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

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Stock Price Prediction and Portfolio Optimization Based on Mean Variance Model and Random Forest Model

Authors
Rundong Chen1, *
1Finance, Jinan University, Guangzhou, 511436, China
*Corresponding author. Email: wangyuping@tzc.edu.cn
Corresponding Author
Rundong Chen
Available Online 24 February 2025.
DOI
10.2991/978-94-6463-652-9_67How to use a DOI?
Keywords
Mean variance model; Random forest model; Portfolio
Abstract

In order to show the applicability and accuracy of different models for predicting stock prices, it is necessary to select classical models for effective comparison. In this paper, the termination model and random forest model are used to analyze and compare five representative American stocks in detail. This paper constructed two different portfolios, each with a unique investment strategy, ranging from maximizing the Sharpe ratio to minimizing risk. To predict future returns and optimize these portfolios, this study utilized the Random Forest method, a robust machine learning algorithm known for its versatility in handling various types of data and its ability to model complex interactions. The analysis of the actual stock market data indicates that the random forest algorithm has a better prediction effect in the stock market quantification. The algorithm can accurately predict the rise and fall trend of stock prices, and can provide the probability of each stock's rise or fall. In a word, it provides more decision basis for investors.

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 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_67How 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  - Rundong Chen
PY  - 2025
DA  - 2025/02/24
TI  - Stock Price Prediction and Portfolio Optimization Based on Mean Variance Model and Random Forest Model
BT  - Proceedings of the International Workshop on Navigating the Digital Business Frontier for Sustainable Financial Innovation (ICDEBA 2024)
PB  - Atlantis Press
SP  - 649
EP  - 655
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-652-9_67
DO  - 10.2991/978-94-6463-652-9_67
ID  - Chen2025
ER  -