Title: Bitcoin price prediction with other commodity prices as exogenous inputs using machine learning techniques

Authors: B. Azhaganathan; Ramasamy Murugesan; V. Shanmugaraja; Balakrishnan Jaikannan Manasvin Surya; Umesh Shide

Addresses: Department of Humanities and Social Sciences, National Institute of Technology, Tiruchirappalli, Tamil Nadu, 620015, India ' Department of Humanities and Social Sciences, National Institute of Technology, Tiruchirappalli, Tamil Nadu, 620015, India ' Department of Humanities and Social Sciences, National Institute of Technology, Tiruchirappalli, Tamil Nadu, 620015, India ' Department of Electronics and Communication Engineering, National Institute of Technology, Tiruchirappalli, Tamil Nadu, 620015, India ' Department of Humanities and Social Sciences, National Institute of Technology, Tiruchirappalli, Tamil Nadu, 620015, India

Abstract: This study addresses a gap in the literature by predicting Bitcoin prices using commodity prices as exogenous variables, a focus previously unexplored. Bitcoin, often referred to as digital gold, has gained significant attention from investors worldwide due to its resilience during financial distress. Prior research primarily utilised macroeconomic indicators, technical indicators, or combinations of commodity prices and macroeconomic factors. However, our study exclusively examines the predictive power of commodity prices - gold, silver, copper, crude oil, and iron ore - on Bitcoin's price, employing machine learning techniques such as random forest, K-nearest neighbours, decision tree, extreme gradient boost, and linear regression. All models showed strong performance when evaluated against 11 error metrics. The findings underscore a robust correlation between Bitcoin and these commodities, with the machine learning models achieving high accuracy in forecasting Bitcoin price fluctuations. These insights hold valuable implications for investors and the broader financial research community.

Keywords: Bitcoin price prediction; exogenic inputs; random forest; K nearest neighbours; extreme gradient boost; decision tree; linear regression; error metrics; commodity prices.

DOI: 10.1504/IJENM.2025.146315

International Journal of Enterprise Network Management, 2025 Vol.16 No.2, pp.179 - 195

Received: 15 Sep 2023
Accepted: 17 Mar 2024
Published online: 21 May 2025 *

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