Predicting Non-Fungible Token (NFT) Collections: A Contextual Generative Approach

• URL: http://arxiv.org/abs/2210.15493v1
• Date: Fri, 14 Oct 2022 12:50:22 GMT
• Title: Predicting Non-Fungible Token (NFT) Collections: A Contextual Generative Approach
• Authors: Wesley Joon-Wie Tann, Akhil Vuputuri, Ee-Chien Chang
• Abstract summary: Non-fungible tokens (NFTs) are digital assets stored on a blockchain representing real-world objects such as art or collectibles. In this paper, we take a contextual generative approach that learns these diverse characteristics of NFT collections. We generate the potential market value predictions of newly minted ones.
• Score: 8.246077490514848
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Non-fungible tokens (NFTs) are digital assets stored on a blockchain representing real-world objects such as art or collectibles. It is a multibillion-dollar market, where the number of NFT collections increased over 100% in 2022; there are currently more than 80K collections on the Ethereum blockchain. Each collection, containing numerous tokens of a particular theme, has its unique characteristics. In this paper, we take a contextual generative approach that learns these diverse characteristics of NFT collections and generates the potential market value predictions of newly minted ones. We model NFTs as a series of transactions. First, meaningful contexts capturing the characteristics of various collections are derived using unsupervised learning. Next, our generative approach leverages these contexts to learn better characterizations of established NFT collections with differing market capitalization values. Finally, given a new collection in an early stage, the approach generates future transaction series for this emerging collection. Comprehensive experiments demonstrate that our approach closely predicts the potential value of NFT collections.

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