Detecting Financial Market Manipulation with Statistical Physics Tools

TL;DR

This paper introduces a physics-inspired framework for analyzing financial markets, effectively detecting manipulation activities like spoofing and layering, and outperforming traditional methods in cryptocurrency markets.

Contribution

It develops a novel statistical physics-based approach to identify market manipulation, providing a new tool for financial anomaly detection.

Findings

Successfully detected spoofing and layering during the LUNA flash crash.

Outperformed Z-score-based methods in identifying market manipulations.

Uncovered widespread manipulation in cryptocurrency markets.

Abstract

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.