Predicting Stock Returns Using Machine Learning: A Hybrid Approach with LightGBM, XGBoost, and Portfolio Optimization

DOI

10.2991/978-94-6463-652-9_94How to use a DOI?

Keywords

Stock Return Prediction; Machine Learning; Finance

Abstract

Predicting stock returns presents a multifaceted challenge in the financial market, as it is influenced by a myriad of variables, including economic indicators, market sentiment, and company performance. Traditional predictive models often fall short in capturing the complexities and dynamics of contemporary markets. As a result, there has been a notable shift towards machine learning approaches, which offer advanced techniques for analyzing vast datasets. This study explores the application of two leading machine-learning models, LightGBM and XGBoost, in forecasting the future performance of S&P 500 stocks. By integrating both fundamental and technical data—such as price trends, financial metrics, and broader market conditions—these models aim to deliver accurate predictions. The insights generated can significantly inform investment strategies, enabling investors to identify favorable long and short-term positions, thereby optimizing their portfolio performance in a highly competitive and rapidly changing financial landscape. This research contributes to the evolving field of quantitative finance, highlighting the potential of machine learning in investment decision-making.

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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TY  - CONF
AU  - Xinrong Yan
PY  - 2025
DA  - 2025/02/24
TI  - Predicting Stock Returns Using Machine Learning: A Hybrid Approach with LightGBM, XGBoost, and Portfolio Optimization
BT  - Proceedings of the International Workshop on Navigating the Digital Business Frontier for Sustainable Financial Innovation (ICDEBA 2024)
PB  - Atlantis Press
SP  - 877
EP  - 882
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-652-9_94
DO  - 10.2991/978-94-6463-652-9_94
ID  - Yan2025
ER  -