Time to Tilt: Harnessing factor cyclicality

Dynamic stock selection: Harnessing cyclicality

The post-pandemic equity market backdrop has proved challenging for active equity managers. Among U.S. large cap stocks, outperforming the index has been particularly difficult due to market volatility driven by vaccine rollouts, unexpected inflation and interest rate hikes. Against this backdrop, BlackRock Systematics’ iShares U.S. Equity Factor Rotation Active ETF (DYNF) has delivered alpha during volatile periods by adeptly navigating changing market conditions. DYNF ranks in the top decile¹ of the peer set in terms of both three-year returns and information ratio, with strong results driven by a dynamic stock selection approach that harnesses factor cyclicality to deliver returns.

Figure 1: Navigating varying market conditions

Because factor performance is cyclical, tactical allocations to factors may increase the return potential of the portfolio and help deliver more consistent returns throughout varying market conditions.

Adapting to Changing Markets

DYNF is an actively managed stock selection strategy that seeks to outperform the broad U.S. market by tactically allocating to six well-known factors: momentum, quality, value, low volatility, size and growth. While returns are primarily driven by these tactical tilts, the strategy also benefits from positive exposure to these factors over the long-term.

Factors are economically intuitive sources of return that have been used by active managers as a source of return for decades. Because each factor is driven by a different economic phenomenon, they have distinct return patterns and respond differently to the same market conditions. Maintaining a diversified allocation across factors can address cyclicality and reduce the potential impact of any single factor on the overall portfolio, designed to create a more consistent return stream – but is it possible to enhance portfolio outcomes beyond strategic diversification?

Because we understand the phenomenon that give rise to factor cyclicality, we can seek to improve upon a well-diversified allocation to factors by anticipating their over or underperformance to achieve incremental returns above long-term factor premiums. While factor timing can be challenging, we believe that a form of timing can be additive. Our approach to factor timing evaluates each factor relative to its historical performance and relative to other factors.³ Over the long run, the portfolio maintains diversified exposure to multiple factors. In the near-term, however, the portfolio tilts into factors with tailwinds and away from factors with headwinds. [Returns, risks and correlations are uncertain; BlackRock cannot guarantee that these potential benefits will be realized.]

A Holistic Approach

Our dynamic factor timing strategy starts by assessing the current economic regime to identify which factors are likely to have long-term tailwinds or headwinds. We then examine the valuation and sentiment of all factors, which influence short-term price behavior. Finally, we incorporate factor-specific indicators to create a robust forward-looking outlook for each factor. While each of these factor timing indicators is individually useful, our research finds that our outlook has been more effective and robust when they are combined. This outlook is translated into a portfolio of U.S. stocks, overweighting stocks that have high exposure to the factors which are in favor. The resulting portfolio adapts to changing market conditions and aims to outperform the market cap weighted benchmark across a variety of market environments.

A Holistic Model

Factor-timing indicators

• Economic regime - The prevailing economic regime has a strong and intuitive link to individual factors, with each factor being rewarded at different times in the economic cycle. We determine the phase of the economic cycle based on our assessment of the level of economic growth, probability of recession, market sentiment, interest rates, and volatility. We then allocate to the factors that have performed best in the current economic environment.
• Valuation - Factors can become over- or under-valued. Our research indicates that undervalued factors tend to outperform their expensive counterparts. Rather than rely on a single valuation metric, we combine multiple valuation metrics, including both trailing and forward-looking insights into a composite valuation indicator. We consider a factor relatively cheap when it has a low valuation relative to its own history and to other factors.
• Sentiment - We see evidence of momentum across groups of securities, including factors. Investors tend to pile into, and thus bid up the prices of, assets that have exhibited strong recent performance. As a result, factors that performed well recently have tended to continue to perform well.
• Factor Specific – The alternating dominance of value and growth factors has driven risk and returns in equity markets for decades. Our approach to growth timing is based on assessing whether growth firms are profitable, investing in innovation, and whether future growth is fully “priced-in.” To position against inflection points when prevailing trends change, we evaluate the stability and crowdedness of the momentum trade.

Better together

To understand the benefits of bringing a robust and impactful set of factor timing signals together, we analyze the Information Ratio (IR) and max drawdown of each cohort of timing insights, as well as the aggregate model. While the IR for the economic regime and growth timing insights are the highest individually, attenuating these views with valuations and sentiment-based timing insights creates a more robust framework. The aggregate model has a significantly higher Information Ratio than any of the individual indicators, and the shallowest drawdowns, presenting the optimal solution.

How do these insights come together to create a portfolio? First, we determine our holistic factor-based outlook by combining all indicators: the economic and market regime, factor valuations, sentiment and factor-specific indicators. We then translate our desired factor positioning into a robust, stock-level representation of our desired factor exposures. This involves analyzing each stock’s inherent factor exposures, favoring those that have simultaneous factor exposures that align with our current factor outlook. Using advanced portfolio construction techniques, we balance our factor-informed return outlook for each stock with risks and transaction costs, while incorporating constraints to ensure the portfolio remains diversified.

How do timing strategies fit into a broader equity allocation?

DYNF can serve as a core or complementary exposure within the portfolio, sitting alongside other actively managed strategies or other broad equity market ETFs. Potential implementation benefits range from, benchmark outperformance to lowering investment risk s and creating a more robust overall portfolio with diversified allocations across factors.

While equity portfolio managers often have style tilts or biases (e.g., value or growth investors), they typically do not change their investment style as macroeconomic conditions fluctuate. As a result, the alpha stream produced by DYNF, which dynamically allocates to U.S. stocks based on their exposure to historically rewarded factors and our forward-looking outlook for those factors, is differentiated from other high performing managers. Data supports this: The average pairwise correlation between DYNF and the other top Information ratio (IR) quintile managers in the U.S. Large Cap category over the trailing 3-year period is less than 0.2, indicating that the strategy is additive and diversifying to a portfolio constructed of the top active managers.⁴ By including several lowly correlated return streams in a portfolio, allocators can seek to improve the overall Sharpe ratio of their active equity allocation

On the other end of the spectrum, many investors use DYNF as a cost and capital efficient way to introduce active returns into a predominantly passively managed portfolio. While the strategy is actively managed by BlackRock Systematic, it is data-driven, transparent, and risk controlled. The higher transparency associated with factor-based strategies and with ETFs may appeal to investors who are most familiar with passive strategies. Figure 3 shows the three-year net of fee returns to four hypothetical portfolios with varying allocations to the S&P500 and DYNF.

Seeking to Generate Alpha with Dynamic Factor Tilts

BlackRock Systematic has over four decades of experience innovating new ways to help deliver better outcomes for clients. DYNF is the latest iteration of our factor rotation strategies that are designed to assess the near-term alpha potential of historically rewarded factors. The dynamic nature of DYNF enables us to identify alpha opportunities with low correlations to other top performing strategies in the U.S. Large Cap category. To learn more about the iShares U.S. Equity Factor Rotation Active ETF (DYNF) click here. For more information about BlackRock Systematic click here.