Exploration of stock index change prediction model based on the combination of principal component analysis and artificial neural network

In order to establish an accurate effective stock forecasting model, the principal component analysis (PCA) was first used to analyze the main financial index data of some listed companies and the comprehensive score of evaluation index was obtained in this study. Then, the financial indicator data...

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Veröffentlicht in:Soft computing (Berlin, Germany) Jg. 24; H. 11; S. 7851 - 7860
Hauptverfasser: Cao, Jiasheng, Wang, Jinghan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2020
Springer Nature B.V
Schlagworte:
Accuracy
Algorithms
Artificial Intelligence
Artificial neural networks
Back propagation networks
Computational Intelligence
Control
Economic development
Engineering
Financial management
Focus
Fourier transforms
Investment policy
Investments
Investors
Market economies
Market prices
Mathematical Logic and Foundations
Mechatronics
Methods
Neural networks
Performance evaluation
Prediction models
Principal components analysis
Regularization
Robotics
Securities markets
Small & medium sized enterprises-SME
Stock exchanges
Support vector machines
Time series
Variables
China
Investment guidance
BP neural network
Bayesian regularization algorithm
Stock price forecasting
Principal component analysis
ISSN:1432-7643, 1433-7479
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Zusammenfassung:In order to establish an accurate effective stock forecasting model, the principal component analysis (PCA) was first used to analyze the main financial index data of some listed companies and the comprehensive score of evaluation index was obtained in this study. Then, the financial indicator data and the transaction indicator data were simultaneously used as the input variables of the stock price prediction research, three back propagation (BP) neural network algorithms were used for experiment, and its prediction situation was compared. Results show that the BP neural network based on Bayesian regularization algorithm has the highest prediction accuracy and can avoid over-fitting phenomenon in the training process of the model; the error between the predicted value and the actual value is small. Finally, this study constructed a stock price prediction study based on PCA and BP neural network algorithm as well as an investment stock selection strategy based on traditional stock selection analysis method. As a result, the proposed model is proved to be effective.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-019-03918-3