Corporate Credit Ratings Forecasting—from Standalone Models to a Heterogeneous Ensemble Model

DOI

10.2991/978-94-6463-823-3_28How to use a DOI?

Keywords

Corporate Credit Ratings; Machine Learning; Ensemble Learning

Abstract

Corporate credit rating prediction remains critical for risk management and capital allocation, yet conventional methodologies face challenges in scalability, objectivity, and timeliness. This study investigates the comparative performance of standalone machine learning models and a heterogeneous ensemble approach using a dataset of 2,029 U.S. corporate ratings from 2005 to 2016. Ten algorithms—including XGBoost, Random Forest, and K-Nearest Neighbors—were evaluated alongside a soft voting classifier optimized through weight tuning. Results demonstrated that the ensemble model achieves superior accuracy and F1-score, outperforming the best standalone model, XGBoost, by synthesizing complementary strengths in gradient boosting, variance reduction, and local pattern recognition. Cross-validation confirmed the ensemble’s robustness, while precision-recall metrics revealed persistent challenges in predicting low-frequency rating tiers. The findings highlight three contributions: empirical validation of ensemble methods for credit risk prediction, quantification of accuracy improvement from algorithmic diversity, and identification of class imbalance as a critical limitation requiring advanced resampling techniques. This research underscores the potential of machine learning-driven ensemble systems to enhance rating objectivity and operational efficiency, providing a foundation for future work on hybrid architectures integrating deep learning and interpretability frameworks.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Ye Nie
PY  - 2025
DA  - 2025/08/31
TI  - Corporate Credit Ratings Forecasting—from Standalone Models to a Heterogeneous Ensemble Model
BT  - Proceedings of the 2025 3rd International Conference on Image, Algorithms, and Artificial Intelligence (ICIAAI 2025)
PB  - Atlantis Press
SP  - 293
EP  - 299
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-823-3_28
DO  - 10.2991/978-94-6463-823-3_28
ID  - Nie2025
ER  -