scholarly journals Assessing the relative efficiency of commercial banks in the Republic of North Macedonia: DEA window analysis

2020 ◽  
Vol 11 (2) ◽  
pp. 217-227
Author(s):  
Violeta Cvetkoska ◽  
Katarina Fotova Cikovic
Keyword(s):  
Commercial Banks ◽  
Relative Efficiency ◽  
Banking Sector ◽  
Efficiency Score ◽  
Data Envelopment ◽  
Window Analysis ◽  
The Republic ◽  
Dea Window Analysis ◽  
Decision Making Units ◽  
Selection Of

The aim of this paper is to assess the relative efficiency of commercial banks in one developing country, i.e. the Republic of North Macedonia by using the data envelopment analysis (DEA) technique-window analysis. The selection of inputs and outputs plays a key role when applying DEA for assessing the efficiency of decision-making units (DMUs). In the conducted research two inputs and two outputs have been selected. The sample consists of 14 commercial banks and the period that is being observed is an eleven year span from 2007 to 2017. According to the average efficiency score for the whole observed period, the most efficient bank belongs to the group of large banks, which simultaneously shows the highest efficiency. The banking sector in the Republic of North Macedonia, as a whole, showed the highest efficiency in 2007, and the lowest efficiency in 2011.

scholarly journals Ranking DMUs by Calculating the Interval Efficiency with a Common Set of Weights in DEA

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Vahideh Rezaie ◽  
Tahir Ahmad ◽  
Siti-Rahmah Awang ◽  
Masumeh Khanmohammadi ◽  
Normah Maan
Keyword(s):  
Decision Maker ◽  
Relative Efficiency ◽  
Efficiency Score ◽  
Upper Bounds ◽  
Data Envelopment ◽  
Lower And Upper Bounds ◽  
Common Set Of Weights ◽  
Overall Performance ◽  
Interval Efficiency ◽  
Decision Making Units

To evaluate the performance of decision making units (DMUs), data envelopment analysis (DEA) was introduced. Basically, the traditional DEA scheme calculates the best relative efficiency score (i.e., the “optimistic” efficiency) of each DMU with the most favorable weights. A decision maker may be unable to compare and fully rank the efficiencies of different DMUs that are calculated using these potentially distinct sets of weights on the same basis. Based on the literature, the assignable worst relative efficiency score (i.e., the “pessimistic” efficiency) for each DMU can also be determined. In this paper, the best and the worst relative efficiencies are considered simultaneously. To measure the overall performance of the DMUs, an integration of both the best and the worst relative efficiencies is considered in the form of an interval. The advantage of this efficiency interval is that it provides all of the possible efficiency values and an expanded overview to the decision maker. The proposed method determines the lower- and upper-bounds of the interval efficiency over a common set of weights. To demonstrate the implementation of the introduced method, a numerical example is provided.

scholarly journals Efficiency Analysis of Macedonian and Croatian Banking Sectors with DEA

2021 ◽  
Vol 2 (2) ◽  
pp. 1-19
Author(s):  
Violeta Cvetkoska ◽  
◽  
Katerina Fotova Čiković ◽  
Keyword(s):  
Relative Efficiency ◽  
Efficiency Analysis ◽  
Average Score ◽  
Data Envelopment ◽  
Operating Expense ◽  
Regulatory Bodies ◽  
The Republic ◽  
Interest Expense ◽  
Over Time ◽  
Selection Of

The aim of this paper is to measure the relative efficiency of commercial banks in two developing countries, the Republic of North Macedonia and the Republic of Croatia under the operating (incomebased) approach by using the leading non-parametric methodology data envelopment analysis (DEA). We follow Banker et al. (2010) in the selection of the approach, variables (two inputs: interest expense and other operating expense, and two outputs: interest revenue and other operating revenue) and the model (output-oriented BCC DEA model) as in their first stage. The observed period is five years (2015-2019) and we use a balanced panel data for both samples (total of 65 Macedonian and 100 Croatian bank-year observations). Outliers are identified and excluded by using the Banker and Gifford (1988) super-efficiency procedure, and the BCC output-oriented model is rerun for both samples (total 55 Macedonian and 95 Croatian bank-year observations). We provide relative efficiency scores for each bank in both sectors, as well as an average score for the banking sectors. In addition, we analyse few banks for both sectors that have decreased or increased the efficiency, or show variable results over time. Besides, we explain how inefficient banks can improve the efficiency in future by setting targets for improvement. Our study provides valuable information for banking management and regulatory bodies.

A ranking system based on inverse data envelopment analysis

2019 ◽  
Vol 31 (3) ◽  
pp. 367-385 ◽  
Author(s):  
Khosro Soleimani-Chamkhorami ◽  
Farhad Hosseinzadeh Lotfi ◽  
Gholamreza Jahanshahloo ◽  
Mohsen Rostamy-Malkhalifeh
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Commercial Banks ◽  
Efficiency Score ◽  
Data Envelopment ◽  
Input Output ◽  
Data Set ◽  
Ranking System ◽  
Numerical Example ◽  
Decision Making Units

Abstract Inverse (DEA) is an approach to estimate the expected input/output variation levels when the efficiency score reminds unchanged. Essentially, finding most efficient decision-making units (DMUs) or ranking units is an important problem in DEA. A new ranking system for ordering extreme efficient units based on inverse DEA is introduced in this article. In the adopted method, here the amount of required increment of inputs by increasing the outputs of the unit under evaluation is obtained through the proposed models. By obtaining these variations, this proposed methodology enables the researcher to rank the efficient DMUs in an appropriate manner. Through the analytical theorem, it is proved that suggested models here are feasible. These newly introduced models are validated through a data set of commercial banks and a numerical example.

Relative Efficiency of Decision Making Units Producing Both Desirable and Undesirable Outputs: A Case of Textile Processing Units in Pakistan.

2011 ◽  
Vol 50 (4II) ◽  
pp. 685-698
Author(s):  
Samina Khalil
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Relative Efficiency ◽  
Value Added ◽  
Data Envelopment ◽  
Undesirable Outputs ◽  
Textile Processing ◽  
Decision Making Unit ◽  
Desirable And Undesirable Outputs ◽  
Decision Making Units

This paper aims at measuring the relative efficiency of the most polluting industry in terms of water pollution in Pakistan. The textile processing is country‘s leading sub sector in textile manufacturing with regard to value added production, export, employment, and foreign exchange earnings. The data envelopment analysis technique is employed to estimate the relative efficiency of decision making units that uses several inputs to produce desirable and undesirable outputs. The efficiency scores of all manufacturing units exhibit the environmental consciousness of few producers is which may be due to state regulations to control pollution but overall the situation is far from satisfactory. Effective measures and instruments are still needed to check the rising pollution levels in water resources discharged by textile processing industry of the country. JEL classification: L67, Q53 Keywords: Data Envelopment Analysis (DEA), Decision Making Unit (DMU), Relative Efficiency, Undesirable Output

DEA Window Analysis of Insurance Sector Efficiency in the Republic of Serbia

2020 ◽  
Vol 58 (3) ◽  
pp. 291-310
Author(s):  
Zlata Đurić ◽  
Milena Jakšić ◽  
Ana Krstić
Keyword(s):  
Financial Stability ◽  
Theoretical Research ◽  
Insurance Companies ◽  
Scale Efficiency ◽  
Financial Investment ◽  
Main Research ◽  
Insurance Sector ◽  
Window Analysis ◽  
The Republic ◽  
Dea Window Analysis

Abstract Insurance market is characterized by growing competition. This has imposed needs relating to the continuous capacity building of insurance companies, the continuous improvement of operating results and the assessment of the effects of insurers’ financial investment. The ultimate goal of these activities is to implement the planned goals and achieve positive business results. It is evident that the financial stability and efficiency of the insurance sector strengthens the confidence of citizens in this type of financial intermediaries. Bearing in mind the importance of the insurance sector for the financial system and economic system growth and development, the research subject is the analysis of the insurance sector efficiency in the Republic of Serbia. The main research objective is to look at the insurance sector efficiency through the performance analysis of nine selected insurance companies in the period 2007-2018, using DEA window analysis. The analysis and systematization of theoretical research findings, along with empirical data interpretation, description and comparison yielded results pointing to very poor performance of the insurance sector as a whole, because in all years of the observed period the relative average efficiency (technical, pure technical and scale efficiency) was below 100%, especially in the period 2015-2018.

scholarly journals Cost efficiency of the Czech and Slovak banking sectors: an application of the data envelopment analysis

2016 ◽  
Author(s):  
Iveta Palecková
Keyword(s):  
Data Envelopment Analysis ◽  
Cost Efficiency ◽  
Commercial Banks ◽  
Banking Sector ◽  
Banking Industry ◽  
Returns To Scale ◽  
Data Envelopment ◽  
Cost Efficient ◽  
The Cost ◽  
Variable Returns To Scale

The aim of the paper is to estimate the cost efficiency of the Czech and Slovak commercial banks within the period 2010-2014. For empirical analysis the Data Envelopment Analysis input-oriented model with variable returns to scale is applied on the data of the commercial banks. The intermediation approach is adopted to define the inputs and outputs. The Czech commercial banks are more cost efficient than Slovak commercial banks. The development of average cost efficiency is similar in the Czech and Slovak banking industry. The most efficient Czech banks are Ceská sporitelna and Sberbank in the Czech banking sector, the most efficient Slovak bank is Privatbanka with 100% efficiency.

Assessing most productive scale size with fuzzy stochastic data envelopment analysis

2022 ◽  
pp. 1-11
Author(s):  
Hooshang Kheirollahi ◽  
Mahfouz Rostamzadeh ◽  
Soran Marzang
Keyword(s):  
Relative Efficiency ◽  
Data Envelopment ◽  
Random Inputs ◽  
Dea Model ◽  
Scale Size ◽  
Stochastic Dea ◽  
Probability Constraints ◽  
Most Productive Scale Size ◽  
Decision Making Units ◽  
Fuzzy Random

Classic data envelopment analysis (DEA) is a linear programming method for evaluating the relative efficiency of decision making units (DMUs) that uses multiple inputs to produce multiple outputs. In the classic DEA model inputs and outputs of DMUs are deterministic, while in the real world, are often fuzzy, random, or fuzzy-random. Many researchers have proposed different approaches to evaluate the relative efficiency with fuzzy and random data in DEA. In many studies, the most productive scale size (mpss) of decision making units has been estimated with fuzzy and random inputs and outputs. Also, the concept of fuzzy random variable is used in the DEA literature to describe events or occurrences in which fuzzy and random changes occur simultaneously. This paper has proposed the fuzzy stochastic DEA model to assess the most productive scale size of DMUs that produce multiple fuzzy random outputs using multiple fuzzy random inputs with respect to the possibility-probability constraints. For solving the fuzzy stochastic DEA model, we obtained a nonlinear deterministic equivalent for the probability constraints using chance constrained programming approaches (CCP). Then, using the possibility theory the possibilities of fuzzy events transformed to the deterministic equivalents with definite data. In the final section, the fuzzy stochastic DEA model, proposed model, has been used to evaluate the most productive scale size of sixteen Iranian hospitals with four fuzzy random inputs and two fuzzy random outputs with symmetrical triangular membership functions.

SIDE

2014 ◽  
Vol 5 (1) ◽  
pp. 39-58 ◽  
Author(s):  
Rashmi Malhotra
Keyword(s):  
Relative Efficiency ◽  
Search Space ◽  
Objective Measures ◽  
Data Envelopment ◽  
Multiple Sources ◽  
Swarm Optimization ◽  
Different Dimensions ◽  
Analytical Power ◽  
Decision Making Units ◽  
Analyze Data

To make sound decisions, managers analyze data from multiple sources using different dimensions and eventually integrate the results of their analysis. This study proposes the design of a multi-attribute-decision-support-system that combines the analytical power of two different tools: data envelopment analysis (DEA) and particle swarm optimization (PSO), one of the major algorithms using swarm intelligence. DEA measures the relative efficiency of decision making units that use multiple inputs and outputs to provide non-objective measures without making any specific assumptions about data. On the other hand PSO's main strength lies in exploring the entire search space. This study proposes a modeling technique that jointly uses the two techniques to benefit from the two methodologies.

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