Prediction Market Index by Combining Financial Time-Series Forecasting and Sentiment Analysis Using Soft Computing

In recent years, a lot of research is focusing on predicting real-world outcomes using Social networks data (for example, Twitter Data). Sentiment Analysis of the twitter data thus has become one of the key aspects of making predictions involving human sentiments. Stock market movements are very sensitive and it affects investment of the investors because of this prediction is the main interest of the researchers. Soft computing approaches and nature-inspired computing has a lot of potential in predicting the market movement. In this paper, soft computing techniques are used to predict market trends using sentiments extracted from market data. The results indicate that by selecting suitable neural networks architecture and selecting suitable regression coefficients can improve the overall accuracy and correlation of the predictions. Stock market information people use for investment decisions. Forecasting must be accurate otherwise it will not be effective in the decision. There are techniques like trend based classification, adaptive indicators selection and market trading signals are used in forecasting.