Entrepreneurial equity financing and securities regulation: An empirical analysis
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.029 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".