  How the online social media, like Twitter or its variant Weibo, interacts
with the stock market and whether it can be a convincing proxy to predict the
stock market have been debated for years, especially for China. As the
traditional theory in behavioral finance states, the individual emotions can
influence decision-makings of investors, it is reasonable to further explore
these controversial topics systematically from the perspective of online
emotions, which are richly carried by massive tweets in social media. Through
thorough studies on over 10 million stock-relevant tweets and 3 million
investors from Weibo, it is revealed that inexperienced investors with high
emotional volatility are more sensible to the market fluctuations than the
experienced or institutional ones, and their dominant occupation also indicates
that the Chinese market might be more emotional as compared to its western
counterparts. Then 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 prediction model significantly outperforms the baseline model,
including the one taking purely financial time series as input features, on
predicting five attributes of the stock market under the $K$-means
discretization. We also employ this prediction model in the scenario of
realistic online application and its performance is further testified.
