Investment Performance of Machine Learning: Analysis of S&P 500 Index - Chen, Chia-Cheng; Chen, Chun-Hung; Liu, Ting-Yin (International Journal of Economics and Financial Issues, 2019)
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. Keywords: ANN, SVM, Random Forest, Machine Learning, Investment Performance JEL Classifications: C11; C15; C53; G17 DOI: https://doi.org/10.32479/ijefi.8925