Do Share Buy Back Announcements Convey Firm Specific or Industry-wide Information? A Test of the Undervaluation Hypothesis
Bibliographic record
Abstract
In this paper, we examine the information content (firm specific) and information transfer (other firm) effects of share buy back announcements using a unique Australian data where the stated reason for the buy back is undervaluation of the firm's stock price. Consistent with the US studies, we find that such share buy back announcements signal positive information about the announcers and their rivals, suggesting that both the announcing firms and their industry rivals were previously undervalued by the market. The results show that while shareholders of firms announcing share buy backs that are motivated by undervaluation of the share price earned statistically significant abnormal returns of 1.25 % on the announcement day, the shareholders of rival firms earned significant abnormal returns of 0.39 % on day +2. The market reaction of industry counterparts thus appears to occur with a lag. For the three days surrounding the announcement, announcing firms ' shareholders earned a statistically significant abnormal return of 4.30%, while rival firms ' shareholders earned abnormal returns of 1.39%. Consistent with our conjecture, we find that the magnitude of the abnormal returns of the share repurchases that are motivated by undervaluation of shares are larger than have been documented for these US market where managers are not legally required to disclose the motives for the share repurchase. We also examine whether the first share buy back announcement in the industry or in a buy back program conveys more
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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 | 0.010 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".