| As the pillar industry of China’s economy,manufacturing industry affects the development of China’s national economy.From the global financial crisis in 2008 to 2012,there was a surplus of products in manufacturing companies,and the inventory backlog gradually appeared.The company’s revenue decreased and its profits gradually deteriorated.By 2012,the production capacity has gradually declined.The company is facing severe inventory pressure,financial pressure and pressure for innovation and upgrading.Coupled with the increasing labor costs,China’s manufacturing industry is facing challenges.Listed companies in manufacturing have the highest loan amount in commercial banks and are important customers of commercial banks.The profitability of manufacturing companies will deteriorate,which will likely result in credit defaults and ultimately affect the entire economic environment.It can be seen that the analysis of the profitability and credit risk of listed companies in manufacturing has important economic significance.This paper first introduces the background and significance of the research.By summarizing the breakthroughs in credit risk research at home and abroad,the research plan of the thesis is planned.Then it expounds the theory of credit risk.On the basis of predecessors,it analyzes various credit scoring methods and models,and finally establishes the DEA-Tobit model to objectively and effectively evaluate manufacturing credit risk.In this paper,the main component index is used as the output index,and the index system is used comprehensively to improve the accuracy.In the model selection,the BCC model and the cross-efficiency evaluation method are comprehensively selected,and the credit efficiency is studied from the two perspectives of self-assessment and evaluation.Increase the accuracy of the results.In the empirical study,according to the securities industry classification criteria,120 listed listed companies from 2013 to 2017 were selected as sample companies,including 40 textile industry,40 resource processing industry and 40 machinery and electronics manufacturing.In the selection of input and output indicators,the principal component analysis was used to measure the six main components of the selected 19 indicators as output indicators,and the asset-liability ratio and the cost of sales rate were used as input indicators to measure credit risk.Firstly,the DEA-BCC model is used to measure the credit scores of 120 samples.Based on the results of the model,the credit risk analysis is carried out from the perspectives of comprehensive credit efficiency,pure technical efficiency and scale efficiency,and combined with the situation of different sub-industries.It shows that the comprehensive efficiency and pure technical efficiency value of the three industries are low,and the scale efficiency is high,and the credit risk of the resource processing industry is the lowest.Further analysis of the shortage and redundancy of each input and output indicators,to understand the reasons for the invalidity of each company’s credit efficiency and improvement measures.Then,using the cross-efficiency evaluation method,120 companies were credit scored,and relatively objective credit scores were obtained and sorted.Finally,this paper comprehensively analyzes the factors that may affect credit risk from the macro level and the micro level.Finally,10 variables are selected as explanatory variables,cross efficiency results are used as explanatory variables,and the Tobit model is constructed to verify the relationship between these factors and manufacturing credit efficiency.The relationship shows that the credit efficiency of listed companies in the manufacturing industry is negatively affected by per capita GDP,asset-liability ratio and operating cost ratio,and is positively affected by the weighted average interest rate of loans,employee reserves and equity concentration.According to the full text of the research,this paper summarizes and proposes countermeasures at the end of the article. |
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