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Research On Credit Risk Evaluation Of Agricultural Listed Companies Based On Catastrophe Model

Posted on:2019-11-26Degree:MasterType:Thesis
Country:ChinaCandidate:L YangFull Text:PDF
GTID:2429330566471382Subject:Finance
Abstract/Summary:PDF Full Text Request
The management and development of agricultural enterprises are of great significance for strengthening the status of agriculture in China,stabilizing the agricultural industry chain,and promoting the healthy development of the economy.Under the mode of debt management,agricultural enterprises have become one of the major risks in the development process.Agricultural listed companies are the locomotives of agricultural enterprises.Once a credit default occurs,it will directly affect the immediate interests of the company's investors,and it is related to the stability of China's economic development;agricultural listed companies,as important customers of China's commercial banks,have weaker resistance to risks.Its credit defaults have brought great losses and adverse effects to the social economy and commercial banks.Therefore,it is of great theoretical and practical significance to strengthen the research on the credit risk of China's agricultural listed companies and establish a credit risk assessment model for agricultural listed companies in China.At present,domestic and foreign scholars focus on the manufacturing industry,real estate industry,and energy industry for corporate credit risk research.Research on the credit risk of listed agricultural companies is relatively weak.As China's agricultural listed companies have the characteristics of relying on government support,small overall assets,unreasonable debt structure,and the phenomenon of diversified operation,it is necessary to establish an evaluation index system that reflects the characteristics of agricultural listed companies,and identify suitable characteristics of agricultural listed companies.Credit risk assessment model.This paper starts with the status quo of credit risk of listed agricultural companies,analyzes the characteristics and influencing factors of credit risk of listed agricultural companies,and builds a credit risk evaluation index system for listed agricultural companies from the perspective of macroeconomics and finance.According to the above index system,the catastrophe theory model is used to evaluate the credit risk of listed agricultural companies.The comparison of evaluation results with professional rating agencies shows that the accuracy is high.Finally,the countermeasures and suggestions to improve the credit risk of listed agricultural companies are put forward.To sum up,the credit risk assessment index system and model constructed in this study can effectively help commercial banks identify corporate credit risk during the credit approval process,reduce non-performing loan ratios,and at the same time provide an agricultural listed company with its own credit risk management system.Strong supporting support,finding weak links to improve their own credit risk management and improving their competitiveness.The innovation of this paper lies in the first consideration of the fact that the credit risk of listed agricultural companies is greatly affected by the macroeconomic environment,and the introduction of four macroeconomic indicators that are closely related to agricultural listed companies establishes risk indicators,profit indicators,development indicators and The four aspects of macroeconomic indicators are the credit risk assessment systems for agricultural listed companies in China.Second,through the introduction of the catastrophe theory model,the credit risk of listed agricultural companies will be evaluated,and their credit risk rankings will be ranked from high to low and six levels will be separated.This is more accurate and more accurate than traditional credit risk analysis methods.The credit risk assessment method avoids the limitations of traditional method evaluation.
Keywords/Search Tags:Agricultural Listed Companies, Credit Risk, Mutation Theory
PDF Full Text Request
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