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.

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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  -