The Dark Side of NFTs: A Large-Scale Empirical Study of Wash Trading

TL;DR

This paper provides a comprehensive analysis of NFT wash trading, identifying different types, proposing detection algorithms, and revealing significant financial impacts and behavioral insights across the NFT ecosystem.

Contribution

It introduces the most extensive multi-dimensional study of NFT wash trading, including new detection algorithms and detailed analysis of trading behaviors and impacts.

Findings

Identified 370 address pairs involved in wash trading.

Detected over 5,300 wash trade events totaling nearly $9 million.

Provided insights into marketplace design and user behavior affecting wash trading.

Abstract

NFTs (Non-Fungible Tokens) have seen significant growth since they first captured public attention in 2021. However, the NFT market is plagued by fake transactions and economic bubbles, e.g., NFT wash trading. Wash trading typically refers to a transaction involving the same person or two colluding individuals, and has become a major threat to the NFT ecosystem. Previous studies only detect NFT wash trading from the financial aspect, while the real-world wash trading cases are much more complicated (e.g., not aiming at inflating the market value). There is still a lack of multi-dimension analysis to better understand NFT wash trading. Therefore, we present the most comprehensive study of NFT wash trading, analyzing 8,717,031 transfer events and 3,830,141 sale events from 2,701,883 NFTs. We first optimize the dataset collected via the OpenSea API. Next, we identify three types of NFT wash trading and propose identification algorithms. Our experimental results reveal 824 transfer events and 5,330 sale events (accounting for a total of \$8,857,070.41) and 370 address pairs related to NFT wash trading behaviors, causing a minimum loss of \$3,965,247.13. Furthermore, we provide insights from six aspects, i.e., marketplace design, profitability, NFT project design, payment token, user behavior, and NFT ecosystem.