Trade size, high-frequency trading, and colocation around the world
Bibliographic record
Abstract
We examine the impact of changes in market microstructure, particularly algorithmic trading (AT) and high-frequency trading (HFT), on trade size across 24 stock exchanges around the world. Using colocation services as a proxy for AT and HFT, we find mixed results on the impact of AT and HFT on the average trade size. Furthermore, we test whether the presence of HFT leads to the introduction of colocation services. The data are consistent with the view that HFT pre-dates colocation by at least eight months on most exchanges, and has strong power in explaining the introduction of colocation services. In effect, our results show that colocation services do not properly measure effective AT and HFT; rather, colocation services are the result of HFT. Exchanges choose to offer colocation services due to the fact HFT requires higher speed transactions. Finally, we show there have been substantial changes in trade size in other countries such as China where there is no HFT and offer explanations for these changes and suggest avenues for future research.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".