When Herding and Contrarianism Foster Market Efficiency : A Financial Trading Experiment
Bibliographic record
Abstract
While herding has long been suspected to play a role in financial market booms and busts, theoretical analyses have struggled to identify conclusive causes for the effect. Recent theoretical work shows that informational herding is possible in a market with efficient asset prices if information is bi-polar, and contrarianism is possible with single-polar information. We present an experimental test for the validity of this theory, contrasting with all existing experiments where rational herding was theoretically impossible and subsequently not observed. Overall we observe that subjects generally behave according to theoretical predictions, yet the fit is lower for types who have the theoretical potential to herd. While herding is often not observed when predicted by theory, herding (sometimes irrational) does occur. Irrational contrarianism in particular leads observed prices to substantially differ from the efficient benchmark. Alternative models of behavior, such as risk aversion, loss aversion or error correction, either perform quite poorly or add little to our understanding.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".