Research on Stock Market Investment Strategy based on Deep Q-Network and Strategy Gradient Method

Authors

  • Zifeng Ye

Keywords:

Stock Market Investment Strategy; Merrill Cycle; Industrial Selection; Value Perception.

Abstract

As China enters a new era and the stock market investment strategy enters the stage of high-quality development around adhering to people-centered development, it is worth highlighting the following recommendations: investment strategies that better meet investors' risk and return needs. Based on the dynamic evolution of stock market development and according to the internal logic of the Merrill Lynch cycle, a theoretical analysis framework for stock market investment strategies is constructed, which can explain the stock market investment mechanism jointly generated by the supply and demand relationship and the price and capital circulation mechanism involved in market sentiment. And continue to explore the possibility of moving towards high-quality development goals from the perspective of style changes and practical interpretations of stock market development. A stock market investment strategy aims to provide investors with returns that meet expected standards, continuously improve return quality, and increase risk-adjusted returns. To this end, it is necessary to strengthen the industry selection based on the industry cycle, build an interaction and feedback mechanism between individual stocks and investors' value perception, and establish an evaluation system for the macroeconomy and the stock market, to achieve high-quality development of investment strategies in the stock market, stimulate the transformation and upgrade of China's economy, and truly satisfy the people. 

Downloads

Download data is not yet available.

References

Lee T K, Cho J H, Kwon D S, et al. Global stock market investment strategies based on financial network indicators using machine learning techniques[J]. Expert Systems with Applications, 2019, 117: 228-242.

Li Z, Deng G, Che H. Patent-based predictive EPS on increasing investment performance of China Stock Market [C]// 2021 IEEE International Conference on Power Electronics, Computer Applications (ICPECA). IEEE, 2021: 562-566.

Gao Z, Gao Y, Hu Y, et al. Application of deep q-network in portfolio management[C]//2020 5th IEEE International Conference on Big Data Analytics (ICBDA). IEEE, 2020: 268-275.

Alabdullah M H, Abido M A. Microgrid energy management using deep Q-network reinforcement learning[J]. Alexandria Engineering Journal, 2022, 61(11): 9069-9078.

Sun C, Abbas H S M, Xu X, et al. Role of capital investment, investment risks, and globalization in economic growth[J]. International Journal of Finance & Economics, 2023, 28(2): 1883-1898.

Carvache-Franco M, Alvarez-Risco A, Carvache-Franco O, et al. Perceived value and its influence on satisfaction and loyalty in a coastal city: a study from Lima, Peru[J]. Journal of Policy Research in Tourism, Leisure and Events, 2022, 14(2): 115-130.

Downloads

Published

24-10-2023

How to Cite

Ye, Z. (2023). Research on Stock Market Investment Strategy based on Deep Q-Network and Strategy Gradient Method. Transactions on Economics, Business and Management Research, 1, 55-61. https://wepub.org/index.php/TEBMR/article/view/102