Research Affiliates: A Smoother Path To Outperformance With Multi-Factor Smart Beta Investing

February 06, 2017

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.

 

ETFExplainerSKYY

(For a larger view, please click on the image above)

 

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.

 

Find your next ETF

CLEAR FILTER