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Crypto Wash Trading: Direct vs. Indirect Estimation

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  • Brett Hemenway Falk
  • Gerry Tsoukalas
  • Niuniu Zhang

Abstract

Recent studies using indirect statistical methods estimate that around 70% of traded value on centralized crypto exchanges like Binance, can be characterized as wash trading. This paper turns to NFT markets, where transaction transparency, including analysis of roundtrip trades and common wallet activities, allows for more accurate direct estimation methods to be applied. We find roughly 30% of NFT volume and between 45-95% of traded value, involve wash trading. More importantly, our approach enables a critical evaluation of common indirect estimation methods used in the literature. We find major differences in their effectiveness; some failing entirely. Roundedness filters, like those used in Cong et al. (2023), emerge as the most accurate. In fact, the two approaches can be closely aligned via hyper-parameter optimization if direct data is available.

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  • Brett Hemenway Falk & Gerry Tsoukalas & Niuniu Zhang, 2023. "Crypto Wash Trading: Direct vs. Indirect Estimation," Papers 2311.18717, arXiv.org.
    • 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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