Interval network data envelopment analysis model for classification of investment companies in the presence of uncertain data

The main purpose of this paper is to propose an approach for performance measurement, classification and ranking the investment companies (ICs) by considering internal structure and uncertainty. In order to reach this goal, the interval network data envelopment analysis (INDEA) models are extended. This model is capable to model two-stage efficiency with intermediate measures in a single implementation. Additionally, the proposed INDEA models of the paper are implemented for a real case study of ICs in Tehran Stock Exchange (TSE) and Illustrative results show that proposed INDEA models are effective in order to classification and ranking of investment companies.