Amazon Stock Price Prediction Using Machine Learning

DOI

10.2991/978-94-6463-823-3_80How to use a DOI?

Keywords

Amazon Stock Price Prediction; Machine Learning; XGBoost

Abstract

In this project, machine learning (ML) models were applied to predict Amazon’s stock prices using historical data from 2014 to 2019 obtained from Kaggle. The predictive performance of Linear Regression (LR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) was evaluated using metrics such as Root Mean Squared Error (RMSE) and the coefficient of determination (R²). The dataset was enhanced through the inclusion of lag variables and technical indicators to better capture time series patterns and market behavior. Experimental results demonstrated that LR significantly outperformed the more complex tree-based ensemble methods, achieving the lowest RMSE and highest R². This finding suggests that, for certain financial forecasting scenarios with well-structured features, simpler ML models can yield comparable or even superior performance, offering both interpretability and reduced computational cost. The study underscores the importance of model selection based on data characteristics rather than algorithmic complexity alone. These insights can guide investors and analysts in choosing appropriate modeling strategies that balance accuracy, efficiency, and practical implementation in real-world trading environments.

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  - Haoyang Yao
PY  - 2025
DA  - 2025/08/31
TI  - Amazon Stock Price Prediction Using Machine Learning
BT  - Proceedings of the 2025 3rd International Conference on Image, Algorithms, and Artificial Intelligence (ICIAAI 2025)
PB  - Atlantis Press
SP  - 805
EP  - 812
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-823-3_80
DO  - 10.2991/978-94-6463-823-3_80
ID  - Yao2025
ER  -