Herding, Information Cascades and Volatility Spillovers \nin Futures Markets \n
Bibliographic record
Abstract
Economists and financial analysts have begun to recognise the importance of the actions of other agents in the decision-making process. Herding is the deliberate mimicking of the decisions of other agents. Examples of mimicry range from the choice of restaurant, fashion and financial market participants, to academic research. Herding may conjure negative images of irrational agents sheepishly following the actions of others, but such actions can be rational under asymmetric information and uncertainty. This paper uses futures position data in nine different markets of the Commodity Futures Trading Commission (CFTC) to provide a direct test of herding behaviour, namely the extent to which small traders mimic the positions of large speculators. Evidence consistent with herding among small traders is found for the Canadian dollar, British pound, gold, S&P 500 and Nikkei 225 futures. Consistent with survey-based results on technical analysis, the positions are significantly correlated with both current and past market returns. Using various time-varying volatility models to accommodate conditional heteroskedasticity, the empirical results are found to be robust to alternative models and methods of estimation. When a test of causality-in-variance is used to analyse if volatility among small traders spills over into spot markets, it is found that spillovers occur only with Nikkei 225 futures. The policy implications of the findings are also discussed.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".