Proceedings of the 2025 3rd International Conference on Image, Algorithms, and Artificial Intelligence (ICIAAI 2025)

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Stock Portfolio Optimization: Selection Methods and Redeployment under Different Preferences

Authors
Zhiyuan Xu1, *
1Massey Institute, Nanjing University of Finance and Economics, Nanjing, 210023, China
*Corresponding author. Email: 2120231157@stu.nufe.edu.cn
Corresponding Author
Zhiyuan Xu
Available Online 31 August 2025.
DOI
10.2991/978-94-6463-823-3_79How to use a DOI?
Keywords
Portfolio; US Presidential Election; PEST Analysis; Quantitative Analysis; Sharpe Ratio
Abstract

When different investors choose stocks, they will have different preferences. Many researchers have optimized investment return and risk control capabilities by improving genetic algorithms or combining new algorithms with genetic algorithms. However, how to select suitable stocks for investment portfolios and how to screen them again based on different preferences have not been explained in detail. Thus, this research aims to help investors select stocks based on macroeconomic outlook and quantitative analysis to form a portfolio, and use machine learning to evaluate and optimize them. The findings of this research show that as of the end of 2024, the high-risk and high-profit industries worth investing in include the technology industry and the energy industry. Industries that can balance risks include defence, consumer staples, and utilities. After evaluation and optimization, the Maximum Sharpe portfolio outperforms others in the Sharpe Ratio, and the naive 1/N (or equally weighted) portfolio beats the minimum volatility portfolios.

Copyright
© 2025 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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Volume Title
Proceedings of the 2025 3rd International Conference on Image, Algorithms, and Artificial Intelligence (ICIAAI 2025)
Series
Advances in Computer Science Research
Publication Date
31 August 2025
ISBN
978-94-6463-823-3
ISSN
2352-538X
DOI
10.2991/978-94-6463-823-3_79How to use a DOI?
Copyright
© 2025 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

risenwbib
TY  - CONF
AU  - Zhiyuan Xu
PY  - 2025
DA  - 2025/08/31
TI  - Stock Portfolio Optimization: Selection Methods and Redeployment under Different Preferences
BT  - Proceedings of the 2025 3rd International Conference on Image, Algorithms, and Artificial Intelligence (ICIAAI 2025)
PB  - Atlantis Press
SP  - 796
EP  - 804
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-823-3_79
DO  - 10.2991/978-94-6463-823-3_79
ID  - Xu2025
ER  -