Using Data Envelopment Analysis in Credit Risk Evaluation of ICT Companies

The aim of the paper is to explore possibilities of diagnosis corporate credit risk through DEA and design an appropriate model for diagnosis of credit risk, which can be used in different sectors of national economy (e.g. agricultural, service sector or industry and innovation sector). The model differs from the conventional application of DEA because of variables selection and construction of production-possibility frontier. We illustrate application of models on sample 110 randomly selected companies during the 2013-2017 period. The reason for choosing the ICT companies is the fact that this sector is considered to be driving force behind the growth of the economy. The data has been obtained from Finstat. The results are divided into identification of 3 zones of corporate financial health with a different stage of credit risk. They show that DEA achieves a satisfactory value of a correct classification into the relevant zone (financial health, grey, and financial distress zone), but also the relatively high error rate of the DEA in the identification of companies in financial distress.

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The metaefficiency of trade shows: a benchmarking analysis by sector

Benchmarking An International Journal ◽

10.1108/bij-06-2017-0129 ◽

2018 ◽

Vol 25 (8) ◽

pp. 2875-2891 ◽

Cited By ~ 1

Author(s):

Pilar Alberca ◽

Laura Parte ◽

Ainhoa Rodríguez

Keyword(s):

Consumer Goods ◽

Service Sector ◽

Strategic Decision ◽

Data Envelopment ◽

Content Type ◽

Services Sector ◽

Trade Show ◽

Homogeneous Production ◽

Using Data ◽

Trade Shows

Purpose The purpose of this paper is to analyze the efficiency of trade shows and provide insights for trade show exhibitors using data envelopment analysis (DEA). The paper also offers a benchmarking analysis of the business factors for the most efficient trade shows in each sector. Design/methodology/approach The paper uses the metafrontier DEA methodology and identifies several frontiers according to the sector in which the trade show operates since different sectors could not share homogeneous production technology for exhibitor firms. Findings The main findings reveal different profiles of individual sectors. The investment sector presents a more homogenous profile than either the consumer goods or the services sector. The consumer goods sector is more heterogeneous but it is also possible to find common characteristics for the most efficient trade shows. The service sector is characterized by a high variability and as such it is more difficult to identify benchmarking elements for the most efficient trade shows. Research limitations/implications The main limitation of the study is that the sample only includes audited trade shows. Future studies could extend the period under study in order to obtain a more complete picture on the evolution of trade show efficiency. Originality/value This paper extends the DEA results by profiling the most efficient trade shows in each sector so that this information can be used as a benchmarking tool to define exhibitors’ strategic decision making.

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New approaches for the financial distress classification in agribusiness

Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis ◽

10.11118/actaun201361041177 ◽

2013 ◽

Vol 61 (4) ◽

pp. 1177-1182 ◽

Cited By ~ 6

Author(s):

Jan Vavřina ◽

David Hampel ◽

Jitka Janová

Keyword(s):

Financial Distress ◽

Bankruptcy Prediction ◽

Set Covering ◽

Data Envelopment ◽

Financial Health ◽

It Industry ◽

Data Set ◽

New Approaches ◽

Recent Financial Crisis ◽

Visegrad Group

After the recent financial crisis the need for unchallenged tools evaluating the financial health of enterprises has even arisen. Apart from well-known techniques such as Z-score and logit models, a new approaches were suggested, namely the data envelopment analysis (DEA) reformulation for bankruptcy prediction and production function-based economic performance evaluation (PFEP). Being recently suggested, these techniques have not yet been validated for common use in financial sector, although as for DEA approach some introductory studies are available for manufacturing and IT industry. In this contribution we focus on the thorough validation calculations that evaluate these techniques for the specific agribusiness industry. To keep the data as homogeneous as possible we limit the choice of agribusiness companies onto the area of the countries of Visegrad Group. The extensive data set covering several hundreds of enterprises were collected employing the database Amadeus of Bureau van Dijk. We present the validation results for each of the four mentioned methods, outline the strengths and weaknesses of each approach and discuss the valid suggestions for the effective detection of financial problems in the specific branch of agribusiness.

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A novel dynamic credit risk evaluation method using data envelopment analysis with common weights and combination of multi-attribute decision-making methods

Computers & Operations Research ◽

10.1016/j.cor.2021.105223 ◽

2021 ◽

Vol 129 ◽

pp. 105223

Author(s):

Jalil Heidary Dahooie ◽

Seyed Hossein Razavi Hajiagha ◽

Shima Farazmehr ◽

Edmundas Kazimieras Zavadskas ◽

Jurgita Antucheviciene

Keyword(s):

Decision Making ◽

Data Envelopment Analysis ◽

Credit Risk ◽

Risk Evaluation ◽

Evaluation Method ◽

Data Envelopment ◽

Common Weights ◽

Multi Attribute Decision Making ◽

Using Data

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Assessment of Technical Efficiency of Indian B-Schools

Advances in Secure Computing, Internet Services, and Applications - Advances in Information Security, Privacy, and Ethics ◽

10.4018/978-1-4666-4940-8.ch007 ◽

2014 ◽

pp. 128-139

Author(s):

Sreekumar ◽

Gokulananda Patel

Keyword(s):

Service Sector ◽

Management Education ◽

Weighted Average ◽

Efficiency Analysis ◽

Data Envelopment ◽

Education In India ◽

Using Data ◽

Management Institutions ◽

Indian Students

In the present economy, both at national and international front service sector, is playing a pivotal role as a major contributor towards the GDP. The importance of service sector necessitates the efficiency measurement of various service units. The opening of Indian economy (Liberalisation – Privitisation – Globalisation) has affected every segment of Indian industry and service sector, education being no exception. Today, management education is one of the most sought after higher education options for Indian students. Management education in India has also undergone many changes in the last decade or so, meeting the need of industries. Meeting this growing demand has lead to proliferation of management institutions, and in many a cases the quality of education is compromised. Some popular Indian magazines and journals started ranking the Indian B-Schools intending to give information to all the stake holders involved. All these methods either use weighted average or clustering method to rank the institutes. This chapter proposes an alternative method based on efficiency analysis using Data Envelopment Analysis to rank the Indian B-Schools. The B-schools are observed over multiple periods of time, and the variations of efficiency are used to draw a conclusion about the performance of B-schools. Window analysis is used to compare the performance of B-schools over the period of time.

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Data Envelopment Analysis (DEA) in the Psychological Sciences

10.31234/osf.io/9j2rv ◽

2019 ◽

Author(s):

Jeffrey A. Shero ◽

Sara Ann Hart

Keyword(s):

Data Envelopment Analysis ◽

Aggressive Behavior ◽

Personality Traits ◽

Educational Administration ◽

Latent Variable ◽

Organizational Management ◽

Data Envelopment ◽

Targeted Interventions ◽

Component Skills ◽

Using Data

Using methods like linear regression or latent variable models, researchers are often interested in maximizing explained variance and identifying the importance of specific variables within their models. These models are useful for understanding general ideas and trends, but often give limited insight into the individuals within said models. Data envelopment analysis (DEA), is a method with roots in organizational management that make such insights possible. Unlike models mentioned above, DEA does not explain variance. Instead, it explains how efficiently an individual utilizes their inputs to produce outputs, and identifies which input is not being utilized optimally. This paper provides readers with a brief history and past usages of DEA from organizational management, public health, and educational administration fields, while also describing the underlying math and processes behind said model. This paper then extends the usage of this method into the psychology field using two separate studies. First, using data from the Project KIDS dataset, DEA is demonstrated using a simple view of reading framework identifying individual efficiency levels in using reading-based skills to achieve reading comprehension, determining which skills are being underutilized, and classifying and comparing new subsets of readers. Three new subsets of readers were identified using this method, with direct implications leading to more targeted interventions. Second, DEA was used to measure individuals’ efficiency in regulating aggressive behavior given specific personality traits or related skills. This study found that despite comparable levels of component skills and personality traits, significant differences were found in efficiency to regulate aggressive behavior on the basis of gender and feelings of provocation.

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