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

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Published Jun 13, 2024
DOI https://doi.org/10.22440/wjae.10.1.2

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Özge Çamalan
Atılım University Department of Economics
https://orcid.org/0000-0002-7196-8882
Şahika Gökmen
Hacı Bayram Veli University, Department of Econometrics and Uppsala University, Department of Statistics
https://orcid.org/0000-0002-4127-7108
Sibel Atan
Hacı Bayram Veli University, Department of Econometrics
https://orcid.org/0000-0002-4868-753X

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.


JEL codes: G12, C53


Keywords: Financial assets, Non-fungible tokens, Machine learning

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How to Cite
Çamalan, Özge, Gökmen, Şahika and Atan, S. (2024) “Using Advanced Machine Learning Techniques to Predict the Sales Volume of Non-Fungible Tokens”, World Journal of Applied Economics, 10(1), pp. 17-27. doi: 10.22440/wjae.10.1.2.
Issue
Vol 10 No 1: June 2024
Section
Research Articles
 
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