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Research On Credit Risk Evaluation Of SME In Pig Breeding Industry Under The Model Of Supply Chain Finance

Posted on:2024-03-06Degree:MasterType:Thesis
Country:ChinaCandidate:X YangFull Text:PDF
GTID:2543307109480894Subject:Finance
Abstract/Summary:PDF Full Text Request
The emergence of supply chain finance effectively alleviates the financing dilemma faced by smes,which is difficult and expensive to obtain financing.Pig industry is an important industry related to the national economy and people’s livelihood,as well as an important pillar of agricultural economy.Focusing on pig breeding industry under the model of supply chain finance has greatly enriched the research object and content.However,due to the information asymmetry under the supply chain finance model,small and medium-sized enterprises have a large credit risk,how to evaluate and control the credit risk is particularly important.Therefore,it is of practical significance for this paper to evaluate the credit risk of small and medium-sized pig breeding enterprises under the supply chain finance model.Firstly,this paper defines the concepts of supply chain finance and supply chain finance credit risk,and details relevant theories of supply chain finance credit risk,including information asymmetry theory,credit risk management theory and transaction cost theory.Secondly,it expounds the development status of pig breeding industry and small and mediumsized enterprises.Thirdly,the credit risks between pig breeding enterprises,upstream feed enterprises and downstream slaughtering and processing enterprises are identified.Then according to the external and internal two aspects of credit risk factors in-depth analysis.Finally,the credit risk mechanism is analyzed.Based on theoretical research on credit risk identification and influencing factor analysis,this paper constructs the credit risk evaluation index system of pig breeding enterprises under the supply chain finance model from five aspects: qualification of financing enterprises,qualification of core enterprises,assets under financing,overall condition of supply chain and macroeconomic environment.The 2017-2021 data of 23 small and medium-sized pig breeding enterprises were selected as sample data,and downstream pig slaughtering,processing and consumption enterprises with business relations with them were selected as core enterprises on the main board.Through principal component analysis and Logistic regression,the credit risk evaluation model of small and medium-sized enterprises is constructed,and the credit risk of small and medium-sized pig breeding enterprises is measured,so that commercial banks can consider whether to provide financing to small and medium-sized enterprises.The empirical results show that the credit risk of an enterprise is mainly related to the qualification of the financing enterprise,the characteristics of accounts receivable and the solvency of the core enterprise,while the macroeconomic environment and the overall condition of the supply chain have a weak influence on the credit risk of an enterprise.Based on the research conclusions,this paper puts forward feasible suggestions from the perspectives of regulators,financial institutions and financing enterprises: Regulators should improve relevant laws and regulations and the market access mechanism of supply chain finance,and strengthen the market supervision of pig breeding industry;As fund providers,financial institutions should build an information sharing platform,improve the credit risk diversification mechanism,optimize credit management strategies,dynamically track cooperative objects,and build a comprehensive credit risk assessment system.As the capital demand side,financing enterprises should focus on core business,focus on improving their own strength level,strengthen information and assets management,establish risk control system,explore high-quality manufacturers,and establish stable cooperative relations.
Keywords/Search Tags:Supply chain finance, Small and medium-sized pig breeding enterprises, Credit risk evaluation
PDF Full Text Request
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