Cliff Asness’s latest on why factor timing is not all it’s cracked up to be and why some forecasts of doom for factors and “smart beta” (yeah, he’s finally using the term) are likely wrong (though there will be crazy times!):
The Siren Song Of Factor Timing
AQR Capital Management, LLC
[drizzle]April 12, 2016
Everyone seems to want to time factors. Often the first question after an initial discussion of factors is “ok, what’s the current outlook?” And the common answer, “the same as usual,” is often unsatisfying. There is powerful incentive to oversell timing ability. Factor investing is often done at fees in between active management and cap-weighted indexing and these fees have been falling over time. Factor timing has the potential of reintroducing a type of skill-based “active management” (as timing is generally thought of this way) back into the equation. I think that siren song should be resisted, even if that verdict is disappointing to some. At least when using the simple “value” of the factors themselves, I find such timing strategies to be very weak historically, and some tests of their long-term power to be exaggerated and/or inapplicable.
Smart Beta: The Siren Song Of Factor Timing - Introduction
"Factors" aka "Smart Beta" aka "Styles"
While consensus might be too strong a word, modern financial researchers have mostly coalesced behind a set of “factors” that both explain security returns and deliver a positive return premium (not necessarily the same things). A “factor” is the spread between the return on one set of securities, systematically and clearly defined, versus another. Perhaps the most famed and basic one is the market factor or the spread of the capitalization weighted stock market over the risk free rate. Other factors, and the ones I’ll discuss here, compare some stocks to other stocks.2 These include such well-known examples as: the spread between the return of small vs. large stocks (“size”), cheap vs. expensive stocks (“value”), recent winners vs. losers (“momentum”), higher vs. lower yielding securities (“carry”)3 and low risk and more profitable companies vs. high risk and less profitable companies (“quality”).4
"Smart beta" as a term is a relatively recent relabeling of factor investing. It usually comes with a focus on the simplest versions of known factors implemented in a long-only (i.e., performance vs. a benchmark) fashion and mostly, to date, in individual stocks. It’s also common to call groups of factors that are driven by a common theme (e.g., value or momentum) “styles.” While this essay will stick with discussing “factors” the semantic wars rage on and unless otherwise noted these comments should be considered applicable to factors, smart beta, styles, and probably other labels.
Research has mostly focused on the average returns to these factors (size and statistical significance). How strong are they? How robust are they? “Robustness” meaning do they pass a series of tests of reasonableness including working out-of-sample and fitting a sensible economic story.5 If successful, robustness tests like these may increase our belief that the average results are real and not random fluctuations found by computers too powerful and databases too vast for our own good. Furthermore, research has also focused on what particular combination of these factors one should hold in an optimal portfolio. There is some broad agreement on the set of candidate factors and some major overlap in recommended combinations but rarely do independent researchers agree precisely. This story will continue.
Finding a factor with high average returns is not the only way to make money. Another possibility is to “time” the factor. To own more of it when its conditional expected return is higher than normal, and less when lower than normal (even short it if its conditional expected return is negative).6 An extreme form of factor timing is to declare a previously useful factor now forever gone. For instance, if a factor worked in the past because it exploited inefficiencies and either those making the exploited error wised up or far too many try to exploit the error (factor crowding) one could imagine the good times are over and possibly not coming back. I think of these as the “supply and demand” for investor error!7 Factor efficacy could go away either because supply went away or demand became too great.
Why do I call factor timing a “siren song” in my title? Well, factor timing is very tempting and, unfortunately, very difficult to do well. Nary a presentation about factors, practitioner or academic, does not include some version of “can you time these?” or “is now a good time to invest in the factor?” I believe the accurate answer to the first question is “mostly no.” However, my answer is usually met with at least mild disappointment and even disbelief. Tempting indeed.
I argue that factor timing is highly analogous to timing the stock market. Stock market timing is difficult and should be done in very small doses, if at all. For instance, Asness, Ilmanen, and Maloney (2015) call market timing a “sin” and recommend, using basic value and trend indicators, to only “sin a little.” The decision of how much average passive stock market exposure to own is far more important than any plausibly reasonable amount of market timing. Given my belief in the main factors described above – that is I do not think they’re the result of data mining or will disappear in the future – the implication is to maintain passive exposures to them with small if any variance through time.8 Good factors and diversification easily, in my view, trump the potential of factor timing.
Asness, Friedman, Krail and Liew (2000), henceforth AFKL, introduced the idea of measuring the “value spread” of the value factor itself as a basic timing tool for the value factor. The “value spread” is the valuation measure (in that paper book-to-price but this can, and has, been extended to any valuation measure) of the long portfolio divided by the same measure for the short portfolio. While the value spread for value factors (if the spread and the factor are constructed using the same measure, like book-to-price, sales-to-price, etc.) will always be over 100%, as that’s how the factor is constructed, there is considerable variation through time. The basic idea was that the factor may perform better than normal when it’s ex ante cheaper than normal and vice versa. This idea has been used and extended to other factors in work such as Cohen, Polk and Vuolteenaho (2003), Asness (2015), and Arnott, Beck, Kalesnik and West (2016), henceforth ABKW.
See full PDF below.