[1]陈可心,黄 刚.CStock:一种结合新闻与股价的股票走势预测模型[J].计算机技术与发展,2020,30(09):18-22.[doi:10. 3969 / j. issn. 1673-629X. 2020. 09. 004]
　CHEN Ke-xin,HUANG Gang.Cstock:A Stock Trend Forecasting Model Combining News and Stock Price[J].,2020,30(09):18-22.[doi:10. 3969 / j. issn. 1673-629X. 2020. 09. 004]
点击复制

CStock:一种结合新闻与股价的股票走势预测模型()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
30
期数:
2020年09期
页码:
18-22
栏目:
智能、算法、系统工程
出版日期:
2020-09-10

文章信息/Info

Title:
Cstock:A Stock Trend Forecasting Model Combining News and Stock Price
文章编号:
1673-629X(2020)09-0018-05
作者:
陈可心; 黄 刚
南京邮电大学 计算机学院,江苏 南京 210023
Author(s):
CHEN Ke-xin; HUANG Gang
School of Computer Science,Nanjing University of Posts and Telecommunications,Nanjing 210023,China
关键词:
股票预测; 深度学习; LSTM; BiLSTM; CLSTM
Keywords:
stock prediction; deep learning; LSTM; BiLSTM; CLSTM
分类号:
TP391
DOI:
10. 3969 / j. issn. 1673-629X. 2020. 09. 004
摘要:
股票是一种高风险、高收益的常见理财产品,为了更好地进行股票投资分析,获得有效的选股方案,文中提出了一种预测股票走势的模型 CStock。 与现有的股票走势预测模型相比,CStock 模型结合新闻和股价走势进行预测,不但利用了股票市场中的交易数据,同时考虑到财经以及政治新闻对于股票市场的影响。 CStock 模型主要由 BiLSTM 和 CLSTM 混合构建,BiLSTM 提取股票交易数据的相关特征,CLSTM 对新闻的语境特征进行整合和处理,最终通过全连接层输出预测结果。 在实验模型中,对股票走势采用分类方法进行实验,得到分类为股票上升的概率和股票下降的概率。 实验使用美股数据作为数据集合。 通过准确率和收益率进行预测效果评估,实验结果表明,CStock 模型在一定程度上能够准确有效地对股票走势进行预测。
Abstract:
Stock is a high-risk,high-yield common financial product. In order to better conduct stock investment analysis and obtain an effective stock selection plan, we propose a model CStock for predicting the stock trend. Compared with the existing stock trend forecasting model,the CStock model combines news and stock price trend to predict. It not only makes use of trading data in the stock market,but also takes into account the influence of financial and political news on the stock market. The CStock model is mainly constructed by mixing BiLSTM and CLSTM. BiLSTM extracts the relevant characteristics of stock trading data,CLSTM integrates and processes the contextual features of news,and finally outputs the predicted results through the fully connected layer. In the experimental model,the stock trend is tested by a classification method,which is classified as the probability of stock rise and the probability of stock decline. The US stock data is used as a data set in the experiment. The prediction results are evaluated by the accuracy rate and the rate of return. The experiment shows that the CStock model can accurately and effectively predict the stock trend to a certain extent.

相似文献/References:

[1]刘利 何先平 袁文亮.股票趋势预测中Wrapper方法的研究与应用[J].计算机技术与发展,2010,(01):209.
　LIU Li,HE Xian-ping,YUAN Wen-liang.Research and Application of Wrapper Approach to Stock Trend Prediction[J].,2010,(09):209.
[2]陈强锐,谢世朋.基于深度学习的肺部肿瘤检测方法[J].计算机技术与发展,2018,28(04):201.[doi:10.3969/ j. issn.1673-629X.2018.04.043]
　CHEN Qiang-rui,XIE Shi-peng.Lung Cancer Detection Method Based on Deep Learning[J].,2018,28(09):201.[doi:10.3969/ j. issn.1673-629X.2018.04.043]
[3]施泽浩,赵启军.基于全卷积网络的目标检测算法[J].计算机技术与发展,2018,28(05):55.[doi:10.3969/j.issn.1673－629X.2018.05.013]
　SHI Ze-hao,ZHAO Qi-jun.Object Detection Algorithm Based on Fully Convolutional Neural Network[J].,2018,28(09):55.[doi:10.3969/j.issn.1673－629X.2018.05.013]
[4]黄法秀,张世杰,吴志红,等.数据增广下的人脸识别研究[J].计算机技术与发展,2020,30(03):67.[doi:10. 3969 / j. issn. 1673-629X. 2020. 03. 013]
　HUANG Fa-xiu,ZHANG Shi-jie,WU Zhi-hong,et al.Research on Face Recognition Based on Data Augmentation[J].,2020,30(09):67.[doi:10. 3969 / j. issn. 1673-629X. 2020. 03. 013]
[5]陈浩翔,蔡建明,刘铿然,等. 手写数字深度特征学习与识别[J].计算机技术与发展,2016,26(07):19.
　CHEN Hao-xiang,CAI Jian-ming,LIU Keng-ran,et al. Deep Learning and Recognition of Handwritten Numeral Features[J].,2016,26(09):19.
[6]高翔,陈志,岳文静,等.基于视频场景深度学习的人物语义识别模型[J].计算机技术与发展,2018,28(06):53.[doi:10.3969/ j. issn.1673-629X.2018.06.012]
　GAO Xiang,CHEN Zhi,YUE Wen-jing,et al.Human Semantic Recognition Model Based on Video Scene Deep Learning[J].,2018,28(09):53.[doi:10.3969/ j. issn.1673-629X.2018.06.012]
[7]贺飞翔,赵启军. 基于深度学习的头部姿态估计[J].计算机技术与发展,2016,26(11):1.
　HE Fei-xiang,ZHAO Qi-jun. Head Pose Estimation Based on Deep Learning[J].,2016,26(09):1.
[8]徐 融,邱晓晖.一种改进的 YOLO V3 目标检测方法[J].计算机技术与发展,2020,30(07):30.[doi:10. 3969 / j. issn. 1673-629X. 2020. 07. 007]
　XU Rong,QIU Xiao-hui.An Improved YOLO V3 Object Detection[J].,2020,30(09):30.[doi:10. 3969 / j. issn. 1673-629X. 2020. 07. 007]
[9]曾志平[] [],萧海东[],张新鹏[]. 基于DBN的金融时序数据建模与决策[J].计算机技术与发展,2017,27(04):1.
　ZENG Zhi-ping[] [],XIAO Hai-dong[],ZHANG Xin-peng[]. Modeling and Decision-making of Financial Time Series Data with DBN[J].,2017,27(09):1.
[10]李全兵,文 钊*,田艳梅*,等.基于 WGAN 的音频关键词识别研究[J].计算机技术与发展,2021,31(08):26.[doi:10. 3969 / j. issn. 1673-629X. 2021. 08. 005]
　LI Quan-bing,WEN Zhao *,TIAN Yan-mei *,et al.Research on Audio Keywords Recognition Based on WassersteinGenerative Adversarial Network[J].,2021,31(09):26.[doi:10. 3969 / j. issn. 1673-629X. 2021. 08. 005]

更新日期/Last Update: 2020-09-10