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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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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DOI: https://doi.org/10.1007/978-3-030-56725-5_10
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