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

Koresh Galil, Ami Hauptman, Rosit Levy Rosenboim

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Article number104648
JournalFinance Research Letters
Volume58
DOIs
StatePublished - 1 Dec 2023

Keywords

  • CART
  • Classification and regression tree
  • Corporate ratings
  • Machine learning
  • SVR
  • Size
  • Support Vector Regression

ASJC Scopus subject areas

  • Finance

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