Crypto Wash Trading

50 Pages Posted: 2 Mar 2020 Last revised: 14 Aug 2023

See all articles by Lin William Cong

Lin William Cong

Cornell University - Samuel Curtis Johnson Graduate School of Management; National Bureau of Economic Research (NBER)

Xi Li

University of Reading - Henley Business School,

Ke Tang

Institute of Economics, School of Social Sciences, Tsinghua University

Yang Yang

University of Bristol - Department of Computer Science

Multiple version iconThere are 2 versions of this paper

Date Written: July 1, 2023

Abstract

We present a systematic approach to detect fake transactions on cryptocurrency exchanges by exploiting robust statistical and behavioral regularities associated with authentic trading. Our sample consists of 29 centralized exchanges, among which the regulated ones feature transaction patterns consistently observed in financial markets and nature. In contrast, unregulated exchanges display abnormal first-significant-digit distributions, size rounding, and transaction tail distributions, indicating widespread manipulation unlikely driven by specific trading strategy or exchange heterogeneity. We then quantify the wash trading on each unregulated exchange, which averaged over 70% of the reported volume. We further document how these fabricated volumes (trillions of dollars annually) improve exchange ranking, temporarily distort prices, and relate to exchange characteristics (e.g., age and user base), market conditions, and regulation. Overall, our study cautions against potential market manipulations on centralized crypto exchanges with concentrated power and limited disclosure requirements, and highlights the importance of FinTech regulation.

Online appendix available at: https://ssrn.com/abstract=4529817

Keywords: Bitcoin; CeFi; Cryptocurrency; Forensic Finance; Fraud Detection; Regulation

JEL Classification: G18, G23, G29

Suggested Citation

Cong, Lin and Li, Xi and Tang, Ke and Yang, Yang, Crypto Wash Trading (July 1, 2023). Available at SSRN: https://ssrn.com/abstract=3530220 or http://dx.doi.org/10.2139/ssrn.3530220

Lin Cong (Contact Author)

Cornell University - Samuel Curtis Johnson Graduate School of Management ( email )

Ithaca, NY 14853
United States

HOME PAGE: http://www.linwilliamcong.com/

National Bureau of Economic Research (NBER) ( email )

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Xi Li

University of Reading - Henley Business School, ( email )

Whiteknights
Reading, Berkshire RG6 6AH
United Kingdom

Ke Tang

Institute of Economics, School of Social Sciences, Tsinghua University ( email )

No.1 Tsinghua Garden
Beijing, 100084
China

Yang Yang

University of Bristol - Department of Computer Science ( email )

Bristol
United Kingdom

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