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Detecting and Quantifying Crypto Wash Trading (Extended Abstract)

Authors Lin William Cong, Xi Li, Ke Tang, Yang Yang

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Author Details

Lin William Cong
  • Samuel Curtis Johnson Graduate School of Management, Cornell University SC Johnson College of Business, Ithaca, NY, USA
Xi Li
  • Newcastle University Business School, UK
Ke Tang
  • Tsinghua University Institute of Economics, Beijing, China
Yang Yang
  • Tsinghua University Institute of Economics, Beijing, China

Funding

This research was funded in part by the Ewing Marion Kauffman Foundation and National Science Foundation of China, and the authors have no affiliation with or research support from any cryptocurrency exchanges.

Acknowledgements

The authors are especially grateful to Deeksha Gupta, Kose John, Evgeny Lyandres, and Tao Li for repeated discussions and detailed feedback. We also thank Marlene Amstad, Mykola Babiak, Kevin Dowd, Valeria Ferrar, Itay Goldstein, Hanna Halaburda, Angel Hernando-Veciana, Andrew Karolyi, Dongyongp Lee, Minhyuk Lee, Jiasun Li, Laura Xiaolei Liu, Roger Loh, Emmanouil Platanakis, Fahad Saleh, Amin Shams, Donghwa Shin, Rajeev Singhal, Baolian Wang, Shang-jin Wei, Wei Xiong, Scott Yonker and seminar and conference participants and reviewers at the Alibaba Group Luohan Academy Webinar, Australasian Banking and Finance Conference, Behavioral Finance/Corporate Finance/Digital Finance (BF/DF/CF) Seminar Group, Cornell University, Cowles Foundation for Research In Economics Conference on the Economics of Cryptocurrencies, 11th CSBF Conference (National Taiwan University), Cowles Foundation Economics of Cryptocurrencies (Macroeconomics) Conference, 1st Crypto and Blockchain Economics Research Conference, Durham University Department of Economics and Finance, Econometric Society World Congress (Bocconi University), 2021 Eastern Finanace Association Annual Meeting, IIF International Research Conference & Award Summit, 13th International Risk Management Conference, Inaugural Machine Laywering Conference: “Human Sovereignty and Machine Efficiency in the Law,” 18th Paris December Finance Meeting, Paris FinTech and Crypto Webinar, 60th Southwestern Finance Association Meeting, Sun Yat-sen University, 3rd Toronto FinTech Conference, Tsinghua University PBC School of Finance, 3rd UWA Blockchain and Cryptocurrency Conference, 11th Financial Markets and Corporate Governance Conference, European Financial Management Association Annual Meeting 2021, China International Conference in Finance 2021, 17th Asia-Pacific Association of Derivatives Annual Conference, World Finance Conference 2021, 2021 Financial Management Association Annual Meeting, 3rd International Conference on Blockchain Economics (Tokenomics2021), 2021 Global AI Finance Research Conference, Xi’an Jiaotong University, and the Zhongnan University of Economics and Law for helpful comments.

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Lin William Cong, Xi Li, Ke Tang, and Yang Yang. Detecting and Quantifying Crypto Wash Trading (Extended Abstract). In 3rd International Conference on Blockchain Economics, Security and Protocols (Tokenomics 2021). Open Access Series in Informatics (OASIcs), Volume 97, pp. 10:1-10:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)
https://doi.org/10.4230/OASIcs.Tokenomics.2021.10

Abstract

We introduce systematic tests exploiting robust statistical and behavioral patterns in trading to detect fake transactions on 29 cryptocurrency exchanges. Regulated exchanges feature patterns consistently observed in financial markets and nature; abnormal first-significant-digit distributions, size rounding, and transaction tail distributions on unregulated exchanges reveal rampant manipulations unlikely driven by strategy or exchange heterogeneity. We 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 userbase), market conditions, and regulation.

Subject Classification

ACM Subject Classification
  • Security and privacy → Cryptography
Keywords
  • Bitcoin
  • Cryptocurrency
  • FinTech
  • Forensic Finance
  • Fraud Detection
  • Regulation

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