Can Online Emotions Predict the Stock Market in China?

By Zhenkun Zhou, Jichang Zhao, Ke Xu. 2016

40 citations recorded by the source corpus.

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.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Jiang Bian
2. Ziniu Hu
3. Tie-Yan Liu
4. Weiqing Liu
5. Xuanzhe Liu
6. Olivier Chapelle
7. Xu Tan
8. Tao Qin

Citations in the corpus

• Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction (2017, background): “Zhou et al. [29] studies particularly the Chinese stock market.”
• Stock closing price prediction based on sentiment analysis and LSTM (2019, unknown): “Zhou et al. [33] based on the SVM-ES model and other online emotions, such as disgust, joy, sadness and fear, predicted the stock market and achieve an accuracy of 64.15%.”