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

This paper proposes the interval network data envelopment analysis (INDEA) approach under constant return to scale (CRS) and variable return to scale (VRS) assumptions which can assess the performance of investment companies (ICs) by considering uncertainty and internal structure. The presented approach of the paper is capable to model two-stage efficiency with intermediate measures in a single implementation. Finally, a real-life case study from Tehran stock exchange (TSE) is implemented to demonstrate applicability and exhibit the efficiency and effectiveness of the presented INDEA approach for performance measurement, ranking and classification of ICs in the presence of uncertain data.