  Market by order (MBO) data - a detailed feed of individual trade instructions
for a given stock on an exchange - is arguably one of the most granular sources
of microstructure information. While limit order books (LOBs) are implicitly
derived from it, MBO data is largely neglected by current academic literature
which focuses primarily on LOB modelling. In this paper, we demonstrate the
utility of MBO data for forecasting high-frequency price movements, providing
an orthogonal source of information to LOB snapshots. We provide the first
predictive analysis on MBO data by carefully introducing the data structure and
presenting a specific normalisation scheme to consider level information in
order books and to allow model training with multiple instruments. Through
forecasting experiments using deep neural networks, we show that while
MBO-driven and LOB-driven models individually provide similar performance,
ensembles of the two can lead to improvements in forecasting accuracy --
indicating that MBO data is additive to LOB-based features.
