International Journal of Data Envelopment Analysis
Volume:9 Issue: 1, Feb 2021

Financial firms and institutes are evaluated based on comparisons between financial ratios. There are various techniques for evaluating the performance of firms based on ratios such as the liquidity ratio, current ratio, quick ratio, and so forth. In this study, firms with a two-stage network structure are evaluated using the Ratio Analysis technique. One advantage to the models proposed within a network structure herein is that they provide a comparison between the nonparametric method data envelopment analysis and ratio analysis in the analysis of financial ratios.

One of the drawbacks of Data Envelopment Analysis ( DEA ) is the problem of lack of discrimination among efficient Decision Making Units ( DMUs ) and hence yielding many numbers of Decision Making Units as efficient. The main purpose of this paper is to overcome this inability. In this paper, we explain new methods based on stability ranges for weights of inputs and outputs, these weights are the weights, that Decision Making Units remain efficient. For illustration numerical example is given.

This paper develops a non-radial linear programming model based on the concept of directional distance function to examine the profit efficiency (inefficiency) of decision making units (DMUs) in the case where the market prices are available but can be controlled by units, and DMUs can influence simultaneously the prices and quantities of inputs and outputs to maximize the total profit. The optimal solution of the proposed model can help decision makers to evaluate and improve the profit efficiency of DMUs by changing the prices and quantities of inputs and outputs. The proposed model satisfies the important properties, units invariance and translation invariance. We present a decomposition of the profit inefficiency obtained by the optimal solution of the proposed model which can individualize quantity and price contributions to the profit inefficiency of under evaluation DMU. Finally, an empirical application to a set of 50 bank branches will be presented to illustrate the proposed approach.

Endogeneity and its impact on estimating economic models can be seen in many economic studies. Data envelopment analysis is one of the most common non-parametric methods in which different axioms are used to estimate the production function (efficient frontier). However, the issue of endogeneity and its impact on estimating the efficient frontier is less considered. Cordero et al (2016) indicated that standard models of data envelopment analysis do not perform well in the presence of positive and high endogeneity. In this article, a model based on relaxing convexity axiom is presented in which the Cobb-Douglas function is considered as a real production function. Then, the efficiency of the proposed model is compared with the standard models of the data envelopment analysis and Cobb-Douglas function under positive and high endogeneity. The results show that the proposed model outperforms the counterparts. In addition, by comparing the models in different modes of return to scale, it is observed that the type of return to scale is also effective in determining the efficiency and efficiency of the proposed model compared to its economic counterpart.

Data envelopment analysis (DEA) is a body of research methodologies to evaluate overall efficiencies, identify the sources, and estimate the amounts of inefficiencies in inputs and outputs.efficient DMUs all have an efficiency of one, so that for these units no ranking can be given. Since in evaluating by traditional DEA models many DMUs are classified as efficient, a large number of methods for fully ranking both efficient and inefficient DMUs have been proposed. In the last decade, ranking DEA efficient units has become the interests of many DEA researchers and a variety of models (called super-efficiency models) were developed to rank DEA efficient units. Super efficiency data envelopment analysis model can be used in ranking the performance of efficient DMUs. While the models developed in the past are interesting and meaningful, they have the disadvantages of being infeasible or instable occasionally. But the main problem of super-efficient models is lack of differentiation between non- extreme efficient DMUs, so these models cannot rank these DMUs. In this paper, we propose a new method for Ranking Non-extreme Efficient Decision making units in Data Envelopment Analysis based on benchmark. One of the main advantages of our approach is that, this method doesn’t apply any new models, rather this model applies a combination of the well-known models for ranking DMUs. Therefore, understanding our proposed method is easy for readers. One numerical example is examined to illustrate the potential applications of the proposed method.

Considering the important role of the construction industry in the form of one of the expandable industries for economic growth, increasing GDP and job creation, as well as the great relationship between construction and its required tools, such as machinery, materials Construction, tools, etc., scientific study of each of these sectors, provides the possibility of better development in the construction industry, the results of which can suggest solutions to boost national production. Since cement as an underlying commodity is a widely used material in construction and concreting, and investing in cement companies can play an important role in improving their process, so in this The paper examines the efficiency of symbols of cement companies in the Tehran Stock Exchange using data envelopment analysis. Due to the fact that some of the indicators extracted from the stock market are usually integers, in this study, a data envelopment analysis model in mixed integer programming class (MILP) has been used, which is capable of processing continuous data. And has the integer together. The results presented in this article can be effective on managers' decisions to increase capital or on how to invest in cement companies.