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Detecting crypto wash trades via machine learning

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  • Falk, Brett Hemenway
  • Tsoukalas, Gerry
  • Zhang, Niuniu

Abstract

Existing studies of crypto wash trading often rely on indirect statistical methods or leaked private data. We develop a machine learning framework that achieves trade-level detection using public on-chain data alone. In three major NFT marketplaces, on-chain filters flag 38% of trades and 60% of traded value as wash trades, with substantial variation across platforms. We engineer a set of trade-level features and train tree-based and deep learning models on these labels, achieving accurate transaction-level classification. The framework accommodates prior aggregate estimators as features, including the trade-size roundedness regression of Cong et al. (2023b). The methodology also applies to fungible-token trade data, as demonstrated on the Mt. Gox Bitcoin dataset. Together, these results provide a scalable basis for on-chain market surveillance.

Suggested Citation

  • Falk, Brett Hemenway & Tsoukalas, Gerry & Zhang, Niuniu, 2026. "Detecting crypto wash trades via machine learning," Research Policy, Elsevier, vol. 55(7).
    • Handle: RePEc:eee:respol:v:55:y:2026:i:7:s0048733326000995
    DOI: 10.1016/j.respol.2026.105508
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