Predictors of NFT Prices: An Automated Machine Learning Approach

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Author(s): Ilan Alon (University of Ariel, Israel), Vanessa P. G. Bretas (Dublin City University, Ireland)and Villi Katrih (Signex, Israel)
Copyright: 2023
Volume: 31
Issue: 1
Pages: 18
Source title: Journal of Global Information Management (JGIM)
Editor(s)-in-Chief: Zuopeng (Justin) Zhang (University of North Florida, USA)
DOI: 10.4018/JGIM.317097

Keywords: Computer Science & IT / Global Information Technology / Information Science Reference / IT Research and Theory

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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.

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Predictors of NFT Prices: An Automated Machine Learning Approach

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