  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-making 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
show 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.
