Paper: arXiv 2312.05346

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.

Complexity vs Empirical Score

  • Math Complexity: 4.5/10
  • Empirical Rigor: 7.0/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: 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.

Research Flowchart

  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"]

Cited References (19)

  1. Mapping the NFT revolution: market trends, trade networks, and visual features (2021)
  2. The non-fungible token (NFT) market and its relationship with Bitcoin and Ethereum (2022)
  3. Is non-fungible token pricing driven by cryptocurrencies? (2022)
  4. Alternative investments in the Fintech era: The risk and return of Non-Fungible Token (NFT) (2021)
  5. Heterogeneous rarity patterns drive price dynamics in NFT collections (2022)
  6. Non-fungible Token Price Prediction with Multivariate LSTM Neural Networks (2022)
  7. Forecasting NFT coin prices using machine learning: Insights into feature significance and portfolio strategies (2023)
  8. Anticipating cryptocurrency prices using machine learning (2018)
and 11 more
  1. Tweetboost: Influence of social media on nft valuation (2022)
  2. Price determinants of non-fungible tokens in the digital art market (2022)
  3. How rarity shapes the NFT market (2022) — link
  4. NFT Appraisal Prediction: Utilizing Search Trends, Public Market Data, Linear Regression and Recurrent Neural Networks (2022) — link
  5. A time-series analysis of how google trends searches affect cryptocurrency prices for decentralized finance and non-fungible tokens (2021)
  6. Show me your NFT and I tell you how it will perform: Multimodal representation learning for NFT selling price prediction (2023)
  7. Investor experience matters: Evidence from generative art collections on the blockchain (2022)
  8. Cultural Price Premium: Evidence from CryptoPunks (2023)
  9. NFT Primary Sale Price and Secondary Sale Prediction via Deep Learning (2023)
  10. ZkCNN: Zero knowledge proofs for convolutional neural network predictions and accuracy (2021)
  11. Zero knowledge proofs for decision tree predictions and accuracy (2020)

0 of 19 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.