Bibliographic record
Abstract
This research primarily aims to study the impact of dividend announcements on the stock price of companies listed in the Indian stock market. Incidental to the study, it is necessary to understand whether the market trends have any role in affecting the changes in share prices due to dividend announcements. The companies listed on the stock market are diverse in terms of the industry, market capitalization, and performance. We analyze the S&P BSE 500 index stocks, which declare cash dividend every year without fail for ten years from 2008 – 17. Total 1755 sample was tested for dividend announcement and sample divided into large, medium, and small sample sizes based on the market capitalization of the stocks to test the market trend effect. Event methodology market model used to calculate the abnormal returns on the dividend announcement day. The present research study examined the impact of dividend announcements on stocks in the Indian stock market. The results observe in twenty-four times based on market capitalization wise and market trend-wise dividend announcements. The results of the study are not the same for all dividend announcement observations. The study found positive abnormal returns on event day in most of the dividend announcement observations and it is similar to Litzenberger and Ramaswamy (1982), Asquith and Mullins Jr (1983), Grinblatt, Masulis, and Titman (1984), Chen, Nieh, Da Chen, and Tang (2009) and many previous research results studied in major developed stock markets and emerging stock markets. Full sample, large-cap, and small-cap final dividend average abnormal returns are positively significant only in bull market trend (period 2) similar to Below and Johnson (1996) and other market trends final dividend announcement abnormal returns are positive in most of the observations, but returns are not significant. Average abnormal returns are sensitive to market trends, especially abnormal small-cap returns more vulnerable to market trends.
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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 | 0.002 |
| 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) | 0.003 | 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".