Category Archives: ETFs

U.S. Smart Beta Crowding

Rapid asset flows into smart beta strategies have led to concerns about froth and a vigorous debate among systematic portfolio vendors. At the same time, few discussions of smart beta crowding are burdened by data on the aggregate risk of smart beta strategies. This article attempts to remedy this data vacuum. We survey the risk factors and the stocks responsible for U.S. smart beta crowding. In doing so, we identify the exposures that would benefit the most from further asset flows into smart beta portfolios and those that would suffer the most from any outflows:

  • The most crowded factor (systematic) exposures are short Size (overweight smaller companies) and short (underweight) Technology Factors.
  • The most crowded residual (idiosyncratic, or stock-specific) exposures are short (underweight) FAANG stocks.
  • Exposures to dumb factors account for over 80% of smart beta crowding.

Our findings refute many common beliefs about smart beta crowding.

It follows that the vigorous performance of Technology shares and FAANG stocks specifically has been in spite of, rather than due to, the asset flows into smart beta strategies. Instead, the principal beneficiaries of such inflows and the most crowded smart beta bet have been smaller companies. Smart beta strategies tend to overweight smaller companies relative to the passive (capitalization-weighted) Market Portfolio. Consequently, smaller companies are most at risk should the flows into smart beta reverse. Given its importance, allocators and portfolio managers should pay particular attention to this Size Factor crowding.

Identifying Smart Beta Crowding

This article applied AlphaBetaWorks pioneering analysis of Hedge Fund Crowding to U.S. smart beta equity ETFs. We aggregated the Q2 2017 positions of over 300 U.S. smart beta equity ETFs with approximately $700 billion in total assets. We combined all portfolios into a single position-weighted portfolio – U.S. Smart Beta Aggregate (USSB Aggregate). We then used the AlphaBetaWorks (ABW) Statistical Equity Risk Model an effective predictor of future risk – to analyze USSB Aggregate’s risk relative to the iShares Russell 3000 ETF (IWV) benchmark. The benchmark is a close proxy for the passive U.S. Equity Market portfolio.

We find that a small number of active bets are behind the aggregate risk and performance of U.S. smart beta ETFs. Our analysis assumes that the U.S. smart beta ETF universe is a good proxy for the total smart beta strategy universe. In this case, the analysis captures overall U.S. smart beta crowding.

Factor and Residual Components of U.S. Smart Beta Crowding

USSB Aggregate has approximately 1% estimated future volatility (tracking error) relative to the Market. Over 80% of this relative risk is due to factor exposures, or factor crowding. This high share of factor crowding is consistent with our earlier findings that the bulk of absolute and relative risk of most smart beta ETFs is due to traditional, or dumb, factors such as Market and Sectors:

Chart of the factor (systematic) and residual (idiosyncratic) components of U.S. smart beta crowding on 6/30/2017

Components of U.S. Smart Beta Crowding in Q2 2017

Source Volatility (ann. %) Share of Variance (%)
Factor 0.84 82.45
Residual 0.39 17.55
Total 0.92 100.00

Given how close the aggregate smart beta ETF portfolio is to the Market, closet indexing is a concern, especially for diversified smart beta portfolios. The little active risk that remains is primarily due to the two dumb factor exposures discussed below.

U.S. Smart Beta Factor (Systematic) Crowding

The following chart shows the main factor exposures of USSB Aggregate in red relative to U.S. Market’s exposures in gray:

Chart of the factor exposures contributing most to the factor variance of U.S. Smart Beta Aggregate relative to U.S. Market on 6/30/2017

Significant Absolute and Relative Factor Exposures of U.S. Smart Beta Aggregate in Q2 2017

The main active bet of the smart beta universe is short exposure to the Size Factor (overweighting of smaller companies). Thus, smart beta crowding is largely a bet against market-cap weighting and in favor of smaller companies. This crowded bet is the natural consequence of most modified-weighting schemes that de-emphasize larger companies.

Chart of the main contributions to the factor variance of U.S. Smart Beta Aggregate relative to U.S. Market on 6/30/2017

Factors Contributing Most to Relative Factor Variance of U.S. Smart Beta Aggregate in Q2 2017

Factor Relative Exposure Factor Volatility Share of Relative Factor Variance Share of Relative Total Variance
Size -5.58 9.62 42.83 35.31
Technology -6.85 6.39 40.74 33.59
Utilities 1.88 12.72 7.44 6.14
Energy 0.84 13.06 4.73 3.90
Real Estate 0.99 12.40 3.79 3.13
Industrials 1.22 4.72 2.67 2.20
FX 2.49 6.77 2.46 2.03
Materials 0.80 7.88 1.39 1.15
Financials -1.28 7.90 -1.70 -1.40
Value -1.08 15.20 -4.04 -3.33

(Relative exposures and relative variance contribution. All values are in %. Volatility is annualized.)

The short Size Factor smart beta crowding accounts for approximately twice the risk of stock-specific crowding. Consequently, smaller companies stand to lose, and larger companies stand to benefit, on average, from smart beta strategy outflows. The short (underweight) Technology bet is nearly as important.

U.S. Smart Beta Residual (Idiosyncratic) Crowding

The remaining fifth of U.S. smart beta crowding as of 6/30/2017 was due to residual (idiosyncratic, stock-specific) risk:

Chart of the main contributors to the residual variance of U.S. Smart Beta Aggregate relative to U.S. Market on 6/30/2017

Stocks Contributing Most to Relative Residual Variance of U.S. Smart Beta Aggregate in Q2 2017

Symbol Name Relative Exposure Residual Volatility Share of Relative Residual Variance Share of Relative Total Variance
AAPL Apple Inc. -1.43 13.37 24.43 4.29
FB Facebook, Inc. A -0.67 24.17 17.39 3.05
AMZN Amazon.com, Inc. -0.74 18.23 12.17 2.13
GOOGL Alphabet Inc. A -0.52 12.83 3.04 0.53
MSFT Microsoft Corporation -0.56 11.78 2.96 0.52
GOOG Alphabet Inc. C -0.54 10.72 2.25 0.39
BRK.B Berkshire Hathaway B -0.69 7.55 1.82 0.32
BAC Bank of America Corporation -0.46 11.08 1.77 0.31
CASH Meta Financial Group 0.22 22.74 1.68 0.29
NFLX Netflix, Inc. -0.11 40.32 1.23 0.21

(Relative exposures and relative variance contribution. All values are in %. Volatility is annualized.)

The main source of residual U.S. smart beta crowding is short (underweight) exposure to AAPL, FB, AMZN, and GOOGL – the principal members of the FAANG club. The Strong performance of these stocks has been despite, rather than due to, flows into smart beta strategies. On a relative basis, FAANGs have suffered from smart beta asset inflows and, all else equal should outperform in the case of outflows from smart beta strategies.

Summary

  • Factor (systematic) exposures that capture risks shared by many stocks, rather than individual stocks, are responsible for over 80% of U.S. smart beta crowding.
  • The most crowded smart beta bet is short Size Factor exposure (overweighting of smaller companies). Thus, smaller companies stand to lose from smart beta strategy outflows.
  • The most crowded residual smart beta bet is short exposure to (underweighting of) FAANG stocks. Therefore, FAANG stocks should be relative beneficiaries of any smart beta outflows.
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.
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Replicating Fundamental Indexing with Factor Tilts

The proliferation of smart beta strategies has raised questions about the relationship between the core risk factors that have formed the foundation of quantitative investment analysis for decades and the growing factor zoo of strategies. Whereas some state that “smart beta is the vehicle to deliver factor investing” others argue that “factor tilts are not smart ‘smart beta’”. A central question is how well dumb beta factors such as Market, Sectors/Industries, and Style (Value/Growth, Big/Small) can replicate the zoo’s residents. The answers drive risk modeling, performance evaluation, and portfolio construction. This article studies replicating fundamental indexing with factor tilts over the past ten years and illustrates how well it has worked. We will discuss the other popular smart beta strategies in subsequent articles.

In this 10-year replication example, the reality falls between the extreme viewpoints above:

  • Factor tilts replicate virtually all (>96%) of the absolute variance.
  • In a typical market environment, factor tilts replicate most (60-75%) of the relative variance. In fact, in this environment, fundamental indexing is substantially replicated with sector rotation. Indeed, most U.S. and international smart beta strategies are largely instances of sector rotation.
  • In periods of stress, factor tilts fail to replicate the relative variance.
  • The difference in returns between the target and replicating portfolios is statistically insignificant.
  • The relative return of the target and replicating portfolios does not follow a random walk – fundamental indexing goes through periods of outperformance and underperformance relative to the replicating portfolio.

We illustrate and quantify the systematic performance that is attainable, and the residual performance that is unattainable, when replicating fundamental indexing with a portfolio of atomic and liquid factor tilts. The replication uses only the rudimentary factors that most commercial equity risk models implement: Market, Sectors, and optionally Style. We also show the environments when replication is more or less successful. It is up to the user to determine whether a given tracking error is adequate for a given application.

Prior Research on Replication

When assumptions and idiosyncrasies of a factor replication exercise are not made explicit, confusion can arise. Any test replicating smart beta with dumb factor tilts is a joint test of the following components:

  • The equity risk model and the optimizer used to create replicating factor tilt portfolios,
  • The securities used to implement replicating portfolios,
  • The portfolio constraints.

Failure of replication based on a flawed model or flawed factors (such as the simple three Fama-French factors suffering from multicollinearity) merely shows the replication process to be flawed; it does not prove the replication impossible.

Replicating Smart Beta Strategies with Dumb Factor Tilts

This article considers fundamental indexing as implemented by the PowerShares FTSE RAFI US 1000 Portfolio (PRF).

We constructed quarterly replicating portfolios using PRF’s position filings. We lagged positions by two months and risk model data by one month to account for filing and processing delays. For example, pre-1/31/2017 PRF positions and 2/28/2017 AlphaBetaWorks U.S. Equity Risk Model were used to construct the 3/31/2017 portfolio, which was next rebalanced on 6/30/2017. The optimizer aimed to minimize estimated future tracking error of the replicating portfolio to PRF, subject to portfolio constraints. Results were unaffected by changes in rebalance delay of a few months.

We attempted to use the simplest replication methodology that is practical and sound. Results vary depending on the security universe, the equity risk model, the optimizer, and the portfolio parameters.

Replicating Fundamental Indexing with Market and Sector Factor Tilts

Market and Sector Factor portfolios were constructed using a Market ETF and nine top-level sector ETFS:

SPY SPDR S&P 500 ETF Trust
XLY Consumer Discretionary Select Sector SPDR Fund
XLP Consumer Staples Select Sector SPDR Fund
XLE Energy Select Sector SPDR Fund
XLF Financial Select Sector SPDR Fund
XLV Health Care Select Sector SPDR Fund
XLI Industrial Select Sector SPDR Fund
XLB Materials Select Sector SPDR Fund
XLK Technology Select Sector SPDR Fund
XLU Utilities Select Sector SPDR Fund

Position constraint for Sector ETFs was 0 to +100%, and for SPY -100% to +100%. Negative Market weight is necessary to target Market exposures that are not attainable by a long-only portfolio of Sector ETFs. Advanced sector indices, such as the NYSE Pure Exposure Indices, avoid this problem and enable long-only replicating portfolios targeting any combination of exposures.

Fundamental Indexing vs. Market and Sector Tilts: Absolute Performance

The following chart plots cumulative and relative returns for the fundamental indexing portfolio and the replicating Market and Sector Factor portfolio:

Chart of the absolute and relative returns of PowerShares FTSE RAFI US 1000 Portfolio (PRF) and a replicating Market and Sector Factor tilt portfolio

Replicating fundamental indexing with Market and Sector Factor tilts: absolute performance

R-squared       0.9615
Correlation     0.9805
Tracking Error  3.50%

We performed a t-test on the difference in monthly log returns to determine whether they are statistically different from zero:

t = -0.3439
p-value = 0.7315
95 percent confidence interval = (-0.1976, 0.1391)
sample estimate mean = -0.0293

We also performed a Ljung-Box Test on the difference in monthly log returns to determine whether it is a random walk (specifically, whether there are non-0 autocorrelations of the time series):

X-squared = 6.0837
p-value = 0.0136

Though the mean difference in log returns is not statistically different from 0, relative performance is not a random walk. The relative returns of PRF and the replicating portfolio are autocorrelated, as is evident in the chart above.

Fundamental Indexing vs. Market and Sector Tilts: Relative Performance

The following chart plots cumulative and relative returns for the fundamental indexing portfolio, the replicating Market and Sector Factor portfolio, and the SPDR S&P 500 ETF (SPY):

Chart of the absolute and relative returns of PowerShares FTSE RAFI US 1000 Portfolio (PRF), a replicating Market and Sector Factor tilt portfolio, and SPDR S&P 500 ETF (SPY)

Replicating fundamental indexing with Market and Sector Factor tilts: relative performance

R-squared       0.4929
Correlation     0.7020
Tracking Error  3.50%

Performance difference mostly comes from a few months during the 2008-2009 crisis when residual variance spikes. The replicating portfolio first sharply outperforms and then sharply underperforms. In a calmer environment post-2009, the fit is much closer:

R-squared       0.6124
Correlation     0.7825
Tracking Error  1.75%

Replicating Fundamental Indexing with Market, Sector, and Style Factor Tilts

Market, Sector, and Style Factor tilt portfolios add a Growth/Value ETF pair and a Small-Cap ETF to capture the additional Style Factor exposures:

IWF iShares Russell 1000 Growth ETF
IWD iShares Russell 1000 Value ETF
IWM iShares Russell 2000 ETF

Fundamental Indexing vs. Market, Sector, and Style Tilts: Absolute Performance

The following chart plots cumulative and relative returns for a fundamental indexing portfolio and the replicating Market, Sector, and Style Factor portfolio:

Chart of the absolute and relative returns of PowerShares FTSE RAFI US 1000 Portfolio (PRF) and a replicating Market, Sector, and Style Factor tilt portfolio

Replicating fundamental indexing with Market, Sector, and Style Factor tilts: absolute performance

R-squared       0.9619
Correlation     0.9807
Tracking Error  3.61%

The t-test results are similar to those of the Market and Sector replicating portfolio:

t = -0.0062504
p-value = 0.995
95 percent confidence interval = (-0.1802, 0.1791)
sample estimate mean = -0.0006

The Ljung-Box test results are also similar:

X-squared = 6.1228
p-value = 0.0134

Fundamental Indexing vs. Market, Sector, and Style Tilts: Relative Performance

The following chart plots cumulative and relative returns for the fundamental indexing portfolio, the replicating Market, Sector, and Style Factor portfolio, and SPY:

Chart of the absolute and relative returns of PowerShares FTSE RAFI US 1000 Portfolio (PRF), a replicating Market, Sector and Style Factor tilt portfolio, and SPDR S&P 500 ETF (SPY)

Replicating fundamental indexing with Market, Sector, and Style Factor tilts: relative performance

Over the entire 10-year history, the style factors do not materially change the quality of replication:

R-squared       0.4794
Correlation     0.6924
Tracking Error  3.61%

Post-2009, the style factors do improve the fit:

R-squared        0.7501
Correlation      0.8661
Tracking Error   1.44%

Conclusions

  • Whether the tracking error of replication is acceptable depends on the application.
  • Though fully replicating fundamental indexing without any residual tracking error is impossible, even simple Market and Sector factor tilts replicate over 96% of the absolute variance for the fundamental indexing portfolio over the past ten years.
  • In a normal market environment, factor tilts replicate most (60-75%) of the relative variance for the fundamental indexing portfolio.
  • In periods of market stress, the tracking error of the replicating portfolio relative to the target is substantial.
  • The difference in returns between the target and replicating portfolios is statistically insignificant, but it does not follow a random walk and exhibits cycles.
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.
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What Fraction of International Smart Beta is Dumb Beta?

Though many smart beta ETFs do provide valuable exposures, others mainly re-shuffle familiar dumb beta factors. Our earlier article showed that traditional, or dumb, Market and Sector Betas account for over 92% of monthly return variance for most U.S. equity smart beta ETFs. This article extends the analysis to international smart beta ETFs.

It turns out that international smart beta ETFs are even more heavily dominated by dumb beta factors than their U.S. counterparts. Consequently, rigorous quantitative analysis is even more critical when deploying smart beta strategies internationally. With capable analytics, investors and allocators can detect unnecessarily complex and expensive re-packaging of dumb international factors as smart beta, identify products that do provide unique exposures, and control for unintended international dumb factor exposures.

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

We started with approximately 800 smart beta ETFs. Since our focus was on the broad international equity strategies, we removed portfolios with over 90% invested in U.S. equities and portfolios dominated by a single sector. We also removed portfolios for which returns estimated from historical positions did not reconcile closely with actual returns. We were left with 125 broad international equity smart beta ETFs, covering all the popular international smart beta strategies.

For each ETF, we estimated monthly positions and then used these positions to calculate portfolio factor exposures to traditional (dumb beta) factors such as global Regions (regional equity markets) and Sectors.  These ex-ante dumb factor exposures provided us with replicating portfolios composed solely of traditional dumb beta factors. For each international smart beta ETF, we compared replicating portfolio returns to actual returns over the past 10 years, or over the ETF history, whichever was shorter.

The correlation between replicating dumb factor portfolio returns and actual ETF returns quantifies the influence of dumb beta factors on international smart beta ETFs. The higher a correlation, the more similar an ETF is to a portfolio of traditional, simple, and dumb systematic risk factors.

The Influence of Region Beta on International Smart Beta ETFs

Our simplest test used a single systematic risk factor for each security – 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 dumbest traditional international factors and also the cheapest to invest in. Since Market Beta is the dominant factor behind portfolio performance, even a very simple model measuring exposures to regional equity markets with robust statistical techniques delivered 0.95 mean and 0.96 median correlations between replicating dumb factor portfolio returns and actual monthly returns for international smart beta ETFs:

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

International Smart Beta Equity ETFs: Correlation between replicating Region Factor portfolio returns and actual monthly returns

  Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
0.6577  0.9390  0.9645  0.9461  0.9818  0.9975

In short: For most broad international smart beta ETFs, Region Market Betas account for at least 93% (0.9645²) of monthly return variance.

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

We next tested a two-factor model that added Sector Factors. Each security belongs to one of 10 broad sectors (e.g., Energy, Technology). Region and Sector Betas, estimated with robust methods, delivered 0.96 mean and 0.97 median correlations between replicating dumb factor portfolio returns and actual monthly returns for international smart beta ETFs:

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

International Smart Beta Equity ETFs: Correlation between replicating Region and Sector Factor portfolio returns and actual monthly returns

  Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
0.7017  0.9526  0.9722  0.9578  0.9849  0.9941

In short: For most broad international equity smart beta ETFs, Regional Market and Sector Betas account for over 94% (0.9722²) of monthly return variance. Put differently, only less than 6% of the variance is not attributable to simple Region and Sector factors.

International Smart Beta Variance and International Dumb Beta Variance

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

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

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

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

 Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
49.24   90.74   94.52   91.95   97.00   98.83  

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

 Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
1.174   3.004   5.484   8.052   9.258  50.760 

Note that market timing of dumb beta exposures can generate an active return. This return is still due to traditional dumb factor exposures, but it adds value through smart variation in such exposures. Market timing is a relatively small source of return for most international smart beta ETFs and is beyond the scope of this article.

Our analysis excludes Value/Growth and Size Factors, which are decades old and 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 over 94% of variance for most international smart beta ETFs.
  • Smart beta, unexplained by the traditional Region and Sector Betas, accounts for under 6% of variance for most international smart beta ETFs.
  • With proper analytics, investors and allocators can guard against elaborate re-packaging of dumb international beta as smart beta and spot the products that actually do provide international smart beta exposures.
  • Investors and allocators can monitor and manage unintended dumb factor exposures of international smart beta 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.
 
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What Fraction of Smart Beta is Dumb Beta?

Our earlier articles discussed how some smart beta strategies turn out to be merely high beta strategies, and how others actively time the market, requiring careful monitoring. We also showed that the returns of popular factor ETFs such as Momentum and Quality are mostly attributable to exposures to traditional Market and Sector Factors. We now quantify the influence of traditional factors, or dumb beta, on all broad U.S. equity smart beta ETFs.

Though many smart beta ETFs do provide valuable exposure to idiosyncratic factors, many others mostly re-shuffle exposures to basic dumb factors. To successfully use smart beta products, investors and allocators must apply rigorous quantitative analysis. With capable analytics, they can guard against elaborate (and often expensive) re-packaging of dumb beta as smart beta, identify smart beta products that time dumb beta factors effectively, and monitor smart beta allocations to control for unintended dumb factor exposures.

Measuring the Influence of Dumb Beta Factors on Smart Beta ETFs

We started with approximately 800 U.S. Smart Beta ETFs. Since our focus was on the broad U.S. equity strategies, we removed non-U.S. portfolios and sector portfolios. (Later articles will cover global equity portfolios.) We also removed portfolios for which returns estimated from historical positions did not reconcile closely to reported performance. We were left with 215 broad U.S. equity smart beta ETFs. This nearly complete sample contains all the popular smart beta strategies.

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.  These ex-ante factor exposures can be used to predict or explain the following months’ returns.

The correlation between returns predicted by dumb beta factor exposures and actual returns quantifies the influence of dumb beta factors. 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. This is the dumbest traditional factor and also the cheapest to invest in. Since Market Beta is the dominant factor behind portfolio performance, even a very simple 1-factor model built with robust statistical methods delivered 0.92 mean and 0.94 median correlation between predicted and actual monthly returns for smart beta ETFs:

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

U.S. Smart Beta Equity ETFs: Correlation between a single-factor statistical equity risk model’s predictions and actual monthly returns

  Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
0.5622  0.8972  0.9393  0.9174  0.9693  0.9960

Put differently: For most broad U.S. equity smart beta ETFs, U.S. Market Beta accounts for over 88% of monthly return variance.

The Influence of Market and Sector Betas on Smart Beta ETFs

Since traditional sector/industry allocation is a staple of portfolio construction and risk management, we next tested a two-factor model that added a Sector Factor. Each security belongs to one of 10 broad sectors (e.g., Energy, Technology). Market and Sector Betas, estimated with robust methods, delivered 0.95 mean and 0.96 median correlation between predicted and actual monthly returns for smart beta ETFs:

Chart of the correlations between predicted returns constructed using a two-factor statistical equity risk model and actual historical returns for over 200 U.S. smart beta equity ETFs

U.S. Smart Beta Equity ETFs: Correlation between a two-factor statistical equity risk model’s predictions and actual monthly returns

  Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
0.5643  0.9320  0.9634  0.9452  0.9805  0.9974

Put differently: For most broad U.S. equity smart beta ETFs, U.S. Market and Sector Betas accounts for over 92% of monthly return variance.

Smart Beta Variance and Dumb Beta Variance

Rather than measuring the correlation between returns predicted by dumb beta exposures and actual returns, we can instead measure the fraction of variance unexplained by dumb beta exposures. This (in blue below) is the fraction of smart beta ETFs’ variance that is unrelated to dumb beta:

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

U.S. Smart Beta Equity ETFs: Percentage of Variance Explained and Unexplained by Dumb Beta Factors

Percentage of Variance Explained by Dumb Beta Factors

Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
1.85   86.87   92.81   89.71   96.14   99.47 

Percentage of Variance Unexplained by Dumb Beta Factors

Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 .53    3.86    7.19   10.29   13.13   68.15

Note that some smart beta strategies do provide value by timing the dumb beta factors. This market timing can generate a positive active return, but it still consists of traditional dumb factor exposures and their variation. Market timing by smart beta ETFs is beyond the scope of this article.

The high explanatory power of dumb beta exposures above was achieved with a primitive model using Market and Sector Factors only. If one incorporates Value/Growth and Size factors that are decades old and considered dumb beta by some, smart beta variance shrinks further.

Conclusions

  • Traditional, or dumb, Market and Sector Betas account for over 92% of variance for most U.S. equity smart beta ETFs.
  • Smart beta, unexplained by the traditional Market and Sector Betas, accounts for under 8% of variance for most U.S. equity smart beta ETFs.
  • With proper analytics, investors and allocators can guard against elaborate re-packaging of dumb beta as smart beta.
  • With proper analytics, investors and allocators can monitor smart beta allocations to control for unintended dumb factor exposures.
  • Equity risk models can adequately describe and predict the performance of most smart beta strategies with traditional dumb risk factors such as Market and Sectors.
The information herein is not represented or warranted to be accurate, correct, complete or timely.
Past performance is no guarantee of future results.
Copyright © 2012-2016AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved.
Content may not be republished without express written consent.
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Are Momentum ETFs Delivering Momentum Returns?

There is a large difference between momentum strategies in theory and in practice. Given that much of its model performance derives from illiquid securities and high turnover, the academic momentum factor is a theoretical ideal that is not directly investable. Consequently, real-world momentum products, such as momentum ETFs, are restricted to investable liquid securities and usually reduce the approximately 200% annual turnover of theoretical momentum portfolios. After these modifications, their idiosyncratic momentum returns mostly vanish.

We consider a popular momentum ETF and illustrate that its historical performance is almost entirely attributable to passive exposures to simple non-momentum factors, such as Market and Sectors. Investors may thus be able to achieve and even surpass the performance of popular momentum ETFs with transparent, passive, and potentially lower-cost portfolios of simpler funds.

Attributing the Performance of Momentum ETFs to Simpler Factors

We analyzed iShares MSCI USA Momentum Factor ETF (MTUM) using the AlphaBetaWorks Statistical Equity Risk Model – a proven tool for forecasting portfolio risk and performance. We estimated monthly positions from regulatory filings, retrieved positions’ factor (systematic) exposures, and aggregated these. This produced a series of monthly portfolio exposures to simple investable risk factors such as Market, Sector, and Size. The factor exposures at the end of Month 1 and factor returns during Month 2 are used to calculate factor returns during Month 2 and any residual (security-selection, idiosyncratic, stock-specific) returns un-attributable to factors.

There are only two ways for a fund to deviate from a passive portfolio: residual returns un-attributable to factors and factor timing returns due to variation in factor exposures over time. We define and measure both components below.

iShares MSCI USA Momentum Factor (MTUM): Performance Attribution

We used iShares MSCI USA Momentum Factor (MTUM) as an example of a practical implementation of a theoretical momentum portfolio. MTUM is a $1.1bil ETF that seeks to track an index of U.S. large- and mid-cap stocks with high momentum. The fund’s turnover, around 100% annually, is about half that of the theoretical momentum factor.

iShares MSCI USA Momentum Factor (MTUM): Factor Exposures

The following factors are responsible for most of the historical returns and variance of MTUM:

Chart of exposures to the risk factors contributing most to the historical performance of MSCI USA Momentum Factor (MTUM)

MSCI USA Momentum Factor (MTUM): Significant Historical Factor Exposures

Latest Mean Min. Max.
Market 88.44 84.12 65.46 96.03
Health 23.73 30.28 23.73 34.94
Consumer 74.02 32.53 13.10 74.06
Industrial 1.69 9.71 1.13 24.51
Size -10.47 -1.04 -11.09 7.67
Oil Price -2.90 -2.45 -4.94 -0.04
Technology 17.72 16.56 1.50 32.29
Value -4.86 -2.13 -8.00 5.20
Energy 0.00 1.86 0.00 4.12
Bond Index 6.51 1.08 -22.90 23.64

iShares MSCI USA Momentum Factor (MTUM): Active Return

To replicate MTUM with simple non-momentum factors, one can use a passive portfolio of these simple non-momentum factors with MTUM’s mean exposures 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 is the active return due to residual stock performance and factor timing:

Chart of the cumulative historical active return from security selection and factor timing of MSCI USA Momentum Factor (MTUM)

MSCI USA Momentum Factor (MTUM): Cumulative Passive and Active Returns

MTUM’s performance closely tracks the passive replicating portfolio. Pearson’s correlation between Total Return and Passive Return is 0.96. Consequently, 93% of the variance of monthly returns is attributable to passive factor exposures, primarily to Market and Sector factors.

Once passive exposures to simpler factors have been removed, MTUM’s active return is negligible. Since MTUM’s launch, the cumulative return difference from such passive replicating portfolio has been approximately 1%:

2013 2014 2015 Total
Total Return 16.73 14.62 8.50 45.18
  Passive Return 16.06 16.48 4.55 41.34
  αβReturn 1.11 -2.46 2.54 1.12
    αReturn 3.91 0.05 0.29 4.27
    βReturn -2.71 -2.52 2.23 -3.05

This active return can be further decomposed into security selection (αReturn) and factor timing (βReturn). These active return components generated low volatility, around 1% annually, mostly offsetting each other as illustrated below:

iShares MSCI USA Momentum Factor (MTUM): Active Return from Security Selection

AlphaBetaWorks’ measure of residual security selection performance is αReturn – performance relative to a factor portfolio that matches the funds’ historical factor exposures. αReturn is the return a fund would have generated if markets had been flat. MTUM has generated approximately 4% cumulative αReturn, primarily in 2013, compared to roughly 1.5% decline for the average U.S. equity ETF:

Chart of the cumulative historical active return from security selection of MSCI USA Momentum Factor (MTUM)

MSCI USA Momentum Factor (MTUM): Cumulative Active Return from Security Selection

iShares MSCI USA Momentum Factor (MTUM): Active Return from Factor Timing

AlphaBetaWorks’ measure of factor timing performance is βReturn – performance due to variation in factor exposures. βReturn is the fund’s outperformance relative to a portfolio with the same mean, but constant, factor exposures as the fund. MTUM generates approximately -3% cumulative βReturn, compared to a roughly 1% decline for the average U.S. equity ETF:

Chart of the cumulative historical active return from factor timing of MSCI USA Momentum Factor (MTUM)

MSCI USA Momentum Factor (MTUM): Cumulative Active Return from Factor Timing

These low active returns are consistent with our earlier findings that 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

  • Theoretical, or academic, momentum portfolios are not directly investable.
  • A popular momentum ETF, MSCI USA Momentum Factor (MTUM), did not deviate significantly from a passive portfolio of simpler non-momentum factors.
  • Investors may be able to achieve and surpass the performance of the popular momentum ETFs with transparent, passive, and potentially lower-cost portfolios of simpler index funds and ETFs.
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.
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