Research on Early Warning System of Financial Risk in China-Based on K-means Clustering Algorithm and BP Neural Network
LI Meng-y · Zhongyang Caizheng Jinrong Xueyuan xuebao · 2012
Based on the analysis of economic statistics in China from 1994 to 2011,this article applies principal components analysis to 16 economic variables related to the stability of finance in order to compartmentalize these variables into macroeconomics,financial system and international business and economics.According to the result of K-means clustering algorithm,we divide financial risk into 4 categories.Then the early warning model of financial system is established by BP Neural Network and the forecast of financial risk in 2012 is conducted using the statistics in 2011.The result demonstrates that the financial risk of China belongs to mild risk status and the main problems which influence the stability of finance is the decline on total demand and the shrink on asset bubbles.Finally policy recommendations are proposed in how to predict and prevent financial risk.