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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
黄丽明1, 陈维政1, 闫宏飞1*, 陈翀2
HUANG Liming1,CHEN Weizheng1,YAN Hongfei1*,CHEN Chong2
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。
中图分类号:
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