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Cryptocurrency price forecasting – A comparative analysis of ensemble learning and deep learning methods
,
Petr Hajek,
Kunpeng Yuan
International Review of Financial Analysis, Volume: 92, Start page: 103055
Swansea University Author:
Mohammad Abedin
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© 2023 The Authors. This is an open access article under the CC BY license.
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DOI (Published version): 10.1016/j.irfa.2023.103055
Abstract
Cryptocurrency price forecasting is attracting considerable interest due to its crucial decision support role in investment strategies. Large fluctuations in non-stationary cryptocurrency prices motivate the urgent need for accurate forecasting models. The lack of seasonal effects and the need to me...
| Published in: | International Review of Financial Analysis |
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| ISSN: | 1057-5219 |
| Published: |
Elsevier BV
2024
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa65393 |
| Abstract: |
Cryptocurrency price forecasting is attracting considerable interest due to its crucial decision support role in investment strategies. Large fluctuations in non-stationary cryptocurrency prices motivate the urgent need for accurate forecasting models. The lack of seasonal effects and the need to meet a number of unrealistic requirements make it difficult to make accurate forecasts using traditional statistical methods, leaving machine learning, particularly ensemble and deep learning, as the best technology in the area of cryptocurrency price forecasting. This is the first work to provide a comprehensive comparative analysis of ensemble learning and deep learning forecasting models, examining their relative performance on various cryptocurrencies (Bitcoin, Ethereum, Ripple, and Litecoin) and exploring their potential trading applications. The results of this study reveal that gated recurrent unit, simple recurrent neural network, and LightGBM methods outperform other machine learning methods, as well as the naive buy-and-hold and random walk strategies. This can effectively guide investors in the cryptocurrency markets. |
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| Keywords: |
Cryptocurrency; Bitcoin; Forecasting; Ensemble learning; Deep learning; Neural networks |
| College: |
Faculty of Humanities and Social Sciences |
| Funders: |
This research paper was made possible by a grant from the Czech Sciences Foundation (No. 22-22586S). |
| Start Page: |
103055 |