ETF Specialist

A Framework for Analyzing Multifactor Funds

Alex Bryan, CFA

Multifactor funds are among the most complex index investments, more closely resembling active than passive management. As such, it is necessary to apply a similar level of rigor to evaluate their portfolio-construction processes. In June, Morningstar’s Manager Research team published “A Framework for Analyzing Multifactor Funds.” What follows is a summary of that framework, which should help investors assess these funds’ approaches to portfolio construction to better navigate the landscape.

What Is the Fund’s Selection Universe?
The selection universe, also referred to as a parent index, is the collection of potential stocks that a fund whittles down to build its investment portfolio. This is typically a broad index, like the Russell 1000 Index. The selection universe should serve as a benchmark for the fund’s performance. It may also offer insight into the fund’s potential to outperform its parent index and/or Morningstar Category peers. For example, the payoff to most investment factors has historically been the greatest among the smallest stocks. This may be because they are more likely to be mispriced than larger stocks. So—all else equal—funds that start with a universe of large- and mid-cap stocks (as most multifactor funds do) likely have less poten­tial to outperform than those that start with an all-cap universe or a group of small-cap stocks.

Which Factors Does the Fund Target?
There are only a handful of investment factors that truly matter. These include:

  • Value (including dividend yield)
  • Small size
  • Momentum
  • Quality, and
  • Low volatility

Each of these factors has been extensively and independently vetted in academic research and has tended to pay off in nearly every geographic market studied over the long term. But more important, there are reasonable economic explanations as to why each of these factors has paid off and will likely continue to do so. These include compensation for risk, behaviorally driven mispricing, and institutional frictions.

In contrast to the other four factors, low volatility doesn’t aim to deliver higher returns than the market, but rather reduce risk and, in turn, potentially deliver better risk-adjusted performance than the market. While the low-volatility factor can help diversify the others, it can disproportionately affect a fund’s performance (unless the fund explicitly limits active risk from this factor). It can also reduce the fund’s long-term return potential.

While there are myriad other factors, they either are not widely accepted, are not investable at scale (like illiquidity), or just repackage one or more of these core factors. It is best to stick to funds that target a combination of the core factors.

How Does the Fund Measure Its Targeted Factors?
There are many ways to measure stocks’ exposure to each factor. Sometimes one metric or set of metrics will work better than another, but it isn’t clear that there is an optimal way to define value. What matters is that the chosen metrics are:

  • Simple
  • Transparent, and
  • Clearly representative of the investment style

The specific metrics chosen tend to move the needle less than whether the fund measures each stock’s factor characteristics relative to its sector peers or the entire universe. There is a trade-off between these two approaches. A sector-relative approach leads to less-pronounced sector biases than the universe-relative approach. Sector bias can be a source of uncompensated active risk that often isn’t necessary to capture the targeted factor. A sector-relative approach can also improve comparability across stocks (particularly for the value and quality factors), as firms in the same sector tend to have more similar balance sheets and profitability than firms in different sectors. The drawback is that it may reduce the fund’s factor purity, causing it to own stocks with weaker absolute factor characteristics than it would if it measured each stock against the entire universe.

One approach isn’t clearly better than the other, but funds that don’t control for sector differences would likely benefit from sector constraints, which can help improve diversification. After all, diversification is one of the core reasons to own a multifactor fund.

How Does the Fund Combine Its Targeted Factors?
There are two main approaches to combining multiple factors in a portfolio: mixing and integration. Funds that follow the mixing approach split their portfolios into individual sleeves that each target a distinct factor. For example, if a fund uses the mixing approach to combine value and momentum, it might dedicate half the portfolio to targeting value stocks (ignoring their momentum characteristics) and the other half to momentum (ignoring value). This approach is similar to combining individual factor funds, but it offers the advantage of lower turnover by allowing trades to partially offset as stocks move across sleeves.

The mixing approach is simple, transparent, and facilitates clean performance attribution, making it easy to gauge the impact of each factor on the fund’s performance. That said, it can dilute the fund’s overall factor exposures because there is usually little overlap between the holdings in the different sleeves.

Funds that use the integration approach can achieve stronger factor exposures. They don’t necessarily target the stocks that score the best on any single factor. Rather, they pursue stocks with the best overall combination of factor characteristics. This allows them to allocate the entire portfolio to stocks with exposure to the targeted factors.

The downside of the integration approach is that it can lead to greater active risk, which increases both the potential for outperformance as well as underperformance. It is also more complex, and in some cases less transparent, than the mixing approach, making it harder to attribute portfolio performance to distinct factors.

How Aggressively Does the Fund Target the Factors?
Funds with greater exposure to their targeted factors have greater potential to outperform the market than their less-aggressive counterparts when those factors are in favor and greater risk of underperformance when they are not. Just as stocks don’t always outperform bonds, even though they tend to do so over the long term, factors experience their own unique cycles of out- and underperformance versus one another and the broader market.

The risk of underperformance is a necessary trade-off to capture the performance advantages factors might offer.

Investors who are comfortable with the risk of underperforming a benchmark (active risk) to capture those potential return advantages should favor funds with pronounced exposure to their targeted factors. Funds with smaller factor exposures are probably more suitable for those who prefer to limit active risk while keeping the door open to the potential for modest outperformance.

The strength of a multifactor fund’s factor exposures is driven by its:

  • Stock-selection threshold
  • Weighting approach
  • Portfolio constraints
  • Rebalancing frequency, and
  • Factor-timing adjustments (if applicable)

Portfolios with higher thresholds for stock selection should have higher factor exposures and more compactness than those with less-demanding criteria. For example, if a fund assigns composite factor scores to all stocks in its selection universe and targets the highest-ranking third, it should have greater exposure to its targeted factors than a fund that filters out the lowest-ranking third.

Funds can also strengthen their factor exposures through their stock-weighting approach. Those that incorporate the strength of each holding’s factor characteristics into their weightings tend to have more-pronounced factor exposures than funds that don’t. Constraints on sector, country, and stock weightings, turnover, and risk are often beneficial (more on that later), but they can also reduce the strength of a fund’s factor exposures by causing it to own stocks with weaker absolute factor character­istics than it otherwise would.

More-frequent portfolio rebalancing tends to strengthen a fund’s factor exposures. Quickly removing stocks whose factor characteristics have weakened and replacing them with stocks that look better on those metrics can help keep these funds homed in on their targeted factors. However, more turnover also leads to higher transaction costs, so it is important to understand how the index balances these considerations.

Some funds explicitly seek to time their factor exposures based on forecasts of how each factor is expected to perform going forward. The larger these tactical adjustments are, the more aggressive the fund tends to be.

Measuring How Aggressively a Fund Pursues Factors
To get a better handle on how aggressively a fund pursues its targeted factors, it is useful to evaluate its active risk. Funds with greater active risk tend to be more aggressive. There are two ways to measure a fund’s active risk relative to its starting universe: tracking error and active share. Tracking error shows how the fund’s construction approach has affected its performance. Active share shows how different the fund’s hold­ings are from its starting universe.

Directionally, active share tends to line up with tracking error, though factor funds with low active share can still exhibit a fair bit of tracking error. When these signals conflict, tracking error is usually more informative. Tracking error also tends to be a more reliable indicator for funds with higher turnover, where the current holdings may not reflect what the fund will own in the future.

While active risk is a good proxy for the strength of a fund’s factor tilts, it does not directly measure them. There are two ways to directly measure the strength of a fund’s factor exposures: holdings-based analysis and returns-based analysis (factor regression). Holdings-based analysis compares how the portfolio’s holdings stack up on the fund’s targeted metrics against a market-cap-weighted benchmark. For example, if a fund tilts toward value and smaller-cap stocks, it can be helpful to compare the average price/earnings and market capitalization of its holdings against those of its starting universe. Factor regression analysis is a complementary tool that shows how the fund’s performance was influenced by its factor tilts. For more information on how to conduct holdings- and return-based factor analysis, please consult the full report.

Are There Any Constraints on the Portfolio?
The most common portfolio constraints applied by multifactor funds include limits on sector weightings, stock weightings, country weightings, risk, and turnover. These constraints can help improve diversification, reduce risk, and reduce transaction costs. However, they also reduce a portfolio’s exposure to the factors it targets by causing it to own stocks with weaker factor exposures than it otherwise would to stay within the limits set by the constraints.

Not all multifactor funds apply such constraints, though it is typically preferable to put limits on sector and country weightings. These are sources of active risk that often are not necessary to capture the targeted factors, and historically they have not been well compensated (unless they were driven by momentum).

The Big Picture
Multifactor funds require a similar level of due diligence to traditional actively managed funds. Resist the urge to assess a fund’s merit solely on its performance. A robust investment process is far more important, though it is more difficult to evaluate. The key things are to be comfortable with the level of active risk the fund is taking, to stay diversified, and to avoid paying too much. For those who aren’t comfortable analyzing multifactor funds, there’s nothing wrong with sticking to low-cost market-cap-weighted funds—that’s better than investing in a strategy you don’t understand.

 

 

 

 

 

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