广西师范大学学报(自然科学版) ›› 2019, Vol. 37 ›› Issue (1): 13-22.doi: 10.16088/j.issn.1001-6600.2019.01.002

• 第二十四届全国信息检索学术会议专栏 • 上一篇    下一篇

基于循环神经网络和深度学习的股票预测方法

黄丽明1, 陈维政1, 闫宏飞1*, 陈翀2   

  1. 1.北京大学信息科学与技术学院,北京100871;
    2.北京师范大学政府管理学院,北京100875
  • 收稿日期:2018-10-14 发布日期:2019-01-08
  • 通讯作者: 闫宏飞(1973—),男,吉林扶余人,北京大学副教授,博士。E-mail: yanhf@pku.edu.cn
  • 基金资助:
    国家自然科学基金(61772044,U1536201)

A Stock Prediction Method Based on Recurrent Neural Network and Deep Learning

HUANG Liming1,CHEN Weizheng1,YAN Hongfei1*,CHEN Chong2   

  1. 1. School of Computer Science and Technology, Peking University, Beijing 100871, China;
    2. School of Government, Beijing Normal University, Beijing 100875, China
  • Received:2018-10-14 Published:2019-01-08

摘要: 本文提出一种基于多路循环神经网络与深度学习的股票预测方法。针对股票的涨跌预测问题,使用分布式向量表示方法提取出股票相关的新闻文本特征,同时考虑到股票相关信息的时序性以及新闻影响的持续性特质,使用多路循环神经网络模型对所提取的特征与交易信息进行协同训练,从而获得历史信息的低维向量表示。最后将多个循环神经网络的输出进行拼接,利用深度神经网络共同对股票的涨跌进行分类预测。本文使用上证A股的价格与新闻数据进行实验,实验结果表明,本文所提出的方法在股票预测任务上具有明显的优越性。

关键词: 股票预测, 循环神经网络, 深度学习, 长短期记忆

Abstract: A stock prediction method based on multiple recurrent neural network and deep learning is proposed in this paper. Aiming at predicting the rise and fall of stocks, the method in this paper extracts the features of news corpus information by distributed vector representation methods. Considering the natural of time-series stock related information and the persistence of news impact, multiple recurrent neural networks are used to collaborate process the features and stock trading information to obtain the history information embedding. Finally, the outputs of all recurrent neural networks are concatenated together to predict a stock. The data of Shanghai A-share market stock are used to do case study, which indicates that our method significantly outperforms the other baselines.

Key words: stock prediction, recurrent neural network, deep learning, long short term memory

中图分类号: 

  • TP181
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