Long-run Relationship between Islamic Stock Indices and US Macroeconomic Variables
Bibliographic record
Abstract
The aim of this paper is to examine the long-run relationship between Islamic stock indices (Dow Jones and FTSE) and US macroeconomic variables (economic uncertainty index, federal funds rate, money supply, volatility fear index, consumer price index, Treasury bill and Brent oil price). Daily closing stock prices for the period January 2006 – December 2017 were used selected from US, Europe, Canada, Japan, Turkey, Malaysia, China India, Qatar, Kuwait, and Taiwan. Johansen test for Cointegration and Vector Error Correction Model (VECM) were employed for the analysis. The study found the existence of a long run relationship between the selected Islamic indices, the broad market index (represented by Dow Jones Industrial Average) and the set of US macroeconomic variables. Results from the VECM showed slow speed of adjustments indicating the series were highly volatile and took long time to converge to equilibrium. It is recommended that investors should be concerned with the economic policies of US as it has the tendency to affect the expected returns of Islamic Dow Jones and FTSE in the selected countries.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".