Using Advanced Machine Learning Techniques to Predict the Sales Volume of Non-Fungible Tokens
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Abstract
Suggested Citation
DOI: 10.22440/wjae.10.1.2
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References listed on IDEAS
- Yousaf, Imran & Yarovaya, Larisa, 2022. "Static and dynamic connectedness between NFTs, Defi and other assets: Portfolio implication," Global Finance Journal, Elsevier, vol. 53(C).
- 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.
- Dowling, Michael, 2022. "Is non-fungible token pricing driven by cryptocurrencies?," Finance Research Letters, Elsevier, vol. 44(C).
- Aharon, David Y. & Demir, Ender, 2022. "NFTs and asset class spillovers: Lessons from the period around the COVID-19 pandemic," Finance Research Letters, Elsevier, vol. 47(PA).
- Maouchi, Youcef & Charfeddine, Lanouar & El Montasser, Ghassen, 2022. "Understanding digital bubbles amidst the COVID-19 pandemic: Evidence from DeFi and NFTs," Finance Research Letters, Elsevier, vol. 47(PA).
- Lennart Ante, 2022. "The Non-Fungible Token (NFT) Market and Its Relationship with Bitcoin and Ethereum," FinTech, MDPI, vol. 1(3), pages 1-9, June.
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"A note on the determinants of NFTs returns,"
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24-07, Rimini Centre for Economic Analysis.
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More about this item
Keywords
Financial assets; Non-fungible tokens; Machine learning;All these keywords.
JEL classification:
- G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
Statistics
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