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. This paper was accepted by Professor Bruno Biais, finance. Funding: J. Li acknowledges support by the U.S. Department of Homeland Security [Grant 205187] through the Criminal Investigations and Network Analysis Center. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2021.01448 .
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