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From Prediction to Profit: A Comprehensive Review of Cryptocurrency Trading Strategies and Price Forecasting Techniquesopen access

Authors
Otabek, SattarovChoi, Jaeyoung
Issue Date
Jun-2024
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Keywords
Cryptocurrency; Reviews; Predictive models; Biological system modeling; Accuracy; Prediction algorithms; Adaptation models; Machine learning; Cryptocurrency trading; price prediction; trading strategies; machine learning
Citation
IEEE ACCESS, v.12, pp 87039 - 87064
Pages
26
Journal Title
IEEE ACCESS
Volume
12
Start Page
87039
End Page
87064
URI
https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/92081
DOI
10.1109/ACCESS.2024.3417449
ISSN
2169-3536
2169-3536
Abstract
The rapid evolution of cryptocurrency markets and the increasing complexity of trading strategies necessitate a comprehensive understanding of price-prediction models and their direct impact on trading efficacy. While extensive research has been conducted separately on price prediction methods and trading strategies, there remains a significant gap in studies explicitly correlating precise price forecasts with successful trading outcomes. This review paper addresses this gap by critically examining the role of accurate cryptocurrency price predictions in enhancing trading strategies. We conducted a systematic review of sufficient scholarly articles and web resources, focusing on the methodologies and effectiveness of various predictive models and their integration into cryptocurrency trading strategies. Our selection criteria ensured the inclusion of papers that demonstrate methodological rigor, relevance, and recent contributions to the field, spanning from economic theories and statistical models to advanced machine learning techniques. The findings reveal that precise price predictions significantly contribute to the development of adaptive and risk-managed trading strategies, which are crucial in the highly volatile cryptocurrency market. The review also identifies current challenges and proposes directions for future research, emphasizing the need for interdisciplinary approaches and ethical considerations in predictive modeling. This synthesis aims to bridge the existing research gap and guide future studies, thereby fostering more sophisticated and profitable trading strategies in the cryptocurrency domain.
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College of IT Convergence (Department of AI)
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