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DOI: 10.4230/OASIcs.Tokenomics.2021.10

Detecting and Quantifying Crypto Wash Trading (Extended Abstract)

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

Published in: OASIcs, Volume 97, 3rd International Conference on Blockchain Economics, Security and Protocols (Tokenomics 2021)

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.

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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)

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@InProceedings{cong_et_al:OASIcs.Tokenomics.2021.10,
  author =	{Cong, Lin William and Li, Xi and Tang, Ke and Yang, Yang},
  title =	{{Detecting and Quantifying Crypto Wash Trading}},
  booktitle =	{3rd International Conference on Blockchain Economics, Security and Protocols (Tokenomics 2021)},
  pages =	{10:1--10:6},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-220-4},
  ISSN =	{2190-6807},
  year =	{2022},
  volume =	{97},
  editor =	{Gramoli, Vincent and Halaburda, Hanna and Pass, Rafael},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.Tokenomics.2021.10},
  URN =		{urn:nbn:de:0030-drops-159072},
  doi =		{10.4230/OASIcs.Tokenomics.2021.10},
  annote =	{Keywords: Bitcoin, Cryptocurrency, FinTech, Forensic Finance, Fraud Detection, Regulation}
}

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Detecting and Quantifying Crypto Wash Trading (Extended Abstract)

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