Abnormal returns on Canadian insider purchases before press releases
Bibliographic record
Abstract
Purpose This study aims to examine whether insider purchases made within 30 days prior to the publication of various kinds of press releases earn higher abnormal returns (AR) than those in the absence of such announcements. It also attempts to identify the factors that explain ARs. Design/methodology/approach This study considers data for Canadian insider purchases made on the Toronto Stock Exchange 60 Index. An event study methodology is used to calculate AR, and a mixed regression model is used to evaluate the effect of corporate news on AR. Findings The empirical results indicate that insiders achieve greater ARs when they purchase stock prior to press releases; findings also show that these returns are specifically related to purchases made before the announcements of mergers and acquisitions, ongoing projects, financial structure, financial results and asset disposals. This is because of the firm effect. Practical implications These findings have important implications for Canadian market regulatory authorities, especially the Ontario Securities Commission and other market participants who are interested in corporate governance, such as boards of directors and shareholders. Originality/value The present findings show that regulatory bodies must work with companies to raise awareness of improper insider trading.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| 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) | 0.003 | 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".