Macroeconomic Shocks and Economic Performance in Malaysia: A Sectoral Analysis
Bibliographic record
Abstract
Many shocks, including COVID-19, wars, inflation, contractionary U.S. monetary policy, and oil price hikes, have recently buffeted the world economy. The literature has reported mixed results concerning how these shocks impact Malaysian stock returns. Some studies found that U.S. monetary policy mattered for Malaysia, while others reported that it did not. This paper, employing two U.S. monetary policy measures over the 2001–2019 period, finds that U.S. policy matters little for Malaysian equities. Some studies found that oil price hikes increased Malaysian stock returns while others reported that they did not. This paper, employing updated data, reports that oil price increases, driven by both world demand shocks and oil supply shocks, raise Malaysian stock returns. The paper also compares the performance of Malaysian equities since the pandemic began, with returns forecasted based on macroeconomic variables. The period since the pandemic started has been labeled the megacrisis era. Interconnected crises, including the pandemic, wars, rising commodity prices, and climate events, all overlapped. The results indicate that industrial metals and banks have performed well since the pandemic began. Food producers, healthcare providers, medical equipment suppliers, tourist-related companies, and semiconductor firms have suffered. This paper considers several steps that could help these sectors to recover.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".