Key technical indicators for stock market prediction
File(s)
Published version
Author(s)
Mostafavi, Seyed Mostafa
Hooman, Ali Reza
Type
Journal Article
Abstract
The use of technical indicators for forecasting the stock market is widespread among investors and researchers. It is crucial to determine the optimal number of input technical indicators to predict the stock market successfully. However, there is no consensus on which collection of technical indicators is most suitable. The selection of technical indicators for a given forecasting model continues to be an active area of research. To our knowledge, there is limited published work on the importance of technical indicators in various categories such as momentum, trend, volatility, and volume. To identify the key technical indicators for stock market prediction, we employed XGBoost, Random Forest, Support Vector Regression, and LSTM regression techniques using 88 technical indicators as input data. We also used the PCA method for dimension reduction. The results reveal the most significant technical indicators within the momentum, trend, volatility, and volume categories. Our findings provide evidence that the proposed model is highly effective in predicting daily prices (with and without lag in Close price) on the S&P 500 stock index.
Date Issued
2025-06-01
Date Acceptance
2025-02-16
Citation
Machine Learning with Applications, 2025, 20
ISSN
2666-8270
Publisher
Elsevier BV
Journal / Book Title
Machine Learning with Applications
Volume
20
Copyright Statement
Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Identifier
10.1016/j.mlwa.2025.100631
Publication Status
Published
Article Number
100631
Date Publish Online
2025-03-01