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

Author

Listed:
  • 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.)

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.

Suggested Citation

  • Chia-Cheng Chen & Chun-Hung Chen & Ting-Yin Liu, 2020. "Investment Performance of Machine Learning: Analysis of S&P 500 Index," International Journal of Economics and Financial Issues, Econjournals, vol. 10(1), pages 59-66.
    • Handle: RePEc:eco:journ1:2020-01-8
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    References listed on IDEAS

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    Cited by:

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    More about this item

    Keywords

    ANN; SVM; Random Forest; Machine Learning; Investment Performance;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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