stock evaluation under mixed uncertainties using robust dea model

Data envelopment analysis (dea) is one of the popular and applicable techniques for assessing and ranking the stocks or other financial assets. it should be noted that in the financial markets, most of the times, the inputs and outputs of dea models are accompanied by uncertainty. accordingly, in this paper, a novel robust data envelopment analysis (rdea) model, which is capable to be used in the presence of discrete and continuous uncertainties, is presented. the proposed novel rdea model in the paper was implemented in a real case study of tehran stock exchange (tse). the results showed that the proposed new rdea model was effective in the assessment and ranking of the stocks under different scenarios with interval values.