Linkages Between Oil Price Shocks and Stock Returns Revisited
Bibliographic record
Abstract
In this paper, we revisit the debate on the relationship between oil price shocks and stock market returns by replicating the quantile-on-quantile (QQ) regression model for the US stock market in Sim and Zhou (2015, Journal of Banking and Finance), and extending it to 15 countries. The classification of these countries as oil importers or oil exporters depends on their net position in crude oil trade. Our results indicate that the finding by Sim and Zhou (2015) that large negative oil price shocks can bolster stock returns when markets are performing well is only partially supported by the three largest oil importers in our sample-China, Japan and India-during the period 1988:1-2007:12. However, when extending the study to more recent data (period 1988:1-2016:12), we find that China and India experience higher returns when markets perform well and there is a large positive oil price shock. Also, large positive oil price shocks often lead to higher stock market returns when markets perform well for both oil exporting countries-Canada, Russia, Norway-and moderately oil dependent countries-such as Malaysia, Philippines and Thailand. These findings highlight that the relationship between the distributions of oil price shocks and stock market returns is not stable over time in most countries studied. Furthermore, the asymmetric effect of oil price shocks observed in the US market by Sim and Zhou (2015) is less evident in most countries for both the baseline and extended periods.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".