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Stock Trend Prediction by Classifying Aggregative Web Topic-Opinion

  • Li Xue
  • Yun Xiong
  • Yangyong Zhu
  • Jianfeng Wu
  • Zhiyuan Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7819)

Abstract

According to the Efficient Market Hypothesis(EMH) theory, the stock market is driven mainly by overall information instead of individual event. Furthermore, the information about hot topics is believed to have more impact on stork market than that about ordinary events. Inspired by these ideas, we propose a novel stock market trend prediction method by Classifying Aggregative Web Topic-Opinion(CAWTO), which predicts stocks movement trend according to the aggregative opinions on hot topics mentioned by financial corpus on the web. Several groups of experiments were carried out using the data of Shanghai Stock Exchange Composite Index(SHCOMP) and 287,686 financial articles released on SinaFinance, which prove the effectiveness of our proposed method.

Keywords

Opinion Mining Aggregative Opinion Stock Prediction 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Li Xue
    • 1
  • Yun Xiong
    • 1
  • Yangyong Zhu
    • 1
  • Jianfeng Wu
    • 2
  • Zhiyuan Chen
    • 3
  1. 1.School of Computer ScienceFudan UniversityShanghaiP.R. China
  2. 2.Shanghai Stock ExchangesShanghaiP.R. China
  3. 3.Department of Information SystemsUniversity of Maryland Baltimore CountyBaltimoreUSA

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