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

Chia-Cheng Chen, Yisheng Liu and Ting-Hsin Hsu
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Chia-Cheng Chen: Department of Finance, Ling Tung University of Science and Technology, Taiwan
Yisheng Liu: Department of Finance, National Yunlin University of Science and Technology, Taiwan,
Ting-Hsin Hsu: Department of Finance, National Taichung University of Science and Technology, Taiwan

International Journal of Economics and Financial Issues, 2019, vol. 9, issue 4, 1-10

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 Na ve-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 Na ve-Bayes coming in last.

Keywords: Naive-Bayes; ANN; SVM; Random Forest; Machine Learning; Investment Performance (search for similar items in EconPapers)
JEL-codes: C11 C53 C63 G11 (search for similar items in EconPapers)
Date: 2019
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