标题:Stock market index prediction using deep neural network ensemble
作者:Yang, Bing ;Gong, Zi-Jia ;Yang, Wenqi
通讯作者:Gong, ZiJia
作者机构:[Yang, Bing ;Gong, Zi-Jia ] School of Mathematics and Statistics, Shandong University, Weihai, Weihai; 264209, China;[Yang, Wenqi ] School of Mathemat 更多
会议名称:36th Chinese Control Conference, CCC 2017
会议日期:26 July 2017 through 28 July 2017
来源:Chinese Control Conference, CCC
出版年:2017
页码:3882-3887
DOI:10.23919/ChiCC.2017.8027964
关键词:ensemble learning; machine learning; neural network; stock index; time series forecasting
摘要:In this paper, we put forward deep neural network ensemble to model and predict Chinese stock market index (including Shanghai composite index and SZSE component index), based on the input indices of recent days. A set of component networks are trained by historical data for this task, where Backpropagation and Adam algorithm are used to train each network efficiently. Bagging approach combines these component networks to generate ensemble, which reduces the generalization error. Indices of test examples are predicted with the model, and the trend predictions is calculated based on the predicted indices. Finally, relative errors between actual indices and predicted indices, as well as accuracy of trend predictions are computed to measure the performance of predictions. It turns out that the model proposed in this paper can partially model and predict the Chinese stock market. The accuracy of trend predictions of the daily barycenter, high, low are 71.34%, 74.15%, 74.15% respectively for Shanghai composite index and 75.95%, 73.95%, 72.34% respectively for SZSE component index. But the predictions on close are unsatisfactory. © 2017 Technical Committee on Control Theory, CAA.
收录类别:EI;SCOPUS
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032186889&doi=10.23919%2fChiCC.2017.8027964&partnerID=40&md5=f1e63d9c395cb959a6eeef43f9643675
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