State Dependent Parallel Neural Hawkes Process for Limit Order Book Event Stream Prediction and Simulation

Abstract

The majority of trading in financial markets is executed through a limit order book (LOB). The LOB is an event-based continuously-updating system that records contemporaneous demand (`bids' to buy) and supply (`asks' to sell) for a financial asset. Following recent successes in the literature that combine stochastic point processes with neural networks to model event stream patterns, we propose a novel state-dependent parallel neural Hawkes process to predict LOB events and simulate realistic LOB data. The model is characterized by: (1) separate intensity rate modelling for each event type through a parallel structure of continuous time LSTM units; and (2) an event-state interaction mechanism that improves prediction accuracy and enables efficient sampling of the event-state stream. We first demonstrate the superiority of the proposed model over traditional stochastic or deep learning models for predicting event type and time of a real world LOB dataset. Using stochastic point sampling from a well trained model, we then develop a realistic deep learning-based LOB simulator that exhibits multiple stylized facts found in real LOB data.

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