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A New Strategy for Short-Term Stock Investment Using Bayesian Approach


Tai Vo-Van, Ha Che-Ngoc, Nghiep Le-Dai, Thao Nguyen-Trang

Source title: 
Computational Economics, 2021 (ISI)
Academic year of acceptance: 

In this paper, an application of the Bayesian classifier for short-term stock trend prediction is presented. In order to use Bayesian classifier effectively, we transform the daily stock price time series object into a data frame format where the dependent variable is the stock trend label and the independent variables are the stock variations of the last few days. Based on the posterior probability density function, we propose a new method for stock selection and then propose a new stock trading strategy. The numerical examples demonstrate the potential of the proposed strategy for application to short-term stock trading.