جستجوی مقالات مرتبط با کلیدواژه « تحلیل پوششی داده ها » در نشریات گروه « مالی »

 Investment in projects is one of the factors of economic growth and sustainable development in countries. Since investors are usually exposed to different risks and due to the importance of risk in selecting projects, it has always been focused on the optimization of project selection and optimization techniques to reduce risks. The main risks associated with investment projects are divided into market and political risk groups, which can lead to decrease in productivity and increased cost in a capital project .Market risk consists of four risk factors: commercial risk, project progress risk, vector gain risk and construction risk. The aim of this paper is evaluation and ranking of market risks in selecting different types of capital projects based on mathematical programming model with DEA / AHP combined technique so that according to the relative weights, the overall impact of market risk on project prioritization is determined. After the implementation of the proposed algorithm, the risk of advancing the first priority, risk of construction / completion of second priority, risk taking and commercial risk were placed third and fourth priority so that investors can identify and reduce their effects in projects.

The purpose of this study is to determine the efficiency limit and evaluate the performance of the stock portfolio in issues with numerical limitations in the Tehran Stock Exchange and IRAN Farabors in year 1398, using data envelopment analysis method. The efficient frontier in the issue of stock portfolio with numerical constraints is discrete and asymmetric. It is not possible to use the data envelopment analysis model. In this study, the efficient boundary is categorized into several continuous boundaries and the "data envelopment analysis" model is used. For this purpose, first the beginning and end points of each continuous boundary are determined as "data segment points" based on the proposed algorithm, and then the data envelopment analysis model is used for each continuous interval. 188 stock exchange and OTC symbols listed in Tehran Stock Exchange and IRAN Farabors were examined in 1398, and with the limitation of selecting 5 to 10 stocks from 188 stocks based on different weights (total stock weight equal to 1), the efficient limit of the stock portfolio was determined. Due to the lack of fast and accurate computational methods in determining the efficiency limit of stock portfolio with numerical constraints, data envelopment analysis method to determine the efficiency limit and evaluate stock portfolio performance of particle swarm algorithm, genetic algorithm and imperialist competition algorithm And the efficient border can be drawn and used.

Optimizing the investment portfolio is one of the vital issues in investment management. The various fluctuations of the financial markets and the uncertainty of the parameters make using classical models a severe challenge. Therefore, the optimization of financial models in conditions of uncertainty to adapt to the real world has been the focus of researchers. In the present research, a hybrid optimization model has been developed by applying data envelopment analysis and robust programming to assess risk with uncertain inputs and outputs. The statistical population of the research was extracted from the Coin Marketcap portal, where the updated data of the adjusted price of 37 selected top cryptocurrencies was used to estimate the risk and create an optimal portfolio. A two-step approach for selecting and optimizing the stock portfolio, increasing the stability of the investment process, and comprehensive evaluation of stocks based on financial criteria is proposed. In the first stage, the performance assessment of the selected stocks is done using the robust programming-data envelopment analysis (RDEA) method. Then, in the second phase, the investment amount in eligible stocks is determined using the half-variance average and absolute standard deviation models. The performance of the proposed approach is evaluated in a case study of cryptocurrency data with increasing uncertainty. The comparative results of robust peer models with two risk measures show that the mean semi-variance model performs better in choosing and optimizing the investment portfolio.

The present study was conducted with the aim of combining multi-criteria decision making approach and mathematical optimization in evaluating financial efficiency and refining the affecting factors. In this research, based on a quantitative analysis, a distinction is made between capital decision supporting and decision-making, and data envelopment analysis has been used in selecting the investment option. The type of research has been done in terms of theoretical-applied purpose and with survey research design. Using the fuzzy Dematel method, the most effective factors of financial efficiency was: 1) Current ratio, 2) Liquidity of assets, 3) Ownership ratio, 4) Operating cost rate, 5) Firm size, 6) Operating return on assets, 7) Net return Assets, 8) return on equity, 9) current asset turnover, 10) average monthly return, 11) daily price to earnings per share, 12) earnings growth. The statistical population of the research in the refinement of inputs and outputs includes professors and experts and, 183 selected companies of Tehran Stock Exchange in the selection of the optimal capital combination. The evaluation of financial efficiency led to the identification of 42 companies as justified investment options.

It is very important to choose an efficient monitoring system to assess the financial distress of banks, therefore, one of the most important monitoring systems used to assess the financial distress of banks is the CAMELS monitoring system. Which includes six indicators; Capital adequacy, asset quality, management quality, revenue quality, liquidity, market risk sensitivity. Therefore, in this study, the criterion of financial helplessness of banks is CAMELS indicators. Initially, 17 banks listed on the Tehran Stock Exchange in the fiscal year 1399 were ranked and divided into healthy and helpless financial groups by CAMELS indicators. Then, models, Data Envelopment Analysis and logistic Regression were used to predict the financial distress of banks. Then, with the pairwise comparison test (T), the prediction accuracy of both models was investigated. In logistic regression method, binary model with ForwardlR method was used. And in data envelopment analysis method, SBM model with different application was used. The results showed that the overall accuracy of the logistic regression model is higher than the data envelopment analysis model in assessing financial distress and also the CAMELS monitoring system can be a good assessor for banks' financial distress.

Today, measuring efficiency is one of the most important methods of performance evaluation in any organization. The outputs obtained in all inputs represent the efficiency. Due to the comprehensiveness of the banking sector, it can be introduced as one of the main areas of economic development. In this paper, a data envelopment analysis (DEA) model in a Free Disposal Hull (FDH) network is presented considering the undesirable output for handling the status of the financial deposits in Saman Bank branches. Since the basic models calculate efficiency by considering the overall output and input and ignoring the internal relationships in the banking process flow system; therefore, by considering this issue, in this paper, we present a DEA model that calculates the efficiency score of the branches with the high accuracy. Then the proposed model is compared to the basic CCR model. Finally, the cross-performance method is used to rank the units of the branches.

Data envelopment analysis method is one of the most widely used methods to performance evaluation of banks. According to extensive studies in the field of bank performance evaluation, various data envelopment analysis models such as basic CCR, BCC models, multi-stage and network models have been presented. According to the models and developments, have been used various and numerous variables. In this research, first, the types of variables in the bank’s evaluation were identified and categorized. On the other hand, the presence of many variables for evaluation in the context of data envelopment analysis would violate the general rule that confirmed relation between decision maker units and evaluation variables. Therefore, an a priori approach was used to reduce the variable before implementing the data envelopment analysis model. The approach was used based on structural equation modeling and confirmatory factor analysis, which was analyzed using AMOS and SPSS software. Elementary analyze result show that model fit indices not confirmed and some of variables non- significance at 90% confidence level and have low factor loading. Therefore, Due to the non-significance of a number of variables, the variable was adjusted. The results of the modified model analysis confirmed it. Using this approach, extracted the effective and key inputs and outputs that capable to make distinguish between banks performance. Finally, model variable reduced from 22 to 8. As a result, this approach can be applied for identification key variables from variable lake in the banks evaluation and other evaluation system with many indicators.

In the past, attempts have been made to conceptualize all purchases based on the LBO model and through the lens of agency theory. This view of the past has severely limited the ability of financial engineers to create models and tools for entrepreneurship motivation. ETA is a window to revitalize businesses by acquiring small businesses with entrepreneurial capacity instead of starting a start-up business.In this study, according to behavioral, financial, economic, social, and legal indicators, the efficiency degree of entrepreneurship through acquisition models using the non-parametric method of data envelopment analysis based on output-oriented BCC and CCR models from the entrepreneurs and investors' perspectives in the Iranian market. We have demonstrated a comprehensive ranking of the models based on the Anderson and Peterson (A&P) model finally.The results show that based on the output-oriented BCC and CCR models, the crowdfunding model from the perspective of entrepreneurs, and the incubator model from the perspective of inefficient investors. According to Anderson and Peterson model, the optimal model from the point of view of investors is the sponsor model and from the entrepreneurs' view, the self-fund model was considered with the highest efficiency and compliance according to the indicators

In the present study, he presented a statistical sample related to the information of legal and credit customers of Tejarat Bank, accepted in the stock exchange during the years 1398 to 1399. Using factor analysis technique and Delphi method, the variables affecting credit risk were selected and entered into the data envelopment analysis model, and the performance scores of law firms were obtained using them, and then ranked by the Fitch Institute model. Performing and using the results to predict the movement of customers in different groups using the Markov chain process. The results of data envelopment analysis indicate that 7 companies were identified as efficient in the financial approach and 12 companies in the combined approach. The results of the Markov chain show that the average probability of stopping at the current rank in 1400 in the financial condition is 46% and in the combined mode is 53%, the average probability of improving the situation of companies is 23% and the average probability of falling is 20%.

The purpose of this study is to select the optimal stock portfolio using data envelopment analysis and to carry out the project of stock exchange information about the top 50 active companies in the summer of 1398 and trace the analysis of the same data in the spring of 1398. To calculate the relative efficiency and progress of companies, a developed data envelopment analysis (SORM) model has been used, where inputs can be changed only at a limited and specified distance, and with the same assumption, the output can be limited to a limited distance. The negative data can be analyzed and the final model facilitates the efficiency and optimization of stock portfolio selection according to the information extracted from the community under discussion. The results of this analysis show that 13 companies achieved a performance equal to 1, which indicates the highest level of efficiency, and Bank Saderat, including 1.32 Fanavaran Petrochemical Company, with the figure of 1.15, has been classified as having the highest progress and growth rate, respectively, on the first and second floors of productivity.Keywords: negative data, data envelopment analysis, ranking indicators, super performance, progress and regression.

Data envelopment analysis is a non-parametric method for measuring the performance score of a set of units under evaluation. Recently, the application of data envelopment analysis models in networked or multi-stage structures has been considered by researchers. This paper seeks to reinforce the first steps taken to develop DEA network models based on asynchronous technology. To this end, it provides a way to consider undesirable outputs in an asynchronous technology. The models presented in this paper, while calculating the overall performance score in a network system, are able to calculate the performance of each step separately in the presence of undesirable factors without any additional calculation and provide it to the system administrators. Also, to show the accuracy of the proposed model compared to the basic CCR model, we conclude the computational accuracy of the model due to the identification of a number of less efficient units than the CCR model. According to the results, despite the fact that some units are considered as efficient, but due to inefficiency in some stages are considered inefficient and only the National Industrial Holding is considered as the only efficient unit due to the fact that it is efficient in both stages.

In analyzing relative performance, especially at the institutional level, the traditional data envelopment analysis models do not recognize vastly different and important activities as separate functions and therefore cannot identify which function may be the main source of inefficiency. Mutual fund families are a group of mutual funds that are established through one investment company. The aim of this paper is to evaluate the relative performance of 18 mutual fund families in Iran during the years 1396 and 1397, by implementing a two-stage DEA model that decomposes the overall efficiency of each decision making unit into two components of operational management efficiency and portfolio management efficiency. The results show that by decomposing the overall efficiency into two components, one can determine mutual funds with good performance from those of bad or poor performance. Also, the frontier projections of mutual fund families with poor performance is calculated in order to help management in managing the relative performance of each family. According to assessment results of 18 mutual fund families in Iran, they have poor performance in operational management which means that management of costs of the funds is poor.

Evaluation of Tehran metro stations based on multiple indices has been studied in this paper. Subway stations have always been considered one of the service centers. Considering that the design and construction of the metro station focuses on the creation of infrastructure, facilities and equipment and then the operational part uses it for the first service, it therefore has two stages of construction and operation. Data envelopment analysis with two-step structure is designed for this topic. The data were collected over five years and attacked four Tehran metro stations. Based on the results of the designed model, the progress and remission of all stations were calculated at each stage and its relationship with financial investment in each metro station was investigated.

insurance companies as one of the most important financial institutions have a special place in economic growth and development , so that efficient performance of this sector will affect other economic sectors.Therefore, evaluating the performance of companies operating in this industry and identifying their strengths and weaknesses is of great importance. One of the performance appraisal techniques that has received a lot of attention in recent years is data envelopment analysis. In this paper, using the two-stage model of SORM-SBM data envelopment analysis, the performance evaluation of 21 insurance companies operating in Iran with financial indicators has been performed, which in addition to total efficiency, calculates and shows the efficiency of each company in both marketing and profitability. The company's performance is efficient if it has been efficient in both areas. The model values for each company are also calculated and the relationship between these values and total performance is shown

Despite the large number of mutual funds available to investors, paying attention to their ratings is important. Many ranking approaches use subjective weights to integrate fund performance across different time horizons, which can lead to completely different ranking. So far, the efficiency of the funds has been calculated in the valuation of the funds with the help of data envelopment analysis, and then the portfolio is selected from the efficient funds. In this paper, a new approach and application of the philosophy of data envelopment analysis (input reduction and output increase) is applied and generalized to the concept of risk and return. The proposed model is a new nonparametric technique that focuses on the simultaneous estimation of contractile (risk) and expansion (return) potentials. This approach eliminates any need for mental such as the importance, weighting, and meaningfulness of actions across different time horizons. This approach, based on the performance of the underlying fund relative to other funds, assigns a score to the underlying fund, on which it can rank the funds. This model has been applied to a sample community of 26 mutual funds. The clear results and unique ratings indicate the ability of this model to evaluate and ranking.

Banks play an important role in the economic development of any country as the main part of the financial system. Calculating efficiency and finding the strengths and weaknesses of branches has a significant impact on increasing the efficiency of banks. Data envelopment analysis is one of the performance evaluation techniques that is able to introduce benchmarks for inefficient decision-making units in addition to estimating relative efficiency. In this paper, using a data envelopment analysis, a model for evaluating efficiency, studying sensitivity analysis of 18 branches of one of the commercial banks of Iran with financial ratios is presented. For this purpose, a model is proposed for output estimating with ratio data. By changing the input values the output values can be estimated. The amount of changes required in the outputs to maintain the efficiency as well as maintaining the rank is possible by using the proposed model with ratio data.

Among the important producers and financial organizations in the national economy, the country's major producers, including petrochemicals and refineries, have an important role in improving efficiency and preventing crude oil. In this regard, managers of these organizations are struggling to improve efficiency in light of the economic conditions. The classic approach of data envelopment analysis models takes into account the linear pricing performance for inputs and outputs in the evaluation. In this paper, we examined the efficiency of the top ten petrochemical companies in the year 1395 by modeling with the help of auxiliary variables and nonlinear values ​​of inputs and outputs. An important feature of the proposed model is that it considers nonlinear values ​​of inputs and outputs. In the DEA total analysis, all weights are taken linearly They can not show the reality of the issue in many practical applications.

In today's highly competitive world, the condition of survival and participation in the field of activity, functioning and efficient and effective, is not achieved except through continuous planning, monitoring, control and evaluation. In this regard, we tried to present a mathematical hybrid model for selecting and planning an optimal composition of the shares according to the goals and priorities, in order to obtain the highest compatibility between the final selection and the initial ranking of each share. The proposed model consists of three steps and several steps, the SBM method of data envelopment analysis (DEA) (for initial stock revisions, multi-factor decision-making (TOPSIS)) in uncertainty conditions, for the assessment and ranking of shares in two individual steps and categorized and integer linear programming (IP) for choosing the best stock portfolio with increased scores according to the organization's priorities and constraints. Collect information from reputable sites of five industries active automotive, pharmacy, petrochemical, cement and food industries to the best stock portfolio for investment, due to the impact of algorithms and methods.

Logical investment decisions require attention to different factors and different criteria at the same time. This goal can be achieved using various methods and algorithms. The purpose of this study is to develop an optimal stock portfolio model using a combination of data envelopment analysis methods, anomaly clustering algorithm and MLP neural networks.The statistical population of the research is the accepted companies in Tehran Stock Exchange during the period of 1386 to 1396. To create an optimal stock portfolio, all available criteria were grouped to reach the optimal stock portfolio.Then, the results were compared in different approaches based on the Sharp ratio. The results of the research indicate that using the combination of data envelopment analysis, anomaly clustering, MLP neural networks and accounting metrics in the provision of an optimal portfolio of stocks led to Increasing Sharp's ratio compared to other approaches (Risk and Efficiency, Value-Based, and Balanced Scorecard). In general, the simultaneous use of hybrid optimization techniques and comprehensive criteria derived from accounting reports can provide a more efficient basket of portfolios and more desirability for the investors.

Decision making on buying stock is a complex process. Comparing various companies based on the industry they belong to and the identification of the superior industries through their performance assessment can be a useful guide for stakeholders. Since a set of parameters is considered for decision, in this study beside identification of factors affecting decision-making process and determining input and output parameters affecting the ranking of selected companies that are active in Tehran stock exchange market, data envelopment analysis (DEA) has been used to evaluate the performance of companies in several different industries. This method is a nonparametric method to measure and assess the relative efficiency of decision-making units with multiple inputs and outputs. Identified inputs and outputs include net profit margin, quick ratio, fixed asset turnover, equity rate, return on assets, equity, current debt, current assets, and debt ratios. First, input orientation CCR method is used to determine efficient and deficient companies. Entropy method is used to determine the weights of indicators. Finally, VIKOR method is used to rank efficient companies and with the ranking of inefficient companies, models have been presented to reach the efficiency frontier.