IDEAS home Printed from https://ideas.repec.org/a/taf/apeclt/v20y2013i14p1310-1317.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/13504851.2013.806771
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/13504851.2013.806771?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Premachandra, I.M. & Bhabra, Gurmeet Singh & Sueyoshi, Toshiyuki, 2009. "DEA as a tool for bankruptcy assessment: A comparative study with logistic regression technique," European Journal of Operational Research, Elsevier, vol. 193(2), pages 412-424, March.
    2. Avkiran, Necmi K., 2011. "Association of DEA super-efficiency estimates with financial ratios: Investigating the case for Chinese banks," Omega, Elsevier, vol. 39(3), pages 323-334, June.
    3. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    4. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    5. Kao, Chiang & Liu, Shiang-Tai, 2004. "Predicting bank performance with financial forecasts: A case of Taiwan commercial banks," Journal of Banking & Finance, Elsevier, vol. 28(10), pages 2353-2368, October.
    6. Joseph Paradi & Mette Asmild & Paul Simak, 2004. "Using DEA and Worst Practice DEA in Credit Risk Evaluation," Journal of Productivity Analysis, Springer, vol. 21(2), pages 153-165, March.
    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.
    8. Yung-Ho Chiu & Chun-Mei Ma & Ming-Yuan Sun, 2010. "Efficiency and credit rating in Taiwan banking: data envelopment analysis estimation," Applied Economics, Taylor & Francis Journals, vol. 42(20), pages 2587-2600.
    9. Kelly Rae Chi, 2010. "A systems approach," Nature, Nature, vol. 464(7291), pages 1090-1091, April.
    10. Charnes, A. & Cooper, W. W. & Seiford, L. & Stutz, J., 1982. "A multiplicative model for efficiency analysis," Socio-Economic Planning Sciences, Elsevier, vol. 16(5), pages 223-224.
    11. John Ruggiero, 2004. "Data envelopment analysis with stochastic data," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(9), pages 1008-1012, September.
    12. Psillaki, Maria & Tsolas, Ioannis E. & Margaritis, Dimitris, 2010. "Evaluation of credit risk based on firm performance," European Journal of Operational Research, Elsevier, vol. 201(3), pages 873-881, March.
    13. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhiyong Li & Chen Feng & Ying Tang, 2022. "Bank efficiency and failure prediction: a nonparametric and dynamic model based on data envelopment analysis," Annals of Operations Research, Springer, vol. 315(1), pages 279-315, August.
    2. 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.
    3. Mousavi, Mohammad M. & Ouenniche, Jamal & Xu, Bing, 2015. "Performance evaluation of bankruptcy prediction models: An orientation-free super-efficiency DEA-based framework," International Review of Financial Analysis, Elsevier, vol. 42(C), pages 64-75.
    4. Premachandra, I.M. & Chen, Yao & Watson, John, 2011. "DEA as a tool for predicting corporate failure and success: A case of bankruptcy assessment," Omega, Elsevier, vol. 39(6), pages 620-626, December.
    5. Necmi Avkiran & Lin Cai, 2014. "Identifying distress among banks prior to a major crisis using non-oriented super-SBM," Annals of Operations Research, Springer, vol. 217(1), pages 31-53, June.
    6. Eling, Martin & Jia, Ruo, 2018. "Business failure, efficiency, and volatility: Evidence from the European insurance industry," International Review of Financial Analysis, Elsevier, vol. 59(C), pages 58-76.
    7. Mohammad Mahdi Mousavi & Jamal Ouenniche, 2018. "Multi-criteria ranking of corporate distress prediction models: empirical evaluation and methodological contributions," Annals of Operations Research, Springer, vol. 271(2), pages 853-886, December.
    8. Ioannis E. Tsolas, 2021. "Firm Credit Scoring: A Series Two-Stage DEA Bootstrapped Approach," JRFM, MDPI, vol. 14(5), pages 1-12, May.
    9. Róbert Štefko & Jarmila Horváthová & Martina Mokrišová, 2021. "The Application of Graphic Methods and the DEA in Predicting the Risk of Bankruptcy," JRFM, MDPI, vol. 14(5), pages 1-19, May.
    10. Róbert Štefko & Jarmila Horváthová & Martina Mokrišová, 2020. "Bankruptcy Prediction with the Use of Data Envelopment Analysis: An Empirical Study of Slovak Businesses," JRFM, MDPI, vol. 13(9), pages 1-15, September.
    11. Huang, Chao & Dai, Chong & Guo, Miao, 2015. "A hybrid approach using two-level DEA for financial failure prediction and integrated SE-DEA and GCA for indicators selection," Applied Mathematics and Computation, Elsevier, vol. 251(C), pages 431-441.
    12. Mai, Nhat Chi, 2015. "Efficiency of the banking system in Vietnam under financial liberalization," OSF Preprints qsf6d, Center for Open Science.
    13. Mai, Feng & Tian, Shaonan & Lee, Chihoon & Ma, Ling, 2019. "Deep learning models for bankruptcy prediction using textual disclosures," European Journal of Operational Research, Elsevier, vol. 274(2), pages 743-758.
    14. Kavčáková, Michaela & Kočišová, Kristína, 2020. "Using Data Envelopment Analysis in Credit Risk Evaluation of ICT Companies," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 10(3), December.
    15. Sueyoshi, Toshiyuki & Goto, Mika, 2009. "Methodological comparison between DEA (data envelopment analysis) and DEA-DA (discriminant analysis) from the perspective of bankruptcy assessment," European Journal of Operational Research, Elsevier, vol. 199(2), pages 561-575, December.
    16. Sueyoshi, Toshiyuki & Goto, Mika, 2009. "DEA-DA for bankruptcy-based performance assessment: Misclassification analysis of Japanese construction industry," European Journal of Operational Research, Elsevier, vol. 199(2), pages 576-594, December.
    17. Jamal Ouenniche & Kaoru Tone, 2017. "An out-of-sample evaluation framework for DEA with application in bankruptcy prediction," Annals of Operations Research, Springer, vol. 254(1), pages 235-250, July.
    18. Ravi Kumar, P. & Ravi, V., 2007. "Bankruptcy prediction in banks and firms via statistical and intelligent techniques - A review," European Journal of Operational Research, Elsevier, vol. 180(1), pages 1-28, July.
    19. fernández, María t. Tascón & gutiérrez, Francisco J. Castaño, 2012. "Variables y Modelos Para La Identificación y Predicción Del Fracaso Empresarial: Revisión de La Investigación Empírica Reciente," Revista de Contabilidad - Spanish Accounting Review, Elsevier, vol. 15(1), pages 7-58.
    20. Premachandra, I.M. & Bhabra, Gurmeet Singh & Sueyoshi, Toshiyuki, 2009. "DEA as a tool for bankruptcy assessment: A comparative study with logistic regression technique," European Journal of Operational Research, Elsevier, vol. 193(2), pages 412-424, March.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:apeclt:v:20:y:2013:i:14:p:1310-1317. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RAEL20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.