Deep Learning for Dynamic NFT Valuation
Computational Finance
2023-12-12 v1
Abstract
I study the price dynamics of non-fungible tokens (NFTs) and propose a deep learning framework for dynamic valuation of NFTs. I use data from the Ethereum blockchain and OpenSea to train a deep learning model on historical trades, market trends, and traits/rarity features of Bored Ape Yacht Club NFTs. After hyperparameter tuning, the model is able to predict the price of NFTs with high accuracy. I propose an application framework for this model using zero-knowledge machine learning (zkML) and discuss its potential use cases in the context of decentralized finance (DeFi) applications.
Keywords
Cite
@article{arxiv.2312.05346,
title = {Deep Learning for Dynamic NFT Valuation},
author = {Mingxuan He},
journal= {arXiv preprint arXiv:2312.05346},
year = {2023}
}
Comments
Code available at https://github.com/mingxuan-he/NFT-pred