Detailed Information

Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

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

Full metadata record
DC Field Value Language
dc.contributor.authorOtabek, Sattarov-
dc.contributor.authorChoi, Jaeyoung-
dc.date.accessioned2024-07-27T11:00:27Z-
dc.date.available2024-07-27T11:00:27Z-
dc.date.issued2024-06-
dc.identifier.issn2169-3536-
dc.identifier.issn2169-3536-
dc.identifier.urihttps://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/92081-
dc.description.abstractThe 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.-
dc.format.extent26-
dc.language영어-
dc.language.isoENG-
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC-
dc.titleFrom Prediction to Profit: A Comprehensive Review of Cryptocurrency Trading Strategies and Price Forecasting Techniques-
dc.typeArticle-
dc.identifier.wosid001258797500001-
dc.identifier.doi10.1109/ACCESS.2024.3417449-
dc.identifier.bibliographicCitationIEEE ACCESS, v.12, pp 87039 - 87064-
dc.description.isOpenAccessY-
dc.identifier.scopusid2-s2.0-85196759855-
dc.citation.endPage87064-
dc.citation.startPage87039-
dc.citation.titleIEEE ACCESS-
dc.citation.volume12-
dc.type.docTypeArticle-
dc.publisher.location미국-
dc.subject.keywordAuthorCryptocurrency-
dc.subject.keywordAuthorReviews-
dc.subject.keywordAuthorPredictive models-
dc.subject.keywordAuthorBiological system modeling-
dc.subject.keywordAuthorAccuracy-
dc.subject.keywordAuthorPrediction algorithms-
dc.subject.keywordAuthorAdaptation models-
dc.subject.keywordAuthorMachine learning-
dc.subject.keywordAuthorCryptocurrency trading-
dc.subject.keywordAuthorprice prediction-
dc.subject.keywordAuthortrading strategies-
dc.subject.keywordAuthormachine learning-
dc.subject.keywordPlusNEURAL-NETWORKS-
dc.subject.keywordPlusTECHNICAL ANALYSIS-
dc.subject.keywordPlusBITCOIN-
dc.subject.keywordPlusMODEL-
dc.subject.keywordPlusVOLATILITY-
dc.relation.journalResearchAreaComputer Science-
dc.relation.journalResearchAreaEngineering-
dc.relation.journalResearchAreaTelecommunications-
dc.relation.journalWebOfScienceCategoryComputer Science, Information Systems-
dc.relation.journalWebOfScienceCategoryEngineering, Electrical & Electronic-
dc.relation.journalWebOfScienceCategoryTelecommunications-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
Files in This Item
There are no files associated with this item.
Appears in
Collections
ETC > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher Choi, Jaeyoung photo

Choi, Jaeyoung
College of IT Convergence (Department of AI)
Read more

Altmetrics

Total Views & Downloads

BROWSE