The effect of additions to or deletions from the TSE 300 Index on Canadian share prices
Bibliographic record
Abstract
In this paper we examine shares that have been added to or deleted from the TSE 300 Index to determine whether abnormal price movements have occurred. We apply the dummy variable approach to event study methodology and adjust the estimated standard errors for arbitrary heteroscedasticity and clustering of events. We also use a non‐parametric method of inference. Like authors of U.S. studies, we find that the market reacts positively to inclusion and negatively to deletion, albeit not significantly in the latter case. The information content of inclusion does not account for the entire share price response, lending support to the hypothesis of increased purchases by index funds. JEL Classification: G14 Ce texte examine les titres qui ont été ajoutés ou soustraits de l'indice TSE 300 pour déterminer si des mouvements anormaux de prix s'en sont suivis. On utilise l'approche des variables fictives dans le cadre d'une méthodologie qui étudie l'impact d'événements, et on ajuste les erreurs standards pour tenir compte de l'hétéroskédasticité arbitraire et de l'agglomération d'événements. On utilise aussi une méthode non‐paramétrique d'inférence. Comme dans des études américaines du même type, on découvre que les marché réagit positivement à l'inclusion et négativement à la soustraction d'un titre, mais que l'effet n'est pas significatif dans ce dernier cas. Le contenu informationnel de l'inclusion n'explique pas entièrement le mouvement dans le prix du titre, ce qui apporte un support à l'hypothèse de l'impact des achats accrus par des fonds indexés.
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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 | 0.001 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".