Deep Learning for Market by Order Data

TL;DR

This paper explores the use of Market by Order data for high-frequency price movement forecasting, demonstrating its additive value to traditional limit order book models through deep learning techniques.

Contribution

It introduces the first predictive analysis of MBO data, including data normalization and multi-instrument training, showing its complementary role to LOB data in forecasting.

Findings

MBO data can improve price movement predictions when combined with LOB data.

Deep neural networks effectively utilize MBO data for forecasting.

Ensemble models outperform individual MBO or LOB models.

Abstract

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.