Author
Sefer, Emre, Pala, Mustafa
Publication Date
2024-12
Publication Place
-
Elsevier
Subject
Temporal price prediction, Transfer learning, Deep learning, Blockchain, NFTs
Type
Periodical
Language
English
Digital
Yes
Manuscript
No
Library
Özyeğin University
Library Asset ID
2405-9188
Record ID
5fc45ff2-987f-4875-92f1-60c68f5eddf2
Library Location
Computer Science
Date
2024-12
Sample Text
Non-fungible tokens (NFTs) are unique digital assets whose possession is defined over a blockchain. NFTs can represent multiple distinct objects such as art, images, videos, etc. There was a recent surge of interest in trading them which makes them another type of alternative investment. The inherent volatility of NFT prices, attributed to factors such as over-speculation, liquidity constraints, rarity, and market volatility, presents challenges for accurate price predictions. For such analysis and forecasting, machine learning methods offer a robust solution framework. Here, we focus on three related prediction problems over NFTs: Predicting NFTs sale price, inferring whether a given NFT will participate in a secondary sale, and predicting NFT's sale price change over time. We analyze and learn the visual characteristics of NFTs by deep pre-trained models and combine such visual knowledge with additional important non-visual attributes such as the sale history, seller's and buyer's centralities in the trading network, and collection's resale probability. We categorize input NFTs into six categories based on their characteristics. Across detailed experiments, we found visual attributes obtained from deep pre-trained models to increase the prediction performance in all cases, and EfficientNet seems to perform the best. In general, CNN and XGBoost consistently outperformed the rest of them across all categories. We also publish our novel NFT dataset with temporal price knowledge, which is the first dataset to have NFT prices over time rather than at a single time point.
DOI
10.1016/j.jfds.2024.100148
Cilt
10
