Defensive vs growth stocks : interaction effects between quarter report announcement with stock price / Azam Ali
Bibliographic record
Abstract
This paper aim study the effectiveness of company quarter performance toward stock price of Defensive industries and Growth industries of public listed company in Malaysia and also want to know the variable which effects the most for each of the industries for better understanding toward stock price movement. This study focuses on public listed company in Malaysia for Defensive industries and Growth industries for every quarter for six years, started from 1st quarter of 2010 until 4th quarter of 2015. Unlike previous study done on effectiveness of financial earnings announcement toward stock price, this study focusses on quarter financial earnings announcement that might influence the stock price. The internal factors such as firm's revenue, profit before tax, net profit, earnings per share, dividend and net tangible asset that updated quarterly in the financial report are used as the independent variables for the research. Hence, all six independent variables were used in this study to examine the effectiveness of company quarter performance toward stock price. In order to answer the first objective of this study, the statistical analysis like descriptive statistic, normality test, correlation analysis, heteroskedasticity test, and panel ordinary least square analysis were done individually for each type of industries. This is to identify which factors can actually influence the changes in stock price. To answer the second research objective, panel least square regression is used to measure the level of significant of the variables used to know which variables have the stronger effect towards firm stock price.
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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 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.009 | 0.001 |
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".