data envelopment analysis (dea) is a method to evaluate the relative efficiency of decision making units (dmus). in this method, the issue has always been to determine a set of weights for each dmu which often caused many problems. since the dea models also have the multi-objective linear programming (molp) problems nature, a rational relationship can be established between molp and dea problem...

the present study is an attempt toward evaluating the performance of portfolios and asset selectionusing cross-efficiency evaluation. cross-efficiency evaluation is an effective way of ranking decisionmaking units (dmus) in data envelopment analysis (dea). conventional dea models assume nonnegativevalues for inputs and outputs. however, we know that unlike return and skewness, varianceis the on...

This paper empirically examines multifactor asset pricing models for the returns and expected returns on eighteen national equity markets. The factors are chosen to measure global economic risks. Although previous studies do not reject the unconditional mean variance efficiency of a world market portfolio, our evidence indicates that the tests are low in power, and the world market betas do not...

This paper develops a test for Pareto-elliciency in the local public sector using the analytical result that aggregate property value is maximized at the public output level which satisfies the Samuelson condition for efficiency. By using cross-section data, it is possible to deduce whether a representative community provides its public goods in a property-value-maximizing (and hence efficient)...

Cross efficiency evaluation was developed as an extension of DEA. But the traditional DEA models usually have alternative optimal solutions and, as a result, cross efficiency scores may not be unique. It is recommended that without changing the DEA efficiency scores, the secondary goal should be introduced for optimization of the inputs/outputs weights.  Several reports evaluated the perfo...

A fundamental weakness of the Data Envelopment Analysis (DEA) is its weak discrimination in cases when a small number of decision making units are compared. Therefore, in such cases the basic DEA model (optimistic and pessimistic) is used in combination with other methods or additional constraints are added to the model. In this paper, the cross-efficiency method was combined with a self-rankin...

The fundamental weakness of a Data Envelopment Analysis (DEA) is its weak discrimination in cases when small number of Decision Making Units (DMU) have been compared. Therefore in such cases the basic DEA model is used in combination with other methods or additional constraints are added to the model. In this paper, the Cross-efficiency method has been combined with the Potential Method (PM).