Examining wash trading in NFT collections: case of two collections

Abstract

Wash trading is a major issue in non-fungible token (NFT) markets that distorts transaction volumes and returns. In this work, we examined the effect of wash trading by focusing on two specific NFT collections. First, we implemented a multi-layered wash trading detection algorithm to identify wash trades. Using regression analysis, we then showed that weekly transaction volumes of a heavily wash-traded collection can be magnified by two orders of magnitude compared to a cleaner collection. Moreover, wash trading resulted in positive returns in a collection that has extensive wash trading; in contrast, wash trading in the cleaner collection was penalized with negative returns, suggesting heterogeneity in both incidence and profitability of wash trading. Our findings provide a better understanding on the impact of wash trading on NFT markets and highlight the need to improve security in NFT markets and other decentralized financial technology systems. In particular, by extending transaction-level detection to collection characteristics, we could assess the impact of wash trading. This approach is easily replicable and enables market stakeholder to identify inauthentic activity and policy makers to provide adequate safeguards for these products.