Predicting impact of news on stock price: An evaluation of neuro fuzzy systems

Investors react to news, particularly to earnings and dividend announcements released by respective firms, and consequently stock prices move. Thus, news has an impact on stock prices. However, the price adjustment process is a complex one. While neural fuzzy systems have advantages over statistical methods in modeling and predicting complex relationships generally, not many neural fuzzy systems share the same level of competence and capabilities. In this study, we evaluate the effectiveness of four neural fuzzy systems - feed forward neural network (FFNN), adaptive neuro fuzzy inference system (ANFIS), radial basis function network (RBFN) and rough set based pseudo outer product rule (RSPOP), respectively - in predicting the impact of news on stock price movements. We found that rough set based pseudo outer product rule (RSPOP) is the most effective system in the study undertaken, and is a candidate for further evaluation as a financial intelligence system.