Tag Archives: factor exposures

The Effect of ESG Constraints on Systematic Risk

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ESG constraints and overlays can create significant systematic exposures within equity portfolios. Whereas some of these exposures may be intentional long and short industry bets, others are unintentional bets on the overall equity market or other macroeconomic factors. In order to deploy ESG strategies successfully, it is vital to identify, quantify, and manage the effect of ESG constraints on systemic risk. That is the only way to avoid unintended hidden risks and the poor performance that results.

Background and Methodology

Over the past decade, institutional investors have been increasingly incorporating Environmental, Social, and Governance (ESG) metrics into their portfolios. Common approaches include separate ESG mandates and ESG constraints applied across entire portfolios. The investors who have implemented these strategies have too often paid little attention to the changes they cause in their portfolios’ factor exposures. This oversight is regrettable, since such changes in systematic risk can dominate subsequent tracking error.

To illustrate a typical ESG overlay effect, we created a constrained portfolio using the FTSE-Russell ESG ratings for S&P 500 Index constituent. The constrained portfolio excluded approximately 18% of the index’s market capitalization with low ESG ratings (below 2.5). A highly predictive multi-factor equity risk model allowed us to analyze the constrained portfolio’s tracking error relative to the unconstrained index.

Tracking Error Due to ESG Constraints

This ESG overlay resulted in an estimated 1.2% tracking error:

The Effect of ESG Constraints on Systematic Risk: Factor and Residual Shares of Tracking Error

Approximately 80% of the tracking error was due to relative factor exposures – relative systematic bets of the constrained portfolio.

Relative Factor Exposures Due to ESG Constraints

Some relative factor bets – for instance, oil price and Energy sector exposures – are reasonable and expected consequences of the overlay. Others look like risky unintended macro tilts:

The Effect of ESG Constraints on Systematic Risk: Key Factors Contributing to Systematic Risk

At nearly 50%, the Equity Market is by far the largest relative factor bet. Market, FX, and Style factor exposures are frequent unintended side-effects of ESG overlays. Fortunately, this risk to strategy’s long-term viability is easy to mitigate. Cheap passive investment vehicles such as index funds, ETFs, futures, and swaps can offset unintended effects of ESG constraints.

The above simple example is representative of the impact of ESG constraints on systematic risk in the real world. Such rules often create factor bets that are undiscovered until they lead to costly underperformance. The effect can be so dramatic that a completely different benchmark could be necessary for this type of strategy. Yet, this solution may not always be feasible and leaves the sacrificed performance unaddressed. Instead, institutions that deploy ESG criteria would benefit from analyzing the resulting exposures and managing any unintended factor bets. Predictive multi-factor models built with liquid investable factors are powerful tools for this task. Such models can identify the inadvertent market bets and guide their mitigation with cheap passive investments, thus avoiding style drift and costly underperformance.

Conclusions

  • ESG constraints and portfolio overlays can lead to unintended systematic bets.
  • Some effects of ESG constraints on systematic risk, such as reducing energy sector exposure, may be intentional.
  • Other effects of ESG constraints may be unintentional long/short market and macro bets.
  • A robust and predictive equity risk model built with investable factors can identify such unintended effects of ESG constraints and guide their mitigation with cheap and liquid passive instruments.

This article was produced in collaboration among AlphaBetaWorks, a division of Alpha Beta Analytics, LLC, Beacon Pointe, and Peer Analytics.

The information herein is not represented or warranted to be accurate, correct, complete or timely. Past performance is no guarantee of future results. Copyright © 2012-2021, AlphaBetaWorks, Beacon Pointe, and Peer Analytics. All rights reserved. Content may not be republished without express written consent.

The Explanatory Power of Sectors and Style

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Factor analysis is a popular and effective technique that explains and forecasts security returns. The factor models prevalent in academic circles (Fama-French, Carhart) tend to rely heavily on the size and value style factors. Meanwhile, effective industry models often attribute risk to sector and industry factors before style. Which approach is more effective? Though claims that style explains stock returns are common, they usually lack evidence – there is a paucity of research that compares the explanatory power of sectors and style.

This paper provides the missing data and analyzes the explanatory power of sectors and style for U.S. stocks. We find that, after controlling for the market exposure, sectors are slightly more effective than size and approximately four times more effective than value in explaining monthly returns.

Measuring the Explanatory Power of Sectors and Style for U.S. Stock Returns

  • We analyze the monthly returns of U.S. stocks over the 15-year interval 4/30/2004-4/30-2019. We start with approximately 6,000 stocks that pass minimum market capitalization and liquidity thresholds – a universe similar to the Russell 3000 Index.
  • For each month in the historical interval and each stock in the sample, we estimate the stock’s market beta.
  • We calculate (out-of-sample) market residuals (alphas) for a given month using the prior month’s market beta for each stock.
  • We restrict the analysis to stocks with at least five years of defined market residuals to have a significant sample. This final sample comprises approximately 3,600 stocks.
  • We construct sector, size, and value factors as follows:
    • The sector factors are cap-weighted portfolios of market residuals for the nine sectors equivalent to the top-level GICS sectors.
    • The size factor is long the cap-weighted portfolio of stocks in the top 25% and short stocks in the bottom 25%, as ranked by market capitalization.
    • The value factor is long the cap-weighted portfolio of stocks in the top 25% and short stocks in the bottom 25%, as ranked by Book/Price.

The results below are insensitive to the specific factor definition and hold across different sector and style portfolios.

  • For sector, size, and value factors, we regress stocks’ market residuals on the corresponding factor returns and measure each regression’s R². This measures the theoretical (in-sample) explanatory power of sectors and style.
  • We estimate factor exposures by robust regression. We fit models with iterated re-weighted least squares (IRLS). Observations are exponentially weighted with a half-life of approximately 36-months.

A Comparison of the Explanatory Power of Sectors and Style for U.S. Stock Returns

The chart below plots the distributions of R² from the regressions of U.S. stocks’ market residuals against the sector, size, and value factors. The x-axis plots the intervals of regression R², and the y-axis plots the number of stocks in each interval. Since the distributions approximately follow a power law, we use a log y-axis:

Chart of the explanatory power of sectors, size, and value -- the distributions of the R² (the coefficient of determination) from regressions U.S. stocks’ Market residuals (alphas) on the Sector, Size, and Value Factors
U.S. Stocks: The Distributions of R² for the Regressions of U.S. Stock Market Residuals (Alphas) on the Sector, Size, and Value Factors

The distributions above illustrate the higher explanatory power of sectors compared to style: Whereas sector factors explain over 25% of the variance in market residuals for hundreds of stocks, style factors do so for only a small handful.

The chart below plots the mean R² values:

Chart of the explanatory power of market, sectors, size, and value -- mean R² (the mean coefficient of determination) from regressions U.S. stocks’ returns and Market residuals (alphas) on the Market, Sector, Size, and Value Factors


U.S. Stocks: Mean R² for the Regressions of U.S. Stock Returns and Market Residuals on the Various Factors

Reasons for Sectors’ Higher Explanatory Power

This higher explanatory power of sectors is unsurprising, given that commentary on style performance usually relies on sector factors: “Value underperformed because oil price crashed, and oil producer stocks, which are cheap, suffered.” On the other hand, even the most ideologically pure believer in the primacy of style would not make the statement: “Oil price crashed because oil producer stocks are cheap and value has recently underperformed.” Whereas sector factors can generally explain the reasons for style factor returns, style factors cannot explain the reasons for sector factor returns.

Since style factors capture systematic risk less effectively, portfolio construction from style building blocks can lead to significant unintended exposures. Studies of common smart beta strategies do indeed find such risks and significant market timing. On the other hand, sector and industry exposures offer superior control of systematic risk and more effective building blocks for portfolio construction.

In the sections that follow, we share the statistics on the explanatory power of the various factors.

The Explanatory Power of Market for U.S. Stock Returns

The chart below plots the distribution of R² from the regressions of U.S. stocks’ returns against the Market factor. This step of the analysis allows us to control for market risk and to analyze the explanatory power specific to the other factors:

Chart of the explanatory power of Market -- the distributions of the R² (the coefficient of determination) from regressions U.S. stocks’ returns on the Market Factor
U.S. Stocks: The Distribution of R² for the Regressions of U.S. Stock Returns on the Market Factor
Min.    1st Qu.  Median    Mean    3rd Qu.    Max.
0.0000  0.1117   0.1770    0.1914  0.2563     0.6525

Market explains approximately 20% of the (in-sample) variance of stock returns. The tests below analyze the out-of-sample (investable) market residuals that this step produces.

The Explanatory Power of Sectors for U.S. Stock Returns

The chart below plots the distribution of R² from the regressions of U.S. stocks’ market residuals against sector factors:

Chart of the explanatory power of sectors -- the distributions of the R² (the coefficient of determination) from regressions U.S. stocks’ Market residuals (alphas) on the Sector Factors
U.S. Stocks: The Distribution of R² for the Regressions of U.S. Stock Market Residuals on Sector Factors
Min.    1st Qu.  Median    Mean    3rd Qu.    Max.
0.0000  0.0070   0.0267    0.0618  0.0767     0.6166

For most stocks, sectors explain 2.7% or more of return variance, after controlling for market risk. The average effectiveness is statistically much higher, since sectors explain a large fraction of return variance for some stocks (e.g., Energy sector for Exxon Mobil).

The Explanatory Power of Size for U.S. Stock Returns

The following chart plots the distribution of R² from the regressions of U.S. stocks’ market residuals against the Size factor:

Chart of the explanatory power of size -- the distributions of the R² (the coefficient of determination) from regressions U.S. stocks’ Market residuals (alphas) on the Size Factor
U.S. Stocks: The Distribution of R² for the Regressions of U.S. Stock Market Residuals on the Size Factor
Min.    1st Qu.  Median    Mean    3rd Qu.    Max.
0.0000  0.0089   0.0290    0.0415  0.0599     0.3366

These results support the popularity of the size factor in academic research. For most stocks, the size factor explains 2.9% or more of return variance, after controlling for market risk. Nevertheless, the average explanatory power of sectors is approximately 1.5 times greater.

The Explanatory Power of Value for U.S. Stock Returns

The following chart plots the distribution of R² from the regressions of U.S. stocks’ market residuals against the Value factor:

Chart of the explanatory power of value -- the distributions of the R² (the coefficient of determination) from regressions U.S. stocks’ Market residuals (alphas) on the Value Factor
U.S. Stocks: The Distribution of R² for the Regressions of U.S. Stock Market Residuals on the Value Factor
Min.    1st Qu.  Median    Mean    3rd Qu.    Max.
0.0000  0.0020   0.0092    0.0202  0.0251     0.3350

Contrary to its vogue in academic research, the explanatory power of Value is low, even in these in-sample results. The Value factor explains less than 1% of return variance, after controlling for market risk. Even for the 25% of stocks where the value factor has the greatest explanatory power, it only explains about 2.5% of return variance.

Notes on the Quantitative Methodology

This study controls for market risk before analyzing the explanatory power of sector and style factors. This two-step approach is necessary to avoid the multicollinearity problems that plague academic research into style factors. Since small and large companies typically have different market betas, and since cheap and expensive companies also typically have different market betas, the Fama-French and Carhart factors are collinear. Though this multicollinearity does not necessarily undermine the overall model, it does render individual factor betas and associated statistics meaningless.

We measured the in-sample explanatory power of various factors, similarly to typical academic research on the subject. These results are theoretical and do not represent practically attainable investment outcomes – they are the upper bound for out-of-sample explanatory power: This approach calculates factor exposures and residuals using a regression of stock returns on one or more factors. For instance, the regression of AAPL in the 4-factor Carhart model for 2010-2015 produces betas and alphas that are un-investable. To realize this alpha, one would need to know 2014 returns in order to effectively hedge AAPL in 2010. We use a similar approach in this study, and our analysis suffers from the same limitations – the results are in-sample.

Conclusions

  • Academic analysis favors factors with less explanatory power than industry’s real-world modeling.
  • The explanatory power of sectors is slightly higher than that of size, and approximately four times greater than that of value/growth.
  • Portfolio construction and manager allocation with sector, rather than style building blocks, provide greater control over systematic risk.
  • Risk models that seek to capture effectively systematic risk should account for sector or industry risk before style risk.
  • Sectors’ higher explanatory power holds across different industry classifications and style factor definitions.

The information herein is not represented or warranted to be accurate, correct, complete or timely. Past performance is no guarantee of future results. Copyright © 2012-2019, AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved. Content may not be republished without express written consent.

U.S. Smart Beta Crowding

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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.

Hedge Fund Crowding Update – Q2 2017

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Whereas hedge fund crowding primarily consists of systematic factor bets, most analysis of hedge fund crowding focuses solely on popular positions. Moreover, such analysis usually assumes that hedge fund crowding in individual stocks is a bullish indicator. This article illustrates the flaws of these common assumptions, identifies the principal sources of hedge fund crowding, and discusses the opportunities that this crowding presents:

  • Factor (systematic) exposures, rather than individual stocks, account for 70% of the crowding.
  • Residual (idiosyncratic, or stock-specific) bets account for just 30% of hedge fund crowding.
  • Simplistic analysis of crowding in individual stocks overlooks the majority of crowding risk.
  • Crowded hedge fund factor bets have been experiencing steep losses since 2015 and have been attractive shorts.
  • Crowded hedge fund stock-specific bets have been attractive longs following the 2014-2015 liquidations and the subsequent recovery, but the trend appears to have ended.

Identifying Hedge Fund Crowding

The analysis of hedge fund crowding in this article follows the approach of our earlier studies: We started with a decade of hedge fund Form 13F filings. Form 13F discloses positions of firms with long U.S. assets over $100 million. We only considered funds with a sufficiently low turnover to be analyzable from filings, and our database is free of survivorship bias. This sample included approximately 1,000 firms. We combined all portfolios into a single position-weighted portfolio – HF Aggregate. We then used the AlphaBetaWorks (ABW) Statistical Equity Risk Model – an effective predictor of future risk – to analyze HF Aggregate’s risk relative to the U.S. Market (represented by the iShares Russell 3000 ETF (IWV) benchmark), identify the crowded exposures, and analyze their performance trends.

Factor and Residual Components of Hedge Fund Crowding

Virtually all of HF Aggregate’s absolute risk is systematic. Thus, the aggregate long U.S. equity holdings of hedge funds will very nearly track a passive factor portfolio with similar risk:

Chart of the factor (systematic) and residual (idiosyncratic) components of absolute U.S. long equity hedge fund crowding on 6/30/2017

Components of U.S. Hedge Fund Aggregate’s Absolute Risk in Q2 2017

Source Volatility (ann. %) Share of Variance (%)
Factor 11.80 97.78
Residual 1.78 2.22
Total 11.93 100.00

HF Aggregate has 2.8% estimated future volatility (tracking error) relative to the Market. Approximately 70% of this relative risk is due to factor crowding:

Chart of the factor (systematic) and residual (idiosyncratic) components of U.S. long equity hedge fund crowding relative to the Market on 6/30/2017

Components of U.S. Hedge Fund Aggregate’s Relative Risk in Q2 2017

Source Volatility (ann. %) Share of Variance (%)
Factor 2.37 69.90
Residual 1.55 30.10
Total 2.83 100.00

Consequently, simplistic analysis of hedge fund crowding that focuses on the popular holdings and position overlap is fatally flawed. It will capture less than a third of the crowding risk that is stock-specific and will overlook the bulk that is systematic.

Stock Picking and Market Timing Returns from Crowding

Crowded factor and residual bets go through cycles of outperformance and underperformance, depending on capital flows. These trends can provide attractive investment opportunities: short during liquidation, and long during expansion.

The following chart shows HF Aggregate’s cumulative βReturn (risk-adjusted returns from factor timing). Crowded hedge fund factor bets have experienced steep and accelerating losses since 2015. We identified these factors in earlier research as attractive short candidates, and the downtrend has persisted:

Chart of the cumulative risk-adjusted return from factor timing (variation in factor exposures) of the U.S. Hedge Fund Aggregate portfolio

Historical Risk-Adjusted Return from Factor Timing of U.S. Hedge Fund Aggregate

The following chart shows HF Aggregate’s cumulative αReturn (risk-adjusted returns from security selection). Following the unprecedented losses on crowded residual bets during 2011-2015, we advised long exposures to these beaten-down stocks in late-2015. The subsequent recovery has been spectacular but now appears over. Thus, the crowded hedge fund residual bets no longer seem to offer clear long or short opportunities:

Chart of the cumulative risk-adjusted return from security selection (stock picking) of the U.S. Hedge Fund Aggregate portfolio

Historical Risk-Adjusted Return from Security Selection of U.S. Hedge Fund Aggregate

The rest of this article considers the crowded factor and residual bets responsible for the above trends.

Hedge Fund Factor (Systematic) Crowding

The following chart shows the main sources of hedge fund factor crowding as of 6/30/2017 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. Hedge Fund Aggregate Portfolio relative to U.S. Market on 6/30/2017

Significant Absolute and Relative Factor Exposures of U.S. Hedge Fund Aggregate in Q2 2017

The dominant hedge fund long equity bet is Market (high Beta). Thus, the most crowded bet is high overall market risk, rather than a specific stock. Like a leveraged ETF, HF Aggregate outperforms when the Market is up and underperforms when it is down.

Chart of the main contributions to the factor variance of U.S. Hedge Fund Aggregate Portfolio relative to U.S. Market on 6/30/2017

Factors Contributing Most to Relative Factor Variance of U.S. Hedge Fund Aggregate in Q2 2017

Factor Relative Exposure Factor Volatility Share of Relative Factor Variance Share of Relative Total Variance
Market 14.05 9.80 47.63 33.29
Health Care 8.72 7.75 13.41 9.37
FX -8.49 6.77 11.35 7.93
Real Estate -2.54 12.40 6.11 4.27
Oil Price 1.11 29.50 5.30 3.71
Utilities -3.17 12.72 4.89 3.42
Bond Index -8.12 3.41 3.86 2.70
Financials -5.29 7.90 2.23 1.56
Industrials -4.15 4.72 2.17 1.51
Consumer Discretionary 8.11 4.31 1.51 1.06

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

In fact, crowding in the simple Market Factor alone accounts for more risk than all the stock-specific crowding combined.

Despite recent losses, HF Aggregate’s Health Care Factor exposure remains near the recent record levels.

Hedge Fund Residual (Idiosyncratic) Crowding

The remaining 30% of hedge fund 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. Hedge Fund Aggregate Portfolio relative to U.S. Market on 6/30/2017

Stocks Contributing Most to Relative Residual Variance of U.S. Hedge Fund Aggregate in Q2 2017

Symbol Name Relative Exposure Residual Volatility Share of Relative Residual Variance Share of Relative Total Variance
BABA Alibaba Group Holding ADR 2.23 23.93 11.82 3.56
LNG Cheniere Energy, Inc. 1.44 27.51 6.54 1.97
HLF Herbalife Ltd. 0.92 36.18 4.59 1.38
CHTR Charter Communications, Inc. 1.66 19.10 4.15 1.25
AVGO Broadcom Limited 1.32 21.20 3.24 0.98
AAPL Apple Inc. -1.96 13.37 2.84 0.86
ALXN Alexion Pharmaceuticals, Inc. 0.85 29.14 2.52 0.76
FB Facebook, Inc. Class A 1.01 24.17 2.47 0.74
EXPE Expedia, Inc. 1.08 21.31 2.21 0.66
FWONK Liberty Media Corporation Formula One 0.91 24.90 2.11 0.64

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

Following the 2011-2015 losses and the subsequent gains, these crowded bets do not offer clear long or short opportunities. Moreover, residual crowding accounts for a small fraction of the industry’s risk. While systematic hedge fund crowding continues to dominate, investors and allocators should focus on managing the crowded factor exposures. Without a firm grasp of factor crowding, investors and fund followers may blindly follow losing bets.

Summary

  • Factor (systematic) exposures that capture risks shared by many stocks, rather than individual stocks, are responsible for the majority of hedge fund crowding.
  • The main sources of Q2 2017 hedge fund crowding were long U.S. Market (high Beta), long Health Care, and short USD (preference for exporters over importers).
  • The crowded factor bets have been in a multi-year bearish trend.
  • The crowded residual bets have recovered from steep losses and no longer offer clear opportunities.
  • Without a robust analysis of the factor and residual components of crowding, a hedge fund investor, follower, or allocator may be missing the bulk of crowding risk and investing in a generic passive factor portfolio.
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.

 

Replicating Fundamental Indexing with Factor Tilts

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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.

Hedge Fund Crowding Update – Q1 2017

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A typical analysis of hedge fund crowding considers large, popular, and concentrated hedge fund long equity holdings. Such analysis usually assumes that crowding comes from stock-specific bets and that it is a bullish indicator. These assumptions are incorrect and have cost investors dearly:

  • Residual, idiosyncratic, or stock-specific bets now account for less than a third of hedge fund crowding. Factor (systematic) risk, rather than the risk from individual stocks, is driving hedge funds’ active returns. Consequently, simplistic analysis of hedge fund crowding that focuses on specific stocks misses the bulk of funds’ active risk and return.
  • The returns of crowded hedge fund factor and residual bets vary over time as the funds go through cycles of capital inflows and outflows. Consequently, generic analysis of hedge fund crowding can herd investors into losing bets on the wrong side of a cycle. For instance, depending on the trend, investors may desire long exposure to the crowded factor exposures in one year and short exposure in another.

This article reviews hedge fund long equity crowding at the end of Q1 2017. We identify the dominant systematic exposures and the top residual bets that will have the largest impact on investor performance. We also explore the current trends in returns from crowding that indicate profitable positioning.

Identifying Hedge Fund Crowding

This article follows the approach of our earlier studies of hedge fund crowding: We start with a survivorship-free database of SEC filings by over 1,000 U.S. hedge funds spanning over a decade. This database contains all funds that had ever filed 13F Reports (which disclose long U.S. assets over $100 million). We only consider funds with a sufficiently low turnover to be analyzable from filings. We combine all fund portfolios into a single position-weighted portfolio (HF Aggregate). The analysis of HF Aggregate’s risk relative to the U.S. Market reveals its active bets and the industry’s crowding. The AlphaBetaWorks (ABW) Statistical Equity Risk Model – an effective predictor of future risk – identifies and quantifies the crowded exposures driving HF Aggregate’s performance.

Factor and Residual Components of Hedge Fund Crowding

The 3/31/2017 HF Aggregate had 2.6% estimated future volatility (tracking error) relative to the U.S. Market (represented by the iShares Russell 3000 ETF (IWV) benchmark). Approximately 30% of this was due to residual crowding, and approximately 70% was due to factor crowding:

Chart of the factor (systematic) and residual (idiosyncratic) components of US hedge fund crowding on 03/31/2017

Components of the Relative Risk for U.S. Hedge Fund Aggregate in Q1 2017

Source Volatility (ann. %) Share of Variance (%)
Factor 2.19 69.01
Residual 1.47 30.99
Total 2.64 100.00

Hedge fund crowding analysis that focuses on the popular holdings and position overlap thus captures less than a third of the total risk and overlooks over two-thirds of crowding that is due to factors – a fatal flaw. Since similar factor exposures can cause funds with no shared positions to correlate closely, a simplistic analysis of holdings and position overlap fosters dangerous complacency.

Stock Picking and Market Timing Returns from Crowding

A precise understanding of crowding is critical to investors and allocators since, depending on the capital flows, crowded bets can generate large and unexpected gains or losses.

The following chart shows cumulative βReturn (risk-adjusted returns from factor timing, or from the variation of factor exposures) of HF Aggregate. Crowded hedge fund factor bets have underperformed since 2011, and losses from hedge fund factor crowding have accelerated since 2015. The crowded factor bets below could have been attractive short candidates. In aggregate, hedge funds’ long equity portfolios would have made approximately 10% more since 2015 had they kept their factor exposures constant:

Chart of the cumulative risk-adjusted return from factor timing (variation in systematic exposures) due to U.S. long equity hedge fund crowding

Historical Risk-Adjusted Return from Factor Timing of U.S. Hedge Fund Aggregate

Crowded hedge fund residual bets have also underperformed since 2011. The following chart shows cumulative αReturn (risk-adjusted returns from security selection) of HF Aggregate. HF Aggregate experienced massive losses from security selection during 2011-2015. Given the unprecedented losses, we advised long exposures to the crowded residual bets in late-2015, and these have indeed recovered:

Chart of the cumulative risk-adjusted return from security selection (stock picking) due to U.S. long equity hedge fund crowding

Historical Risk-Adjusted Return from Security Selection of U.S. Hedge Fund Aggregate

We now turn to the specific crowded factor and residual bets behind the trends above.

Hedge Fund Factor (Systematic) Crowding

The following chart illustrates the main sources of factor crowding. HF Aggregate’s factor exposures are in red. The U.S. Market’s (defined as the iShares Russell 3000 ETF (IWV) benchmark) is in gray:

Chart of the factor exposures contributing most to the factor variance of U.S. Hedge Fund Aggregate Portfolio relative to U.S. Market on 03/31/2017

Significant Absolute and Residual Factor Exposures of U.S. Hedge Fund Aggregate in Q1 2017

The dominant bet of hedge funds’ long equity portfolios is Market (high Beta). The most crowded hedge fund bet is thus not a particular stock, but high overall market risk. HF Aggregate partially behaves like a leveraged market ETF, outperforming during bullish regimes and underperforming during bearish ones.

Chart of the main factors and their cumulative contribution to the factor variance of U.S. Hedge Fund Aggregate Portfolio relative to U.S. Market on 03/31/2017

Factors Contributing Most to Relative Factor Variance of U.S. Hedge Fund Aggregate in Q1 2017

Factor Relative Exposure Factor Volatility Share of Relative Factor Variance Share of Relative Total Variance
Market 13.25 10.67 54.00 37.26
Health Care 8.55 7.62 15.93 10.99
Utilities -3.05 12.86 8.16 5.63
Real Estate -2.69 12.87 7.91 5.46
Bond Index -7.33 3.59 6.02 4.15
Consumer Staples -5.04 8.04 4.64 3.20
Size -2.02 9.35 2.45 1.69
Oil Price 0.53 30.36 2.38 1.64
Industrials -4.28 4.96 1.85 1.27
FX 2.59 6.77 -1.83 -1.26

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

Crowding into a single factor (Market) accounts for more hedge fund risk than all their stock-specific and other factor bets combined. The three top sector bets are long Health Care, short Utilities, and short Real Estate.

HF Aggregate’s exposures to Market, Health Care, and Bond Factors remained near record levels reached recently.

Hedge Fund Residual (Idiosyncratic) Crowding

The remaining third of hedge fund crowding as of 3/31/2017 was due to residual (idiosyncratic, stock-specific) risk:

Chart of the main stock-specific bets and their cumulative contribution to the residual variance of U.S. Hedge Fund Aggregate Portfolio relative to U.S. Market on 03/31/2017

Stocks Contributing Most to Relative Residual Variance of U.S. Hedge Fund Aggregate in Q1 2017

Symbol Name Relative Exposure Residual Volatility Share of Relative Residual Variance Share of Relative Total Variance
CHTR Charter Communications, Inc. Class A 2.53 18.64 10.31 3.19
LNG Cheniere Energy, Inc. 1.41 29.37 7.96 2.47
BABA Alibaba Group Holding Ltd. Sponsored ADR 1.17 26.26 4.40 1.36
FB Facebook, Inc. Class A 1.02 28.05 3.76 1.17
FLT FleetCor Technologies, Inc. 1.19 22.02 3.19 0.99
HCA HCA Holdings, Inc. 1.12 21.36 2.66 0.82
AAPL Apple Inc. -1.72 13.87 2.63 0.81
NXPI NXP Semiconductors NV 0.78 28.62 2.31 0.71
PYPL PayPal Holdings Inc 1.26 17.48 2.26 0.70
ATVI Activision Blizzard, Inc. 0.98 21.53 2.05 0.64

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

While systematic hedge fund crowding continues to dominate, investors and allocators should focus on the factor exposures. Without a firm grasp of factor crowding, allocators to a supposedly diversified hedge fund portfolio may be paying high active management fees for what is effectively a leveraged ETF book. Also, investors and fund followers may blindly follow losing factor bets.

Nevertheless, residual hedge fund crowding can be a profitable long and short indicator. The 25% decline in 2010-2015 was followed by a 15% gain.

Summary

  • Factor (systematic) exposures and risks shared across stocks, rather than individual positions, are the primary drivers of hedge fund industry’s long equity risk.
  • The main sources of Q1 2017 hedge fund crowding were long U.S. Market (high Beta), long Health Care, short Utilities, and short Real Estate Factor exposures.
  • Without a robust analysis of factor and residual crowding, a hedge fund investor, follower, or allocator may be investing in a generic passive factor portfolio, likely with leverage.
  • The crowded factor bets have been in a bearish trend and may represent attractive short candidates.
  • The crowded residual bets have been recovering from steep losses and may continue to represent attractive long candidates, though less so than in 2016.
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.

Hedge Fund Crowding Update – Q4 2016

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A typical analysis of hedge fund crowding surveys popular equity holdings. Yet, such residual, idiosyncratic, or stock-specific bets account for only 31% of current hedge fund crowding. Factor (systematic) risk, rather than a few specific stocks, is driving absolute and relative returns. Consequently, most analysis of hedge fund crowding focuses on a small fraction of crowding, missing its bulk.

Nearly 70% of the hedge fund industry’s long equity risk comes from factor crowding. Market exposure (high Beta) constitutes half of that – more than all the remaining factor bets and more than all the stock-specific bets combined. Since the consensus factor exposures can be obtained cheaply via ETFs and do not warrant the same compensation as idiosyncratic insights, it is vital for investors and allocators to understand and manage these crowded exposures. In addition, crowded factor bets are vulnerable to damaging liquidations.  This article reviews hedge fund long equity bets at the end of 2016 and focuses on the dominant systematic exposures that will have the largest impact on investor performance.

Identifying Hedge Fund Crowding

This article follows the approach of our earlier studies of hedge fund crowding: We started with a 10-year survivorship-free database of SEC filings by over 1,000 U.S. hedge funds. This database contains all funds that had long U.S. assets in excess of $100 million and sufficiently low turnover to be analyzable from their filings. We then combined all fund portfolios into a single position-weighted portfolio (HF Aggregate). The analysis of HF Aggregate’s risk relative to the U.S. Market revealed its main active bets. The AlphaBetaWorks (ABW) Statistical Equity Risk Model – an effective predictor of future risk – identified and quantified these crowded exposures driving HF Aggregate’s performance.

Hedge Fund Industry’s Risk

The 12/31/2016 HF Aggregate had 2.7% estimated future volatility (tracking error) relative to the U.S. Market. Approximately a third of this tracking error was due to residual crowding, and the remaining two thirds  was due to factor crowding:

Chart of the factor (systematic) and residual (idiosyncratic) components of the U.S. Hedge Fund Aggregate’s variance relative to U.S. Market on 12/31/2016

Components of the Relative Risk for U.S. Hedge Fund Aggregate in Q4 2016

Source

Volatility (ann. %)

Share of Variance (%)
Factor

2.25

68.89

Residual

 1.51

31.11
Total

2.71

100.00

The low 1.5% residual volatility, less than a third of the total, illustrates the challenges of hedge fund crowding analysis that focuses on the popular holdings and position overlap. Such stock-specific view overlooks the two thirds of crowding that is due to factors – a fatal flaw. As a result, simplistic analysis of popular holdings and of position overlap fosters dangerous complacency when funds with no shared positions correlate highly due to similar factor exposures.

Hedge Fund Factor (Systematic) Crowding

The following chart illustrates the main sources of factor risk. HF Aggregate’s factor exposures are in red. The U.S. Market’s (defined as the iShares Russell 3000 ETF (IWV) Benchmark) is in gray:

Chart of the factor exposures contributing most to the factor variance of U.S. Hedge Fund Aggregate Portfolio relative to U.S. Market on 12/31/2016

Significant Absolute and Residual Factor Exposures of U.S. Hedge Fund Aggregate in Q4 2016

The dominant bet of hedge funds’ long equity portfolios is Market (high Beta). The most crowded hedge fund bet is thus not a particular stock, but high overall market risk. HF Aggregate thus partially behaves like a leveraged market ETF, outperforming during bullish regimes and underperforming during bearish ones.

Chart of the main factors and their cumulative contribution to the factor variance of U.S. Hedge Fund Aggregate Portfolio relative to U.S. Market on 12/31/2016

Factors Contributing Most to Relative Factor Variance of U.S. Hedge Fund Aggregate in Q4 2016

Factor Relative Exposure Factor Volatility Share of Relative Factor Variance Share of Relative Total Variance
Market 12.92 10.52 50.26 34.62
Health Care 10.14 7.61 19.26 13.26
Bond Index -9.71 3.59 7.32 5.04
Utilities -3.19 12.53 7.14 4.92
Real Estate -2.51 12.88 7.07 4.87
Industrials -5.00 4.96 3.09 2.13
Consumer Staples -4.97 7.75 2.27 1.56
Oil Price 0.56 30.34 2.27 1.56
FX -1.12 6.87 1.02 0.70
Financials -2.38 7.71 -0.83 -0.57

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

Hedge funds’ Market Factor crowding accounts for more risk than all their stock-specific bets combined. This dominance of a single systematic risk illustrates how asset managers’ and allocators’ endurance increasingly depends on their grasp of systematic crowding. It also illustrates the dangers of fixation on individual holdings.

HF Aggregate’s exposures to Market, Health Care, and Bond Factors remained near record levels reached recently. We analyze these in their order of importance below.

Hedge Fund U.S. Market Factor (Beta) Crowding

Hedge Fund Aggregates’ U.S. Market exposure decreased slightly from the mid-2016 record level:

Chart of the historical exposure of U.S. Hedge Fund Aggregate’s to the U.S. Equity Market Factor through 12/31/2016

U.S. Hedge Fund Aggregate’s U.S. Market Factor Exposure History

Even following this decrease in risk, the average dollar of hedge fund long U.S. equity capital carries approximately 20% more market risk than S&P 500. Thus, hedge fund portfolios move in concert with the market, but with heightened sensitivity to it. Consequently, simple comparison of hedge fund returns to broad equity benchmarks and identification of nominal outperformance with alpha remain dangerous. Further, simple equation of capital invested in (dollar exposures to) a market or sector with actual portfolio risk remains flawed.

Hedge Fund Health Care Crowding

Hedge Fund Aggregates’ Health Care exposure also decreased sharply from its 2016 record:

Chart of the historical exposure of U.S. Hedge Fund Aggregate’s to the U.S. Health Care Sector Factor through 12/31/2016

U.S. Hedge Fund Aggregate’s U.S. Health Care Factor Exposure History

Even after this decrease, HF Aggregate continues to carry almost twice the Health Care exposure of the Market. The Health Care Factor remains the second most significant hedge fund long equity bet.

Hedge Fund Short Bonds/Long Interest Rates Factor Crowding
HF Aggregate’s Short Bonds/Long Interest Rates Factor exposure was profitable in late-2016. This exposure decreased by half in the second half of the year, following the election catalyst:

Chart of the historical exposure of U.S. Hedge Fund Aggregate’s to the U.S. Long Bonds/Short Interest Rates Factor through 12/31/2016

U.S. Hedge Fund Aggregate’s U.S. Long Bonds/Short Interest Rates Factor Exposure History

Short bond exposure is a natural consequence of hedge funds’ interest in “cheap call options”, often highly financially leveraged companies with asymmetric profit and loss potential. We discussed the fundamental sources of this Bonds/Interest Rates Factor exposure in more detail in a prior article.

Hedge Fund Residual (Idiosyncratic) Crowding

The remaining third of hedge fund crowding on 12/31/2016 was due to residual (idiosyncratic, stock-specific) risk. Though this is a minor component of the total crowding, we survey it for completeness and to facilitate comparisons with the basic surveys of crowding found elsewhere:

Chart of the main stock-specific bets and their cumulative contribution to the residual variance of U.S. Hedge Fund Aggregate Portfolio relative to U.S. Market on 12/31/2016

Stocks Contributing Most to Relative Residual Variance of U.S. Hedge Fund Aggregate in Q4 2016

Symbol Name Relative Exposure Residual Volatility Share of Relative Residual Variance Share of Relative Total Variance
CHTR Charter Communications, Inc. 2.62 19.03 10.87 3.38
LNG Cheniere Energy, Inc. 1.40 29.27 7.29 2.27
FLT FleetCor Technologies, Inc. 1.27 23.17 3.80 1.18
AGN Allergan plc 1.44 18.31 3.05 0.95
AAPL Apple Inc. -1.85 13.81 2.86 0.89
FB Facebook, Inc. Class A 0.91 27.00 2.63 0.82
BABA Alibaba Group Holding Ltd. ADR 1.02 23.23 2.47 0.77
HCA HCA Holdings, Inc. 1.09 21.27 2.34 0.73
HUM Humana Inc. 1.05 21.88 2.29 0.71
WMB Williams Companies, Inc. 0.83 25.52 1.94 0.60

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

Though these exposures are sensitive to asset flows, they generally constitute minor risks within the crowded portfolios. While systematic hedge fund crowding continues to dominate, investors and allocators should focus on the factor exposures. Without a firm grasp of factor crowding, a supposedly diversified hedge fund portfolio may be charging high active management fees for what is effectively a leveraged ETF book.

Summary

  • Factor (systematic) exposures and risks shared across stocks, rather than individual positions, are driving hedge fund industry’s long equity risk. Exposure to these crowded bets can be obtained much more cheaply via ETFs and other passive products.
  • The main sources of Q4 2016 hedge fund crowding were long U.S. Market (high Beta), long Health Care, and short Bonds/long Interest Rates Factor exposures.
  • Without a robust analysis of factor and residual crowding, a hedge fund investor, follower, or allocator may be investing in a generic passive factor portfolio, likely with leverage.
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.

Hedge Fund Crowding Update – Q3 2016

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Whereas most analysis of hedge fund crowding focuses on individual stocks, over 85% of hedge funds’ recent long equity variance has been due to their factor (systematic) risk. Residual, idiosyncratic, or stock-specific bets accounted for less than 15%. Thus, factor crowding has dominated hedge fund industry’s absolute and relative returns. This article reviews the most crowded hedge fund long equity bets at 9/30/2016.

Understanding and quantifying this factor crowding is vital for hedge fund investors and allocators: Factor exposures that are shared by the entire hedge fund industry and that can be obtained cheaply with passive funds do not warrant the same compensation as the distinctive insights of gifted managers. Even worse, crowded bets expose investors to damaging stampedes during liquidations.

Identifying Hedge Fund Crowding

This article’s approach follows our earlier studies of hedge fund crowding: We started with a 10-year survivorship-free dataset of SEC filings by over 1,000 hedge funds. We then created a position-weighted portfolio (HF Aggregate) comprising all hedge fund long U.S. equity portfolios that can examined using the filings. We analyzed HF Aggregate’s risk and its historical exposures relative to the U.S. Market. The top contributors to hedge fund industry’s relative risk are the industry’s most crowded bets. Factor exposures were analyzed using the AlphaBetaWorks (ABW) Statistical Equity Risk Model – an effective predictor of future risk.

Hedge Fund Industry’s Risk

The 9/30/2016 HF Aggregate had 3.9% estimated future volatility (tracking error) relative to the U.S. Market. Less than 20% of this risk came from individual stocks, or from stock-specific crowding; the remainder – more than 80% – came from factor (systematic) crowding:

Factor (systematic) and residual (idiosyncratic) components of the U.S. Hedge Fund Aggregate’s variance relative to U.S. Market on 9/30/2016

Components of the Relative Risk for U.S. Hedge Fund Aggregate in Q3 2016

Source Volatility (ann. %) Share of Variance (%)
Factor 3.50 82.19
Residual 1.63 17.81
Total 3.86 100.00

Since residual risk accounts for just 18% of the total, basic analysis of hedge fund crowding that examines popular holdings and position overlap is misguided. Such stock-specific analysis of crowding covers less than 20% of the industry’s risk, overlooking the dominant 80% of hedge fund crowding that is due to factors – a fatal flaw. Even funds with no shared positions correlate highly when they have similar factor exposures, so simplistic analysis of popular holdings and of position overlap understates portfolio risk and fosters complacency.

Hedge Fund Factor (Systematic) Crowding

Below are HF Aggregate’s principal factor exposures (in red). The U.S. Market, defined as the iShares Russell 3000 ETF (IWV) is the Benchmark (in gray). These factors are the primary sources of risk in the table above:

Chart of the factor exposures contributing most to the factor variance of U.S. Hedge Fund Aggregate Portfolio relative to U.S. Market on 9/30/2016

Significant Absolute and Residual Factor Exposures of U.S. Hedge Fund Aggregate in Q3 2016

The dominant bet of hedge funds’ long equity portfolios is Market (high Beta):

Chart of the main factors and their cumulative contribution to the factor variance of U.S. Hedge Fund Aggregate Portfolio relative to U.S. Market on 9/30/2016

Factors Contributing Most to Relative Factor Variance of U.S. Hedge Fund Aggregate in Q3 2016

Factor Relative Exposure Factor Volatility Share of Relative Factor Variance Share of Relative Total Variance
Market 20.65 10.59 46.19 37.97
Size -14.67 8.62 19.06 15.66
Health Care 12.68 7.54 16.22 13.33
Bond Index -19.34 3.37 8.94 7.35
Consumer Staples -7.87 7.24 5.28 4.34
Utilities -3.40 12.46 5.00 4.11
FX 10.70 6.80 -4.22 -3.47
Real Estate -1.86 12.80 2.97 2.44
Oil Price 1.03 30.15 2.75 2.26
Financials -4.84 7.05 -2.09 -1.72

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

This high U.S. Market exposure alone is twice as influential as all the stock-specific bets combined. Given this importance of factor crowding compared to residual crowding, popular fascination with fund holdings and position overlap is especially dangerous. Asset managers’ and allocators’ endurance thus depends increasingly on their edge in assessing systematic crowding.

Hedge Fund Short Bonds/Long Interest Rates Factor Crowding

HF Aggregate’s exposures to Market, Size, and Health Factors were near their peak levels seen in recent quarters. In addition to these, their Short Bonds/Long Interest Rates Factor exposure has also recently reached historic extremes:

Chart of the historical exposure of U.S. Hedge Fund Aggregate’s to the U.S. Long Bonds/Short Interest Rates Factor

U.S. Hedge Fund Aggregate’s U.S. Long Bonds/Short Interest Rates Factor Exposure History

We discussed the fundamental sources of this Bonds/Interest Rates Factor exposure in a prior article. Short bond risk is a natural consequence of hedge funds’ fondness for financially leveraged companies, often viewed as “cheap call options.” A company’s indebtedness creates economic and statistically observable short bond exposure. Given the Q4 2016 moves in yields, this bet should prove profitable for the hedge fund industry.

Hedge Fund Residual (Idiosyncratic) Crowding

A fifth of hedge fund crowding on 9/30/2016 was due to residual (idiosyncratic, stock-specific) risk. The following stocks were the main contributors to residual hedge fund crowding:

Chart of the main stock-specific bets and their cumulative contribution to the residual variance of U.S. Hedge Fund Aggregate Portfolio relative to U.S. Market on 9/30/2016

Stocks Contributing Most to Relative Residual Variance of U.S. Hedge Fund Aggregate in Q3 2016

Symbol Name Relative Exposure Residual Volatility Share of Relative Residual Variance Share of Relative Total Variance
LNG Cheniere Energy, Inc. 1.63 28.94 8.43 1.50
VRX Valeant Pharmaceuticals International Inc 0.89 43.63 5.76 1.02
AGN Allergan plc 1.86 18.17 4.32 0.77
WMB Williams Companies, Inc. 1.28 25.87 4.12 0.73
CHTR Charter Communications, Inc. Class A 1.69 19.27 4.01 0.71
FLT FleetCor Technologies, Inc. 1.62 17.40 3.02 0.54
AAPL Apple Inc. -1.90 14.29 2.78 0.50
EXPE Expedia, Inc. 1.04 24.57 2.47 0.44
BABA Alibaba Group Holding Ltd. Sponsored ADR 1.07 23.61 2.43 0.43
HCA HCA Holdings, Inc. 1.10 21.96 2.22 0.40

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

The importance of residual crowding diminished in recent quarters as factor crowding increased. Consequently, hedge fund stock-picking has faded in importance relative to market timing. The most crowded stocks are sensitive to asset flows in and out of the industry, but they are not the main threat to crowded portfolios. In the current environment of extreme systematic hedge fund crowding, investors and allocators should focus on the factor exposures. Without an accurate view of factor crowding, investors in a supposedly diversified hedge fund portfolio often end up paying high active fees for a passive factor portfolio.

Summary

  • At Q3 2016, over 80% of hedge fund industry’s relative long equity risk was due to factor, or systematic, crowding.
  • The main sources of Q3 2016 hedge fund crowding were high U.S. Market, short Size, long Health, and short Bonds/long Interest Rates Factor exposures.
  • Short Bonds/Long Interest Rates Factor exposure reached historic extremes.
  • Systematic exposures and risks shared across stocks, rather than individual positions, are driving 80% of the hedge fund industry’s long equity risk.
  • Only robust analysis of factor and residual crowding can determine whether a hedge fund investor, follower, or allocator is investing in exceptional insights or in a generic passive factor portfolio.
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.

What Fraction of International Smart Beta is Dumb Beta?

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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.
 

What Fraction of Smart Beta 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, 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 © 2012-2016, AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved.
Content may not be republished without express written consent.