Mapping the NFT revolution: market trends, trade networks and visual features

TL;DR

This paper analyzes a large dataset of NFT trades to uncover market structure, trader behavior, visual object clustering, and sale price predictability, providing insights into the NFT ecosystem's dynamics and characteristics.

Contribution

It offers a comprehensive analysis of NFT market data, revealing trader clustering, visual homogeneity in collections, and sale price predictability using machine learning.

Findings

Traders tend to form clusters around similar objects.

NFT collections are visually homogeneous.

Sale history and visual features predict prices effectively.

Abstract

Non Fungible Tokens (NFTs) are digital assets that represent objects like art, collectible, and in-game items. They are traded online, often with cryptocurrency, and are generally encoded within smart contracts on a blockchain. Public attention towards NFTs has exploded in 2021, when their market has experienced record sales, but little is known about the overall structure and evolution of its market. Here, we analyse data concerning 6.1 million trades of 4.7 million NFTs between June 23, 2017 and April 27, 2021, obtained primarily from Ethereum and WAX blockchains. First, we characterize statistical properties of the market. Second, we build the network of interactions, show that traders typically specialize on NFTs associated with similar objects and form tight clusters with other traders that exchange the same kind of objects. Third, we cluster objects associated to NFTs according to their visual features and show that collections contain visually homogeneous objects. Finally, we investigate the predictability of NFT sales using simple machine learning algorithms and find that sale history and, secondarily, visual features are good predictors for price. We anticipate that these findings will stimulate further research on NFT production, adoption, and trading in different contexts.