Research Affiliates: Why Factor Tilts Are Not Smart ‘Smart Beta’

May 19, 2017

This article is the second in a series we are publishing in 2017. The first article of the series showed that the factor returns realized by mutual fund managers can be very different from the returns investors might expect based on the funds’ factor loadings. We find that the performance of the market, value, and momentum factors in live portfolios is sharply lower than their performance in theoretical model portfolios. If the results of long–short factor paper portfolios used in regression analysis to judge manager skill are not replicable with live assets, bad decisions may be made. This may be contributing to many of the new live factor strategies faring as poorly as they are, even though the periods over which their performance is being measured are too short to draw any meaningful conclusions.

In this article, we challenge the common view that smart beta strategies and factor tilts are equivalent. Initially, the term “smart beta” referred to strategies that broke the link between the price of a stock and its weight in the portfolio or index. Capitalization weighting does not do that—neither does a portfolio that applies factor tilts to a cap-weighted starting portfolio. 

Some have suggested that certain smart beta strategies are essentially factor tilt strategies in disguise, which can be replicated with factor tilts applied to a cap-weighted market portfolio. We test this assertion by replicating three first-generation smart beta strategies—Fundamental Index™, equal weight, and minimum variance—with factor tilts. Creating factor-replicated portfolios that match the factor loadings of these smart beta strategies is easy, but the factor-replicated portfolios are poor substitutes for their smart beta counterparts: performance is poor, turnover is high, and capacity is terrible. Why? The simple answer is that construction details matter in achieving both lower trading costs and higher performance.

In the third article of the series, we will examine whether expected factor returns based on relative valuation can forecast mutual fund performance better than existing models, whose typical inputs are fees, turnover, and past returns. The fourth paper in our series will take a deep dive into momentum to explore why live results for momentum strategies are so starkly inferior to the results of theoretical model portfolios and to ask how momentum can be preserved as a value-added strategy.

A walk along Canal Street in New York City on a typical day winds around numerous vendors selling replica Rolexes at bargain prices. The replicas’ quality varies from vendor to vendor, but for the most part they all look very much like the real deal and might even keep time reasonably well. But a buyer of a replica Rolex accepts certain risks avoided when buying an original: the watch is not guaranteed, may break easily, and may even contain toxic chemicals used to simulate gold that can turn skin green. To state the obvious: all Rolexes are watches, but not all watches are Rolexes.

We assert the same logic holds for smart beta investment strategies. All smart beta strategies have factor tilts (useful in that factors can educate investors about strategy tendencies and return drivers), and factor tilt strategies can reasonably replicate the short-term performance of smart beta strategies. We show, however, that simple factor tilts based on the factor construction popularized in the academic literature are a poor way to capture the long-term performance of smart beta strategies. Smart beta strategies—as originally defined by Towers Watson—generally deliver superior performance, both before and after trading costs, and have more favorable portfolio characteristics, such as turnover, trading costs, and capacity. Although all smart beta strategies have factor tilts, not all factor tilts are smart beta strategies. 

Smart Beta Return Performance
Towers Watson coined the term “smart beta” around 2009, inspired by the Fundamental Index and other strategies, to encompass an array of strategies that break the link between the price of a stock and its weight in the portfolio. Towers Watson found many examples, including among them equal weight, Fundamental Index, minimum variance, low volatility, EDHEC’s Risk-Efficient strategy, and TOBAM’s Maximum Diversification strategy. A unifying attribute of these strategies is that they exploit a simple fact: market capitalization–weighted strategies weight every stock that is currently overvalued (hence, destined to underperform in the future) in the portfolio above its fair-value weight, and underweight every undervalued stock. 

Advocates of cap-weighted indexing correctly observe we cannot know which stock is overvalued and which is undervalued because we cannot know fair value, and accordingly we cannot know fair-value weight. They argue this seeming Achilles’ heel of capitalization weighting does not present a problem. But if we can break the link between the price of a stock and its portfolio weight, we will no longer assuredly overweight overvalued stocks and underweight undervalued stocks. An over- or undervalued stock is roughly equally as likely to be above as below its fair-value weight, so the errors cancel! This has been referred to as rebalancing alpha and is a shared attribute of all generation-one smart beta strategies.


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