Hidden Orders and Optimal Submission Strategies in a Dynamic Limit Order Market
Bibliographic record
Abstract
Recent empirical evidence on tradersorder submission strategies in electronic limit order markets (LOB) shows the growing use of hidden orders. This paper provides a theory of the optimal order submission strategies in an LOB, where traders can choose among limit, market and hidden orders. The dynamic model we propose allows for strategic interaction of traders on the two sides of the LOB, asset volatility and varying order sizes. Hence, it allows traders take a simultaneous three-dimensional strategic choice of price, quantity and exposure. We nd that hidden orders increase the liquidity of the LOB. The use of hidden orders increases with volatility, order size, and relative depth on the opposite side of the market, and it decreases with time-to-shock. Agents use order exposure and price aggressiveness as complements. Toronto University and Bocconi University, Milan. With thanks to Ulf Axelson, Bruno Biais, Mauro Buti, Fabio Deotto, Thierry Foucault, Gene Kandel, Laurence Lescourret and Enrico Perotti for their precious comments and suggestions. The usual disclaimer applies. We acknowledge nancial support from Bocconi University (Ricerca di Baseproject). Contact author: sabrina.buti@rotman.utoronto.ca
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".