Dynamic Business Network Analysis for Correlated Stock Price Movement Prediction
Though much research is dedicated to the analysis and prediction of people’ behavior in social networks, very few studies analyze companies’ performance with respect to business networks. Empowered by recent research on the automated mining of business networks, this article illustrates the planning of a unique business network-primarily based model known as the energy cascading model (ECM) for predicting directional stock worth movements of connected corporations. Additional specifically, the proposed network-based mostly predictive analytics model considers both influential business relationships and Twitter sentiments to infer a firm’s middle to long-term directional stock worth movements. The reported empirical experiments are based mostly on a publicly offered financial corpus and social media postings that reveal the proposed ECM model to be effective for predicting directional stock price movements. It outperforms the simplest baseline model, the Pearson correlation-primarily based prediction model, in upward stock price movement prediction by 11.seven % in terms of F-live.
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