Forecasting Bitcoin: A Comparative Analysis of Traditional versus Machine Learning Approach
Muhammad Arslan, Akmal Shahzad, Anum Shafique, Wajid Shakeel Ahmed · Transformations in banking, finance and regulation · 2025
This study attempts to forecast Bitcoin using both traditional and machine learning approaches to determine which methods are more robust. For the traditional method, the GARCH method is used to forecast Bitcoin returns. For the machine learning method, LSTM is used. A hybrid approach of GARCH–LSTM is also applied to the data to compare the results. Hourly data for Bitcoin are obtained from Coin-MarketCap for three years, from 2019 to 2022. The findings of the study reveal that machine learning methods outperform traditional methods. The study has useful implications for researchers.