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。
[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.
作者
张梦吉
杜婉钰
郑楠
Zhang Mengji;Du Wanyu;Zheng Nan(School of Management Science and Engineering,Dongbei University of Finanee and Economics, Dalian 116025,China)
出处
《数据分析与知识发现》
CSSCI
CSCD
北大核心
2019年第5期11-18,共8页
Data Analysis and Knowledge Discovery
