Bayesian Change-Point Detection for Order Flow and Market Impact
Summary
The paper models persistent buy and sell order flow as evidence of changing market regimes, which can arise as large orders are divided and executed over time. It proposes a Bayesian online change-point detection method with a score-driven model that allows serial dependence and parameters to vary within each regime. The goal is to detect shifts in real time and improve forecasts of both order flow and price impact.
An empirical application to NASDAQ data reports better out-of-sample predictions than regime models that assume independent observations, and residual checks indicate that the proposed model captures distributional and temporal features reasonably well. The authors also find that within regimes, price dynamics relate concavely to time and volume, and that incorporating regime information improves online forecasts over models without it. These findings are specific to the studied data and model comparisons; the document does not provide implementation details or evidence across other markets.
Key ideas
- Order flow persistence can reflect the gradual execution of large orders.
- Bayesian online change-point detection can identify order flow regime shifts as they occur.
- A score-driven model accommodates temporal dependence and changing parameters within regimes.
- The NASDAQ application reports improved forecasts of order flow and market impact when regime information is included.
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Full text
# Online Learning of Order Flow and Market Impact with Bayesian Change-Point Detection Methods # Online Learning of Order Flow and Market Impact with Bayesian Change-Point Detection Methods 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.
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