Direct Evidence of Bitcoin Wash Trading

• 成果类型: Article
• 署名作者: Aloosh, Arash; Li, Jiasun
• 署名单位: George Mason University
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2021.01448
• 发表日期: 2024
• 关键词: Bitcoin; Cryptocurrency; Exchanges; forensics; Market manipulation; regulation

摘要

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.