基于循环神经网络和深度学习的股票预测方法

doi:10.16088/j.issn.1001-6600.2019.01.002

Abstract

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

CLC Number:

TP181

HUANG Liming,CHEN Weizheng,YAN Hongfei,CHEN Chong. A Stock Prediction Method Based on Recurrent Neural Network and Deep Learning[J].Journal of Guangxi Normal University(Natural Science Edition), 2019, 37(1): 13-22.

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

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