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

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Listed:
  • Galil, Koresh
  • Hauptman, Ami
  • Rosenboim, Rosit Levy

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.

Suggested Citation

  • Galil, Koresh & Hauptman, Ami & Rosenboim, Rosit Levy, 2023. "Prediction of corporate credit ratings with machine learning: Simple interpretative models," Finance Research Letters, Elsevier, vol. 58(PD).
    • Handle: RePEc:eee:finlet:v:58:y:2023:i:pd:s1544612323010206
    DOI: 10.1016/j.frl.2023.104648
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    References listed on IDEAS

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    More about this item

    Keywords

    Corporate ratings; Machine learning; Classification and regression tree; Support Vector Regression; CART; SVR; Size;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G24 - Financial Economics - - Financial Institutions and Services - - - Investment Banking; Venture Capital; Brokerage
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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