Predicting Stock Trends with CNN-BiLSTM Based Multi-Feature Integration Model
Xu Yuemei1(),Wang Zihou2,Wu Zixin1
1School of Information Science and Technology, Beijing Foreign Studies of University, Beijing 100089, China 2National Computer Network Emergency Response Technical Team/Coordination Center of China, Beijing 100029, China
[Objective] Based on the traditional financial data analysis, this paper explores the impacts of online news on stock market, aiming to improve the accuracy of predicting stock trends. [Methods] First, we used the Convolutional Neural Network (CNN) and Bi-directional Long Short-Term Memory (Bi-LSTM) to extract news events and their sentiment orientations. Then, we proposed a prediction model for stock trends, which combines the stock numerical data and the news event sentiments. Finally, we examined the feasibility of this model with two individual stocks (GREE Electric Appliance in the household appliance industry and ZTE in the electronic appliance industry). [Results] The prediction accuracy of our model was 11.6% and 25.6% higher than the exiting algorithms. [Limitations] We did not evaluate the impacts of prediction period on the performance of the proposed model. [Conclusions] The news events and their sentiment orientations could lead to the fluctuation of stock prices.
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