IDEAS home Printed from https://ideas.repec.org/a/wut/journl/v32y2022i1p25-47id2643.html
   My bibliography  Save this article

A comparative study of corporate credit ratings prediction with machine learning

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://ord.pwr.edu.pl/assets/papers_archive/2643%20-%20published.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.37190/ord220102?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
    ---><---

    References listed on IDEAS

    as
    1. Ederington, Louis H, 1985. "Classification Models and Bond Ratings," The Financial Review, Eastern Finance Association, vol. 20(4), pages 237-262, November.
    2. Lawrence Fisher, 1959. "Determinants of Risk Premiums on Corporate Bonds," Journal of Political Economy, University of Chicago Press, vol. 67(3), pages 217-217.
    3. 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.
    4. 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".
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. Dominique Guegan, 2018. "Credit Risk Analysis Using machine and Deep Learning Models," Post-Print halshs-01889154, HAL.
    10. Dominique Guegan & Peter Martey Addo & Bertrand Hassani, 2018. "Credit Risk Analysis Using Machine and Deep Learning Models," Post-Print halshs-01835164, HAL.
    11. 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.
    12. 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.
    13. 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.
    14. 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).
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    20. 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.
    21. 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.
    22. 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.
    23. Dominique Guegan, 2018. "Credit Risk Analysis Using machine and Deep Learning Models," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01889154, HAL.
    24. Peter Martey Addo & Dominique Guegan & Bertrand Hassani, 2018. "Credit Risk Analysis using Machine and Deep Learning models," Post-Print halshs-01719983, HAL.
    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.
    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. Davidescu Adriana AnaMaria & Agafiței Marina-Diana & Strat Vasile Alecsandru & Dima Alina Mihaela, 2024. "Mapping the Landscape: A Bibliometric Analysis of Rating Agencies in the Era of Artificial Intelligence and Machine Learning," Proceedings of the International Conference on Business Excellence, Sciendo, vol. 18(1), pages 67-85.

    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. Dan Wang & Zhi Chen & Ionut Florescu, 2021. "A Sparsity Algorithm with Applications to Corporate Credit Rating," Papers 2107.10306, arXiv.org.
    2. Ștefan Ionescu & Nora Chiriță & Ionuț Nica & Camelia Delcea, 2023. "An Analysis of Residual Financial Contagion in Romania’s Banking Market for Mortgage Loans," Sustainability, MDPI, vol. 15(15), pages 1-32, August.
    3. 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.
    4. Roy Cerqueti & Francesca Pampurini & Annagiulia Pezzola & Anna Grazia Quaranta, 2022. "Dangerous liasons and hot customers for banks," Review of Quantitative Finance and Accounting, Springer, vol. 59(1), pages 65-89, July.
    5. Keerthana Sivamayil & Elakkiya Rajasekar & Belqasem Aljafari & Srete Nikolovski & Subramaniyaswamy Vairavasundaram & Indragandhi Vairavasundaram, 2023. "A Systematic Study on Reinforcement Learning Based Applications," Energies, MDPI, vol. 16(3), pages 1-23, February.
    6. Amirhosein Mosavi & Yaser Faghan & Pedram Ghamisi & Puhong Duan & Sina Faizollahzadeh Ardabili & Ely Salwana & Shahab S. Band, 2020. "Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics," Mathematics, MDPI, vol. 8(10), pages 1-42, September.
    7. Anastasios Petropoulos & Vasilis Siakoulis & Evaggelos Stavroulakis & Aristotelis Klamargias, 2019. "A robust machine learning approach for credit risk analysis of large loan level datasets using deep learning and extreme gradient boosting," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Are post-crisis statistical initiatives completed?, volume 49, Bank for International Settlements.
    8. Anastasios Petropoulos & Vasilis Siakoulis & Evaggelos Stavroulakis & Aristotelis Klamargias, 2019. "A robust machine learning approach for credit risk analysis of large loan-level datasets using deep learning and extreme gradient boosting," IFC Bulletins chapters, in: Bank for International Settlements (ed.), The use of big data analytics and artificial intelligence in central banking, volume 50, Bank for International Settlements.
    9. Nenad Milojević & Srdjan Redzepagic, 2021. "Prospects of Artificial Intelligence and Machine Learning Application in Banking Risk Management," Journal of Central Banking Theory and Practice, Central bank of Montenegro, vol. 10(3), pages 41-57.
    10. Irving Fisher Committee, 2019. "The use of big data analytics and artificial intelligence in central banking," IFC Bulletins, Bank for International Settlements, number 50.
    11. Yaseen Ghulam & Kamini Dhruva & Sana Naseem & Sophie Hill, 2018. "The Interaction of Borrower and Loan Characteristics in Predicting Risks of Subprime Automobile Loans," Risks, MDPI, vol. 6(3), pages 1-21, September.
    12. Li-Chen Cheng & Wei-Ting Lu & Benjamin Yeo, 2023. "Predicting abnormal trading behavior from internet rumor propagation: a machine learning approach," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-23, December.
    13. Roman P. Bulyga & Alexey A. Sitnov & Liudmila V. Kashirskaya & Irina V. Safonova, 2020. "Transparency of credit institutions," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 7(4), pages 3158-3172, June.
    14. Revathi Bhuvaneswari & Antonio Segalini, 2020. "Determining Secondary Attributes for Credit Evaluation in P2P Lending," Papers 2006.13921, arXiv.org.
    15. 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.
    16. K. S. Naik, 2021. "Predicting Credit Risk for Unsecured Lending: A Machine Learning Approach," Papers 2110.02206, arXiv.org.
    17. Kolesnikova, A. & Yang, Y. & Lessmann, S. & Ma, T. & Sung, M.-C. & Johnson, J.E.V., 2019. "Can Deep Learning Predict Risky Retail Investors? A Case Study in Financial Risk Behavior Forecasting," IRTG 1792 Discussion Papers 2019-023, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    18. Xu Chen & Chunhong Liu & Changchun Gao & Yao Jiang, 2021. "Mechanism Underlying the Formation of Virtual Agglomeration of Creative Industries: Theoretical Analysis and Empirical Research," Sustainability, MDPI, vol. 13(4), pages 1-21, February.
    19. Chen, Shunqin & Guo, Zhengfeng & Zhao, Xinlei, 2021. "Predicting mortgage early delinquency with machine learning methods," European Journal of Operational Research, Elsevier, vol. 290(1), pages 358-372.
    20. 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).

    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:wut:journl:v:32:y:2022:i:1:p:25-47:id:2643. 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: Adam Kasperski (email available below). General contact details of provider: https://edirc.repec.org/data/iopwrpl.html .

    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.