Financial investors make decisions based on the information available about the market. The information about a company and the expectations of other market participants are incorporated into the news. The release of major news items often produces speculation among traders which results in price movements.
In recent years, stock price predictions which utilize the combination of historical prices with news have been actively studied. However, exploration of a fully integrated model linking both news information and numerical data is limited and requires more investigations. This project aims to develop an intelligent technique for effective integration of information from financial news articles with historical time series data, such that the approach outperforms current methods.