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.
Original language | English |
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Article number | 7061664 |
Pages (from-to) | 26-33 |
Number of pages | 8 |
Journal | IEEE Intelligent Systems |
Volume | 30 |
Issue number | 2 |
DOIs | |
Publication status | Published - 1 Mar 2015 |
Keywords
- 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