Next up in the Cornell-Citi webinar series is Dr. Zihao Zhang, who spoke on Tuesday, Oct. 26.

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.

Speaker Bio: Dr. Zihao Zhang is a postdoctoral researcher at the Oxford-Man Institute and Machine Learning Research Group at the University of Oxford. Zihao’s research focuses on quantitative finance with a special emphasis on applying deep learning models to financial time series modelling. Zihao’s current projects include portfolio optimisation and reinforcement learning. Zihao holds a PhD and MSc in Applied Statistics from the University of Oxford and a BSc in Economics and Statistics from University College London.