Research On The Early Warning System Of Local Government Debt Risk Based On BP Model

In the past few years, as region economy of China continues to developing, the total amount of local government debt continues to increase,until the end of June 2013, the debt has reached 17.9 trillion yuan. The local government debt itself is a double edged sword, it can promote social and economic development of the region, but also may lead to financial crisis and financial risks of local government. In October 2014, "issued by the State Council on strengthening local government debt management’s views ", emphasized that for the scale of local government debt management and risk control, can be seen at this stage for the study of local government debt risk increasing urgency. How to correctly understand the existence of local government debt 、 debt risk classification and study of the impact, give full play to local government debt of "duality" to build risk warning mechanism has a full-bore index system, the greatest degree of avoid debt risk that a research direction of this paper. This paper summarizes the basic theory of local government debt and risk qualitatively analyzed the current situation and characteristics of China’s local government debt, combined with the impact of local government debt four levels of risk: the local macroeconomic development, local public development, local financial and local debt designed system of government debt risk indicators full aperture within the range of data available through cluster analysis indicators were screened constructed to reflect the local government debt risk warning indicator system. Using artificial neural networks(BP) was constructed of local government debt risk warning model, select 2006- 2012 fiscal relevant statistical data of 31 provinces to analysis, local government overall debt risks were faced grade evaluation. Finally to against the risk of local government debt, suggest some measures. This article is an early warning mechanism for local government debt risk study aimed to pass local government debt risk warning method for full view.