Statistical Physics Measures for Detecting Spoofing and Layering

Summary

The document proposes representing order book dynamics as the motion of particles and summarizing the market state with a momentum measure. This statistical physics framing is intended to capture market behavior that may signal manipulation, particularly spoofing and layering. The method is applied to order book activity around the LUNA cryptocurrency flash crash, where the authors report finding widespread examples of both behaviors.

The document also reports comparisons with conventional Z-score anomaly detection across LUNA and Bitcoin markets, claiming better identification of manipulative activity with the proposed approach. It offers a high-level account rather than implementation details: the text does not define the momentum measure, explain parameter choices, or provide quantitative performance results. Its findings therefore indicate a possible detection framework, but the summary alone does not establish how robust it is across venues, time periods, or other forms of manipulation.

Key ideas

• Order book activity is modeled as particle motion inspired by statistical physics.
• A system-level momentum measure is used to summarize market state.
• The approach is applied to detecting spoofing and layering in LUNA and Bitcoin markets.
• The authors report stronger identification than Z-score anomaly detection, without giving performance details here.

Tags

# Detecting Financial Market Manipulation with Statistical Physics Tools


# Detecting Financial Market Manipulation with Statistical Physics Tools









We take inspiration from statistical physics to develop a novel conceptual framework for the analysis of financial markets. We model the order book dynamics as a motion of particles and define the momentum measure of the system as a way to summarise and assess the state of the market. Our approach proves useful in capturing salient financial market phenomena: in particular, it helps detect the market manipulation activities called spoofing and layering. We apply our method to identify pathological order book behaviours during the flash crash of the LUNA cryptocurrency, uncovering widespread instances of spoofing and layering in the market. Furthermore, we establish that our technique outperforms the conventional Z-score-based anomaly detection method in identifying market manipulations across both LUNA and Bitcoin cryptocurrency markets.

Shown in full with attribution under the source's licence. Licence: abstract CC0

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