IDEAS home Printed from https://ideas.repec.org/a/kap/compec/v66y2025i1d10.1007_s10614-024-10700-7.html

Understanding and Attaining an Investment Grade Rating in the Age of Explainable AI

Author

Listed:
  • Ravi Makwana

    (DA-IICT)

  • Dhruvil Bhatt

    (DA-IICT
    University of California)

  • Kirtan Delwadia

    (DA-IICT
    University of Southern California)

  • Agam Shah

    (Georgia Institute of Technology)

  • Bhaskar Chaudhury

    (DA-IICT)

Abstract

Specialized agencies issue corporate credit ratings to evaluate the creditworthiness of a company, serving as a crucial financial indicator for potential investors. These ratings offer a tangible understanding of the risks associated with the credit investment returns of a company. Every company aims to achieve a favorable credit rating, as it enables them to attract more investments and reduce their cost of capital. Credit rating agencies typically employ unique rating scales that are broadly categorized into investment-grade or non-investment-grade (junk) classes. Given the extensive assessment conducted by credit rating agencies, it becomes a challenge for companies to formulate a straightforward and all-encompassing set of rules which may help to understand and improve their credit rating. This paper employs explainable AI, specifically decision trees, using historical data to establish an empirical rule on financial ratios. The rule obtained using the proposed approach can be effectively utilized to understand as well as plan and attain an investment-grade rating. Additionally, the study investigates the temporal aspect by identifying the optimal time window for training data. As the availability of structured data for temporal analysis is currently limited, this study addresses this challenge by creating a large and high-quality curated dataset. This dataset serves as a valuable resource for conducting comprehensive temporal analysis. Our analysis demonstrates that the empirical rule derived from historical data, yields a high precision value, and therefore highlights the effectiveness of our proposed approach as a valuable guideline and a feasible decision support system.

Suggested Citation

  • Ravi Makwana & Dhruvil Bhatt & Kirtan Delwadia & Agam Shah & Bhaskar Chaudhury, 2025. "Understanding and Attaining an Investment Grade Rating in the Age of Explainable AI," Computational Economics, Springer;Society for Computational Economics, vol. 66(1), pages 105-126, July.
    • Handle: RePEc:kap:compec:v:66:y:2025:i:1:d:10.1007_s10614-024-10700-7
    DOI: 10.1007/s10614-024-10700-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10614-024-10700-7
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10614-024-10700-7?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Mizen, Paul & Tsoukas, Serafeim, 2012. "Forecasting US bond default ratings allowing for previous and initial state dependence in an ordered probit model," International Journal of Forecasting, Elsevier, vol. 28(1), pages 273-287.
    2. Gonzalez, F. & Haas, F. & Johannes, R. & Persson, M. & Toledo, L. & Violi, R. & Zins, C. & Wieland, M., 2004. "Market dynamics associated with credit ratings: a literature review," Financial Stability Review, Banque de France, issue 4, pages 53-76, June.
    3. Parisa Golbayani & Dan Wang & Ionut Florescu, 2020. "Application of Deep Neural Networks to assess corporate Credit Rating," Papers 2003.02334, arXiv.org.
    4. Kim, Hong Sik & Sohn, So Young, 2010. "Support vector machines for default prediction of SMEs based on technology credit," European Journal of Operational Research, Elsevier, vol. 201(3), pages 838-846, March.
    5. 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.
    6. Niklas Bussmann & Paolo Giudici & Dimitri Marinelli & Jochen Papenbrock, 2021. "Explainable Machine Learning in Credit Risk Management," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 203-216, January.
    7. 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.
    8. Ken Hung & Hui Wen Cheng & Shih-shen Chen & Ying-Chen Huang, 2013. "Factors that Affect Credit Rating: An Application of Ordered Probit Models," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 94-108, December.
    9. 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.
    10. Gonzalez, F. & Haas, F. & Johannes, R. & Persson, M. & Toledo, L. & Violi, R. & Zins, C. & Wieland, M., 2004. "Market dynamics associated with credit ratings: a literature review," Financial Stability Review, Banque de France, issue 4, pages 53-76, June.
    Full references (including those not matched with items on IDEAS)

    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. Mizen, Paul & Tsoukas, Serafeim, 2012. "Forecasting US bond default ratings allowing for previous and initial state dependence in an ordered probit model," International Journal of Forecasting, Elsevier, vol. 28(1), pages 273-287.
    2. Alina Mihaela Dima & Simona Vasilache, 2016. "Credit Risk modeling for Companies Default Prediction using Neural Networks," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 127-143, September.
    3. Long Ren & Shaojie Cong & Xinlong Xue & Daqing Gong, 2024. "Credit rating prediction with supply chain information: a machine learning perspective," Annals of Operations Research, Springer, vol. 342(1), pages 657-686, November.
    4. Eleimon Gonis & Salima Paul & Jon Tucker, 2012. "Rating or no rating? That is the question: an empirical examination of UK companies," The European Journal of Finance, Taylor & Francis Journals, vol. 18(8), pages 709-735, September.
    5. Dalila Boughaci & Abdullah A. K. Alkhawaldeh & Jamil J. Jaber & Nawaf Hamadneh, 2021. "Classification with segmentation for credit scoring and bankruptcy prediction," Empirical Economics, Springer, vol. 61(3), pages 1281-1309, September.
    6. Ruey-Ching Hwang, 2013. "Forecasting credit ratings with the varying-coefficient model," Quantitative Finance, Taylor & Francis Journals, vol. 13(12), pages 1947-1965, December.
    7. Barth, Mary E. & Beaver, William H. & Landsman, Wayne R., 2001. "The relevance of the value relevance literature for financial accounting standard setting: another view," Journal of Accounting and Economics, Elsevier, vol. 31(1-3), pages 77-104, September.
    8. Akkoç, Soner, 2012. "An empirical comparison of conventional techniques, neural networks and the three stage hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) model for credit scoring analysis: The case of Turkish credit card data," European Journal of Operational Research, Elsevier, vol. 222(1), pages 168-178.
    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. Huaming Du & Cancan Feng & Yuqian Lei & Chenyang Zhang & Guisong Liu & Gang Kou & Carl Yang & Yu Zhao, 2022. "A Comprehensive Survey on Enterprise Financial Risk Analysis from Big Data and LLMs Perspective," Papers 2211.14997, arXiv.org, revised Mar 2026.
    11. David Veganzones, 2022. "Corporate failure prediction using threshold‐based models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(5), pages 956-979, August.
    12. Barth, Mary E. & Beaver, William H. & Landsman, Wayne R., 1998. "Relative valuation roles of equity book value and net income as a function of financial health," Journal of Accounting and Economics, Elsevier, vol. 25(1), pages 1-34, February.
    13. Poon, Winnie P. H. & Firth, Michael & Fung, Hung-Gay, 1999. "A multivariate analysis of the determinants of Moody's bank financial strength ratings," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 9(3), pages 267-283, August.
    14. Jiaqi Kuang & Zihao Guo & Jinghan Wang & Yezhen Wang & Kaiwen Zhang, 2026. "Financial distress prediction using signatures: evidence from Chinese listed firms," Risk Management, Palgrave Macmillan, vol. 28(1), pages 1-27, February.
    15. repec:hum:wpaper:sfb649dp2013-002 is not listed on IDEAS
    16. 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.
    17. Van Laere, Elisabeth & Baesens, Bart, 2010. "The development of a simple and intuitive rating system under Solvency II," Insurance: Mathematics and Economics, Elsevier, vol. 46(3), pages 500-510, June.
    18. Ken Hung & Hui Wen Cheng & Shih-shen Chen & Ying-Chen Huang, 2013. "Factors that Affect Credit Rating: An Application of Ordered Probit Models," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 94-108, December.
    19. Katarina Valaskova & Dominika Gajdosikova & Jaroslav Belas, 2023. "Bankruptcy prediction in the post-pandemic period: A case study of Visegrad Group countries," Oeconomia Copernicana, Institute of Economic Research, vol. 14(1), pages 253-293, March.
    20. 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.
    21. Iulian Viorel Brasoveanu & Florin Dobre & Laura Brad, 2014. "Increasing Financial Audit Quality Using A New Model To Estimate Financial Performance," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 88-107, October.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;

    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:kap:compec:v:66:y:2025:i:1:d:10.1007_s10614-024-10700-7. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.