Investment Performance of Machine Learning: Analysis of S&P 500 Index

Chia-Cheng Chen, Chun-Hung Chen, Ting-Yin Liu


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


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