Deep Learning for Dynamic NFT Valuation

Mingxuan He

Papers from arXiv.org

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.

Date: 2023-12
New Economics Papers: this item is included in nep-big, nep-cmp, nep-cul, nep-mst and nep-pay
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