- Researchers have identified hundreds of factors that purport to predict equity returns; we find a half dozen that provide an opportunity to outperform the market.
- To maximize risk-adjusted returns, diversify across smart beta strategies that access the value, low beta, profitability, investment, momentum, and size factors.
- Systematic rebalancing to fixed weights—reducing exposure to popular factors that have outperformed over recent years, while increasing exposure to the out-of-favor factors that have underperformed—in a portfolio of smart betas will likely improve performance relative to a buy-and-hold weighting.
- Dynamically rebalancing factor exposures using short-term momentum and long-term reversal signals further improves the return.
Factor investing, also called smart beta, is rapidly displacing traditional stock picking—and for good reason. Traditional active management of equity mutual funds has delivered returns persistently below passive benchmarks. In contrast, many factor-based smart beta strategies have persistently outperformed the same capitalization-weighted benchmarks. As you consider migrating your public equity holdings away from traditional active management to smart beta, two portfolio construction questions come to the fore: which smart beta strategies should you include, and how should you manage those strategy allocations through time? We find that a smart beta strategy diversified across factors substantially reduces tracking error relative to the average of the single-factor strategies, and dynamic rebalancing materially increases expected return relative to rebalancing to equal weights.
Look Before You Leap
The advantages associated with systematic factor investing, such as low costs and transparency, have driven rapid growth in the number of smart beta funds. At the end of 2015 we counted more than 800 smart beta ETFs, not including mutual funds, separately managed accounts, and other investment vehicles. This nascent smart beta category does not come without its challenges, however.
Many factors are mirages that result from datamining. According to Harvey, Liu, and Zhu’s (2015) survey of the literature, top-tier academic journals document over 300 distinct factors and the number grows every year. We are not surprised. Professional success of an army of professors, assistant professors, and graduate students depends on publishing articles that “discover” new factors. Because the number of potential factors is practically unlimited, and stock price changes are largely random, hundreds of false positives are inevitable. To combat these datamining outcomes, academia is increasing the pressure on publications to institute stricter criteria in evaluating research that purports to identify new factors.
Many seemingly robust factors are simply exhausted past opportunities. Active quantitative investors are constantly searching for investment opportunities, and by the time academic researchers document a factor, investors have often already recognized it and deployed sufficient capital to eliminate its future profitability. No surprise that MacLean and Pontiff (2015) document significant reduction of factor efficacy after publication.
Some otherwise robust factors may even be dangerous. After a factor strategy has proved sufficiently profitable, investment flows attracted by its popularity can drive up the prices of stocks with that factor characteristic. Factors thereby become overvalued. Arnott, Beck, and Kalesnik (2016a,b) and Arnott et al. (2016) empirically demonstrate that strategies with rich valuations tend to provide poor subsequent performance. To avoid such underperformance, we suggest you look before you leap.
The Distinction between Factors and Smart Betas
Before we continue, let’s clarify how we define the following terms: factor, factor portfolio, smart beta, and smart beta strategy. Factor is a generic label for company and stock price characteristics that provide the common sources of return across the broad universe of equity securities.
We construct factor portfolios to measure and study factor returns. Factor portfolios are long stocks with the desired characteristic and short stocks with the undesired characteristic. For example, the value factor portfolio is long cheap stocks and short expensive stocks, and the size factor portfolio is long small stocks and short large stocks. An investor cannot practically invest in factor portfolios because of restrictions on shorting and leverage.
Smart beta is a label for simple, transparent, low-cost, systematic investment strategies, often designed to exploit factor research. Smart beta strategies are long-only portfolios that can be carefully engineered to avoid excessive implementation costs. Smart beta strategies are easy and inexpensive to invest in.
The Six Factors with the Most Robust Returns
Our research leads us to conclude that only a handful of factors represent genuine future return opportunities—strategies with the potential to outperform in the decades ahead. Following the findings of Fama and French (1993, 2012, 2015), we include in this group of six the four factors in their current model (looking beyond the market factor)—value, profitability, investment, and size—as well as low beta and momentum, two factors widely deemed robust in academic publications (Frazzini and Pedersen, 2014, and Carhart, 1997).
We construct these six factor portfolios in accordance with widely accepted academic practice. We summarize our factor portfolio construction method in Appendix A. All six factors demonstrate both statistically and practically significant returns. The average annualized factor return in the United States over our study period July 1973–September 2016 is 4.86%. The correlations across these factor returns are predominantly low or negative, with an average cross-correlation of 0.08, suggesting they are independent and thus able to provide strong diversification benefits.
In Appendix B we replicate the same six factor portfolios in three international markets—Japan, the United Kingdom, and Europe ex UK—over the period July 1993–September 2016. We choose these markets for their lengthy history and homogeneity of corporate domicile. Whereas not all factors display positive returns in all geographies, taken as a group the factors do show consistently strong out-of-sample returns. We intend to elaborate on our research validating these six factors in future publications; many of our detailed research findings on these equity factors are beyond the scope of this article.
A Few Observations about Factors
Drawing on the abundant literature dealing with factors, as well as our own research, we can make several observations relevant to factor portfolio construction; we refer primarily here to the US market. First, the low beta factor’s exceptionally strong return can be explained in part by its rising valuations. From today’s baseline of elevated prices in low-volatility stocks, the low beta factor may well provide disappointing returns over the next decade.
A second observation is that, on its own, profitability generates a low return. Despite its low return, however, profitability’s low and negative correlation with the other factors makes it a helpful addition to a diversified portfolio of factor strategies. Perhaps for this reason many smart beta providers combine profitability with other characteristics to create a composite quality factor.1
We also observe the relatively strong correlation between value and investment factor returns, which suggests that in combination the two factors may be redundant. This higher correlation is primarily the result of similar sector exposures. But, controlling for sector composition, we find that value and investment are robust independent factors.
We find that smaller stocks do not necessarily provide higher returns than larger stocks, consistent with Shumway and Warther (1999). We find that small size does provide such excess returns, however, when combined with other factors. Beck and Kalesnik (2014) argue that other factors provide stronger returns when applied to small companies because of the higher volatility and less-efficient pricing of small stocks. Similarly, Asness et al. (2015) find