1. We previously used the term “situational alpha,” but others have suggested “revaluation alpha,” which we rather like better than our own nomenclature! We’re embracing the change in terminology in this third article of our series.
2. We do not mean this in any pejorative way. We’re all data miners, even if inadvertently, merely in the act of seeking ideas that can add value. While there’s (usually) nothing nefarious about it, we owe it to ourselves and to our clients to acknowledge we’re engaged in data mining and to try to minimize the extent our decisions rely on it.
3. As we’ve shown in previous articles, factor tilts explain most of structural alpha. This is not to say these alphas could be recreated with factor tilts! As we’ll explore in a future article, factor-tilt strategies deliver factor alpha minus implementation shortfall. The fact that smart beta strategies mostly have alpha, over and above the alpha explained by factor tilts, is actually a huge “win.”
4. Value investing first appeared in the academic literature in Basu (1977).
5. We distinguish between factor tilts and smart beta strategies for reasons outlined in Arnott and Kose (2014). We’re clearly losing this battle as the term “smart beta” is stretched to encompass factor-tilt strategies and a host of ideas, some smart, some not smart. If the term smart beta encompasses almost everything, then the term means nothing.
6. We examine the Fundamental Index™, an equally weighted index, a low-volatility index, the FTSE RAFI™ Low Volatility Index, a quality index, a dividend-weighted index, a risk-efficient index, and a maximum-diversification index.
7. We examine value (Fama–French HML), low beta, gross profitability, momentum (UMD), size (SMB), illiquidity, and investment. As a robustness check we test two versions of value. One is constructed using the price-to-book ratio (the most common academic definition of value), and one is based on a blend of four valuation metrics: price-to-five-year-earnings, price-to-five-year-sales, price-to-five-year dividends, and price-to-book ratios. With two versions of value we have a total of eight factors which we use as a starting point in our analysis.
8. All smart beta strategies are constructed from the largest 1,000 stocks by market capitalization to make comparison less vulnerable to idiosyncrasies unrelated to index methodology. The only exception is the Fundamental Index where, following methodology of Arnott, Hsu, and Moore (2005), we use the top 1,000 names by fundamental measures of company size. With the exception of the momentum factor portfolio, which is rebalanced monthly, all other factors (and all smart beta strategies) are rebalanced annually at yearend.
9. Slippage can be huge. The momentum factor has delivered a 5% return (up stocks beating down stocks by 5% a year) since the last momentum “shock” during the global financial crisis. Despite this, we are not aware of any momentum funds that have delivered a positive alpha, let alone 5%.
10. On closer examination we find most of the popular smart beta strategies are positively correlated to the Fundamental Index and the dividend index, indications of a strong element of value and small-cap exposure relative to the benchmark. The benchmark assigns weights proportional to company capitalization, overweighting overpriced growth companies and underweighting underpriced value companies. The value exposure almost automatically arises as the byproduct of many smart beta strategies not using capitalization to assign weights to individual stocks.
11. The Opportunity Set (OS) is defined by Grinold and Taylor (2009) as , where r is the vector of excess returns and Ω is the covariance matrix. While OS is technically the maximum ex post Sharpe ratio that could have been obtained by optimal allocation (in our case allocation across the eight smart beta strategies or eight factors), it is also a useful measure of the effective breadth of a portfolio. Portfolios can achieve breadth and increase their opportunity set by including more assets, especially if they have low correlations with each other. For example, making investment decisions across 10 uncorrelated assets will provide more opportunity for higher performance than with only 5 uncorrelated assets. Likewise, 10 uncorrelated assets will provide more opportunity than 10 assets with correlation near 1.0 (having correlation near 1.0 would be similar to having the breadth of just 1 asset). Similarly, portfolios with more volatile assets have more breadth and a larger opportunity set. For example, 10 volatile assets will provide more opportunity than 10 assets whose prices don’t move; without changing prices, even the most skilled investor could not outperform. We find that our set of eight factors provides more opportunity to take advantage of timing signals than our set of eight smart beta strategies. We, therefore, expect a wider spread between timing well versus timing poorly in factors than we do in smart betas. This is, in fact, exactly what we see.
12. Hsu, Myers, and Whitby (2016) show that investors are measurably destroying value by selling funds at cheap levels and buying at expensive levels. The poor timing of purchases and sales by investors destroys value, resulting in their underperforming the broad market.
13. For illustrative purposes, we show two charts from our prior work updated through August 2016. Please refer to the first two articles in this series for a complete set of these analyses.
14. This comparison is of the relative valuation of a factor or strategy to its own prior norm with no look-ahead bias. The statistical significance is particularly interesting because this would be expected to degrade the statistical significance, relative to an in-sample test.
15. We have fallen prey to this error, too! When Jason Hsu, Philip Moore, and I published “Fundamental Indexation” in 2005, it did not occur to us to test whether RAFI™ was newly expensive at that time or to test if the past performance of RAFI was partly driven by rising valuations. Had we done so, we would have discovered that a modest fraction of the historical alpha of RAFI was revaluation alpha, and that RAFI was trading a little rich at the time. This gives us special satisfaction to observe that RAFI has added value since its introduction, all over the world, despite a headwind of becoming cheaper—much cheaper—over the subsequent decade. We can’t wait to see how it works when it finally enjoys a tailwind from value winning!
16. In our earlier articles, our analysis began in 1967. Our current analysis begins in 1977 because we use trailing 10-year performance as one of the selection criteria in our strategy and factor-timing tests. We also use valuation relative to that factor or strategy’s own history; starting in 1977 allows us to start with 10 years of historical valuation data.
17. Harvey, Liu, and Heqing (2015) cannot recall in their survey a single published article about a new factor or smart beta strategy that did not reportedly generate alpha.
18. According to Brightman, Li, and Liu (2015), ETF providers evidently take investors’ preference for winners into account by predominatelylaunching funds whose underlying indices are outperforming at the time they make new product decisions.
19. Beck et al. (2016) provide an examination of factor robustness and implementation costs.
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