scholarly journals Selecting the Best of Portfolio Using OWA Operator Weights in Cross Efficiency-Evaluation

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Masoud Sanei ◽  
Shokoofeh Banihashemi
Keyword(s):  
Evaluation Process ◽  
Efficiency Score ◽  
Weighted Averaging ◽  
Data Envelopment ◽  
Efficiency Evaluation ◽  
Owa Operator ◽  
Public And Private ◽  
The Cross ◽  
Asset Selection ◽  
Public And Private Sector

The present study is an attempt toward evaluating the performance of portfolios and asset selection using cross-efficiency evaluation. Cross-efficiency evaluation is an effective way of ranking decision making units (DMUs) in data envelopment analysis (DEA). The most widely used approach is to evaluate the efficiencies in each row or column in the cross-efficiency matrix with equal weights into an average cross-efficiency score for each DMU and consider it as the overall performance measurement of the DMU. This paper focuses on the evaluation process of the efficiencies in the cross-efficiency matrix and proposes the use of ordered weighted averaging (OWA) operator weights for cross-efficiency evaluation. The OWA operator weights are generated by the minimax disparity approach and allow the decision maker (DM) or investor to select the best assets that are characterized by an orness degree. The problem consists of choosing an optimal set of assets in order to minimize the risk and maximize return. This method is illustrated by application in mutual funds and weights are obtained via OWA operator 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.

scholarly journals A Cross-Efficiency Evaluation Method Based on Evaluation Criteria Balanced on Interval Weights

Symmetry ◽  
2019 ◽  
Vol 11 (12) ◽  
pp. 1503 ◽  
Author(s):  
Hailiu Shi ◽  
Yingming Wang ◽  
Xiaoming Zhang
Keyword(s):  
Evaluation Method ◽  
Evaluation Criteria ◽  
Evaluation Criterion ◽  
Peer Evaluation ◽  
Data Envelopment ◽  
Efficiency Evaluation ◽  
Common Set Of Weights ◽  
The Cross ◽  
The Difference ◽  
Evaluation Approaches

Cross-efficiency evaluation approaches and common set of weights (CSW) approaches have long been suggested as two of the more important and effective methods for the ranking of decision making units (DMUs) in data envelopment analysis (DEA). The former emphasizes the flexibility of evaluation and its weights are asymmetric, while the latter focuses on the standardization of evaluation and its weights are symmetrical. As a compromise between these two approaches, this paper proposes a cross-efficiency evaluation method that is based on two types of flexible evaluation criteria balanced on interval weights. The evaluation criteria can be regarded as macro policy—or means of regulation—according to the industry’s current situation. Unlike current cross-efficiency evaluation methods, which tend to choose the set of weights for peer evaluation based on certain preferences, the cross-efficiency evaluation method based on evaluation criterion determines one set of input and output weights for each DMU. This is done by minimizing the difference between the weights of the DMU and the evaluation criteria, thus ensuring that the cross-evaluation of all DMUs for evaluating peers is as consistent as possible. This method also eliminates prejudice and arbitrariness from peer evaluations. As a result, the proposed cross-efficiency evaluation method not only looks for non-zero weights, but also ranks efficient DMUs completely. The proposed DEA model can be further extended to seek a common set of weights for all DMUs. Numerical examples are provided to illustrate the applications of the cross-efficiency evaluation method based on evaluation criterion in DEA ranking.

Efficiency evaluation in data envelopment analysis using strong defining hyperplanes

OR Spectrum ◽  
2021 ◽  
Author(s):  
Andreas Dellnitz ◽  
Elmar Reucher ◽  
Andreas Kleine
Keyword(s):  
Data Envelopment Analysis ◽  
Efficiency Analysis ◽  
Efficiency Score ◽  
Second Step ◽  
Data Envelopment ◽  
Efficiency Evaluation ◽  
Real World Data ◽  
Computational Burden ◽  
Numerical Investigations ◽  
Subjective Judgement

AbstractCross-efficiency analysis in DEA generally uses a two-stage procedure: first, optimize a DMU’s individual efficiency score; next, push the efficiencies of all DMUs in a desired direction. In order to reduce subjective judgement in the second step, we provide a cross-efficiency method using only strong defining hyperplanes of the underlying technology. We develop a new DEA-based algorithm, and prove its finiteness and correctness. To reduce the computational burden, we show how one can combine the procedure with a known beneath-and-beyond procedure. Numerical investigations—comprising real-world data—demonstrate the superiority of the new method.

scholarly journals Gap Minimization for Peer-Evaluation in DEA Cross-Efficiency

2014 ◽  
Vol 2014 ◽  
pp. 1-7
Author(s):  
Anrong Yang ◽  
Zigang Zhang ◽  
Yishi Zhang ◽  
Dunliang Chen
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Peer Evaluation ◽  
Data Envelopment ◽  
Efficiency Evaluation ◽  
Self Evaluation ◽  
Criterion A ◽  
The Cross ◽  
Similarity Distance ◽  
Decision Making Units

Cross-efficiency evaluation is an effective and widely used method for ranking decision making units (DMUs) in data envelopment analysis (DEA). Gap minimization criterion is introduced in aggressive and benevolent cross-efficiency methods to avoid possible extreme efficiency from peer-evaluation and to get equitable results. On the basis of this criterion, a weighted cross-efficiency method with similarity distance that, respectively, considers the aggressive and the benevolent formulations is proposed to determine cross-efficiency. The weights of the cross-evaluation determined by this method are positively influenced by self-evaluation and thus are propitious to resolving conflict. Numerical demonstration reveals the feasibility of the proposed method.

scholarly journals Game Performance Versus Competitive Performance in the World Championship of Handball 2011

2013 ◽  
Vol 36 (1) ◽  
pp. 137-147 ◽  
Author(s):  
Óscar Gutiérrez ◽  
José L. Ruiz
Keyword(s):  
Data Envelopment ◽  
Efficiency Evaluation ◽  
Competitive Performance ◽  
World Championship ◽  
The World ◽  
Overall Performance ◽  
The Cross ◽  
Using Data ◽  
World Champion ◽  
Game Performance

This article assesses the game performance of the teams participating in the Men’s World Championship of Handball of 2011 by using Data Envelopment Analysis (DEA) and the cross-efficiency evaluation. DEA uses Linear Programming to yield a measure of the overall performance of the game of particular teams, and allows us to identify relative strengths and weaknesses by means of benchmarking analysis. The cross-efficiency evaluation provides a peerappraisal of the teams with different patterns of game, and makes it possible to rank them. Comparisons between this ranking and the final classification in the championship provide an insight into the game performance of the teams versus their competitive performance. We highlight the fact that France, which is the world champion, is also identified as an “all-round” performer in our game performance assessment.

scholarly journals Comparative Efficiency Analysis of Public and Private Colleges of Multan District: Data Envelope Approach Analysis

2016 ◽  
Vol 2 (1) ◽  
pp. 69-80
Author(s):  
Sumaira Batool ◽  
Imran Abbs ◽  
Fatima Farooq ◽  
Ishtiaq Ahmad
Keyword(s):  
Private Colleges ◽  
Public Colleges ◽  
Scale Efficiency ◽  
Data Envelopment ◽  
Public And Private ◽  
Non Profit ◽  
Comparative Efficiency ◽  
Using Data ◽  
Public And Private Sector ◽  
Dominant Paradigm

The purpose of this paper is to evaluate the efficiency of public and private sector colleges in Multan district. We use output oriented data envelopment analysis to measure technical and scale efficiency of a sample of 40 colleges, using data for the year 2014. DEA, which is the most popular technique used to measure the relative efficiency of non-profit organizations due to the absence of prices or relative values of educational outputs, is  employed to compare efficiency of both types of colleges. Moreover, it can handle multiple inputs and outputs with great ease. As public and private colleges are working under similar environmental conditions, we have used a single frontier, incorporating four educational inputs and four outputs. The results of the data demonstrate that private colleges lag behind public colleges in terms of CRS and VRS technical efficiency scores and scale efficiency scores. Our study of colleges is in contrast with the dominant paradigm that private colleges outperform the state-run colleges.  

Gangless cross-evaluation in DEA: an application to stock selection

2019 ◽  
Vol 53 (2) ◽  
pp. 645-655 ◽  
Author(s):  
Gholam R. Amin ◽  
Amar Oukil
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Stock Market ◽  
Data Envelopment ◽  
Efficiency Evaluation ◽  
Stock Selection ◽  
Evaluation Approach ◽  
The Cross ◽  
The Impact ◽  
Decision Making Units

This paper discusses the impact of ganging decision making units (DMUs) on the cross-efficiency evaluation in data envelopment analysis (DEA). A group of DMUs are said to be ganging-together if the minimum and the maximum cross-efficiency scores they give to all other DMUs are identical. This study demonstrates that the ganging phenomenon can significantly influence the cross-efficiency evaluation in favour of some DMUs. To overcome this shortcoming, we propose a gangless cross-efficiency evaluation approach. The suggested method reduces the effect of ganging and generates a more diversified list of top performing units. An application to the Tehran stock market is used to show the benefits of gangless cross-evaluation.

scholarly journals Hybrid cross-efficiency approach based on Ideal and Anti-Ideal points and the CRITIC method for ranking decision-making units: A case study on ranking the methods of rice weevil disinfestation

2021 ◽  
Vol 10 (3) ◽  
pp. 375-392
Author(s):  
Pariwat Nasawat ◽  
Sukangkana Talangkun ◽  
Sirawadee Arunyanart ◽  
Narong Wichapa
Keyword(s):  
Decision Making ◽  
Evaluation Method ◽  
Data Envelopment ◽  
Efficiency Evaluation ◽  
New Approach ◽  
Comprehensive Method ◽  
Ideal Points ◽  
The Cross ◽  
Decision Making Units

A new approach is applied in the process of measuring the efficiency of decision-making units (DMUs) through the cross-efficiency evaluation method. Ideal and Anti-Ideal models are generated to form a comprehensive method based on the cross-efficiency evaluation method. The two models are formulated and combined to the Data Envelopment Analysis using the CRITIC method. In a comparative analysis based on three numerical examples, the proposed approach can lead to achieving a more reliable result than one based on an individual method.

Integration of Data Envelopment Analysis and Clustering Methods

2020 ◽  
Vol 19 (01) ◽  
pp. 2040006
Author(s):  
Hassan Najadat ◽  
Ahmad Alaiad ◽  
Sanaa Abu Alasal ◽  
Ghadeer Anwar Mrayyan ◽  
Izzat Alsmadi
Keyword(s):  
Data Envelopment Analysis ◽  
Evaluation Process ◽  
Public Hospitals ◽  
Data Envelopment ◽  
Efficiency Evaluation ◽  
Clustering Methods ◽  
Multiple Input ◽  
Mining Methods ◽  
Crs Model ◽  
Decision Making Units

Data Envelopment Analysis (DEA) has been applied creatively in various study domains to compare and evaluate different Decision Making Units (DMUs) based on multiple input–output attributes. In this paper, the performance of Jordanian public hospitals is assessed via a methodology combining DEA with data mining methods, specifically, clustering. Initially, inputs of inefficient hospitals were altered to check for waste in the allocated resources. Then, the number of inputs–outputs was manipulated to test if the number is strongly influencing the productivity of the DMUs. The number of DMUs used was 27 public hospitals and the applicable efficiency measurements used were constant return to scale (CRS) and variable return to scale (VRS) through the DEAP software. Experiments showed that the efficiency of a hospital might be more meaningfully assessed if it is compared with a group of hospitals that are similar in some factors. More specifically, results of applying the CRS model proved that 77% of the hospitals were efficient. Additionally, we found that the inefficiencies of some hospitals are linked to weak resource utilization. It is concluded that number of inputs–outputs inserted in the efficiency evaluation process impacts the resulted values.

scholarly journals Efficiency Analysis of Public and Private Sector Schools of Multan District: A Non-Parametric Approach

2015 ◽  
Vol 1 (1) ◽  
pp. 45-56
Author(s):  
Sumaira Batool ◽  
Fatima Farooq ◽  
Imran Abbas ◽  
Muhammad Abbas
Keyword(s):  
Public Schools ◽  
Private Sector ◽  
Private Schools ◽  
Scale Efficiency ◽  
Data Envelopment ◽  
Public And Private ◽  
Non Profit ◽  
Using Data ◽  
Public And Private Sector ◽  
Dominant Paradigm

The purpose of this paper is to evaluate the efficiency of public and private sector secondary and higher secondary schools in Multan district. We use output oriented data envelopment analysis to measure technical and scale efficiency of a sample of 100 public and private sector schools, using data for the year 2014. DEA is employed to compare efficiency of both types of schools because it is the most popular technique used to measure the relative efficiency of non-profit organizations due to the absence of prices or relative values of educational outputs. Moreover, it can handle multiple inputs and outputs with great ease. As public and private schools are working under similar environmental conditions, we have used a single frontier, incorporating four educational inputs and four outputs. The results of the data demonstrate that public schools lag behind private schools in terms of CRS and VRS technical efficiency scores and scale efficiency scores. Our study of schools validates the dominant paradigm that private schools outperform the state-run institutes.

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