An Explanation of the Trends in Aggregate Corporate Payout Policy
Bibliographic record
Abstract
We would like to thank seminar participants at Indiana University for helpful The use of stock repurchases has fluctuated dramatically over the last two decades: Aggregate repurchases peaked in 1999, when the use of repurchases came close to surpassing the use of dividends, and reached a low in 1991, when the repurchases amounted to only a quarter of dividends. Though several researchers document this trend in repurchases, there has been little explanation provided for why it occurs. In this paper, we investigate why stock repurchases occur in waves by explaining how the trends in aggregate payout policy relate to earnings and the overall economy. Specifically, we estimate the cointegrating relation between earnings and GDP and use the residual from this relation, the deviation in earnings from its trend, as a measure of transitory earnings. We find that repurchases increase with increases in both permanent and transitory earnings. However, the change in dividends paid is not related to transitory earnings but rather only permanent shifts in earnings that result from changes in the macro-economy. Further, transitory earnings are the primary driver in the choice between repurchases in
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".