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 |
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| Sprache: | Englisch |
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Berlin/Heidelberg
Springer Berlin Heidelberg
01.06.2020
Springer Nature B.V |
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| ISSN: | 1432-7643, 1433-7479 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Cao, Jiasheng Wang, Jinghan |
| Author_xml | – sequence: 1 givenname: Jiasheng surname: Cao fullname: Cao, Jiasheng email: jasoncao109@163.com organization: University of Science and Technology of China – sequence: 2 givenname: Jinghan surname: Wang fullname: Wang, Jinghan organization: Illinois Institute of Technology |
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| Keywords | Investment guidance BP neural network Bayesian regularization algorithm Stock price forecasting Principal component analysis |
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| SubjectTerms | 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 |
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| Title | Exploration of stock index change prediction model based on the combination of principal component analysis and artificial neural network |
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