A Stock Trend Prediction Approach based on Chinese News and Technical Indicator Using Genetic Algorithms

被引:0
作者
Chen, Chun-Hao [1 ]
Shih, Ping [1 ]
机构
[1] Tamkang Univ, Dept Comp Sci & Informat Engn, Taipei, Taiwan
来源
2019 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC) | 2019年
关键词
Chinese news; genetic algorithm; stock trend prediction; text mining; trading strategy; MARKET PREDICTION; SENTIMENT; MEDIA;
D O I
10.1109/cec.2019.8790177
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Stock trend prediction problem is an attractive research topic since many factors should be considered simultaneously. Due to the fact that financial news articles are a combination of influential information and noises, extracting valuable insights in between noises is an interesting and rewarding task. Hence, lots of approaches have been proposed for stock trend prediction based on financial news. However, they focused on analysis of English news. In this paper, we thus first depict the Chinese news-based stock trend prediction framework. Based on the framework, we then propose the stock trend prediction (STP) algorithm that takes Chinese news and technical indicator into consideration for predicting stock trends. Because parameters setting is an optimization problem, we then modify the framework and design the second stock trend prediction algorithm to determine the optimal trading situation using genetic algorithms, namely GATSP. At last, experiments were conducted on real news and stock data to verify the effectiveness of the proposed approach.
引用
收藏
页码:1468 / 1472
页数:5
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