How machine learning helps identify convertible bond market opportunities
Schroders’ proprietary tool predicts rating outcomes for convertible bonds and reaches into less covered parts of the market.
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Summary:
- Schroders’ proprietary machine learning-driven tool predicts likely credit rating outcomes for convertible bonds using a blend of issuer fundamentals and bond-specific features.
- It improves consistency and scale across large universes, helps detect earlier signals of potential credit drift, and supports forward-looking scenario analysis.
- It is designed to complement not replace analyst coverage, with a focus on new issues and less well covered parts of the market.
- We use it to assess new or less-covered bonds, to understand drivers behind rating changes and predict future ratings.
1. Limitations of traditional analyst-driven approaches
Traditional credit rating approaches are well established and widely relied upon, but they face growing challenges in today’s more complex and fast‑moving markets. Heavy reliance on qualitative judgement and committee processes can lead to inconsistency. Because ratings are updated periodically, they may also be slow to reflect changes in underlying fundamentals. In addition, coverage is uneven across the market: smaller issuers, emerging sectors, and new borrowers often take time to be picked up, limiting visibility where change and innovation are most pronounced.
Taken together, these factors can make it harder for traditional ratings alone to keep pace with evolving risks and market dynamics.
2. A machine learning framework for rating prediction
A machine‑learning‑based framework for rating prediction offers a powerful complement to traditional approaches. By combining a wide range of qualitative and quantitative signals, these models can capture complex, non‑linear relationships that are difficult to assess through conventional analysis alone. The approach is inherently consistent, applying the same framework across issuers. It can also be updated more frequently, enabling real‑time monitoring, and earlier warning of emerging credit risks. Machine‑learning models can operate at scale, assessing thousands of issuers in minutes rather than days or weeks.
3. Our approach: data and feature design
The model learns from a large training set of composite ratings, incorporating ratings from all the main agencies, and holding issuer fundamentals constant. The input features are a range of signals pertaining to both the security and the issuer designed to learn what the agencies would use: for example, the seniority and time to maturity of the bond at time of rating, as well as the key fundamental ratios for the issuing company.
We then use the latest machine-learning techniques to build a model that understands the complex relationships between the features of the bond and issuing company and the rating given, including interactions between these features (for example, how the relationship between a particular fundamental ratio and the credit rating varies by sector or region).
Model interpretability, a critical component of the model-building process, can be achieved using techniques such as SHAP (Shapley Additive Explanations). SHAP evaluates the contribution of each feature to a model's prediction for individual observations, helping investment professionals understand the drivers of predicted credit risk.
The top chart below shows the relationship between a company's debt-to-assets ratio and its contribution to the predicted credit risk score. Higher levels of leverage generally increase the model's predicted credit risk, with the contribution becoming positive at debt-to-assets ratios of approximately 0.35–0.40 and rising thereafter. Vice versa. Similarly, the second chart below shows the relationship between a company's interest coverage ratio and its contribution to the predicted credit risk score. Higher levels of interest coverage generally reduce the model's predicted credit risk, with the contribution becoming negative at interest coverage ratios of approximately 7-8.
Figure 1: Relationship between a company's debt-to-assets ratio and its contribution to the predicted credit risk score
Figure 2: Relationship between a company's interest coverage ratio and its contribution to the predicted credit risk score
Source: Schroders, May 2026
The model outputs are packaged into an interactive application where our analysts and fund managers can use to see the predicated ratings, rate new bonds, and inspect and understand the specific relationships the model has learned.
Figure 3: Example of model output
Source: Schroders, May 2026
4. Practical implications
Machine‑learning models are not without limitations. Like any data‑driven approach, they can struggle during unprecedented periods or sharp regime shifts, when historical patterns become less reliable guides to future outcomes. For this reason, machine learning is most effective when used as a complement rather than a substitute for human judgement. By combining computational scale, consistency, and timeliness with expert oversight and interpretation, this hybrid approach has the potential to materially enhance how credit risk is assessed and monitored. It provides early warning capabilities while retaining the judgement needed to navigate periods of structural change.
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