Masters in Computational Social Science (MACSS) Theses
Published May 2024 | Version v1
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Deep Learning for Dynamic NFT Valuation

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  • 1. University of Chicago

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Description

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.

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oai:uchicago.tind.io:12009

UChicago Information

Division(s)
Social Sciences Division
Department(s)
Computational Social Sciences (MACSS)
Center(s) or Institute(s)
Becker Friedman Institute for Economics