Systematic fixed income investing

Systematic fixed income investing

Systematic investing is uncovering new opportunities in fixed income by combining vast datasets, scientific research, and deep human expertise to make sense of market complexity.

What is systematic fixed income investing?

Systematic investing, often called quantitative investing, is a rapidly developing field in fixed income. Systematic fixed income strategies employ an objective, disciplined and repeatable process to identify, test, and implement investment ideas.

Our core investment principles

BlackRock's systematic strategies seek to generate consistent, high quality outcomes by applying a scientific process that validates fundamentally-oriented market insights with quantitative research.
Rigorous testing
Rigorous testing
We take fundamental insights and quantitatively test and validate them through a scientific process
Systematic construction
Systematic construction
We build portfolios through a disciplined, methodical process with a distinct focus on taking compensated risks
Targeted outcomes
Targeted outcomes
We seek to deliver strategies with a clear understanding and delineation between alpha, factor, and beta return sources

Systematic fixed income solutions

A systematic approach can be implemented across fixed income asset classes and investment styles. It can range from cap-weighted indexing, to factor-based and actively-screened strategies, or even long/short alternatives. Our solutions are available in a variety of structures like mutual funds, ETFs and other institutional vehicles.

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Our systematic alpha-seeking fixed income strategies seek high quality alpha through a consistent, repeatable process that validates fundamentally oriented market insights with quantitative research.

Core Alpha

  • Core bond strategy that seeks to generate attractive risk-adjusted returns by employing a diversified set of scientifically-tested investment insights

ESG Bond

  • Core-plus bond strategy that seeks to generate attractive risk-adjusted returns while incorporating a holistic approach to sustainable investing


Our factor-based funds target broad and persistent sources of returns that have historically driven fixed income markets. These funds seek to track indexes that pursue outcome-oriented and differentiated results compared to market-cap weighted indexes.

Balanced Risk

  • Multi-sector bond strategies that balance macro factors like credit and interest rate risk to seek improved risk-adjusted returns

Style Factors

  • Investment grade and high yield strategies using factor insights to target high quality and undervalued securities to seek improved risk-adjusted returns


Our systematic alternative strategies employ our best ideas using multiple, independent and risk controlled alpha models. These strategies seek uncorrelated returns to traditional asset classes like equities and fixed income.


  • Seek total return through a diversified mix of global assets and strategies using a systematic, credit-oriented approach
  • Unique credit insights aim to provide returns in both up and down markets with low correlation to stocks and bonds


Our fixed income index funds help access broad or specific market cap-weighted exposures in efficient and cost-effective solutions.


  • Broadly diversified holdings for core, low-cost portfolio allocations


  • Access credit markets such as investment grade and high yield corporates

Other Targeted Exposures

  • Provide exposure to specific sectors, regions, maturities or credit qualities

Term Maturity Bonds

  • ETFs designed to mature like a bond, trade like a stock, and diversify like a fund

Dig deeper into systematic fixed income

  • An alpha-seeking systematic approach would have been extremely difficult to implement just a few decades ago, as there simply wasn’t enough data available to build a sophisticated investment model.1 Today that has changed completely.

    Both the amount, and velocity of data that is available to be analyzed has exploded. New technologies like artificial intelligence and natural language processing2 are helping to efficiently analyze these datasets and transform them into insights. Now, limited or stale market information can be augmented with more robust datasets to create real-time sentiment, fundamental, and economic indicators. These techniques are powerful new tools for quant investors to identify and capture alpha opportunities.

  • A common perception of quantitative bond investing is that it is a “black box,” with trade ideas being spit out by a machine. In practice, experienced professionals are critical to generating and refining investment ideas.  

    BlackRock’s systematic idea generation in action:

    1. Idea generation – Researchers identify investment ideas and any dislocations in markets.
    2. Qualitative testing – Top ideas are evaluated to see if they are sensible, predictive, consistent, and additive to the portfolio.
    3. Quantitative testing – If the idea fits all four criteria, it is tested quantitatively through an academically-oriented process.
    4. Development of model – The idea is built into a model that incorporates estimated risk, return and trade costs. Modelling ideas ensures that they are implemented in a systematic and unbiased manner.

    Once in a portfolio, a model is evaluated using a detailed return analysis which creates a feedback loop to continuously refine it.

  • Compared to fundamental fixed income approaches, systematic strategies may have distinctive features including:

    • Minimal bias: In a systematic approach, insights are applied consistently through time, through markets, and across securities. Cognitive bias and emotion are essentially removed. A model can improve through time as it is finely tuned and informed by more data.
    • Wide portfolio breadth: A systematic strategy is not limited by the constraints of human attention and processing capability. It can monitor thousands of changes in metrics, prices, and market conditions a day. Compared to fundamental strategies that focus on a few concentrated positions, a quant bond strategy allows for a wider range of insights and investment decisions across a broadly diversified portfolio.
    • Defensive-orientation: Many systematic alpha-seeking strategies tend to have defensive return characteristics during equity market downturns. For example, when investing in credit, the focus is on fully valuing the firm and its overall chances of default, from there debt can be evaluated relative to its price. Done correctly, this approach can identify and underweight bonds more likely to default, resulting in less downside risk in the portfolio.
  • As a relatively new approach to fixed income, quantitative strategies can face a set of unique portfolio challenges including:

    • Trading hurdles: Many academic articles are written identifying investment models that appear to deliver strong results. In reality many of them do not work because transaction costs in fixed income outweigh the potential excess returns gained by trading. Having a process to realistically measure trade costs is what separates successful real-world strategies from theoretical strategies.
    • Data deluge: The availability of data is no longer a gating factor in fixed income, but more data does not mean outperforming is easier, it just provides more opportunities to uncover and test insights. Alpha can only be created by professionals with a deep understanding of the data who can skillfully model it to pursue opportunities.

Why BlackRock for systematic fixed income investing?

Innovative Drive
Over 20 years of innovation across quantitative fixed income investment styles*
Expansive Scale
Manage over $1 trillion in systematic alpha-seeking, factor, and index strategies*
Technological Leader
Backed by Aladdin®, our sophisticated risk management platform