Investors are warming to systematic processes in bond markets. In this new 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, aiming to generate outperformance (alpha) through bottom-up security selection. This contrasts with traditional active fixed-income approaches, which mostly prioritize duration and credit-market exposure (beta) and sector tilts.
The attractions: systematic fixed-income investing represents a different and complementary approach to traditional active bond investing. It creates the potential for attractive, consistent active returns and has demonstrated both low correlations with traditional active fixed-income approaches and lower volatility, in our analysis.
The potential pitfalls: systematic fixed-income investing needs specialist skills and resources that are hard to acquire. In our view, the key drivers of success in systematic bond investing are: abundant, reliable data; factor management skills; and cutting-edge liquidity analysis and trading tools. These can take many years to develop.
In such a highly specialized area, insightful manager selection is key. We think there are seven questions investors should raise with their prospective investment manager.
Does your process manage factors dynamically, and how many factors does it use?
Because the choice of predictive factors drives systematic investment performance, and because factor efficacy changes over time, investment managers need to demonstrate skill in factor management.
We find that systematic strategies using a dynamically weighted and wide range of factors are positioned to deliver better outcomes than those that rely on fewer factors and that use static factor weights.
Have you embedded liquidity anlaysis in your investment process?
Systematic managers that can execute bond trades successfully and at speed have a significant advantage.
Compared with equity trading, fixed income is much more problematic: more manual, less transparent and less liquid. That’s a problem, especially for equity managers attempting to transition to bond investing.
Consequently, it’s important to assess liquidity at the start. Advanced systematic approaches incorporate liquidity information to help source the exact bonds their model selects to buy. Overly basic approaches may send a list of bonds that meet certain criteria to their trading desk and buy those they can source—even if they’re not their model’s top choices. And if there aren’t enough sourceable bonds to buy, trading may be delayed as the process starts over again.
How extensive are your data?
Abundant, clean data compiled rigorously and covering many years’ history are the foundation of robust systematic investing. Beware managers that have short data histories and/or have bought in lower-quality data sets.
How do you integrate AI into your process?
Managers can add value with machine learning techniques at many levels of a systematic process. AI enhancements range from time-saving and efficiency gains (identifying price patterns to impute missing data quickly and reliably) to qualitative advances (improving analytics across multiple valuation factors to help find new signals and make existing signals more effective). Managers that don’t harness the power of AI will quickly be left behind.
Is your investment approach exclusively quantitative?
We believe that integrating quantitative experts within a broader fixed-income team that includes fundamental active bond professionals such as portfolio managers and traders can bring big benefits. This broader experience can improve execution, thus lowering transaction costs, and provide a wider perspective, practical insights and sanity checks to help evaluate factors and fine-tune models. The team can also highlight risks and opportunities that might not have been previously back-tested, such as the benefits of incorporating the new-issue market into a systematic process.
Does your process create a black box?
Systematic fixed income is an active approach that depends on discovery, selection and monitoring of predictive factors and that demands human input. While a model determines the factor weights and is instrumental in back-testing the data, human involvement is critical in testing the factors and deciding to add new ones and remove others.
Your manager should demonstrate not only an understanding of factors but also the ability to identify new factors, to evaluate factor performance and to modify the portfolio’s factor mix as conditions change.
How would you describe your results?
Investors should expect robust systematic approaches to exhibit lower credit risk than traditional active products because their focus is more on security selection, and they control sector and credit risks more rigorously. For the same reasons, we expect that strong systematic managers will also exhibit better upside and lower downside capture, with high information ratios.
Assessing a very different investment process needs a tailored evaluation approach. But in our view, allocating time and resource to understanding a systematic fixed-income process can be well worth the effort. We think this approach can offer a compelling opportunity for bond investors, for its potential both to generate consistent alpha and to improve diversification by introducing different return streams to traditional active fixed-income portfolios.
For further information, read our white paper, Systematic Fixed-Income Investing Comes of Age.
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. Views are subject to revision over time.
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