Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/10273
Title: Exploring key properties and predicting price movements of cryptocurrency market using social network analysis
Authors: Dr. HO Kin-Hon, Roy 
Hou, Yun 
Georgiades, Michael 
Fong, Ken C. K. 
Issue Date: 2024
Source: IEEE Access, 2024, vol. 12, pp. 65058-65077.
Journal: IEEE Access 
Abstract: The emerging cryptocurrency market is one of the largest financial markets in the world, with a market capitalization that is already surpassing the gross domestic product of many developed economies. Cryptocurrencies are increasingly being adopted as a means of transaction and ownership in the digital domain, particularly in areas like decentralized finance and non-fungible tokens. Known for its high volatility, this market offers investors the potential for higher returns than traditional financial markets like stocks, foreign exchange, and commodities. However, it remains underexplored in academic research. In this paper, we propose the use of social network analysis to effectively model and analyze the cryptocurrency market and conduct a comprehensive numerical study to explore its key properties, including correlation structure, topological characteristics, stability, and influence. Furthermore, we propose the use of centrality measures as novel indicators to improve the accuracy of cryptocurrency price movement predictions. Our research introduces a novel method for understanding and navigating the cryptocurrency market, enabling investors to integrate advanced analytical tools into their decision-making processes.
Description: Open access
Type: Peer Reviewed Journal Article
URI: http://hdl.handle.net/20.500.11861/10273
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2024.3397723
Appears in Collections:Business Administration - Publication

Show full item record

Google ScholarTM

Impact Indices

Altmetric

PlumX

Metrics


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