CEO Overconfidence and Dividend Payout Policy
Bibliographic record
Abstract
The payout policy depends on not only the company's development strategy but also the CEO's personal characteristics, and CEO overconfidence is one of the attributes that are widely discussed. This study investigates the impact of CEO overconfidence on dividend payout policy. We analyze the traditional dividend hypothesis, the irrational behaviors caused by CEO overconfidence, and how these irrational behaviors affect the payout policy. \n \nThe study carried out the estimation based on 1238 U.S. and Canadian active and inactive publicly companies from 2010-2019. We use the method Holder67 of Malmendier and Tate (2005) to measure CEO overconfidence and follow the research method of Deshmukh et al. (2013). We first prove that overconfident CEO is 6.5% less likely to initiate dividend payments, which is consistent with the cater hypothesis. Then we find that CEO overconfidence is positively related to the amount of dividend payment. Specifically, overconfident CEOs tend to distribute $62.35 million more dividends than non-overconfidence. \n \nWe also analyze life-cycle theories of dividends and find that the growth opportunities have no impact on dividend payout independently, and the impact of CEO overconfidence on divide payout is greater in high-growth firms. Moreover, we differentiate companies into innovative and non-innovative firms based on the amount of R&D investment, and we find that the increase in dividend payments associated with CEO overconfidence is larger in innovative firms.
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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 | 0.002 | 0.013 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".