Random Forest in Stock Market Prediction: A Data-Driven Approach

DOI

10.2991/978-94-6239-801-6_61How to use a DOI?

Keywords

Random Forest; Stock Market Prediction; Machine Learning; Financial Forecasting; Trend Prediction

Abstract

Forecasting stock price movements remains a core research area within the modern financial industry. When applied to financial forecasting tasks, machine learning algorithms, particularly Random Forest (RF), deliver reliable prediction outcomes, run steadily and rarely fall into overfitting issues. This work adopts the Random Forest algorithm to judge whether stock prices will rise or fall in the short run. The research dataset combines daily trading data including opening price, closing price, highest price, lowest price and trading volume, alongside mainstream technical indicators such as RSI and moving averages. The paper partitioned the full dataset into a 70% training set and a 30% test set for model training and validation. The final experimental results show the model achieves 87.3% overall accuracy, 85.6% precision, 86.1% recall and an F1-score of 85.8%. After analyzing feature contribution, the paper found trading volume and the 14-day RSI are the two most influential factors affecting prediction results. The results prove that Random Forest can effectively capture the inherent rules behind stock price fluctuations and offer practical guidance for quantitative investment work. Although sudden market emergencies will cause a slight drop in prediction accuracy, this algorithm is still a practical choice for trend judgment and investment plan design.

Copyright

© 2026 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  - Yiwen Yang
PY  - 2026
DA  - 2026/10/23
TI  - Random Forest in Stock Market Prediction: A Data-Driven Approach
BT  - Proceedings of the 2026 2nd International Conference on Financial Innovation and Marketing Management (FIMM 2026)
PB  - Atlantis Press
SP  - 572
EP  - 578
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6239-801-6_61
DO  - 10.2991/978-94-6239-801-6_61
ID  - Yang2026
ER  -