Volatility Prediction in Cryptocurrency UsingNFTs

Saeed Arshad ()
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Saeed Arshad: FAST-NUCES Lahore campus

International Journal of Innovations in Science & Technology, 2024, vol. 6 Special Issue: 7, issue 7, 22-31

Abstract: The cryptocurrency market has evolved in unprecedented ways over the past decade. However, due to the high price volatility associated with cryptocurrencies, predicting their prices remains an attractive research topic. While many researchers have focused on predicting cryptocurrency prices, there has been relatively little attention given to the latest trend in blockchain applications, specifically non-fungible tokens (NFTs). In this study, we have prepared a dataset comprising NFT sales and transaction data, along with information from other cryptocurrencies. This dataset is utilized to forecast the future price of Bitcoin using several machineslearning models, including Linear Regression, Random Forest, and XGBoost. The results highlight the prediction accuracy of these models. Among the three, the Random Forest regressor demonstrates the highest accuracy, followed closely by the XGBoost regressor and Linear Regression. These findings may assist investors in making informed decisions when investing in cryptocurrencies.

Keywords: Cryptocurrency; Machine Learning; NFT; Price prediction; Volatility (search for similar items in EconPapers)
Date: 2024
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