Bibliographic record
Abstract
which are now running at extremely high levels.For example, according to the AMF surveillance department, three firms that accounted for 39.6 percent of orders on the CAC-40 in April 2010 cancelled 96.5 percent of these orders.This tactic of order cancellation, which can be used for aggressive or defensive purposes, has played a core role in the divergence of executable liquidity and net executed volume, an imbalance known as the concept of "disappearing liquidity."For example, the joint Commodity Futures Trading Commission (CFTC)-SEC report on the 6 May "flash crash" (in discussing the 14-second "hot potato" period) notes that "high trading volume is not necessarily a reliable indicator of market liquidity."5 On the European side, a recent study of Chi-X traded Dutch stocks stated that the introduction of a new HFT "middleman" "lowers bid-ask spreads but also lowers volume."6 Although HFT firms would note that their strategies are modifications of such well-known trading strategies as directional trading, arbitrage trading, and market making, these headline techniques raise concerns.What matters is not so much the descriptive name of the strategy but rather how that strategy is implemented at the tactical and operational levels.For example, the "market-making strategy" is self-descriptive and crucially non-contractual, 7 and, as a result, high-frequency traders have decisionmaking control over when to provide liquidity for stocks of their choosing and at a price that is suitable for them, as opposed to the formal regulatory obligations that we are familiar with under the banner of "market making."Consequently, this particular topic is now the subject of much regulatory debate.Because HFT accounts for the lion' s share of trading volume, it naturally has some considerable impacts on the capital market.Some heavyweight buy-side firms, such as Principal Global Investors with more than US$225 billion in assets under management, have recently voiced concern over the consequences of HFT for market trust, confidence, and efficiency.The major impact is on liquidity.HFT has led to a reduction in bid-ask spreads and an increase in trading volume in the definitive sense, and indeed, both institutional and retail investors can certainly benefit from lower bid-ask spreads.This benefit comes with a caveat, however.As already noted, trading volume is not necessarily a reliable indicator of market liquidity, especially in times of significant volatility.The automated execution of large orders by institutional investors, which often use trading volume as the proxy for liquidity, could help trigger excessive price movement and extraordinary losses, as evidenced in the 6 May flash crash.
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 | 0.005 | 0.049 |
| 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 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".