Tag Archives: ETFs

What Fraction of International Smart Beta Tracking Error is Dumb Beta?

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In earlier articles, we showed that the returns of most U.S. and international smart beta ETFs are primarily due to their dumb beta (dumb factor) exposures. Thus, smart beta ETFs turn out to actively time dumb beta factors. In fact, most smart beta strategies are primarily different approaches to sector rotation. After readers’ requests to extend the above analysis to relative returns and tracking error, we showed that the findings hold for U.S. smart beta tracking error. This article extends the analysis to international smart beta tracking error.

The results hold for international smart beta tracking error: Though some international smart beta ETFs do provide valuable exposures to idiosyncratic factors, most primarily re-shuffle basic dumb Region and Sector Factors.  In fact, dumb beta exposures are even more influential for international smart beta equity ETFs than for their U.S. peers. Most international smart beta equity ETFs are mainly region and sector rotation strategies in disguise. Consequently, investors and allocators must guard against fancy re-packaging of dumb international risk factors as smart beta and should perform rigorous region and sector factor analysis of their smart beta allocations. Further, many international smart beta strategies can be substantially replicated and blended using simple passive Region and Sector Factors.

Measuring the Influence of Dumb Beta Factors on International Smart Beta ETFs

We used the same dataset of International Smart Beta Equity ETFs as our earlier article on international smart beta ETFs’ dumb factor exposures. We estimated monthly positions of each ETF and then used these positions to calculate portfolio factor exposures to traditional factors such as Regions and Sectors.  These ex-ante factor exposures at the end of month m1 were used to predict returns during the subsequent month m2. We then calculated predicted and actual returns relative to the Global Equity Market (defined as the iShares MSCI ACWI Index Fund – ACWI). The correlation between actual and predicted relative returns quantified the influence of dumb beta factors on smart beta tracking error. The higher the correlation, the more similar a smart beta ETF is to a portfolio of traditional, simple, and dumb systematic risk factors.

The Influence of Region Beta on International Smart Beta ETFs

The simplest systematic risk factor to describe each security is its Region (Region Market Beta). Region Beta measures exposure to one of 10 broad regional equity markets (e.g., North America, Developing Asia). These are the most basic and cheap passive international factors. Even this single-factor model estimated with robust statistical techniques delivered 0.67 mean and 0.68 median correlations between predicted and actual relative returns:

Chart of the correlations between relative returns of replicating portfolios constructed using Region Factors and actual relative returns for international smart beta equity ETFs

International Smart Beta Equity ETFs: Correlation between relative returns predicted using Region Factors and actual relative returns

  Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
0.2490  0.5662  0.6791  0.6655  0.7805  0.9185

For a quarter of smart beta ETFs, Region Factor alone replicates 61% (0.7805²) of tracking error. For this group of strategies, international smart beta tracking error is largely due to region rotation.

The Influence of Region and Sector Betas on International Smart Beta ETFs

We next extended the model with Sector Factors. Sector Beta measures exposure to one of 10 broad sectors (e.g., Energy, Technology). Region and Sector Betas, estimated with robust methods, delivered 0.75 mean and 0.77 median correlations between predicted and actual relative returns:

Chart of the correlations between relative returns of replicating portfolios constructed using Region and Sector Factors and actual relative returns for international smart beta equity ETFs

International Smart Beta Equity ETFs: Correlation between relative returns predicted using Region and Sector Factors and actual relative returns

  Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
0.2604  0.6807  0.7673  0.7461  0.8523  0.9494

For most international smart beta ETFs, Region and Sector Factors replicate 59% (0.7673²) of tracking error. Thus, for most international smart beta equity ETFs, over half of tracking error is due to region and sector rotation.

International Smart Beta Variance and International Dumb Beta Variance

Rather than measure correlations between relative returns of replicating dumb beta portfolios and ETFs, we can instead measure the fractions of their (relative) variances unexplained by dumb beta exposures. The Dumb Beta Variance (in red below) is the distribution of ETFs’ relative variances due to their dumb Region and Sector Factor exposures. The Smart Beta Variance (in blue below) is the distribution of ETFs’ relative variances unrelated to their dumb beta exposures:

Chart of the percentage of international smart beta tracking error explained by traditional, non-smart, or dumb beta Region and Sector Factors and the percentage of tracking error unexplained by these factors for international smart beta equity ETFs

International Equity Smart Beta ETFs: Percentage of tracking error explained and unexplained by the Region and Sector dumb beta exposures

Percentage of international equity smart beta ETFs’ relative variances due to dumb beta exposures:

Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
6.78   46.34   58.88   57.70   72.65   90.13

Percentage of international equity smart beta ETFs’ relative variances unrelated to dumb beta exposures:

Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
9.87   27.35   41.12   42.30   53.66   93.22

For a quarter of strategies, over 2/3 of international smart beta tracking error is due to region and sector rotation, and for some, it reaches 90%.

Our analysis focused on the most basic Region and Sector Factors and excluded Value/Growth and Size Factors, which are decades old and also considered dumb beta by some. If one expands the list of dumb beta factors, smart beta variance shrinks further.

Conclusions

  • Traditional, or dumb, Region and Sector Betas account for the majority of international smart beta tracking error.
  • Smart beta effects, unexplained by the traditional Region and Sector Betas, account for 41% of variance or less for most international smart beta ETFs.
  • With proper analytics, investors and allocators can identify products that do provide unique international smart beta exposures and can guard against fancy re-packaging of dumb international beta.
  • Investors and allocators can monitor the majority of international smart beta ETF relative risk by focusing on their Region and Sector Factor exposures.
  • Most international smart beta strategies can be combined using Region and Sector Factor portfolios.
The information herein is not represented or warranted to be accurate, correct, complete or timely.
Past performance is no guarantee of future results.
Copyright © 2012-2017, AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved.
Content may not be republished without express written consent.

What Fraction of Smart Beta Tracking Error is Dumb Beta?

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Our earlier articles discussed how some smart beta strategies turn out to be merely high beta strategies and how others actively time the market. We also showed that, for the majority of smart beta ETFs, returns are mostly attributable to the traditional dumb Market and Sector Factors. Consequently, the absolute performance of most smart beta strategies can be substantially captured by sector rotation. We received questions about these studies’ focus on absolute performance: The attribution of absolute performance to the Market and Sector factors tells little about tracking error and relative variance. Perhaps smart beta volatility is attributable to dumb factors, but smart beta tracking error is not?

This article addresses the above criticism and analyzes smart beta tracking error rather than (absolute) volatility. The results hold: Though some smart beta ETFs do provide valuable exposures to idiosyncratic factors, most primarily re-shuffle basic dumb factors. Whether one considers their absolute or relative performance, most smart beta equity ETFs are largely sector rotation strategies in disguise. Consequently, investors and allocators must guard against elaborate re-packaging of dumb factors as smart beta and perform rigorous sector and industry analysis of their smart beta allocations. Further, dozens of smart beta strategies can be substantially replicated and blended using simple sector factor portfolios.

Measuring the Influence of Dumb Beta Factors on Smart Beta ETFs

We used the same U.S. Smart Beta ETF dataset as our earlier study of smart beta ETFs’ dumb factor exposures. For each ETF, we estimated monthly positions and then used these positions to calculate portfolio factor exposures for traditional (dumb beta) factors such as Market and Sectors.  The ex-ante factor exposures at the end of each month were used to predict the following month’s returns. The correlation between actual and predicted returns relative to the U.S. Equity Market (defined as the iShares Russell 3000 ETF – IWV) quantified the influence of dumb beta factors on smart beta tracking error. The higher the correlation, the more similar a smart beta ETF is to a portfolio of traditional, simple, and dumb systematic risk factors.

The Influence of Market Beta on Smart Beta ETFs

Our simplest test used a single systematic risk factor – Market Beta. Median correlation between predicted and actual relative returns of smart beta ETFs was 0.31:

Chart of the correlations between predicted relative returns constructed using a single Market factor and actual relative historical returns for over 200 U.S. smart beta equity ETFs

U.S. Smart Beta Equity ETFs: Correlation between predicted relative returns and actual relative returns using a single Market factor

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-0.1089  0.2129  0.3050  0.3096  0.4053  0.8899

The influence of Market Beta on tracking error is much lower than its influence on absolute variance since it only measures relative performance due to deviations from Market’s risk.

The Influence of Market and Sector Betas on Smart Beta ETFs

We next tested a two-factor model that added a Sector Factor. Each security belonged to one of 10 broad sectors (e.g., Energy, Technology). Market and Sector Betas, estimated with robust methods, delivered 0.68 mean and median correlations between predicted and actual relative returns of smart beta ETFs:

Chart of the correlations between predicted relative returns constructed using Market and Sector factors and actual historical returns for over 200 U.S. smart beta equity ETFs

U.S. Smart Beta Equity ETFs: Correlation between predicted relative returns and actual relative returns using Market and Sector factors

  Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
0.0355  0.5983  0.6821  0.6801  0.7981  0.9630

Put differently: For most broad U.S. equity smart beta ETFs, U.S. Market and Sector Betas alone account for approximately half of tracking error and relative variance.

The Influence of all Dumb Factor Betas on Smart Beta ETFs

For the final tests, we added additional dumb factors such as Bonds, Value, and Size. All dumb factor betas delivered 0.74 mean and 0.78 median correlations between predicted and actual relative returns of smart beta ETFs:

Chart of the correlations between predicted relative returns constructed using dumb risk factors and actual historical returns for over 200 U.S. smart beta equity ETFs

U.S. Smart Beta Equity ETFs: Correlation between predicted relative returns and actual relative returns using dumb beta factors

  Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
0.2094  0.6875  0.7759  0.7447  0.8387  0.9715

Thus, for most broad U.S. equity smart beta ETFs, dumb beta factors account for the majority of tracking error.

Smart Beta Tracking Error and Dumb Beta Tracking Error

Rather than measure correlations between relative returns predicted by dumb beta exposures and actual relative returns, we can instead measure the fraction of relative variance unexplained by dumb beta exposures. This value (in blue below) is the fraction of smart beta tracking error that is unrelated to dumb beta factors:

Chart of the percentage of tracking error or variance explained by traditional, non-smart, or dumb beta factors and the percentage of tracking error unexplained by these factors for over 200 U.S. smart beta equity ETFs

Percentage of Smart Beta Tracking Error Explained and Unexplained by Dumb Beta Factors

Percentage of Tracking Error Explained by Dumb Beta Factors

 Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
4.384  47.268  60.197  57.141  70.346  94.381

Percentage of Tracking Error Unexplained by Dumb Beta Factors

 Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
5.619  29.654  39.803  42.859  52.732  95.616

Conclusions

  • Dumb factor exposures are responsible for over 60% of the smart beta tracking error for most strategies.
  • Market and Sector Factors alone account for half of the tracking error for most U.S. equity smart beta ETFs.
  • Most smart beta strategies can be viewed as (largely) various approaches to sector rotation. They can also be substantially replicated and blended using sector rotation.
The information herein is not represented or warranted to be accurate, correct, complete or timely.
Past performance is no guarantee of future results.
Copyright © 2012-2017, AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved.
Content may not be republished without express written consent.
 

Do Equity Risk Models Need a Quality Factor?

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It is common to augment risk models with numerous interrelated factors. This causes problems: Size, Value, Quality, Volatility, and their kin have much in common. At best, overzealous addition of related factors leads to unnecessarily bloated models. At worst, it leads to overfitting, multicollinearity, and questionable statistical analysis.

Fortunately, most complex factors derive virtually all of their volatility and performance from more basic ones such as Market, Sectors, and Size. Therefore, simple statistical equity risk models that capture a few intuitive investable factors with robust statistics usually suffice to describe and predict the performance of investable portfolios of more complex factors. We illustrate this with a popular Quality Factor ETF.

Attributing the Performance of a Quality ETF to Simpler Factors

We analyzed a popular Quality ETF using the AlphaBetaWorks Statistical Equity Risk Model – a proven tool for forecasting portfolio risk and performance. We estimated monthly positions from regulatory filings and aggregated positions’ factor (systematic) exposures. This produced a series of monthly portfolio exposures to simple investable risk factors such as Market, Sector, and Size. The factor exposures and subsequent factor returns were used to calculate future residual (security-selection, idiosyncratic, stock-specific) returns un-attributable to these simple investable factors.

iShares MSCI USA Quality Factor (QUAL): Performance Attribution

We used iShares MSCI USA Quality Factor (QUAL) as an example of a practical implementation of a quality factor portfolio. QUAL is a $1.7bil ETF that seeks to track an index of U.S. large- and mid-cap stocks with high return on equity, high earnings variability, and low debt-to-equity ratio.

iShares MSCI USA Quality Factor (QUAL): Factor Exposures

The following non-Quality factors are responsible for most of the historical returns and variance of QUAL within the parsimonious statistical equity risk model used:

Chart of exposures to the risk factors contributing most to the historical performance of iShares MSCI USA Quality Factor (QUAL)ETF

iShares MSCI USA Quality Factor (QUAL): Significant Historical Factor Exposures

Latest Mean Min. Max.
Market 88.99 85.98 81.07 89.44
Technology 19.93 33.39 19.93 37.35
Health 15.56 17.44 12.34 20.54
Consumer 35.08 33.41 28.70 35.50
Industrial 13.60 11.40 8.86 13.92
Energy 4.14 5.76 3.05 12.02
Size 9.11 6.27 2.91 9.11
Value -0.93 -0.78 -1.54 0.10
Oil Price -1.83 -0.09 -1.83 1.17
Finance 4.95 -1.22 -2.57 4.95

For instance, since Quality companies tend to be larger, some of QUAL’s performance is due to its long exposure to the Size Factor (overweighting of stocks that behave like large-capitalization companies):

Chart of the historical exposures to the Size Factor of iShares MSCI USA Quality Factor (QUAL)ETF

iShares MSCI USA Quality Factor (QUAL): Historical Size Factor Exposures

iShares MSCI USA Quality Factor (QUAL): Active Return

To replicate QUAL with simple non-momentum factors, one can use a passive portfolio of these simple non-momentum factors with QUAL’s mean exposures to them as weights. This portfolio defined the Passive Return in the following chart. Active return, or αβReturn, is the performance in excess of this passive replicating portfolio. It in turn is the sum of active return from residual stock-specific performance (αReturn) and active return from variation in factor exposures, or factor timing (βReturn):

Chart of the cumulative historical active return from security selection and factor timing of iShares MSCI USA Quality Factor (QUAL)ETF

iShares MSCI USA Quality Factor (QUAL): Cumulative Passive and Active Returns

QUAL’s performance closely tracks the passive replicating portfolio. Pearson’s correlation between Total Return and Passive Return is 0.97 – 94% of the variance of monthly returns is attributable to passive factor exposures, primarily to Market, Sector, and Size factors. Active return – performance due to idiosyncratic Quality effects rather than simpler factors – is negligible. Even without a factor to identify quality, the model comprehensively captures the risk and performance of QUAL.

QUAL offers convenient and cheap exposure to quality companies. We cite it here as an example of the reduction of the Quality Factor to simpler non-Quality factors. More elaborate, non-transparent, and expensive smart beta strategies can be hazardous. Many “smart beta” funds are merely high-beta and offer no value over portfolios of conventional dumb-beta funds. It is thus vital to test any new resident of the Factor Zoo to determine whether they are merely exotic breeds of its more boring residents.

Conclusion

  • Investable portfolios based on complex factors such as Quality, tend to derive virtually all of their volatility and performance from more basic factors, such as Market, Sectors, and Size.
  • A popular Quality ETF, iShares MSCI USA Quality Factor (QUAL), has had 0.97 correlation with a passive replicating portfolio of basic non-quality factors.
  • Even simple statistical equity risk models capturing a few intuitive and investable factors with robust statistics may adequately describe and predict the performance of Quality portfolios.
The information herein is not represented or warranted to be accurate, correct, complete or timely.
Past performance is no guarantee of future results.
Copyright © 2012-2016, AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved.
Content may not be republished without express written consent.