An Empirical Investigation of Trading on Asymmetric Information and Heterogeneous Prior Beliefs
Bibliographic record
Abstract
The purpose of this study is to analyze inter-temporal trading patterns attributable to informed trading, and distinguish between trading due to asymmetric information and trading due to heterogeneous prior beliefs. Although liquidity and asymmetric information motives for trading are well established in the literature, there is much less consensus about the role played by heterogeneous beliefs. If trading on heterogeneous prior beliefs describes actual order flows, then this motive could be a source of considerable trading volume and may be responsible for previously-documented trading patterns. We apply the econometric procedures of Easley, Kiefer, O'Hara, Paperman (1996 Journal of Finance 51, 1405-1436) to the testable hypotheses of Wang's (1998 Journal of Financial Markets 1, 321-352) informed trader model. The empirical findings confirm the existence of trading on heterogeneous prior beliefs and generally support the inter-temporal patterns proposed by Wang (1998).
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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 | 0.004 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".