The information content of Canadian open market repurchase announcements
Bibliographic record
Abstract
Purpose This study seeks to examine the role of firm characteristics and insider private information in affecting Canadian firms’ repurchase decision and the associated announcement period stock return. Design/methodology/approach Past studies of announcement returns employ a standard event‐study methodology, which produces biased parameter estimates when the corporate event is voluntary. This study employs the conditional event study methodology, which is free of self‐selection bias. The conditional model also provides a direct test of whether private information is conveyed through the announcement. Findings It is found that firms are more likely to buy back shares if they have greater free cash flows, lower market‐to‐book ratios, poor prior stock performance, and their insiders have large shareholdings. It is shown that the announcement period returns are strongly and positively related to the private information possessed by company insiders. The market reacts to the reason given for the repurchase and reacts less positively to repeat repurchases. Overall, the evidence is consistent with Isagawa's model which argues that repurchases signal that the insiders are not the type to waste their free cash flow. Research limitations/implications This methodology should also be applied to US open market repurchases. Originality/value This is the first study to: explicitly test whether the abnormal return is attributable to private information; employ the conditional event study methodology in examining the announcement return; and study the returns to Canadian repurchase announcements.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".