Modelling high-frequency limit order book dynamics with support vector machines

DOI10.1080/14697688.2015.1032546zbMath1406.91511OpenAlexW1505330957MaRDI QIDQ4619497

Yuan Zhang, Alec Norton

Publication date: 6 February 2019

Published in: Quantitative Finance (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/14697688.2015.1032546

zbMATH Keywords

machine learningsupport vector machines (SVMs)multi-class classifiershigh-frequency limit order book

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Estimation and detection in stochastic control theory (93E10) Signal detection and filtering (aspects of stochastic processes) (60G35) Data smoothing in stochastic control theory (93E14) Stochastic calculus of variations and the Malliavin calculus (60H07) Actuarial science and mathematical finance (91G99)

Related Items (8)

ESTIMATION OF A HIGH-DIMENSIONAL COUNTING PROCESS WITHOUT PENALTY FOR HIGH-FREQUENCY EVENTS ⋮ A generative model of a limit order book using recurrent neural networks ⋮ Deep order flow imbalance: Extracting alpha at multiple horizons from the limit order book ⋮ Forecasting jump arrivals in stock prices: new attention-based network architecture using limit order book data ⋮ Deep learning for limit order books ⋮ Exploiting social media with higher-order Factorization Machines: statistical arbitrage on high-frequency data of the S&P 500 ⋮ Encoding of high-frequency order information and prediction of short-term stock price by deep learning ⋮ Learning multi-market microstructure from order book data

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