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A comparative study of corporate credit ratings prediction with machine learning

Author

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  • Seyyide Doğan
  • Yasin Büyükkör
  • Murat Atan

Abstract

Credit scores are critical for financial sector investors and government officials, it is important to develop reliable, transparent and appropriate tools for obtaining ratings. The aim of this study is to predict company credit scores with machine learning and modern statistical methods, both in sectoral and aggregated data. Analyzes are made on 1881 companies operating in three different sectors that applied for loans from Turkey's largest public bank. The results of the experiment are compared in terms of classification accuracy, sensitivity, specivity, precision, and Mathews correlation coefficient. When credit ratings are estimated on sectoral basis, it is observed that the classification rate changes considerably. Considering the analysis results, it is seen that Logistic Regression Analysis, Support Vector Machines, Random Forest and XGBoost have better performance than Decision Tree and k-Nearest Neighbour for all data sets.

Suggested Citation

  • Seyyide Doğan & Yasin Büyükkör & Murat Atan, 2022. "A comparative study of corporate credit ratings prediction with machine learning," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 32(1), pages 25-47.
    • Handle: RePEc:wut:journl:v:32:y:2022:i:1:p:25-47:id:2643
    DOI: 10.37190/ord220102
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    References listed on IDEAS

    as
    1. Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
    2. Ederington, Louis H, 1985. "Classification Models and Bond Ratings," The Financial Review, Eastern Finance Association, vol. 20(4), pages 237-262, November.
    3. Lawrence Fisher, 1959. "Determinants of Risk Premiums on Corporate Bonds," Journal of Political Economy, University of Chicago Press, vol. 67, pages 217-217.
    4. Parisa Golbayani & Ionuc{t} Florescu & Rupak Chatterjee, 2020. "A comparative study of forecasting Corporate Credit Ratings using Neural Networks, Support Vector Machines, and Decision Trees," Papers 2007.06617, arXiv.org.
    5. Dominique Guegan & Peter Martey Addo & Bertrand Hassani, 2018. "Credit Risk Analysis Using Machine and Deep Learning Models," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01835164, HAL.
    6. Dominique Guegan, 2018. "Credit Risk Analysis Using machine and Deep Learning Models," Post-Print halshs-01889154, HAL.
    7. Peter Martey Addo & Dominique Guegan & Bertrand Hassani, 2018. "Credit Risk Analysis using Machine and Deep learning models," Working Papers 2018:08, Department of Economics, University of Venice "Ca' Foscari".
    8. P. Kalaivani & K.L. Shunmuganathan, 2016. "Feature selection based on genetic algorithm and hybrid model for sentiment polarity classification," International Journal of Data Mining, Modelling and Management, Inderscience Enterprises Ltd, vol. 8(4), pages 315-329.
    9. Peter Martey Addo & Dominique Guegan & Bertrand Hassani, 2018. "Credit Risk Analysis using Machine and Deep Learning models," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01719983, HAL.
    10. Seung Hun Han & Michael S. Pagano & Yoon S. Shin, 2012. "Rating Agency Reputation, the Global Financial Crisis, and the Cost of Debt," Financial Management, Financial Management Association International, vol. 41(4), pages 849-884, December.
    11. Dominique Guegan & Peter Martey Addo & Bertrand Hassani, 2018. "Credit Risk Analysis Using Machine and Deep Learning Models," Post-Print halshs-01835164, HAL.
    12. Gentry, James A & Whitford, David T & Newbold, Paul, 1988. "Predicting Industrial Bond Ratings with a Probit Model and Funds Flow Components," The Financial Review, Eastern Finance Association, vol. 23(3), pages 269-286, August.
    13. Dominique Guegan, 2018. "Credit Risk Analysis Using machine and Deep Learning Models," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01889154, HAL.
    14. Peter Martey Addo & Dominique Guegan & Bertrand Hassani, 2018. "Credit Risk Analysis using Machine and Deep Learning models," Post-Print halshs-01719983, HAL.
    15. Maisa Cardoso Aniceto & Flavio Barboza & Herbert Kimura, 2020. "Machine learning predictivity applied to consumer creditworthiness," Future Business Journal, Springer, vol. 6(1), pages 1-14, December.
    16. Pinches, George E & Mingo, Kent A, 1973. "A Multivariate Analysis of Industrial Bond Ratings," Journal of Finance, American Finance Association, vol. 28(1), pages 1-18, March.
    17. Peter Martey Addo & Dominique Guégan & Bertrand Hassani, 2018. "Credit Risk Analysis using Machine and Deep learning models," Documents de travail du Centre d'Economie de la Sorbonne 18003, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    18. Iryna Yanenkova & Yuliia Nehoda & Svetlana Drobyazko & Andrii Zavhorodnii & Lyudmyla Berezovska, 2021. "Modeling of Bank Credit Risk Management Using the Cost Risk Model," JRFM, MDPI, vol. 14(5), pages 1-15, May.
    19. Ionuț Nica & Daniela Blană Alexandru & Simona Liliana Paramon Crăciunescu & Ștefan Ionescu, 2021. "Automated Valuation Modelling: Analysing Mortgage Behavioural Life Profile Models Using Machine Learning Techniques," Sustainability, MDPI, vol. 13(9), pages 1-27, May.
    20. G. V. Kass, 1980. "An Exploratory Technique for Investigating Large Quantities of Categorical Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 29(2), pages 119-127, June.
    21. Takeaki Kariya & Yoshiro Yamamura & Koji Inui, 2019. "Empirical Credit Risk Ratings of Individual Corporate Bonds and Derivation of Term Structures of Default Probabilities," JRFM, MDPI, vol. 12(3), pages 1-29, July.
    22. Golbayani, Parisa & Florescu, Ionuţ & Chatterjee, Rupak, 2020. "A comparative study of forecasting corporate credit ratings using neural networks, support vector machines, and decision trees," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    23. Peter Martey Addo & Dominique Guegan & Bertrand Hassani, 2018. "Credit Risk Analysis Using Machine and Deep Learning Models," Risks, MDPI, vol. 6(2), pages 1-20, April.
    24. Jian Shi & Benlian Xu, 2016. "Credit Scoring by Fuzzy Support Vector Machines with a Novel Membership Function," JRFM, MDPI, vol. 9(4), pages 1-10, November.
    25. 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.
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