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

Chia-Cheng Chen, Yisheng Liu, Ting-Hsin Hsu


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


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