Hybrid Model for Stock Trading System

Background/Objectives: Stock Trading is the action of buying or selling the products. In this complex habitat, generating the future trend is such a difficult process. Methods: Methods such as Sentiment survey, Technical survey and Trend based segmentation method are used in this system. The segmentation process, a hybrid model using Support Vector Regression and Naïve Bayes classification is used to extract the intensity levels of the gathered information. Findings: This is the application where the trading decisions can be automatically predicted to gain profit. Based on the reviews and ratings of a product from users, the system concludes whether to invest in that product or not. By using hybrid model, the predictive signals may be accurate. Decision for investors for investing in a better profit earning product is given by the system itself. The previous method uses the sentiments from the news articles and social networks, which is less reliable than getting the feedbacks from the users or the investors of that product. Applications: This framework may be used in real time applications such as trading activities, stock exchange in the stock market, etc.