Three Decades of IPO Markets in Canada: Evolution, Risk and Return
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This article explores the extent that the long‐run returns following initial public offerings (IPOs) can explain the asserted decrease in IPOs in Canada. The causes of such a decrease remain controversial, in part because of our limited knowledge of this market. We first describe in detail the evolution of Canadian IPOs on the senior and the venture stock exchanges over three decades (1986–2016). This evolution differs considerably between natural resource and non‐natural resource firms. Second, using other junior markets as a benchmark, we show that the Canadian IPO market is very particular, mainly because it lists very small firms at an early development stage. Third, using 2,145 Canadian IPOs, we provide evidence that these IPOs generate three‐year negative average abnormal returns, and more than 70 percent report negative abnormal returns. Large issuers reporting profits constitute the only subsample that provides fair returns, but they account for less than 5 percent of IPOs. Such a market probably survived for many decades because of investors' preference for skewness and the characteristics of the returns' distribution. We observe a high level of skewness of abnormal returns, consistent with the behavioral finance proposition that investors are often unduly optimistic when valuing lottery stocks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it