Score based financial forecasting method by incorporating different sources of information flow into integrative river model

Nature and behavior of data required for the financial market forecasting specially in the stock market is not only restricted to the stock prices. Data scientists had studied market behavior by applying behavior study tools like Google-Profile of Mood States (GPOMS) and OpinionFinder on information available through news and social media platforms like twitter. But behavior finance is still at a novice state and growing with a substantial pace. Data required for the market is big, heterogeneous and mammoth. It consists of prices of stock exchanges as well as socio - political - economic data from all over the globe. Green database design will help to increase the efficiency of the database towards green drive but restricted to the prices of the stock. In continuation of our previous work on green computing in financial market, we are proposing a model as score based financial forecasting method by incorporating different sources of integrated information flow into integrative river model.