Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/239630 
Year of Publication: 
2021
Citation: 
[Journal:] Journal of Risk and Financial Management [ISSN:] 1911-8074 [Volume:] 14 [Issue:] 5 [Publisher:] MDPI [Place:] Basel [Year:] 2021 [Pages:] 1-12
Publisher: 
MDPI, Basel
Abstract: 
This paper employs a cross-sectional research design to collect quantitative data for a group of Greek pharmaceutical companies in order to evaluate their credit risk. The data are processed using a variety of quantitative approaches, including series two-stage data envelopment analysis (DEA) combined with bootstrap and hierarchical clustering. The results of the two-stage DEA bootstrapped analysis indicate that the key problem with the firms' performance is a lack of effectiveness rather than operating efficiency. The lack of a correlation between operating efficiency and effectiveness indicates that the firms' performance metrics are unrelated. As a result, a bootstrapped DEA-based synthetic indicator is developed to be used with the other performance metrics as inputs to hierarchical clustering to divide sample firms into credit risk clusters. The series two-stage DEA bootstrapped approach used in this study could aid firms in evaluating their performance and increasing their competitive advantages.
Subjects: 
data envelopment analysis
credit risk
bootstrap
Greece
pharmaceutical firms
Persistent Identifier of the first edition: 
Creative Commons License: 
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Document Type: 
Article

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