Investigating Predatory Trading in the NFT Market
Year: 2026
Type: article
Abstract: Since the inception of cryptocurrencies, trading has been a major use case. As cryptocurrency evolved, new financial instruments, such as lending and borrowing protocols, currency exchanges, fungible and non-fungible tokens (NFT) have emerged. However, unlike traditional money markets, blockchain-based markets and decentralized finance (DeFi) instruments are mostly unregulated, which, in turn, lured bad actors to flock to the market seeking quick profit through illicit activities. This situation is further exacerbated by the fact that there are sophisticated actors who turn their domain knowledge and market inefficiencies to their strategic advantage, thus extracting value through automated high-frequency trades. Though a large body of work has already studied the unfairness in trades involving fungible tokens, the intricacies of NFT trades present unique challenges that prohibit applying the same techniques to NFTs—which motivated this research. Our work is complementary to the prior research in the NFT space, which focuses on low-frequency malicious trades, such as wash trading, shill bidding, bid shielding, and so on, while we exclusively explore the unfairness issues in high-frequency trades. In this article, we first identify the distinctive features of an unfair trade by observing the dynamics of the NFT market and previously reported instances of wrongdoings. Next, under our notion of unfairness, we study several such trading practices, collectively referred to as predatory trading . Predatory traders are sophisticated actors who employ automated, high-frequency strategies to execute unfair trades, that are malicious, disruptive, or deceptive. Specifically, we explore three such broad classes of strategies, viz. , acquisition, instant profit generation, and loss minimization, aiming to realize distinct trading objectives.
DOI: 10.1145/3845612
Institution: University of California, Santa Barbara
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7208758146