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

TL;DR

This paper introduces a novel generative model that predicts future NFT transactions and market value based on early transaction data, using context extraction to handle diverse collection characteristics.

Contribution

It presents a two-step contextual generative approach that models NFT collection transactions to project future market value, addressing diversity in collection characteristics.

Findings

Effective projection of NFT collection transactions

Generates realistic future transaction sequences

Accurately estimates potential market value

Abstract

Non-fungible tokens (NFTs) are digital assets stored on a blockchain representing real-world objects such as art or collectibles. An NFT collection comprises numerous tokens; each token can be transacted multiple times. It is a multibillion-dollar market where the number of collections has more than doubled in 2022. In this paper, we want to obtain a generative model that, given the early transactions history (first quarter Q1) of a newly minted collection, generates subsequent transactions (quarters Q2, Q3, Q4), where the generative model is trained using the transaction history of a few mature collections. The goal is to use the generated transactions to project the potential market value of this newly minted collection over the next few quarters. A technical challenge exists in that different collections have diverse characteristics, and the generative model should generate based on the appropriate "contexts" of the collection. Our method takes a two-step approach. First, it employs unsupervised learning on the early transactions to extract characteristics (which we call contexts) of NFT collections. Next, it generates future transactions of each token based on these contexts and the early transactions, projecting the target collection's potential market value. Comprehensive experiments demonstrate our contextual generative approach's NFT projection capabilities.