RETRACTED ARTICLE: Financial information prediction and information sharing supervision based on trend assessment and neural network
Abstract In order to help financial users to invest, and to provide users with comprehensive and accurate information about financial securities, information about financial securities from multi-heterogeneous information is obtained. The characteristics of financial information are analyzed to prov...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Gao, Xingyu [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag GmbH Germany, part of Springer Nature 2019 |
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| Übergeordnetes Werk: |
Enthalten in: Soft Computing - Springer-Verlag, 2003, 24(2019), 11 vom: 26. Juni, Seite 8087-8096 |
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| Übergeordnetes Werk: |
volume:24 ; year:2019 ; number:11 ; day:26 ; month:06 ; pages:8087-8096 |
| Links: |
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| DOI / URN: |
10.1007/s00500-019-04176-z |
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SPR039615189 |
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| 520 | |a Abstract In order to help financial users to invest, and to provide users with comprehensive and accurate information about financial securities, information about financial securities from multi-heterogeneous information is obtained. The characteristics of financial information are analyzed to provide valuable investment advice to users. According to the financial characteristics of the user’s interest, the characteristics of the investor’s interest are extracted from the heterogeneous information. Then, the multi-level model is proposed to analyze the characteristics. Through the multi-level model, the conversion of convertible bonds and the net value of closed funds are predicted. In the first level, based on the characteristics of convertible bonds and closed funds, three models of trend evaluation model, SVR (Support Vector Regression) model and neural network backpropagation network (BPN) model are used to predict financial characteristics. In the second level, the results produced by the three models in the first level are fused by the neural network. The third level optimizes the neural network based on the second level. The optimal initial weights and thresholds are selected by genetic algorithm to obtain better prediction results. The results show that the model can predict the characteristics of convertible bonds and closed funds more accurately. Therefore, the model provides a certain reference for financial users’ investment. | ||
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| 700 | 1 | |a Jiang, Hui |4 aut | |
| 700 | 1 | |a Zhang, Zhuo |4 aut | |
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10.1007/s00500-019-04176-z doi (DE-627)SPR039615189 (SPR)s00500-019-04176-z-e DE-627 ger DE-627 rakwb eng Gao, Xingyu verfasserin aut RETRACTED ARTICLE: Financial information prediction and information sharing supervision based on trend assessment and neural network 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2019 Abstract In order to help financial users to invest, and to provide users with comprehensive and accurate information about financial securities, information about financial securities from multi-heterogeneous information is obtained. The characteristics of financial information are analyzed to provide valuable investment advice to users. According to the financial characteristics of the user’s interest, the characteristics of the investor’s interest are extracted from the heterogeneous information. Then, the multi-level model is proposed to analyze the characteristics. Through the multi-level model, the conversion of convertible bonds and the net value of closed funds are predicted. In the first level, based on the characteristics of convertible bonds and closed funds, three models of trend evaluation model, SVR (Support Vector Regression) model and neural network backpropagation network (BPN) model are used to predict financial characteristics. In the second level, the results produced by the three models in the first level are fused by the neural network. The third level optimizes the neural network based on the second level. The optimal initial weights and thresholds are selected by genetic algorithm to obtain better prediction results. The results show that the model can predict the characteristics of convertible bonds and closed funds more accurately. Therefore, the model provides a certain reference for financial users’ investment. Multi-level model (dpeaa)DE-He213 Predictive analysis (dpeaa)DE-He213 Financial information (dpeaa)DE-He213 Zhang, Pu aut Huang, Guanhua aut Jiang, Hui aut Zhang, Zhuo aut Enthalten in Soft Computing Springer-Verlag, 2003 24(2019), 11 vom: 26. Juni, Seite 8087-8096 (DE-627)SPR006469531 nnns volume:24 year:2019 number:11 day:26 month:06 pages:8087-8096 https://dx.doi.org/10.1007/s00500-019-04176-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 24 2019 11 26 06 8087-8096 |
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10.1007/s00500-019-04176-z doi (DE-627)SPR039615189 (SPR)s00500-019-04176-z-e DE-627 ger DE-627 rakwb eng Gao, Xingyu verfasserin aut RETRACTED ARTICLE: Financial information prediction and information sharing supervision based on trend assessment and neural network 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2019 Abstract In order to help financial users to invest, and to provide users with comprehensive and accurate information about financial securities, information about financial securities from multi-heterogeneous information is obtained. The characteristics of financial information are analyzed to provide valuable investment advice to users. According to the financial characteristics of the user’s interest, the characteristics of the investor’s interest are extracted from the heterogeneous information. Then, the multi-level model is proposed to analyze the characteristics. Through the multi-level model, the conversion of convertible bonds and the net value of closed funds are predicted. In the first level, based on the characteristics of convertible bonds and closed funds, three models of trend evaluation model, SVR (Support Vector Regression) model and neural network backpropagation network (BPN) model are used to predict financial characteristics. In the second level, the results produced by the three models in the first level are fused by the neural network. The third level optimizes the neural network based on the second level. The optimal initial weights and thresholds are selected by genetic algorithm to obtain better prediction results. The results show that the model can predict the characteristics of convertible bonds and closed funds more accurately. Therefore, the model provides a certain reference for financial users’ investment. Multi-level model (dpeaa)DE-He213 Predictive analysis (dpeaa)DE-He213 Financial information (dpeaa)DE-He213 Zhang, Pu aut Huang, Guanhua aut Jiang, Hui aut Zhang, Zhuo aut Enthalten in Soft Computing Springer-Verlag, 2003 24(2019), 11 vom: 26. Juni, Seite 8087-8096 (DE-627)SPR006469531 nnns volume:24 year:2019 number:11 day:26 month:06 pages:8087-8096 https://dx.doi.org/10.1007/s00500-019-04176-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 24 2019 11 26 06 8087-8096 |
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10.1007/s00500-019-04176-z doi (DE-627)SPR039615189 (SPR)s00500-019-04176-z-e DE-627 ger DE-627 rakwb eng Gao, Xingyu verfasserin aut RETRACTED ARTICLE: Financial information prediction and information sharing supervision based on trend assessment and neural network 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2019 Abstract In order to help financial users to invest, and to provide users with comprehensive and accurate information about financial securities, information about financial securities from multi-heterogeneous information is obtained. The characteristics of financial information are analyzed to provide valuable investment advice to users. According to the financial characteristics of the user’s interest, the characteristics of the investor’s interest are extracted from the heterogeneous information. Then, the multi-level model is proposed to analyze the characteristics. Through the multi-level model, the conversion of convertible bonds and the net value of closed funds are predicted. In the first level, based on the characteristics of convertible bonds and closed funds, three models of trend evaluation model, SVR (Support Vector Regression) model and neural network backpropagation network (BPN) model are used to predict financial characteristics. In the second level, the results produced by the three models in the first level are fused by the neural network. The third level optimizes the neural network based on the second level. The optimal initial weights and thresholds are selected by genetic algorithm to obtain better prediction results. The results show that the model can predict the characteristics of convertible bonds and closed funds more accurately. Therefore, the model provides a certain reference for financial users’ investment. Multi-level model (dpeaa)DE-He213 Predictive analysis (dpeaa)DE-He213 Financial information (dpeaa)DE-He213 Zhang, Pu aut Huang, Guanhua aut Jiang, Hui aut Zhang, Zhuo aut Enthalten in Soft Computing Springer-Verlag, 2003 24(2019), 11 vom: 26. Juni, Seite 8087-8096 (DE-627)SPR006469531 nnns volume:24 year:2019 number:11 day:26 month:06 pages:8087-8096 https://dx.doi.org/10.1007/s00500-019-04176-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 24 2019 11 26 06 8087-8096 |
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10.1007/s00500-019-04176-z doi (DE-627)SPR039615189 (SPR)s00500-019-04176-z-e DE-627 ger DE-627 rakwb eng Gao, Xingyu verfasserin aut RETRACTED ARTICLE: Financial information prediction and information sharing supervision based on trend assessment and neural network 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2019 Abstract In order to help financial users to invest, and to provide users with comprehensive and accurate information about financial securities, information about financial securities from multi-heterogeneous information is obtained. The characteristics of financial information are analyzed to provide valuable investment advice to users. According to the financial characteristics of the user’s interest, the characteristics of the investor’s interest are extracted from the heterogeneous information. Then, the multi-level model is proposed to analyze the characteristics. Through the multi-level model, the conversion of convertible bonds and the net value of closed funds are predicted. In the first level, based on the characteristics of convertible bonds and closed funds, three models of trend evaluation model, SVR (Support Vector Regression) model and neural network backpropagation network (BPN) model are used to predict financial characteristics. In the second level, the results produced by the three models in the first level are fused by the neural network. The third level optimizes the neural network based on the second level. The optimal initial weights and thresholds are selected by genetic algorithm to obtain better prediction results. The results show that the model can predict the characteristics of convertible bonds and closed funds more accurately. Therefore, the model provides a certain reference for financial users’ investment. Multi-level model (dpeaa)DE-He213 Predictive analysis (dpeaa)DE-He213 Financial information (dpeaa)DE-He213 Zhang, Pu aut Huang, Guanhua aut Jiang, Hui aut Zhang, Zhuo aut Enthalten in Soft Computing Springer-Verlag, 2003 24(2019), 11 vom: 26. Juni, Seite 8087-8096 (DE-627)SPR006469531 nnns volume:24 year:2019 number:11 day:26 month:06 pages:8087-8096 https://dx.doi.org/10.1007/s00500-019-04176-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 24 2019 11 26 06 8087-8096 |
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10.1007/s00500-019-04176-z doi (DE-627)SPR039615189 (SPR)s00500-019-04176-z-e DE-627 ger DE-627 rakwb eng Gao, Xingyu verfasserin aut RETRACTED ARTICLE: Financial information prediction and information sharing supervision based on trend assessment and neural network 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2019 Abstract In order to help financial users to invest, and to provide users with comprehensive and accurate information about financial securities, information about financial securities from multi-heterogeneous information is obtained. The characteristics of financial information are analyzed to provide valuable investment advice to users. According to the financial characteristics of the user’s interest, the characteristics of the investor’s interest are extracted from the heterogeneous information. Then, the multi-level model is proposed to analyze the characteristics. Through the multi-level model, the conversion of convertible bonds and the net value of closed funds are predicted. In the first level, based on the characteristics of convertible bonds and closed funds, three models of trend evaluation model, SVR (Support Vector Regression) model and neural network backpropagation network (BPN) model are used to predict financial characteristics. In the second level, the results produced by the three models in the first level are fused by the neural network. The third level optimizes the neural network based on the second level. The optimal initial weights and thresholds are selected by genetic algorithm to obtain better prediction results. The results show that the model can predict the characteristics of convertible bonds and closed funds more accurately. Therefore, the model provides a certain reference for financial users’ investment. Multi-level model (dpeaa)DE-He213 Predictive analysis (dpeaa)DE-He213 Financial information (dpeaa)DE-He213 Zhang, Pu aut Huang, Guanhua aut Jiang, Hui aut Zhang, Zhuo aut Enthalten in Soft Computing Springer-Verlag, 2003 24(2019), 11 vom: 26. Juni, Seite 8087-8096 (DE-627)SPR006469531 nnns volume:24 year:2019 number:11 day:26 month:06 pages:8087-8096 https://dx.doi.org/10.1007/s00500-019-04176-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 24 2019 11 26 06 8087-8096 |
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Abstract In order to help financial users to invest, and to provide users with comprehensive and accurate information about financial securities, information about financial securities from multi-heterogeneous information is obtained. The characteristics of financial information are analyzed to provide valuable investment advice to users. According to the financial characteristics of the user’s interest, the characteristics of the investor’s interest are extracted from the heterogeneous information. Then, the multi-level model is proposed to analyze the characteristics. Through the multi-level model, the conversion of convertible bonds and the net value of closed funds are predicted. In the first level, based on the characteristics of convertible bonds and closed funds, three models of trend evaluation model, SVR (Support Vector Regression) model and neural network backpropagation network (BPN) model are used to predict financial characteristics. In the second level, the results produced by the three models in the first level are fused by the neural network. The third level optimizes the neural network based on the second level. The optimal initial weights and thresholds are selected by genetic algorithm to obtain better prediction results. The results show that the model can predict the characteristics of convertible bonds and closed funds more accurately. Therefore, the model provides a certain reference for financial users’ investment. © Springer-Verlag GmbH Germany, part of Springer Nature 2019 |
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Abstract In order to help financial users to invest, and to provide users with comprehensive and accurate information about financial securities, information about financial securities from multi-heterogeneous information is obtained. The characteristics of financial information are analyzed to provide valuable investment advice to users. According to the financial characteristics of the user’s interest, the characteristics of the investor’s interest are extracted from the heterogeneous information. Then, the multi-level model is proposed to analyze the characteristics. Through the multi-level model, the conversion of convertible bonds and the net value of closed funds are predicted. In the first level, based on the characteristics of convertible bonds and closed funds, three models of trend evaluation model, SVR (Support Vector Regression) model and neural network backpropagation network (BPN) model are used to predict financial characteristics. In the second level, the results produced by the three models in the first level are fused by the neural network. The third level optimizes the neural network based on the second level. The optimal initial weights and thresholds are selected by genetic algorithm to obtain better prediction results. The results show that the model can predict the characteristics of convertible bonds and closed funds more accurately. Therefore, the model provides a certain reference for financial users’ investment. © Springer-Verlag GmbH Germany, part of Springer Nature 2019 |
| abstract_unstemmed |
Abstract In order to help financial users to invest, and to provide users with comprehensive and accurate information about financial securities, information about financial securities from multi-heterogeneous information is obtained. The characteristics of financial information are analyzed to provide valuable investment advice to users. According to the financial characteristics of the user’s interest, the characteristics of the investor’s interest are extracted from the heterogeneous information. Then, the multi-level model is proposed to analyze the characteristics. Through the multi-level model, the conversion of convertible bonds and the net value of closed funds are predicted. In the first level, based on the characteristics of convertible bonds and closed funds, three models of trend evaluation model, SVR (Support Vector Regression) model and neural network backpropagation network (BPN) model are used to predict financial characteristics. In the second level, the results produced by the three models in the first level are fused by the neural network. The third level optimizes the neural network based on the second level. The optimal initial weights and thresholds are selected by genetic algorithm to obtain better prediction results. The results show that the model can predict the characteristics of convertible bonds and closed funds more accurately. Therefore, the model provides a certain reference for financial users’ investment. © Springer-Verlag GmbH Germany, part of Springer Nature 2019 |
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RETRACTED ARTICLE: Financial information prediction and information sharing supervision based on trend assessment and neural network |
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https://dx.doi.org/10.1007/s00500-019-04176-z |
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Zhang, Pu Huang, Guanhua Jiang, Hui Zhang, Zhuo |
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2024-07-04T00:45:36.478Z |
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