Bibliographic record
Abstract
High frequency trading (HFT) is taking world capital markets by storm, notably in the United States and the United Kingdom, where it accounted for about 50 percent of equities trading in 2012, and to a growing extent in other parts of Europe and in Canada. Are high frequency traders angels or devils in terms of the impact on capital markets? Critics claim the latter and charge that they put retail and institutional investors at a disadvantage. Critics also blame high frequency trading for the “flash crash” on the Dow of May 6 2010 and say it has increased the likelihood of such events happening again. A closer examination of these views is in order. In this Commentary, I first look at what HF traders do and how HFT differs from traditional market making. I then explore the empirical evidence relating to the effect of HFT on capital markets, and canvass the policy issues that HFT raises. In the final section, I list some recommendations for policymakers with respect to HFT. After surveying empirical studies of HFT, I conclude that it enhances market quality. For example, it lowers bid/ask spreads, reduces volatility, improves short-term price discovery, and creates competitive pressures that reduce broker commissions. Despite being at a pronounced speed disadvantage, retail traders have realized a net gain from the presence of HF traders in the world’s capital markets. Maintain the Order Protection Rule and Contain the Spread of Dark Pools: To prevent abusive trading practices, protect client interests, and create a level playing field among different trading venues, policymakers should defend the consolidated order book by maintaining and policing the order protection rule and minimizing the leakage of trading from the “lit” markets to “dark pools.” Do Not Interfere with Maker/Taker Pricing Models: Some observers say maker/taker pricing raises higher trading costs for retail traders, because retail trade orders are typically on the active side of the market, and associated fees are passed on to customers. However, retail traders are about as likely to be on the active as the passive side of the market. Maker/taker pricing may raise costs on the margin, but also lowers bid/ask spreads. Focus on Circuit Breakers to Prevent “Flash Crashes”: HF traders did not cause the “flash crash, ” and instead supply liquidity when markets become volatile. Canadian regulators concerned with preventing similar events should focus on circuit breakers to stop market anomalies before they turn into “flash crashes.”
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".