@inbook{fee80600687e4995a2edbc1c9b6e714b,
title = "Detecting Financial Market Manipulation with Statistical Physics Tools",
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.",
author = "Haochen Li and Maria Polukarov and Carmine Ventre",
note = "Funding Information: Haochen Li acknowledges support from the Alan Turing Institute (grant no. EP/N510129/1) through its Enrichment Scheme. And the authors would like to thank the valuable feedback of Adam Ostaszewski, Merlin Mei, Ke Chen, and Yue Xiao. Publisher Copyright: {\textcopyright} 2023 ACM.",
year = "2023",
month = nov,
day = "27",
doi = "10.1145/3604237.3626871",
language = "English",
series = "ICAIF 2023 - 4th ACM International Conference on AI in Finance",
pages = "565--573",
booktitle = "ICAIF 2023 - 4th ACM International Conference on AI in Finance",
}
