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数据分析与知识发现  2019, Vol. 3 Issue (5): 11-18    DOI: 10.11925/infotech.2096-3467.2018.0871
  专题 本期目录 | 过刊浏览 | 高级检索 |
引入新闻短文本的个股走势预测模型
张梦吉(),杜婉钰,郑楠
东北财经大学管理科学与工程学院 大连 116025
Predicting Stock Trends Based on News Events
Mengji Zhang(),Wanyu Du,Nan Zheng
School of Management Science and Engineering, Dongbei University of Finance and Economics, Dalian 116025, China
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摘要 

【目的】结合深度学习, 分析股市数值数据和财经新闻, 提高股票涨跌预测准确率。【方法】建立基于事件的新闻分类模型, 使用多输入的循环神经网络建立基于新闻事件、资金流向和公司财务的个股走势预测模型, 提升股票预测准确率。【结果】引入新闻文本后模型预测准确率进一步提升, 其中, 采矿业准确率达到76.22%, 医药制造业准确率达到77.36%。【局限】未验证新闻标题与新闻文章对股价影响程度的差异, 且新闻事件的分类是基于一年内的新闻数据集进行人工划分, 数据集不具备完整性和代表性。【结论】引入新闻事件作为股票预测模型的特征之一, 能够提升预测的准确率。

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张梦吉
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关键词 个股走势预测深度学习文本挖掘    
Abstract

[Objective] This paper tries to predict stock trends with the help of deep learning models, financial data and related news events. [Methods] First, we built a classification model for news events. Then, we used the recurrent neural networks to construct a forecasting model for stock trends based on news, capital flows and corporate financial reports. [Results] The prediction accuracy was improved by the proposed model (76.22% and 77.36% for the mining and pharmaceutical manufacturing industries). [Limitations] We did not examine the different impacts of news headlines and full-texts on stock market. We only chose news events from the past one year, which needs to be expanded. [Conclusions] News events could improve the accuracy of predicting stock trends.

Key wordsStock Trend Forecast    Deep Learning    Text Mining
收稿日期: 2018-08-06     
引用本文:   
张梦吉,杜婉钰,郑楠. 引入新闻短文本的个股走势预测模型[J]. 数据分析与知识发现, 2019, 3(5): 11-18.
Mengji Zhang,Wanyu Du,Nan Zheng. Predicting Stock Trends Based on News Events. Data Analysis and Knowledge Discovery, DOI:10.11925/infotech.2096-3467.2018.0871.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2018.0871
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