Early Detection of Latent Microstructure Regimes in Limit Order Books

2026-04-24 · Source: stat.ML updates on arXiv.org · Field: Finance & Economics — Capital Markets & Investment Management, FinTech & Digital Financial Services · Depth: Expert, extended

Summary

A new study introduces a trigger-based detector for early identification of latent microstructure regimes in limit order books (LOBs), aiming to predict liquidity stress before it becomes visible. The researchers formalize a three-regime causal data-generating process (stable → latent build-up → stress) and prove that the latent build-up phase is identifiable. They derive theoretical guarantees, including a sufficient drift-to-noise condition for positive expected lead-time (Proposition 1) and a lower bound on detection probability before stress onset (Proposition 2). The proposed detector, which combines MAX aggregation of uncertainty and drift channels, a rising-edge condition, and an adaptive threshold, achieved a mean lead-time of +18.6 ± 3.2 timesteps, precision of 1.00 ± 0.00, and coverage of 0.54 ± 0.06 across 200 simulation runs. In a preliminary real-data application on one week of BTC/USDT order book data, it showed a mean lead-time of +38 ± 21 seconds, precision of 1.00, and coverage of 0.80, outperforming CUSUM, BOCPD, HMM thresholding, and imbalance/volatility baselines.

Key takeaway

For Machine Learning Engineers building financial early warning systems, prioritize signals causally upstream of stress, such as LOB depth erosion and HMM entropy, over reactive metrics like volatility or imbalance. Your system should incorporate a rising-edge condition and adaptive thresholds to achieve positive lead-time and maintain precision, especially in high-frequency trading environments where timely detection of liquidity stress is critical.

Key insights

Latent LOB microstructure regimes can be detected early to predict liquidity stress with positive lead-time.

Principles

• Early warning signals must target precursors, not consequences.
• Positive lead-time requires sufficient drift-to-noise ratio.
• Coverage and lead-time exhibit a fundamental trade-off.

Method

A trigger-based detector uses MAX aggregation of HMM entropy, depth erosion, spread drift, and order flow momentum, combined with a rising-edge condition and adaptive thresholding to identify latent build-up.

In practice

• Prioritize depth erosion and HMM entropy for early detection.
• Use adaptive thresholds for robustness across market regimes.
• Implement a rising-edge condition to target onset, not peak.

Topics

• Limit Order Books
• Latent Microstructure Regimes
• Liquidity Stress Detection
• Early Warning Systems
• Hidden Markov Models

Code references

• prakulhiremath/LOB-Latent-Regimes

Best for: AI Scientist, Research Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by stat.ML updates on arXiv.org.