Determinants of Repurchase Size: Evidence from the UK
Bibliographic record
Abstract
The paper focuses on the factors that determine the size of an open market share repurchase in the UK. The testing covers the time period 1985–2014 and tests if the traditional motives for repurchasing shares also determine the size of the repurchase. The testing also checks if the influences of these determinants are non-linear, U-shaped or inverted U-shaped, which, to the best of our knowledge, is also a novel empirical approach. The consideration of non-linear influences on repurchase size is relevant due to the overlapping of repurchase determinants. For instance, if the distribution of excess cash is the motive for undertaking the repurchase and not replacing dividend distribution, then the influence of dividend distribution on repurchase size may conflict with the traditional expectation of repurchases being used as dividend replacements. The testing finds that the motive of using repurchases for signalling stock undervaluation has the most consistent influence on repurchase size, followed by the motives of adjusting the reported EPS when earnings are negative and for distributing surplus cash. The motive for using repurchases to adjust the capital structure to increase the debt exposure has a U-shaped influence on repurchase size, while board independence has an inverted U-shaped influence. Overall, when compared to the current literature, this paper is able to demonstrate that there is a strong consistency between the motives that lead to repurchases in the UK, and the determinants of repurchase size.
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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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".