Can Regulatory Reform Reverse the Decline of Public Markets in Canada? Assessing the Factors Impacting Decisions by Corporate Leaders to Avoid Canadian Public Listings
Bibliographic record
Abstract
The decline in the number of operating public companies in Canada over the past decade is startling and the trend shows no sign of reversing. Since robust public markets are widely understood as serving a critical role in a healthy economy, the decline is particularly concerning for Canadian policy makers. Moreover, the Canadian trend is reflective of similar declines in the United States and Western Europe.\nMany possible contributing factors have been posited to explain public company decline based on speculation and anecdotal evidence. Amongst the factors most frequently cited as contributing to public company decline is regulatory overreach. As such, participants in the public company ecosphere have been advocating for regulatory reform to streamline the IPO process and reduce the cost and complexity of ongoing public company compliance. To this end, Canadian securities regulators have recently undertaken an analysis of public company burden reduction through CSA Consultation Paper 51-404, spawning the ongoing Ontario regulatory reform process under OSC Notice 11-784.\nYet, no significant effort has been as of yet undertaken to empirically validate whether regulatory overreach is indeed the primary factor in the public company decline phenomenon or to determine which of the other potential factors are, in fact, most influential for key decision-makers in making the go-public / stay-private decision. The research project underpinning this dissertation addresses this critical knowledge gap, comprising an extensive survey of senior business decision-makers and other key public markets influencers in Canada. Using both qualitative and quantitative survey methodologies, the study evidences that the phenomenon of public company decline is complex and multi-factorial. Although regulatory overreach is certainly a relevant factor in the mind of business decision-makers, it is only one of a number of interrelated factors. Moreover, many of these factors are unrelated to increased costs and regulatory complexity and therefore cannot be addressed directly through regulatory reform at the securities commission level. As such, it is naïve to expect that regulatory streamlining and cost reduction initiatives alone will be successful in stemming the further decline of operating public companies. Rather, preservation of robust public markets in Canada requires an integrated and aggressive multi-pronged intervention supported by federal and provincial governments, securities regulators and other key players in the public markets ecosphere.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".