Bibliographic record
Abstract
According to a review of the literature, there is no study that examines how the price impact of informed trades is related to liquidity levels in the U.S. Treasury markets. Using variance decomposition and regime-switching methodologies, we find that the price impact of informed trades is higher in more liquid markets. In the case of on-the-run and off-the-run spot markets, the price impact of informed trades is higher in 2-year and 5-year T-notes markets. In the case of T-notes futures markets, the price impact of informed trades is higher in 10-year futures market. We find that the price impact of uninformed (informed) individual trades decreases (increases) as the time scale increases. The results indicate that the price impact of informed trades is greater between 8:00 am and 3:00 pm when the market is more liquid, and smaller between 3:00 pm and 5:00 pm when the market is less liquid.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".