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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Change history
24 February 2022
This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s00500-022-06904-4
References
Blasques F, Koopman SJ, Mallee M et al (2016) Weighted maximum likelihood for dynamic factor analysis and forecasting with mixed frequency data. J Econom 193(2):405–417
Cheng CH, Chen CH (2018) Fuzzy time series model based on weighted association rule for financial market forecasting. Expert Syst 35(2):e12271
Jaramillo J, Velasquez JD, Franco CJ (2017a) Research in financial time series forecasting with SVM: contributions from literature. IEEE Latin America Trans 15(1):145–153
Jaramillo J, Velasquez JD, Franco CJ (2017b) Research in Financial Time Series Forecasting with SVM: contributions from Literature. IEEE Latin America Trans 15(1):145–153
Jie W, Wang J (2017) Forecasting stochastic neural network based on financial empirical mode decomposition. Neural Netw 90:8–20
Khashei M, Hajirahimi Z (2017) Performance evaluation of series and parallel strategies for financial time series forecasting. Financ Innov 3(1):24
Kumar D, Meghwani SS, Thakur M (2016) Proximal support vector machine-based hybrid prediction models for trend forecasting in financial markets. J Comput Sci 17:1–13
Maciel L, Ballini R, Gomide F (2016) Evolving granular analytics for interval time series forecasting. Granul Comput 1(4):1–12
Maragoudakis M, Serpanos D (2016) Exploiting financial news and social media opinions for stock market analysis using MCMC Bayesian inference. Comput Econ 47(4):589–622
Marcek D (2018) Forecasting of financial data: a novel fuzzy logic neural network based on error-correction concept and statistics. Complex Intell Syst 4(2):95–104
Mo H, Wang J (2017) Return scaling cross-correlation forecasting by stochastic time strength neural network in financial market dynamics. Soft Comput 22:1–13
Pradeepkumar D, Ravi V (2017) Forecasting financial time series volatility using particle swarm optimization trained quantile regression neural network. Appl Soft Comput 58:S1568494617301862
Rosas-Romero R, Etcheverry G (2016) Forecasting of stock return prices with sparse representation of financial time series over redundant dictionaries. Expert Syst Appl 57(C):37–48
Salhi K, Deaconu M, Lejay A et al (2016) Regime switching model for financial data: empirical risk analysis. Phys A 461:148–157
Singh P (2016) Rainfall and financial forecasting using fuzzy time series and neural networks-based model. Int J Mach Learn Cybernet 9(3):1–16
Tregub IV (2016) On the applicability of the random walk model with stable steps for forecasting the dynamics of prices of financial tools in the Russian market. J Math Sci 216(5):716–721
Wang Y (2017) Stock market forecasting with financial micro-blog based on sentiment and time series analysis. J Shanghai Jiaotong Univ 22(2):173–179
Wei LY (2016) A hybrid ANFIS model based on empirical mode decomposition for stock time series forecasting. Appl Soft Comput 42:368–376
Zhang N, Lin A, Shang P (2017) Multidimensional k-nearest neighbor model based on EEMD for financial time series forecasting. Phys A 477:161–173
Zhao ZY, Xie M, West M (2016) Dynamic dependence networks: financial time series forecasting and portfolio decisions. Appl Stoch Models Bus Ind 32(3):311–332
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
All authors declare that they have no conflict of interest.
Ethical approval
This article does not contain any studies with human participants or animals performed by any of the authors.
Informed consent
Informed consent was obtained from all individual participants included in the study.
Additional information
Communicated by Mu-Yen Chen.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This article has been retracted. Please see the retraction notice for more detail:https://doi.org/10.1007/s00500-022-06904-4.
About this article
Cite this article
Gao, X., Zhang, P., Huang, G. et al. RETRACTED ARTICLE: Financial information prediction and information sharing supervision based on trend assessment and neural network. Soft Comput 24, 8087–8096 (2020). https://doi.org/10.1007/s00500-019-04176-z
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-019-04176-z