The volatile and stochastic characteristics of securities make it challenging to predict even tomorrow's stock prices. Better estimation of stock trends can be accomplished using both the significant and well-constructed set of features. Moreover, the prediction capability will gain momentum as we build the right model to capture unobservable attributes of the varying tendencies. In this paper, we propose a Binary Stock Event Model (BSEM) and generate features sets based on it in order to better predict the future trends of the stock market. We apply two learning models such as a Bayesian Naive Classifier and a Support Vector Machine to prove the efficiency of our approach in the aspects of prediction accuracy and computational cost. Our experiments demonstrate that the prediction accuracies are around 70-80% in one day predictions. In addition, our back-testing proves that our trading model outperforms well-known technical indicator based trading strategies with regards to cumulative returns by 30%-100%. As a result, this paper suggests that our BSEM based stock forecasting shows its excellence with regards to prediction accuracy and cumulative returns in a real world dataset.