Aftermarket Volatility and Underpricing of Canadian Initial Public Offerings
Bibliographic record
Abstract
Abstract This paper investigates aftermarket volatility and underpricing of the Initial Public Offerings (IPOs) listed on the Toronto Stock Exchange during the period 1990–1999. Our results indicate that aftermarket volatility has increased significantly but the average degree of underpricing has not increased. The decomposition of the aggregate aftermarket volatility in market, industry, and firm‐specific components reveals that the increase is mostly attributable to firm‐specific risk. We also find that the volatility of IPOs is smaller than that of comparable (matched) seasoned stocks. Although our results show an overall positive relationship between IPO underpricing and aftermarket volatility, we find that this relationship has become considerably weaker in recent years. Résumé Cet article étudie la volatilité après‐bourse et la sousévaluation des titres suite à un appel public à l'épargne (APE) à la bourse de Toronto durant la période 1990–1999. Nos résultats indiquent que la volatilité aprèsbourse a augmenté de manière significative, mais que le niveau moyen de la sous‐évaluation quant à lui est resté intact. La décomposition de la volatilité totale aprèsbourse selon ses composantes de marché, d'industrie et celle propre aux firmes, indique que l'augmentation de la volatilité peut être attribuée aux risques spécifiques aux firmes. Nous trouvons aussi que la volatilité des APE est plus faible que celle des émissions d'actions comparables déjà listées. Bien que nos résultats montrent une relation positive globale entre la sous‐évaluation des APE et la volatilité subséquente à l'émission, nous estimons que cette relation s'est considérablement affaiblie au cours des dernières années.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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 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".