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A Framework For Assessing Factors And Implementing Smart Beta Strategies

A Framework for Assessing Factors and Implementing Smart Beta Strategies by Institutional Investor Journals

Jason Hsu, Vitali Kalesnik, and Vivek Viswanathan

The assets invested in smart beta strategies have grown at a breathtaking pace,1 as have the variety of smart beta products and the number of allegedly premium-bearing factors underlying smart beta indexes. Today, among the more reputable journals, one can find some 250 factors and, extrapolating from recent experience, one could expect that number to increase by 40 factors per year. In an earlier, simpler time—about 20 years ago—there were only five equity factors (the market, value, small-cap, momentum, and low beta factors). It is most unlikely that 250 factors are now driving equity returns. Indeed, given that some equity factors might have been behavioral in nature, while others are simply artifacts of historical data, one would actually expect the number of equity return factors to decline over time!

A number of finance researchers2 argue that many of the recently discovered factors may be the results of data mining and thus unlikely to produce future excess returns. Indeed, with thousands of finance professors, doctoral candidates, and quantitative analysts running thousands of backtests and predictive regressions, the discovery of positive outliers is inevitable. Simply put, given the natural cross-sectional variance in returns, a portfolio strategy whose mean excess return is 0 with a tracking error of 4% has roughly a 5% chance of outperforming its benchmark by 1% in a 40-year backtest. Without careful robustness verifications, 1 in 20 portfolio simulations would accidentally look attractive.

A casual student of the empirical literature on factors might blithely mix a batch of factors to form a portfolio with multiple sources of excess return. Such a portfolio might well appear to have a Sharpe ratio greater than 2.0. Indeed, the more exotic and obscure a factor, the more valuable is its inclusion in the mix due to the low correlation in excess returns. However, if there is a meaningful probability that the factor is really just a data artifact, then including it is no different than adding casino bets to an investment portfolio. They, too, are uncorrelated with standard investment strategies; they, too, can have a run of positive outcomes that might fool less sophisticated individuals into believing that the odds are in the speculator’s favor.

Thus, identifying an actual return factor in a zoo of factors, many of which are simply noise, is a critical step in selecting smart beta products that could deliver on the promise of long-term outperformance over traditional capitalization-weighted market beta.3 In this article, we offer a practical framework to help investors separate the wheat from the chaff.

Determining Factor Robustness

Harvey et al. [2015] and Pukthuanthong and Roll [2014] offer stringent criteria for qualifying factors.4 However, the average practitioner might find their statistical methods technically complex. We suggest a simple three-step heuristic for establishing the robustness of a factor premium. In our view, a robust factor is, first, one whose economic underpinnings and persistence have been debated and validated in numerous research papers published in top-tier journals. Second, the effect should persist across time periods and be statistically significant in most countries. Third, the effect should survive reasonable perturbations in the definition of the factor strategy. In the following subsections, we illustrate this validation framework by applying it to some of the more popular factors.

A Deep Literature Debating and Vetting the Factor

When a factor has been vigorously debated and vetted in the literature over a lengthy period, highly trained economists have thoroughly investigated the data and explored various economic rationales behind the existence and persistence of the factor premium. This process ensures that the effect is not a coding error and can be replicated by other researchers potentially using slightly different databases and construction methodologies. It is surprising how many published results cannot be replicated (Bailey et al. [2014 and 2015]).

While a lengthy literature surrounding a given factor does not necessarily guarantee a consensus on the origin or persistence of the premium, it does provide investors with a number of credible hypotheses to evaluate. Is the factor premium driven by risk or behavioral bias? If the latter, why might it persist? If there is no plausible explanation on the basis of risk or investor behavior, a dearth of follow-up literature will often reveal that a factor lacks a theoretical foundation. In this context, it is useful to understand how academic publishing works in general. Negative results are typically not published, even if they reject a previously reported factor—unless it is one of the classic factors such as smallcap.5 Thus, the absence of vibrant follow-up research is a telltale sign that a purported factor has no real standing with financial researchers.

A scan of the existing literature finds many studies exploring the origin and application of factor strategies like value, momentum, low beta, and illiquidity; their existence does not appear to be in question. A search of the Social Science Research Network (SSRN) yields 2,306 hits for “value factor,” 450 for “momentum factor,” 260 collectively for “low-volatility factor” and “low beta factor,” and 568 for “liquidity factor.” These factors are debated and discussed to such an extent that we cannot attribute them to coding errors or one very particular definition of the factor.

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