Mission possible: Timing equity factors

Two mantras exist among investors when it comes to equity factors. First, you hear that “factors tend to outperform the broader market in the long run”. True. Second, you hear that “timing factors is almost impossible, and that you would be better off with a static allocation”. False. In fact, dynamic timing approaches to factor premia management adds significant benefits over static allocation approaches and in this piece, we show you why.

Think about it like this: just like a soccer team’s performance can vary season to season, factor returns fluctuate over time, making the key to success not just the selection of players (factors), but how you allocate them based on changing field (market) dynamics. A great coach (aka multifactor model) knows when certain players (factors) can perform the best. Timing them requires a good coach who can recognize the different environments and implement the most appropriate strategy.

Before we start, let us add, as it relates to the first mantra, that factor outperformance is more pronounced in certain markets such as Switzerland for instance.

Quality Shines Over the Long Run—But It's Not the Whole Story

Over extended investment horizons, the Quality factor has delivered the highest performance among its peers, outperforming the Swiss Performance Index (SPI) by an average of 3.6 percentage points annually. With such compelling returns, one might reasonably ask: Why not just invest in Quality and ignore the rest?

The answer lies in the nature of factor performance. While all major factors—Quality, Value, Momentum, and Low Volatility—have historically outperformed the SPI over the long run, their returns fluctuate significantly over shorter periods. Figure 1 illustrates this point, showing the one-year rolling excess returns of each factor versus the SPI. The chart makes clear that no factor outperforms consistently; each moves in and out of favor depending on prevailing market conditions. Consider for instance 2022, an inflationary period in which one would have expected Quality to shine again. Quite the opposite, Quality had a drawdown of 11 percentage points relative to the broader market.

Understanding Factor Behavior Across Market Cycles

As shown in the first row of Table 1, equity factors offer a good base for outperformance, since they systematically outperform the market over the long run. However, different factors tend to perform better under specific market and macroeconomic conditions, reflecting their distinct underlying drivers. For example, Value which focuses on companies trading below their intrinsic worth—often performs best in early-cycle recoveries or environments characterized by low interest rates, when investor appetite for discounted growth prospects tends to rise. Momentum, on the other hand, typically excels in strong, upward-trending markets, where price persistence is reinforced by investor sentiment and herd behavior. Quality stands out as a more balanced, all-weather factor. It exhibits resilience during periods of market stress, thanks to its focus on companies with strong balance sheets and consistent profitability but also participates meaningfully in market upswings—offering both defensive and offensive characteristics within a portfolio. Low Volatility, by contrast, is more defensively oriented and shows particular strength in downturns due to its emphasis on stable, low-risk equities, natural safe havens when risk aversion dominates.

To better understand these dynamics, Table 1 breaks down factor returns and risk characteristics by market regime. We define an up-market as any month in which the SPI posts positive returns, and a down-market as one with negative returns. The results underscore the importance of regime awareness in factor investing, as both returns and betas shift meaningfully depending on broader market direction.

In rising markets, Momentum, Quality, and Value tend to outperform the SPI by 50 to 60 basis points per month. These periods reward risk-taking and reward factors that lean into market trends or valuation opportunities. Conversely, Low Volatility, which is inherently more defensive, typically lags the broader market by around 30 basis points per month. Risk-adjusted returns, however, are not simply a function of return differentials. Market beta plays a crucial role. Most factors display betas below 1, indicating lower sensitivity to overall market movements—except for Value, which tends to have a beta above 1 during upswings.

During down-markets, the dynamics shift notably: Low Volatility stands out as a truly defensive factor, with a beta consistently below 1 and an average monthly outperformance of 100 basis points versus the SPI. Momentum and Quality hold up relatively well, delivering returns roughly in line with the market. Value, however, tends to underperform by about 30 basis points per month—highlighting its pro-cyclical nature and vulnerability during drawdowns.

These statistics highlight the episodic nature of factor performance and raise an important question: could a well-designed combination of factors help smooth out this variability and increase the probability of consistent outperformance? The evidence strongly suggests that the answer is yes.

The Case for Multi-Factor Investing

Relying on a single factor—even one with a strong long-term track record—can expose investors to unnecessary cyclicality and performance swings. A thoughtfully constructed multi-factor portfolio, combining complementary factors such as Quality, Value, Momentum, and Low Volatility, can help smooth the ride and goes a long way in solving the problem shown in Figure 1. By balancing exposure across factors with different risk profiles and sensitivities to market regimes, investors can achieve more stable returns and improved risk-adjusted performance over time.

A simple yet effective way to build a diversified multi-factor portfolio is to allocate equal weights to each factor. In the case of Momentum, Quality, Value, and Low Volatility, this would mean a 25% allocation to each. While this naïve equal-weighting approach may result in slightly lower absolute returns than investing in the top-performing Quality factor alone, it typically delivers the highest risk-adjusted performance, as measured by the Sharpe ratio. This highlights the value of diversification—not just for risk reduction, but also for more efficient return generation.

Interestingly, a more refined risk-based approach, such as weighting factors inversely by their long-term volatility to approximate equal risk contribution, does not lead to substantially different results than the naive equal-weighting. Therefore, there is no additional benefit in going for more complexity. Specifically, we found that both equal-weight and inverse-volatility strategies produce multi-factor portfolios with superior Sharpe ratios compared to most standalone factors and these findings hold across various market environments.

Embracing Time Variability: The Case for Dynamic Allocation

While static diversification already offers significant benefits, a fully dynamic weighting approach can further enhance outcomes by adapting the portfolio to changing market and economic conditions.

Our dynamic multi-factor strategy allocates capital flexibly across the four core factors—Momentum, Quality, Value, and Low Volatility—based on three core principles: understanding the broader economic landscape, identifying persistent market trends, and recognizing valuation extremes. Together, these principles form a cohesive framework that seeks to enhance returns while managing risk.

Three Perspectives, One Cohesive Framework

As outlined above in Table 1, factor returns depend very much on the market regime, which in turn is shaped by the broader economic backdrop, including business cycles, interest rates, and investor sentiment. By staying attuned to these shifts and relying on observable indicators such as the steepness of the global yield curve or the credit spread of corporate bonds, a dynamic strategy can adjust allocations to better align with the prevailing environment, helping to manage risks and capture opportunities as they arise.

Because economic regimes typically evolve gradually, market trends as well as factor trends often persist over time as investors gradually respond to new information. By identifying factors that exhibit strong momentum, our dynamic approach can capitalize on these trends, positioning the portfolio to benefit from sustained performance patterns.

Markets are not always efficient, and factors can become over- or under-valued relative to their historical norms, due to investor crowding for instance. Our dynamic allocation framework seeks to identify these valuation extremes, reducing exposure to expensive factors that may be at risk of reversal and increasing exposure to attractively priced factors with stronger potential for long-term returns.

Aggregating insights into a unified portfolio

To take up again the analogy of the coach of a successful sports team, our allocation framework rotates in to and out of factors as it sees fit—just as a coach would with her players—timing them so that they can play out their strengths and assigning them to the bench if their weaknesses dominate.

Within our dynamic allocation framework, we ensure that each perspective—whether economic regime, market trend or valuation— gets the same say and that no single perspective dominates.

Figure 2 illustrates how the factor composition of a multi-factor portfolio on Swiss equities would have evolved over time. The result is a dynamic, data-driven investment approach that not only leverages the diversification benefits of multi-factor investing but also seeks to systematically enhance performance through active allocation across different market environments. Figure 3 demonstrates the dynamic allocation’s performance effect, delivering 1.45% p.a. (as a difference between 10.40% p.a. and 8.95% p.a. as illustrated in Figure 3) over a static, equal weighted approach and 3.21% p.a. over the broad market index SPI (as a difference between 10.40% p.a. and 7.19% p.a. in Figure 3).

Conclusion

Factor investing is not just a framework—it’s a transformative approach to building diversified, resilient equity portfolios. Momentum, Quality, Value, and Low Volatility are four factors that have consistently demonstrated the potential to outperform the market over the long run, offering institutional investors a solid foundation for success.

However, while these factors deliver clear long-term benefits, their short-term performance can be cyclical and less predictable, influenced by shifting market and economic conditions. This is where dynamic factor allocation comes into play. By rotating into more defensive factors, such as Low Volatility and Quality, ahead of downturns—using our advanced algorithms—we can unlock additional value and enhance portfolio resilience.

Our dynamic multi-factor strategy combines insights into economic trends, market behavior, and valuation extremes to capture the time-varying nature of factor premia. The result? More consistent outperformance across market cycles.

For institutional investors seeking to navigate complexity and build robust portfolios, this disciplined, data-driven approach represents a compelling evolution of traditional equity investing. With Momentum, Quality, Value, and Low Volatility as your foundation—and dynamic allocation as your edge—you can confidently embrace the future of factor investing.