The Logical Framework of Industry Analysis and Stock Investment

DOI

10.2991/aebmr.k.220307.302How to use a DOI?

Keywords

Industry Analysis; Stock Investment; Factor Analysis; Risk Control

Abstract

In recent years, China’s economy has continued to grow at a high speed, which has led to a substantial increase in the performance of listed companies, laying a solid foundation for my country’s stock market. Especially in 2007, my country’s stock market surpassed most people’s imagination and completed a historic leap in the development of my country’s stock market. From the perspective of mature international markets, my country’s securities market is an emerging market. However, with the continuous growth and development of my country’s securities market, my country’s securities market will continue to be full of vitality in the future. The function of optimizing the allocation of resources in the securities market by excellent domestic enterprises is more reflected. With the maturity of my country’s stock market, how investors can invest under such stock market conditions has become a problem. Generally speaking, investors need to solve two problems when investing: one is how to analyze the industry and choose an excellent investment plan; the other is how to determine the investment ratio when investors choose multiple excellent stocks. The purpose of this article is to study the logical framework of industry analysis and stock investment, and using actual data, first use the simulated annealing algorithm to experiment, observe the rate of return and risk when the preference coefficient increases, and then base the algorithm on the basis of this algorithm. A particle swarm algorithm simulation experiment was carried out on the data. The experiment proved that the particle swarm simulation algorithm has a higher return on stock investment and lower risk than normal stock investment, with a return rate of 0.41 and a risk of 0.8. The expected results of the experiment are achieved, and the stock investment based on the algorithm is effectively proved.

Copyright

© 2022 The Authors. Published by Atlantis Press International B.V.

Open Access

This is an open access article under the CC BY-NC license.

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TY  - CONF
AU  - Shunye Zha
PY  - 2022
DA  - 2022/03/26
TI  - The Logical Framework of Industry Analysis and Stock Investment
BT  - Proceedings of the 2022 7th International Conference on Financial Innovation and Economic Development (ICFIED 2022)
PB  - Atlantis Press
SP  - 1837
EP  - 1842
SN  - 2352-5428
UR  - https://doi.org/10.2991/aebmr.k.220307.302
DO  - 10.2991/aebmr.k.220307.302
ID  - Zha2022
ER  -