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Equity Perspectives

All that these highly touted investment strategies have done is “rediscover” small cap and value investing. 

 

In Brief

  • Smart-beta strategies, which seek to outperform cap-weighted indexes by employing alternative weighting systems, are likely to attract $5.7 trillion in assets by 2017, according to Cerulli Associates, surpassing the $2 trillion in hedge funds.
  • But these “new” products offer nothing new. They merely have “rediscovered” risk factors such as value and size that were already well-known sources of equity returns.
  • A recent attribution analysis of smart beta strategies reveals that their outperformance can be completely explained by their implicit tilts toward these well-known risk factors.  
  • Smart-beta strategies are unable to provide alpha that comes from traditional approaches, such as sector rotation and stock selection, which is available only via active management.   

Interest in so-called smart-beta strategies continues to surge, and although estimates vary widely, The Economist puts the total assets in such strategies at nearly $142 billion.1 This is small compared to the assets in hedge funds (more than $2 trillion), the magazine notes, but it is only likely to grow. The research group Cerulli Associates forecasts that smart-beta strategies could accumulate $5.7 trillion in assets by 2017.2

Smart beta refers to funds and indexes that seek to outperform traditional cap-weighted indexes by weighting their holdings either equally or by fundamentals, such as sales or dividend yield. Others may weight by volatility or some other aspect of a stock’s performance. Smart-beta funds take a mechanical, rules-based approach to investing. One common strategy, an equal-weighted portfolio, buys equal amounts of every stock in an index. It then rebalances periodically, selling those that have increased in price and buying those that have decreased. 

Smart beta’s rules-based approach contrasts sharply with more traditional equity investing. Typically, equity managers seek to outperform the market via one of two approaches: factor timing or stock selection. Factor-timing techniques include sector rotation and similar approaches applied to style (value versus growth) and market capitalization. Stock selection, on the other hand, seeks to discover individual securities that are likely to perform well because of fundamentals such as earnings growth and valuation.

Smart-beta products have received attention largely because they’ve had some success—at least in historical back tests— in outperforming traditional capitalization-weighted indexes. But are these products truly something new, or do they merely repackage well-known drivers of equity performance? Discussing this subject is Walter Prahl, Lord Abbett Partner & Director of Quantitative Research.

Beta and Beyond

Traditionally, the performance of an equity portfolio has been attributed to the market return, or beta, and to manager skill, or alpha. The terms “alpha” and “beta” come from the Capital Asset Pricing Model (CAPM), a framework developed in the 1960s, which earned its developers the Nobel Prize in Economics, and which has been dominant in equity investing for decades. CAPM explains a stock’s performance as the result of one factor: its sensitivity to the broader market.

But in the eyes of some, CAPM left a lot of a stock’s performance unexplained. So, in the 1970s researchers began to identify other risk factors that added explanatory power to the CAPM model. In the early 1990s, professors Eugene Fama and Kenneth French presented research that empirically verified the importance of two new factors: size (market capitalization) and “value” (as measured by a stock’s price-to-book value ratio).

This confirmed what many had known for a long time: that over extended periods, small caps tended to outperform large caps, and value stocks, those with a low price-to-book value ratio, tended to beat growth stocks. Later, a fourth factor, price momentum, which finds that stocks with strong past performance tend to perform well in the future, was added. Today, the Fama-French Four-Factor Model is the most widely accepted multi-factor model of stock returns among equity investors and academics.  

Why have factors such as size and value been associated with outperformance historically? The value effect is arguably a compensation for the greater exposure to downturns in the business cycle. (See “The Value of Growth,” December 4, 2013. We’ll return to the subject of factor return performance and the possible explanations for that in a future article.)

To distinguish alpha that is measured relative to the original CAPM model from alpha measured relative to the new model, some researchers use the terms “CAPM alpha” and “four-factor alpha.” Similarly, instead of referring to “beta,” some distinguish “market beta” from beta that refers to exposure to other risk factors. (See Chart 1.)
 

Chart 1. Portfolio Returns: The Evolution of Alpha

Source: MSCI, Research Affiliates, and Lord Abbett.
 

Researchers continue to search for other risk factors that have the same sort of persistent influence on portfolio performance that these widely accepted factors have. Some argue that low volatility, for example, as well as “quality” (as indicated by low debt, stable growth, and other measures) and dividend yield are both significant risk factors.

Smart Beta—Risk Factors in Disguise

Whether acknowledged or not, smart-beta strategies owe the historical outperformance found in back tests to overweighting, or “loading” on, these risk factors. In other words, whether a smart-beta strategy is described as equal-weighted, fundamental-weighted, low-volatility, dividend growth, or maximum diversification, its outperformance versus cap-weighted portfolios comes from its exposure to risk factors, primarily value and size.

“If the various smart-beta products had identified some new factor that seemed to have a persistent return, that would be interesting,” said Prahl. “But they haven’t done that. They’re constructed on well-known factors.”

In an instructive article in the Journal of Portfolio Management, Robert Arnott and his colleagues at Research Affiliates showed that smart-beta strategies have succeeded in beating cap-weighted indexes not because of their professed philosophy but because they implicitly tilt toward these risk factors. “Our findings suggest that the investment beliefs upon which many investment strategies are ostensibly based play little or no role in their outperformance,” Arnott’s team wrote.3  

Arnott’s research shows, for example, that one common smart-beta strategy—fundamental weighting—would have generated an annual return of 11.6% between 1964 and 2012, outperforming the 9.66% return posted by a cap-weighted portfolio. The CAPM alpha produced by this strategy would have been 2.3%, and would have been statistically significant. (See Table 1.)
 

Table 1. After Risk Factors Are Accounted for, Smart Beta Strategies Produce No Outperformance
Performance of U.S.-based portfolios, 1964-2012

Source: Robert, Jason Hsu, Vitali Kalesnik, and Phil Tindall, "The Surprising Alpha From Malkiel's Monkey and Upside-Down Strategies," Journal of Portfolio Management, Summer 2013.
*Statistically significant at .05 level.
Past performance is no guarantee of future results.
For illustrative purposes only and does not reflect the performance of any Lord Abbett mutual fund or particular investment.
 

This appears impressive. But that outperformance can be completely explained by the strategy’s loading on the four risk factors. (The loading coefficient indicates how strongly a factor influences portfolio returns.) Once these risk factors are taken into account, the outperformance is reduced to just 0.64% (and is statistically indistinguishable from zero). In fact, just one factor, value, with a loading of 0.37, explains most of the outperformance (after market sensitivity, at 1.01). That is, the strategy weights holdings by fundamentals such as cash flow, sales, dividends, and book value, and as a result, it implicitly tilts toward value stocks.

Similar results are evident for other smart-beta strategies. Equal-weighted strategies tend to load heavily on the size factor (0.38), and minimum variance, a low-volatility strategy, tends to load heavily on value (0.34) and, to a lesser extent, on size (0.13). And once these exposures are factored in, the alpha produced by the strategy is statistically indistinguishable from zero.

“The most immediate question about smart beta is, is there anything new there?” said Prahl. “And as you can see, once you do the attribution analysis, which attributes the performance to well-known factors such as value and size, there’s nothing left. That’s pretty unambiguous and conclusive evidence.”

The Arnott results suggest that smart beta is not a new strategy but just another way to tap into well-known risk factors. But if that’s all it is, there is no need for it. “If all you’re interested in doing is accessing the size effect or the value effect, there are plenty of ways to do that that have nothing to do with smart beta,” said Prahl.  

Yet, purveyors of smart beta continue to offer more “innovative” products. A new equal-weighted buyback index, for example, was recently launched that tracks companies that are likely to engage in stock buybacks.4 But because the index is equal-weighted, its “backfilled” historical performance was virtually guaranteed to beat a broad cap-weighted index due to the small cap tilt that results from the equal weighting, says Prahl. 

“If somebody claims to have found a new effect or factor, and they impound it in an equal-weighted index, you know it will likely show historical outperformance because we know that an equal-weighted index overweights small caps, and small caps have historically outperformed relative to cap-weighted indexes,” said Prahl.

However, although the factor tilts that explain the back-test performance5 of smart beta have outperformed historically, investors should not assume they will always provide similar returns in the future. For example, with the exception of 2012, the Russell 3000 Value® Index has lagged the Russell 3000 Growth® Index every year since 2009.6 So, the value effect that is implicitly responsible for much of the historical performance of smart-beta strategies has not provided a performance advantage in recent years.

Indeed, as one observer notes in the Financial Times, “Tempting though it may be, smart beta should never be thought of as a perpetual motion alpha generator. . . . [R]ules-based strategies or factor biases that will beat the market over long periods—before or after expenses—do not exist. If they did, the benefit would be arbitraged away.”7 To put it differently, investors should not expect these strategies to consistently capture CAPM alpha.

Smart Beta versus Timing and Selection Alpha

Alpha—even four-factor alpha—is available, however, and it can be achieved in two ways, by factor timing and stock selection—in other words, via active management. “One thing that active management can do that a purely factor-oriented construction can’t do is factor timing—putting you into a style when the fund manager believes that style is going to outperform,” said Prahl.

“The other thing that none of these products can do—whether they’re size and style indexes or smart beta—is give you alpha from stock selection,” Prahl continued. “That’s something we know that none of these strategies can do, and they don’t claim to. None of them claim to add selection alpha on top of their factor exposure. That’s our claim as active managers, that we can deliver alpha that none of these other approaches can.”

—Reported by Ron Vlieger


1 “The Rise of Smart Beta,” The Economist, July 6, 2013.
2 Madison Marriage, “’Smart Beta’ Funds Eye $5 trillon Windfall,” Financial Times, ft.com, July 14, 2013.
3 Robert D. Arnott, Jason Hsu, Vitali Kalesnik, and Phil Tindall, “The Surprising Alpha from Malkiel’s Monkey and Upside-Down Strategies,” Journal of Portfolio Management 39:4 (Summer 2013).
4 “S&P 500 Buyback Index Launched by S&P Dow Jones Indices,” PR Newswire, May 6, 2013.
5 Back-testing is a process used to test the effectiveness of an investment strategy. It uses historical data to show how a strategy would have performed if it had been used in previous time periods. The validity of a back-test depends in part on the length of the historical period over which a strategy is tested. Of course, there is no guarantee that a strategy will perform in a similar manner in the future.
6 Source: Bloomberg.
7 Jeffrey Molitor, “How to Get Smart about ‘Smart Beta,’” Financial Times, ft.com, May 27, 2012.

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