The Stock market forecasters focus on developing a successful approach to predict stock prices. The vital idea to successful stock market prediction is not only achieving best results but also to minimize the inaccurate forecast of stock prices. This paper attempts to design and implement a predictive system for guiding stock market investment. The novelty of our approach is the combination of both sensex points and Really Simple Syndication (RSS) feeds for effective prediction. Our claim is that the sentiment analysis of RSS news feeds has an impact on stock market values. Hence RSS news feed data are collected along with the stock market investment data for a period of time. Using our algorithm for sentiment analysis, the correlation between the stock market values and sentiments in RSS news feeds are established. This trained model is used for prediction of stock market rates. In our experimental study the stock market prices and RSS news feeds are collected for the company ARBK from Amman Stock Exchange (ASE). Our experimental study has shown an improvement of 14.43% accuracy prediction, when compared with the standard algorithm of ID3, C4.5 and moving average stock level indicator.
A predictive system is designed for SPP using sentimental analysis REF .
Sentiment Analysis for Effective Stock Market Prediction
International Journal of Intelligent Engineering and Systems
Economics