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

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Predicting Stock Prices and Optimizing Portfolios: A Random Forest and Monte Carlo-Based Approach Using NASDAQ-100

Authors
Hanyi Zhao1, *
1Business School, University of Edinburgh, Edinburgh, EH8 9YL, UK
*Corresponding author. Email: H.Zhao-25@sms.ed.ac.uk
Corresponding Author
Hanyi Zhao
Available Online 24 February 2025.
DOI
10.2991/978-94-6463-652-9_95How to use a DOI?
Keywords
Empirical Asset Pricing; Random Forest; Portfolio Optimization; NASDAQ-100
Abstract

In recent years, machine learning has gained substantial traction in financial markets, particularly in predicting stock prices and optimizing investment portfolios. Traditional methods for stock prediction, such as fundamental and technical analysis, have limitations in capturing complex market patterns. This study explores the application of the Random Forest model in stock price prediction and portfolio optimization using NASDAQ-100 constituent stocks. By combining return predictions from the Random Forest model with Monte Carlo simulations for portfolio construction, the research aims to create portfolios that maximize returns while maintaining controlled risk levels. The results indicate that the constructed portfolios significantly outperformed the NASDAQ-100 benchmark in annualized returns, though they exhibited higher volatility and risk, particularly during market downturns. While the machine learning approach performed well in normal conditions, certain limitations became evident during extreme market environments. Future research could address these issues by incorporating broader diversification and more advanced risk management techniques to enhance portfolio stability.

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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<Previous 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_95How 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  - Hanyi Zhao
PY  - 2025
DA  - 2025/02/24
TI  - Predicting Stock Prices and Optimizing Portfolios: A Random Forest and Monte Carlo-Based Approach Using NASDAQ-100
BT  - Proceedings of the International Workshop on Navigating the Digital Business Frontier for Sustainable Financial Innovation (ICDEBA 2024)
PB  - Atlantis Press
SP  - 883
EP  - 892
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-652-9_95
DO  - 10.2991/978-94-6463-652-9_95
ID  - Zhao2025
ER  -