Detecting Financial Market Manipulation with Statistical Physics Tools
Using statistical physics to detect financial market manipulation, outperforming traditional methods in cryptocurrency markets.
What it examines
This paper introduces a statistical physics approach to analyze financial markets, focusing on detecting market manipulation in decentralized digital markets. By modeling order book dynamics as particle motion, the study aims to identify spoofing and layering activities, particularly during the LUNA cryptocurrency flash crash.
What it concludes
The research offers a new method for detecting market manipulation, with potential applications in enhancing AI models for financial markets. Future research could explore additional physical concepts and apply this approach to other asset classes like equities and futures.
Evidence objects
The research offers a new method for detecting market manipulation, with potential applications in enhancing AI models for financial markets. Future research could explore additional physical concepts and apply this approach to other asset classes like equities and futures.
key_findings bullet 1 · key_findings · validation V0
Raw abstract and provenance
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 mar… ▽ More 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. △ Less
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