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.
