Ứng dụng phân tích cảm xúc tin tức và mô hình học sâu GRU trong dự đoán giá cổ phiếu trên thị trường chứng khoán Việt Nam

Antweiler, W., & Frank, M. Z. (2004). Is all that talk just noise? The information content of internet stock message boards. Journal of Finance, 59(3), 1259-1294. https://doi.org/10.1111/j.1540-6261.2004.00662.x.

Appel, G. (2005). Technical analysis: Power tools for active investors. FT Press.

Baker, M., & Wurgler, J. (2006). Investor sentiment and the cross-section of stock returns. Journal of Finance, 61(4), 1645-1680. https://doi.org/10.1111/j.1540-6261.2006.00885.x.

Barber, B. M., & Odean, T. (2008). All that glitters: The effect of attention and news on the buying behavior of individual and institutional investors. Review of Financial Studies, 21(2), 785-818. https://doi.org/10.1093/rfs/hhm079.

Bengio, Y., Simard, P., & Frasconi, P. (1994). Learning long-term dependencies with gradient descent is difficult. IEEE Transactions on Neural Networks, 5(2), 157-166. https://doi.org/10.1109/72.279181.

Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of Computational Science, 2(1), 1-8. https://doi.org/10.1016/j.jocs.2010.12.007.

Brock, W., Lakonishok, J., & LeBaron, B. (1992). Simple technical trading rules and the stochastic properties of stock returns. The Journal of Finance, 47(5), 1731-1764. https://doi.org/10.1111/j.1540-6261.1992.tb04681.x.

Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078. https://doi.org/10.3115/v1/D14-1179.

Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014). Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555. https://arxiv.org/abs/1412.3555.

Đào Lê Kiều Oanh, & Nguyễn Thị Minh Châu. (2024). Dự báo chỉ số chứng khoán bằng học máy: Bằng chứng thực nghiệm từ thị trường chứng khoán Việt Nam. Tạp chí Kinh tế và Dự báo.

De Long, J. B., Shleifer, A., Summers, L. H., & Waldmann, R. J. (1990). Noise trader risk in financial markets. Journal of Political Economy, 98(4), 703-738.

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) (pp. 4171-4186). Association for Computational Linguistics. https://doi.org/10.18653/v1/N19-1423.

Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. Journal of Finance, 25(2), 383-417. https://doi.org/10.2307/2325486.

Fischer, T., & Krauss, C. (2018). Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research, 270(2), 654-669. https://doi.org/10.1016/j.ejor.2017.11.054.

Gao, Y., Wang, R., & Zhou, E. (2021). Stock prediction based on optimized LSTM and GRU models. Scientific Programming, 2021(1), 4055281. https://doi.org/10.1155/2021/4055281.

Hirshleifer, D., & Teoh, S. H. (2003). Limited attention, information disclosure, and financial reporting. Journal of Accounting and Economics, 36(1-3), 337-386. https://doi.org/10.1016/j.jacceco.2003.10.002.

Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735.

Hu, Z., Liu, W., Bian, J., Liu, X., & Liu, T. Y. (2018). Listening to chaotic whispers: A deep learning framework for news-oriented stock trend prediction. In Proceedings of the eleventh ACM international conference on web search and data mining (pp. 261-269). https://doi.org/10.1145/3159652.3159690.

Loughran, T., & McDonald, B. (2011). When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks. Journal of Finance, 66(1), 35-65. https://doi.org/10.1111/j.1540-6261.2010.01625.x.

Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781. https://doi.org/10.48550/arXiv.1301.3781.

Thu Minh. (2026). Năm 2026 thị trường chứng khoán phân hóa cực mạnh. VnEconomy. https://vneconomy.vn/nam-2026-thi-truong-chung-khoan-phan-hoa-cuc-manh-phai-chon-mat-gui-vang.htm.

Mohan, S., Mullapudi, S., Sammeta, S., Vijayvergia, P., & Anastasiu, D. C. (2019). Stock price prediction using news sentiment analysis. In 2019 IEEE 5th International Conference on Big Data Computing Service and Applications (pp. 205-208). IEEE. https://doi.org/10.1109/BigDataService.2019.00035.

Murphy, J. J. (1999). Technical analysis of the financial markets: A comprehensive guide to trading methods and applications. Penguin.

Nelson, D. M. Q., Pereira, A. C. M., & de Oliveira, R. A. (2017). Stock market's price movement prediction with LSTM neural networks. In 2017 International joint conference on neural networks (IJCNN) (pp. 1419-1426). IEEE. https://doi.org/10.1109/IJCNN.2017.7966019.

Nguyen, D. Q., & Nguyen, A.-T. (2020). PhoBERT: Pre-trained language models for Vietnamese. In Findings of the Association for Computational Linguistics: EMNLP 2020 (pp. 1037-1042). Association for Computational Linguistics. https://doi.org/10.48550/arXiv.2003.00744.

Nguyen, T. H., Shirai, K., & Velcin, J. (2015). Sentiment analysis on social media for stock movement prediction. Expert Systems with Applications, 42(24), 9603-9611. https://doi.org/10.1016/j.eswa.2015.07.052.

Nguyễn Văn Nghĩa. (2024). Áp dụng mô hình học máy xây dựng ứng dụng dự đoán chứng khoán Việt Nam [Đồ án tốt nghiệp, Học viện Công nghệ Bưu chính Viễn thông]. https://dlib.ptit.edu.vn/bitstream/HVCNBCVT/3609/1/Do%20an%20Nguyen%20Van%20Nghia%20D19%20HTTT.pdf.

Pennington, J., Socher, R., & Manning, C. D. (2014). Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) (pp. 1532-1543).

Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533-536. https://doi.org/10.1038/323533a0.

Shiller, R. J. (1981). Do stock prices move too much to be justified by subsequent changes in dividends?. American Economic Review, 71(3), 421-436.

Si[CE1] , J., Mukherjee, A., Liu, B., Li, Q., Li, H., & Deng, X. (2013). Exploiting topic based twitter sentiment for stock prediction. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) (pp. 24-29).

Sunny, M. A. I., Maswood, M. M. S., & Alharbi, A. G. (2020). Deep learning-based stock price prediction using LSTM and bi-directional LSTM model. In 2020 2nd novel intelligent and leading emerging sciences conference (NILES) (pp. 87-92). IEEE. https://doi.org/10.1109/NILES50944.2020.9257950.

Tetlock, P. C. (2007). Giving content to investor sentiment: The role of media in the stock market. The Journal of Finance, 62(3), 1139-1168. https://doi.org/10.1111/j.1540-6261.2007.01232.x.

Tetlock, P. C., Saar-Tsechansky, M., & Macskassy, S. (2008). More than words: Quantifying language to measure firms' fundamentals. Journal of Finance, 63(3), 1437-1467. https://doi.org/10.1111/j.1540-6261.2008.01362.x.

Trần Đăng Tuyên. (2024). Đánh giá hiệu suất mô hình phức hợp LSTM-GRU: Nghiên cứu điển hình về dự báo chỉ số đo lường xu hướng biến động giá cổ phiếu trên sàn giao dịch chứng khoán Hồ Chí Minh. Tạp chí Khoa học Đại học Cần Thơ, 60(1), 235-249.

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30.

Vũ Văn Đức, Trần Thị Minh Ngọc, Nguyễn Tuấn Dương, Nguyễn Khánh Huy, Lưu Ngân Hà, & Hoàng Thái Vân Trang. (2024). Dự báo cảm xúc nhà đầu tư Việt Nam bằng phương pháp phân tích cảm xúc. Tạp chí Khoa học và Công nghệ Trường Đại học Hòa Bình, 12, 59-65.

Wilder, J. W. (1978). New concepts in technical trading systems. Trend Research.

Xu[CE2] , Y., & Cohen, S. B. (2018). Stock movement prediction from tweets and historical prices. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 1970-1979). https://doi.org/10.18653/v1/P18-1183.

Yang, Y., UY, M. C. S., & Huang, A. (2020). FinBERT: A pretrained language model for financial communications. arXiv preprint arXiv:2006.08097. https://doi.org/10.48550/arXiv.2006.08097.

Yang[CE3] , Z., Yang, D., Dyer, C., He, X., Smola, A., & Hovy, E. (2016). Hierarchical attention networks for document classification. In Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: human language technologies (pp. 1480-1489).