An Empirical Analysis of the Relationship between Oil Prices and Stock Markets
Bibliographic record
Abstract
This paper investigates the relationship between oil prices and stock market returns for the G7 and the BRIC countries for the period 1991-2016 using cointegration and a vector error correction model. Results reveal that there is no long-run relationship between oil prices and the stock market indices of the G7 countries. However, they also reveal that there is a long-run relationship between oil prices and the stock market indices of three out of the four BRIC countries (Brazil, China and Russia). This result appears to be broadly aligned with the idea that over the past quarter of a century emerging countries have been more exposed to oil prices (either as producers or consumers) than developed ones. Furthermore, from an investments’ and international portfolio management perspective, it seems that there might be benefits from diversification when holding the stock market index of a G7 country or India and oil assets since these appear to be segmented. On the other hand, such benefits might not be applicable in the case of the stock markets of Brazil, China or Russia and oil assets as these seem to be integrated.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".