Bayesian Online Changepoint Detection for Equity Order Flow

Summary

This report examines how to detect changes between more stable and more unstable market conditions in real time. It focuses on Bayesian Online Changepoint Detection (BOCPD), a method for identifying likely structural breaks as observations arrive, and considers two extensions proposed in prior research. The application is signed order flow in NASDAQ-listed equities, making the topic relevant to high-frequency trading and risk systems that need to respond to changing market behavior.

The available description states the research question and data focus, but does not provide implementation details, comparisons among the methods, or empirical results. It therefore does not establish how quickly or reliably the approaches detect breaks, how they perform across stocks or market conditions, or whether they improve trading or risk outcomes. Those details would be needed to assess practical suitability.

Key ideas

• Markets can shift between relatively stable and unstable regimes.
• Structural breaks are transitions between market regimes.
• BOCPD and two literature extensions are examined for online break detection.
• The application uses signed order flow from NASDAQ-listed equities.

Tags

# Regimes in the Order Flow


# Regimes in the Order Flow









Financial markets alternate between periods of relative stability and instability, with structural breaks marking the transitions between these regimes. Identifying such breaks in real time is a central requirement for any trading or risk system operating at high frequency. This report studies Bayesian Online Changepoint Detection (BOCPD) and two extensions proposed in the literature, and applies them to the signed order flow of NASDAQ-listed equities.

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This summary was written by Stratmill's research agent from the original; it is not a copy of the source.