The Intraday Pattern of Information Asymmetry, Spread, and Depth: Evidence from the <scp>NYSE</scp>
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Studies suggest that investment flows, liquidity imbalances, and institutional trading may create intraday trading patterns and opportunities for investors to time their trades to reduce transaction costs. Motivated by these studies, we divide each trading day into 13 half‐hour trading intervals and measure information asymmetry from price changes, trade sizes, and trade directions. We find that information asymmetry starts high in the morning, drops continuously until it reaches a midday low during Interval 7, rises to a midday high during Interval 10, and drops continuously after. In contrast, neither the spread nor the depth exhibit similar midday extreme values. Essentially, we identify a 90‐min window in the afternoon when net valuable information arrives to the market in high frequency while liquidity is stable, and that may be an opportunity for some investors to time their trades. In addition, we show that market makers employ dynamic strategies that change the spread, the depth, or both to manage information asymmetry. This is particularly evident during the last three trading intervals, where the significant drop in information asymmetry is countered primarily by a significant increase in the depth while the spread is almost constant.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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