Construction of artificial neural network economic forecasting model based on the consideration of state transition diagram

  • Xiaofang LuoEmail author
Machine Learning - Applications & Techniques in Cyber Intelligence


In order to quantify the time-varying dependent structure between the assets and forecast the portfolio risk accurately, the difference in the preferences for asset risk is taken into consideration in this paper. It is assumed that the new interest rate of asset return sequence is subject to the standard t distribution. A kind of artificial neural network economic forecasting model is put forward. The two-step state transition diagram estimation method for the economic forecasting is deduced, and the forecasting method for the profile risk is obtained. Finally, Shanghai Securities Composite Index and Standard & Poor’s 500 Index are selected to verify the feasibility and superiority of the model and method put forward in this paper. At the same time, the model can accurately quantify the time-varying dependent structural characteristics of the two indices after the subprime mortgage crisis.


State transition diagram Artificial neural network Economic forecasting model Risk management 



The work has been sponsored by Project Supported by the National Natural Science Foundation of China (No. 71601087) and the Humanities and Social Sciences Fund of the Ministry of Education (No. 15YJC630088). The authors gratefully acknowledge this support.

Compliance with ethical standards

Conflict of interest

The author declares that she has no conflicts of interest.


  1. 1.
    Wang Ying, Cuijie Lu, Zuo Cuiping (2015) Coal mine safety production forewarning based on improved BP neural network. Int J Min Sci Technol 25(2):319–324CrossRefGoogle Scholar
  2. 2.
    Yin L et al (2017) A calculation method for CO2 emission in utility boilers based on BP neural network and carbon balance. Energy Procedia 105:3173–3178CrossRefGoogle Scholar
  3. 3.
    Mu Z, Hu J, Min J (2016) EEG-based person authentication using a fuzzy entropy-related approach with two electrodes. Entropy 18(12):432CrossRefGoogle Scholar
  4. 4.
    Mu Z, Hu J, Yin J (2017) Driving fatigue detecting based on eeg signals of forehead area. Int J Pattern Recognit Artif Intell 31(05):40–44CrossRefGoogle Scholar
  5. 5.
    Mostafa MM, El-Masry AA (2016) Oil price forecasting using gene expression programming and artificial neural networks. Econ Model 54(1):40–53CrossRefGoogle Scholar
  6. 6.
    Sokolov-Mladenović S, Milovančević M, Mladenović I, Alizamir M (2016) Economic growth forecasting by artificial neural network with extreme learning machine based on trade, import and export parameters. Comput Hum Behav 65:43–45CrossRefGoogle Scholar
  7. 7.
    Günay ME (2016) Forecasting annual gross electricity demand by artificial neural networks using predicted values of socio-economic indicators and climatic conditions: case of turkey. Energy Policy 90:92–101CrossRefGoogle Scholar
  8. 8.
    Zhang S, Liu W, Deng XL, Xu Z, Choo KKR (2018) Micro-blog topic recommendation based on knowledge flow and user selection. J Comput Sci 26:512–521CrossRefGoogle Scholar
  9. 9.
    Kordanuli B, Barjaktarović L, Jeremić L, Alizamir M (2017) Appraisal of artificial neural network for forecasting of economic parameters. Phys A 465:515–519CrossRefGoogle Scholar
  10. 10.
    Abdellatif ME, Osman YZ, Elkhidir AM (2015) Comparison of artificial neural networks and autoregressive model for inflows forecasting of roseires reservoir for better forecast of irrigation water supply in Sudan. Int J River Basin Manag 13(2):203–214CrossRefGoogle Scholar
  11. 11.
    Nabavi-Pelesaraei A, Fatehi F, Mahmoudi A (2014) Prediction of yield and economic indices for tangerine production using artificial neural networks based on energy consumption. Int J Agron Agric Res 4(5):57–64Google Scholar
  12. 12.
    Deng JJ, Leung CHC (2015) Dynamic time warping for music retrieval using time series modeling of musical emotions. IEEE Trans Affect Comput 6(2):137–151CrossRefGoogle Scholar
  13. 13.
    Chuang FK, Hung CY, Chang CY, Kuo KC (2013) Deploying arima and artificial neural networks models to predict energy consumption in Taiwan. Sens Lett 11(12):2333–2340CrossRefGoogle Scholar
  14. 14.
    Orimi MG, Farid A, Amiri R, Imani K (2015) Cprecip parameter for checking snow entry for forecasting weekly discharge of the Haraz river flow by artificial neural network. Water Resour 42(5):607–615CrossRefGoogle Scholar
  15. 15.
    Zhang Shunxiang, Wei Zhongliang, Wang Yin, Liao Tao (2018) Sentiment analysis of Chinese micro-blog text based on extended sentiment dictionary. Future Gener Comput Syst 81:395–403CrossRefGoogle Scholar
  16. 16.
    Zhang ZS, Sun YZ, Gao DW, Lin J, Cheng L (2013) A versatile probability distribution model for wind power forecast errors and its application in economic dispatch. IEEE Trans Power Syst 28(3):3114–3125CrossRefGoogle Scholar
  17. 17.
    García-Alonso CR, Arenas-Arroyo E, Pérez-Alcalá GM (2012) A macro-economic model to forecast remittances based on monte-carlo simulation and artificial intelligence. Expert Syst Appl 39(9):7929–7937CrossRefGoogle Scholar
  18. 18.
    Embrechts X, Cuartas BM, Muñiz ASG (2016) A grey neural network and input-output combined forecasting model. primary energy consumption forecasts in spanish economic sectors. Energy 115:1042–1054CrossRefGoogle Scholar

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© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Jiangsu University of Science and TechnologySchool of Economics and ManagementZhenjiangChina

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