Abstract
This study aims to explore the prediction of S&P 500 stock price movement and conduct an analysis of its investment performance. Based on the S&P 500 index, the study compares three machine learning models: ANN, SVM, and Random Forest. With a performance evaluation of S&P 500 index historical data spanning from 2014 to 2018, we find: (1) By overall performance measures, machine learning models outperform benchmark market index. (2) By risk-adjusted measures, the empirical results suggest that Random Forest generates the best performance, followed by SVM and ANN.
Highlights
In recent years, deep learning, machine learning, and artificial intelligence (AI) are mainstream
The main objective of the research is to input the results of ten technical analysis indicators into artificial neural networks (ANN), support vector machines (SVM), and Random Forest models to predict stock price movement and evaluate investment performance and risk measurement
Random forests or random decision forests are an ensemble learning method for classification, regression and other tasks that operates by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classification
Summary
Deep learning, machine learning, and artificial intelligence (AI) are mainstream. The main objective of the research is to input the results of ten technical analysis indicators into artificial neural networks (ANN), support vector machines (SVM), and Random Forest models to predict stock price movement and evaluate investment performance and risk measurement. Based on the S&P 500 (GSPC) Index from 2014 to 2018, this research compares the investment performance among the machine learning models.
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