The main goal of this paper is to propose a new approach for efficiency measurement and ranking of stocks. Data envelopment analysis (DEA) is one of the popular and applicable techniques that can be used to reach this goal. However, there are always concerns about negative data and uncertainty in financial markets. Since the classical DEA models cannot deal with negative and imprecise values, in this paper, possibilistic range directional measure (PRDM) model is proposed to measure the efficiencies of stocks in the presence of negative data and uncertainty with input/output parameters. Using the data from insurance industry, this model is also implemented for a real case study of Tehran stock exchange (TSE) in order to analyse the performance of the proposed method.
