Forecasting Bitcoin: A Comparative Analysis of Traditional versus Machine Learning Approach

Muhammad Arslan, Akmal Shahzad, Anum Shafique and Wajid Shakeel Ahmed

Chapter 10 in Digital Banking and Finance:A Handbook, 2025, pp 257-279 from World Scientific Publishing Co. Pte. Ltd.

Abstract: 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.

Keywords: FinTech; Digital Era; Financial Industry; Digital Technology; Digital Financial Industry; Digital Finance; Financial Inclusion; Bank Stability; Emerging Economies; Bibliometric Analysis; Digital Finance Revolution; Global Impacts; Digital Innovation; Insurance; Big Data Applications; Digital Assets in Disarray; Forecasting Bitcoin; Machine Learning Approach; Economic Policy Uncertainty; Cryptocurrency; Bank Shares; Digital Age; Corporate Governance; Risks; Rewards; Assets Tokenization; Future of Money; Central Bank Digital Currencies; Bank Innovation; Risk-Taking Perspective (search for similar items in EconPapers)
JEL-codes: G1 G2 G24 M41 O3 O32 O33 (search for similar items in EconPapers)
Date: 2025
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