Increasing interest rates signify that the bond market is once again offering valuable real returns. Simultaneously, challenging economic conditions are expanding the opportunities for active fixed-income security selection. The question arises: How can investors consistently capitalize on these opportunities? We contend that systematic approaches to fixed-income investing can provide a solution, offering a high degree of customization.
What Is a Systematic Approach to Fixed-Income Markets?
Systematic fixed-income investing represents an active strategy aimed at surpassing bond-market benchmarks. This approach relies on a dynamic multifactor process to guide investment decisions, utilizing predictive factors with proven connections to outperformance. Employing quantitative and AI-driven decision-making, each bond in the market is ranked based on its alignment with these predictive factors, aiming to achieve outperformance through a bottom-up selection of securities.
This systematic methodology stands in contrast to traditional active approaches, which often prioritize factors such as duration, credit-market exposure, and sector allocations.
As systematic approaches rely on distinct performance drivers, their returns are likely to differ from, and complement, those of traditional active strategies. The returns generated from security selection in systematic strategies are intentionally uncorrelated with both the benchmark and significant risk premiums. Consequently, these strategies can act as effective diversifiers within a fixed-income portfolio. Furthermore, systematic approaches offer competitive fee structures, potentially making them a cost-effective means of outperforming bond benchmarks. Additionally, they can be tailored to align precisely with client preferences while retaining performance potential.
How Do Predictive Factors Operate?
Predictive factors, whether market value-based (e.g., value and momentum) or fundamental and company-specific (e.g., quality), identify the avenues through which market dynamics affect security prices. Systematic strategies aim to identify factors with predictive power that consistently identify securities with the best risk-adjusted return potential. These predictive factors are employed to systematically analyze extensive historical market data, pinpointing securities with the right attributes to demonstrate an above-average probability of outperforming the market.
Managing Factor Strategies in Practice
With access to our comprehensive research databases, AB can identify and access numerous proprietary factors. Not all of these factors are continuously implemented in a systematic portfolio. Instead, they can be rotated based on market conditions as investment environments change and the effectiveness of the factors evolves.
Predictive factor-based approaches originated in equity markets, where constructing benchmarks is relatively straightforward, and pricing transparency is high. These approaches were introduced more recently to the fixed-income markets, which are larger, more intricate, and fragmented across various trading platforms. These characteristics make the discovery of liquidity and pricing in bond markets more challenging.
For these reasons, advanced technology and analytics play a crucial role in the effectiveness of systematic approaches within fixed-income markets. While academic research supports the use of predictive factors in fixed-income investing, successful portfolio creation requires rigorous testing and practical implementation skills.
Creating a Systematic Portfolio: Incorporating Predictive Factors
In a systematic approach, each bond within the benchmark is assessed based on a range of predictive factors, resulting in a set of scores for each security. For example, a bond may score high on value but low on momentum. A factor combination model aggregates these factor scores to generate a single composite total factor score for each security.
The model employs two criteria to construct a portfolio using these factor scores: predictive effectiveness and correlation with other factors. These criteria are weighted using an algorithm determined by a machine learning technique. This ranks the total factor score for each bond while adhering to other optimization and risk constraints, including bond, issuer, sector, ESG, duration, spread, liquidity, and transaction cost limits. In this manner, the model seeks to achieve superior risk-adjusted returns by balancing predictive effectiveness with rigorous risk management.
Three Key Success Factors
In this relatively new field, the performance of some providers’ systematic fixed-income products has fallen short of expectations, with actual returns not matching back-tested results. We believe that three common shortcomings account for this: reliance on static factors, utilization of unreliable data, and difficulties in sourcing liquidity and executing ideas effectively. These underscore the significance of three fundamental “pillars” for successful systematic strategies:
- A Dynamic Factor Approach: Market conditions are constantly changing, and the efficacy of factors varies across different markets and over time. Therefore, it is vital to continuously assess and dynamically manage factors.
- Abundance of Data: Reliable data are essential for effective systematic strategies, and compiling this data involves extensive research and data gathering across various bond metrics and fixed-income classes.
- Liquidity Considerations: The ability to assess a bond’s liquidity is crucial for implementing investment ideas effectively. Utilizing technology to consolidate fixed-income trading platforms is essential for finding sufficient liquidity at attractive prices, a prerequisite for managing factor weights effectively.
An Idea Whose Time Has Arrived
We firmly believe that systematic fixed-income investing is a concept whose time has arrived. It offers an active, cost-efficient means of achieving consistent, uncorrelated, and risk-adjusted returns by:
Selecting and structuring individual bond holdings from the bottom up.
Implementing robust risk control measures that make systematic portfolios relatively resilient to significant drawdowns caused by adverse interest rates, credit, and other single-factor events.
This dual focus on achieving outperformance through individual security selection and rigorous risk management makes systematic approaches a valuable complement to most traditional active fixed-income strategies and potential diversifiers for portfolios.
In today’s rapidly changing world, characterized by increased data availability and the emergence of AI, we believe that a cutting-edge systematic approach leveraging these advancements can offer an objective, evidence-based path to achieving more consistent risk-adjusted returns in fixed-income investments.