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NFT Appraisal Prediction: Utilizing Search Trends, Public Market Data, Linear Regression and Recurrent Neural Networks

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  • Shrey Jain
  • Camille Bruckmann
  • Chase McDougall

Abstract

In this paper we investigate the correlation between NFT valuations and various features from three primary categories: public market data, NFT metadata, and social trends data.

Suggested Citation

  • Shrey Jain & Camille Bruckmann & Chase McDougall, 2022. "NFT Appraisal Prediction: Utilizing Search Trends, Public Market Data, Linear Regression and Recurrent Neural Networks," Papers 2204.12932, arXiv.org.
    • Handle: RePEc:arx:papers:2204.12932
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    File URL: http://arxiv.org/pdf/2204.12932
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    References listed on IDEAS

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    1. 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.
    2. Wenjie Lu & Jiazheng Li & Yifan Li & Aijun Sun & Jingyang Wang, 2020. "A CNN-LSTM-Based Model to Forecast Stock Prices," Complexity, Hindawi, vol. 2020, pages 1-10, November.
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    Cited by:

    1. Ali, Omar & Momin, Mujtaba & Shrestha, Anup & Das, Ronnie & Alhajj, Fadia & Dwivedi, Yogesh K., 2023. "A review of the key challenges of non-fungible tokens," Technological Forecasting and Social Change, Elsevier, vol. 187(C).
    2. Mingxuan He, 2023. "Deep Learning for Dynamic NFT Valuation," Papers 2312.05346, arXiv.org.
    3. Zhang, Luyao & Sun, Yutong & Quan, Yutong & Cao, Jiaxun & Tong, Xin, 2023. "On the Mechanics of NFT Valuation: AI Ethics and Social Media," OSF Preprints qwpdx, Center for Open Science.

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