Cryptocurrency price forecasting - A comparative analysis of ensemble learning and deep learning methods
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F24%3A39922534" target="_blank" >RIV/00216275:25410/24:39922534 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S1057521923005719#ac0005" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1057521923005719#ac0005</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.irfa.2023.103055" target="_blank" >10.1016/j.irfa.2023.103055</a>
Alternative languages
Result language
angličtina
Original language name
Cryptocurrency price forecasting - A comparative analysis of ensemble learning and deep learning methods
Original language description
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 re-quirements 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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
50206 - Finance
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
International Review of Financial Analysis
ISSN
1057-5219
e-ISSN
1873-8079
Volume of the periodical
92
Issue of the periodical within the volume
March
Country of publishing house
US - UNITED STATES
Number of pages
12
Pages from-to
103055
UT code for WoS article
001165877200001
EID of the result in the Scopus database
2-s2.0-85181245581