The field of production frontier estimation is divided between the parametric Stochastic Frontier Analysis (SFA) and the deterministic, nonparametric Data Envelopment Analysis (DEA). This paper explores an amalgam of DEA and SFA that melds a nonparametric frontier with a stochastic composite error. Our model imposes the standard SFA assumptions for the inefficiency and noise terms. The frontier...

Recently, different methods have been proposed for portfolio optimization and decision making on investment issues. This article aims to present a new method for portfolio formation based on Data Envelopment Analysis (DEA) and Entropy function. This new portfolio optimization method applies DEA in association with a model resulting from the insertion of the Entropy function directly into the op...

in recent years, the existing competitions between investment companies have been increased largely by entering private investors in capital market. large and powerful companies try to achieve the goals predicted to increase the competition capacity. to analyze the efficiency of investment companies, parametric and non-parametric methods are used. in this research, based on the dissociation pow...

Bankruptcy assessment provides valuable information for the governments and investors to base their decisions in order to prevent possible financial losses. Data envelopment analysis (DEA) has generally been used to assess relative efficiency of decision making units. Recently, several approaches have appeared that reformulate DEA as a bankruptcy prediction tool. However, only several studies h...

Data Envelopment Analysis (DEA) is an approach based on linear programming to assess the relative efficiency of peer Decision Making Units (DMUs). Typically, each DMU is free to choose the weights of the factors used in its evaluation. However, the evaluator’s preferences may not warrant so much freedom. Several approaches have been proposed to allow the incorporation of managerial preferences ...

Supplier selection problem (SSP) is a problem to select the best among suppliers based on input and output data of the suppliers. Since different uncontrollable and unpredictable parameters are affecting selection, choosing the best supplier is a complicated process. Data Envelopment Analysis (DEA) is a method for measuring efficiency and inefficiencies of Decision Making Units (DMUs). While, t...

This study evaluates operation performances of 67 international tourist hotels in Taiwan by using the Context-dependent DEA model of Date Envelopment Analysis (DEA) in order to find out the performance of these hotels and their competitive advantages. This study differentiates the levels of efficient frontier with the efficiency of each international tourist hotel. Based on the efficiency, the ...

Data envelopment analysis (DEA) is a non-parametric approach to evaluate a set of decision making units (DMUs) consuming multiple inputs to produce multiple outputs. Formally, DEA use to estimate the efficiency score into the empirical efficient frontier. Also, DEA can be used to allocate resources and set targets for future forecast. The data are continuous in the standard DEA model whereas th...

The paper presents results of computer-assisted portfolio management simulation based on using a DEACascor mathematical model. The model uses the Data Envelopment Analysis (DEA) ratio as a neuron with memory and combines it with Cascade Correlation Neural Network (Cascor) to forecast stock prices. The model is designed for using in high-frequency stock trading. It utilizes ability of DEA to con...

We present a theoretical and computational study of the impact of inserting a new attribute and removing an old attribute in a DEA model. Our objective is to obviate a portion of the computational effort needed to process such model changes by studying how the efficient/inefficient status of DMUs is affected. Reducing computational efforts is important since DEA is known to be computationally i...