
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsInitial public offeringBusinessVenture capitalRevenueStock exchangeQuality (philosophy)Capital marketValue (mathematics)Finance
DOInot available

Abstract

fetched live from OpenAlex

This article shows that a junior market can be an effective financing strategy for growth-oriented entrepreneurs who want to list on a senior stock exchange. We analyze 209 graduations from the Canadian junior market (TSXV) benchmarked with 191 initial public offerings (IPOs) on the senior exchange (TSX). Graduations are as frequent as IPOs, and the probability of reaching the TSX is significantly higher for TSXV firms than for venture capital-backed firms. The growth rate of revenues is significantly higher before graduations than before IPOs, allowing TSXV firms to reach the TSX earlier. Investors value both groups of firms similarly, indicating comparable perceived quality. In Canada, the junior market is a valuable financing strategy for growth-oriented entrepreneurs. It fulfills its role of fostering the development of innovative firms and feeding the senior exchange. However, the choice of the TSXV reduces entrepreneur ownership interest compared with the IPO strategy.

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)0.004
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 score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.031
GPT teacher head0.310
Teacher spread0.278 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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