Analysis & Opinion

How Can 'Smart Beta' Go Horribly Wrong?

Many investors are performance chasers, creating valuation levels that inflate past performance

New data from Research Affiliates’ Rob Arnottt, Noah Beck, Vitali Kalesnik & John West

The following is an excerpt from the February 2016 Research Affiliates’ new series on the future of ‘smart beta’ investing. Read the entire article here. Reprinted with permission. Visit

Because active equity management has largely failed to deliver on investors’ expectations,1 investors have acquired a notable appetite for any ideas that seem likely to boost returns.

In this environment, impressive past results for so-called smart beta strategies, even if only on paper, are attracting enormous inflows. Investors often choose these strategies, as they previously chose their active managers, based on recent performance. If the strong performance comes from structural alpha, terrific! If the performance is due to the strategy becoming more and more expensive relative to the market, watch out!

Performance chasing, the root cause of many investors’ travails, has three inextricably linked components. Rising valuation levels of a stock, sector, asset class, or strategy inflate past performance and create an illusion of superiority. At the same time, rising valuations reduce the future return prospects of that stock, sector, asset class, or strategy, even if the new valuation levels hold. Finally, the higher valuations create an added risk of mean reversion to historical valuation norms.

Many of the most popular new factors and strategies have succeeded solely because they have become more and more expensive. Is the financial engineering community at risk of encouraging performance chasing, under the rubric of smart beta? If so, then smart beta is, well, not very smart.

Are we being alarmist? We don’t believe so. If anything, we think it’s reasonably likely a smart beta crash will be a consequence of the soaring popularity of factor-tilt strategies. This provocative statement—especially by one of the original smart beta practitioners—requires careful documentation. In this article we examine the impact of rising valuations on many popular smart beta categories.

A Risk Premium Parable: The “New Paradigm” of 1999

A quick look back to 1999 is instructive. Over the second-half of the 20th century, the S&P 500 Index produced a 13.5% return (an annualized real return of 9.2%) and 10-year Treasuries a 5.7% return (an annualized real return of 1.6%). During this 50-year period, stocks delivered an excess return relative to bonds, let alone cash, of almost 7.5% a year!2 The investing industry embraced these historical returns as gospel in setting future return expectations—at the top of the tech bubble, pension fund discount rates and return assumptions were the highest ever, before or since, for stocks and balanced portfolios. In the late 1990s, many proclaimed a “new paradigm”: profits were no longer needed, and equity valuations could rise relentlessly. Remember “Dow 36,000”? We’re still waiting.

The problem with these forecasts is that fully 4.1% of the annualized 50-year (1950–1999) stock market return—nearly half of the real return!—came from rising valuations as the dividend yield tumbled from 8% to 1.2%. The Shiller PE ratio more than quadrupled from the post-war doldrums of 10.5x to a record 44x.3 Reciprocally, as bond yields tripled over the same period, from 1.9% to 6.6%, real bond returns were trimmed by an average 0.7% a year, creating modest capital losses atop skinny real yields. If we subtract nonrecurring capital gains (for stocks) and losses (for bonds) from market returns, the adjusted historical excess return falls to 2.5%.4,5 Thus, over this stupendous half-century for stocks, the true equity premium was 2.5%. The 7.5% gap between stocks and bonds was an unsustainable change in relative values!

Is the financial engineering community at risk of encouraging performance chasing, under the rubric of smart beta? If so, then smart beta is, well, not very smart.

The lofty past returns not only laid a foundation for lofty expectations, but also led to valuations that virtually guaranteed far lower future returns. As noted by Arnott and Bernstein (2002), investors in 1999 should not only have adjusted past returns to remove the impact of rising valuation levels, they should also have adjusted expectations to reflect the lowest-ever stock market yields and the above-average real bond yields.6 Investors could even have gone further, adjusting expectations to reflect the substantial likelihood of mean reversion. The higher equity valuations of today continue to translate into lower future returns than most investors expect.

Nowadays, astute observers increasingly “get it,” at least to the point of subtracting valuation gains from past returns. A 2015 survey of investment consultant return expectations produced an average forward “long-term” (10-year) U.S. nominal equity return of 6.8% a year7; at the start of the century, return expectations over a similar horizon were in the double digits.8,9 After 15 years and two punishing bear markets, investors are figuring out past returns need to be adjusted for the sometimes large impact of rising valuations, and expected returns need to be adjusted for the sometimes large impact of mean reversion. Even after the stellar bull market since early 2009, the annualized real return on U.S. stocks from 2000 to 2015 has averaged a scant 1.9% (not even matching the average dividend yield), while U.S. bonds have delivered an outsized real return of 3.6%. The “excess return” for stocks has been negative by a daunting 1.7% a year.

Our parable holds a relevant lesson for smart beta investors: a lengthy return history, even 50 years, does not guarantee a correct conclusion. Investors need to look under the hood to understand how a strategy or factor produced its alpha. We compare several popular strategies’ current valuations relative to history, and find that for many, much of the historical value-add—in some cases, all!—has come primarily from the “alpha mirage” of rising valuations.

Academia is no less prone than the practitioner community to be a slave to past returns. Anomalies and factor returns tend to appear and then fade, depending on recent performance. Of course, no one will bother to publish a factor or a strategy that fails to add value historically; this encourages data mining and selection bias. In recent years, several hundred “factors” have been published, most showing statistically significant “alpha” and a path to higher future returns.10

Value-add can be structural (hence, plausibly a source of future alpha) or situational (a consequence of rising enthusiasm for, and valuation of, the selected factor or strategy). Few, if any, of the research papers in support of newly identified factors make any effort to determine whether rising valuations contributed to the lofty historical returns. The unsurprising reality is that many of the new factors deliver alpha only because they’ve grown more expensive—absent rising relative valuations, there’s nothing left!

The Impact of Valuations on Returns: The Value Factor

The value effect was first identified in the late 1970s, notably by Basu (1977), in the aftermath of the Nifty Fifty bubble, a period when value stocks were becoming increasingly expensive, priced at an ever-skinnier discount relative to growth stocks. More recently, for the past eight years, value investing has been a disaster with the Russell 1000 Value Index underperforming the S&P 500 by 1.6% a year, and the Fama–French value factor in large-cap stocks returning −4.8% annually over the same period. But, the value effect is far from dead! In fact, it’s in its cheapest decile in history. In Figure 1 we compare the performance of the classic Fama–French value factor11 (black line) with changes in its relative price-to-book (P/B) valuation levels (red line) from January 1967 to September 2015. When the black line is rising, value stocks are becoming more richly priced (i.e., the market is paying a shrinking premium for growth) and value is outperforming. Conversely, when the black line is falling, value stocks are almost always getting cheaper (i.e., the market is paying up for growth stocks) and value is underperforming. (Click Here for a full description of the simulation methodology.)


The red line shows the relative P/B valuation level (the average P/B ratio for the value portfolio divided by the average P/B ratio for the growth portfolio) as it changes over time. Because value always trades cheaper than growth—by its very definition—the valuation ratio, shown on the right scale, often is far lower than 1.0. When the red line is rising, value is winning (i.e., getting more richly priced than it was before, relative to growth), and when the red line is falling, growth is winning (i.e., getting more expensive, relative to value). Not surprisingly the black and red lines move up and down together. The lines diverge, however, which means value has historically had a structural alpha, not wholly reliant on becoming more expensive.

How many practitioners who rely on the value factor take the time to gauge whether the factor is expensive or cheap relative to historical norms? If they took the time to do so today, they would find value is currently cheaper than at any time other than the height of the Nifty Fifty13 (1972–73), the tech bubble (1998–2003), and the global financial crisis (2008–09).

Relative Valuation Levels in the “Factor Zoo”

Relative valuation affects factor returns throughout the factor zoo.14 We find that the efficacy of a factor-based strategy or a factor tilt (included by many under the smart beta umbrella) is strongly linked to changes in relative valuation, that is, whether the strategy is in vogue (becoming more richly priced) or out of favor (becoming cheaper).

How do most investors assess whether these factors and strategies work? The same way they figure out the effectiveness of conventional active managers: past performance! How do academics determine which factors can get them published? Again, past performance! What do most investors and academics miss? The effects of changing relative valuation levels, of course!

Read the entire report here.

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 Horribly-Wrong-Endnote-12-Formula-1.jpg and for smart beta strategies as Horribly-Wrong-Endnote-12-Formula-2.jpg
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.