A Comparative Study of Stock Price Movement Prediction Methods

DOI

10.2991/978-94-6463-823-3_82How to use a DOI?

Keywords

Stock price movement prediction; Machine Learning; Ensemble Learning; Deep Learning; Comparative case analysis

Abstract

Stock price fluctuations are highly complex and uncertain, it is crucial for investment decision-making and finance risk management to accurately predict stock price movement of direction. In recent years, despite Machine Learning methods and Deep Learning algorithms having made progress in the field of financial prediction, there is a lack of systematic comparison analysis of multiple methods due to most research being restricted to single-model applications. To address this research gap, this study selects three types of methods: traditional Machine Learning, Ensemble Learning, and Deep Learning for case comparison, surrounding the subject of stock price direction prediction. According to case analysis, including the data of Vietnam VN30 index, U.S. stocks and Tesla stocks, and application of Logistic Regression, SVM, ANN, Random Forest, Extra Trees, as well as LSTM, GRU, and Transformer, this study collects accuracy, stability and adaptability of models, and discuss performance variance of different prediction methods under conditions of various markets and data. The results show that SVM has a high accuracy rate (92.48%), Ensemble Learning methods tend to show outstanding robustness in the prediction of multi-stock tasks, and LSTM achieves the best fitting effect in single-stock long-term series, with R-squared at 98%. Through comprehensive analysis, future stock price movement prediction is supposed to pay more attention to multi-feature fusion, hybrid model construction, and adaptive optimization strategy, to further improve the accuracy, robustness, and applicability of the model.

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  - Youran Chen
PY  - 2025
DA  - 2025/08/31
TI  - A Comparative Study of Stock Price Movement Prediction Methods
BT  - Proceedings of the 2025 3rd International Conference on Image, Algorithms, and Artificial Intelligence (ICIAAI 2025)
PB  - Atlantis Press
SP  - 822
EP  - 829
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-823-3_82
DO  - 10.2991/978-94-6463-823-3_82
ID  - Chen2025
ER  -