The volatilities of the factor portfolios are a measure of the volatility of a long–short portfolio; in other words, these volatilities measure the volatility of the return difference between the long and the short portfolios. Take, for example, the low beta factor in the United States, which has a volatility second only to the momentum factor. Does this mean that low beta stocks have high volatility? No. The factor portfolio that goes long in low beta stocks and short in high beta stocks carries with it a substantial negative net beta, which contributes to the volatility of the factor.8
The volatility of the low beta factor in this long–short framework therefore suggests that a long-only low beta investor should expect large tracking error with respect to the market, even if the portfolio is much less risky than the market. Momentum also typically leads to high tracking error, while the investment factor leads to low tracking error. Viewing projected alpha and relative risk together gives us an insight into the likely information ratios currently available in these factors.
Factors with negative forecasted alpha. Forecasted alphas for low beta factors are negative in all markets. Having experienced a strong bull market from 2000 through early 2016, and even after a large pullback over the second half of 2016, low beta factors are still quite expensive relative to their historical valuation norms. We hesitate to speculate if this is due to the rising popularity of the factor driving the relative valuation higher or the soaring valuation driving the rising popularity. As anyone in the social sciences knows, correlation is not causation. Either way, the data suggest we should not expect low beta strategies to add much value to investor portfolios until their valuations are more consistent with their past norms.
We also hesitate to dismiss the low beta factor solely because of its relative valuation. Diversification and the quest for return are both important goals. Even at current valuation levels, low volatility can serve an important role in both reducing and diversifying risk. A sensible response is to rely on the low beta factor less than we might have in the past.
Alpha forecasts for the size factor (small cap versus large cap) are negative in all markets. Put another way, the size factor in all regions is expensive relative to its own historical average. In the United States this relationship has flipped from a year ago: the Russell 2000 Index beat the Russell 1000 Index by over 1,000 bps in the second half of 2016. This huge move takes the size factor (in the United States) from somewhat cheap a year ago to neutral now. Size has lower long-term historical performance compared to other factors in most regions, so modest overvaluation (outside the United States) is enough to drive our alpha forecasts negative. Other factors with less attractive projected alphas are illiquidity in the US market and gross profitability in the developed markets, both forecast to have close to zero expected return over the next five years.
Factors with positive forecasted alphas. Value outperformed handily in 2016, but not enough to erase the relative cheapness of the strategy in most markets, especially in the emerging markets. Increasing valuation dispersion around the globe has opened up many great opportunities for the patient value investor, the mirror image—tumbling popularity, tumbling relative valuations, and tumbling historical returns—of the picture painted by low beta.
We look at value two ways. The first, a composite, is one of the factors with the highest projected expected returns across all regions. The composite is constructed using four valuation metrics, each measuring the relative valuation multiples of the long portfolio (value) relative to the short portfolio (growth): Price to book value (P/B), price to five-year average earnings (P/E), price to five-year average sales (P/S), and price to five-year average dividends (P/D).
The second value factor we construct is based on P/B, the classic measure most favored in academe. Unlike the value composite, it has close to zero projected return. The lower forecasted return may be associated with the big gap in profitability observed among companies today versus in the past. A strategy favoring high B/P companies may favor less profitable companies, increasing investor exposure to “value traps”—those companies that look cheap on their way to zero!
After a lousy second half of 2016, momentum has flipped from overpriced to underpriced. Is this because momentum underperformed so drastically that it’s now cheap? No. Its composition changed. A year ago, the FANGs (Facebook, Amazon, Netflix, and Google) had great momentum—the momentum factor was signaling “buy.” Value stocks are handily outpacing growth now, and value has the momentum. It turns out that, although for most factors relative valuation plays out slowly over a number of years, valuation is a pretty good short-term predictor for momentum performance. Across all markets, we expect momentum to deliver respectable future performance slightly above historical norms. The “signal” changes pretty rapidly from year to year (and sometimes even from month to month).
Finally, we are projecting good performance for gross profitability in the US market over the next five years, a switch from last spring. Quality’s disappointing performance in the second half of 2016 sowed the seeds for this turn in relative attractiveness.
Our return forecasts are all before trading costs and fees. In the real world, these anticipated costs should be subtracted from return forecasts to reflect the investor’s true expected return. In the case of momentum, trading costs can dwarf fees.
Smart Beta Strategies
In addition to factors, which are theoretical difficult-to-replicate long–short portfolios, we estimate the expected risk–return characteristics for a selection of the more-popular smart beta strategies. The list of strategies and the description of their methodologies is available in Appendix B. In order to produce forecasts we replicate the strategies using the published methodologies of the underlying indices. Any replication exercise is subject to deviation from the original due to differences in databases, rebalancing dates, interpretations of the written methodologies, omitted details in the methodology description, and so forth; our replication is no exception.9 The results of the replicated exercise, albeit imprecise, should be informative of the underlying strategies.10
The results for the smart beta strategies yield a number of interesting observations, some of which are quite similar to our observations about factors. Like popular factors, all popular strategies in all regions (with the exception of small cap in emerging markets) have positive historical returns. Again, this should not be surprising because these strategies would not be popular without strong historical returns! Note many of the strategies are simulated backtests for most of the historical test span. Accordingly, as with factors, the high historical returns for long-only investment strategies should be adjusted downward for selection bias.11
The historical and expected alphas for the smart beta strategies, as well as their respective tracking errors, implied by current US valuation levels are shown in the scatterplots in Figure 3. Appendix D presents the same data for the developed and emerging markets. (The data are also provided in tabular form later in the article in Table 2, Panel B.)