  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.
