Can Online Emotions Predict the Stock Market in China?

  • Zhenkun Zhou
  • Jichang Zhao
  • Ke Xu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10041)


Whether the online social media, like Twitter or its variant Weibo, can be a convincing proxy to predict the stock market has been debated for years, especially for China. However, as the traditional theory in behavioral finance states, the individual emotions can influence decision-makings of investors, so it is reasonable to further explore this controversial topic from the perspective of online emotions, which is richly carried by massive tweets in social media. Surprisingly, through thorough study on over 10 million stock-relevant tweets from Weibo, both correlation analysis and causality test demonstrate that five attributes of the stock market in China can be competently predicted by various online emotions, like disgust, joy, sadness and fear. Specifically, the presented model significantly outperforms the baseline solutions on predicting five attributes of the stock market under the K-means discretization. We also employ this model in the scenario of realistic online application and its performance is further testified.


Social media Stock market Sentiment analysis Causality test 


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.State Key Laboratory of Software Development EnvironmentBeihang UniversityBeijingChina
  2. 2.School of Economics and ManagementBeihang UniversityBeijingChina

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