Direct Evidence of Bitcoin Wash Trading

Arash Aloosh and Jiasun Li ()
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Arash Aloosh: Léonard de Vinci Pôle Universitaire, Research Center, 92916 Paris La Défense, France
Jiasun Li: George Mason University, Fairfax, Virginia 22030

Management Science, 2024, vol. 70, issue 12, 8875-8921

Abstract: We use the internal trading records of a major Bitcoin exchange leaked by hackers to detect and characterize wash trading—a type of market manipulation in which a single trader clears the trader’s own limit orders to “cook” transaction records. Our finding provides direct evidence for the widely suspected “fake volume” allegation against cryptocurrency exchanges, which has so far only been backed by indirect estimation. We then use our direct evidence to evaluate various indirect techniques for detecting the presence of wash trades and find measures based on Benford’s law, trade size clustering, lognormal distributions, and structural breaks to be useful, whereas ones based on power law tail distributions to give opposite conclusions. We also provide suggestions to effectively apply various indirect estimation techniques.

Keywords: bitcoin; cryptocurrency; exchanges; forensics; market manipulation; regulation (search for similar items in EconPapers)
Date: 2024
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