  Previous attempts to predict stock price from limit order book (LOB) data are
mostly based on deep convolutional neural networks. Although convolutions offer
efficiency by restricting their operations to local interactions, it is at the
cost of potentially missing out on the detection of long-range dependencies.
Recent studies address this problem by employing additional recurrent or
attention layers that increase computational complexity. In this work, we
propose Axial-LOB, a novel fully-attentional deep learning architecture for
predicting price movements of stocks from LOB data. By utilizing gated
position-sensitive axial attention layers our architecture is able to construct
feature maps that incorporate global interactions, while significantly reducing
the size of the parameter space. Unlike previous works, Axial-LOB does not rely
on hand-crafted convolutional kernels and hence has stable performance under
input permutations and the capacity to incorporate additional LOB features. The
effectiveness of Axial-LOB is demonstrated on a large benchmark dataset,
containing time series representations of millions of high-frequency trading
events, where our model establishes a new state of the art, achieving an
excellent directional classification performance at all tested prediction
horizons.
