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Sequence classification of the limit order book using recurrent neural networks - 2018

Research Area:  Machine Learning

Abstract:

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.

Author(s) Name:  Matthew Dixon

Journal name:  Journal of Computational Science

Conferrence name:  

Publisher name:  ELSEVIER

DOI:  10.1016/j.jocs.2017.08.018

Volume Information:  Volume 24, January 2018, Pages 277-286