Deep Learning for Market by Order Data
This work addresses the need for better predictive models in financial markets by exploring an orthogonal data source, though it is incremental as it builds on existing LOB methods.
The paper tackles the problem of forecasting high-frequency price movements by utilizing market by order (MBO) data, an underused granular source, and shows that ensembles combining MBO and limit order book (LOB) models improve forecasting accuracy, indicating additive information.
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.