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Using DEA and financial ratings for credit risk evaluation: an empirical analysis

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  • Gianpaolo Iazzolino
  • Maria Elena Bruni
  • Patrizia Beraldi

Abstract

The article deals with the methodologies for credit risk evaluation. It describes an empirical analysis carried out on a sample of Italian firms belonging to the leather manufacturing and wholesale industry. The study uses the efficiency, calculated through data envelopment analysis (DEA), and the credit rating at the same time. As long as efficiency is calculated by using inputs and outputs strictly linked to the credit reliability of the firm, the study confirms that there is a relationship between efficiency and credit rating, and then that efficiency can be considered as an early warning index for evaluating credit risk.

Suggested Citation

  • Gianpaolo Iazzolino & Maria Elena Bruni & Patrizia Beraldi, 2013. "Using DEA and financial ratings for credit risk evaluation: an empirical analysis," Applied Economics Letters, Taylor & Francis Journals, vol. 20(14), pages 1310-1317, September.
    • Handle: RePEc:taf:apeclt:v:20:y:2013:i:14:p:1310-1317
    DOI: 10.1080/13504851.2013.806771
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    References listed on IDEAS

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    7. Bruni, M.E. & Conforti, D. & Beraldi, P. & Tundis, E., 2009. "Probabilistically constrained models for efficiency and dominance in DEA," International Journal of Production Economics, Elsevier, vol. 117(1), pages 219-228, January.
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    Cited by:

    1. Dimitris Andriosopoulos & Michalis Doumpos & Panos M. Pardalos & Constantin Zopounidis, 2019. "Computational approaches and data analytics in financial services: A literature review," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(10), pages 1581-1599, October.
    2. Aziza ZHUPAROVA & Rimma SAGIYEVA & Dinara ZHAISANOVA, 2019. "The Impact Of The Knowledge Economy Indicators On Regional Economic Growth: Evidence From Kazakhstan," Proceedings of the INTERNATIONAL MANAGEMENT CONFERENCE, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, vol. 13(1), pages 514-520, November.
    3. P. Beraldi & M. E. Bruni, 2020. "Efficiency evaluation under uncertainty: a stochastic DEA approach," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 43(2), pages 519-538, December.
    4. Ioannis E. Tsolas, 2021. "Firm Credit Scoring: A Series Two-Stage DEA Bootstrapped Approach," JRFM, MDPI, vol. 14(5), pages 1-12, May.
    5. Galymkair Mutanov & Aziza Zhuparova & Dinara Zhaisanova, 2020. "Measuring the Knowledge-Based Performance Efficiency in the Oil-Exported Countries," Montenegrin Journal of Economics, Economic Laboratory for Transition Research (ELIT), vol. 16(3), pages 109-122.
    6. Zhang, Faming & Tadikamalla, Pandu R. & Shang, Jennifer, 2016. "Corporate credit-risk evaluation system: Integrating explicit and implicit financial performances," International Journal of Production Economics, Elsevier, vol. 177(C), pages 77-100.
    7. Gianpaolo Iazzolino & Rossella Gabriele, 2016. "Energy Efficiency and Sustainable Development: An Analysis of Financial Reliability in Energy Service Companies Industry," International Journal of Energy Economics and Policy, Econjournals, vol. 6(2), pages 222-233.
    8. Ioannis Tsolas, 2015. "Firm credit risk evaluation: a series two-stage DEA modeling framework," Annals of Operations Research, Springer, vol. 233(1), pages 483-500, October.
    9. Yali Cao & Yue Shao & Hongxia Zhang, 2022. "Study on early warning of E-commerce enterprise financial risk based on deep learning algorithm," Electronic Commerce Research, Springer, vol. 22(1), pages 21-36, March.
    10. Liu, Jiawen & Gong, Yeming (Yale) & Zhu, Joe & Zhang, Jinlong, 2018. "A DEA-based approach for competitive environment analysis in global operations strategies," International Journal of Production Economics, Elsevier, vol. 203(C), pages 110-123.
    11. Antonio Angelo Romano & Giuseppe Scandurra & Alfonso Carfora, 2016. "Estimating the Impact of Feed-in Tariff Adoption: Similarities and Divergences among Countries through a Propensity-score Matching Method," International Journal of Energy Economics and Policy, Econjournals, vol. 6(2), pages 144-151.

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