The case for multifactor investing
By Lukas J. Smart, CFA, and Joel P. Schneider
Published March 2017
This paper presents a framework for identifying factors worth pursuing, structuring portfolios to pursue them, and implementing them in a cost-effective way within an ETF structure.
Theoretical and empirical research in finance has led to an evolution in our understanding of how financial markets work. For instance, 45 years ago, most financial economists and some market participants thought that sensitivity to the market was the only driver of expected returns; they thought the market was the only factor needed to systematically explain differences in expected returns among securities. Today, we recognize several other factors in addition to the market itself. Economists have also done a great deal of work related to financial intermediation and the institutions of exchange. This academic research is highly relevant to financial market participants because it has improved our understanding of what drives expected returns.
The existence of multiple factors presents additional challenges to investors’ asset allocation decisions. In the past, those decisions were relatively simple: To build their own portfolios, investors had to decide (1) how to split their money between fixed income and equities and (2) whether to invest in index funds, conventional active funds, or individual securities. Today, while investors still need to make those decisions, they can make better choices by taking into account, among other things, the additional factors that help explain returns in the equity markets and how those factors interact with each other, as well as how much emphasis they want to place on each factor. A multifactor world presents better opportunities to meet investors’ needs and pursue improved outcomes, but doing so effectively requires a greater degree of expertise to evaluate and manage the trade-offs between expected returns on the one hand and risks and costs on the other.
Backed by decades of theoretical and empirical research, Dimensional Fund Advisors’ investment philosophy was founded on the idea that security prices contain reliable information about differences in expected returns among securities. These prices reflect the expectations of all market participants who trade voluntarily with one another at prices they see as fair, given their expectations of risk and return and the information available (otherwise they would not transact).1 As a result, the daily activity of market participants drives prices toward equilibrium.
Research has also shown there are differences in expected returns among securities.2 Those differences are driven by the prices investors pay and the cash flows they expect to receive. Investors have different preferences and opportunities. They face, and are willing to bear, different risks. Put simply, the lower the relative price an investor pays, the higher the expected return.
Because markets are very effective at aggregating and disseminating investors’ knowledge and expectations into market prices, we can use information in prices, together with fundamental data and our understanding of asset pricing theory, to systematically identify differences in expected returns among securities. At Dimensional, we refer to the systematic identification of those differences as dimensions of expected returns. We use the term dimensions instead of factors because, in our view, not all factors qualify as dimensions worth pursuing. Dimensions point to systematic differences in expected returns among securities and are the core investment insight behind relevant factors.
Systematic differences in expected returns among securities are explained by several dimensions. Within equities, company size, relative price, and profitability are variables that allow us to identify differences in expected returns. The first two are price-driven dimensions, while the third gives us a proxy for expected future cash flows to investors.
The company size dimension reflects the excess return that investors demand for investing in small- capitalization stocks relative to large-capitalization stocks. The premium associated with this dimension is the small-cap premium. The relative price dimension reflects the excess return that investors expect from investing in low relative price, or value, stocks (as measured, for instance, by the price-to-book ratio) relative to high relative price, or growth, stocks. The premium associated with this dimension is the value premium. The profitability dimension provides a way to discern the expected returns of companies with similar price-driven characteristics. If two companies trade at the same relative price, the one with higher profitability should have a higher expected return. The premium associated with this dimension is the profitability premium.
To avoid chasing data-mined results, we have high hurdles to clear before a premium can be considered a dimension. These dimensions are supported by theoretical and empirical research3: They are sensible, persistent over time, pervasive across markets, robust to multiple definitions, and can be captured in cost-effective ways in well-diversified portfolios. While there is no guarantee the premiums will be positive in the future, these rigorous criteria increase our confidence that these premiums are likely to continue in the future and can be pursued in a real trading environment. That is why we build our investment strategies around these dimensions.
The list of identified premiums is long. We have evaluated many of the premiums that academics have uncovered over the years and found that most are redundant or do not meet our criteria. When we do add a new dimension, it is done with scientific rigor and involves careful analysis by Dimensional researchers and portfolio managers, as well as the financial economists with whom we maintain close ties. But the investment process goes beyond identifying the drivers of expected returns. It also requires expertise in structuring and implementing cost-effective investment solutions.
Integrated solutions that incorporate all dimensions of expected returns can potentially increase the reliability of outcomes by providing more information about securities’ expected returns. However, in balancing the trade-offs among competing premiums, diversification, and costs, successful integration requires a deep understanding of the interaction among the dimensions.
Pursuing one premium without taking into account how that will affect a strategy’s emphasis on the other premiums can hurt a portfolio’s performance. For instance, more profitable companies tend to have higher relative prices than less profitable companies. Consequently, if we seek to capture the profitability premium without taking into account how it interacts with the value premium, it could hinder our ability to capture the value premium.
Likewise, the integration of relative price and profitability allows us to separate, for instance, more profitable securities from less profitable securities within the low relative price segment of the market. This differentiation, in turn, allows us to select or overweight more profitable securities and exclude or underweight less profitable securities in an effort to improve expected returns. It is difficult yet essential to properly account for the interaction among the different dimensions and the costs associated with implementing investment solutions along several dimensions.
We realize that not all securities contribute equally to the premiums. As Eugene F. Fama and Kenneth R. French (2007),4 among others, have shown, some securities do extremely well while others have average returns or perform poorly. Research has also shown that it is not possible to reliably predict which securities are going to do well on an individual basis, because in many cases news about why they will do well has not arrived yet (e.g., a new discovery or a new need by some other company), so it is not yet in the price. For that reason, the most reliable way to capture the premiums is to have a diversified strategy that emphasizes securities with higher expected returns (lower market cap, low relative price, and high profitability stocks). Concentrated portfolios may inadvertently exclude securities that ultimately generate most of those premiums, whereas broadly diversified portfolios are more likely to include those securities and capture those expected premiums.
In addition, it is almost impossible to reliably determine when premiums may be realized. Under these conditions, it is useful to expect the premiums to be earned every day. Thus, to increase the reliability of outcomes and the likelihood of capturing different market premiums on a daily basis, strategies should have a continual and accurate focus on the dimensions of expected returns. This need is, in fact, why we require that a premium be persistent to be considered a dimension that we use in our portfolios.
A premium that can be pursued with a large number of stocks in a relatively low turnover strategy is going to be much more attractive than a premium that is concentrated on a small set of stocks in a relatively high turnover strategy. A good portfolio design will recognize that difference and will focus on premiums that can be captured with a large number of securities with relatively low turnover. Targeting these investable premiums makes implementation more efficient because it allows us to treat securities with similar characteristics as close substitutes for one another, at least over short timeframes and provided we maintain appropriate diversification. To screen out stocks that may have a detrimental effect on the performance of the portfolio, good portfolio design will also recognize that noninvestable premiums must be taken into account when managing and implementing strategies.
The body of research is rich, but long-term results for investors depend on how effectively the insight can be implemented as strategies in competitive, real-world financial markets, either through a mutual fund, an ETF, or another investment vehicle. Implementation through an ETF, a vehicle well suited to our systematic and transparent investment approach, begins by building a custom index that takes into account the foregoing considerations.
The application of Dimensional’s investment philosophy to John Hancock Multifactor ETFs required careful design of the indexes tracked by the ETFs. Each ETF will track a specific index designed to target higher expected returns within a specific segment of the market in a broadly diversified way. For example, the John Hancock Dimensional Large Cap Index5 targets the top 750 to 800 companies based on market capitalization. Within that segment, it then weights securities based on the dimensions of higher expected returns: smaller market capitalization, lower relative price, and higher profitability. Regardless of the targeted segment, the indexes are designed to consider the dimensions of expected returns when defining those segments and weighting securities within them.
Trading costs have a direct impact on investors’ returns, so the indexes are constructed to allow for cost-effective trading. In the index design, we work to reduce trading costs by adding what we call Index Memory™. For an index with no memory, the companies held in the index prior to rebalancing would have no bearing on the new construction of the index. In our design, the index remembers what was held previously. To help illustrate this concept, consider a company that is currently in the index and can still be held at its current weight without meaningfully changing the overall index’s characteristics. Index Memory enables the index to continue to hold that company, which minimizes unnecessary turnover. Again, the idea is to target a particular segment of the market, capture dimensions of expected returns, and simultaneously be aware of costs that hurt returns.
These trade-offs, intended to minimize unnecessary turnover for the ETF portfolios, have also been incorporated into the reconstitution rules of the indexes. Reconstitution, the process by which the list of stocks and/or their weight in the indexes changes, happens twice per year. As with any decision we make regarding portfolio design and implementation, we considered multiple trade-offs in determining how often to rebalance the indexes. Our aim for the indexes is to maintain consistent focus on the dimensions of expected returns while keeping turnover low and otherwise limiting the costs associated with pursuing the premiums associated with those dimensions. Reconstituting twice per year allows us to balance those competing objectives.
The ETFs are designed to fully replicate the indexes as efficiently as possible. In addition to trading the ETF portfolio so that it tracks the index closely, portfolio management activities include managing cash and corporate actions. Dimensional’s experience with portfolio design, management, and execution provides useful knowledge applicable to both the design of the index and the ongoing management of the ETFs.
In liquid and competitive markets, security prices reflect the aggregate expectations of all market participants. As a result, we can use information in market prices to systematically identify differences in expected returns among securities along multiple dimensions—market, company size, relative price, and profitability—and to structure and implement investment strategies along those dimensions.
The premiums associated with those dimensions are largely unpredictable over short periods, both in terms of when they will show up and which individual securities will be the drivers of those premiums. For those reasons, we believe the best way to invest is to structure broadly diversified portfolios with a consistent focus on the desired dimensions, seeking to capture the expected premiums associated with them.
This multidimensional approach increases the likelihood of capturing the premiums associated with each dimension. It requires expertise in understanding how the premiums interact and compete with each other, because stocks are often exposed to more than one premium. It also requires expertise in balancing the trade-offs among diversification, trading costs, and other market frictions.
In the end, we believe a deep commitment to theoretical and empirical research, combined with a focus on effective implementation in competitive and complex markets, can increase an investor’s chance of capturing the higher expected returns supported by financial theory.