A Study of the Abnormal Dividend Decisions of New Zealand Firms during COVID-19
Bibliographic record
Abstract
We investigated the stock return risk associated with the various types of dividend decisions announced by New Zealand firms during the COVID-19 pandemic in 2020. The sample includes a group of firms that initially announced cash dividends but a number of days later made announcements cancelling their payments. Using multinomial logistic regression analysis, we found that higher pre-pandemic payout policy significantly increased the likelihood of a cancellation, an omission or an increase decision. Higher growth and higher profitability reduced the probability of an omission and a reduction decision, respectively. Moreover, higher stock return volatility increased the likelihood of an omission, a reduction or an increase decision. Further event study analysis revealed that investors reacted more feverishly to the announcements of cancellation decisions than any other types of dividend decisions. Moreover, we report strong evidence of negative abnormal returns around the cancellation announcements followed by positive post-announcement price reversals, a pattern that is not observed for the omission announcements. This paper contributes to the literature by studying a cancellation sample and reveals, for the first time, significant shareholder risk associated with cancellation decisions, which was not observed for omission decisions. We alert managers to carefully weigh the costs and benefits of breaking a promise of dividend payout.
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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 | 0.008 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".