Early Warning Model of Financial Risks and Empirical Study Based on Neural Network
Qiao Lon · Jinrong luntan · 2011
Based on the evaluation criteria of every indicator for early warning of financial risk (EWFR),this paper produces the efficient number of training data sets,verification data sets and testing data sets for back-propagation neural networks (BPNN) model and,following the principles and basic steps of BPNN model,establishes a BPNN model with a good generalization for early warning of financial risk.The empirical study of China's financial risks from 1994 to 2010 shows that BPNN model can be better used in the research on the early warning of China's financial risks and empirical results can better match the actual operation of China's finance.Except for the financial risks in 2008 and 2010,which are in Rank III(guard),the financial risks in other years are in Rank II(subordinate safe).The BPNN model overcomes the shortages of both factor analysis (FA) and the method of FA combined with BPNN and can analyses the nonlinear relationship between evaluation indicators and financial risks and the sensitivity of evaluation indicators,etc.