Abstract
This study constructs a FinBERT-LSTM model to improve the accuracy of stock price prediction in the Chinese financial market. The FinBERT model, a Chinese language-specific, finance-oriented pre-trained model, is utilized to extract sentiment from financial texts. This sentiment data is combined with historical stock price data and input into an LSTM network to predict future stock prices. The model is trained and tested on the real data set with Kweichow Moutai as an example. The experimental results show that the FinBERT-LSTM model achieves better prediction accuracy than the traditional machine learning and deep learning models. This study provides a more accurate stock price prediction model by combining sentiment analysis with quantitative data, offering valuable insights for investors and researchers in the Chinese financial market.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Li, Y.: Short-term stock price forecast based on arma model – a case study of guling gongjiu stock. J. Shanxi Finance Taxation Vocational College. 24(1), 25–30 (2022)
Xie, H., Shang, Y.: Stock price forecast based on ARMA model analysis – a case study of citic bank. Northern Econ. Trade. 12, 122–124 (2020)
Shi, S.: Analysis and prediction of stock price time series. Master's Thesis of Dalian University of Technology (2013)
Jaiwang, G., Jeatrakul, P.: A forecast model for stock trading using support vector machine. In: International Computer Science and Engineering Conference, pp. 1–6 (2016)
Lin, S.: Stock prediction research based on LSTM. Master's Thesis of Guangzhou University (2019)
Bao, Z., Guo, J., Xie, Y., Zhang, W.: Stock price rise and fall prediction model based on LSTM-GA. Comput. Sci. 47(S1), 467–473 (2020)
Bustos, O., Pomares-Quimbaya, A.: Stock market movement forecast: a systematic review. Expert Syst. Appli. 156, 113464 (2020)
Bollen, J., Mao, H., Zeng, X.: Twitter mood predicts the stock market. J. Comput. Sci. 2(1), 1–8 (2011)
Lin, P., Zhou, J., Wen, Y.: SCONV: a sentiment analysis-based financial market trend prediction method. Comput. Res. Developm. 57(8), 1769–1778 (2020)
Tian, H., Jin, S., Yan, H.: A stock prediction method based on TCN and news sentiment. Inform. Technol. Inform. 6, 17–21 (2021)
Yan, D., He, W., Chen, Z.: Research on stock price prediction based on RoBERTa-TCN integrated with sentiment features. Data Analy. Knowl. Dis. 6(12), 123–133 (2022)
Liu, W., Jiang, S., Jiang, H., et al.: Sentiment analysis method of stock bar comments based on FinBERT-CNN. Integrated Technol. 11(1), 27–39 (2022)
Lu, D., Wu, H., Liang, J., et al.: Bbt-fin: comprehensive construction of Chinese financial domain pre-trained language model, corpus and benchmark. arXiv preprint arXiv:2302.09432. (2023)
Antweiler, W., Frank, M.Z.: Is all that talk just noise? The information content of internet stock message boards. J. Financ. 59(3), 1259–1294 (2004)
Acknowledgments
This work is supported by “the Fundamental Research Funds for the Central Universities”, Zhongnan University of Economics and Law, Project Name (2722024BY023)).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Fan, S., Chen, X., Wang, Xa. (2024). Stock Price Prediction Based on FinBERT-LSTM Model. In: Barolli, L. (eds) Complex, Intelligent and Software Intensive Systems. CISIS 2024. Lecture Notes on Data Engineering and Communications Technologies, vol 87. Springer, Cham. https://doi.org/10.1007/978-3-031-70011-8_4
Download citation
DOI: https://doi.org/10.1007/978-3-031-70011-8_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-70010-1
Online ISBN: 978-3-031-70011-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)