Queuing and inventories in limit order markets
Bibliographic record
Abstract
Limit order markets use a queuing system in which limit orders must wait in line to execute. We show that the queue position of a limit order influences its adverse selection risk and inhibits inventory risk management. Trade may worsen market maker risk sharing, unlike many protocols without queuing. We uncover a crowding-out effect: An inventory shock reduces liquidity provision by market makers later in the queue. Using futures data, we confirm both low risk sharing and the crowding-out effect. These two results imply a trade-off, as the queuing sequence that optimizes risk sharing decreases quoted depth up to 8.4%. • Queue position affects adverse-selection risk and inventory management. • Market-maker risk sharing may worsen due to queuing. • Inventory shocks reduce liquidity provision later in the queue. • Canadian futures data confirm low risk sharing and crowding-out effects. • Optimizing risk sharing lowers quoted depth by up to 8.4%.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".