Systematic Investing | BlackRock

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INVESTMENT STRATEGY

By combining the power of big data, data science, and deep human expertise to modernize the way we invest, systematic investing is unlocking new ways to seek consistent portfolio outcomes in an evolving market landscape.

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Investing, evolved

Systematic strategies combine human expertise with quantitative, data-driven techniques to construct portfolios aligned with specific investment objectives across asset classes.

Human x machine

Innovative investment insights are validated through rigorous quantitative testing—amplifying the decision-making of our investment experts.

Engineered for scale

Technology-driven process helps scale investment insights across vast sets of securities, enabling high-breadth portfolios for equities, fixed income, and alternatives.

Targeted outcomes

Systematic tools help our investors balance complex risk and return trade-offs with precision—targeting the investment outcomes that you expect.

Explore Systematic strategies

Our systematic funds are designed to pursue a range of client portfolio objectives, including outperformance, diversification, and income.

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The performance quoted represents past performance and does not guarantee future results. Investment return and principal value of an investment will fluctuate so that an investor’s shares, when sold or redeemed, may be worth more or less than the original cost. Current performance may be lower or higher than the performance quoted. Performance data current to the most recent month end may be obtained by visiting www.iShares.com or www.blackrock.com.

To obtain more information on the fund(s) including the Morningstar time period ratings and standardized average annual total returns as of the most recent calendar quarter and current month end, please click on the fund tile. The Morningstar Rating for funds, or "star rating", is calculated for managed products (including mutual funds, variable annuity and variable life subaccounts, exchange-traded funds, closed-end funds, and separate accounts) with at least a three-year history. Exchange-traded funds and open-ended mutual funds are considered a single population for comparative purposes. It is calculated based on a Morningstar Risk-Adjusted Return measure (excluding any applicable sales charges) that accounts for variation in a managed product's monthly excess performance, placing more emphasis on downward variations and rewarding consistent performance. The top 10% of products in each product category receive 5 stars, the next 22.5% receive 4 stars, the next 35% receive 3 stars, the next 22.5% receive 2 stars, and the bottom 10% receive 1 star. The Overall Morningstar Rating for a managed product is derived from a weighted average of the performance figures associated with its three-, five-, and 10-year 60-119 months of total returns, and 50% 10-year rating/30% five-year rating/20% three-year rating for 120 or more months of total returns. While the 10-year overall star rating formula seems to give the most weight to the 10-year period, the most recent three-year period actually has the greatest impact because it is included in all three rating periods.

The performance quoted represents past performance and does not guarantee future results. Investment return and principal value of an investment will fluctuate so that an investor’s shares, when sold or redeemed, may be worth more or less than the original cost. Current performance may be lower or higher than the performance quoted. Performance data current to the most recent month end may be obtained by visiting www.iShares.com or www.blackrock.com.

To obtain more information on the fund(s) including the Morningstar time period ratings and standardized average annual total returns as of the most recent calendar quarter and current month end, please click on the fund tile. The Morningstar Rating for funds, or "star rating", is calculated for managed products (including mutual funds, variable annuity and variable life subaccounts, exchange-traded funds, closed-end funds, and separate accounts) with at least a three-year history. Exchange-traded funds and open-ended mutual funds are considered a single population for comparative purposes. It is calculated based on a Morningstar Risk-Adjusted Return measure (excluding any applicable sales charges) that accounts for variation in a managed product's monthly excess performance, placing more emphasis on downward variations and rewarding consistent performance. The top 10% of products in each product category receive 5 stars, the next 22.5% receive 4 stars, the next 35% receive 3 stars, the next 22.5% receive 2 stars, and the bottom 10% receive 1 star. The Overall Morningstar Rating for a managed product is derived from a weighted average of the performance figures associated with its three-, five-, and 10-year 60-119 months of total returns, and 50% 10-year rating/30% five-year rating/20% three-year rating for 120 or more months of total returns. While the 10-year overall star rating formula seems to give the most weight to the 10-year period, the most recent three-year period actually has the greatest impact because it is included in all three rating periods.

The performance quoted represents past performance and does not guarantee future results. Investment return and principal value of an investment will fluctuate so that an investor’s shares, when sold or redeemed, may be worth more or less than the original cost. Current performance may be lower or higher than the performance quoted. Performance data current to the most recent month end may be obtained by visiting www.iShares.com or www.blackrock.com.

To obtain more information on the fund(s) including the Morningstar time period ratings and standardized average annual total returns as of the most recent calendar quarter and current month end, please click on the fund tile. The Morningstar Rating for funds, or "star rating", is calculated for managed products (including mutual funds, variable annuity and variable life subaccounts, exchange-traded funds, closed-end funds, and separate accounts) with at least a three-year history. Exchange-traded funds and open-ended mutual funds are considered a single population for comparative purposes. It is calculated based on a Morningstar Risk-Adjusted Return measure (excluding any applicable sales charges) that accounts for variation in a managed product's monthly excess performance, placing more emphasis on downward variations and rewarding consistent performance. The top 10% of products in each product category receive 5 stars, the next 22.5% receive 4 stars, the next 35% receive 3 stars, the next 22.5% receive 2 stars, and the bottom 10% receive 1 star. The Overall Morningstar Rating for a managed product is derived from a weighted average of the performance figures associated with its three-, five-, and 10-year 60-119 months of total returns, and 50% 10-year rating/30% five-year rating/20% three-year rating for 120 or more months of total returns. While the 10-year overall star rating formula seems to give the most weight to the 10-year period, the most recent three-year period actually has the greatest impact because it is included in all three rating periods.

Our history is built on asking what’s possible. Our future depends on asking what’s next.

For decades, BlackRock Systematic has brought together investment insight, data, technology and research to rethink how investing is done. That same spirit of experimentation continues to shape what comes next.

40th Anniversary Video Transcript

Raffaele Savi, Global Head of BlackRock Systematic

I like to think that team has been -- active for over 40 years and I've been here for at least half of those.

Jeff Shen, PhD, Co-head and Co-CIO of Systematic Active Equity

I think, one thing that could be surprising is, how smart people are, but also how nice people are.

Ronald Kahn, PhD, Global Head of Systematic Investment Research

You know, one of the great things about the investment business is we do bring in people with a lot of different backgrounds, and that makes it I mean, I think it helps us make better investment decisions, but I also think it makes it a more interesting place to work.

Tom Parker, CFA, Chief Investment Officer, Systematic Fixed Income

You know, I think the most interesting thing is that we've always worried when people leave, we go, how are we going to replace them? What are we going to do? But, you know, quantitative techniques have one advantage is, the insights don't go down in the elevator. They stay in your model. And I always call them the ghost in the machine.

Raffaele:

We work really hard to turn innovation into consistency.

Ron:

We’re really a group that turns, you know, basically turns research and data into alpha.

And so research is at the heart of what we do. And so we have to keep innovating because the ideas that we use, they're all about informational advantages. So if you buy most Blackrock systematic products there's not a risk premia or behavioral, anomaly component to them. Most of what we do is informational advantage.

We identify data that tells us things that the market doesn't understand, but we know the market figures these things out eventually. So for us, we always need to be coming up with new things. And so I think we've never had a stronger culture of innovation and creativity that we have now.

Jeff:

Clients look at BlackRock and think it's a big place. So we always say that, while it's a big place at the same time, it's all about delivering the benefit of a scale for our clients. How can we leverage a scale of BlackRock, of Systematic, for the benefit of our clients?

And I think we can think of all this benefit of a scale in multiple dimensions, in a sense, and allow us to really get data that we wouldn't be able to get before, to really allow us to trade much more efficiently for our clients to leverage the benefit the scale, and also to have a diverse team that can cut across multiple disciplines, not only having the best of a computer scientist in the world, but also having the best economists in the world and have them work together to provide solutions for our clients.

Tom:

Alpha is the hardest game in town. You know, you're competing against Nobel Prize winners. You have geniuses, you know, large tech teams. And it's just everybody wants to be in that game and it's a zero sum game. And so that's always been the challenge. And we even gave ourselves two harder challenges.

So alpha is this amazingly hard challenge, doing alpha in fixed income where there's very little idiosyncratic risk is even a bigger challenge. And then saying you can actually gonna be market neutral is kind of an amazing challenge. And sometimes you kind of wish, oh, maybe we shoulda compromised some more or added more factor exposure out of more beta.

But there also is just a great feeling of satisfaction when you actually get things that work and you go, wow, we're competing with the best people in the world, the best finance people in the world, and winning.

Raffaele:

It's remarkable, I feel that truly the best is always yet to come. The best is always our next idea our next iteration. We've done incredible work, that's the team. I've been fortunate to be part of that team where a lot of people did a lot of incredible work-- there's so many more things that this team can do so I'm looking forward to all the future milestones.

Investing involves risks, including possible loss of principal.

Stock and bond values fluctuate in price so the value of your investment can go down depending upon market conditions. The two main risks related to fixed income investing are interest rate risk and credit risk. Typically, when interest rates rise, there is a corresponding decline in the market value of bonds. Credit risk refers to the possibility that the issuer of the bond will not be able to make principal and interest payments. The principal on mortgage- or asset-backed securities may be prepaid at any time, which will reduce the yield and market value of these securities. Obligations of US Government agencies and authorities are supported by varying degrees of credit but generally are not backed by the full faith and credit of the US Government. Investments in non-investment-grade debt securities (high-yield bonds or junk bonds) may be subject to greater market fluctuations and risk of default or loss of income and principal than securities in higher rating categories. Income from municipal bonds may be subject to state and local taxes and at times the alternative minimum tax.

The information and opinions contained in this material are derived from proprietary and non-proprietary sources deemed by BlackRock to be reliable, are not necessarily all inclusive and are not guaranteed as to accuracy. Investments named within this material may not necessarily be held in any accounts managed by BlackRock. Reliance upon information in this material is at the sole discretion of the reader. Statements concerning financial market trends are based on current market conditions, which will fluctuate. There is no guarantee that these investment strategies will perform well under all market conditions. Outlook and strategies are subject to change without notice.

Prepared by BlackRock Investment LLC. Member FINRA. © 2026 BlackRock, Inc. or its affiliates. All Rights Reserved. BLACKROCK is a trademark of BlackRock, Inc. or its affiliates. All other trademarks are those of their respective owners.

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Built on a legacy of innovation

For the last 40 years, BlackRock’s Systematic team has been at the forefront of data-driven investing, transforming advances in research and technology into new ways of understanding markets.

Explore our approach Explore our approach

Raffaele Savi: Markets today are definitely awash in information. The quantity of information we have is something that is increasing significantly every year.

And for an investor, the key challenge and opportunity is always, how do we make sense of all of this? How do you filter out noise and focus on the signal that is underneath all this change?

Ronald Kahn: Our whole business is turning data into outperformance for clients, investment performance for clients.

And we've always used data. When I started years ago, our data was mainly financial statements and a few other things, things that fit neatly into spreadsheets. Now there's been an explosion of data.

We saw that early on, and we've been using as much data as we can get our hands on for more than a decade. I mean, for our whole history, really, but with the explosion of available data, we've harnessed all of that to deliver performance for clients.

Raffaele Savi: For a mathematically inclined person, systematic investing is a dream field. And I say that because it combines three aspects that make modeling always interesting every day when you start working on it.

One, it's non-stationary — the world changes every day. Two, it's a competitive environment and everybody is trying to have a better forecast, and prices react to that. And third, in general in the space, we say that there's a low signal-to-noise ratio. To find that gram of insight, you have to sift through a lot of data and ideas.

Ronald Kahn: When I think about the impact of technology generally. What people often don't realize is the impact of Moore's Law — that every 18 months to two years, the power of technology doubles. And if you think about what we can do with AI today, if you're not happy with it or impressed with it now, a year or two from now it's going to be a lot more powerful.

And what we've learned from technology is that sort of trumps everything else, whether you look at playing Go, playing chess, image recognition — all of it. It's all powered by the increasing power of technology itself.

Jeff Shen: Machines are very good at achieving a sense of breadth and velocity, so that we can look at a lot of things at once.

I think the artificial intelligence revolution really allows us to think about both breadth and depth together. You can actually look at many, many different stocks or many different securities all at once, but with a tremendous amount of depth.

So this combination of breadth and depth is certainly what AI is promising for us on a forward-looking basis.

Ronald Kahn: Our view is that this is a very challenging problem. How do we outperform markets in this highly competitive environment?

And I believe we need all of these different skills. We need people with backgrounds in accounting and economics and finance. But we also need people with backgrounds in computer science, data science, statistics, and AI. It's been a big effort on our part to bring all these people together. And I think we have a kind of unique culture around bringing all these people together and getting them to work together because they're all technical, they're all highly skilled, but they come at problems different ways.

Raffaele Savi: One thing that is interesting about systematic investing: when I started, which is almost 30 years ago, it was a business by experts, for experts.

It was perceived to be complicated. It was perceived to be something only the largest institutions in the world would have an interest in. And as with many trends in finance, I think we've seen over the years and over the decades a democratization of access.

And so now I would say that systematic investing is a business for everybody. We have among our clients some of the largest and most sophisticated institutions in the world.

And we've also built and manage active ETFs and mutual funds that my mom is invested in. And that's quite special about systematic investing at BlackRock as well. Other teams in our space, the most sophisticated competitors, might be focused only on institutions. We think that this is a style of investing that should be accessible to everybody.

Jeff Shen: Our fundamental belief is that today's alpha is tomorrow's beta. Maybe tomorrow’s smart beta.

Therefore, it's critical for systematic strategies to stay current, to think about what's on the horizon, and how we can evolve to adapt to the new market regime.

It's about investing in data, in technology, in people, and in the process. So it's actually about continually thinking about how we innovate on all these dimensions to evolve the process going forward.

Ronald Kahn: I think success requires three things. It requires people, culture, and scale.

We've brought together people with all different backgrounds in a fairly unique way. And then on scale, this is a scale game. We have to buy tons of data. We spend a lot of money on data. We have to build technology to do this. We use the infrastructure of BlackRock to trade effectively.

All of these things put us in a position to lead in data-driven investing.

Build what’s next in investing

The next generation of investing will be shaped at the intersection of data, technology, AI and investment research. Our team brings these disciplines together to discover new signals, test new ideas and build investment solutions at global scale.

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Systematic Insights

Our investment experts take on some of the most talked about investment themes today and decode what is happening in the markets.

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Frequently asked questions

BlackRock integrates artificial intelligence (AI) and machine learning into its systematic investing processes. AI helps analyze vast amounts of data, identify patterns, and generate insights that inform investment decisions, ultimately aiming to enhance performance and manage risks.
We believe that systematic investing has the potential to deliver consistent alpha to investors, regardless of market cycles or macro conditions. There are several advantages ranging from scale, to repeatability, to flexibility.
Systematic investing can be beneficial in various market conditions. Its data-driven approach allows for adaptability and responsiveness to market changes, making it a valuable strategy during periods of volatility and uncertainty.
Systematic investing, like all investment approaches, involves risk. Performance depends on market behavior, the assumptions embedded in investment models, and the availability of data, and may vary across different market environments.