- 10 June 2026
Airlines are under pressure from rising fuel costs — but aircraft lessors occupy a structurally different...
How machine learning helps identify convertible bond market opportunities
- PROFESSIONAL INVESTORS
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- 11.06.26
Credit ratings are a cornerstone of fixed income investing — but traditional approaches have well-documented limitations. Periodic updates, uneven coverage of smaller or newer issuers, and reliance on qualitative judgement can mean that emerging risks aren't always visible until it's too late.
This latest insight from Schroders explores how the firm is using a proprietary machine learning framework to address those gaps in the convertible bond market. The model draws on a broad range of issuer fundamentals and bond-specific features to predict likely credit rating outcomes — consistently, at scale, and with the ability to update in near real-time.
Importantly, the tool is designed to complement rather than replace analyst expertise. It is particularly focused on new issues and less well-covered parts of the market, where traditional coverage can be slower to develop. The article also explains how interpretability techniques allow analysts and fund managers to understand the drivers behind any prediction — not just the output itself.
For advisers and wealth managers with client exposure to convertible bonds or broader fixed income, this is a useful window into how quantitative techniques are being applied to improve credit risk monitoring in practice.
