The Behavior of Prices, Trades and Spreads for Canadian IPO's
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
Microstructure effects for 359 TSX listed IPO’s in the period 1984-2002 are examined. Based on first day returns, earning positive mean returns is very difficult even when most IPO’s are purchased at the offer price. Mean daily trade volume for the first five days of IPO trading is large relative to the means for the first thirty days and for longer periods. The dollar volume of sells is always significantly larger than that of buys suggesting that institutional investors are active on the sell side in the aftermarket. Liquidity as measured by quoted depth is initially large and decays rapidly over time. Gross returns are often low or negative, and average round-trip trade costs increase from 1.5% to 2.9% and 1.8% to 3.7% for more and less patient traders, respectively, over the first nine months of trading for an average IPO. Early amortized spreads are relatively large due to large initial share turnover.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| 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