The Market Reaction to Repurchase Announcements
Bibliographic record
Abstract
This paper investigates the drivers of the market’s reaction to share repurchase announcements in the UK and the related abnormality in stock performance. It uniquely captures the impact of globalisation in tandem with a variety of firm-level and macro-level determinants. We undertake multivariate OLS regression to test the determinants of the market’s reaction and find a negative influence when repurchases are tax-friendlier than dividends if there is high debt exposure and economic globalisation is rising, with a positive influence when the company has a history of distributing above average dividends. To quantify the short-term price abnormality, we employ event study analysis, and the findings compute positive (insignificant) stock price abnormality for nonfinancial (financial) firms. For long-term stock price abnormality, we compare against the FTSE 100 by computing annual geometric stock performances. The findings indicate a negative (insignificant) stock price abnormality for nonfinancial (financial) firms. The results can aid corporate management in improving repurchase timing, aid in the decision making of financial practitioners when trading or investing in repurchasing firms, and assist policymakers in mapping more efficient fiscal and cross-market trade frameworks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".