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A quantum artificial neural network for stock closing price prediction

被引：31
|
作者：
Liu, Ge [1 ,2 ]
Ma, Wenping [1 ,2 ]
机构：
[1] Xidian Univ, Sch Telecommun Engn, Xian 710071, Peoples R China
[2] State Key Lab Cryptol, POB 5159, Beijing 100878, Peoples R China
来源：
INFORMATION SCIENCES | 2022年 / 598卷
基金：
美国国家科学基金会; 国家重点研发计划;
关键词：
Elman neural network; Quantum computing; Stock market; ALGORITHM;
D O I：
10.1016/j.ins.2022.03.064
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In practice, stock market behavior is difficult to predict accurately because of its high volatility. To improve market forecasts, a method inspired by Elman neural network and quantum mechanics is presented. To render the network sensitive to dynamic information, the internal self-connection signal that is extremely useful for system modeling is introduced to the proposed technique. Double chains quantum genetic algorithm is employed to tune the learning rates. This model is validated by forecasting closing prices of six stock markets, the simulation results indicate that the proposed algorithm is feasible and effective. Accordingly, generalizing the method is deemed advantageous.(c) 2022 Elsevier Inc. All rights reserved.
引用
收藏
页码：75 / 85
页数：11
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