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 Na?ve 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. |