Research On The "Industry Risk Factors" In The Index System Of Machinery Enterprise Credit Rating

Machinery industry is a basic industry in China, and it plays a very importantrole in our national economic development, so it really makes sense for machineryindustrial enterprises to get credit rating. But now, the credit rating index system failsto reasonably reflect the impacts of external industry risks and the differences ofexternal industry risk among the sub-sectors of machinery industry. Therefore, toimprove the system is of great significance.From the view of independent third-party credit rating agencies, the paperimproves the existing credit rating index system for our country’s machineryindustry association at this stage. By observing industrial operating data in China’sEconomic Website, the viewpoint that the index external industry risk should beadded to the index system is proposed in this paper. Using a method of combiningqualitative and quantitative indexes, external industry risk is subdivided into twoparts,"the industrial warning index" and "industrial policy". So it distinguishes theimpact of external industrial risks for the machinery industry. Then, after calculatingthe data’s reliability and validity by the software of SPSS, the six first-class indexeswhich include external industry risks, comprehensive staff quality, financialstatus, management level, competitive ability and social credit records arearranged together to form the Discrimination Matrix A, while the two second-classindexes which include external industry risk and industrial policy are gottogether to form the discrimination matrix A’. So using the Analytic HierarchyProcess, we can calculate the weight of each index in the primary and secondaryindexes by operating the order in the software of Matlab.In addition, accounting to the questionnaires of experts, the paper get the fuzzyevaluation matrix of fuzzy judgment factors for the industrial risk in the sub-sectorsof mechanical industry, which are related with three evaluation set----low level,medium level and high level of risk. Then figure out the membership degree relatedwith the above three risk levels of the13sub-industries, using the max-mincomposition operation of Fuzzy Comprehensive Method. Finally, according to therisk weight of different level of risk determined by the secondary expertquestionnaire, the paper multiplies them with the level’s membership degrees to getrespective external risk weight in13sub-industries, so that adjusts the averageexternal risk weight calculated by Analytic Hierarchy Process into13different indexweights of external industry risk to reflect the different impact of external industry risk on different sub-sectors of machinery industry. At the same time, the amounts ofweight change of the index external industry risk are equally allocated to theweights of other first-class indexes originally figured out by the Analytic HierarchyProcess, thus it designs a weight distribution system that13sub-industries haverespective different weight allocating system.This refinement improves the existing credit rating index system, and gets theinternal and external factors, which affect the operation of an enterprise, into anorganic combination. So in this system, credit rating can reflect the true state of theenterprise.