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Can AI Detect Wash Trading? Evidence from NFTs

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  • Brett Hemenway Falk
  • Gerry Tsoukalas
  • Niuniu Zhang

Abstract

Existing studies on crypto wash trading often use indirect statistical methods or leaked private data, both with inherent limitations. This paper leverages public on-chain NFT data for a more direct and granular estimation. Analyzing three major exchanges, we find that ~38% (30-40%) of trades and ~60% (25-95%) of traded value likely involve manipulation, with significant variation across exchanges. This direct evidence enables a critical reassessment of existing indirect methods, identifying roundedness-based regressions \`a la Cong et al. (2023) as most promising, though still error-prone in the NFT setting. To address this, we develop an AI-based estimator that integrates these regressions in a machine learning framework, significantly reducing both exchange- and trade-level estimation errors in NFT markets (and beyond).

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  • Brett Hemenway Falk & Gerry Tsoukalas & Niuniu Zhang, 2023. "Can AI Detect Wash Trading? Evidence from NFTs," Papers 2311.18717, arXiv.org, revised Mar 2025.
    • Handle: RePEc:arx:papers:2311.18717
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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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