razieh mehrjoo

Several studies have used the data envelopment analysis method in the field of portfolio optimization or efficiency evaluation of companies listed on the Tehran Stock Exchange, but less attention has been paid to the network data envelopment analysis method and indicators simultaneously derived from technical and fundamental analysis. Therefore, considering the importance of the issue, the purpose of this article is to evaluate the relative and integrated efficiency of the Tehran Stock Exchange using "production process" and "financial production process" with the approaches of Fundamental Analysis (FA) and Technical Analysis (TA). To achieve this, the network data envelopment analysis model (NDEA) was used for the period 2014-2021. In addition, FAHP and SOCP are developed, respectively, to determine the weights of the first and second stages and to solve the nonlinear problem in the AED model. In addition, the efficiency of the year 2021 is predicted based on the efficiency values of the previous years by using simple regression analysis. The results showed: the production process is more important in defining the overall efficiency of a company; there is a significant difference in the efficiency distribution among different industries; Kh-Sapa and Khodro achieved 100% overall efficiency and the Technical and Engineering Services industry achieved the highest overall efficiency; the predicted efficiency values are very close to the actual efficiency values in 2021. Based on the results, it is suggested that investors pay more attention to the fundamental analysis in order to create an investment portfolio. Additionally, focus more on the companies with higher first and second stage efficiency, since they are investment proposals.

The purpose of this article is to evaluate the technical efficiency of listed companies in the pharmaceutical industry in 2021. To achive this, the additive model of two-stage network data envelopment analysis was investigated with semi-positive and negative indicators and simultaneously considering the production process(Fundamental Analysis) in the first stage and the financial production process (Technical Analysis) in the second stage. In addition, FAHP applied to determine the weights of the first and second stages. The results showed: the production process is more important in defining the total efficiency of a company in the Stock Exchange. In the first stage, %33 and in the second stage, %25 of the pharmaceutical industry companies achieved full efficiency of one. According to the efficiency one of %13 companies of the studied industry in both first and second stages, the efficiency of the mentioned companies was also full and found to be one; These companies include Toliddaru, Exir Pharmaceutical and Loghman Pharmaceutical & Hygienic Companies. The lowest total efficiency belongs to Sobhan Pharmaceutical Group. In general, 88% companies of the studied industry have an efficiency higher than 0.5.

The estimating returns to scale (RTS) and identifying the area in which the type of returns to the scale of a single decision maker unit (DMU) remains sustainable helps managers to have more accurate predictions about the size variations of a DMU. Contrary to theoretical studies and attitudinal surveys, the RTS-based econometric estimates of the panel data are derived from the time changes observed in inputs and outputs. Therefore the description of returns-to-scale based on the panel data is closer to those that are related to changes in demand and changes in the policy of managers during the sample period. it's clear that the use of the panel data, in addition to estimating returns to scale closer to the view of the managers, leads to a significant increase in the number of inputs and outputs for each DMU. Since we know that the DEA models have computational complexity for the great practical issues, use particularly when there are large numbers of outputs and inputs reducing the number of models will greatly help to solve these issues. In most articles, the returns-to-scale estimates for decision-making units (DMU) are performed with at least two LP models. Furthermore have problems in some cases and may not be able to investigate all DMUs. In this paper, a method has been proposed which determines returns-to-scale and identifies boundary units by solving only one model. In fact, unlike some articles which explore the boundary DMUs, the assessment area in this paper is all the DMUs observed in PPS (Production Possible Set). Therefore, based on this approach, identifying boundary units and estimating their returns-to-scale will be possible by solving only one LP model. Further, according to the proposed dual model, a different perspective has been proposed to identify and evaluate boundary units and to estimate the returns-to-scale by solving a single LP model. Additionally and according to numerous observations, the dual model is able to estimate the returns-to-scale of the image point of the inefficient units in the BCC model in the input orientation. The correctness of all content has been shown theoretically as well as intuitively, with due substantiation.

The inverse data envelopment analysis is an inverse optimization problem, which can be used as an appropriate planning tool for management decisions. The typical DEA mainly focuses on post-operative evaluation of an organizational performance. Sometimes economic conditions such as economic prohibitions on exports or imports are imposed on a system. These prohibitions prevent decision-making units from best performance (efficiency one). In this case, if the system has the best performance (with a less than one efficiency score) then it will be considered as an efficient system. So, the efficiency frontier change’s problem must be studied. So by making change in definition of the best efficiency amount of a system, it still has the best performance. In these situations, the inefficient units can select a real pattern instead of reaching an unrealistic pattern that is presented in ideal terms to achieve the best conditions (the best efficiency value is one). So a long-term management plan can be developed. the efficiency frontier change will be expressed inputs and outputs as a coefficient of efficiency. The frontier change looks at the changes in inputs and outputs to reach the new frontier. One of the purposes of the data envelopment analysis is the investigation of input’s and output’s amounts by changing the amount of efficiency. So far, many models must be solved to calculating these changes. Efficiency frontier problem can replace a simple mathematical model with these models. All of these advantages can improve calculating input and output’s changes and RTS will be unchanged and decision maker can estimate unit’s RTS without solving any model. So a unit will be stayed MPSS by reducing inputs. In other frontier change methods some hyperplanes and extreme units had been deleting but our method transforms them on new frontier. So all extreme units and RTS can estimate easily. The efficiency frontier changes can delete some inefficient units so system’s cost will be reduced.  For this purpose, in this paper, the change in the efficiency frontier, its properties and its effect on the inverse data envelopment analysis is examined.

In the environment of business‐to‐business e‐commerce, Buyers and sellers in mature industrial markets can turn single transactions into long-term beneficial relationships by a deeper understanding of the complex connection between the two and buyers and sellers are uncertain about their roles. A “must-do” for the sellers, in particular, is to understand patterns of investment and reward, and effectively manage the process that defines the dynamics of buyer-seller evolution. This paper tries to use data envelopment analysis as a reliable and achievable tool for performance evaluating, quality and performance improvement of Buyer-Seller Relationship, in the situation where the information flows are imprecise data between buyers and sellers.