A Review: Predictive Models and Behaviour of Cryptocurrencies Price

This study aims to assess the knowledge flow within the research field and provide recommendations for further investigation. Specifically, this study conducts a thematic analysis of articles published in peer-reviewed journals between 2014 and 2022. Two primary themes emerge from the co-occurring k...

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Published in:Journal of Advanced Research in Applied Sciences and Engineering Technology
Main Author: Rashid N.A.; Ismail M.T.
Format: Article
Language:English
Published: Semarak Ilmu Publishing 2025
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200674405&doi=10.37934%2faraset.48.2.148167&partnerID=40&md5=d9f38a057e48435532a0bcbd9b9a5be8
id 2-s2.0-85200674405
spelling 2-s2.0-85200674405
Rashid N.A.; Ismail M.T.
A Review: Predictive Models and Behaviour of Cryptocurrencies Price
2025
Journal of Advanced Research in Applied Sciences and Engineering Technology
48
2
10.37934/araset.48.2.148167
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200674405&doi=10.37934%2faraset.48.2.148167&partnerID=40&md5=d9f38a057e48435532a0bcbd9b9a5be8
This study aims to assess the knowledge flow within the research field and provide recommendations for further investigation. Specifically, this study conducts a thematic analysis of articles published in peer-reviewed journals between 2014 and 2022. Two primary themes emerge from the co-occurring keywords: (1) cryptocurrency behaviour and (2) cryptocurrency price prediction models. The findings reveal the use of various methods for predicting cryptocurrency prices, including econometric and statistical approaches, machine learning (ML), deep learning (DL), and hybrid models. The overarching objective of all these models is to achieve optimal results in addressing the various challenges associated with predicting cryptocurrency prices. However, it is important to note that no single model can effectively address all the behavioural nuances within cryptocurrency price prediction datasets. To bridge this gap, we recommend that future researchers explore the development of a hybrid model that combines a statistical model with deep learning. Such a hybrid model has the potential to accurately address the behavioural challenges encountered in cryptocurrency price prediction data series. © 2025, Semarak Ilmu Publishing. All rights reserved.
Semarak Ilmu Publishing
24621943
English
Article

author Rashid N.A.; Ismail M.T.
spellingShingle Rashid N.A.; Ismail M.T.
A Review: Predictive Models and Behaviour of Cryptocurrencies Price
author_facet Rashid N.A.; Ismail M.T.
author_sort Rashid N.A.; Ismail M.T.
title A Review: Predictive Models and Behaviour of Cryptocurrencies Price
title_short A Review: Predictive Models and Behaviour of Cryptocurrencies Price
title_full A Review: Predictive Models and Behaviour of Cryptocurrencies Price
title_fullStr A Review: Predictive Models and Behaviour of Cryptocurrencies Price
title_full_unstemmed A Review: Predictive Models and Behaviour of Cryptocurrencies Price
title_sort A Review: Predictive Models and Behaviour of Cryptocurrencies Price
publishDate 2025
container_title Journal of Advanced Research in Applied Sciences and Engineering Technology
container_volume 48
container_issue 2
doi_str_mv 10.37934/araset.48.2.148167
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200674405&doi=10.37934%2faraset.48.2.148167&partnerID=40&md5=d9f38a057e48435532a0bcbd9b9a5be8
description This study aims to assess the knowledge flow within the research field and provide recommendations for further investigation. Specifically, this study conducts a thematic analysis of articles published in peer-reviewed journals between 2014 and 2022. Two primary themes emerge from the co-occurring keywords: (1) cryptocurrency behaviour and (2) cryptocurrency price prediction models. The findings reveal the use of various methods for predicting cryptocurrency prices, including econometric and statistical approaches, machine learning (ML), deep learning (DL), and hybrid models. The overarching objective of all these models is to achieve optimal results in addressing the various challenges associated with predicting cryptocurrency prices. However, it is important to note that no single model can effectively address all the behavioural nuances within cryptocurrency price prediction datasets. To bridge this gap, we recommend that future researchers explore the development of a hybrid model that combines a statistical model with deep learning. Such a hybrid model has the potential to accurately address the behavioural challenges encountered in cryptocurrency price prediction data series. © 2025, Semarak Ilmu Publishing. All rights reserved.
publisher Semarak Ilmu Publishing
issn 24621943
language English
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