References
[1]. Kercheval, A. et al.(2014). Support Vector Machine Models for High-Frequency Limit Order Book Prediction. Journal of Financial Markets , 17(3), 1–25.
[2]. Wang, L. et al. (2018). High-Frequency Time Series Forecasting Using Stacked Denoising Autoencoders. Quantitative Finance, 18(9), 1453–1472.
[3]. Financial Technology Research Group, Chongqing Branch of the People’s Bank of China (2020). Real-Time Risk Detection System for AI-Driven Financial Regulation. Contemporary Financial Research, (06), 61–67.
[4]. Wang, L. et al. (2016). Dynamic Training Dataset Updating for Deep Neural Networks in High-Frequency Trading. IEEE Transactions on Neural Networks, 27(12), 2563–2572.
[5]. Zhou, X. et al. (2018). Stock Volatility Prediction Using Generative Adversarial Networks. Journal of Computational Finance, 22(4), 89–112.
[6]. Zhang, X. H. (2023). Accelerating Financial Information Processing with NLP: A DID Analysis of AI-Enhanced Funds. Management World, 39(5), 45–60.
[7]. Wang, Y. M. et al. (2023). Algorithmic Failure Risk in Extreme Markets: Lessons from the 2020 U.S. Stock Meltdown. China Finance, 45(7), 33–49.
[8]. Li, M. et al. (2023). Algorithmic Convergence Effect in China’s Growth Enterprise Market. Journal of Financial Innovation, 15(3), 78–95.
[9]. International Monetary Fund (IMF) (2023). Comparative Study of AI Regulatory Frameworks: China’s "Technological Controllability" vs. U.S. "Algorithmic Transparency". IMF Working Paper No. WP/23/189.
[10]. International Monetary Fund (IMF) (2023). Comparative Study of AI Regulatory Frameworks: China’s "Technological Controllability" vs. U.S. "Algorithmic Transparency". IMF Working Paper No. WP/23/189.
[11]. Zhou, G. W. et al. (2023). Quantum Reinforcement Learning for Stock Trend Prediction: A Case Study of CSI 300 Index. China FinTech Press, 12(1), 55–72.
[12]. Zhang, X. H. (2023). ESG Rating Innovation Through Government Data Integration: A Comparative Study of China and the EU. Sustainable Finance Review, 6(2), 88–104.
Cite this article
Yue,H. (2025). Exploring the Impact of AI on the Stock Market. Advances in Economics, Management and Political Sciences,191,47-52.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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References
[1]. Kercheval, A. et al.(2014). Support Vector Machine Models for High-Frequency Limit Order Book Prediction. Journal of Financial Markets , 17(3), 1–25.
[2]. Wang, L. et al. (2018). High-Frequency Time Series Forecasting Using Stacked Denoising Autoencoders. Quantitative Finance, 18(9), 1453–1472.
[3]. Financial Technology Research Group, Chongqing Branch of the People’s Bank of China (2020). Real-Time Risk Detection System for AI-Driven Financial Regulation. Contemporary Financial Research, (06), 61–67.
[4]. Wang, L. et al. (2016). Dynamic Training Dataset Updating for Deep Neural Networks in High-Frequency Trading. IEEE Transactions on Neural Networks, 27(12), 2563–2572.
[5]. Zhou, X. et al. (2018). Stock Volatility Prediction Using Generative Adversarial Networks. Journal of Computational Finance, 22(4), 89–112.
[6]. Zhang, X. H. (2023). Accelerating Financial Information Processing with NLP: A DID Analysis of AI-Enhanced Funds. Management World, 39(5), 45–60.
[7]. Wang, Y. M. et al. (2023). Algorithmic Failure Risk in Extreme Markets: Lessons from the 2020 U.S. Stock Meltdown. China Finance, 45(7), 33–49.
[8]. Li, M. et al. (2023). Algorithmic Convergence Effect in China’s Growth Enterprise Market. Journal of Financial Innovation, 15(3), 78–95.
[9]. International Monetary Fund (IMF) (2023). Comparative Study of AI Regulatory Frameworks: China’s "Technological Controllability" vs. U.S. "Algorithmic Transparency". IMF Working Paper No. WP/23/189.
[10]. International Monetary Fund (IMF) (2023). Comparative Study of AI Regulatory Frameworks: China’s "Technological Controllability" vs. U.S. "Algorithmic Transparency". IMF Working Paper No. WP/23/189.
[11]. Zhou, G. W. et al. (2023). Quantum Reinforcement Learning for Stock Trend Prediction: A Case Study of CSI 300 Index. China FinTech Press, 12(1), 55–72.
[12]. Zhang, X. H. (2023). ESG Rating Innovation Through Government Data Integration: A Comparative Study of China and the EU. Sustainable Finance Review, 6(2), 88–104.