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Neural Computing and Applications
Published in: Neural Computing and Applications 12/2019

28-01-2019 | Machine Learning - Applications & Techniques in Cyber Intelligence

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

Author: Xiaofang Luo

Published in: Neural Computing and Applications | Issue 12/2019

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Abstract

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.
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Metadata
Title
Construction of artificial neural network economic forecasting model based on the consideration of state transition diagram
Author
Xiaofang Luo
Publication date
28-01-2019
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 12/2019
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-019-04038-7

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