Subsidizing liquidity: The impact of make/take fees on market quality, SSRN eLibrary
Bibliographic record
Abstract
In recent years most equity trading platforms moved to subsidize the provision of liquidity, using a so-called make/take fee structure. Under this structuresubmittersoflimitorderstypicallyreceivearebateuponexecutionoftheir orders, and the exchange covers its costs by charging a higher fee for executed market orders. We provide the first empirical study on the impact of this, now prevalent, fee structure on market quality, market efficiency, and trading activity by analyzing the introduction of liquidity rebates on the Toronto Stock Exchange. Using a proprietary dataset, we find that the liquidity rebate structure leads to decreased spreads, increased depth, increased volume, and intensified competition in liquidity provision. Explicitly accounting for exchange fees and rebates, we further find that trading costs for market orders did not decrease and that per share revenues for liquidity providers increase, despite the reduced bid ask spreads. Finally, we find no evidence for changes in intermediation or market efficiency. JEL Classification: G12, G14.
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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 | 0.001 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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".