Systematic Fixed Income: A Breakthrough in Bond Investing

05 September 2023

3 min read

Bernd Wuebben| Director—Systematic Investing and Quantitative Research

Bond investors have been looking for an approach that delivers attractive, repeatable, uncorrelated active returns. Is their wait over?

Higher rates mean bond markets once more offer worthwhile real returns, while more challenging economic conditions are creating greater scope for active fixed-income security selection. But how can investors harvest these opportunities consistently? We believe systematic fixed-income investing approaches can help provide the answer—and offer a high degree of customizability.

What Is a Systematic Approach to Fixed-Income Markets?

Systematic fixed-income investing is an active approach that aims to outperform bond-market benchmarks. In this approach, a dynamic multifactor process drives the investment decisions, using predictive factors with demonstrable links to outperformance. A quantitative and AI driven decision process ranks each bond in the market based on its alignment with these predictive factors, and so aims to generate outperformance (alpha) through bottom-up security selection.

This systematic methodology contrasts with traditional active approaches, which mostly prioritize duration and credit-market exposure (beta) and sector tilts (Display).

Systematic Fixed Income Differs from Other Solutions

Systematic Strategies Prioritize Security Selection While Most Traditional Active Strategies Prioritize Beta

A pyramid shows systematic strategies’ security selection alpha at the top, with traditional timing strategies at the base.

For informational purposes only
As of December 31, 2022
Source: AllianceBernstein (AB)

Because systematic approaches depend on different performance drivers, their returns will likely be different from—and complementary to—traditional active strategies. The active returns from security selection in systematic strategies are by design largely uncorrelated both with the benchmark and with important risk premia. As a result, these strategies can be effective diversifiers in a fixed-income portfolio. In addition, systematic approaches can enable competitive fee structures, making them a potentially lower-cost way to beat bond benchmarks. They can also be readily customized, allowing managers to tailor portfolios exactly in line with client preferences while retaining performance potential.

How Do Predictive Factors Work?

Risk factors, such as interest-rate risk (duration) or spread duration (the sensitivity of a bond’s price to changes in its credit spread) identify the ways that market drivers can influence security prices. Systematic strategies aim to discover factors that have predictive power to repeatably find securities with the best risk-adjusted return potential—that is, predictive factors. These can be market value–based (for instance, value and momentum) or fundamental, company-specific factors (such as quality). Predictive factor insights are used to systematically analyze large volumes of historical market data and single out securities with the right characteristics to have an above-average probability of outperforming the market (Display).

How Do Systematic Strategies Differ from Traditional Active Ones?

Systematic Strategies Are Research-Driven, Evidence-Based and Focus on Security Selection

A table shows how systematic approaches are quantitative research- and process-driven, and contrasts traditional approaches.

For informational purposes only
As of December 31, 2022
Source: AB

Managing Factor Strategies in Practice

With our deep research databases, AB is able to identify and access hundreds of proprietary factors. Not all will be implemented continuously in a systematic portfolio, but the manager can rotate them according to market conditions as investment regimes change and the efficacy of the factors varies with them.

Predictive factor–based approaches originated in equity markets, where benchmarks are relatively straightforward to construct, and pricing is largely transparent. Factor-based approaches arrived more recently in fixed-income markets, which are larger, more complex and fragmented across disparate trading pools. All these features make liquidity and pricing harder to discover in bond markets.

For these reasons, advanced technology and analytics are vital to making systematic approaches work in fixed-income markets. And although academic research supports the case for predictive factors in fixed-income investing, it takes rigorous testing and practical implementation skills to create successful portfolios.

Creating a Systematic Portfolio: Combining Predictive Factors

With a systematic approach, each bond in the benchmark is scored on a range of predictive factors. This results in an array of scores for each security. For instance, a bond might have a high score on value but a low score for momentum. A factor combination model then rolls up the different factor scores to produce a single composite total factor score for each security.

The model adopts two criteria to create a portfolio using the factor scores: predictive efficacy and correlation with other factors. It weights them using an algorithm determined by a machine learning technique. This ranks the total factor score for each bond subject to other optimization and risk constraints, principally: bond, issuer, sector, ESG, duration, spread, liquidity and transaction cost limits. In this way, the model seeks superior risk-adjusted returns by balancing predictive efficacy with rigorous risk controls.

Three Critical Success Factors

In this relatively new field, the performance of some providers’ systematic fixed-income products has proved disappointing, with live returns failing to live up to back-tested results. We think there are three frequent failings: relying on static factors, using unreliable data, and an inability to source liquidity and to execute ideas effectively. These illustrate the importance of three “pillars” for effective systematic strategies:

A Dynamic Factor Approach. Market conditions are always changing, and factor efficacy varies from market to market and over time. For instance, carry (yield) may be a strong factor in investment-grade bond markets but not in high yield, where default risk is a more important performance driver. Consequently, it’s crucial to evaluate factors continuously and manage them dynamically.

Abundance of Data. Reliable data are the indispensable building blocks of effective systematic strategies. Systematic investors need vast quantities of data that are clean (free of anomalies and inconsistencies), extensive and have a very long history. Compiling those data is a laborious, research-intensive task. It involves amassing data that extend across a wide range of bond metrics, include point-in-time analytics for companies’ financials across multiple fields and cover many fixed-income classes globally.

Liquidity Considerations. Firms that can’t effectively assess a bond’s liquidity won’t be able to implement their investment ideas. To keep up in a marketplace that will digest and react to every new bit of information faster and faster, successful fixed-income managers need to use technology that pulls all external fixed-income trading platforms together in one place. Finding adequate liquidity to execute desired trades is a precondition for managing a portfolio’s factor weights. And finding enough liquidity at attractive prices is paramount because systematic strategies only execute trades if they pass tests for transaction-cost effectiveness.

An Idea Whose Time Has Come

We believe systematic fixed-income investing is an idea whose time has come. It provides an active, cost-effective way to achieve attractive, repeatable and uncorrelated risk-adjusted returns through:

Bottom-up selection and structuring of many independent bond holdings

A high level of risk control that makes systematic portfolios relatively less vulnerable to big drawdowns resulting from adverse interest-rate, credit and other single-factor events

This dual emphasis on outperforming through individual security selection and rigorous risk control makes systematic approaches complementary to most traditional active fixed-income strategies, and potentially powerful portfolio diversifiers.

Investors today face a world where conditions are rapidly changing, with increased availability of data and the advent of AI shifting familiar paradigms. We believe a leading-edge systematic approach that utilizes these new developments can help provide an objective, evidence-based path to more consistent risk-adjusted fixed-income returns.

For further information, read our White Paper here.

The views expressed herein do not constitute research, investment advice or trade recommendations, and do not necessarily represent the views of all AB portfolio-management teams and are subject to change over time.

About the Authors

Scott DiMaggio, CFA

Scott DiMaggio is a Senior Vice President, Head of Fixed Income and a member of the Operating Committee. As Head of Fixed Income, he is responsible for the management and strategic growth of AB’s fixed-income business and investment decisions across the department. DiMaggio has previously served as director of Global Fixed Income and continues to be a portfolio manager across numerous multi-sector and multi-currency strategies. Prior to joining AB’s Fixed Income portfolio-management team, he performed quantitative investment analysis, including asset-liability, asset-allocation, return attribution and risk analysis for the firm. Before joining the firm in 1999, DiMaggio was a risk management market analyst at Santander Investment Securities. He also held positions as a senior consultant at Ernst & Young and Andersen Consulting. DiMaggio holds a BS in business administration from the State University of New York, Albany, and an MS in finance from Baruch College. He is a member of the Global Association of Risk Professionals and a CFA charterholder. Location: New York

Bernd Wuebben

Bernd Wuebben oversees the Quantitative Research Group for Fixed Income, where he is responsible for developing the quantitative models and methods that are an important aspect of the firm's investment process. Prior to joining AB, Wuebben worked from 2008 to 2018 as a quantitative investment portfolio manager, managing portfolios that invested in liquid traded assets across all major geographic locations and asset classes. His quantitative investment process made heavy use of big data and machine learning. Prior to this, Wuebben worked for 10 years as a macro and relative-value strategist as well as a proprietary trader for major investment banks including Morgan Stanley, Bear Stearns and Deutsche Bank, where he started his career in 1998 as a fixed-income strategist. He holds a BS in mathematics and a BS in physics from Heidelberg University, Germany, and an MS in mathematics and an MS in computer science from Cornell University. Wuebben also passed the doctoral exam in mathematics at Cornell University, where his research focused on the mathematical aspects of superstring theory. Location: New York

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