A Machine Learning Based Study on Amazon Stock Price Prediction

DOI

10.2991/978-94-6463-823-3_83How to use a DOI?

Keywords

Stock forecasting; Random forest; Support vector machine; LSTM; Time series analysis

Abstract

Stock market price prediction is a significant research area in finance, and accurate predictions can help investors make better trading decisions. With the advancement of machine learning (ML) technology, data-driven methodologies have progressively superseded classic statistical models. Based on Yahoo Finance’s 2015–2024 Amazon stock data, it utilises Random Forest (RF), Support Vector Machine (SVM), and Long Short-Term Memory Network (LSTM) to build a prediction model and analyse its performance using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). According to the experimental results, LSTM outperforms RF and SVM in terms of time series feature capture and prediction accuracy. In short-term prediction, RF performs consistently, but SVM is generally poor. The study implies that deep learning approaches have benefits in stock prediction, and that adding more market components (e.g., news sentiment analysis) may increase prediction performance and serve as a reference for financial market research.

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  - Ting Xiao
PY  - 2025
DA  - 2025/08/31
TI  - A Machine Learning Based Study on Amazon Stock Price Prediction
BT  - Proceedings of the 2025 3rd International Conference on Image, Algorithms, and Artificial Intelligence (ICIAAI 2025)
PB  - Atlantis Press
SP  - 830
EP  - 837
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-823-3_83
DO  - 10.2991/978-94-6463-823-3_83
ID  - Xiao2025
ER  -