Is grey relational analysis superior to the conventional techniques in predicting financial crisis?
This study proposes a new approach for analyzing the credit risks of banking industry based on the modeling of grey relational analysis (GRA). In order to construct a financial crisis warning system for banking industry, a GRA approach is developed and applied to the real data set with 111 samples. The results of the current model are compared to those of traditional ones, logistic regression and back-propagation neural network. The results illustrate that in the prediction of financially crisis as well as financially sound banks, the proposed GRA model demonstrates better prediction accuracy than the conventional ones. The results also imply that the financial data set one year before the crisis leads to the best accuracy. It is helpful for the establishment of early warning models of financial crisis for banks. The current results show that the proposed GRA provides a novel approach in handling financial crisis warning tasks.
financial crisis credit risk grey relational analysis (gra) warning system grey system