Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/12189
Title: Dynamic business network analysis for correlated stock price movement prediction
Authors: Zhang, W
Li, C
Ye, Y
Li, W 
Ngai, EWT 
Keywords: Business network mining
Intelligent systems
Network-based inference
Predictive analytics
Stock movement prediction
Twitter sentiments
Issue Date: 2015
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE Intelligent systems, 2015, v. 30, no. 2, 7061664, p. 26-33 How to cite?
Journal: IEEE intelligent systems 
Abstract: Although much research is devoted to the analysis and prediction of individuals' behavior in social networks, very few studies analyze firms' performance with respect to business networks. Empowered by recent research on the automated mining of business networks, this article illustrates the design of a novel business network-based model called the energy cascading model (ECM) for predicting directional stock price movements of related firms. More specifically, the proposed network-based predictive analytics model considers both influential business relationships and Twitter sentiments to infer a firm's middle to long-term directional stock price movements. The reported empirical experiments are based on a publicly available financial corpus and social media postings that reveal the proposed ECM model to be effective for predicting directional stock price movements. It outperforms the best baseline model, the Pearson correlation-based prediction model, in upward stock price movement prediction by 11.7 percent in terms of F-measure.
URI: http://hdl.handle.net/10397/12189
ISSN: 1541-1672
EISSN: 1941-1294
DOI: 10.1109/MIS.2015.25
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