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NFT Wash Trading Detection

Author

Listed:
  • Derek Liu
  • Francesco Piccoli
  • Katie Chen
  • Adrina Tang
  • Victor Fang

Abstract

Wash trading is a form of market manipulation where the same entity sells an asset to themselves to drive up market prices, launder money under the cover of a legitimate transaction, or claim a tax loss without losing ownership of an asset. Although the practice is illegal with traditional assets, lack of supervision in the non-fungible token market enables criminals to wash trade and scam unsuspecting buyers while operating under regulators radar. AnChain.AI designed an algorithm that flags transactions within an NFT collection history as wash trades when a wallet repurchases a token within 30 days of previously selling it. The algorithm also identifies intermediate transactions within a wash trade cycle. Testing on 7 popular NFT collections reveals that on average, 0.14% of transactions, 0.11% of wallets, and 0.16% of tokens in each collection are involved in wash trading. These wash trades generate an overall total price manipulation, sales, and repurchase profit of \$900K, \$1.1M, and negative \$1.6M respectively. The results draw attention to the prevalent market manipulation taking place and inform unsuspecting buyers which tokens and sellers may be involved in criminal activity.

Suggested Citation

  • Derek Liu & Francesco Piccoli & Katie Chen & Adrina Tang & Victor Fang, 2023. "NFT Wash Trading Detection," Papers 2305.01543, arXiv.org.
    • Handle: RePEc:arx:papers:2305.01543
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    File URL: http://arxiv.org/pdf/2305.01543
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    References listed on IDEAS

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    1. Friedhelm Victor & Andrea Marie Weintraud, 2021. "Detecting and Quantifying Wash Trading on Decentralized Cryptocurrency Exchanges," Papers 2102.07001, arXiv.org.
    2. Phillip G. Bradford, 2015. "Foundations for Wash Sales," Papers 1511.03704, arXiv.org, revised Jun 2016.
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    Cited by:

    1. Guruprakash Jayabalasamy & Cyril Pujol & Krithika Latha Bhaskaran, 2024. "Application of Graph Theory for Blockchain Technologies," Mathematics, MDPI, vol. 12(8), pages 1-45, April.
    2. Priyanka Bose & Dipanjan Das & Fabio Gritti & Nicola Ruaro & Christopher Kruegel & Giovanni Vigna, 2023. "Exploiting Unfair Advantages: Investigating Opportunistic Trading in the NFT Market," Papers 2310.06844, arXiv.org.

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