Price Aggressiveness and Quantity: How are They Determined in a Limit Order Market?
Bibliographic record
Abstract
Dealers in a limit order market must choose both the price aggressiveness and the quantity. We empirically investigate how investors jointly make these decisions using a simultaneous equations model in the foreign exchange market. An ordered probit model is implemented to account for the discrete nature of price aggressiveness and a censored regression model is implemented to capture the clustering of orders placed at $1 million. We find evidence of a clear trade-off between price aggressiveness and quantity: more aggressive orders tend to be smaller in size when submitted or cancelled. The competition from increased depth on the same (opposite) side of the market leads to less (more) aggressive orders in smaller (larger) size. Similarly, the prices of submitted orders become more (less) aggressive if there were more aggressive orders submitted on the opposite (same) side of the market. Although the impact of the submission and cancellation of off-best orders on the depth of the market are not observable to traders, they impact the price aggressiveness of the orders submitted.
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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 | 0.007 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".