Predictors of NFT Prices: An Automated Machine Learning Approach

【Author】 Alon, Ilan; Bretas, Vanessa P. G.; Katrih, Villi

【Source】JOURNAL OF GLOBAL INFORMATION MANAGEMENT

【影响因子】3.474

【Abstract】This article aims to broaden the understanding of the non-fungible tokens (NFTs) pricing determinants by investigating features, both market-and network-related aspects. NFTs are uniquely identifiable digital assets stored on the blockchain. Ownership is assigned through smart contracts and can be transferred or resold by the owner. The authors analyzed a comprehensive dataset from Signex.io with over 19,183 datapoints on NFT prices and NFT social communities using automated machine learning (AML), a suitable technique to investigate the most impactful factors due to a lack of knowledge on the exact determinants. Findings show that network factors are the most important pricing determinants: Twitter members followed by Discord members. Online communities drive the price of NFTs, but not in a linear fashion. Given the newness of the phenomenon and no agreed upon pricing models, this article contributes by using AML to discover the most relevant determinants of non-fungible tokens (NFT) prices.

【Keywords】AML; Artificial Intelligence; Digital Assets; NFTs; Non-fungible Tokens; Pricing; Social Metrics; Signex; io

【发表时间】2023

【收录时间】2023-03-30

【文献类型】实证数据

【主题类别】

区块链治理-市场治理-价格预测