# Do Weibo platform experts perform better at predicting stock market?

arXiv: https://arxiv.org/abs/2403.00772 · HTML: https://mlsanity.com/paper/2403.00772/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 34 references that are in the map.
- First posted on arXiv 12 Feb 2024.

## Builds on (most cited first, 7 of 7)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [How to Fine-Tune BERT for Text Classification?](https://mlsanity.com/paper/1905.05583/) (2019)
- [Audio ALBERT: A Lite BERT for Self-supervised Learning of Audio Representation](https://mlsanity.com/paper/2005.08575/) (2020)
- [Adversarial Training for Aspect-Based Sentiment Analysis with BERT](https://mlsanity.com/paper/2001.11316/) (2020)
- [Impact of Training Dataset Size on Neural Answer Selection Models](https://mlsanity.com/paper/1901.10496/) (2019)
- [Can Online Emotions Predict the Stock Market in China?](https://mlsanity.com/paper/1604.07529/) (2016)

## Abstract (arXiv)

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.