NFT Wash Trading: Quantifying suspicious behaviour in NFT markets
Not on the map. Why?
Abstract (arXiv)
The smart contract-based markets for non-fungible tokens (NFTs) on the Ethereum blockchain have seen tremendous growth in 2021, with trading volumes peaking at 3.5b in September 2021. This dramatic surge has led to industry observers questioning the authenticity of on-chain volumes, given the absence of identity requirements and the ease with which agents can control multiple addresses. We examine potentially illicit trading patterns in the NFT markets from January 2018 to mid-November 2021, gathering data from the 52 largest collections by volume. Our findings indicate that within our sample 3.93% of addresses, processing a total of 2.04% of sale transactions, trigger suspicions of market abuse. Flagged transactions contaminate nearly all collections and may have inflated the authentic trading volumes by as much as 149,5m for the period. Most flagged transaction patterns alternate between a few addresses, indicating a predisposition for manual trading. We submit that the results presented here may serve as a viable lower bound estimate for NFT wash trading on Ethereum. Even so, we argue that wash trading may be less common than what industry observers have previously estimated. We contribute to the emerging discourse on the identification and deterrence of market abuse in the cryptocurrency markets.
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). First posted on arXiv 7 Feb 2022.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2023: 5; 2024: 0; 2025: 2; 2026: 1.
8 papers.
- 2023Live Graph Lab: Towards Open, Dynamic and Real Transaction Graphs with NFTcited by 10
- 2023Show me your NFT and I tell you how it will perform: Multimodal representation learning for NFT selling price predictioncited by 6
- 2023EX-Graph: A Pioneering Dataset Bridging Ethereum and Xcited by 5
- 2025Agentic AI for Financial Crime Compliancecited by 1
- 2026Early Rug Pull Warning for BSC Meme Tokens via Multi-Granularity Wash-Trading Pattern Profilingcited by 1
- 2023Visually Wired NFTs: Exploring the Role of Inspiration in Non-Fungible Tokenscited by 0
- 2023Abnormal Trading Detection in the NFT Marketcited by 0
- 2025Clustering and analysis of user behaviour in blockchain: A case study of Planet IXcited by 0
- 2026Early Rug Pull Warning for BSC Meme Tokens via Multi-Granularity Wash-Trading Pattern Profiling14 Mar 2026
- 2025Agentic AI for Financial Crime Compliance16 Sep 2025
- 2025Clustering and analysis of user behaviour in blockchain: A case study of Planet IX16 Apr 2025
- 2023Live Graph Lab: Towards Open, Dynamic and Real Transaction Graphs with NFT18 Oct 2023
- 2023EX-Graph: A Pioneering Dataset Bridging Ethereum and X2 Oct 2023
- 2023Abnormal Trading Detection in the NFT Market25 May 2023
- 2023Visually Wired NFTs: Exploring the Role of Inspiration in Non-Fungible Tokens29 Mar 2023
- 2023Show me your NFT and I tell you how it will perform: Multimodal representation learning for NFT selling price prediction3 Feb 2023