Dynamic Business Network Analysis for Correlated Stock Price Movement Prediction

Wenping Zhang, Chunping Li, Yunming Ye, Wenjie Li, Wai Ting Ngai

Research output: Journal article publicationJournal articleAcademic researchpeer-review

29 Citations (Scopus)


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.
Original languageEnglish
Article number7061664
Pages (from-to)26-33
Number of pages8
JournalIEEE Intelligent Systems
Issue number2
Publication statusPublished - 1 Mar 2015


  • business network mining
  • intelligent systems
  • network-based inference
  • predictive analytics
  • stock movement prediction
  • Twitter sentiments

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Artificial Intelligence


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