Before and After Wash Trade Detection in Ethereum NFTs: Evidence for the Mixture of Distribution Hypothesis and Sequential Information Arrival Hypothesis and Effect of Collection Characteristics

Abstract

NFT market is nascent and thus prone to manipulative behavior. This paper examines the impact of wash trading on the relationships between NFT returns, volume, and volatility via Mixture of Distributions Hypothesis (MDH) and Sequential Information Arrival Hypothesis (SIAH), and the role of collection characteristics in these dynamics via Hedonic Pricing Theory (HPT). By comparing the full dataset and those devoid of cyclical wash trades, we find that MDH and SIAH hold across samples. Notably, the return-volatility relationship shifts from significantly negative to significantly positive post-cleaning, confirming that manipulative trades distort true market risk-return dynamics. In contrast, support for HPT weakens after applying stricter wash trade detection, suggesting collection features had overstated influence due to manipulation. These findings highlight the need for robust wash trading detection to ensure data reliability. Policymakers should consider ensuring market data reliability by enhancing transparency regulations around suspected wash trade transactions.