Deep Learning for Dynamic NFT Valuation

ArXiv ID: 2312.05346 “View on arXiv”

Authors: Unknown

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: Non-Fungible Tokens (NFTs), Deep Learning, Zero-Knowledge Machine Learning (zkML), Decentralized Finance (DeFi)

Complexity vs Empirical Score

• Math Complexity: 4.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Street Traders
• Why: The paper applies standard deep learning models to blockchain data with clear feature engineering, showing practical data implementation and backtesting preparation. While the methodology involves standard ML training and hyperparameter tuning, the mathematics remains at an applied level rather than introducing novel theoretical derivations.

  flowchart TD
    A["Research Goal: Dynamic NFT Valuation"] --> B["Data Collection<br>Ethereum & OpenSea"]
    B --> C["Feature Engineering<br>Market, Traits, Rarity"]
    C --> D["Deep Learning Model<br>Training & Hyperparameter Tuning"]
    D --> E["Key Finding: High Price Prediction Accuracy"]
    E --> F["Application Framework<br>zkML + DeFi Integration"]