Estimating the gains from trade in limit-order markets, The Journal of Finance 61
Bibliographic record
Abstract
We present a method to estimate the gains from trade in limit-order markets and pro-vide empirical evidence that the limit-order market is a good market design. Using observations on order submissions and execution and cancellation histories, we esti-mate both the distribution of traders ’ unobserved valuations for the stock and latent trader arrival rates. We use the resulting estimates to compute the current gains from trade, the gains from trade in a perfectly liquid market, and the gains from trade with a monopoly liquidity supplier. The current gains are 90 % of the maximum gains and 150 % of the monopolist gains. THE MAJORITY OF THE WORLD’S STOCK EXCHANGES operate some form of a limit-order market. A feature of good market design is that traders realize most of the potential gains from trade. We develop a method for identifying and estimating the gains from trade in a limit-order market and apply our method to a sample from a particular limit-order market, the Vancouver Stock Exchange (VSE). We find that gains from trade in the VSE are approximately 90 % of the gains from trade in a perfectly liquid market and approximately 50 % more than gains from trade in a market in which liquidity is supplied by a profit-maximizing monopolist. Our results therefore provide new empirical evidence that the limit-order market is a good market design. A large number of experimental studies document that the gains from trade in a double auction are close to the maximum gains from trade. See, for ex-ample, Cason and Friedman (1996) or the survey by Holt (1995). Similarly, our empirical results show that the limit-order market—a market design similar to ∗Hollifield and Miller are from Carnegie Mellon University, Sanda s is from the University of
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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 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".