  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 and expanding the universe
of alpha discovery. 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.
