Comparison of Actor-Critic Reinforcement Learning Models for Formulating Stock Trading Strategy
- DOI
- 10.2991/978-94-6463-370-2_22How to use a DOI?
- Keywords
- Reinforcement Learning; Stock Trading Strategy; Deep Learning
- Abstract
The development of a stock trading strategy holds significant importance inside investing organizations. Investors can enhance their trading performance and attain their financial objectives by comprehending market behaviour, creating returns, effectively managing risk, and making well-informed judgments. Nonetheless, formulating a proficient plan within the intricate and perpetually evolving stock market poses a formidable undertaking. Machine learning and deep learning have been widely recognized as useful methodologies for stock trading. This research presents a comparative analysis of four distinct reinforcement learning models. The algorithms under consideration are Deep Deterministic Policy Gradient (DDPG), Proximal Policy Optimization (PPO), Advantage Actor Critic (A2C), and the ensemble method. These models are tested within the context of the actual stock market environment. The findings indicate that the ensemble strategy outperforms the other three algorithms, suggesting that the ensemble model holds promise to improve the effectiveness of stock trading techniques.
- Copyright
- © 2024 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Chendi Li PY - 2024 DA - 2024/02/14 TI - Comparison of Actor-Critic Reinforcement Learning Models for Formulating Stock Trading Strategy BT - Proceedings of the 2023 International Conference on Data Science, Advanced Algorithm and Intelligent Computing (DAI 2023) PB - Atlantis Press SP - 200 EP - 206 SN - 1951-6851 UR - https://doi.org/10.2991/978-94-6463-370-2_22 DO - 10.2991/978-94-6463-370-2_22 ID - Li2024 ER -