Open AccessOrder flow dynamics for prediction of order cancelation and applications to detect market manipulation
Author(s) -
Martínez Miranda Enrique,
Phelps Steve,
Howard Matthew J.
Publication year - 2019
Publication title -
high frequency
Language(s) - English
Resource type - Journals
ISSN - 2470-6981
DOI - 10.1002/hf2.10026
Subject(s) - order (exchange) , computer science , limit (mathematics) , order book , binary number , flow (mathematics) , artificial intelligence , prediction market , adversary , machine learning , data mining , econometrics , economics , computer security , mathematics , finance , mathematical analysis , geometry , arithmetic
In this work, a methodology is proposed to detect and predict the intention of cancelation of a large order at an optimal event‐time horizon by analyzing real order flow market data. We achieve this by reconstructing the full history of the limit order book and formulate the case as a binary classification supervised learning problem. The results presented in this study suggest that using the information at the microstructure level of the order book is highly efficient for predicting and detecting the cancelation of the large order than using information at the macrostructure level, and that predicting the cancelation is marginally outperformed by the detection case. With this, we make a step forward in identifying potential orders related to price manipulation but the results can be used by institutional traders to anticipate adversary market impact produced by large orders.