Research On Credit Risk Of Agriculture Leading Enterprise Under The Perspective Of The Financial Link

Agriculture is an important part of Chinese industrial structure, and agricultureleading enterprise is the outstanding representative of the agricultural economydevelopment in our country.to evaluate the default condition of agricultural leadingenterprises in China have important application value. Involved in financial linkagriculture leading enterprise, its different with the general enterprise, its ownproduction capacity, obtain funds from the formal financial institutions, to the system,also of the order or loan guarantee way to fund money to farmers, farmers was thereal user of funds, so the credit risk is there is a difference.This paper with the agricultural credit subsidies theory, rural financial markettheory and imperfect competition market theory and so on rural finance theory as thetheoretical basis, analyzes the basic situation of development of agricultural leadingenterprises, summarizes the three modes of agricultural leading enterprises toparticipate in the financial connection, summarizes the involved in financialconnection of the characteristics of the agricultural leading enterprises, put forwardthe financial link agricultural leading enterprises under the perspective of credit riskfrom agricultural leading enterprises themselves, financial coupling process and theexternal environment and other aspects. The second combed the credit riskmeasurement method, by comparing credit risk measurement model, it is concludedthat KMV model is the financial evaluation of agricultural leading enterprises underthe perspective of feasible credit risk method. Then47is calculated by using theKMV model of agricultural listed companies from2000to2013, the default distance,analyze the distance to default to the following conclusion:First, poor agricultural leading enterprises credit status or extremely well are few,the vast majority of cases, there is a certain degree of credit risk. Secondly, differentyears of agricultural leading enterprises in the credit risk is different, the defaultdistance listed or restructuring listed for the first time that year, good credit standing. Thirdly, in different parts of the agricultural leading enterprises of different defaultdistance, default distance, the largest in eastern west default minimum distance, themore the economy developed area of the smaller credit risk. Then the default distanceof agricultural leading enterprises of different industries, animal husbandry biggestdefault distance, default distance minimum of forestry and animal husbandry creditrisk small, forestry big. The last ST agriculture leading enterprise credit risk of defaultdistance is much smaller than the agriculture leading enterprise of ST, ST agriculturalleading enterprises in six credit risk, forecasting default distance8lines of credit.In this paper, the financial institutions should establish agricultural leadingenterprises default database, USES the default distance this risk measure dynamicindex to strengthen agricultural leading enterprises to participate in the financialconnection monitoring, and further verify the default distance threshold, according todifferent areas, different industries of agriculture leading enterprises financialinstitutions should adopt different credit risk evaluation standard, countries shouldstandardize and develop securities market actively, credit risk management forfinancial institutions to create a good financial environment, to provide real andeffective data.In this paper, we study mainly the following two deficiencies: one is to obtaindata, this paper only select the listed agricultural leading enterprises as samples tostudy its credit risk, for non-listed companies not involved in this article, in fact thenon-listed companies, information more opaque and unlisted companies, the privatecompany credit risk evaluation is more important and urgent, secondly, this papercalculated the only47listed agricultural leading enterprises in the default distance for13years, on the basis of the analysis result has certain representativeness, butrelatively less, also failed to establish a default database, predict the default distancethreshold, also need to be more empirically.