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

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Application of Machine Learning in Dynamic Multi-Factor Stock Selection

Authors
Wendi Ouyang1, *
1School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, China
*Corresponding author. Email: wendiouyang@link.cuhk.edu.cn
Corresponding Author
Wendi Ouyang
Available Online 31 August 2025.
DOI
10.2991/978-94-6463-823-3_65How to use a DOI?
Keywords
Machine Learning; Dynamic Multi-Factor Strategy; Stock Selection; Clustering Analysis; Portfolio Optimization
Abstract

The stock market has long been a crucial component of modern economic systems, significantly impacting the lives of both individual and institutional investors. Investment strategies have increasingly evolved from static to dynamic and adaptive approaches, thanks to advancements in data science, to navigate the inherent complexities of the market. This paper presents a dynamic multi-factor stock selection strategy enhanced by machine learning techniques, including clustering and dynamic factor weighting. By regularly updating the factors and their corresponding weights, this strategy aligns with the current market conditions, such as varying levels of volatility, market trends, and overall uncertainty. Additionally, the study includes comprehensive backtesting that demonstrates the superior performance of the machine learning-based dynamic strategy over a static multi-factor approach and the traditional S&P 500 index benchmark. Notably, the dynamic strategy achieved an annualized return (CAGR) of 47.57%, significantly exceeding the S&P 500’s 14.41% and the passive strategy’s 14.41%, while also delivering a more favorable Sharpe ratio, indicating improved risk-adjusted returns despite experiencing greater volatility and drawdowns.

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_65How 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  - Wendi Ouyang
PY  - 2025
DA  - 2025/08/31
TI  - Application of Machine Learning in Dynamic Multi-Factor Stock Selection
BT  - Proceedings of the 2025 3rd International Conference on Image, Algorithms, and Artificial Intelligence (ICIAAI 2025)
PB  - Atlantis Press
SP  - 653
EP  - 674
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-823-3_65
DO  - 10.2991/978-94-6463-823-3_65
ID  - Ouyang2025
ER  -