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Combining accounting data and a structural model for predicting credit ratings: Empirical evidence from European listed firms

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  • Doumpos, Michael
  • Niklis, Dimitrios
  • Zopounidis, Constantin
  • Andriosopoulos, Kostas

Abstract

Ratings issued by credit rating agencies (CRAs) play an important role in the global financial environment. Among other issues, past studies have explored the potential for predicting these ratings using a variety of explanatory factors and modeling approaches. This paper describes a multi-criteria classification approach that combines accounting data with a structural default prediction model in order to obtain improved predictions and test the incremental information that a structural model provides in this context. Empirical results are presented for a panel data set of European listed firms during the period 2002–2012. The analysis indicates that a distance-to-default measure obtained from a structural model adds significant information compared to popular financial ratios. Nevertheless, its power is considerably weakened when market capitalization is also considered. The robustness of the results is examined over time and under different rating category specifications.

Suggested Citation

  • Doumpos, Michael & Niklis, Dimitrios & Zopounidis, Constantin & Andriosopoulos, Kostas, 2015. "Combining accounting data and a structural model for predicting credit ratings: Empirical evidence from European listed firms," Journal of Banking & Finance, Elsevier, vol. 50(C), pages 599-607.
    • Handle: RePEc:eee:jbfina:v:50:y:2015:i:c:p:599-607
    DOI: 10.1016/j.jbankfin.2014.01.010
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    Cited by:

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    2. Modina, Michele & Pietrovito, Filomena & Gallucci, Carmen & Formisano, Vincenzo, 2023. "Predicting SMEs’ default risk: Evidence from bank-firm relationship data," The Quarterly Review of Economics and Finance, Elsevier, vol. 89(C), pages 254-268.
    3. 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.
    4. Charlie X. Cai & Paul B. McGuinness & Qi Zhang, 2018. "Credit scores and the performance of newly-listed stocks: an exploration of the Chinese A-share market," Review of Quantitative Finance and Accounting, Springer, vol. 51(1), pages 79-111, July.
    5. Carbone, Sante & Giuzio, Margherita & Kapadia, Sujit & Krämer, Johannes Sebastian & Nyholm, Ken & Vozian, Katia, 2021. "The low-carbon transition, climate commitments and firm credit risk," Working Paper Series 2631, European Central Bank.
    6. Carlo Alberto Magni & Stefano Malagoli & Andrea Marchioni & Giovanni Mastroleo, 2020. "Rating firms and sensitivity analysis," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 71(12), pages 1940-1958, December.
    7. Jiang, Cuiqing & Lyu, Ximei & Yuan, Yufei & Wang, Zhao & Ding, Yong, 2022. "Mining semantic features in current reports for financial distress prediction: Empirical evidence from unlisted public firms in China," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1086-1099.
    8. Kadziński, Miłosz & Ciomek, Krzysztof, 2021. "Active learning strategies for interactive elicitation of assignment examples for threshold-based multiple criteria sorting," European Journal of Operational Research, Elsevier, vol. 293(2), pages 658-680.
    9. Balios, Dimitris & Thomadakis, Stavros & Tsipouri, Lena, 2016. "Credit rating model development: An ordered analysis based on accounting data," Research in International Business and Finance, Elsevier, vol. 38(C), pages 122-136.
    10. Yan Liu & Zhan-jiang Li & Xue-jun Zhen, 2018. "Empirical Study on Indicators Selection Model Based on Nonparametric -Nearest Neighbor Identification and R Clustering Analysis," Complexity, Hindawi, vol. 2018, pages 1-9, April.
    11. Sim, Jaehun & Kim, Chae-Soo, 2019. "The value of renewable energy research and development investments with default consideration," Renewable Energy, Elsevier, vol. 143(C), pages 530-539.
    12. Yukiko Konno & Yuki Itoh, 2016. "An alternative to the standardized approach for assessing credit risk under the Basel Accords," Cogent Economics & Finance, Taylor & Francis Journals, vol. 4(1), pages 1220119-122, December.
    13. Jaspreet Kaur & Madhu Vij & Ajay Kumar Chauhan, 2023. "Signals influencing corporate credit ratings—a systematic literature review," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 50(1), pages 91-114, March.
    14. Sermpinis, Georgios & Tsoukas, Serafeim & Zhang, Ping, 2018. "Modelling market implied ratings using LASSO variable selection techniques," Journal of Empirical Finance, Elsevier, vol. 48(C), pages 19-35.
    15. Alessandro Bitetto & Stefano Filomeni & Michele Modina, 2021. "Understanding corporate default using Random Forest: The role of accounting and market information," DEM Working Papers Series 205, University of Pavia, Department of Economics and Management.
    16. Doumpos, Michalis & Figueira, José Rui, 2019. "A multicriteria outranking approach for modeling corporate credit ratings: An application of the Electre Tri-nC method," Omega, Elsevier, vol. 82(C), pages 166-180.

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    More about this item

    Keywords

    Credit ratings; Rating agencies; Black–Scholes–Merton model; Multi-criteria decision making;
    All these keywords.

    JEL classification:

    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • G24 - Financial Economics - - Financial Institutions and Services - - - Investment Banking; Venture Capital; Brokerage
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing

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