摘要
股票市场的预测一直是一个具有挑战性的问题,其波动会受国家政策、公司财报、行业表现、投资者情绪等因素的影响。本文基于股市图像(Stock Charts)方法将股票的连续时间信息进行处理,根据不同的信息丰富度以及预测时间间隔将原始数据分为了多个类别,依次作为深度卷积神经网络(Deep Convolutional Neural Network, DCNN)训练集;并利用深度卷积神经网络对股票市场进行预测,分析在不同分类方法下的精度差异。结果表明,当在标记间隔为30天,使用包含成交量的蜡烛图作为输入时,对美国NDAQ交易所的股票走势预测可以达到59.7%的准确度。
The prediction of the stock market has always been a challenging issue, because many factors will cause the market uncertainty such as national policies, company financial reports, industry per-formance, investor sentiment, social media sentiment, and economic factors. In this paper, based on the stock charts method, the continuous time stock information is processed. According to different information richness, prediction time interval and classification method, the original data is divided into multiple categories as the training set of DCNN (Deep Convolutional Neural Network). The re-sults show that the method has the best performance when the forecast time interval is 30 days. Moreover, this method can accurately predict the stock trend of the US NDAQ exchange for 59.7%.
出处
《金融》
2020年第4期334-342,共9页
Finance
关键词
股市预测
卷积神经网络
蜡烛图
Stock Market Predicted
Convolutional Neural Network
Stock Charts
