Assessing Country Risks Based on Hybrid Neural Network Models
Guo Yuanyua · Jinrong jianguan yanjiu · 2014
Based on the advantages of econometric analysis methods in dealing with multiple indicators data and the advantages of neural network models in deriving optimal solutions,we build hybrid neural network models to predict country risks.We get two important predicting indicators—EBI(Ease of doing business index) and RED(Total reserves/total external debt) by the methods of correlation analysis and logistic regressions.The predicting accuracy of the binary-classification multi-layer perception network model is better than that of the probabilistic neural network model,the former reaches 100%and the latter is 90.91%.The predicting results of the two network models support and validate each other,which prove the reliability of hybrid neural network models in predicting country risk.