Sequence classification of the limit order book using recurrent neural networks

By Matthew F. Dixon. 2017

92 citations recorded by the source corpus.

Recurrent neural networks (RNNs) are types of artificial neural networks (ANNs) that are well suited to forecasting and sequence classification. They have been applied extensively to forecasting univariate financial time series, however their application to high frequency trading has not been previously considered. This paper solves a sequence classification problem in which a short sequence of observations of limit order book depths and market orders is used to predict a next event price-flip. The capability to adjust quotes according to this prediction reduces the likelihood of adverse price selection. Our results demonstrate the ability of the RNN to capture the non-linear relationship between the near-term price-flips and a spatio-temporal representation of the limit order book. The RNN compares favorably with other classifiers, including a linear Kalman filter, using S&P500 E-mini futures level II data over the month of August 2016. Further results assess the effect of retraining the RNN daily and the sensitivity of the performance to trade latency.

Subfields: Economics and Econometrics, Management Science and Operations Research

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Citations in the corpus

Deep Learning for Financial Applications : A Survey (2020, methodology): “[70] used RNN for the sequence classification of the limit order book to predict a next event price-flip.”
Universal features of price formation in financial markets: perspectives from deep learning (2018, methodology): “This is one of the reasons why, except in a few instances (Sirignano et al. 2016, Kolanovic and Krishnamachari 2017, Dixon 2018a, 2018b, Sirignano 2019), largescale machine learning methods such as Deep Learning (Goodfellow et al. 2017) have not yet been deployed for quantitative modeling in…”
DeepLOB: Deep Convolutional Neural Networks for Limit Order Books (2018, background): “Unlike CNNs which are less widely applied in financial markets, the LSTM has been popular in recent years, [42, 28, 43, 44, 45, 46, 47, 20] all utilising LSTMs to analyse financial data.”
A High Frequency Trade Execution Model for Supervised Learning (2017, unknown): “Following Kercheval and Zhang (2015); Sirignano (2016); Dixon (2017), we compose our feature set of ﬁve levels of prices, volumes and number of limit orders on both the ask and bid side of the book.”