Journal
INFORMATION SCIENCES
Volume 553, Issue -, Pages 305-330Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2020.10.023
Keywords
Attention mechanism; Long short-term memory network; Stock price prediction; Heterogeneous information fusion; Machine learning
Categories
Funding
- National Natural Science Foundation of China [11701144, 71801057, 11901113]
- Natural Science Foundation of Guangdong Province [2018A030313968, 2019A1515011649]
- Guangdong Youth Innovation Talent Project in China [2018KQNCX086]
- Project of Guangdong Province Innovative Team [2020WCXTD011]
- Higher Education School Young Backbone Teacher Training Program of Henan Province [2017GGJS020]
- Guangzhou Science and Technology Plan Project [201904010225, 202002030231]
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This study presents an analytical framework based on multiple data sources, demonstrates the prediction of BGI Genomics stock price, and applies an LSTM network, showing that performance can be significantly improved through multisource information fusion.
The recent availability of enormous amounts of both data and computing power has created new opportunities for predictive modeling. This paper compiles an analytical framework based on multiple sources of data including daily trading data, online news, derivative technical indicators, and time-frequency features decomposed from closing prices. We also provide a real-life demonstration of how to combine and capitalize on all available information to predict the stock price of BGI Genomics. Moreover, we apply a long short-term memory (LSTM) network equipped with an attention mechanism to identify long-term temporal dependencies and adaptively highlight key features. We further examine the learning capabilities of the network for specific tasks, including forecasting the next day's price direction and closing price and developing trading strategies, comparing its statistical accuracy and trading performance with those of methods based on logistic regression, support vector machine, gradient boosting decision trees, and the original LSTM model. The experimental results for BGI Genomics demonstrate that the attention enhanced LSTM model remarkably improves prediction performance through multisource heterogeneous information fusion, highlighting the significance of online news and time-frequency features, as well as exemplifying and validating our proposed framework. (C) 2020 Elsevier Inc. All rights reserved.
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