Adverse Selection in a High-Frequency Trading Environment
Bibliographic record
Abstract
Fear of adverse selection has been cited by buy-side traders as one of the reasons for the decline in block market share, and concerns of adverse selection from executing in dark pools and with high frequency trading firm contras have also been raised. The authors describe and define adverse selection for both block and non-block executions. They define some quantitative metrics to characterize the degree of adverse selection exhibited by Canadian dark executions as well as to capture both the idiosyncratic volatility of the stock being measured and the size of the execution. In the next step, they look at actual executions, both block and non-block, and characterize the level of observed adverse selection. The authors compare their results to a randomized control group for the block trades and compute previously published adverse selection metrics for the non-block execution set. They find statistically significant levels of adverse selection for both block and non-block executions, more traditional adverse selection in the open access dark ATS than in the buy-side-only dark ATS, and a small but statistically significant amount of adverse selection for midsize trades in the open access ATS as a result of resting liquidity in the dark pool interacting with continuous flow passing through. Finally, the authors discuss the implications of the results on algorithmic trading and transaction cost analysis. TOPICS:Statistical methods, exchanges/markets/clearinghouses, equity portfolio management
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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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".