Forecasting Volatility in Stock Market Considering Sentiments of Macroeconomic News

Abstract

Stock price forecasting is an important and growing topic in the financial engineering. Due to volatile and nonlinear nature of the global equity market it is very tough to forecast the accurate price of a particular stock in the equity market. And also, with the increased computational capabilities and introduction of artificial intelligence, and different programming methods the prediction of the stock price of a particular stock have proven efficient. For predicting the stock price, it requires the evaluation of huge amount of data. For that we have taken past four years stock data and the recent social media mainly the twitter data which is basically the data which is particularly affecting the price of the data in future. Final we compare our algorithmic outcome result with the actual Next’s day actual close price. The penultimate goal of our project is to predict the behaviors of the stock equity market for the future via sentimental analysis of twitter dataset which contains set of different tweets over the past couple of days. The final results were promising, and we also found the correlation between the tweets, data of the particular stock and the stock price.