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

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

Author

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  • Rundong Chen

    (Jinan University, Finance)

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.

Suggested Citation

  • Rundong Chen, 2025. "Stock Price Prediction and Portfolio Optimization Based on Mean Variance Model and Random Forest Model," Advances in Economics, Business and Management Research, in: Junfeng Lu (ed.), Proceedings of the International Workshop on Navigating the Digital Business Frontier for Sustainable Financial Innovation (ICDEBA 2024), pages 649-655, Springer.
    • Handle: RePEc:spr:advbcp:978-94-6463-652-9_67
    DOI: 10.2991/978-94-6463-652-9_67
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