Economic Diversification and the State of Oil Dependency of UAE Stock Returns-An Analysis of ADX Indices 2014-2019
Bibliographic record
Abstract
Impact of commodity price risk on stock return remains an important forecasting parameters across stock markets of developed and emerging markets. In recent times the subdued oil price poses a challenge to the economic imbalance among oil producing countries, and thus non-oil diversification has been adopted as an economic solution. Amongst the GCC countries, the intensity of non-oil diversification has been found to be higher in the UAE which prompted to conduct a separate study of impact of oil price volatility on stock returns of Abu Dhabi Securities Market General Index and various sectoral indices. This study examines whether UAE stock returns are still associated with changes in oil price as reported in earlier research despite significant improvements in non-oil sector GDP contributions. The empirical assessment is based on weekly returns of the Abu Dhabi Stock Market General Index and four sectoral indices, namely, banking, industrial, energy and real estate in relation to variations in weekly WTI prices for the period between 1st week of 2012 to 29th week of 2019, i.e., for a period of 392 weeks applying Vector Error Correction model and Granger Causality test. It is found that there exists both long run and short run association between oil price volatility and stock return except model misspecification in respect of industrial and energy sectors arising out of serial correlation. Two lagged weekly oil price movements are found to be strong explanatory variables of stock returns.
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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 | 0.000 | 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 | 0.000 |
| 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".