The Impact of High-Frequency Trading on Markets
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,049 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,003 |
| Communication savante | 0,007 | 0,008 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».