Firm credit scoring: A series two-stage DEA bootstrapped approach

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) combin...

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Bibliographic Details
Published in:Journal of risk and financial management 2021-05, Vol.14 (5), p.1-12
Main Author: Tsolas, Ioannis E
Format: Article
Language:English
Subjects:
Artificial intelligence
Bankruptcy
bootstrap
Business metrics
Credit ratings
Credit risk
Credit scoring
Data envelopment analysis
Decision making
Default
Discriminant analysis
Efficiency
Greece
Network topologies
Neural networks
Performance evaluation
pharmaceutical firms
Pharmaceutical industry
Regression analysis
Risk assessment
Statistical methods
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Summary: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.
ISSN:1911-8074
1911-8066
1911-8074
DOI:10.3390/jrfm14050214