Impact of foreign exchange currencies towards Malaysian stock market (Kuala Lumpur Composite Index) / Wan Mohd Nursyakirin Wan Mohd Tarmizi
Bibliographic record
Abstract
The objective of this paper is to investigate the impact of foreign exchange currencies towards Malaysian stock market which means Kuala Lumpur Composite Index (KLCI). This study will focus on Kuala Lumpur Composite Index (FBMKLCI) stock price movement. The factors that may influence the stock market price will be observer closely. The dependent variable for this research is Kuala Lumpur Composite Index (KLCI). While the 5 independent variables that has been selected are foreign exchange rates of United States (USD), Japan (JPY), Canada (CAD), Great Britain (GBP) and European Union (EU). The data that has been taken are pooled for 10 years (January 2007 –November 2016). The data is from quarterly data from those years. Total number of observations is 38. The data was obtained from Datastream. This study used quantitative secondary data which is time series data and multiple regression model represented by the ordinary least squares (OLS). It involves the Malaysian stock price index as dependent variable and the independent variables are USD, JPY, CAD, GBP and EU. The result of this data has been revealed that only USD, JPY and EU have significant relationship with the Malaysian stock market itself while the other two variables CAD and GBP do not have significant relationship with the KLCI.
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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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".