Mapping the Landscape of Machine Learning Applications in Bitcoin Price Forecasting: A Bibliometric Analysis

The growing utilization of machine learning techniques for forecasting has generated significant interest among individuals involved in Bitcoin investing, trading, and portfolio management. This research presents a comprehensive bibliometric analysis of 123 English- language journal articles indexed in Scopus, focusing on the use of machine learning techniques for forecasting Bitcoin prices. The study provides valuable insights into the field’s progression, patterns, and key contributors. Noteworthy authors include Gupta R, Li J, Li X, and Li Y, while prominent institutions include "King Abdulaziz University", "Mansoura University", and "Sungkyunkwan University". China, Korea, and India are the leading countries in terms of article productivity. The top publishing outlets are identified as "IEEE Access", "Computational Economics", "Entropy", and "Finance Research Letters". The study’s findings cater to the interests of stakeholders in this field. This study conducts the key- word co-occurrence and co-citation analysis to identify potential clustering of topics. The findings have significant implications for academics, industry practitioners, regulators, and policymakers.