2026-07-29T14:03:16Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:15907 2024-03-06T10:31:38Z ddc:004 open_access

Detecting and Quantifying Crypto Wash Trading (Extended Abstract) Cong, Lin William Li, Xi Tang, Ke Yang, Yang Bitcoin Cryptocurrency FinTech Forensic Finance Fraud Detection Regulation 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. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Lin William Cong and Xi Li and Ke Tang and Yang Yang 2022 Is Part Of OASIcs, Volume 97, 3rd International Conference on Blockchain Economics, Security and Protocols (Tokenomics 2021) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/OASIcs.Tokenomics.2021.10 urn:nbn:de:0030-drops-159072 https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.Tokenomics.2021.10 eng https://creativecommons.org/licenses/by/4.0/legalcode