| Financial machinery industry has strong correlation with financial industry. It can improve the production and operation efficiency of the financial industry. Up to now, there have been more than 30 finance machinery enterprises listing in the A-share market. These enterprises play an important role in adjusting traditional industrial structure, improving the techniques and founding the brand.DEA method was put forward by operational research expert Farell. This method constructs the production frontier through linear regression. This method calls the decision unit which fall inside production frontier technical efficiency decision unit. The value of efficiency decision unit is 1, and calls the decision unit which fall outside production frontier technical no-efficiency decision unit. The value of technical no-efficiency decision unit is between 0 and 1. We can calculate the technical efficiency of these enterprises and its rank through DEA method, and give specific learning and benchmarking by solve the model’s equations.Malmquist index, known as the total factor productivity, was put forward by Swedish economists and statistician in 1953 and combined with DEA by Caves, Fare etc. to calculate the change of technical efficiency of different period of enterprises.Our research is based on 36 enterprises which are belongs to financial machinery industry and choses "number of employees" and "total assets" as the invest index, choses "operation revenue" and "total value" as the production index. And we use C2R model and BCC model and Malmquist index decomposition to research the technical efficiency and total factors productive of enterprises which have been listed in A-share market. We also give some suggestions to improve the production efficiency based on the results.This article is divided into six chapters, the specific contents are summarized as follows:The first chapter is the preface. This chapter introduces the research background, research purpose and significance, research methods and ideas, as well as the innovation points and deficiencies of this paper.The second chapter is the theoretical basis and literature review. In this chapter, the concept of efficiency and the evaluation are briefly described. introducing the DEA method, and then reviews the literature on the evaluation of the efficiency of DEA and the relevant literature of China’s financial machinery industry.The third chapter is the DEA method used in this paper and the relevant model to do a detailed introduction. This chapter introduces the C2R model, BCC model and DEA-Malmquist model of the geometric meaning, and gives the model of the expression.The fourth chapter is China’s financial machinery industry sample and input-output index selection. This chapter introduces the reasons for choosing listing Corporation in China financial machinery industry as the research sample, and puts forward the index system of their own.The fifth chapter is the empirical research on the technical efficiency and total factor productivity of listing Corporation in China’s financial machinery industry. The horizontal and the vertical analysis are given based on the modles.The sixth chapter is the summary and suggestions. This chapter summarizes the research results of the fifth chapter, and puts forward suggestions to improve the technical efficiency and total factor production of China’s listed financial machinery companies.Cross-sectional study found that:pure technical efficiency of Chinese overall financial machinery industry is the main reason for the overall impact of technical efficiency. The company which pure technical efficiency value is less than 0.7, still has 14, accounting for 38.9% of the total sample between 2012 and 2014. It indicated that a large part of the company’s technical and management level is relatively low. There are 17 companies at the stage of increasing returns to scale, accounting for 47.2% of the total samples, indicating that a large part of the financial machinery industry is small scale, and have no economies of scale.Longitudinal study found that:company’s overall average TFP of China’s financial machinery industry rose from 0.878 to 1.140 between 2012 and 2014.The overall average technical index enhanced from 0.996 to 1.004, technological overall average index increased from 0.881 to 1.135, technological change during the three years are the main factors causing changes in total factor productivity.Based on the results of the study, the paper put forward five suggestions: Firstly, to strengthen human resources management and improve overall quality of staff; secondly, Inefficient enterprises should learn from efficient enterprises; Thirdly, optimize the allocation of resources to improve resource utilization rate; fourthly, increase efforts to improve the technical TFP contribution rate; fifthly, the Government should make relevant policies to promote the healthy development of the industry. |
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