Oil prices fluctuations and stock market performance: does uncertainty matter? / Noor Zahirah Mohd Sidek
Bibliographic record
Abstract
Bloomberg reported that Malaysia is one of the most ‘boring’ stock market where it is the only market losing out in Asia and stock market remained ‘bearish’ since “… there is nothing to be positive about over the next 12 months” (Bloomberg, 15 April 2019). In the first quarter of 2019, the Malaysian stock market saw an exodus of more than USD500 million. It is alleged that changes in the ruling party in the 14th General Election followed by unclear policy directions being one of the contributing factors. The first week after the 14th General Elections witnessed the normal downturn of 75 points to 1,841 points, as foreign investors start to sell the shares and continued to plunge down to 1,660 points when investors start to make ‘technical corrections’, account for the risks following announcements of cancellation of projects under previous government and other policy uncertainties. The fluctuations did not dissipate and up to March 2019, KLCI continue to trade at 1,679.9 point despites predictions that the market should hit 1,900 point. The reasons cited were external shocks perpetuating to Malaysia such as China’s export slump, lower Euro Zone economic growth and inflation forecast and overall fall in the world’s major stock markets. Despite the upward movement of crude oil prices in the first quarter of 2019, the stock market remained gloomy. Changes in oil price directly affect other macroeconomic variables. An increase in oil price may lead to increases in the production costs stemming from increments in transport and raw material costs. Consequently, inflation, interest rates and terms of trade will follow suit which later affects investments and economic growth. Meanwhile, the government revenue is expected to fall due to changes of taxation system from GST to SST. The overestimated oil prices revenue in the last year’s budget of around USD70 per barrel poses a possibility of higher deficit, all of which leads to uncertainty in the 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 | 0.011 |
| 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 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".