Forecasting High Frequency Order Flow Imbalance

TL;DR

This paper introduces a Hawkes process-based method to estimate and forecast high-frequency order flow imbalance (OFI), improving prediction accuracy by accounting for dependencies in order flow data.

Contribution

It develops a novel approach using Hawkes processes for OFI estimation and forecasting, along with a model comparison framework for high-frequency trading data.

Findings

Hawkes process with Sum of Exponentials kernel outperforms other models in forecasting OFI.

The method effectively captures lagged dependencies in bid and offer order flows.

Application to real market data demonstrates practical forecasting improvements.

Abstract

Market information events are generated intermittently and disseminated at high speeds in real-time. Market participants consume this high-frequency data to build limit order books, representing the current bids and offers for a given asset. The arrival processes, or the order flow of bid and offer events, are asymmetric and possibly dependent on each other. The quantum and direction of this asymmetry are often associated with the direction of the traded price movement. The Order Flow Imbalance (OFI) is an indicator commonly used to estimate this asymmetry. This paper uses Hawkes processes to estimate the OFI while accounting for the lagged dependence in the order flow between bids and offers. Secondly, we develop a method to forecast the near-term distribution of the OFI, which can then be used to compare models for forecasting OFI. Thirdly, we propose a method to compare the forecasts of OFI for an arbitrarily large number of models. We apply the approach developed to tick data from the National Stock Exchange and observe that the Hawkes process modeled with a Sum of Exponential's kernel gives the best forecast among all competing models.