2016/04/26 by Zhenkun Zhou, Zhou, Zhenkun, Jichang Zhao +3 · 1 citation
Computer Science · Decision Sciences · #Advanced Text Analysis Techniques #Computers and Society (cs.CY) #FOS: Computer and information sciences #Sentiment Analysis and Opinion Mining #Social and Information Networks (cs.SI) #Stock Market Forecasting Methods #cs.CY #cs.SI
paper · pdf · doi:10.48550/arxiv.1604.07529
openalex publication_date 2016/04/26 · arxiv created 2016/04/28 · arxiv updated 2016/04/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.