In this paper, an application of Bayesian classifier for short- term stock trend prediction, which is a popular field of study, is presented. In order to use Bayesian classifier ef- fectively, 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 numer- ical example using stock market data of individual firms demonstrates the potential of the proposed method in pre- dicting the short-term stock trend. In addition, to reduce the risk for the investor, a method to adjust the probabil- ity 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 adjust- ing the threshold, identifying the suitable stock and the suitable time for trading, combining the proposed tech- nique with other tools of fundamental analysis and techni- cal analysis, etc.