The smart beta and factor landscape has exploded in recent years. Academics have identified an ever-expanding list of equity factors that may explain excess returns when applied to broad systematic portfolios, and fund providers have launched a variety of strategies that seek to harness and package them. But how can investors gauge whether these strategies, and the factors underlying them, make economic sense and are positioned to produce results over time?

In this Q&A, PIMCO equity strategist Raji Manasseh and Research Affiliates product specialists Brent Leadbetter and Joe Steidl discuss the process behind factor selection and portfolio construction for the PIMCO RAFI Dynamic Multi-Factor funds and the inputs informing factor weightings.

Q: HOW CAN INVESTORS BENEFIT FROM THE RAFI APPROACH?

A: Equity factors should be chosen carefully: Only a handful of the hundreds of factors identified to date are likely to persist in the future, in our view. For this reason, Research Affiliates’ equity factors are grounded in academic-quality research and subject to publication in peer-reviewed journals. These factors are designed with an eye toward simplicity, transparency, a sound economic rationale and reduced transaction costs. We believe applying this approach in the PIMCO RAFI Dynamic Multi-Factor funds leads to efficient implementation of a select subset of robust, persistent and pervasive factor portfolios. Finally, by looking at a factor’s valuation and relative momentum, our fund seeks to efficiently allocate more towards those factors looking most attractive in the current environment. We believe this combination gives investors the potential for higher returns with a smoother ride.

Q: HOW DID YOU DETERMINE WHICH FACTORS TO INCLUDE IN THE RAFI DYNAMIC MULTI-FACTOR FUNDS?

A: Research Affiliates is skeptical of many in the expanding array of equity factors and questions the economic rationale behind some seemingly far-fetched – yet published – factors said to generate a premium (the “creativity in stocks’ ticker symbols” factor comes to mind).¹ However, we do believe a combination of a small subset of thoughtfully constructed portfolios based on factors with a sound economic rationale, grounded in academic-quality research, may offer the potential for excess returns with modest tracking error.

Research Affiliates identified five equity factors – value, quality, low volatility, momentum and size – that we believe meet these criteria. Working together, PIMCO and Research Affiliates combined these factors into a cohesive multi-factor strategy that favors the factors that we believe appear to be most attractively positioned for future excess returns (as measured by a combination of value and momentum signals). We then packaged this strategy in the four PIMCO RAFI Dynamic Multi-Factor funds, each focused on a different region (U.S., developed, international  and emerging markets).

Q: HOW DO YOU CONSTRUCT THE FACTOR PORTFOLIOS?

A: Construction of the PIMCO RAFI Dynamic Multi-Factor fund portfolios begins with the global universe, which we divide into a large and small companies universe. Within each universe we create four distinct factor portfolios: value, quality, low volatility and momentum.

• The value portfolio selects the cheapest companies identified by the ratio of their fundamental size to market capitalization. Companies with the highest ratio exhibit the strongest value signal. We believe this approach provides more diversified exposure to value than a single metric, such as the price-to-book ratio.
• The quality portfolio selects the top companies asmeasured by both profitability and the degree of conservative investment. Again, we believe this combination provides a more robust solution than one relying on a single metric. Selecting companies based solely on their profitability, for instance, would ignore whether firms’ management teams may have wasted those profits on excessive reinvestment (e.g., in the form of empire-building).
• The low-volatility portfolio selects companies with the lowest risk as measured by sector-level, regional and global betas. Diversifying across multiple measures of beta seeks to reduce persistent biases to individual sectors or countries.
• The momentum portfolio selects high-momentum stocks using three distinct measures: standard momentum, beta- adjusted momentum and “fresh” momentum (referring to outperformance that has just begun). We believe companies exhibiting fresh momentum are more likely to outperform those with “stale” momentum – those that have outpaced the market for multiple years and may be running out of steam.
• Finally, the size factor portfolio is an equally weighted combination of the above four factor premiums harvested in the small company universe. It is essentially a multi factor portfolio in its own right.

We weight stocks in the individual value, quality and low- volatility factor portfolios according to each company’s fundamental size (again, as measured by its sales, cash flow, dividends and book value relative to the market’s aggregate size). We believe this approach differentiates the RAFI  factor portfolios from most other factor funds, which we observe tend to weight stocks by market cap. The RAFI approach allows us to keep the liquidity of the portfolios high (i.e., larger companies get larger weights) while breaking the link between price and weight. This introduces a “buy low/sell high” dynamic within the factor portfolios themselves. One exception: We weight stocks in the momentum factor portfolio by market cap to prevent RAFI’s inherently contrarian rebalancing from reducing the exposure to momentum.

Q: HOW DO YOU ASSIGN WEIGHTS TO THE FACTOR PORTFOLIOS?

A: Research Affiliates’ factor weighting methodology offers another point of differentiation, in our view: While most multi- factor strategies equally weight their factor allocations, we dynamically weight factor allocations over time based on relative value and momentum. While some may view this approach as controversial, we believe incorporating valuation into the weighting of factors is no different from the role valuation typically plays when investors allocate to other assets, such as individual stocks, sectors and countries.

Any asset can become relatively cheap or expensive, and we believe future returns are conditional on starting valuations. We recognize that a dynamic allocation based solely on valuation has the potential to trade too early, buying cheap factors before they become even cheaper and selling appreciating factors before they appreciate further. Therefore, we also incorporate momentum when determining the dynamic factor weights in our strategies.

We use what is called “reversal” – returns over the past five years, excluding the most recent year – as a proxy for factor valuations in our calculation (see Figure 2). We believe a factor that has been beaten up for four years relative to other factors is likely relatively cheap (and vice versa). We also use standard momentum – returns over the past year, excluding the most recent month – as an indication of a factor’s recent turnaround. Looking at the two components together, we would generally consider a relatively cheap factor that is in the midst of a turnaround to be an attractive factor to overweight.

Figure 3 shows how this approach translated to factor weights at the most recent quarter-end (30 September 2019) in the GIS RAFI Dynamic Multi-Factor US Equity Fund. The value portfolio exhibited the worst returns over the four years preceding the most recent one (2015—2018), while also experiencing the worst performance of any factor over the past year, suggesting that the underperformance could continue. This combination led to a weighting below the baseline 20% equal-weight position.

Conversely, the low volatility portfolio received the highest weight because its recent price momentum has been far stronger than any other factor, which suggests that it may continue to outperform.

Factor return trends in developed equity markets led to similar allocations for the GIS RAFI Dynamic Multi-Factor Global Developed Equity Fund. As Figure 4 shows, the value portfolio received the smallest weight because it simultaneously exhibited low relative returns over the four years preceding the most recent one while also experiencing the worst returns of any factor over the past year. Conversely, the low volatility factor received the highest weight because although it had relatively high returns over the preceding four years, its strong performance has continued over the past year. While the low volatility portfolio may be relatively expensive versus other factors, its recent continued outperformance justifies an overweight.

Allocations to factor portfolios for the GIS RAFI Dynamic Multi-Factor Emerging Markets Equity Fund reflect Research Affiliates’ decision not to include a size factor portfolio due  to implementation and liquidity limitations. Additionally, Research Affiliates generally avoids small companies in these regions because they tend to be more expensive to trade than their developed market peers. Therefore, a factor’s baseline equal-weight position is 25%.

In the emerging markets, the value factor portfolio has the lowest weight today because it was the strongest performer over four years before running out of steam during the past year. Alternatively, the quality portfolio received the largest weight. Not only did quality lag the other factors for four years (an indicator of its relative cheapness), but its recent turnaround suggests it is an optimal time to be overweight the factor.

The GIS RAFI Dynamic Multi-Factor Europe Equity Fund also utilizes Research Affiliates’ dynamic weighting process to determine factor allocations. The fund and its associated factor portfolios incepted in 2018 and, therefore, have limited performance histories; Research Affiliates utilizes back-tested performance in conjunction with its dynamic weighting process to determine factor weights for this fund.

As Figure 6 shows, the value portfolio received the smallest weight because it exhibited relatively strong performance over the four years preceding the most recent one and then experienced the worst returns of any factor over the past year, suggesting that this recent outperformance could persist.

Alternatively, the momentum factor portfolio received the largest weight due to a combination of weak relative returns over the four years prior to the most recent year and strong performance over the most recent year.

All told, we believe our dynamic approach to factor weighting, applied to a select subset of robust factor portfolios, contributes to higher return potential while helping smooth the path for investors.