Research On Credit Risk Measurement Of Unlisted Construction Companies Based On BP-KMV Model

Since the 21 st century,the corporate bond market has developed rapidly,and the issuance have grown in a high speed.However,behind the prosperous corporate bond market,credit risk is also accumulating.In 2014,the government’s bond market was broken,and corporate bonds defaulted.In order to maintain financial stability and enhance the ability of financial services to the real economy,the report of the 19 th National Congress of the Communist Party of China puts forward the strategic goals of sound financial supervision and prevention of systemic financial risks.In the context of frequent bond defaults,this article attempts to build a set of effective default risk identification,measurement,and control mechanisms for the corporate bond market,to maintain financial stability for regulators,bond issuers to measure their own credit risk,and bond investors to properly review investments Credit risk of varieties provides ideas.This article first analyzes the development of the corporate bond market and the current situation of defaults.It is found that the number and amount of defaults in the construction industry are among the highest in all industries.To this end,debt-issuing companies in the construction industry are selected as research targets.Non-listed companies are the main issuers of default events.Most of the current research focuses on the credit risk research of listed companies.This article identifies non-listed companies as the research target.After reviewing the literature of traditional credit risk measurement models and modern measurement methods,in the comparison of the applicability of modern credit risk measurement models,it is found that the KMV model is currently the method for the Chinese market,and the two technologies of BP neural network model and KMV model can be used.It is complementary in time and accuracy.Therefore,this paper constructs a BP-KMV combination model to measure the default risk of debtors in the construction industry.In the sample selection,this paper selected 60 listed companies and 51 non-listedcompanies in the construction industry.All the stock market data and financial statement data of these companies were collected from the wind database,and calculated Default distances and expected default rates of 51 non-listed companies through the working steps of the BP-KMV combination model.Test the model results with actual default conditions,and find that the model judgments have certain reference to the default prediction.The issuers with higher credit ratings have a larger default distance.The smaller the default risk,the model judgment results and the credit rating agency The rating mechanism can play a similar predictive role.Finally,this paper proposes policy suggestions because problems found during credit risk measurement,for collecting and sorting default data,implementing information disclosure systems,and improving the quality of credit ratings.