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NFT Wash Trading: Direct vs. Indirect Estimation

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  • Brett Hemenway Falk
  • Gerry Tsoukalas
  • Niuniu Zhang

Abstract

Recent studies estimate around 70% of traded value on off-chain crypto exchanges like Binance is wash trading. This paper turns to NFT markets, where the on-chain nature of transactions-a key tenet of Web3 innovation-enables more direct estimation methods to be applied. Focusing on three of the largest NFT marketplaces, we find 30-40% of NFT volume and 25-95% of traded value involve wash trading. We leverage this direct approach to critically evaluate recent indirect estimation methods suggested in the literature, revealing major differences in effectiveness, with some failing altogether. Trade-roundedness filters, as suggested in Cong et al. (2023), emerge as the most accurate indirect estimation method. In fact, we show how direct and indirect approaches can be closely aligned via hyper-parameter fine-tuning. Our findings underscore the crucial role of technological innovation in detecting and regulating financial misconduct in digital finance.

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  • Brett Hemenway Falk & Gerry Tsoukalas & Niuniu Zhang, 2023. "NFT Wash Trading: Direct vs. Indirect Estimation," Papers 2311.18717, arXiv.org, revised Jun 2024.
    • Handle: RePEc:arx:papers:2311.18717
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    References listed on IDEAS

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    1. Eric Zitzewitz, 2012. "Forensic Economics," Journal of Economic Literature, American Economic Association, vol. 50(3), pages 731-769, September.
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