Hawkes-Process Forecasting of High-Frequency Order Flow Imbalance

Summary

The paper studies order flow imbalance (OFI), an indicator based on the asymmetry between bid-side and offer-side events that is associated with short-term price direction. It uses Hawkes processes to estimate OFI while modeling lagged dependence between bid and offer order flows. The authors also introduce a way to forecast the near-term distribution of OFI and methods for comparing forecasts across multiple models.

The approach is applied to tick data from the National Stock Exchange. In that comparison, a Hawkes process with a sum-of-exponentials kernel produces the best forecast among the tested models. The description does not identify the competing models, forecast horizon, evaluation measures, or trading outcomes. Forecasting OFI distributions may support model assessment, but the stated result alone does not establish profitability after execution costs or generalization to other venues.

Key ideas

• Bid and offer event flows may be asymmetric and dependent, and that imbalance can relate to price movement.
• Hawkes processes model lagged dependence between the bid and offer flows used to estimate OFI.
• The paper forecasts the near-term OFI distribution and compares forecasts from multiple models.
• On the described exchange tick data, a sum-of-exponentials Hawkes kernel gives the strongest forecast among the tested models.

Tags

# Forecasting High Frequency Order Flow Imbalance


# Forecasting High Frequency Order Flow Imbalance









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.

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