Stock Portfolio Optimization Based on Reinforcement Learning

DOI

10.2991/978-94-6463-368-9_16How to use a DOI?

Keywords

Stock portfolio optimization; Reinforcement Learning; financial indicators and statistical indicators; value function

Abstract

This paper made a profound study of the application of reinforcement learning in portfolio optimization, using deep learning algorithm, combine various indicators, and analyze the explanatory variables that can effectively improve portfolio risk control through multi-dimensional financial indicators and statistical indicators. Designing a reasonable and effective value function from the reward and punishment mechanism to achieve the optimization goal of income maximization and risk control, mining problems from the perspective of practice, and the research results is of great significance for portfolio management.

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  - Jinglong Li
PY  - 2024
DA  - 2024/02/14
TI  - Stock Portfolio Optimization Based on Reinforcement Learning
BT  - Proceedings of the 2023 5th International Conference on Economic Management and Cultural Industry (ICEMCI 2023)
PB  - Atlantis Press
SP  - 123
EP  - 130
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-368-9_16
DO  - 10.2991/978-94-6463-368-9_16
ID  - Li2024
ER  -