Online Learning of Order Flow and Market Impact with Bayesian Change-Point Detection Methods

TL;DR

This paper introduces a Bayesian online change-point detection method tailored for real-time identification of regime shifts in order flow, improving market impact predictions by capturing persistence and temporal correlations in financial data.

Contribution

It develops a novel score-driven BOCPD model that accounts for temporal correlations and time-varying parameters, enhancing regime detection and prediction accuracy in financial markets.

Findings

Superior out-of-sample predictive performance over existing models

Good model specification regarding distribution and temporal correlations

More accurate online predictions of order flow and market impact

Abstract

Financial order flow exhibits a remarkable level of persistence, wherein buy (sell) trades are often followed by subsequent buy (sell) trades over extended periods. This persistence can be attributed to the division and gradual execution of large orders. Consequently, distinct order flow regimes might emerge, which can be identified through suitable time series models applied to market data. In this paper, we propose the use of Bayesian online change-point detection (BOCPD) methods to identify regime shifts in real-time and enable online predictions of order flow and market impact. To enhance the effectiveness of our approach, we have developed a novel BOCPD method using a score-driven approach. This method accommodates temporal correlations and time-varying parameters within each regime. Through empirical application to NASDAQ data, we have found that: (i) Our newly proposed model demonstrates superior out-of-sample predictive performance compared to existing models that assume i.i.d. behavior within each regime; (ii) When examining the residuals, our model demonstrates good specification in terms of both distributional assumptions and temporal correlations; (iii) Within a given regime, the price dynamics exhibit a concave relationship with respect to time and volume, mirroring the characteristics of actual large orders; (iv) By incorporating regime information, our model produces more accurate online predictions of order flow and market impact compared to models that do not consider regimes.