2014
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3
3
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Portfolio Performance Evaluation in a Modified MeanVarianceSkewness Framework with Negative Data
2
2
The present study is an attempt toward evaluating the performance of portfolios using meanvarianceskewness model with negative data. Meanvariance nonlinear framework and meanvarianceskewness non linear framework had been proposed based on Data Envelopment Analysis, which the variance of the assets had been used as an input to the DEA and expected return and skewness were the output. Conventional DEA models assume nonnegative values for inputs and outputs. However, we know that unlike return and skewness, variance is the only variable in the model that takes nonnegative values. This paper focuses on the evaluation process of the portfolios in a meanvarianceskewness model with negative data. The problem consists of choosing an optimal set of assets in order to minimize the risk and maximize return and positive skewness. This method is illustrated by application in Iranian stock companies and extremely efficiencies are obtained via meanvarianceskewness nonlinear framework with negative data for making the best portfolio. The finding could be used for constructing the best portfolio in stock companies, in various finance organization and public and private sector companies.
1

409
421


Sh.
Banihashemi
Department of Mathematics and Computer Science, Faculty of Econimics Allameh Tabataba’i University.
Department of Mathematics and Computer
Iran


M.
Sanei
Department of Applied Mathematics, Islamic Azad University, Central Tehran Branch, Tehran, Iran.
Department of Applied Mathematics, Islamic
Iran


M.
Azizi
Department of Mathematics and Computer Science, Faculty of Econimics Allameh Tabataba’i University.
Department of Mathematics and Computer
Iran
portfolio
Data envelopment analysis (DEA)
skewness
Efficiency
Negative data
Resource Allocation through Contextdependent data envelopment analysis
2
2
System designs, optimizing resource allocation to organization units, is still being considered as a complicated problem especially when there are multiple inputs and outputs related to a unit. The algorithm presented here will divide the frontiers obtained with DEA. In this way, we investigate a new approach for resource allocation.
1

423
429


N.
Ebrahimkhani Ghazi
(a) Department of Mathematics, Science and Research Branch, Islamic Azad University, Tehran, Iran.
(a) Department of Mathematics, Science and
Iran


M.
Ahadzadeh Namin
Department of Mathematics, Shahre –Qods Branch, Islamic Azad University, Tehran, Iran.
Department of Mathematics, Shahre –Qods
Iran
A Modified Directional Distance Formulation of DEA with Malmquist Index to Assess Bankruptcy
2
2
Bankruptcy in the same amount of time and history is very rampant and therefore the vision of the future can be prevented. Using data envelopment analysis (DEA) and malmquist index can precise evaluating of the performances of many different kinds of decision making units (DMU) such as hospitals, universities, business firms, etc. In this paper, we will modify directional distance formulation of DEA to assess bankruptcy with using malmquist index. This method is the most general nonradial directional distance model. The method measures worst relative efficiency within the interval of zero to one for various period times. Model locates worst performing DMUs and determines an inefficient frontier model simultaneously with decrease of output and increase of input. By using malmquist index we measure the productivity for various years .This study introduces a precise and comprehensive bankruptcy measure that could be used as an early warning system for bankruptcy assessment.
1

431
446


E.
Mirzaie
Department of Mathematics, Faculty of Science, Islamic Azad University, South Tehran Branch, Tehran, Iran
Department of Mathematics, Faculty of Science,
Iran


N.
Malekmohammadi
Department of Mathematics, Faculty of Science, Islamic Azad University, South Tehran Branch, Tehran, Iran
Department of Mathematics, Faculty of Science,
Iran
Data Envelopment Analysis
Directional distance function
Bankruptcy
Productivity
Malmquist
Malmquist Productivity Index with Dynamic Network Structure
2
2
Data envelopment analysis (DEA) measures the relative efficiency of decision making units (DMUs) with multiple inputs and multiple outputs. DEAbased Malmquist productivity index measures the productivity change over time. We propose a dynamic DEA model involving network structure in each period within the framework a DEA. We have previously published the network DEA (NDEA) and the dynamic DEA (DDEA) models separately. Hence, this article is a composite of these two models. Vertically, we deal with multiple divisions connected by links of network structure within each period and, horizontally, we combine the network structure by means of caryover activities between succeeding periods. We also introduce dynamic Malmquist index by which we can compare divisional performances over time
1

447
460


S.
KeikhaJavan
Department of Mathematics, Zabol Branch, Islamic Azad University, Zabol, Iran.
Department of Mathematics, Zabol Branch,
Iran


M.
RostamyMalkhalifeh
Department of Mathematics, Science and Research Branch, Islamic Azad University, Tehran, Iran.
Department of Mathematics, Science and Research
Iran
Malmquist productivity index DEA network overall efficiency
An Extension of Enhanced Russell Measure to deal with Interval Scale Data in DEA
2
2
Data Envelopment Analysis (DEA) models with interval inputs and outputs have been rarely discussed in DEA literature. This paper, using the enhanced Russell measurement proposes an extended model which permits the presence of interval scale variables which can take both negative and positive values. The model is compared with most wellknown DEA models of which include the CCR model, the BCC model and the additive model. An empirical data set is used to illustrate the model.
1

461
472


M.
Mohammadpour
Department of Mathematics, Boukan Branch, Islamic Azad University, Boukan, Iran.
Department of Mathematics, Boukan Branch,
Iran
Data Envelopment Analysis
Decision making
Translation Invariance
interval scale
ratio scale
A Neural Network Model to Solve DEA Problems
2
2
The paper deals with Data Envelopment Analysis (DEA) and Artificial Neural Network (ANN). We believe that solving for the DEA efﬁciency measure, simultaneously with neural network model, provides a promising rich approach to optimal solution. In this paper, a new neural network model is used to estimate the inefficiency of DMUs in large datasets.
1

473
479


S.
Dolatabadi
Department of Mathematics, Islamic Azad University, Mashhad Branch, Mashhad, Iran
Department of Mathematics, Islamic Azad University
Iran


H.
Rezai Zhiani
Department of Mathematics, Islamic Azad University, Mashhad Branch, Mashhad, Iran
Department of Mathematics, Islamic Azad University
Iran
Data envelopment analysis (DEA)
Neural Networks