Dividend Consistency: Rewards, Learning, and Expectations
Bibliographic record
Abstract
The fact that many companies have a long track record of consistent dividend increases suggests that managers believe there is some benefit to establishing and maintaining such a pattern. Many companies, for example, follow a perennial policy of increasing the dividend in a particular quarter, maintaining it at the same level for the next three quarters, and then increasing it in the same quarter of the following year. But does the capital market reward companies for maintaining a consistent dividend policy? Do companies with a history of repeated dividend increases earn long‐term positive abnormal returns; and if so, how long do the returns persist? The authors find that companies earned significantly positive abnormal returns following each of the first five annual dividend increases, over and above the positive announcement‐month returns. Nevertheless, the reward decreases as the track record of dividend increases becomes longer. After the first dividend increase, companies enjoy significantly positive returns for the next two years. Companies that increase the dividend in the same quarter of the following year also enjoy significant positive returns, but returns that are smaller (and less statistically significant) than in the case of first‐time dividend increases. And as the dividend‐increase track record further lengthens, the size and statistical significance of the abnormal returns continues to shrink; and after the sixth dividend increase, the abnormal returns in the next twelve months are statistically indistinguishable from zero. In sum, although there is some support for maintaining a consistent dividend policy, the market response diminishes over time, and investors do not earn abnormal returns by buying stocks whose annual dividend has already been increased six or more times.
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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 | 0.006 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".