Introduction into the Quantitative Analysis and Evaluation of Enterprises
Huang/Luo/Xu/Zhou (2018) refer to the quantitative analysis and evaluation method of the banking industry, combined with the data characteristics of the financial company’s industry. The scientists carry out the quantitative analysis of the efficiency of 79 Chinese enterprise group finance companies from 2011 to 2016 through the data envelopment analysis (DEA) model and the Malmquist index model (MIM). Their results are as follows: From the static point of view (based on the DEA model), the overall efficiency of the chinese financial companies is low. And the efficiency is less affected by scale efficiency than that of pure technical efficiency. From the dynamic point of view (based on the MIM), the overall efficiency of financial companies has been slightly improved and the efficiency is easily influenced by the change of scale efficiency. (cf. Huang et al. 2018, p.1).
Underlying Research Methodology
Due to the similarity in main business activities, the scholars’ research methods of the banking efficiency is implemented for the research methods of industry efficiency of financial companies entailing two main factors; DEA and stochastic frontier approach (SFA). The SFA is a parametric method concerned with determining the unknown parameters in the frontier cost function. DEA is non-parametric method concerned with evaluating the relative efficiency of entities with the same input factors and the same output with the same function. The evaluated entity becomes the Decision Making Unit. The DEA method can obtain the quantitative index of each Decision Making Unit (DMU) comprehensive efficiency, and then classify each DMU according to this. Compared to SFA, DEA method can handle multiple input and output items simultaneously, does not required sample size, more flexible in dealing with data. (cf. Huang et al. 2018, p.6).
Huang, Yanni ; Luo, Sumei ; Xu, Guohu ; Zhou, Guanyou (2018): Quantitative Analysis and Evaluation of Enterprise. Group Financial Company Efficiency in China. University Shanghai et al.