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

Chia-Cheng Chen, Chun-Hung Chen and Ting-Yin Liu
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Chia-Cheng Chen: Department of Finance, Ling Tung University of Science and Technology, Taichung, Taiwan
Chun-Hung Chen: Department of Finance, National Yunlin University of Science and Technology, Doulium, Yunlin County, Taiwan,
Ting-Yin Liu: Department of Business Affairs, Mingdao High School, Taichung, Taiwan.

International Journal of Economics and Financial Issues, 2020, vol. 10, issue 1, 59-66

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.

Keywords: ANN; SVM; Random Forest; Machine Learning; Investment Performance (search for similar items in EconPapers)
JEL-codes: C11 C15 C53 G17 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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