
Key takeaways
A significant share of recent market returns has been driven by a small group of mega-cap stocks, while evolving interest rate expectations and geopolitical risks have contributed to elevated market volatility. As economic conditions, investor sentiment, and valuations change, leadership naturally rotates across investment styles, creating a cyclical pattern of factor performance. This cyclical behavior presents a key challenge: maintaining diversified factor exposure while adapting to changing market leadership.
Factor rotation strategies, like the iShares U.S. Equity Factor Rotation Active ETF (DYNF), seek to address this challenge by dynamically adjusting exposures across factors such as value, momentum, and quality as market conditions evolve. Rather than relying on a single investment style, DYNF seeks to capture opportunities as factor leadership changes while maintaining diversified factor exposure.
Factor rotation is grounded in the observation that different investment styles tend to perform best at different points in the economic cycle. For example, quality and low volatility factors have historically shown resilience during periods of slowing growth or heightened uncertainty, while value and momentum factors have often benefited during recoveries and expansions.1
Source: BlackRock Systematic as of June 30, 2026. For illustrative purposes only
Rather than maintaining static allocations, factor rotation strategies seek to identify these shifts in market leadership and dynamically adjust exposures accordingly. By analyzing a broad set of economic indicators, we identify the current phase of the economic cycle and assess which factors have historically demonstrated the strongest performance in similar market conditions.
By aligning portfolio positioning with prevailing economic and market conditions, investors may be better positioned to capture opportunities as leadership rotates across factors over time.
While precisely timing factors is challenging, we believe a systematic approach to factor timing can add value. Our process aims to evaluate factors both relative to their own history and relative to one another.
Over the long term, the portfolio seeks to maintain diversified exposure across multiple equity factors. In the short term, however, the strategy dynamically adjusts exposures based on changing market conditions, increasing allocations and tilts into factors with tailwinds and away from factors with headwinds.
BlackRock as of April 30, 2026. Chart illustrates monthly active factor exposures of DYNF vs. the S&P 500 Index for 36 months. Factor exposures are measured using the BlackRock Fundamental Risk for Equities Model (BFRE US). Factor exposures are shown as Z-scores, which are statistical measurements of the number of standard deviations the portfolio’s style exposure is away from the estimated total universe. From red to green, z-scores are categorized according to the following 7 ranges: <-0.2, <-0.1, <-0.05, <+0.05, <+0.1, <+0.2, >+0.2. Factors are represented by the style factors of the same name, with the exceptions of quality (profitability style factor), min vol (volatility style factor, inverted), and size (size style factor, inverted). This information should not be relied upon as research, investment advice or a recommendation regarding the Funds or any security in particular. This information is strictly for illustrative and educational purposes and is subject to change. Past performance does not guarantee future results.
Figure 2 illustrates how our factor exposures evolved across different market environments:
BlackRock Systematic as of March 9, 2026. For illustrative purposes only. This information should not be relied upon as research, investment advice or a recommendation regarding the Fund or any security in particular. This information is strictly for illustrative and educational purposes and is subject to change. Past performance does not guarantee future results.
The factor tilts shown in Figure 3 were driven by a systematic process designed to adapt to evolving market conditions. As leadership shifted from AI-driven growth stocks to a more volatile and uncertain environment, the model continuously reassessed which factors were most likely to be rewarded. This adaptive approach is powered by a combination of factor timing signals that work together to inform portfolio positioning.
The fund’s changing factor exposures reflect the output of our broader factor timing framework. Rather than relying on a single market signal, the fund integrates multiple insights to identify potential opportunities and risks across different market environments.
Our dynamic framework incorporates the following complementary factor timing indicators:
By combining these perspectives, the strategy seeks to create a more robust and adaptive stock selection process rather than relying on any single signal alone.
In today’s evolving market environment, investors are rethinking how they allocate across equity factors. DYNF seeks to simplify tactical factor implementation by combining factor timing and stock selection within a single actively managed ETF. Rather than rotating among individual factor ETFs, investors can access a dynamic strategy that adjusts as market conditions change and can serve as a core or complementary exposure within the portfolio.
The fund invests across U.S. large- and mid-cap stocks, seeking exposure to the factors our systematic process identifies as best positioned for the current environment. As market leadership evolves, the portfolio evolves with it — providing investors with a streamlined, actively managed approach to factor investing designed to adapt across market cycles.
