Do Weibo platform experts perform better at predicting stock market?
Ziyuan Ma Conor Ryan Jim Buckley Muslim Chochlov
Sentiment analysis can be used for stock market prediction. However, existing
research has not studied the impact of a user's financial background on
sentiment-based forecasting of the stock market using artificial neural
networks. In this work, a novel combination of neural networks is used for the
assessment of sentiment-based stock market prediction, based on the financial
background of the population that generated the sentiment. The state-of-the-art
language processing model Bidirectional Encoder Representations from
Transformers (BERT) is used to classify the sentiment and a Long-Short Term
Memory (LSTM) model is used for time-series based stock market prediction. For
evaluation, the Weibo social networking platform is used as a sentiment data
collection source. Weibo users (and their comments respectively) are divided
into Authorized Financial Advisor (AFA) and Unauthorized Financial Advisor
(UFA) groups according to their background information, as collected by Weibo.
The Hong Kong Hang Seng index is used to extract historical stock market change
data. The results indicate that stock market prediction learned from the AFA
group users is 39.67% more precise than that learned from the UFA group users
and shows the highest accuracy (87%) when compared to existing approaches.