Big data and predictive computing to support financial market decisions

The financial market is very fickle and investors have the difficult task of following and trying to predict the swings of the market so that their strategies result in better financial returns. With the use of Big Data and Bayesian mathematical statistics based on prior knowledge and examples of training to determine the likelihood of a hypothesis, financial news can be tracked continuously and affecting key financial indicators, Actions and assisting the investor. The objective of this work is to propose a stock price prediction process of a company listed on the Ibovespa to support decisions in the financial market, helping an investor to better buy and sell stocks using a predictive system with Big Data and Natural Processing Language (LPN) to collect real-time information from news sites that may influence stock prices.