Analyzing market performance via social media: a case study of a banking industry crisis View Full Text


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Article Info

DATE

2014-05

AUTHORS

CuiQing Jiang, Kun Liang, Hsinchun Chen, Yong Ding

ABSTRACT

Analyzing market performance via social media has attracted a great deal of attention in the finance and machine- learning disciplines. However, the vast majority of research does not consider the enormous influence a crisis has on social media that further affects the relationship between social media and the stock market. This article aims to address these challenges by proposing a multistage dynamic analysis framework. In this framework, we use an authorship analysis technique and topic model method to identify stakeholder groups and topics related to a special firm. We analyze the activities of stakeholder groups and topics in different periods of a crisis to evaluate the crisis’s influence on various social media parameters. Then, we construct a stock regression model in each stage of crisis to analyze the relationships of changes among stakeholder groups/topics and stock behavior during a crisis. Finally, we discuss some interesting and significant results, which show that a crisis affects social media discussion topics and that different stakeholder groups/topics have distinct effects on stock market predictions during each stage of a crisis. More... »

PAGES

1-18

References to SciGraph publications

  • 2006. Visualizing Authorship for Identification in INTELLIGENCE AND SECURITY INFORMATICS
  • 1987-09. Knowledge acquisition via incremental conceptual clustering in MACHINE LEARNING
  • 2001-01. Unsupervised Learning by Probabilistic Latent Semantic Analysis in MACHINE LEARNING
  • 2003-05-27. Authorship Analysis in Cybercrime Investigation in INTELLIGENCE AND SECURITY INFORMATICS
  • 2001-05. Computer-Based Authorship Attribution Without Lexical Measures in LANGUAGE RESOURCES AND EVALUATION
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