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 |
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