Exploration Of Stock Price Predictability In HFT With An Application In Spoofing Detection
Notice bibliographique
Résumé
Today many brokerage firms use computer algorithms to make trade decisions, submit orders, and manage orders after submission. This algorithmic trading is required to maximize execution speed and so minimize the cost, market impact and risk associated with trading large volumes of securities. Traders place orders to buy or sell a given amount of a security for a specific price on an exchange. These buy and sell orders accumulate in the `order book' until they either find a counter-party for execution or are canceled. All participants can also issue market orders to buy or sell at the best available prices; these orders are immediately executed on a `first come first serve' basis.\nUsing high frequency trading (HFT) data on the Toronto Stock Exchange, provided by the TMX Group, we explore a data driven model to detect a form of high frequency price manipulation -- known as spoofing. A spoofer manipulates prices by placing limit orders which they do not intend to be executed in order to mislead other traders about the available volume of shares. The hope is that this will cause prices to move in their favor. We show that a generalized form of volume imbalance is associated with price movements and this can be manipulated by spoofing strategies. The literature argues spoofing strategies are detrimental to the integrity of markets and new models are necessary for regulators to combat them.\nThe size of the data sets we use definitely qualify for the moniker `Big Data'. The limit order book must be constructed each time an order arrives for a particular stock. This process is implemented on a distributed data system using Pyspark since it would be impossible to do so, efficiently, on a local machine. We discuss some issues and complications that arise from working with very large data sets of this type.\nWe define a generalized volume imbalance as the weight in a convex combination of two price change distributions which forms our price change model. Price changes for different stocks happen at different time scales. We remedy this issue by comparing stocks on time intervals over which they all have the same variance in their price change distributions. Statistical and goodness of fit tests using Cramer's V statistic and Kullback–Leibler divergence, respectively, are implemented to validate our model across a large collection of stocks. The model is then used to test the sensitivity of the limit order book to spoofing and derive relationships between the spoofer's constraints and their optimal decisions. These results could then be implemented by regulators as a way to flag periods of the trading day where market conditions make spoofing possible as a means to improve market surveillance.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,005 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».