The Moderating Impact of Individual Ownership on The Relationship between Dividend Yield and Ex-dividend Day Excess Return
Bibliographic record
Abstract
This study investigates the moderating impact of individual ownership on the relationship between dividend yield and ex-dividend excess return. Our sample includes US listed companies for years 2002 to 2010. A cross-sectional regression analysis is done to reveal the moderating impact of individual ownership. Our findings show that there is a positive relationship between dividend yield and ex-dividend day excess return in line with tax clientele theory. We also found that the relationship between dividend yield and the ex-dividend day excess return is positively moderated by individual ownership. These findings reveal that the positive relationship between dividend yield and ex-dividend day excess return stems from individual investors’ dividend tax misgiving in line with tax clientele theory. Moreover, we found a negative relationship between corporate size and ex-dividend day excess return that supports the short selling theory. We conclude that tax-induced dynamic trading theory is the premier justification of ex-dividend day pricing as the mixture of both tax clientele and short selling theories.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".