What Is the Effect of Oil and Gas Markets (Spot/Futures) on Herding in BRICS? Recent Evidence (2007–2022)
Bibliographic record
Abstract
We investigate the effect of gas/oil markets (spot/futures) on herding in stock markets in BRICS over 15 years (2007–2022). We consider the effect(s) of crises (Global financial, European debt, COVID-19, and Russia–Ukraine war), bull/bearish energy markets, volatility, and speculation. The effect of gas and oil markets on herding in stock markets is minimal, and investors herd selectively during crises. Even during the ongoing Russia–Ukraine war, the effect of energy markets on herding in BRICS is minimal. Causality tests show that oil (spot/futures) Granger causes CSAD during COVID-19 only. Gas (spot/futures) has no effect. We also find that energy (spot/futures) market states (bearish/bullish) have no effect on herding in stock markets. Low volatility in energy markets can trigger herding (consistent with previous research in US, China) in all BRICS. Speculative activities during (non)crises appear to have minimal impact on herding. However, as the degree of intensity (volatility) in speculative activities increases in oil/gas, it causes herding in all countries (India is affected mostly), except Brazil. It is not the speculation activity per se in (non)crises that causes herding, but the intensity/volatility in speculation activity. Overall, oil/gas markets (especially gas markets) appear to have a smaller impact on herding than expected, contrary to public belief; however, as the intensity/volatility in speculative activities increases, then herding also increases, which is expected given the uncertainty that speculation causes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".