Robust Data Envelopment Analysis with Hybrid Uncertainty Approaches and its Applications in Stock Performance Measurement

Performance measurement and ranking the stocks is one of the most important problems in financial markets. Data envelopment analysis (DEA) is one of the tools that can be used to reach this goal. This approach is one of the non-parametric performance measurement techniques in order to estimates the relative efficiency of sets of decision making units (DMUs) using inputs and outputs. With respect to one of the most important features of financial markets is their uncertainty, in this paper, the novel robustDEA Models that are capable to use in the presence of discrete and continuous uncertainties are proposed. Finally, the proposed novel robust DEA (NRDE) of the paper is implemented in a real case study of Tehran Stock Exchange (TSE) and illustrative results show that proposed NRDEA model is effective.

Keywords:

Data Envelopment Analysis (DEA) , Robust Data Envelopment Analysis (RDEA) , Robust Optimization , Convex Uncertainty Set , Scenario Based , Stock Performance Measurement , Tehran Stock Exchange (TSE)