Navigating a Sea of Investment-Grade Credit with GMO

The U.S. investment-grade (IG) credit market has expanded significantly in both issuance and liquidity, creating fertile ground for systematic investment strategies. In contrast, traditional credit approaches—typically anchored in deep fundamental analysis of individual securities or issuers—have found it increasingly difficult to generate distinctive return profiles in recent years.

In its latest paper, Navigating a Sea of Investment-Grade Credit, GMO outlines why it believes a transparent, repeatable, and factor-based quantitative framework—rooted in solid economic research and applied through diversified factor tilts—presents a compelling alternative in today’s IG landscape.

To explore the insights presented in the paper, we spoke with James Donaldson, Portfolio Manager on GMO’s Developed Fixed Income team.

You describe the U.S. investment-grade credit market as having evolved significantly. What are the structural changes that prompted GMO to develop a systematic approach?

Over the past two decades, the U.S. investment-grade credit market has seen substantial corporate issuance, both in size and count, providing a rich dataset for testing ideas and understanding signal behavior. Additionally, liquidity in corporate debt markets has improved substantially, and increased volumes through electronic trading platforms and the reliance on portfolio trading, accompanied by the wider adoption of fixed income ETFs, have provided the market with better price discovery, making a systematic approach more scalable in today’s market. 

GMO leverages quantitative signals to inform investment decisions, employing a transparent, rules-based approach that allows for clear performance attribution of the underlying signals through both traditional and factor-based return attribution. This systematic approach is repeatable, scalable, and transparent, bolstering our confidence in its effectiveness and affording our clients a lens into how and why the strategy is performing.

Could you walk us through how your valuation model estimates fair credit spread and incorporates bond-specific risks?

Our valuation model at GMO estimates fair credit spreads by integrating data from an issuer's balance sheet with specific risks associated with individual bonds. We believe that both issuer default risks and bond-specific risks, such as duration, age, and size, are often mispriced in the market. Our strategy involves identifying and taking positions in bonds that are trading significantly away from their intrinsic fair value. 

Quality and fundamentals are traditionally part of fundamental credit analysis. How does your quantitative implementation of these differ or improve upon human analysis?

At GMO, our quality and company fundamental signals incorporate quantitative measures of default and credit deterioration, which may differ from traditional fundamental analysis. Additionally, our process leverages GMO's expertise in systematic equity investing, incorporating proprietary measures of issuer/equity valuation. While there may be some overlap with fundamental managers in identifying opportunities, we emphasize the repeatability of our quantitative investment approach. Our process is designed to analyze a broad swath of the investment-grade market and exploit several dislocations in a disciplined and repeatable manner.

Once you have bond-level alpha signals, how does the portfolio optimization process integrate them with macro- and issuer-level constraints?

Our portfolio optimization process at GMO tightly manages active exposures to align closely with the broad investment-grade (IG) market. We focus on maximizing portfolio alpha while carefully managing benchmark-relative positions in terms of duration, curve, sector, rating, issuer, and country risk. By constraining our active positioning, we aim to minimize idiosyncratic or unintended macro-related risks while emphasizing credit (bond) selection based on our signal-driven evaluation framework. Portfolio managers continuously monitor the strategy, incorporating new information into the process, creating a virtuous cycle of improvement and refinement that enhances the robustness and adaptability of our approach.

What role does your proprietary risk model play in managing tracking error versus ex-ante alpha potential?

Our proprietary risk model plays a crucial role in monitoring tracking error, but we do not target a specific level of tracking error for the strategy. Instead, we focus on optimizing ex-ante alpha potential by carefully managing risk exposures and ensuring that our investment decisions align with our overall strategy. This approach allows us to balance the pursuit of alpha with effective risk management, enhancing the robustness and adaptability of our investment process.

How does the strategy mitigate idiosyncratic or unintended macro exposure while maintaining enough factor tilt to deliver differentiated performance?

Our strategy mitigates idiosyncratic or unintended macro exposure by tightly managing active exposures within the portfolio construction process, as discussed in question four above. Again, we focus on balancing return potential with effective risk management. Our research indicates that our current portfolio design achieves this balance well, ensuring that we maintain enough factor tilt to deliver differentiated performance while minimizing unintended risks.