Stock Prediction Based on Machine Learning Models

DOI

10.2991/978-94-6463-823-3_78How to use a DOI?

Keywords

Quantitative investment strategies; Machine learning models; Market-relative performance

Abstract

The rapid development of computing power has significantly boosted the adoption of quantitative investment strategies in global financial markets. While Compared with the limitations of traditional analytical methods in complex market environments, machine learning algorithms demonstrate significant advantages in pattern recognition and predictive modeling through high-dimensional data processing capabilities. This technological breakthrough enables investors to shift focus from absolute returns to more sophisticated market-relative performance measurement. By leveraging advanced machine learning techniques, quantitative analysts can now better identify securities that consistently outperform relevant benchmarks, representing a paradigm shift in modern investment strategy formulation. These innovations not only enhance return predictability but also improve risk management in volatile market conditions. The theme of this study is stock prediction based on machine learning models. This research constructs a machine learning stock selection model that integrates Support Vector Machines (SVM), Random Forest, and XGBoost to predict individual stocks’ future market-relative performance. Through experiments and data fitting, the study demonstrates that machine learning models can effectively assist stock trading and generate returns for investors. Therefore, this research concludes that with technological advancements, such quantitative stock selection models will play an increasingly significant role in the investment field.

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  - Rui Huang
PY  - 2025
DA  - 2025/08/31
TI  - Stock Prediction Based on Machine Learning Models
BT  - Proceedings of the 2025 3rd International Conference on Image, Algorithms, and Artificial Intelligence (ICIAAI 2025)
PB  - Atlantis Press
SP  - 787
EP  - 795
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-823-3_78
DO  - 10.2991/978-94-6463-823-3_78
ID  - Huang2025
ER  -