A Stock Decision Model Based on Optimized Neural Network Algorithm

被引：1
|
作者：
Zhang, Qiansheng [1 ]
Zhang, Jingru [2 ]
Chen, Zisheng [2 ]
Zhang, Miao [2 ]
Li, Songying [2 ]
机构：
[1] Guangdong Univ Foreign Studies, Sch Math & Stat, Guangzhou, Peoples R China
[2] Guangdong Univ Foreign Studies, Sch Finance, Guangzhou 510006, Peoples R China
来源：
FUZZY SYSTEMS AND DATA MINING V (FSDM 2019) | 2019年 / 320卷
关键词：
Stock selection model; Kernel Principal Component Analysis; Neural Network Model; Genetic Algorithm;
D O I：
10.3233/FAIA190219
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The stock comprehensive decision indexes are extracted by kernel principal component (KPC) analysis method. An effective stock decision model based on optimized neural network algorithm is presented. The weight and threshold value of the proposed BP neural network with KPC input and stock return output are adjusted and optimized by genetic algorithm. The empirical test results of CSI 300 stock sample show that the proposed stock decision model improves the operational efficiency and possesses strong learning ability with high prediction accuracy. This paper develops a bottom-up stock decision model to conduct data mining in stock returns and risks in order to select stock portfolio from individual companies with high quality.
引用
收藏
页码：523 / 533
页数：11
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