
AI Technology's Application and Impact in the Secondary Market of Virtual Currencies
- 1 Sichuan University of Media and Communications
* Author to whom correspondence should be addressed.
Abstract
With the rapid development of the financial industry and artificial intelligence (AI) technology, the application of AI robots in finance has become a widely discussed topic in the academia. As an important part of the financial market, the secondary trading market of virtual currencies is characterized by high volatility, risk and decentralization, which poses significant challenges for traditional trading methods. AI technologies, especially machine learning and deep learning algorithms, provides a new path to optimize trading strategies and reduce investment risks thanks to their powerful data processing, pattern recognition and real-time analysis capabilities. This paper focuses on the characteristics of AI technology and the secondary trading market of virtual currencies, as well as the practical application of artificial intelligence technology in the financial industry, and explores the potential of applying AI robots to virtual currency trading. The research shows that AI robots can provide more accurate decision support for investors through massive data analysis, automatic trading execution and real-time risk assessment, improving market response speed and investment return rate. If the combination of AI robots and virtual currency trading is successful, it is expected to create more long-term and stable returns for investors, providing important theoretical and practical value for the development of financial investment.
Keywords
artificial intelligence, virtual currencies, secondary trading market
[1]. Tan, H., Huan, Z., & Pu, Y. (2024). Machine learning, text big data, and nowcasting instant prediction. Financial Development Review, (05), 59-74. https://doi.org/10.19895/j.cnki.fdr.2024.05.008
[2]. Wang, A., Kong, L., & Li, Y. (2024). Legal risk inspection and response of digital finance algorithm black box. Financial Development Research, 1-8. https://doi.org/10.19647/j.cnki.37-1462/f.2024.11.007
[3]. Zhu, F., Guo, W., & Yan, X. (2024). Volatility prediction of high-frequency trading financial data based on deep learning. Intelligent Computer and Applications, (09), 82-87. https://doi.org/10.20169/j.issn.2095-2163.240912
[4]. Sun, X. (2011). Research on the volatility impact of warrant listing on underlying stocks in China. Value Engineering, (16), 156. https://doi.org/10.14018/j.cnki.cn13-1085/n.2011.16.207
[5]. Wang, N. (2011). Research on the pricing method of warrants in China's securities market (Master's thesis, Shaanxi Normal University). Retrieved from CNKI
[6]. Zhou, K., Lin, C., & Wu, Z. (2023). The impact of short selling mechanism on enterprise innovation decision-making. Industrial Economic Review, (01), 81-101. https://doi.org/10.14007/j.cnki.cjpl.2023.01.006
[7]. Shi, J. (2024). Research on convertible bond stock price effect and arbitrage (Master's thesis, Zhejiang University). Retrieved from CNKI
Cite this article
Hou,C. (2025). AI Technology's Application and Impact in the Secondary Market of Virtual Currencies. Journal of Applied Economics and Policy Studies,16,26-29.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
About volume
Journal:Journal of Applied Economics and Policy Studies
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).