The Value of a Millisecond: Structural Segmentation of Uninformed Order Flow
Bibliographic record
Abstract
In this paper, we investigate the consequences of segregating retail order flow away from incumbent exchanges on overall market quality and trading costs, as well as identifying the welfare gains and losses for different groups of market participants. We exploit an exogenous shock to the equity market landscape in Canada, where one of the exchange venues, TSX Alpha, implemented a randomized speed bump for marketable orders only with an inverted fee structure. These changes make it attractive for small orders, while simultaneously making it very unattractive for large informed orders that are more likely to impose adverse selection costs. Canada is uniquely suited for this analysis, because it previously disallowed any type of internalization or payment for order flow, and dark trading is limited due to minimum price improvement regulations. We first document that the design changes led to a sizeable increase in the proportion of uninformed order flow on the relaunched Alpha. Second, we analyse market quality after the change. Among the other exchanges, we find widening effective spreads for liquidity demanders at the same time as reduced realized spreads for liquidity suppliers, resulting in welfare losses for both groups. We find that this is driven by increases in the adverse selection costs that liquidity suppliers face due to a higher probability of facing informed traders. The clear winners are liquidity suppliers on new Alpha who benefit from wider spreads and lower adverse selection, which outweigh increases in passive trading fees. Overall, the segmentation of uninformed order flow appears detrimental to market quality and aggregate welfare.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.007 | 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".