Which Trades Move Asset Prices? An Analysis of Futures Trading Data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article examines the information content of trade size and investor performance in a unified framework, using the price contribution (PC) measure proposed by Barclay and Warner (1993). Several interesting results obtained through the analysis of a unique dataset of KOSPI200 futures are presented herein, as follows: (1) evidence is presented against the "stealth trading hypothesis," and it is claimed that medium-size trades are not more informative than trades of other sizes; (2) foreign institutions have an advantage over domestic investors in terms of information, and their investment performance is the best among all investor types; (3) domestic individuals cannot be considered homogeneous investors; and (4) although the PC of the trades by domestic institutions is relatively small on average, the domestic institutional investors outperform other investors at around the futures' maturity dates.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it