Quarter dividend announcement effect on stock price: a case study on technology industries in Malaysia / Aqilah Rosli
Bibliographic record
Abstract
Many studies on the effects of dividend announcement on stock price have been done over the years. Dividend statement has remained the topic of concern for scholars and for professionals. There is general consensus that in situations, of efficient markets, stock price are a good estimate of the value fixed on the security by the market at any point in time. When markets are imperfect, share price may react to changes in dividends. In this case, dividend announcement may be examined to convey implicit information about the firm future earning potential. Stock price reaction to the dividend announcement is also a topic that discusses in number of empirical research in the developed and emerging stock markets over the last several decades. Studies already carried out on the subject of dividends conclude that stock price react to dividend announcement. This implies that dividend announcement carry details information to investors. This study examined the stock price reactions towards company quarter dividend announcement and also to know which variables has the most effects on technology industry for better comprehending towards price movement. This study focuses on public listed company in Malaysia for technology industries in the Biz Stock Malaysia for the period every quarter for six years, started from 1st quarter of 2011 until 4ffi quarter 2016. The internal factors such as firm revenue, net profit, earning per share, dividend and net tangible asset that updated quarterly in financial report are used as independent variable for the research. Hence, five independent variables were used in this study to examine the effectiveness of stock price reactions towards company quarter dividend announcement. The methods that have been used are statistical analysis and panel least square regression is used to measure the level of significance of the variables. The findings conclude all the variables are expected to be positive relationships with the stock price of the technology industry.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".