I really enjoy reading new material from the famed Cliff Assnes of AQR Capital. His words are always insightful and often entertaining from the many open debates he gladly engages with other notable figures in the industry. His latest paper, published in the SSRN discusses what “Factor Timing” really means and whether that’s just a new investing fad or likely has legs under it. I really liked his introduction and breakdown of the basics and thought my SmartestBeta readers should read this too. Some excerpts below with emphasis mine.

“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. 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”) and low risk and more profitable companies vs. high risk and less profitable companies (“quality”).

“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. 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…

—Click here to read the entire paper