Proceedings of the International Workshop on Navigating the Digital Business Frontier for Sustainable Financial Innovation (ICDEBA 2024)

<Previous Article In Volume
Next Article In Volume>

The Application of Ridge Regression, Random Forest and Mean-Variance Model in Portfolio Optimization

Authors
Tianyi Lu1, *
1Department of Economic and Finance, City University of Hong Kong, Hong Kong, 999077, China
*Corresponding author. Email: tianyilu3-c@my.cityu.edu.hk
Corresponding Author
Tianyi Lu
Available Online 24 February 2025.
DOI
10.2991/978-94-6463-652-9_62How to use a DOI?
Keywords
Portfolio Optimization; Ridge Regression; Random Forest; Mean-Variance Model
Abstract

In recent years, with the increasing volatility in global financial markets, which is driven by factors including geopolitical events and policy uncertainty, the need for more effective portfolio optimization techniques has highly intensified. This study explores the use of machine learning, specifically Ridge regression and Random Forest model, under the framework of the Mean-Variance Model to optimize portfolio returns. Historical stock between July 2014 and July 2023 was used. Key technical indicators such as moving averages (MA50, MA200), volatility, and volume-based metrics were utilized as input features. The specific models used in this study were Ridge Regression and Random Forest for comparing the performance of the linear and non-linear models. The predicted returns from both models’ test sets were incorporated into the Mean-Variance portfolio optimization, aiming to maximize the Sharpe ratio. The back-testing results between July 2023 and July 2024 showed that both machine learning-enhanced portfolios outperformed the benchmark portfolio based on actual market returns, with the Random Forest portfolio achieving superior risk-adjusted returns and lower volatility. These findings represent the potential of utilizing machine learning models to enhance traditional portfolio optimization strategies.

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.

Download article (PDF)

<Previous Article In Volume
Next Article In Volume>
Volume Title
Proceedings of the International Workshop on Navigating the Digital Business Frontier for Sustainable Financial Innovation (ICDEBA 2024)
Series
Advances in Economics, Business and Management Research
Publication Date
24 February 2025
ISBN
978-94-6463-652-9
ISSN
2352-5428
DOI
10.2991/978-94-6463-652-9_62How 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  - Tianyi Lu
PY  - 2025
DA  - 2025/02/24
TI  - The Application of Ridge Regression, Random Forest and Mean-Variance Model in Portfolio Optimization
BT  - Proceedings of the International Workshop on Navigating the Digital Business Frontier for Sustainable Financial Innovation (ICDEBA 2024)
PB  - Atlantis Press
SP  - 592
EP  - 598
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-652-9_62
DO  - 10.2991/978-94-6463-652-9_62
ID  - Lu2025
ER  -