Abstract
To solve the stock prediction problem, we propose a deep learning model base on a hierarchical attention network. Our model is divided into two models. The first model is the article selection attention network that transfers the news into a low dimension vector. This model could identify the important factors in the news that affect the stock price. The second model is a time series attention network which combines the output of the first model and the transaction data as input. In this model, we could figure out the potential impact between different dates and summarize the historical data to predict whether the stock price will rise or fall. The most innovative concept in this paper is stock encoding. The model learns the difference between each stock and make predictions more accurate by using the stock encoding. The experimental result shows that the model fully utilize text features and make better predictions than related research papers.
Supported by: NSFC Grant 61772044; MSTC Grant 2019YFC1521203: research, development and demonstration of key technologies for knowledge organization and services for Antiques based on Knowledge Graph; Peking University Grant 2020ZD002.
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References
Kumar, D.A., Murugan, S.: Performance analysis of Indian stock market index using neural network time series model. In: 2013 International Conference on Pattern Recognition Informatics and Mobile Engineering (2013)
Si, Y.-W., Yin, J.: OBST-based segmentation approach to financial time series. Eng. Appl. Artif. Intell. 26(10), 2581–2596 (2013)
Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining (2017)
Hu, H., Qi, G.-J.: State-frequency memory recurrent neural networks. In: Proceedings of the 34th International Conference on Machine Learning, vol. 70 (2017)
Wang, T., Wan, X.: Hierarchical attention networks for sentence ordering. In: Proceedings of the AAAI Conference on Artificial Intelligence (2019)
Kim, R., So, C.H., Jeong, M., Lee, S., Kim, J., Kang, J.: HATS: a hierarchical graph attention network for stock movement prediction. arXiv preprint arXiv:1908.07999 (2019)
Ruiz, E.J., Hristidis, V., Castillo, C., Gionis, A., Jaimes, A.: Correlating financial time series with micro-blogging activity. In: Proceedings of the fifth ACM International Conference on Web Search and Data Mining (2012)
Brachman, R.J., Khabaza, T., Kloesgen, W., Piatetsky-Shapiro, G., Simoudis, E.: Mining business databases. Commun. ACM 39(11), 42–48 (1996)
Harris, Z.S.: Distributional structure. Word 10(2–3), 146–162 (1954)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems (2013)
Pennington, J., Socher, R., Manning, C.D.: GloVe: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2014)
Yang, L., et al.: Explainable text-driven neural network for stock prediction. In: 2018 5th IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS) (2018)
Zhang, Z., Chen, W., Yan, H.: Stock prediction: a method based on extraction of news features and recurrent neural networks. arXiv preprint arXiv:1707.07585 (2017)
Akita, R., Yoshihara, A., Matsubara, T., Uehara, K.: Deep learning for stock prediction using numerical and textual information. In: 2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS) (2016)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems (2017)
Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., Hovy, E.: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (2016)
Abadi, M., et al.: TensorFlow: a system for large-scale machine learning. In: 12th Symposium on Operating Systems Design and Implementation, vol. 16 (2016)
Ding, X., Zhang, Y., Liu, T., Duan, J.: Using structured events to predict stock price movement: an empirical investigation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2014)
Kim, Y.: Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882 (2014)
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Huang, L. et al. (2020). Hierarchical Attention Network in Stock Prediction. In: Dou, Z., Miao, Q., Lu, W., Mao, J., Jia, G. (eds) Information Retrieval. CCIR 2020. Lecture Notes in Computer Science(), vol 12285. Springer, Cham. https://doi.org/10.1007/978-3-030-56725-5_10
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