Deep Learning for Market by Order Data

Zihao Zhang,Bryan Lim,Stefan Zohren

doi:10.1080/1350486x.2021.1967767

Abstract

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 normalization 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.

Highlights

High-frequency microstructure data has received growing attention both in academia and industry with the computerisation of financial exchanges and the increase capacity of data storage
If we look at the Pearson correlation between predictive signals in Figure 3, we can see that predictive signals from the Market by Order (MBO) data are less correlated with limit order books (LOBs)’s signals
In this work we introduce deep learning models for Market by Order (MBO) data

Summary

Introduction

High-frequency microstructure data has received growing attention both in academia and industry with the computerisation of financial exchanges and the increase capacity of data storage. The detailed records of order flow and price dynamics provide us with a granular description of short-term supply and demand, and we can take the dynamics of order books into account during the modelling process. Propelled by the publication of the benchmark dataset (Ntakaris et al 2018) of high-frequency limit order book (LOB) data, there has been a growing interest in research studying LOB data. Recent works by Tsantekidis et al (2017); Sirignano and Cont (2019); Zhang, Zohren, and Roberts (2019a); Briola, Turiel, and Aste (2020) demonstrate that strong predictive performance can be obtained from modelling high-frequency LOB data and with resulting predictions finding applications in market-making and trade execution which have short holding periods. We introduce Market by Order (MBO) data for predictive modelling with deep learning algorithms.

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