Open Access

ARTICLE

Prediction of NFT Sale Price Fluctuations on OpenSea Using Machine Learning Approaches

Zixiong Wang, Qiuying Chen, Sang-Joon Lee^*

Interdisciplinary Program of Digital Future Convergence Service, Chonnam National University, Gwangju, 61186, Korea

* Corresponding Author: Sang-Joon Lee. Email:

Computers, Materials & Continua 2023, 75(2), 2443-2459. https://doi.org/10.32604/cmc.2023.037553

Received 08 November 2022; Accepted 29 December 2022; Issue published 31 March 2023

Abstract

The rapid expansion of the non-fungible token (NFT) market has attracted many investors. However, studies on the NFT price fluctuations have been relatively limited. To date, the machine learning approach has not been used to demonstrate a specific error in NFT sale price fluctuation prediction. The aim of this study was to develop a prediction model for NFT price fluctuations using the NFT trading information obtained from OpenSea, the world’s largest NFT marketplace. We used Python programs to collect data and summarized them as: NFT information, collection information, and related account information. AdaBoost and Random Forest (RF) algorithms were employed to predict the sale price and price fluctuation of NFTs using regression and classification models, respectively. We found that the NFT related account information, especially the number of favorites and activity status of creators, confer a good predictive power to both the models. AdaBoost in the regression model had more accurate predictions, the root mean square error (RMSE) in predicting NFT sale price was 0.047. In predicting NFT sale price fluctuations, RF performed better, which the area under the curve (AUC) reached 0.956. We suggest that investors should pay more attention to the information of NFT creators. We anticipate that these prediction models will reduce the number of investment failures for the investors.

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Keywords

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