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Deep Learning for Dynamic NFT Valuation

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  • Mingxuan He

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.

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  • Mingxuan He, 2023. "Deep Learning for Dynamic NFT Valuation," Papers 2312.05346, arXiv.org.
    • Handle: RePEc:arx:papers:2312.05346
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    References listed on IDEAS

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    1. Henriques, Irene & Sadorsky, Perry, 2023. "Forecasting NFT coin prices using machine learning: Insights into feature significance and portfolio strategies," Global Finance Journal, Elsevier, vol. 58(C).
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    6. Matthieu Nadini & Laura Alessandretti & Flavio Di Giacinto & Mauro Martino & Luca Maria Aiello & Andrea Baronchelli, 2021. "Mapping the NFT revolution: market trends, trade networks and visual features," Papers 2106.00647, arXiv.org, revised Sep 2021.
    7. Dowling, Michael, 2022. "Is non-fungible token pricing driven by cryptocurrencies?," Finance Research Letters, Elsevier, vol. 44(C).
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