Dynamic Business Network Analysis for Correlated Stock Price Movement Prediction

摘要: Although much research is devoted to the analysis and prediction of individuals' behavior in social networks, very few studies analyze firms' performance with respect business networks. Empowered by recent on automated mining this article illustrates design a novel network-based model called energy cascading (ECM) for predicting directional stock price movements related firms. More specifically, proposed predictive analytics considers both influential relationships Twitter sentiments infer firm's middle long-term movements. The reported empirical experiments are based publicly available financial corpus media postings that reveal ECM be effective It outperforms best baseline model, Pearson correlation-based upward movement 11.7 percent terms F-measure.