Is There Any Life After Going Public? <i>Evidence from the Canadian Market</i>
Bibliographic record
Abstract
This article examines the survival profile of Canadian initial public offerings (IPOs). More specifically, the authors develop multinomial logit models based on the information contained in the prospectus and attempt to determine what factors influence the post-issue transition of the IPO firms into survivors, non-survivors, or targets. They find that larger IPOs experience a lower probability of delisting, and higher underpricing implies a lower probability of failure or becoming a target. Further, the presence of venture capitalists at the IPO stage seems to influence the post-IPO transition state. The authors also estimate an accelerated-failure-time model as a robustness test and find that the survival time for IPOs increases with the level of underpricing and decreases during hot issue periods. This latter result suggests that leaving money on the table is not a bad thing as generally perceived and has some beneficial outcomes such as enhancing the survivability of the firm.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".