A new study spanning more than two decades and more than 500,000 issuer-months presents one of the most accurate and forward-looking models of corporate credit rating transitions to date.
By combining machine learning with the SAS® KRIS® 1-year default probability (KDP), the authors show how investors can gain a powerful edge in anticipating downgrades and upgrades – well before the market catches on.
The findings carry far-reaching implications across three key investment domains: systematic credit, systematic equity and fundamental fixed income.
Systematic credit: A forward-looking signal the market misses
For quantitative credit investors, the study confirms that SAS KRIS KDP is more than just a credit metric – it’s a top-three predictor of rating transitions, behind only option-adjusted spreads (OAS) and yield to maturity.
KDP provides valuable incremental insights beyond what has already been priced in. As the study authors explain, “… the KDP adds orthogonal information – for any fixed OAS, a higher KDP drives a larger downgrade probability, indicating the value-add of KDPs above and beyond what is visible in market spreads.”
This means the KDP-based model can identify downgrade risk before it is reflected in bond prices, offering:
- Receiver Operating Characteristic Area Under the Curve (ROC AUC) of 0.90 for downgrades.
- 28× lift over random classification achieved by 20% precision at 20% recall.
- Actionable downgrade alerts and cleaner upgrade signals.
For systematic credit strategies, KDP emerges as a high-value alpha signal that enhances both return potential and risk control.
Systematic equity: Credit risk as a hidden driver of equity returns
The study also shows that KDP is a valuable tool for systematic equity investors. Rating transitions often lead to changes in cost of capital and balance sheet risk – factors that can drive equity underperformance (or outperformance in the case of upgrades).
The study notes, “… we find default probability to be a critical indicator to differentiate issuer downgrade probability within ratings class.”
Because KDP surfaces credit stress before it’s reflected in spreads, it can serve as a forward-looking overlay to traditional equity factors – especially in long/short, factor-based or risk-managed strategies.
Fundamental fixed income: Downgrade defense, quantified
For fundamental portfolio managers (PMs) managing to investment-grade benchmarks, the study offers a compelling case for integrating KDP into screening and sizing workflows.
As the authors note, “… in terms of fundamental indicators of credit risk, we see the SAS Kamakura default probability and current issuer-level rating as critical covariates. Notably, these features drive rating transitions in a non-linear way; the empirical AUC results indicate non-linearity adds substantial value on top of a logistic-linear specification with the same underlying data.”
In practice, this means:
- Advance warning before spreads widen
- Lower downgrade slippage
- Tighter tracking error
- Improved excess return
KDP helps fundamental managers proactively manage downgrade risk – before credits fall out of investment grade (IG)-only mandates.
A signal for all seasons
Whether you’re building systematic models or managing discretionary portfolios, the SAS KRIS 1-year default probability emerges from this study as a versatile, predictive, orthogonal, or independent signal. It enhances alpha generation, risk management and portfolio construction across asset classes.
SAS KRIS also provides a full forecast of the term structure of default probabilities, offering a significantly richer and more nuanced set of signals than the single one-year point used in the study. This expanded insight unlocks the potential for even greater predictive power and more informed investment decisions.
In a world where timing and foresight are everything, KDP offers both.
