# Can Online Emotions Predict the Stock Market in China?

arXiv: https://arxiv.org/abs/1604.07529 · HTML: https://mlsanity.com/paper/1604.07529/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Apr 2016.

## Built on by (most cited first, 5 of 5)

- [HIST: A Graph-based Framework for Stock Trend Forecasting via Mining Concept-Oriented Shared Information](https://mlsanity.com/paper/2110.13716/) (2021)
- [REST: Relational Event-driven Stock Trend Forecasting](https://mlsanity.com/paper/2102.07372/) (2021)
- [News and Load: A Quantitative Exploration of Natural Language Processing Applications for Forecasting Day-ahead Electricity System Demand](https://mlsanity.com/paper/2301.07535/) (2023)
- [Electricity Demand Forecasting through Natural Language Processing with Long Short-Term Memory Networks](https://mlsanity.com/paper/2309.06793/) (2023)
- [Do Weibo platform experts perform better at predicting stock market?](https://mlsanity.com/paper/2403.00772/) (2024)

## Abstract (arXiv)

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.