دغدغه های مدل سازی ریسک و اولویت های بازده در ارزیابی عملکرد: نرم افزار برای بازارهای جهانی سرمایه

A technique used to assess relative performance in a multiple input–output framework is data envelopment analysis (DEA). In basic DEA models, an entity may show its best performance by selecting input and output factor weights different from those selected by the other entities in the sample. Hence, when using basic DEA models, divergence of weighting schemes across the assessed entities cannot be ruled out.Weighting imbalance is another issue encountered in the application of basic DEA models. The assignment of an extremely low or zero weight to an input or an output factor implies that it is disregarded in performance appraisal.We appraise equity market performance using the Assurance Region Global (ARG)–DEA model where weighting divergence may be eliminated while controlling weighting imbalance. We show that risk concerns and returns preferences can be modeled in the ARG–DEA model through the bounds on the virtual input and virtual output ratios. Different combinations of risk concerns and returns preferences assess equity market performance under different risk-adjusted return scenarios and thereby allow sensitivity analysis of performance.