An Analysis on Investment Performance of Machine Learning: An Empirical Examination on Taiwan Stock Market

Authors

  • Chia-Cheng Chen Ling Tung University of Science and Technology
  • Yisheng Liu National Yunlin University of Science and Technology
  • Ting-Hsin Hsu National Taichung University of Science and Technology

Abstract

This study aims to explore the prediction of Taiwan stock price movement and conduct an analysis of its investment performance. Based on Taiwan Stock Market index, the study compares four machine learning models: ANN, SVM, Random Forest and Naïve-Bayes. With a performance evaluation of Taiwan Stock Market 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 ANN generates the best performance, followed by SVM and Random Forest, and Naïve-Bayes coming in last.

Keywords: Naive-Bayes, ANN, SVM, Random Forest, Machine Learning, Investment Performance

JEL Classifications: C11; C53; C63; G11

DOI: https://doi.org/10.32479/ijefi.8129

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Author Biographies

Chia-Cheng Chen, Ling Tung University of Science and Technology

Department of Finance

Yisheng Liu, National Yunlin University of Science and Technology

Department of Finance

Ting-Hsin Hsu, National Taichung University of Science and Technology

Department of Finance

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Published

2019-07-02

How to Cite

Chen, C.-C., Liu, Y., & Hsu, T.-H. (2019). An Analysis on Investment Performance of Machine Learning: An Empirical Examination on Taiwan Stock Market. International Journal of Economics and Financial Issues, 9(4), 1–10. Retrieved from https://econjournals.com/index.php/ijefi/article/view/8129

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Articles