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more than 5 percent from their weight in the regular, capitalization-weighted index. In our view, both approaches are too extreme. The MSCI Minimum Volatility index is overly constrained, while the S&P 500 Low Volatility Index is overly concentrated. Our assessment is that the optimum lies somewhere between these two approaches. Russell recently launched its so-called defensive equity indexes, which can be regarded as a “low-volatility light” alternative. This is because the weight of low-volatility factors in these indexes amounts to only 50 percent. The other 50 percent is based on “quality” factors, such as earnings stability, profitability and leverage. The reason for blending in these other factors is not entirely clear. The backtested index returns indicate that these factors tend to increase, rather than further reduce, volatility. So if volatility does not improve, the benefit should probably come from improved returns. Thus, investors should be convinced that the incremental return from tilting toward quality more than offsets the higher volatility induced by these factors. Maximum Sharpe Ratio Indexes We next discuss two closely related smart-beta indexes; namely, the FTSE/TOBAM Maximum Diversification Index and the FTSE/EDHEC Risk Efficient indexes. Both approaches essentially try to maximize the expected Sharpe ratio, i.e., the ratio of expected return to expected risk. Although the way in which expected risk and return are defined is not identical, the differences are relatively small. For example, the Maximum Diversification index In our view, the main challenge involved with harvesting the momentum premium is how to control the high risk involved with the strategy. to reduce volatility through low correlations. A drawback of optimized low-volatility indexes is their lack of transparency. For example, the most popular minimum-volatility index, the one provided by MSCI, uses the proprietary Barra risk model and optimization algorithm, as a result of which, many investors regard the index to be a “black box.” Another concern is that the raw turnover of minimum-volatility strategies tends to be very high. MSCI addresses this concern by imposing turnover constraints, 8 but this causes a new drawback; namely, path-dependency. This means that today’s composition of the MSCI Minimum Volatility index depends on its past composition—a feature that is undesirable for investors interested in a fresh minimum-volatility portfolio because they wish to invest in the strategy from scratch. A more transparent alternative is provided by the S&P 500 Low Volatility Index, which simply invests in the 100 stocks in the S&P 500 index with the lowest volatility over the preceding 12 months. 9 Empirical studies have shown that this simple ranking approach results in a very similar risk/return profile to more sophisticated optimization approaches. 10 The added value of both approaches comes from their tilt toward low-volatility stocks, which enables them to capture the low-volatility premium. 11 We believe, however, that both represent a suboptimal way of benefiting from the low-volatility premium. Our first concern with low-volatility indexes is their onedimensional view of risk, focusing mainly on past volatility and correlations. We believe that risk cannot be captured by a single number, and our research confirms that a multidimensional approach, which also includes forward-looking risk measures, is able to reduce risk—in particular, tail risk— further. 12 A second concern with low-volatility indexes is that they completely ignore expected return considerations. We find that there is, in fact, a large dispersion in the expected returns of stocks with similar volatility characteristics. For example, stocks that are attractive from a volatility perspective, but that go against other factor premiums—for example, by having unattractive valuation or momentum characteristics—tend to have below-average expected returns; however, low-volatility stocks that are supported by other factor premiums tend to have above-average expected returns. These insights are entirely ignored by generic low-volatility indexes. We also observe some significant differences in the composition of different low-volatility index portfolios. The S&P 500 Low Volatility Index does not constrain sector weights, resulting in a huge sector concentration. For example, at the time of writing, around 60 percent of this index was invested in only two sectors (utilities and consumer staples). The MSCI Minimum Volatility index, on the other hand, does not allow sector weights to deviate assumes that expected returns are proportional to volatility, while the Risk Efficient index assumes that expected returns are proportional to downside volatility. The Maximum Diversification and Risk Efficient indexes are often regarded to be alternative low-volatility approaches. To understand this, note that lowering portfolio volatility helps to maximize the Sharpe ratio, which has volatility in the denominator. However, the indexes actually go against the low-volatility premium by assuming that expected returns are proportional to (downside) volatility, which makes high-risk stocks more attractive in the numerator of the Sharpe ratio. These two opposing forces (i.e., a preference for low-volatility stocks from a risk perspective versus a preference for high-volatility stocks from a return perspective) can cause the indexes to have either a low-volatility or a high-volatility profile. In the long term, the high-volatility profile actually appears to dominate. 13 Compared with the capitalization-weighted index, the indexes also appear to load on the small-cap and value-factor premiums. 14 In sum, it seems that, similar to other smart-beta indexes, classic factor premiums fully explain the added 38 March / April 2013
value of the Maximum Diversification and Risk Efficient indexes. Unlike fundamentally weighted and minimumvolatility indexes, however, the tilt toward factor premiums is less direct and more dynamic in nature. Momentum Indexes Historically, the momentum premium has been at least as large and consistent as the value and low-volatility factor premiums. Momentum indexes are much scarcer though, probably due to the fact that momentum struggled during the most recent decade (while value and lowvolatility strategies showed very strong performance over this period) and because the relatively high turnover of momentum strategies fits less well with the idea of a “passive” index strategy. We believe, however, that momentum deserves more attention, if only because it tends to do well when value and low-volatility struggle simultaneously, such as during the tech bubble of the late 1990s. Although momentum strategies have shown impressive long-term average returns, they can show a large underperformance over shorter periods of time. For example, the generic long/short momentum strategy that is typically considered in the academic literature shows a return of -83 percent over the year 2009. 14 In our view, the main challenge involved with harvesting the momentum premium is how to control the high risk involved with the strategy. AQR, which recently introduced the first serious momentum indexes, seems to do so by limiting the tilt toward momentum stocks. Specifically, they invest in a relatively broad set of stocks (the top 33 percent, based on a ranking on return over the past 12 months, excluding the most recent month) and they weight these stocks in proportion to their market capitalization. Although these choices are indeed effective for controlling the risk of a momentum strategy, they also prevent investors from benefiting from the full potential magnitude of the momentum premium. Our research shows that in order to earn the momentum premium, it is not necessary to be exposed to the large risks involved with naive momentum strategies. Specifically, we find that a more sophisticated momentum strategy is highly effective at eliminating precisely those risks that are not properly rewarded, thereby resulting in significantly better risk-adjusted returns. 15 The essence of our approach is to adjust the momentum of each stock for the part that is driven by its systematic risk characteristics (for example, high-beta stocks are expected to outperform the market in proportion to their beta). By ranking stocks according to their remaining, idiosyncratic momentum, we obtain a more sophisticated momentum strategy, which is much less sensitive to systematic risk, such as a broad market reversal. This enables us to create a portfolio that is tilted more aggressively toward the momentum premium, while staying within the same risk budget. Turnover is also a major concern with momentum strategies, which have relatively high turnover by definition. From this perspective, the AQR momentum indexes are clearly not entirely optimal, because they may involve buying a stock ranked just above the selection threshold and selling it at the next rebalancing, three months later, if its rank has dropped to just below the selection threshold. More sophisticated buy-sell rules may be able to avoid such unnecessary turnover. 16 Equally Weighted Indexes Several index providers, including MSCI and S&P, have introduced equally weighted indexes. These are typically regarded as a means to harvest the small-cap premium, which is another example of a premium that has been extensively documented in the literature. However, we believe that a word of caution is appropriate here. The evidence for a small-cap premium in the literature mainly concerns the smallest, least liquid stocks in the market. Equally weighted indexes do not actually invest in these stocks, but continue to invest in large- and medium-sized firms. For example, the S&P 500 Equal Weight Index still invests in 500 of the largest U.S. stocks, while the total number of U.S. stocks is well over 5,000. Thus, equally weighted indexes are better described as strategies that try to exploit a possible difference in return between large stocks and even larger stocks. Equally weighted indexes are thus able to profit only partly, at best, from the smallcap effect considered in the literature. Another concern with equal weighting is that portfolio weights tend to move continuously away from their target levels, so frequent rebalancing is required to maintain equal weights. As this rebalancing involves selling recent winners and buying recent losers, this tends to go against the momentum effect (e.g., in case of annual or semiannual rebalancing). A nice anecdote in this regard is that back in the early 1970s, when the concept of passive investing was conceived, some of the early adopters in fact chose equally weighted portfolios, but soon abandoned this approach because of these practical issues. 17 In our view, therefore, a traditional capitalization-weighted (buy-and-hold) index of true small stocks is a more appropriate and also a more efficient way to capture the small-cap premium. Summary In smart-beta indexes—such as fundamentally weighted and minimum-volatility indexes—stock weights are based not on their market capitalizations, but on some alternative formula. We have argued that the added value of smart-beta indexes comes from systematic tilts toward classic factor premiums that are induced by these alternative weighting schemes. We also showed that smart-beta indexes are not specifically designed for harvesting factor premiums in the most efficient manner, but primarily for simplicity and appeal. For a number of popular smart-beta indexes, we have discussed the main pitfalls, and how investors may capture factor premiums more efficiently by addressing these concerns. Finally, it is important to remember that although passive management can be used to replicate smart indexes, investors should realize that, without exception, smart indexes themselves always represent active strategies. continued on page 50 March / April 2013 39
Page 1 and 2: Plus an interview with Robert Mayna
Page 3 and 4: www.journalofndexes.com Vol. 16 No.
Page 5 and 6: McGRAW-HILL You can never have too
Page 7 and 8: ©2013 Morningstar, Inc. All rights
Page 9 and 10: THE POWERSHARES DB COMMODITY INDEX
Page 11 and 12: InsideIndexing risk, return & inDex
Page 13 and 14: kets and currencies—as particular
Page 15 and 16: and the Nasdaq-100 Technology Secto
Page 17 and 18: ficially drive up the price of copp
Page 19 and 20: Over 70 years ago, Benjamin Graham
Page 21 and 22: are high as they do when valuations
Page 23 and 24: the U.K.), but most of the other co
Page 25 and 26: JOI: But PERSI’s strategy does se
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Page 29 and 30: Figure 1 Main Index Providers And R
Page 31 and 32: Figure 5 Five Key Issues With Equit
Page 33 and 34: Figure 8 Important Issues When Sele
Page 35 and 36: indexes that do not rely on a buy-a
Page 37 and 38: Figure 13 Relative Risk of Alternat
Page 39: For example, in a recent paper, we
Page 43 and 44: Despite high t-statistics, statisti
Page 45 and 46: There is a growing body of research
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Page 55 and 56: While the global financial crisis t
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Page 63 and 64: Exchange-Traded Dow Jones U.S. Indu
Page 65 and 66: News continued from page 15 Malkiel
Page 67 and 68: ETF Analytics There are over 1,000

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