  We showcase how Quantile Regression (QR) can be applied to forecast financial
returns using Limit Order Books (LOBs), the canonical data source of
high-frequency financial time-series. We develop a deep learning architecture
that simultaneously models the return quantiles for both buy and sell
positions. We test our model over millions of LOB updates across multiple
different instruments on the London Stock Exchange. Our results suggest that
the proposed network not only delivers excellent performance but also provides
improved prediction robustness by combining quantile estimates.
