Key technical indicators for stock market prediction

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.

Keywords

• Technical indicator
• Stock market
• S&P 500 index
• Machine learning
• LSTM
• XGBoost