Prediction of corporate credit ratings with machine learning: Simple interpretative models

Koresh Galil, Ami Hauptman and Rosit Levy Rosenboim

Finance Research Letters, 2023, vol. 58, issue PD

Abstract: This study utilizes machine learning techniques, notably classification and regression trees (CART) and support vector regression (SVR), to predict corporate credit ratings. While SVR marginally outperforms in accuracy, CART offers interpretability. However, unconstrained models can produce non-monotonic relationships between credit ratings and core features, an undesired outcome. To circumvent this, we recommend restricted CART models that ensure interpretable, theory-consistent results. We underscore the importance of company size in credit rating prediction with an ideal model integrating size, interest coverage, and dividends. Although being a large-cap company is crucial, it doesn't guarantee high ratings, and small-cap companies rarely secure investment-grade ratings.

Keywords: Corporate ratings; Machine learning; Classification and regression tree; Support Vector Regression; CART; SVR; Size (search for similar items in EconPapers)
JEL-codes: C45 C53 G24 G32 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1544612323010206
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:58:y:2023:i:pd:s1544612323010206

DOI: 10.1016/j.frl.2023.104648

Access Statistics for this article

Finance Research Letters is currently edited by R. Gençay

More articles in Finance Research Letters from Elsevier
Bibliographic data for series maintained by Catherine Liu ().