Impacts of the quarter dividend announcement on the stock price study on consumer product industries / Muhammad Ihsan Abd Rahim
Bibliographic record
Abstract
In Malaysia, people start to actively involve themselves in investment and stock trading. This is because people realise that they can make a good amount of money when invest in stock. To gain profit in stock price, Investor need to know the correct indicator on predicting the movement of the stock price. The lack of the understanding on the factor that effect the stock price can cause the investor to experience loss on their investment. They need to consider the factor which can help the investor on forecasting the stock price. To solve this problem, this research focus on the effect of the quarter dividend announcement on the stock price. The variables that were chosen from the quarter dividend announcement are the companies' revenue, profit before tax, net profit, earnings per share and dividend. By using these variables, the effectiveness of the quarter dividend announcement on the stock price can be examined. This study can determine which factor that can influence the stock price. This result can help the investor in forecasting the movement of the stock price and can choose the best stock that can give them the most profit. The other researcher can use this research as their reference. This paper can enlighten their thinking on making further research and explore new information on variables that affect the price of the stock.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".