Predicting stock market indexes with world news

With the rapid development of economy, people need to raise the accuracy of predicting prices considering late events. The main tackle in raising the accuracy is to fully use the information in daily reports. Unfortunately, most of the current solutions separate the time series statistics, the events reported in text data and prediction in data mining way from each other. As a result, although they predict the prices accurately in most of the days, they can not grasp the sudden change points of those time series. In this paper, we propose an ensemble framework to take advantage of the news text in predicting change points in stock market indexes as well as traditional prediction works, so that we can improve our prediction sufficiently. Our extensive experimental results shows that we reduce the loss of error predictions and enhance the good prediction results.