Stock Market Prediction using Sequential Events

The stock market prediction is considered to be the most exigent and challenging problem in the domain of finance and time series prediction. In this paper we present problems pertaining stock market prediction and the models of prediction. Further, we also probe into the effect of global events and their influence on the stock prices. It was found that by incorporating the event information in the prediction model, the prediction's accuracy will be escalated. The overall scope of this work is to provide the predictive power to the investor in the web environment so that he could take informed decision of whether he can invest in the company in question, and yield high profits, by considering the effect of the events occurred. We have established that there is a huge impact of negative news on the stock and also we proved that our method outperformed SVM and NBC techniques.