Performance analysis of non-banking finance companies using two-stage data envelopment analysis

This paper analyses the performance of non-banking finance companies (NBFC) in the Indian context using data envelopment analysis (DEA). The underlying objective of this study is to fill the void in the domain of NBFC, although a lot of research has been done on the banking industry in the context of the application of DEA, but none on NBFCs. The paper takes the panel data of the last 5 years (2014–2018) to calculate super-efficiencies in the first stage and then regresses the same on exogenous factors in stage-2. Descriptive statistics are used to estimate the efficiency by carrying out the calculations using both the traditional models (OTE, PTE and SE) and super-efficiency model. A comparison is made by categorizing NBFC’s based on the size of total assets and using non-parametric statistic tests to find whether the efficiency scores are significantly different across different categories. The second stage DEA analysis uses Tobit regression to find the exogenous factors which affect the model significantly. Based on traditional models, the total number of efficient DMUs are 8 out of 43 while there are 15 after considering the super-efficiency algorithm. Malmquist Indices are used to study the productivity indices of NBFCs over the last 5 years, and it gives us a maximum productivity growth of 8.53%. It was noticed that there is a significant difference in the mean efficiency values of different sized NBFC’s which can be explained by the lack of standardization in the NBFC domain and the few companies which are listed on the stock market. The managers should not consider ROE as a significant indicator of efficiency and should instead focus on aspects such as ROA and income diversity. Through the Malmquist analysis, the managers can break down the productivity change into technical and efficiency shifts for further investigation.