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NFT Wash Trading

Quantifying Suspicious Behaviour in NFT Markets

  • Conference paper
  • First Online:
Financial Cryptography and Data Security. FC 2022 International Workshops (FC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13412))

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Abstract

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 decentralized 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 $149,5 m 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.

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Notes

  1. 1.

    https://dune.xyz/sophieqgu/NFT-Marketplaces.

  2. 2.

    https://dune.xyz/sophieqgu/NFT-Marketplaces.

  3. 3.

    The authors will open-source scripts and data upon publication.

  4. 4.

    https://www.cftc.gov/LawRegulation/CommodityExchangeAct/index.htm.

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Author information

Authors and Affiliations

  1. University of Copenhagen, Copenhagen, Denmark

    Victor von Wachter, Johannes Rude Jensen & Omri Ross

  2. eToroX Labs, Copenhagen, Denmark

    Johannes Rude Jensen & Omri Ross

  3. University of Vienna, Vienna, Austria

    Ferdinand Regner

Authors
  1. Victor von Wachter
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  2. Johannes Rude Jensen
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  3. Ferdinand Regner
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  4. Omri Ross
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Corresponding author

Correspondence to Victor von Wachter .

Editor information

Editors and Affiliations

  1. Georgetown University, Washington, DC, USA

    Shin'ichiro Matsuo

  2. Imperial College London, London, UK

    Lewis Gudgeon

  3. Cornell University, Ithaca, NY, USA

    Ariah Klages-Mundt

  4. Imperial College London, London, UK

    Daniel Perez Hernandez

  5. Imperial College London, London, UK

    Sam Werner

  6. The Australian National University, Canberra, ACT, Australia

    Thomas Haines

  7. Western University, London, ON, Canada

    Aleksander Essex

  8. University of Stirling, Stirling, UK

    Andrea Bracciali

  9. University of Trento, Trento, Trento, Italy

    Massimiliano Sala

A Appendix

A Appendix

Algorithm

figure b
Table 2. Results for each collection. Column (A) is the share of suspicious addresses, (B) the share of suspicious transactions, (C1) represents the total volume flagged denominated in USD, (C2) the share of the flagged volume and (D) the share of suspicious NFTs
Full size table
Fig. 4.
figure 4

Wash trading with respect to collections’ lifetime. In absolute terms, wash trading is the highest at the beginning of a collections’ lifetime, however this is also matched by a high amount of organic traffic.

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Fig. 5.
figure 5

Trades and unique trade partners. Each dot represents an address trading on NFT markets, positioned by the amount of trades and unique trade partners. The size of the dot depicts the number of empirically identified suspicious trades.

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von Wachter, V., Jensen, J.R., Regner, F., Ross, O. (2023). NFT Wash Trading. In: Matsuo, S., et al. Financial Cryptography and Data Security. FC 2022 International Workshops. FC 2022. Lecture Notes in Computer Science, vol 13412. Springer, Cham. https://doi.org/10.1007/978-3-031-32415-4_20

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  • DOI: https://doi.org/10.1007/978-3-031-32415-4_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-32414-7

  • Online ISBN: 978-3-031-32415-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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