The Determinants of Implementing and Completing Share Repurchases
Bibliographic record
Abstract
Open-market repurchase is a popular corporate payout method that public limited company (PLCs) use, and once they have made this decision an announcement is made. However, the announcement does not necessarily mean that the firm will implement the payout, or if it is initiated that they will buy back the entire announced volume of shares. Thus, using a sample of firms listed on the London Stock Exchange that announced an open-market repurchase between 1993 and 2014, we test the determinants of repurchase implementation using probit regressions, and if their influence also extends to the payout’s completion using Tobit regressions. The results are not identical in nature, but largely indicate a consistency between the influence patterns. Positive influences are exhibited by firm leverage, the balance sheet’s asset base, independent directors and the repurchase’s tax efficiency over dividends. Additionally, the volume of shares announced for repurchasing has a positive influence on the payout’s implementation, but not its completion, while market capitalisation has a positive influence on the payout’s completion, but not its implementation. The findings are most useful for financial practitioners to optimise their portfolio following a repurchase announcement.
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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 | 0.003 | 0.023 |
| 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 | 0.001 |
| 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 | 0.001 |
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".