文献类型：期刊文献

英文题名：Predicting stock high price using forecast error with recurrent neural network

作者：Bao, Zhiguo[1];Wei, Qing[1,2];Zhou, Tingyu[3];Jiang, Xin[4];Watanabe, Takahiro[3]

第一作者：Bao, Zhiguo

通讯作者：Bao, ZG[1]

机构：[1]Henan Univ Econ & Law, Sch Comp & Informat Engn, Zhengzhou 450046, Henan, Peoples R China;[2]Capital Univ Econ & Business, Sch Management Engn, Beijing 100070, Peoples R China;[3]Waseda Univ, Grad Sch Informat Prod & Syst, Kitakyushu, Fukuoka 8080135, Japan;[4]Kitakyushu Coll, Natl Inst Technol, Kitakyushu, Fukuoka 8020985, Japan

第一机构：河南财经政法大学计算机与信息工程学院

通讯机构：[1]corresponding author), Henan Univ Econ & Law, Sch Comp & Informat Engn, Zhengzhou 450046, Henan, Peoples R China.|[1048412]河南财经政法大学计算机与信息工程学院;[10484]河南财经政法大学;

年份：2021

卷号：6

期号：1

起止页码：283-292

外文期刊名：APPLIED MATHEMATICS AND NONLINEAR SCIENCES

收录：EI(收录号：20212310450880);Scopus(收录号：2-s2.0-85107053642);WOS:【ESCI(收录号:WOS:000672422900027)】；

基金：This work was supported by the National Natural Science Foundation of China (Nos 61602153, 61702161 and 31700858), Scientific and Technological Project of Henan Province (Nos 152300410207, 162102210274, 172102210171, 182102210020 and 182102210213), Key Research Fund for Higher Education of Henan Province (No. 18A520003).

语种：英文

外文关键词：stock price prediction; recurrent neural network; long short-term memory network; gated recurrent unit

摘要：Stock price forecasting is an eye-catching research topic. In previous works, many researchers used a single method or combination of methods to make predictions. However, accurately predicting stock prices is very difficult. To improve the predicting precision, in this study, an innovative prediction approach was proposed by recurrent substitution of forecast error into the historical neural network model through three steps. According to the historical data, the initial predicted value of the next day is obtained through the neural network. Then, the prediction error of the next day is obtained through the neural network according to the historical prediction error. Finally, the initial predicted value and the prediction error are added to obtain the final predicted value of the next day. We use recurrent neural network prediction methods, such as Long Short-Term Memory Network Model and Gated Recurrent Unit, which are popular in the recent neural network study. In the simulations, the past stock prices of China from June 2010 to August 2017 are used as training data, and those from September 2017 to April 2018 are used as test data. The experimental findings demonstrate that the proposed method with forecast error gives a more accurate prediction result for the stock's high price on the next day, which indicates that the performance of the proposed one is superior to that of the traditional models without forecast error.