Predicting Corporate Defaults

Credit risk modeling is a cornerstone of financial risk management, enabling institutions to assess the likelihood of corporate defaults and make informed decisions on lending, pricing, and portfolio management. This study develops a logistic regression model to predict corporate defaults over a five year period (January 1, 2020, to Jan 1, 2025), using a dataset of 2,367 companies with industry proxy ratings applied for 179 companies with no ratings (NR).

The methodology follows a four-step process, aligning with S&P Global Ratings corporate credit rating methodology and addresses challenges such as the lack of financial data and class imbalance. The model produces five year Probabilities of Default (PDs) and extends these to one–five-year horizons, achieving robust performance suitable for credit risk applications. It also benchmarks the term structure PDs against S&P Global Ratings historically observed default rates for the one-to-five-year term structure. This study details each step, the data used, the results obtained, and their quality, providing a comprehensive evaluation of the model’s effectiveness.