Volume 3 • Issue 2 | September 2019
A Technique to Predict Short-term Stock Trend Using Bayesian Classifier
Ho Vu, T. Vo Van, N. Nguyen-Minh, T. Nguyen-Trang
Abstract:
In this paper, an application of Bayesian classifier for shortterm stock trend prediction, which is a popular field of study, is presented. In order to use Bayesian classifier effectively, we transform daily stock price time series object into data frame format where the dependent variable is stock trend label and the independent variables are the stock variations with respect to previous days. The numerical example using stock market data of individual firms demonstrates the potential of the proposed method in predicting the short-term stock trend. In addition, to reduce the risk for the investor, a method to adjust the probability threshold using the ROC curve is investigated. Also, it can be implied that the performance of the new technique mainly depends on the skill of investors, such as adjusting the threshold, identifying the suitable stock and the suitable time for trading, combining the proposed technique with other tools of fundamental analysis and technical analysis, etc.
JEL classification: C11, C15, C3