  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.
