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.
翻译:按顺序排列的市场数据(MBO)数据——对交易所特定股票的个别贸易指示的详细反馈——可以说是微结构信息的最颗粒来源之一。虽然限制订单簿(LABBs)是间接来源,但目前主要侧重于LOB建模的学术文献在很大程度上忽略了MBO数据。在本文中,我们展示了MBO数据对预测高频价格波动的有用性,为LOB快照提供了一个正统的信息来源。我们通过仔细介绍数据结构,对MBO数据提供了第一个预测性分析。我们提出了一个具体的正常化计划,以考虑以书籍为目的的层次信息,并允许用多种仪器进行示范培训。我们通过利用深层神经网络进行预测实验,我们表明,虽然MBO驱动的和LOB驱动的模型单独提供类似的性能,但两者的组合可以导致预测准确性的改进 -- 表明MBO数据是基于LAB特征的添加剂。