1. Active managers have failed to deliver on clients’ return expectations through no fault of their own. This result is almost a tautology. When the capitalization-weighted index strategies are removed from the cap-weighted market, we’re left with more or less the same portfolio, that is, the holdings of active managers and individual investors. Collectively, because of trading costs and management fees, active managers and individual investors cannot beat the market; most will underperform. Certainly, some active managers will win. In fact, Berk and Green (2004) estimate that before fees about 80% of active managers do win, chiefly at the expense of individual investors. Unfortunately, even if active managers do win, Malkiel (2005) estimates that, on average, fees and other expenses consume most of the outperformance, leaving an average investor in active funds slightly worse off than if they had invested in a low-fee passive alternative. Collectively, an active manager’s very important role is to increase market efficiency by identifying mispricing. If investors collectively chose only passive investing, markets would be extremely inefficient both in terms of investment outcomes and aggregate capital allocation. French (2008) estimates investors collectively pay 67 bps in market value annually for this price discovery, a remarkably reasonable societal cost for the efficient allocation of capital in the aggregate economy.
2. Many investors erroneously label this return difference a risk premium. This is a dangerous and expensive mistake. A risk premium is a forward-looking expectation; excess return is a backward-looking historical return difference. Past excess returns and the expected risk premium are not the same thing.
3. The Shiller Price/Earnings (PE) ratio, also known as the cyclically adjusted PE (CAPE), is simply the real level of a market index (or individual stock) divided by the previous 10-year average of real earnings. This simple adjustment assures that our measure of market valuation is not distorted by current peak or trough earnings.
4. Over the 1950–1999 period, if 4.1% of the 9.2% real return for stocks came from rising valuation multiples, then absent that rise in valuation multiples, the real return would have been 4.9% (9.2% minus 4.1% minus 0.2% from the compounding effect). Net of the capital losses associated with rising bond yields, the average real bond return for the same period would have been 2.3% (1.6% plus 0.7%). Subtracting the 2.3% bond return from the 4.9% stock return, and adjusting the difference for compounding, the adjusted historical equity excess return is 2.5%.
5. The market valuation levels cited are as of December 1999.
6. Arnott and Ryan (2000, 2001) argued the risk premium was dead, a position widely dismissed at the time as utterly implausible. What’s been the excess return for U.S. stocks relative to bonds since then, despite current nosebleed valuation levels? Less than zero! Arnott and Ryan readily acknowledged that, with a large enough shift in relative valuation between stocks and bonds, the risk premium could—like the phoenix—come back from the dead, reviving the positive risk premium that finance theory and common sense suggest should prevail.
7. “Survey of Capital Market Assumptions: 2015 Edition,” Horizon Actuarial Services, LLC, July 2015.
8. The Duke CFO Global Business Outlook, a quarterly survey of chief financial officers of public and private companies around the globe, showed an average 10-year nominal equity return forecast of 6.5% as of December 2015. The same survey conducted in June 2000 showed an average 10-year nominal return forecast of 10.5%. Although CFOs are not money managers or consultants, they are usually aware of standard valuation techniques and use them to explain their company’s share performance relative to the market.
9. It is encouraging to see more realistic expectations, but the fact that valuations can detract, sometimes sizably, from long-term equity returns should not be ignored. For a fuller explanation, see Brightman, Masturzo, and Beck (2015).
10. The problems of data mining and identifying spurious factors have attracted a lot of attention recently in both the academic and practitioner communities. Harvey, Liu, and Zhu (2015) and Harvey and Liu (2015) propose a multiple-testing framework to adjust t-statistics as a means of reducing the number of spurious factors that need to be considered. Hsu, Kalesnik, and Viswanathan (2015) propose a practitioner-oriented procedure to identify more robust factors by perturbing factor definitions, examining factor robustness across geographies, and incorporating transaction costs into estimates of excess returns.
11. The performance line (in black) tracks the cumulative return of the Fama–French value, or high-minus-low (HML), factor for large-cap stocks. The factor return series is computed by taking the monthly difference between the return of a cap-weighted portfolio of the 30% of large-cap stocks trading at the highest book-to-price (B/P) ratio (value stocks) versus a cap-weighted portfolio of the 30% of large-cap stocks trading at the lowest B/P ratio (growth stocks). The portfolios are constructed once a year and are not subject to monthly reconstitution or rebalancing.
12. Relative valuation is defined for factors as and for smart beta strategies as
13. The Nifty Fifty refers to 50 NYSE stocks—including stocks such as Xerox, IBM, Polaroid, Mattel, Avon, and Coca-Cola—proclaimed in the 1960s and 1970s to be so dominant in their industry and so reliable in their growth that they were deemed to be good investments at any valuation.
14. Cochrane (2011) first coined the term “zoo of new factors.” Jason Hsu appropriated and abbreviated this to factor zoo.
15. All of the results presented in this article ignore transaction costs. Investors interested in practically implementing these strategies should adjust excess return estimates for the trading costs associated with them. Novy-Marx and Velikov (2014) and Hsu et al. (forthcoming) estimate trading costs for the more common factors and find many associated with intensive trading, such as momentum, do not exhibit excess return after being adjusted for trading costs under an assumption of index-like implementation. To benefit from many of these factors, investors need access to index funds that can materially reduce transaction costs and access to fund managers who can materially reduce transaction costs through careful execution.
16. The importance of current valuations in predicting future value strategy returns was first independently demonstrated by Asness et al. (2000) and Cohen, Polk, and Vuolteenaho (2001). Li and Lawton (2014) and Garcia-Feijóo et al. (2015) demonstrated that valuations are extremely important for low beta/low volatility strategies.
17. In the 1990s, low beta stocks (i.e., the long side of the low beta factor portfolios in our study) posted an average return of 10.0%, underperforming high beta stocks (i.e., the high beta side of the same factor portfolio) by 12.5% over the same period and underperforming S&P 500 by 8.2% annually.
18. We use the methodology of Arnott, Hsu, and Moore (2005) to replicate the Fundamental Index strategy; the methodology of Amenc et al. (2010) to replicate the risk efficient strategy; and the methodology of Choueifaty and Coignard (2008) to replicate the maximum diversification strategy. We rely on the following for low volatility and quality, respectively: S&P Low Volatility Index Methodology and MSCI Quality Indices Methodology.
19. For the Fundamental Index strategy, the selection of the universe is as important as the portfolio weighting method. In our analysis the universe is the 1,000 largest companies, weighted on four fundamental measures of company size: most recent year-end book value and the five-year average of sales, cash flow, and dividends paid. The average of these four measures of company size—not per share, and not looking at the valuation ratios—is the basis for identifying the 1,000 largest businesses and for their weights in the portfolio.
20. We surveyed all MSCI and Russell factor index strategies. As of December 2014, the average index history length was 16.4 years for MSCI and 13.7 for Russell. The average across both index providers was 15.1 years.
21. A popular Wall Street aphorism, is “never mistake a bull market for genius.” This is every bit as applicable in assessing smart beta strategies and factors as it is for assessing manager or market performance.
22. We compute the regression coefficient–adjusted return by regressing factor or smart beta excess returns on a rolling 12-month basis against the concurrent change in relative valuations (i.e., the movement in price to book, relative to the market, over the same span). If done for a randomly constructed market-like portfolio, the beta would be 1.0: if the portfolio beat the market by 10%, it presumably got 10% richer in relative valuation, and vice versa. Because the portfolios are rebalanced and reconstituted annually (or in the case of momentum, monthly), the linkage weakens.
23. We use the following return decomposition to compute the return from a change in valuation:
from which we derive to define the return from a change in valuation. The difference between the return and the return due to a change in valuation is the valuation-adjusted return.
24. Optimists might expect another upward adjustment in valuation of the same level, much like the optimists of the late 1990s extrapolated equity returns, tacitly forecasting ever-higher Shiller PEs.
25. We note that both of these strategies, on average, have significant positive value exposure according to Arnott et al. (2013). Some claim that the Fundamental Index strategy is just a repackaged value strategy. But, how did these strategies manage to outperform over a period when the value factor did so poorly? We conjecture that part of the difference comes from dynamic value exposure: if a strategy has relatively little value exposure when value is expensive, and a lot of value exposure when value is cheap, such a strategy can still have positive performance despite value doing poorly over the period.
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