Abnormal Trading Detection in the NFT Market

Mingxiao Song; Yunsong Liu; Agam Shah; Sudheer Chava

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arXiv · 2306.04643

Abnormal Trading Detection in the NFT Market

Mingxiao Song·Yunsong Liu·Agam Shah·Sudheer Chava

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Abstract

The Non-Fungible-Token (NFT) market has experienced explosive growth in recent years. According to DappRadar, the total transaction volume on OpenSea, the largest NFT marketplace, reached 34.7 billion dollars in February 2023. However, the NFT market is mostly unregulated and there are significant concerns about money laundering, fraud and wash trading. The lack of industry-wide regulations, and the fact that amateur traders and retail investors comprise a significant fraction of the NFT market, make this market particularly vulnerable to fraudulent activities. Therefore it is essential to investigate and highlight the relevant risks involved in NFT trading. In this paper, we attempted to uncover common fraudulent behaviors such as wash trading that could mislead other traders. Using market data, we designed quantitative features from the network, monetary, and temporal perspectives that were fed into K-means clustering unsupervised learning algorithm to sort traders into groups. Lastly, we discussed the clustering results' significance and how regulations can reduce undesired behaviors. Our work can potentially help regulators narrow down their search space for bad actors in the market as well as provide insights for amateur traders to protect themselves from unforeseen frauds.

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Mingxiao Song, Yunsong Liu, Agam Shah, Sudheer Chava. 2023-05-25. Abnormal Trading Detection in the NFT Market. https://arxiv.org/abs/2306.04643

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