Analysis on the Influence of Intelligent Algorithm Trading in Financial Market

Authors

  • Gefan Dai

DOI:

https://doi.org/10.54097/j7r1v853

Keywords:

Intelligent algorithm trading, Influence, Financial market

Abstract

As a means to realize the intellectualization and automation of trading by using advanced mathematical models and computer technology, intelligent algorithm trading significantly improves the trading speed and efficiency, shortens the trading time window through millisecond decision-making and execution, reduces the potential losses caused by market fluctuations, and improves market liquidity and trading accuracy. However, it also brings risks such as technical failure and market manipulation. While emphasizing the trading opportunities of intelligent algorithm, this paper pays attention to its potential risks, and puts forward some suggestions on improving technology and strengthening supervision. The research points out that only through technological innovation, supervision strengthening and risk management can we ensure that intelligent algorithm trading can promote the development of financial markets without endangering market stability. In the future, with the technological progress and market maturity, the application prospect of intelligent algorithm trading will be broader.

Downloads

Download data is not yet available.

References

[1] James, P., Anisoara, C., & Michael, W.(2018). Agent-based modeling for complex financial systems. IEEE Intelligent Systems, 33(2), 74-82.

[2] Pei Haotian, Che Xuemeng, Yang Aijun, & Lin Jinguan. (2024). Research on Financial Asymmetric Log-GARCH Model with Zero Yield Rates. Operations Research and Management, 33(3), 177-183.

[3] Wang Huaiyong, & Deng Ruohan. (2021). Algorithm Convergence Risk: Theoretical Justification and Governance Logic - Analysis Based on Financial Markets. Modern Economic Discussion, 000(001), 113-121.

[4] Wen XinXian. (2023). Research on High-Frequency Quantitative Trading Strategies Based on Deep Reinforcement Learning. Modern Electronic Technology, 46(2), 125-131.

[5] Gong Yajian, Wei Xianhua, Meng Xiangying, & Liu Chenhao. (2020). Can Genetic Programming Strategy Be Applied to Chinese Stock Market? - Research on Index Trading Strategy Based on Multi-Objective Genetic Programming with Randomness. Systems Science and Mathematics, 40(12), 2381-2400.

[6] Liu Min, Zhang Fan, Wang Lin, & Zhu Qing. (2022). Financial Trading Decision Support Model Based on Deep Learning and Signal Decomposition. Management Review, 34(9), 14-26.

[7] Li Xiaohan, Wang Jun, Jia Huading, & Xiao Liu. (2022). Stock Market Volatility Prediction Method Based on Graph Neural Network with Multiple Attention Mechanisms. Computer Applications, 42(7), 2265-2273.

[8] Deng Mingmao, Yang Jiuxiang, & Mei Chun. (2023). Information Network of Fund Group Trading and Tail Risk of Stock Prices. Journal of Financial Economics Research, 38(5), 129-144.

[9] Olorunnimbe, K., & Viktor, H. (2024). Ensemble of temporal transformers for financial time series. Journal of Intelligent Information Systems, 62(4), 1087-1111.

Downloads

Published

07-11-2024

Issue

Section

Articles

How to Cite

Dai, G. (2024). Analysis on the Influence of Intelligent Algorithm Trading in Financial Market. Frontiers in Business, Economics and Management, 17(1), 280-283. https://doi.org/10.54097/j7r1v853