Signaling Hypothesis and Size Anomaly in Indian Stock Market
Bibliographic record
Abstract
The dividend signaling hypothesis means that dividend change announcements send signals to the market about its prospects. Market capitalization anomaly or size effect means small-cap stocks variances and returns are different than the large-cap stocks. The sample was tested for dividend change announcement, and the sample was divided into large, medium, and small sample sizes based on the market capitalization of the stocks to test the size effect. Event methodology market model used to calculate the abnormal returns on the dividend announcement day. We found that dividends send signals to the market, and the market reacts positively to the dividend change announcements on event day (Aharony and Swary 1980, Litzenberger and Ramaswamy 1982, Dhillon and Johnson 1994, Below and Johnson 1996), but results may vary with the size of the company. Small-cap companies' variances are higher than the large-cap and mid-cap companies, and also small-cap variances are not equal to other variances results similar to Wong (1989), Bandara and Samarakoon (2002), Sehgal and Tripathi (2006), and Switzer (2010). Finally, we concluded that the dividend signaling hypothesis and market capitalization or size effect anomaly exist in the Indian stock market
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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 | 0.016 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".