Research on the Optimal Prediction Model of Stock Returns of FAANG + M

DOI

10.2991/978-94-6463-823-3_77How to use a DOI?

Keywords

Stock Return; Linear Regression; Random Forest; XGBoost

Abstract

As machine learning continues to mature and advance, stock prediction has been extensively discussed across diverse fields of study. Compared to pure price, stock return seems to matter more, for people tend to always attach most importance to the chance of winning revenue. However, research that deals with relative return forecasting and focuses on a specific collection of stocks is still limited. In order to promote the wider application in real life and facilitate the process of trading in stock market, this paper examines the optimal prediction model of stock returns by gathering stock price data from 2015 to 2024 in six enterprises (FAANG + M), adding time series parameters based on existing information, selecting NASDAQ-100 Index as the baseline parameter to eliminate market fluctuations, and constructing separate three regression models (Linear Regression, Random Forest and Extreme Gradient Boosting). Overall, Linear Regression possesses the optimal prediction outcomes, while Random Forest takes second place and XGBoost the last. In other words, in the particular context of this paper, there seems to be a positive relationship between model simplicity and result quality. The outcome deviates from conventional expectations and can be partly explained by the complexity of the models themselves and the overfitting issues that ensue. To tackle this problem, regularization and early stopping technology can be further employed to optimize model performance.

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  - Yijun Xia
PY  - 2025
DA  - 2025/08/31
TI  - Research on the Optimal Prediction Model of Stock Returns of FAANG + M
BT  - Proceedings of the 2025 3rd International Conference on Image, Algorithms, and Artificial Intelligence (ICIAAI 2025)
PB  - Atlantis Press
SP  - 779
EP  - 786
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-823-3_77
DO  - 10.2991/978-94-6463-823-3_77
ID  - Xia2025
ER  -