The Survival and Success of Penny Stock IPOs: an Analysis of the Consequences of Low Listing Requirements
Bibliographic record
Abstract
We analyze a large sample of IPOs realized in a country where the minimal listing requirements are amongst the lowest in the world. In Canada, IPOs mainly consist of micro- and penny stocks offered by non-venture-backed companies in the developing stage. The proportion of issuers without revenues (positive earnings) is 45 % (71%) and the median issue price is CAN$0.75, less than Euro0.50. Consequently, Canada offers a very rich context to study the effect of relatively lenient requirements on the survival and success of these issues. We divide issuers into four categories, from the lowest level of requirement (no sales, earnings or history) to the highest level (major exchange listing requirements). Using this classification, we analyze the survival and success of Canadian IPOs based on an original sample of 2,373 issues from 1986 to 2003. Following the TSX Venture Exchange, we consider that a newly listed company succeeds if it graduates to a senior exchange. Using Survival functions and Cox Proportional Hazards models, we test whether the differences between the survival and success rates are linked to the class of minimum listing requirements in which the company is situated at the IPO. Lastly, we estimate the costs and benefits in terms of failure and success associated with easing the new listing requirements. Our research attempts to contribute to the debate surrounding IPO regulation, listing
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".