The Different Dividend Signaling Effect under Tax Deduction around Ex-Right Day: Evidence from Taiwan Stock Exchange
Bibliographic record
Abstract
Dividend tax policy is one of the important tools of government taxation. Observing the dividend tax policy and the behavior of stock prices around ex-rights will not only shed light on investment strategies, but also give us a clearer understanding of the microstructure of the capital market. Taiwan went through dividend tax policy and National Health Insurance (NHI) supplementary premium changes from 2014 to 2016. Therefore, this paper adopts the event study method to conduct empirical research on this major event period. The research conclusion points out: (1) During the research period, the company studied had a positive cumulative abnormal return before and on the ex-right day, and there was a negative cumulative abnormal return after the ex-right day. (2) When the tax reduction effect is more favorable to investors, there will be only a positive relationship with positive abnormal returns. (3) There is no statistical significance between the dividend tax reform policy and the negative abnormal return after ex-rights. The empirical results of this paper can help to better understand the pricing process of stocks by market microstructure systems such as dividend tax policies and help build a more stable stock market transaction structure. On the other hand, investors and companies can also gain their own investment or dividend policy inspiration from this research.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".