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Investors increasingly embrace “smart beta” investing, by which we mean passively following an index in which stock weights are not proportional to their market capitalizations, but based on some alternative weighting scheme. Examples include fundamentallyweighted indices and minimumvolatility indices.
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In this note we first take a critical look at the pros and cons of smart beta investing in general. After this we successively discuss the most popular types of smart indices that have been introduced in recent years.
The argument which is typically used to motivate smart beta investing is that the capitalizationweighted index is inefficient, and that a more efficient portfolio can be constructed by applying some alternative stock weighting scheme. We agree with this view, but we think it is important to understand where the added value of such weighting schemes really comes from. Research has shown that the weighting schemes tend to result in structural tilts towards stocks which score high (or low) on certain factors, and that the premiums which are known to be associated with these factors are driving performance [1]. For example, compared to the capitalizationweighted index, fundamental indices systematically tilt towards value stocks. These exposures enable the strategy to benefit from the wellknown value premium, which, in fact, turns out to fully explain its performance. Similarly, a minimumvolatility index captures the lowvolatility premium by tilting the portfolio towards lowvolatility stocks. Although this may seem obvious to some, many smart beta index providers are still reluctant to acknowledge that their performance is driven by factor exposures, and that their weighting schemes are merely a novel way of establishing exposures towards classic factor premiums.
We are often asked whether smart beta investing is a form of passive investing. It is important to realize that it is not. Although passive management can be used to replicate smart indices, smart indices themselves are essentially active strategies. The only truly passive investment strategy is the capitalization weighted broad market portfolio, which represents the only buyandhold portfolio that could, in principle, be held in equilibrium by every investor. Smart beta indices are fundamentally different, because they require various subjective assumptions and choices.
Their active nature is also illustrated by the fact that they require periodic rebalancing to maintain their profile. It is true that smart beta indices may bear some resemblance with true passive investing, e.g. by investing in a large number of stocks with relatively low turnover, but it is important to realize that their deviations from the capitalizationweighted index, which are the key to their added value, represent active investment decisions.
In sum, so far, smart beta investing is a way to actively tilt a portfolio towards certain factor premiums. As we are proponents of factor investing, this makes smart beta investing a potentially promising investment approach.
For example, in a recent paper we argued that equity investors should strategically allocate a sizable part of their portfolio to the value, momentum and lowvolatility factor premiums [2].
Smart beta investing represents one way in which this could be implemented in practice.
Our view on smart beta investing can be summarized as follows: although smart beta investing may be a good start, we believe that investors can do better. The reason is that the main appeal of smart beta indices, namely their simplicity, is at the same time their biggest weakness. Specifically, we find that the simple tilts towards factor premiums provided by smart beta indices often involve significant risks that are undesirable. In addition, smart beta strategies can be inefficient from a turnover perspective, or can have unattractive exposures to factor premiums other than the one that is primarily targeted.
Another concern with smart beta indices is that they are often based on backtests which only go back ten or fifteen years in time. Investors should therefore be careful to avoid chasing recent performance. In order to properly understand the behavior of a smart index in different environments, we recommend analyzing its performance over long historical periods, covering multiple economic cycles.
Investors should also carefully think about whether the factor premiums which are driving historical smart beta index returns are likely to persist in the future.
In the following sections we will elaborate on these points by discussing the pros and cons of the most popular types of smart beta indices that have been introduced in recent years.
In a fundamental index, stocks are weighted in proportion to their fundamentals, such as book value or earnings. In other words, instead of letting the market decide on the appropriate weight of a stock, one might say that fundamental index investors prefer to rely on the assessment of accountants. The differences in weights between a traditional capitalization weighted index and a fundamental index are, by definition, entirely due to differences in valuation ratios of individual stocks, such as differences in booktoprice or earningstop rice ratios. Compared to the capitalization weighted index, a fundamental index is tilted towards stocks which are cheap on such ratios, i.e. value stocks. Studies have shown that the added value of fundamental indices is, in fact, entirely attributable to this tilt towards the value premium [3].
For a long time, Research Affiliates, the inventors of fundamental indexation, denied that the success of fundamental indexation is critically dependent on the existence of a value premium. Instead they argued that even in the absence of a value premium, random mispricing causes capitalizationweighted indices to be biased towards overvalued stocks, resulting in a structural drag on performance [4].
Nowadays, however, they acknowledge that the value premium does indeed explain most, or all of their performance [5].
Our main concern with straightforward value strategies such as fundamental indexation is that they tilt towards financially distressed firms. To understand this, consider a firm which actually gets into financial distress. As a result, its share price drops, and its weight in the cap weighted index drops correspondingly. Initially, the same happens in a fundamental index.
At a certain point, however, a fundamental index rebalances back to the weight based on past and current fundamentals, which have typically not (or only partly) adapted to the new situation. This exposure to distressed firms might not be a problem if, as some have conjectured, distress risk is the source of the value premium. Studies have shown, however, that the stocks of financially distressed firms tend to underperform and that the tilt to distressed firms of naïve value strategies increases risk and is harmful to returns [6].
This implies that the value premium can be captured more efficiently by avoiding cheap stocks of financially distressed firms.
A related concern is that because rebalancing involves buying stocks which have recently experienced a large price drop, fundamental indices tend to go against the momentum premium. As the momentum premium appears to be at least as strong as the value premium, this suggests that the return of a value strategy may be enhanced by avoiding its natural tendency of going against the momentum premium.
Another concern with fundamental indices is their sensitivity to settings choices. For example, it has been shown that, in certain calendar years, the arbitrary choice of the annual rebalancing moment of the FTSE/RAFI fundamental indices can make the difference between an outperformance of 10% or a small underperformance [7].
The more recently launched fundamental indices of MSCI, called MSCI Value Weighted indices, address this concern by rebalancing every six months, while those of Russell rebalance a quarter of the portfolio every quarter. In light of these developments FTSE recently announced that they will also provide such a staggered quarterly rebalanced variant of the FTSE/RAFI indices in 2013, although these will not replace their current indices, but will exist next to them.
Finally, we note that fundamental indices represent a lowconviction approach to capturing the value premium. To understand this, note that a fundamental index is not concentrated in stocks with the most attractive valuation characteristics. For example, the FTSE/RAFI US and Developed ex US indices each invest in 1,000 stocks, and the MSCI Value Weighted indices invest in all the stocks that are in the regular MSCI indices. In other words, stocks with the least attractive valuations are still included in these indices, only with smaller weights.
Lowvolatility indices are designed to benefit from the lowvolatility premium: the empirical finding that lowrisk stocks have similar or better returns than the market average, with substantially lower risk.
Minimumvolatility indices use optimization techniques to create a portfolio with the lowest expected future volatility. The resulting portfolio tends to consist mainly of stocks with low past volatility, although it may also include some highervolatility stocks if these help to reduce volatility through low correlations. A drawback of optimized lowvolatility indices is their lack of transparency. For example, the most popular minimumvolatility 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 minimumvolatility strategies tends to be very high. MSCI addresses this concern by imposing turnover constraints [8], but this causes a new drawback, namely pathdependency. This means that today’s composition of the MSCI MinimumVolatility index depends on its past composition; a feature which is undesirable for investors who are interested in a fresh minimumvolatility 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 twelve months [9]. Empirical studies have shown that this simple ranking approach results in a very similar riskreturn profile as more sophisticated optimization approaches [10]. The added value of both approaches comes from their tilt towards lowvolatility stocks, which enables them to capture the lowvolatility premium [11]. We believe, however, that both represent a suboptimal way of benefiting from the lowvolatility premium.
Our first concern with lowvolatility indices 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 forwardlooking risk measures, is able to further reduce risk – in particular tail risk [12].
A second concern with lowvolatility indices 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 which are attractive from a volatility perspective, but which go against other factor premiums, e.g. by having unattractive valuation or momentum characteristics, tend to have belowaverage expected returns, while lowvolatility stocks which are supported by other factor premiums tend to have above average expected returns. These insights are entirely ignored by generic lowvolatility indices.
We also observe some significant differences in the composition of different lowvolatility 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% 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 more than 5% from their weight in the regular, capitalizationweighted 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 in the middle.
Russell recently launched its socalled “defensive” equity indices, which can be regarded as a “lowvolatility light” alternative. This is because the weight of lowvolatility factors in these indices amounts to only 50%. The other 50% 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 towards quality more than offsets the higher volatility induced by these factors.
We next discuss two closely related smart beta indices, namely the FTSE/TOBAM Maximum Diversification index and the FTSE/EDHEC Risk Efficient indices. 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 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 indices are often regarded to be alternative lowvolatility approaches. To understand this, note that lowering portfolio volatility helps to maximize the Sharpe ratio, which has volatility in the denominator. However, the indices actually go against the lowvolatility premium by assuming that expected returns are proportional to (downside) volatility, which makes highrisk 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 highvolatility stocks from a return perspective, can cause the indices to either have a lowvolatility or a highvolatility profile.
In the longterm, the highvolatility profile actually appears to dominate [13]. Compared to the capitalizationweighted index, the indices also appear to load on the smallcap and value factor premiums [14].
In sum, it seems that, similar to other smart beta indices, classic factor premiums fully explain the added value of the Maximum Diversification and Risk Efficient indices. Unlike fundamental and minimumvolatility indices, however, the tilt towards factor premiums is less direct and more dynamic in nature.
Historically, the momentum premium has been at least as large and consistent as the value and lowvolatility factor premiums. Momentum indices 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 lowvolatility struggle simultaneously, such as during the tech bubble of the late 1990s.
Although momentum strategies have shown impressive longterm average returns, they can show a large underperformance over shorter periods of time. For example, the generic long short momentum strategy which is typically considered in the academic literature shows a return of 83% over the year 2009 [15]. 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 indices, seems to do so by limiting the tilt towards momentum stocks. Specifically, they invest in a relatively broad set of stocks (the top 33% based on a ranking on return over the past 12months, 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 naïve 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 riskadjusted returns [16]. The essence of our approach is to adjust the momentum of each stock for the part that is driven by its systematic risk characteristics (e.g. highbeta stocks are expected to outperform the market in proportion to their beta). By ranking stocks on 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 which is tilted much more aggressively towards 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 indices 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 buysell rules may be able to avoid such unnecessary turnover [17].
Several index providers, including MSCI and S&P, have introduced equallyweighted indices. These are typically regarded as a means to harvest the smallcap premium, which is another example of a premium which has been extensively documented in the literature.
However, we believe that a word of caution is appropriate here. The evidence for a smallcap premium in the literature mainly concerns the smallest, least liquid stocks in the market.
Equallyweighted indices actually do not invest in these stocks, but continue to invest in large and mediumsized firms. For example, the S&P 500 Equal Weight index still invests in the 500 largest US stocks, while the total number of US stocks is well over 5,000.
Thus, equallyweighted indices are better described as strategies that try to exploit a possible difference in return between large stocks and even larger stocks. Equallyweighted indices are thus able to profit only partly, at best, from the smallcap effect considered in the literature.
Another concern with equalweighting is that portfolio weights continuously tend to move 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 already chose for equally weighted portfolios, but soon abandoned this approach because of these practical issues. In our view, therefore, a traditional capitalization weighted (buyandhold) index of true small stocks is a more appropriate and also a more efficient way to capture the smallcap premium.
In smart beta indices, such as fundamental and minimumvolatility indices, stock weights are not based on their market capitalizations, but on some alternative formula. We have argued that the added value of smart beta indices comes from systematic tilts towards classic factor premiums that are induced by these alternative weighting schemes. We also showed that smart beta indices 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 indices 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 indices, investors should realize that, without exception, smart indices themselves always represent active strategies.
David Blitz , Pim van Vliet , April 2013
Article also available in : English  français
[1] See, for example, Chow, Hsu, Kalesnik & Little (2011), “A Survey of Alternative Equity Index Strategies”, Financial Analysts’ Journal, Vol. 67, No. 5, pp. 3757
[2] Blitz (2012), “Strategic Allocation to Premiums in the Equity Market”, Journal of Index Investing, Vol. 2, No. 4, pp. 4249
[3] See Asness (2006), “The Value of Fundamental Indexation”, Institutional Investor, (October), pp. 9499 and Blitz & Swinkels (2008), “Fundamental Indexation: an Active Value Strategy in Disguise”, Journal of Asset Management, Vol. 9, No. 4, pp. 264269
[4] See Arnott, Hsu & Moore (2005), “Fundamental Indexation”, Financial Analysts’ Journal, Vol. 61, No. 2, pp. 8399
[5] See Chow, Hsu, Kalesnik & Little (2011), “A Survey of Alternative Equity Index Strategies”, Financial Analysts’ Journal, Vol. 67, No. 5, pp. 3757
[6] See de Groot & Huij (2011), “Is the Value Premium Really a Compensation for Distress Risk”, SSRN working paper no. 1840551.
[7] See Blitz, van der Grient & van Vliet (2010), “Fundamental Indexation: Rebalancing Assumptions and Performance”, Journal of Index Investing, Vol. 1, No. 2, pp. 8288
[8] We note that although MSCI aims for a oneway turnover of no more than 20% per annum, they have, on several occasions, relaxed this constraint. For example, a methodology change implemented at the end of 2009 caused a turnover of 45% at that moment.
[9] Stock weights in this index are set inversely proportional to their volatility, so the lowest volatility stocks get the highest weights.
[10] See, for example, Soe (2012), “The Volatility Effect: A Comprehensive Look”, S&P research note
[11] For a discussion of the lowvolatility premium we refer to Blitz & van Vliet (2007), “The Volatility Effect: Lower Risk Without Lower Return”, Journal of Portfolio Management, Vol. 34, No. 1, pp. 102113
[12] See Huij, van Vliet, Zhou & de Groot (2012), “How Distress Improves LowVolatility Strategies: Lessons Learned Since 2006”, Robeco research note
[13] See Clarke, de Silva & Thorley (2011), “Minimum Variance, Maximum Diversification, and Risk Parity: An Analytic Perspective”, SSRN working paper no. 1977577. In their Table 2 they report a volatility of 19.0% for a Maximum Diversification strategy applied to U.S. equities over the 19682010 period, which compares to a volatility of only 15.6% for the capweighted index over the same period.
[14] See Chow, Hsu, Kalesnik & Little (2011), “A Survey of Alternative Equity Index Strategies”, Financial Analysts’ Journal, Vol. 67, No. 5, pp. 3757
[15] Returns for this strategy are publicly available on the website of Prof. Kenneth French: http://mba.tuck.dartmouth.edu/pages....
[16] See Blitz, Huij & Martens (2011), “Residual Momentum”, Journal of Empirical Finance, Vol. 18, No. 3, pp. 506521
[17] In all fairness, AQR also acknowledges that mechanically following their momentum indices would be a suboptimal approach and recognizes the need for a more efficient implementation strategy.
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