Upgrading Credit Pricing and Risk Assessment through Embeddings
This study extracts high-dimensional firm embeddings from corporate bond holdings to enhance credit spread and risk assessment beyond traditional ratings.
What it examines
This study develops a new method using firm embeddings extracted from institutional bond holdings data. Using an asset pricing model and machine learning (ridge regression) to combine credit ratings and distance-to-default, the paper aims to better explain credit spreads and risk variations in fixed income markets compared to traditional measures.
What it concludes
The study shows that firm embeddings predict credit spreads, changes, and volatility more accurately than traditional metrics. Incorporating these embeddings in rating systems can enhance bond pricing, risk management, underwriting, and macroeconomic forecasts. Future research may refine this approach and expand its applications across different asset classes.
Evidence objects
The study unveils firm embeddings from institutional bond holdings that capture firm attributes neglected by traditional credit ratings and distance-to-default models, uncovering notable credit spread variations within uniform BBB ratings.
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Using asset pricing, principal component analysis, and ridge regression with cross-validation, the study develops trained embeddings that boost explanatory power, raising $$R^2$$ from 60% to 71% compared to traditional assessments.
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By predicting credit spread movements, volatility, rating downgrades, and defaults, the method refines risk analysis for fixed income markets, while limitations on sample focus prompt calls for expanded future research.
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Presenting a groundbreaking methodology, this paper extracts embeddings from bond holdings data to reveal latent risk characteristics absent from credit ratings and distance-to-default metrics. Employing $PCA$ and $\text{ridge regression}$ within an asset pricing and machine learning framework, the novel approach substantially improves fixed income risk assessment, pricing, and regulatory evaluation.
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Raw abstract and provenance
- … summary statistics on the corporate bond market and credit spreads. In Section 3, we … We then present summary statistics on the corporate bond market and credit spreads …
Source row: 2109 · abstract type: snippet