Analysis of non-fungible token pricing factors with machine learning

Kin-Hon HO, Yun HOU, Tse Tin David CHAN, Haoyuan PAN

Research output: Chapter in Book/Report/Conference proceedingChapters

Abstract

Rarity is known to be a factor in the price of non-fungible tokens (NFTs). Most investors make their purchasing decisions based on the rarity score or rarity rank of NFTs. However, not all rare NFTs are associated with a higher price, especially for play-to-earn gaming NFTs. In this paper, we studied the top-ranked play-to-earn gaming NFTs on Axie Infinity. We found that, in addition to rarity, utility is also a significant factor influencing the price. Furthermore, we use utility as a predictor to predict the price of Axies using the XGBoost regressor. Our results reveal that, compared to using rarity-based predictors only, leveraging utility-based predictors can improve the prediction accuracy, thus highlighting utility as a price determinant for play-to-earn gaming NFTs. Copyright © 2022 IEEE.

Original languageEnglish
Title of host publicationProceedings of 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
Place of PublicationUSA
PublisherIEEE
Pages1161-1166
ISBN (Electronic)9781665452588
DOIs
Publication statusPublished - 2022

Citation

Ho, K.-H., Hou, Y., Chan, T.-T., & Pan, H. (2022). Analysis of non-fungible token pricing factors with machine learning. In Proceedings of 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 1161-1166). USA: IEEE.

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