Open Access
| Issue |
ITM Web Conf.
Volume 84, 2026
2026 International Conference on Advent Trends in Computational Intelligence and Data Science (ATCIDS 2026)
|
|
|---|---|---|
| Article Number | 02001 | |
| Number of page(s) | 8 | |
| Section | Machine Learning Approaches in FinTech and Economic Forecasting | |
| DOI | https://doi.org/10.1051/itmconf/20268402001 | |
| Published online | 06 April 2026 | |
- X.-Y. Liu, X. Yang, X.-Y. Wang, FinRL: Deep reinforcement learning framework to automate trading in quantitative finance. arXiv preprint arXiv:2111.09395 (2021) [Google Scholar]
- J. Zhang, S. Skiena, Sentiment and knowledge based algorithmic trading with deep reinforcement learning. arXiv preprint arXiv:2001.09403 (2020) [Google Scholar]
- B. Horvath, A. Muguruza, Learning a functional control for high-frequency finance. arXiv preprint arXiv:2006.09611 (2020) [Google Scholar]
- B. Ning, X. Gao, Double deep Q-learning for optimal execution. arXiv preprint arXiv:1812.06600 (2018) [Google Scholar]
- R. Kumar, Y. Chen, The evolution of reinforcement learning in quantitative finance. arXiv preprint arXiv:2408.10932 (2024) [Google Scholar]
- J. Li, H. Zhao, Reinforcement learning for financial portfolio optimization: Dynamic strategies for risk and reward management. arXiv preprint (2024) [Google Scholar]
- Z. Shen, H. Huang, An adaptive quantitative trading strategy optimization framework based on meta reinforcement learning and cognitive game theory. Appl Intell 55, 689 (2025). [Google Scholar]
- X. Zhong, J. Wei, S. Li et al., Deep reinforcement learning for dynamic strategy interchange in financial markets. Appl Intell 55, 30 (2025). [Google Scholar]
- C. Cheng, B. Chen, Z. Xiao et al., Quantum Finance and Fuzzy Reinforcement Learning-Based Multi-agent Trading System. Int. J. Fuzzy Syst. 26, 2224–2245 (2024). [Google Scholar]
- S.K. Sahu, A.S. Mokhade, P.K. Agrawal et al., Utilizing Supervised Machine Learning and Technical Indicators for Quantitative Trading to Improve Stock Market Trading Decisions. SN Comput Sci 6, 498 (2025). [Google Scholar]
- S. Talazadeh, D. Peraković, SARF: Stock Market Prediction with Sentiment-Augmented Random Forest. In: D. Perakovic, L. Knapcikova (eds) Future Access Enablers for Ubiquitous and Intelligent Infrastructures. FABULOUS 2024. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 596. Springer, Cham (2025). [Google Scholar]
- K. Neupane, I. Griva, A Random Forest Approach to Detect and Identify Unlawful Insider Trading. Comput Econ (2025). [Google Scholar]
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