Detecting Financial Market Manipulation with Statistical Physics Tools

ArXiv ID: 2308.08683 “View on arXiv”

Authors: Unknown

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.

Keywords: Statistical Physics, Order Book Dynamics, Anomaly Detection, Spoofing & Layering, Flash Crash Analysis, Cryptocurrency (LUNA & Bitcoin)

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 6.0/10
• Quadrant: Lab Rats
• Why: The paper develops a novel, non-trivial theoretical framework by mapping order book dynamics to particle physics, involving velocity and momentum definitions, but its application is primarily focused on detecting manipulation in historical cryptocurrency data (LUNA and Bitcoin) rather than providing full backtest-ready implementation details.

  flowchart TD
    A["Research Goal<br>Develop a physics-inspired<br>market manipulation detector"] --> B["Methodology<br>Statistical Physics Framework"]
    B --> C["Data<br>High-frequency LUNA &<br>Bitcoin order book data"]
    C --> D["Computation<br>Momentum Measure Calculation<br>Particle Motion Model"]
    D --> E{"Analysis"}
    E --> F["Flash Crash Analysis<br>Detected LUNA crash manipulation"]
    E --> G["Performance Comparison<br>Outperforms Z-score baseline"]
    F & G --> H["Key Findings<br>Novel spoofing/layering detection<br>Physics tools > traditional methods"]