Key Points

  • We find slippage between the factor returns realized by mutual fund managers and the theoretical factor returns “earned” by long–short paper portfolios over the period 1991–2016.
  • The source of the slippage appears to be costs related to implementation, such as trading costs, missed trades, expenses of shorting, manager fees, stale prices, bid–ask spreads, and so forth.
  • Our research shows that over the last quarter-century the real-world return for the value and market factors is halved or worse than theoretical factor returns imply, and the momentum factor has provided no benefit whatever to the end-investor.

“Why, sometimes I’ve believed as many as six impossible things before breakfast.”
—The White Queen, from Lewis Carroll’s Through the Looking Glass

In 2016, Research Affiliates published a series of articles1 challenging the “smart beta” revolution. We pointed out that, while there is merit in many factor tilt and smart beta strategies, performance chasing in these strategies—buying the popular outperforming strategies whose relative valuations are at extremely high levels—can be just as dangerous as performance chasing in other realms of asset management. We observe in factors and smart beta strategies that valuations matter just as they do in stock selection and asset allocation (i.e., lower relative valuations positively correlate with higher subsequent returns, and vice versa).

In this article, the first in a series to be published in 2017, we attempt to measure the slippage between the factor returns realized by fund managers and the theoretical factor returns constructed from long–short paper portfolios, and potential reasons for this slippage, or performance shortfall. Theoretical concepts, such as long–short factor portfolios, although helpful in advancing our understanding of a subject, are typically idealized approximations of the real world, built on a foundation of simplifying core assumptions, which are usually implausible at best.

We find that managers who favor high factor loadings for market beta, value, or momentum generally do not derive nearly as much incremental return, relative to low beta, growth, or contrarian funds, respectively, as factor return histories would suggest. Well over half of the factor return for market beta and for value (defined as HML) disappears, as does essentially all of the momentum factor return. By all appearances, Alice’s “Drink Me” potion, responsible for shrinking her so she can pass through the door to Wonderland, has found its way into real-world factor returns.

A Preview of the 2017 Smart Beta Series
Our 2017 smart beta series is called “Alice in Factorland.” Our next article in the series will challenge the idea that factor tilts—portfolios combining several theoretical factor portfolios—are smart beta. We will show that factor tilts cannot be used to replicate other smart beta strategies, using Fundamental Index™, equal weight, and low-volatility strategies as illustrative examples. The factor tilts of these strategies are easy to replicate, but the resulting portfolios look very different from the original, and the replication portfolios typically have far higher turnover, lower performance especially net of trading costs, and smaller capacity than the originals.

In a third article, we will show that the relative valuations of factor loadings can give us the courage to buy mutual funds when their factor exposures are at their cheapest, hence, the most out of favor. Along with fees, turnover, and past performance—where low fees, low turnover, and low (yes, low!) past performance are predictive of better future returns—factor loadings can help us improve our forecasts of fund returns. We find the best predictor is prior three-year performance, but with the wrong sign: buying the losers is the winningest strategy.

Finally, a fourth article will take a closer look at momentum, where we find the realized alpha in live portfolios is essentially zero compared to a theoretical alpha of around 6% a year. We show why momentum doesn’t work in live portfolios, and also show how momentum can be saved as a useful source of alpha.