Leaving the Big Canadian Senior Market Health Check: Future Relevancy of the Public Markets at Risk as Operating Public Company Decline Accelerates
Bibliographic record
Abstract
Since the peak of operating company listings in 2008, the number of operating companies listed on the Toronto Stock Exchange (the “TSX”) has declined by 42.5%. A short COVID-era Initial Public Offering (“IPO”) boom in 2020-21 offered a brief reprieve in the ongoing operating company decline phenomenon, but the last two and one-half years has seen the worst Canadian IPO market over the multiple decades for data is available. Each passing month now sees a new multi-generational low in the number of operating public companies listed on the TSX. It has become apparent that the public operating public company decline phenomenon in Canada has become systemic and will not be reversed without significant reform in the regulation of capital markets along with other key policy innovations. Academic analysis on thetopic of operating public company decline to date in Canada has largely been focused only onthe twin headline statistics of total operating company listings and IPO volumes. Significant data gaps remain as to the broader nature of the operating public company decline phenomenon in Canada. Filling in these data gaps is necessary to underpin further academic analysis as to which regulatory reforms, and other public policy interventions, are most likely to be successful in reversing the tide of public company decline. This paper picks up the threads of pre-COVID analysis on this important issue, starting with a review of the key reasons why operating public company decline in Canada is a critical public policy issue necessitating intervention. Theories on factors contributing to the phenomenon are also briefly discussed. Finally, this paper presents a variety of key data points examining in more detail the precise nature and extent of the operating public company decline phenomenon in the senior Canadian capital markets.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".