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Using Advanced Machine Learning Techniques to Predict the Sales Volume of Non-Fungible Tokens

Author

Listed:
  • Ozge Camalan

    (Atılım University Department of Economics)

  • Sahika Gokmen

    (Hacı Bayram Veli University, Department of Econometrics and Uppsala University, Department of Statistics)

  • Sibel Atan

    (Hacı Bayram Veli University, Department of Econometrics)

Abstract

Non-fungible tokens (NFTs) are a type of digital asset based on blockchain that contain unique codes verifying the authenticity and ownership of different assets such as art pieces, music, gaming items, collections, and so on. This phenomenon and its markets have grown significantly since the beginning of 2021. This study, using daily data between November 2017 and November 2022, predicts the volume of NFT sales by utilising Random Forest (RF), GBM, XGBoost, and LightGBM methods from the community machine learning methods. In the predictions, several financial variables, including Gold, Bitcoin/USD, Ethereum/USD, S&P 500 index, Nasdaq 100, Oil/USD, Euro/USD, and CDS data, are treated as independent variables. According to the results, XGBoost is found to be the best prediction method for NFT market volume estimation concerning several statistical criteria, e.g., MAE, MAPE, and RMSE, and the most significant influential feature in determining prices is the Ethereum/USD exchange rate.

Suggested Citation

  • Ozge Camalan & Sahika Gokmen & Sibel Atan, 2024. "Using Advanced Machine Learning Techniques to Predict the Sales Volume of Non-Fungible Tokens," World Journal of Applied Economics, WERI-World Economic Research Institute, vol. 10(1), pages 17-27, June.
    • Handle: RePEc:ana:journl:v:10:y:2024:i:1:p:17-27
    DOI: 10.22440/wjae.10.1.2
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    References listed on IDEAS

    as
    1. Yousaf, Imran & Yarovaya, Larisa, 2022. "Static and dynamic connectedness between NFTs, Defi and other assets: Portfolio implication," Global Finance Journal, Elsevier, vol. 53(C).
    2. 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.
    3. Dowling, Michael, 2022. "Is non-fungible token pricing driven by cryptocurrencies?," Finance Research Letters, Elsevier, vol. 44(C).
    4. 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).
    5. 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).
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    Full references (including those not matched with items on IDEAS)

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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

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