Quant meets credit: enhancing fundamental analysis through systematic screening

Systematic strategies have rapidly gained traction in corporate bond investing, marking a significant evolution in the fixed income landscape.¹

We believe the future of fixed income investing lies in combining the strengths of quantitative and conventional fundamental approaches. Where both align, research suggests that risk-adjusted returns can be significantly enhanced.

Market shifts enable systematic approaches

Funds that employ quantitative screening tools in the investment grade and high yield markets now hold between US$90bn and US$140bn in assets under management, and are forecast to continue to grow faster than the overall fixed income market.²

Driving this growth are structural shifts in credit markets including the rise of portfolio and electronic trading, the expansion of credit exchange-traded funds (ETFs), improved bond-level data and analytics, broader adoption of systematic overlays,³ and the application of equity-style factors like value and momentum to fixed income markets.⁴

Research shows that portfolio trading has enhanced the efficiency of the secondary corporate bond market by boosting liquidity in off-the-run securities⁵ and reducing execution costs, both of which are key enablers of the effective implementation of systematic strategies.⁶

Where quantitative and fundamental signals align

Systematic fixed income strategies use data-driven, relative value approaches to exploit cross-sectional dispersion in credit markets and structural inefficiencies across sectors. As well as being less susceptible to behavioral biases, they can more efficiently scan the breadth of the bond universe to identify relative value opportunities – particularly among less-followed names.

This can result in more diversified and uncorrelated streams of potential alpha compared to traditional approaches, where decisions often cluster around macroeconomic themes and the credit cycle.⁷

Evidence suggests that combining quantitative insights and fundamental views in portfolio construction can lead to higher risk-adjusted returns compared to using either method in isolation, especially when both approaches are directionally aligned. However, signal divergence may introduce noise and reduce performance consistency.⁸

Research by Barclays found that when analyst and quantitative signals align, the ability to predict issuer-specific returns in US investment grade credit improves significantly — from 58% to around 66%. When the signals conflict, however, this success rate drops to about 52%.⁹

In this study, focusing on investment decisions where quantitative and analyst views aligned boosted annual excess returns from 2.0% to 3.7%, on average, with information ratios rising from 1.09 to 1.71.¹⁰ Doubling exposure to these aligned signals further enhanced performance, raising excess returns among the study group to 4.1% and the information ratio to 1.83.¹¹

Please note that past performance does not guarantee future results.

Incorporating factor-based analysis

We use quantitative tools in our fixed income strategies to look for relative value opportunities in credit markets.

One of the tools we use is factor-based analysis.¹² By combining advanced data analysis with technology, factor-based analysis helps us spot market inefficiencies and identify undervalued credit opportunities. Each month, bonds are ranked and sorted into deciles based on factor exposures using bond-specific characteristics.

Internal research has shown that factors like carry, size, low risk, value and momentum can help to generate meaningful excess returns in both investment grade and high yield markets beyond what can be explained by conventional credit risk premiums.

As part of our efforts to build more resilient, performance-driven fixed income portfolios, we have integrated four key factors into our quantitative relative value screens to help generate investment ideas:

1) Size: bonds issued by smaller companies tend to outperform those issued by larger firms due to illiquidity premiums;

2) Low-risk: shorter-maturity, higher-rated bonds typically yield better risk-adjusted returns;

3) Value: bonds with high credit spreads relative to a model-implied fair value spread tend to deliver better returns;

4) Momentum: bonds that have recently performed have a historic tendency to continue outperforming.

Enhancing, not replacing active management

The adoption of systematic tools is no replacement for standard fundamental research in active portfolio management.

While algorithms offer scale and consistency, we believe human insight remains crucial for interpreting complex capital structures, conducting covenant analysis and understanding real-time developments including sentiment. Final investment decisions continue to be made by fundamental research analysts and portfolio managers, who apply their judgment and deep credit expertise to select what they believe to be the most compelling relative value opportunities.

Instead, by blending the scale and consistency of quantitative models with the depth and nuance of human insight, we aim to uncover differentiated sources of alpha and construct portfolios built for long-term performance. This is a cornerstone of the Impax Credit Prism, a philosophy that underpins our approach to fixed income investing.

The Impax Credit Prism focuses on three key tenets to generate alpha through strong credit evaluation and selection. Our approach combines deep fundamental credit research, an assessment of relative value complemented by quantitative insights, and a thorough consideration of material sustainability risks to ensure a more comprehensive understanding of an issuer’s risk-return profile. We focus on identifying companies with strong fundamentals and compelling relative valuations, and look to balance material sustainability risks alongside standard risks.

With the advent of increasingly advanced quantitative tools, we believe sophisticated fixed income investors must embrace their potential to enhance conventional, research-led approaches. Integrating complementary approaches should allow investors to capture both persistent, idiosyncratic insights and dynamic, market-wide signals, ultimately enabling the pursuit of more diversified alpha streams and improved portfolio resilience.

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¹ ‘Systematic’ (or ‘quantitative’) approaches to investing use data analysis and rules-based models to dynamically manage portfolios
² Mateer, H. et al., 2024: QPS & Credit Research: Better Together – Fundamental and Systematic Views in Credit Investing. Barclays Quantitative Portfolio Strategy & Credit Research
³ ‘Systematic overlays’ are rules-based strategies that use derivatives to manage specific risk exposures within fixed income portfolios
⁴ ‘Value’ stocks are those that are identified as less expensive, on a relative basis, according to valuation metrics like price-to-earnings ratios. ‘Momentum’ stocks are those that have delivered strong relative performance over a given period
⁵ ‘Off-the-run’ securities are older, less frequently traded bonds that are less liquid than their newly-issued counterparts
⁶ Ali, S. et al., March 2025: The Credit Line: The Changing Face of the Market Microstructure. Goldman Sachs Global Investment Research
⁷ ‘Alpha’ is a measure of an investment portfolio’s ability to generate financial returns in excess of its benchmark
⁸ ‘Signal divergence’ occurs when the price of an asset moves inversely to the direction of a related technical indicator
⁹ Mateer, H. et al., 2024: QPS & Credit Research: Better Together – Fundamental and Systematic Views in Credit Investing. Barclays Quantitative Portfolio Strategy & Credit Research
¹⁰ The ‘information ratio’ is a measure of the active return of a portfolio compared to a benchmark index, taking into account relative risk. The higher the information ratio, the greater a portfolio’s risk-adjusted performance
¹¹ Mateer, H. et al., 2024: QPS & Credit Research: Better Together – Fundamental and Systematic Views in Credit Investing. Barclays Quantitative Portfolio Strategy & Credit Research
¹² ‘Factor-based analysis’ involves the identification of characteristics (or factors) that can explain differences in securities’ financial returns, versus the overall market

Additional references
¹³ Karoui, L., et al., 2021: The Credit Line: Thematic Portfolios and Factor Strategies in Credit. Goldman Sachs Global Investment Research.
¹⁴ Houweling, P., & van Zundert, J., 2017: Factor Investing in the Corporate Bond Market. Financial Analysts Journal

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This material contains past performance information. Past performance and market trends do not guarantee future results.

Nothing presented herein is intended to constitute investment advice and no investment decision should be made solely based on this information. Nothing presented should be construed as a recommendation to purchase or sell a particular type of security or follow any investment technique or strategy. Information presented herein reflects Impax Asset Management’s views at a particular time. Such views are subject to change at any point, and Impax Asset Management shall not be obligated to provide any notice. Forward-looking statements or forecasts herein are subject to known and unknown risks and uncertainties including inaccurate assumptions that could cause actual results to differ materially from those expected or implied by the forward-looking statements. . While Impax Asset Management has made reasonable efforts to obtain information from reliable sources, we make no representations or warranties as to the accuracy, reliability or completeness of third-party information presented herein. No guarantee of investment performance is being provided and no inference to the contrary should be made.