@inproceedings{luan-etal-2024-stock,
    title = "Stock Price Prediction with Sentiment Analysis for {C}hinese Market",
    author = "Luan, Yuchen  and
      Zhang, Haiyang  and
      Zhang, Chenlei  and
      Mu, Yida  and
      Wang, Wei",
    editor = "Chen, Chung-Chi  and
      Liu, Xiaomo  and
      Hahn, Udo  and
      Nourbakhsh, Armineh  and
      Ma, Zhiqiang  and
      Smiley, Charese  and
      Hoste, Veronique  and
      Das, Sanjiv Ranjan  and
      Li, Manling  and
      Ghassemi, Mohammad  and
      Huang, Hen-Hsen  and
      Takamura, Hiroya  and
      Chen, Hsin-Hsi",
    booktitle = "Proceedings of the Joint Workshop of the 7th Financial Technology and Natural Language Processing, the 5th Knowledge Discovery from Unstructured Data in Financial Services, and the 4th Workshop on Economics and Natural Language Processing",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.finnlp-1.16",
    pages = "167--177",
    abstract = "Accurate prediction of stock prices is considered as a significant practical challenge and has been a longstanding topic of debate within the economic domain. In recent years, sentiment analysis on social media comments has been considered an important data source for stock prediction. However, most of these works focus on exploring stocks with high market values or from specific industries. The extent to which sentiments affect a broader range of stocks and their overall performance remains uncertain. In this paper, we study the influence of sentiment analysis on stock price prediction with respect to (1) different market value groups and (2) different Book-to-Market ratio groups in the Chinese stock market. To this end, we create a new dataset that consists of 24 stocks across different market value groups and Book-to-Market ratio categories, along with 12,000 associated comments that have been collected and manually annotated. We then utilized this dataset to train a variety of sentiment classifiers, which were subsequently integrated into sequential neural-based models for stock price prediction. Experimental findings indicate that while sentiment integration generally improve the predictive performance for price prediction, it may not consistently lead to better results for individual stocks. Moreover, these outcomes are notably influenced by varying market values and Book-to-Market ratios, with stocks of higher market values and B/M ratios often exhibiting more accurate predictions. Among all the models tested, the Bi-LSTM model incorporated with the sentiment analysis, achieves the best prediction performance.",
}

Accurate prediction of stock prices is considered as a significant practical challenge and has been a longstanding topic of debate within the economic domain. In recent years, sentiment analysis on social media comments has been considered an important data source for stock prediction. However, most of these works focus on exploring stocks with high market values or from specific industries. The extent to which sentiments affect a broader range of stocks and their overall performance remains uncertain. In this paper, we study the influence of sentiment analysis on stock price prediction with respect to (1) different market value groups and (2) different Book-to-Market ratio groups in the Chinese stock market. To this end, we create a new dataset that consists of 24 stocks across different market value groups and Book-to-Market ratio categories, along with 12,000 associated comments that have been collected and manually annotated. We then utilized this dataset to train a variety of sentiment classifiers, which were subsequently integrated into sequential neural-based models for stock price prediction. Experimental findings indicate that while sentiment integration generally improve the predictive performance for price prediction, it may not consistently lead to better results for individual stocks. Moreover, these outcomes are notably influenced by varying market values and Book-to-Market ratios, with stocks of higher market values and B/M ratios often exhibiting more accurate predictions. Among all the models tested, the Bi-LSTM model incorporated with the sentiment analysis, achieves the best prediction performance.
Luan, Yuchen Zhang, Haiyang Zhang, Chenlei Mu, Yida Wang, Wei
Stock Price Prediction with Sentiment Analysis for Chinese Market