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Firm Credit Scoring: A Series Two-Stage DEA Bootstrapped Approach

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  • Ioannis E. Tsolas

    (School of Applied Mathematics and Physics, National Technical University of Athens, 15780 Athens, Greece)

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.

Suggested Citation

  • Ioannis E. Tsolas, 2021. "Firm Credit Scoring: A Series Two-Stage DEA Bootstrapped Approach," JRFM, MDPI, vol. 14(5), pages 1-12, May.
    • Handle: RePEc:gam:jjrfmx:v:14:y:2021:i:5:p:214-:d:551338
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    References listed on IDEAS

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