An Intensive Empirical Study of Machine Learning Algorithms for Predicting Vietnamese Stock Prices

Predicting stock prices is a challenging task due to the highly stochastic nature of the financial market. Among many proposed quantitative approaches to tackle this problem, machine learning, in recent years, has become one of the most promising methods. However, machine learning is still new to a large part of Vietnamese investors community. This motivated us to take some first steps in using machine learning techniques on Vietnamese stock data, in particular top 20 listed stocks (according to market capitalization) of VN-Index in June 2019. The experimental results suggest that machine learning and hybrid methods give better performances in forecasting stock price fluctuation than ones achieved by traditional methods such as the Autoregressive Integrated Moving-average model. To realize our study, we implement a web-based tool and release its source code.