Hedge Funds’ Best and Worst Sectors

Due to the congestion of their investor base, crowded hedge fund stocks are volatile and vulnerable to mass selling. The risk-adjusted performance of consensus bets tends to disappoint. In two past pieces we illustrated the toll of crowding on exploration and production as well as internet companies. We also reviewed two specific crowded bets: SanDisk and eHealth.

While crowded hedge fund ideas do poorly most of the time, they don’t always. Market efficiency varies across sectors, and some industries are more analytically tractable than others. In this article we survey the sectors with the best and worst hedge fund performance records. We will illustrate when investors should stay clear of crowded ideas and when they can embrace them.

Analyzing Hedge Fund Performance and Crowding

To explore performance and crowding we analyze hedge fund sector holdings (HF Sector Aggregate) relative to the Sector Market Portfolio (Sector Aggregate). HF Sector Aggregate is position-weighted, and Sector Aggregate is capitalization-weighted. This follows the approach of our earlier articles on aggregate and sector-specific hedge fund crowding.

Hedge Funds’ Worst Sector: Miscellaneous Metals and Mining

Historical Hedge Fund Performance: Miscellaneous Metals and Mining

Hedge funds’ worst security selection performance for the past ten years has been in the Miscellaneous Metals and Mining sector. The figure below plots historical HF Miscellaneous Metals and Mining Aggregate’s return. Factor return is due to systematic (market) risk. It is the return of a portfolio that replicates HF Sector Aggregate’s market risk. The blue area represents positive and the gray area represents negative risk-adjusted returns from security selection (αReturn).

Chart of the historical total, factor, and security selection performance of the Hedge Fund Miscellaneous Metals and Mining Sector Aggregate

Hedge Fund Miscellaneous Metals and Mining Sector Aggregate Historical Performance

Even without adjusting for risk, crowded bets have done poorly. They consistently underperformed the factor portfolio, missing out on over 300% in gains.

The HF Sector Aggregate’s risk-adjusted return from security selection (αReturn) is the return it would have generated if markets were flat – all market effects on performance have been eliminated. This idiosyncratic performance of the crowded portfolio is a decline of 87%. Crowded bets in this sector are especially dangerous, given their persistently poor performance:

Chart of the historical security selection performance of the Hedge Fund Miscellaneous Metals and Mining Sector Aggregate

Hedge Fund Miscellaneous Metals and Mining Sector Aggregate Historical Security Selection Performance

In this sector, hedge funds lost $900 million to other market participants. In commodity industries, the recipients of this value transfer are usually private investors and insiders.

Current Hedge Fund Bets: Miscellaneous Metals and Mining

The following stocks contributed most to the relative residual (security-specific) risk of the HF Miscellaneous Metals and Mining Sector Aggregate as of Q3 2014. Blue bars represent long (overweight) exposures relative to the Sector Aggregate. White bars represent short (underweight) exposures. Bar height represents contribution to relative stock-specific risk:

Chart of the top contributors' contribution to the Hedge Fund Miscellaneous Metals and Mining Sector Aggregate's risk

Crowded Hedge Fund Miscellaneous Metals and Mining Sector Bets

The following table contains detailed data on these crowded bets. Large and illiquid long (overweight) bets are most at risk of volatility, mass liquidation, and underperformance:

Exposure (%) Net Exposure Share of Risk (%)
HF Sector Aggr. Sector Aggr. % $mil Days of Trading
ZINC Horsehead Holding Corp. 72.74 2.41 70.33 148.5 15.6 80.55
SLCA U.S. Silica Holdings, Inc. 0.30 9.68 -9.39 -19.8 -0.2 6.45
LEU Centrus Energy Corp. Class A 4.54 0.22 4.32 9.1 17.2 4.85
SCCO Southern Copper Corporation 7.69 70.19 -62.51 -132.0 -2.3 4.18
CSTE CaesarStone Sdot-Yam Ltd. 0.00 5.18 -5.18 -10.9 -0.8 1.14
MCP Molycorp, Inc. 3.84 0.84 3.01 6.3 1.7 0.92
MTRN Materion Corporation 7.15 1.82 5.33 11.3 2.1 0.69
HCLP Hi-Crush Partners LP 0.49 2.90 -2.41 -5.1 -0.2 0.35
CA:URZ Uranerz Energy Corporation 2.00 0.27 1.72 3.6 11.7 0.29
IPI Intrepid Potash, Inc. 0.36 3.38 -3.02 -6.4 -0.5 0.22
OROE Oro East Mining, Inc. 0.00 0.52 -0.52 -1.1 -39.9 0.05
CANK Cannabis Kinetics Corp. 0.00 0.10 -0.10 -0.2 -2.7 0.05
UEC Uranium Energy Corp. 0.00 0.33 -0.33 -0.7 -0.4 0.02
FCGD First Colombia Gold Corp. 0.00 0.09 -0.09 -0.2 -19.0 0.02
MDMN Medinah Minerals, Inc. 0.00 0.16 -0.16 -0.3 -4.8 0.01
QTMM Quantum Materials Corp. 0.00 0.13 -0.13 -0.3 -6.3 0.00
ENZR Energizer Resources Inc. 0.00 0.12 -0.12 -0.3 -11.7 0.00
AMNL Applied Minerals, Inc. 0.00 0.20 -0.20 -0.4 -18.5 0.00
LBSR Liberty Star Uranium and Metals Corp. 0.00 0.03 -0.03 -0.1 -4.9 0.00
Other Positions 0.61 0.21
Total 100.00

Hedge Funds’ Best Sector: Real Estate Development

Historical Hedge Fund Performance: Real Estate Development

Hedge funds’ best security selection performance has been in the Real Estate Development Sector. The figure below plots the historical return of HF Real Estate Development Aggregate. Factor return and αReturn are defined as above:

Chart of the historical total, factor, and security selection returns of the Hedge Fund Real Estate Development Sector Aggregate

Hedge Fund Real Estate Development Sector Aggregate Historical Performance

Since 2004, the HF Sector Aggregate outperformed the portfolio with equivalent market risk by approximately 200%. In a flat market, HF Sector Aggregate would have gained approximately 180%:

Chart of the historical security selection (residual) return of the Hedge Fund Real Estate Development Sector Aggregate

Hedge Fund Real Estate Development Sector Aggregate Historical Security Selection Performance

In this sector, hedge funds gained $1 billion at the expense of other market participants. The Real Estate Development Sector appears less efficient but tractable, providing hedge funds with consistent stock picking gains.

Current Hedge Fund Real Estate Development Bets

The following stocks contributed most to the relative residual (security-specific) risk of the HF Real Estate Development Sector Aggregate as of Q3 2014:

Chart of the contribution to the residual (stock-specific) risk of the various hedge fund Crowded Hedge Fund Real Estate Development Sector bets

Crowded Hedge Fund Real Estate Development Sector Bets

The following table contains detailed data on these crowded bets. Since in this sector hedge funds are “smart money,” large long (overweight) bets are most likely to outperform and large short (underweight) bets at most likely to do poorly:

Exposure (%) Net Exposure Share of Risk (%)
HF Sector Aggr. Sector Aggr. % $mil Days of Trading
HHC Howard Hughes Corporation 28.47 15.98 12.49 326.5 17.5 36.73
CBG CBRE Group, Inc. Class A 52.28 26.54 25.74 672.7 10.8 27.58
JLL Jones Lang LaSalle Incorporated 0.14 15.21 -15.07 -393.9 -8.5 12.86
JOE St. Joe Company 0.04 4.94 -4.91 -128.2 -13.5 8.82
ALEX Alexander & Baldwin, Inc. 0.00 4.71 -4.71 -123.2 -13.5 5.38
HTH Hilltop Holdings Inc. 1.35 4.86 -3.51 -91.8 -18.3 4.29
KW Kennedy-Wilson Holdings, Inc. 3.60 6.11 -2.51 -65.6 -7.6 1.19
TRC Tejon Ranch Co. 3.36 1.55 1.81 47.2 37.9 0.77
EACO EACO Corporation 0.00 0.22 -0.22 -5.7 -436.1 0.65
FOR Forestar Group Inc. 0.62 1.66 -1.05 -27.3 -5.3 0.42
FCE.A Forest City Enterprises, Inc. Class A 8.78 10.56 -1.78 -46.5 -1.9 0.35
SBY Silver Bay Realty Trust Corp. 0.07 1.68 -1.61 -42.0 -8.4 0.23
AVHI A V Homes Inc 0.26 0.87 -0.61 -15.8 -28.7 0.20
MLP Maui Land & Pineapple Company, Inc. 0.00 0.29 -0.29 -7.5 -132.0 0.10
CTO Consolidated-Tomoka Land Co. 0.16 0.77 -0.61 -15.9 -24.5 0.09
RDI Reading International, Inc. Class A 0.02 0.54 -0.52 -13.7 -14.2 0.08
ABCP AmBase Corporation 0.00 0.15 -0.15 -3.8 -130.1 0.06
AHH Armada Hoffler Properties, Inc. 0.00 0.59 -0.59 -15.5 -9.4 0.06
OMAG Omagine, Inc. 0.00 0.07 -0.07 -1.9 -24.7 0.05
FVE Five Star Quality Care, Inc. 0.26 0.49 -0.23 -6.1 -5.1 0.04
Other Positions 0.01 0.07
Total 100.00

Real Estate Development is not the only sector where hedge funds excel. Crowded Coal, Hotels, and Forest Product sector ideas have also done well. Skills vary within each sector: The most skilled funds persistently generate returns in excess of the crowd, while the least skilled funds persistently fall short. Performance analytics built on robust risk models help investors and allocators reliably identify each.

Conclusions

  • With proper data, attention to hedge fund crowding prevents “unexpected” volatility and losses.
  • Market efficiency and tractability vary across sectors – crowded hedge fund bets do poorly in most sectors, but do well in some.
  • Investors should avoid crowded ideas in sectors of persistent hedge fund underperformance, such as Miscellaneous Metals and Mining.
  • Investors can embrace crowded ideas in sectors of persistent hedge fund outperformance, such as Real Estate Development.
  • Funds with significant and persistent stock picking skills exist in most sectors, even those with generally poor hedge fund performance. AlphaBetaWorks’ Skill Analytics identify best overall and sector-specific stock pickers.
The information herein is not represented or warranted to be accurate, correct, complete or timely.
Past performance is no guarantee of future results.
Copyright © 2012-2015, AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved.
Content may not be republished without express written consent.
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Hedge Fund Crowding – Q3 2014

U.S. hedge funds share a few systematic and idiosyncratic long bets. These crowded bets are the main sources of aggregate hedge fund relative performance and of many individual funds’ returns. We survey the risk factors and the stocks behind most of Q3 2014 hedge fund herding.

Investors should treat crowded ideas with caution: Due to the congestion of their hedge fund investor base, crowded stocks tend to be more volatile and are vulnerable to mass selling. In addition, the risk-adjusted performance of consensus bets has been disappointing.

Identifying Crowding

This piece follows the approach of our earlier articles on fund crowding: We created an aggregate position-weighted portfolio (HF Aggregate) consisting of popular securities held by approximately 500 U.S. hedge funds with medium to low turnover. We then evaluated the HF Aggregate risk relative to the U.S. Market (Russell 3000) using AlphaBetaWorks’ Statistical Equity Risk Model and looked for evidence of crowding. Finally, we analyzed risk and calculated each fund’s tracking error relative to HF Aggregate to see which most closely resembled it.

Hedge Fund Aggregate Risk

The Q3 2014 HF Aggregate had 2.7% estimated future tracking error relative to the Market. Risk was evenly split between factor (systemic) and residual (idiosyncratic) bets:

 Source Volatility (%) Share of Variance (%)
Factor 1.99 52.64
Residual 1.89 47.36
Total 2.74 100

This 2.7% tracking error estimate decreased by a fifth since our Q2 2014 estimate of 3.3%.

The HF Aggregate is nearly passive and will have a very hard time earning a typical fee. Because of this, investing in a broadly diversified portfolio of long-biased hedge funds is almost certainly a bad idea.

Hedge Fund Factor (Systemic) Crowding

Below are HF Aggregate’s (red) most significant factor exposures relative to the U.S. Market (gray):

Chart of the current and historical exposures of U.S. Hedge Fund Aggregate to factors contributing most to its risk relative to the U.S. Market.

Factors Contributing Most to the Relative Risk for U.S. Hedge Fund Aggregate

We now consider the sources of HF Aggregate’s factor (systematic) variance relative to the U.S. Market. These are the components of the Factor Volatility in the above table. Market (higher beta) and Oil bets are responsible for over 80% of the factor risk relative to the U.S. Market:

Chart of the variance contribution for factors contributing most to the relative risk of the U.S. Hedge Fund Aggregate

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

The HF Aggregate has become considerably more systematically crowded since Q2 2014: The following factors are the top contributors to the Q3 2014 relative systematic risk:

Factor HF Relative Exposure (%) Portfolio Variance (%²) Share of Systematic Variance (%)
Market 11.23 2.34 59.10
Oil Price 2.52 1.05 26.66
Finance -7.04 0.33 8.46
Utilities -3.19 0.24 6.11
Industrial 5.27 0.14 3.64
Other Factors -0.14 -3.97
Total 3.96 100.00

The following were the top contributors to the Q2 2014 relative systematic risk:

Factor HF Relative Exposure (%) Portfoio Variance (%²) Share of Systematic Variance (%)
Market 14.64 4.01 65.41
Size -9.93 0.90 14.61
Utilities -3.40 0.32 5.25
Technology 6.46 0.27 4.44
Oil Price 0.62 0.23 3.68
Other Factors 0.40 6.61
Total 6.13 100.00

Note that, following the poor performance of this factor throughout 2014, the short Size (small-cap) bet has been liquidated. Instead, hedge funds increased their long oil exposure by almost 2%. This crowded long oil bet has been another costly mistake.

Hedge Fund Residual (Idiosyncratic) Crowding

Turning to HF Aggregate’s residual variance relative to the U.S. Market, just seven stocks are responsible for half of the relative residual (idiosyncratic) risk:

Chart of the contribution to relative residual variance of the most significant residual (stock-specific) bets of the U.S. Hedge Fund Aggregate

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

These stocks may be wonderful individual investments, but they have a lot of sway in the way HF Aggregate and individual funds closely matching it will move. They will also be affected by the whims of capital allocation into hedge funds as an asset class. Investors should be ready for seemingly inexplicable volatility in these names. The list is mostly unchanged from the previous quarter:

Symbol Name Exposure (%) Share of Idiosyncratic Variance (%)
LNG Cheniere Energy, Inc. 1.61 15.28
VRX Valeant Pharmaceuticals International, Inc. 2.36 9.76
MU Micron Technology, Inc. 1.45 6.34
AGN Allergan, Inc. 2.82 6.08
BIDU Baidu, Inc. Sponsored ADR Class A 1.30 3.83
HTZ Hertz Global Holdings, Inc. 1.36 3.68
CHTR Charter Communications, Inc. Class A 1.68 3.67
EBAY eBay Inc. 1.62 2.58
AIG American International Group, Inc. 1.37 2.17
CA:CP Canadian Pacific Railway 1.74 2.02
SHPG Shire PLC Sponsored ADR 1.28 1.70

Investors should be especially careful and perform particularly thorough due-diligence when investing in crowded names, since any losses will be magnified when hedge funds rush for the exits. Fund allocators should thoroughly investigate hedge fund managers’ crowding to avoid investing in a pool of undifferentiated bets.

AlphaBetaWorks assists in both tasks: Our sector crowding reports identify hedge fund herding in each equity sector. Our hedge fund crowding data identifies manager skill and differentiation and is predictive of future performance.

Summary

  • There is both factor (systematic/market) and residual (idiosyncratic/security-specific) crowding of long hedge fund portfolios.
  • Hedge funds have become more crowded and more passive in Q3 2014.
  • The main sources of factor crowding are: Market (higher beta) and Oil.
  • The main sources of residual crowding are: LNG, AGN, VRX, MU, BIDU, and AIG.
  • Our research reveals that, collectively, hedge funds’ long U.S. equity portfolios tend to generate negative risk-adjusted returns. Crowded bets tend to disappoint and hedge fund investors should pay close attention to crowding before allocating capital.
The information herein is not represented or warranted to be accurate, correct, complete or timely.
Past performance is no guarantee of future results.
Copyright © 2012-2015, AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved.
Content may not be republished without express written consent.
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Smart Beta and Market Timing

Why Returns-Based Style Analysis Breaks for Smart Beta Strategies

Smart beta (SB) strategies tend to vary market beta and other factor exposures (systematic risk) over time. Consequently, market timing is an important source of their risk-adjusted returns, at times more significant than security selection. We have previously discussed that returns-based style analysis (RBSA) and similar methods fail for portfolios that vary exposures. Errors are most pronounced for the most active funds:

  • Estimates of a fund’s historical and current systematic risks may be flawed.
  • Excellent low-risk funds may be deemed poor.
  • Poor high-risk funds may be deemed excellent.

Due to the variation in Smart Beta strategies’ exposures over time, returns-based methods tend to fail for these strategies as well.

Three Smart Beta Strategies

We analyze historical risk of three SB strategies as implemented by the following ETFs:

SPLV indexes 100 stocks from the S&P 500 with the lowest realized volatility over the past 12 months. PRF indexes the largest US equities based on book value, cash flow, sales, and dividends. SPHQ indexes the constituents of the S&P 500 with stable earnings and dividend growth.

All three smart beta strategies varied their factor exposures including their market exposures.

Low Volatility ETF (SPLV) – Market Timing

The low-volatility smart beta strategy has varied its market exposure significantly, increasing it by half since 2011. As stocks with the lowest volatility – and their risk – changed over time, the fund implicitly timed the broad equity market.  The chart below depicts the market exposure of SPLV over time:

Chart of this historical U.S. market exposure of the low volatility smart bet (SB) strategy as implemented by PowerShares S&P 500 Low Volatility Portfolio ETF (SPLV)

PowerShares S&P 500 Low Volatility Portfolio ETF (SPLV) – Historical U.S. Market Exposure

Low Volatility ETF (SPLV) – Historical Factor Exposures

SPLV’s market exposure fluctuates due to the changes in its sector bets. Since the market betas of sectors differ from one another, as sector exposures vary so does the fund’s market exposure:

Chart of the historical exposures to significant risk factors of the low volatility smart bet (SB) strategy as implemented by PowerShares S&P 500 Low Volatility Portfolio ETF (SPLV)

PowerShares S&P 500 Low Volatility Portfolio ETF (SPLV) – Significant Historical Factor Exposures

Low Volatility ETF (SPLV) – Returns-Based Analysis

The chart below illustrates the returns-based analysis of SPLV. The regression of SPLV’s monthly returns against U.S. Market’s monthly returns estimates the fund’s U.S. Market factor exposure (beta) at 0.50 – significantly different from the historical risk observed above:

Chart of the regression of the historical returns of PowerShares S&P 500 Low Volatility Portfolio ETF (SPLV) against the Market

PowerShares S&P 500 Low Volatility Portfolio ETF (SPLV) – Historical Returns vs. the Market

This estimate of beta understates SPLV’s historical market beta (0.55) by a tenth and current market beta (0.70) by more than a third. RBSA thus fails to evaluate current and historical risk of the low volatility smart beta strategy. Performance attribution and all other analysis that relies on estimates of historical factor exposures will also fail.

Fundamental ETF (PRF) – Market Timing

The market risk of the Fundamental ETF has been remarkably constant, except from 2009 to 2010. Back in 2009 PRF increased exposure to high-beta (mostly financial) stocks in a spectacularly prescient act of market timing:

Chart of the historical exposures of the fundamental smart beta (SB) strategy as implemented by the PowerShares FTSE RAFI US 1000 Portfolio ETF (PRF) to U.S. and Canadian Markets

PowerShares FTSE RAFI US 1000 Portfolio ETF (PRF) – Historical Market Exposure

Fundamental ETF (PRF) – Historical Factor Exposures

The historical factor exposure chart for PRF illustrates this spike in Finance Factor exposure from the typical 20-30% range to over 50% and the associated increase in U.S. Market exposure:

Chart of the exposures of the fundamental smart beta (SB) strategy as implemented by the PowerShares FTSE RAFI US 1000 Portfolio ETF (PRF) to significant risk factors

PowerShares FTSE RAFI US 1000 Portfolio ETF (PRF) – Significant Historical Factor Exposures

This 2009-2010 exposure spike generated a significant performance gain for the fund. PRF made approximately 20% more than it would have with constant factor exposures, as illustrated below:

Chart of the historical return from market timing (variation in factor exposures) of the PowerShares FTSE RAFI US 1000 Portfolio ETF (PRF)

PowerShares FTSE RAFI US 1000 Portfolio ETF (PRF) – Historical Risk-Adjusted Return from Market Timing

By contrast, PRF’s long-term risk-adjusted return from security selection is insignificant:

Chart of the historical returns from security selection (stock picking) of the PowerShares FTSE RAFI US 1000 Portfolio ETF (PRF)

PowerShares FTSE RAFI US 1000 Portfolio ETF (PRF) – Historical Risk-Adjusted Return from Security Selection

Factor timing turns out to be more important for the performance of some smart beta strategies than security selection.

Fundamental ETF (PRF) – Returns-Based Analysis

The returns-based analysis of PRF estimates historical U.S. market beta around 1.05:

Chart of the regression of the returns of PowerShares FTSE RAFI US 1000 Portfolio ETF (PRF) against the U.S. Market

PowerShares FTSE RAFI US 1000 Portfolio ETF (PRF) – Historical Returns vs. the Market

This 1.05 beta estimate only slightly overstates the fund’s current and historical betas, but misses the 2009-2010 exposure spike. Returns-based analysis thus does a decent job evaluating the average risk of the fundamental indexing smart beta strategy, but fails in historical attribution.

Quality ETF (SPHQ) – Market Timing

The market exposure of the quality smart beta strategy (SPHQ) swung wildly before 2011. It has been stable since:

Chart of the U.S. and Canadian Market exposures of the quality smart beta (SB) strategy as implemented by the PowerShares S&P 500 High Quality Portfolio ETF (SPHQ)

PowerShares S&P 500 High Quality Portfolio ETF (SPHQ) – Historical Market Exposure

Quality ETF (SPHQ) – Historical Factor Exposures

As with the other smart beta strategies, market timing by SPHQ comes from significant variations in sector bets:

Chart of the historical exposures of the quality smart beta (SB) strategy as implemented by the PowerShares S&P 500 High Quality Portfolio ETF (SPHQ) to significant risk factors

PowerShares S&P 500 High Quality Portfolio ETF (SPHQ) – Significant Historical Factor Exposures

Quality ETF (SPHQ) – Returns-Based Analysis

The returns-based analysis of SPHQ estimates historical U.S. market beta around 0.86:

Chart of the regression of the historical returns of PowerShares S&P 500 High Quality Portfolio ETF (SPHQ) against the U.S. Market

PowerShares S&P 500 High Quality Portfolio ETF (SPHQ) – Historical Returns vs. the Market

Given the large variation in SPHQ’s risk over time, this 0.86 beta estimate understates the average historical beta but slightly overstates the current one. While the current risk estimate is close, RBSA fails for historical risk estimation and performance attribution.

Conclusions

  • Low volatility indexing, fundamental indexing, and quality indexing smart beta strategies vary market and other factor exposures (systematic risk) over time.
  • Due to exposure variations over time, returns-based style analysis and similar methods tend to fail for smart beta strategies:
    • Funds’ historical systematic risk estimates are flawed.
    • Funds’ current systematic risk estimates are flawed.
    • Performance attribution and risk-adjusted performance estimates are flawed.
  • Analysis and aggregation of factor exposures of individual holdings throughout a portfolio’s history with a capable multi-factor risk model produces superior risk estimates and performance attribution.
The information herein is not represented or warranted to be accurate, correct, complete or timely.
Past performance is no guarantee of future results.
Copyright © 2012-2015, AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved.
Content may not be republished without express written consent.
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Returns-Based Style Analysis – Overfitting and Collinearity

Plagued by overfitting and collinearity, returns-based style analysis frequently fails, confusing noise with portfolio risk.

Returns-based style analysis (RBSA) is a common approach to investment risk analysis, performance attribution, and skill evaluation. Returns-based techniques perform regressions of returns over one or more historical periods to compute portfolio betas (exposures to systematic risk factors) and alphas (residual returns unexplained by systematic risk factors). The simplicity of the returns-based approach has made it popular, but it comes at a cost – RBSA fails for active portfolios. In addition, this approach is plagued by the statistical problems of overfitting and collinearity, frequently confusing noise with systematic portfolio risk. 

Returns-Based Style Analysis – Failures for Active Portfolios

In an earlier article we illustrated the flaws of returns-based style analysis when factor exposures vary, as is common for active funds:

  • Returns-based analysis typically yields flawed estimates of portfolio risk.
  • Returns-based analysis may not even accurately estimate average portfolio risk.
  • Errors will be most pronounced for the most active funds:
    • Skilled funds may be deemed unskilled.
    • Unskilled funds may be deemed skilled.

These are not the only flaws. We now turn to the subtler and equally critical issues – failures in the underlying regression analysis itself. We use a recent Morningstar article as an example.

iShares Core High Dividend ETF (HDV) – Returns-Based Style Analysis

A recent Seeking Alpha article provides an excellent illustration of problems created by overfitting and collinearity. In this article, Morningstar performed returns-based style analysis of iShares Core High Dividend ETF (HDV).

Morningstar estimated the following factor exposures for HDV using the Carhart model:

Morningstar: Returns-Based Analysis of the iShares Core High Dividend ETF (HDV) Using the Carhart Model

iShares Core High Dividend ETF (HDV) – Estimated Factor Exposures Using the Carhart Model – Source: Morningstar

The Mkt-RF coefficient, or loading, is HDV’s estimated market beta. A beta value of 0.67 means that given a +1% change in the market HDV is expected to move by +0.67%, everything else held constant.

The article then performs RBSA using an enhanced Carhart + Quality Minus Junk (QMJ) model:

Morningstar: Returns-Based Analysis of iShares Core High Dividend ETF (HDV) Using the Carhart + Quality Minus Junk (QMJ) Model

iShares Core High Dividend ETF (HDV) – Estimated Factor Exposures Using the Carhart + Quality Minus Junk (QMJ) Model – Source: Morningstar

With the addition of the QMJ factor, the market beta estimate increased by a third from 0.67 to 0.90. Both estimates cannot be right. Perhaps the simplicity of the Carhart model is to blame and the more complex 5-factor RBSA is more accurate?

iShares Core High Dividend ETF (HDV) – Historical Factor Exposures

Instead of Morningstar’s RBSA approach, we analyzed HDV’s historical holdings using the AlphaBetaWorks’ U.S. Equity Risk Model. For each month, we estimated the U.S. Market exposures (betas) of individual positions and aggregated these into monthly estimates of portfolio beta:

Chart of the historical market exposure (beta) of iShares Core High Dividend ETF (HDV)

iShares Core High Dividend ETF (HDV) – Historical Market Exposure (Beta)

Over the past 4 years, HDV’s beta varied in a narrow range between 0.50 and 0.62.

Both of the above returns-based analyses were off, but the simpler Carhart model did best. It turns out the simpler and a less sophisticated returns-based model is less vulnerable to the statistical problems of multicollinearity and overfitting. The only way to find out that returns-based style analysis failed was to perform holdings-based analysis using a multi-factor risk model.

Statistical Problems with Returns-Based Analysis

Multicollinearity

Collinearity (Multicollinearity) occurs when risk factors used in returns-based analysis are highly correlated with each other. For instance, small-cap stocks tend to have higher beta than large-cap stocks, so the performance of small-cap stocks relative to large-cap stocks is correlated to the market.

Erratic changes in the factor exposures for various time periods, or when new risk factors are added, are signs of collinearity. These erratic changes make it difficult to pin down factor exposures and are signs of deeper problems:

A principal danger of such data redundancy is that of overfitting in regression analysis models.
-Wikipedia

Overfitting

Overfitting is a consequence of redundant data or model over-complexity. These are common for returns-based analyses which usually attempt to explain a limited number of return observations with a larger number of correlated variable observations.

An overfitted returns-based model may appear to describe data very well. But the fit is misleading – the exposures may be describing noise and will change dramatically under minor changes to data or factors. A high R squared from returns-based models may be a sign of trouble, rather than a reassurance.

As we have seen with HDV, exposures estimated by RBSA may bear little relationship to the portfolio risk. Therefore, all dependent risk and skill data will be flawed.

Conclusions

  • When a manager does not vary exposures to the market, sector, and macroeconomic factors, returns-based style analysis (RBSA) using a parsimonious model can be effective.
  • When a manager varies bets, RBSA typically yields flawed estimates of portfolio risk.
  • Even when exposures do not vary, returns-based style analysis is vulnerable to multicollinearity and overfitting:
    • The model may capture noise, rather than the underlying factor exposures.
    • Factor exposures may vary erratically among estimates.
    • Estimates of portfolio risk will be flawed.
    • Skilled funds may be deemed unskilled.
    • Unskilled funds may be deemed skilled.
  • Holdings-based analysis using a robust multi-factor risk model is superior for quantifying fund risk and performance.
The information herein is not represented or warranted to be accurate, correct, complete or timely.
Past performance is no guarantee of future results.
Copyright © 2012-2015, AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved.
Content may not be republished without express written consent.
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When “Smart Beta” is Simply High Beta

WisdomTree Mid Cap Earnings Fund (EZM) vs. PowerShares Dynamic Large Cap Value Portfolio (PWV)

Many “smart beta” funds are merely high-beta, delivering no value over traditional index funds. On the other hand, some smart beta strategies are indeed exceptional and worth their fees.

Most analyses of enhanced index funds and smart beta strategies lack a rigorous approach to risk evaluation and performance attribution. Consequently, risky and mediocre funds are mislabeled as excellent, while conservative and exceptional funds are wrongly considered mediocre. Investors relying on simplistic analyses may end up with mediocre funds, hidden risks, and subpar performance.

The Not-So-Smart Beta

Some smart beta funds deliver consistent outperformance with high liquidity and low tracking error. Others merely deliver high market beta or high exposures to other common risk factors. Analyses of these funds’ performance are usually simplistic, failing to differentiate between the two groups.

Enhanced indexing and smart beta strategies are usually more active than the underlying indices. This can cause their risk to vary dramatically over time. For instance, a fund’s market beta can vary by 40-50% over a few years. This variation makes it difficult to determine whether a particular strategy is smart or merely risky. When a market correction arrives, risky funds suffer outsized losses.

Many estimate the beta of a fund by fitting its returns to the market or a benchmark using a regression, a technique known as returns-based style analysis. This is a flawed approach, which fails to accurately estimate the risk of active strategies. We discussed the flaws of returns-based style analysis in earlier articles.

A robust approach to estimating a fund’s historical risk and risk-adjusted performance is to evaluate its holdings over time. At each period, the risk of individual holdings is aggregated to estimate the risk of the fund. This is AlphaBetaWorks’ approach, implemented in our Performance Analytics Platform. Our analysis reveals that many “smart beta” funds are merely high-beta. These funds deliver no value over traditional index funds. On the other hand, some smart beta strategies are indeed exceptional and worth the fees they charge.

WisdomTree Mid Cap Earnings Fund (EZM) – Historical Risk

On the surface, the returns of the WisdomTree Mid Cap Earnings Fund (EZM) appear strong. The fund has dramatically outperformed its broad benchmark, the Russell Midcap Index (IWR):

Chart of the Cumulative Return of WisdomTree Mid Cap Earnings Fund (EZM) and of the  Benchmark (IWR)

Cumulative Return of WisdomTree Mid Cap Earnings Fund (EZM) vs the Benchmark (IWR)

However, this is nominal outperformance not risk-adjusted outperformance.

The main source of security risk and return is market risk, or beta. With this in mind, we analyzed the holdings of EZM and IWR during each historical period, calculated their holdings’ risk, and calculated the total risk of each fund. Not surprisingly, IWR’s beta has been stable, averaging 1.09 (109% of the risk of U.S. Market). Meanwhile, EZM’s beta has varied in a wide range, averaging 1.18 (118% of the risk of U.S. Market):

Chart of the historical beta of  the WisdomTree Mid Cap Earnings Fund (EZM) compared to the historical beta of the Benchmark (IWR)

Historical Beta of WisdomTree Mid Cap Earnings Fund (EZM) vs the Benchmark (IWR)

EZM had higher returns, but it also consistently took more market risk. With greater risk comes greater volatility, and a down cycle will affect EZM more.

To determine its risk-adjusted return, we must compare the performance of EZM to the performance of a passive portfolio with the same factor exposures.  Below are EZM’s current and historical factor exposures:

Chart of the historical and current factor exposures of the WisdomTree Mid Cap Earnings Fund (EZM)

Historical Factor Exposures of WisdomTree Mid Cap Earnings Fund (EZM)

WisdomTree Mid Cap Earnings Fund (EZM) – Risk-Adjusted Performance

Instead of owning EZM, investors could have owned a passive portfolio with similar risk (a passive replicating portfolio). If EZM had profitably timed the market (varied its risk) or selected securities, it should have outperformed.

EZM’s risk-adjusted performance closely matches a passive replicating portfolio. Relative to its passive equivalent, EZM has generated negligible active return (abReturn in the following chart):

Chart of the historical passive and active returns of the WisdomTree Mid Cap Earnings Fund (EZM)

Historical Passive and Active Return of WisdomTree Mid Cap Earnings Fund (EZM)

PowerShares Dynamic Large Cap Value Portfolio (PWV) – Risk-Adjusted Performance

Let’s contrast the performance of EZM with the results of another smart beta option: PowerShares Dynamic Large Cap Value Portfolio (PWV).

PWV has also varied U.S. Market exposure by approximately 40%:

Chart of the historical and current factor exposures of the PowerShares Dynamic Large Cap Value Portfolio (PWV)

Historical Factor Exposures of PowerShares Dynamic Large Cap Value Portfolio (PWV)

PWV has consistently outperformed its passive replicating portfolio and produced strong active returns due to market timing and security selection:

Chart of the historical passive and active returns of the PowerShares Dynamic Large Cap Value Portfolio (PWV)

Historical Passive and Active Return of PowerShares Dynamic Large Cap Value Portfolio (PWV)

Conclusion

Performance evaluation tools lacking accurate insights into risk may rank the better-performing but riskier EZM ahead of the excellent PWV. The accurate picture of their relative risk-adjusted performance emerges once both funds’ historical holdings are examined with a multi-factor risk model and their excess returns distilled.

Unsuspecting investors relying on simplistic analysis may conclude that a risky and mediocre fund is excellent while a conservative and exceptional fund is mediocre. At best, they will face higher-than-anticipated risks. At worst, they will get a nasty surprise when a correction comes.

The information herein is not represented or warranted to be accurate, correct, complete or timely.
Past performance is no guarantee of future results.
Copyright © 2012-2015, 
AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved.
Content may not be republished without express written consent.
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Upgrading Fund Active Returns

And Not Missing Out

Maybe your fund took extra risk to keep up with its benchmark. Maybe your fund should have made more – much more – given the risks taken. By the time market volatility reveals underlying exposures, it may be too late to avoid severe losses. There is a better way: Investors can continuously monitor a fund’s risk, the returns it should be generating, and the value it creates. This value should matter most to investors and allocators. Regrettably, most fund analysis tools and services pay no attention to it.

To illustrate, we analyze two funds: one that did much worse than it should have, and one that did better.

PRSCX – Negative Active Returns

The T. Rowe Price Science & Technology Fund (PRSCX) manages approximately $3 billion. This fund generally tracks its benchmark and it gets 3 star rating from a popular service. Notwithstanding this, PRSCX has produced persistently negative active returns. Given its historical risk, PRSCX should have made investors far more money: Over the past ten years, an investor would have made 50-80% more owning a passive portfolio with PRSCX’s risk profile.

Chart of the historical cumulative passive and active returns of T. Rowe Price Science & Technology Fund (PRSCX)

T. Rowe Price Science & Technology Fund (PRSCX) – Passive and Active Return History

While we seem to bolster arguments for passive investing, reality is more complex: Active returns (both positive and negative) persist over time. Thus, upgrading from PRSCX to a fund with persistently positive active returns is a superior move. We will provide one candidate.

PRSCX – Historical Risk

The chart below shows PRSCX’s historical risk (exposures to significant risk factors). The red dots indicate monthly exposures (as a percentage of assets) over the past 10 years; the black diamonds indicate latest exposures:

Chart of the historical exposures of T. Rowe Price Science & Technology Fund (PRSCX) to significant risk factors

T. Rowe Price Science & Technology Fund (PRSCX) – Exposure to Significant Risk Factors

PRSCX varied its exposures over time. U.S. Market is the most important exposure, reaching 200% (market beta of 2) at times. As expected for a technology fund, its U.S. Technology exposure has been near 100%. Also note PRSCX’s occasional short bond exposure. Many equity funds carry large hidden bond bets due to the risk profile of their equity holdings. Most investors and portfolio managers are no aware of these bets. For these funds, bond risk is a key driver of portfolio returns and volatility.

PRSCX – Historical Active Returns

The above exposures define a passive replicating portfolio matching PRSCX’s risk. The fund manager’s job is to outperform this passive alternative by generating active returns.

To isolate active returns, we quantify passive factor exposures, estimate the passive return, and then calculate the remaining active return – αβReturn. We further break down αβReturn into risk-adjusted return from security selection, or stock picking (αReturn), and market timing (βReturn):

Component 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Total 1.6 2.46 7.1 11.88 -43.8 67.83 21.25 -4.51 6.25 43.7 9.39
Passive -0.69 1.4 5.45 19.26 -46.13 77.52 23.21 -3.42 20.36 51.54 13.61
αβReturn 0.98 -0.69 -2.2 -7.45 2.71 -11.61 -2.22 -8.24 -13.76 -14.14 -5.84
αReturn -1.95 -3.29 -6.45 -2.79 7.92 0.78 4.17 -12.31 -11.47 -4.54 1.11
βReturn 2.94 2.6 4.25 -4.66 -5.2 -12.39 -6.39 4.07 -2.29 -9.6 -6.95
Undefined 1.3 1.75 3.85 0.07 -0.38 1.92 0.26 7.15 -0.35 6.3 1.61

Note that we are unable to account for trades behind some of the returns – the “Undefined” component. It may be due to private securities or intra-period trading; it may be passive or active. Yet, even if we assume that all undefined returns above are active, PRSCX still delivered persistently negative αβReturn over the past ten years. Furthermore, the compounding of negative αβReturn leaves investors missing out on 50-80% in gains.

FSCSX – An Upgrade Option with Similar Historical Risk

While a passive portfolio would have been superior to PRSCX, it is not the best upgrade. Allocators and investors can do better owning a fund with consistently positive αβReturns, since αβReturns persist. One candidate is Fidelity Select Software & Computer Services Portfolio (FSCSX):

Chart of the historical exposures of Fidelity Select Software & Computer Services Portfolio (FSCSX) to significant risk factors

Fidelity Select Software & Computer Services Portfolio (FSCSX) – Exposure to Significant Risk Factors

Currently, FSCSX and PRCSX have similar exposures. AlphaBetaWorks’ risk analytics estimate the current annualized tracking error between the two funds at a 5.29% (about the same volatility as bonds, and less than one half of market volatility).

FSCSX – Historical Active Returns

FSCSX’s 3-year trailing average annual return of 23% is slightly ahead of PRSCX’s 20%. But most importantly, given its lower historical risk, FSCSX has delivered positive αβReturns versus PRSCX’s significantly negative ones. The chart below shows FSCSX’s ten-year performance. The purple area is the positive αβReturn. The gray area is FSCSX’s passive return:

Chart of the historical cumulative passive and active returns of Fidelity Select Software & Computer Services Portfolio (FSCSX)

Fidelity Select Software & Computer Services Portfolio (FSCSX) – Passive and Active Return History

FSCSX is superior to a passive portfolio with similar risk and to PRSCX. Mind you, this is not a sales pitch for FSCSX but merely a consequence of its positive αβReturn and αβReturn persistence.

Few fund investors and allocators possess the tools to quantify active returns. Yet, this knowledge is an essential competitive advantage, leading to improved client returns, client retention, and asset growth. Unfortunately, many are content to pick funds based on past nominal returns and to suffer the consequences: picking yesterday’s winners tends to pick tomorrow’s losers. AlphaBetaWorks spares clients from the data processing headaches, financial modeling, and statistical analysis of thousands of portfolios, delivering predictive risk and skill analytics on thousands of funds.

Conclusions

  • Analyzing a fund’s performance relative to a benchmark ignores the most important question: What should you have made given its risk?
  • Some mutual funds produce persistently negative active returns; others produce persistently positive active returns.
  • Upgrading from a fund with persistently negative active return (αβReturn) to a replicating passive portfolio tends to improve performance.
  • Upgrading from a passive portfolio to a fund with persistently positive αβReturn also tends to improve performance.
  • Tools that accurately estimate fund risk and active returns provide enduring competitive advantages for investors and professional allocators, leading to improved client returns, client retention, and asset growth.
The information herein is not represented or warranted to be accurate, correct, complete or timely.
Past performance is no guarantee of future results.
Copyright © 2012-2014, 
AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved.
Content may not be republished without express written consent.
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Hedge Fund E&P Crowding – Q2 2014

U.S. hedge funds share a few systematic and idiosyncratic long bets – a phenomenon called “crowding.” Hedge fund crowding within specific sectors can be heavy; bets on exploration and production (E&P) companies are particularly crowded. Hedge fund E&P bets are the subject of this article. Eight stocks are responsible for three quarters of the herding.

Crowding is costly to investors, fund managers, and allocators: over the past 10 years the aggregate hedge fund E&P portfolio underperformed the market E&P portfolio by 23% while taking more risk. The risk-adjusted return was even worse – a loss of 52%.

Identifying Hedge Fund E&P Crowding

To evaluate hedge fund (HF) exploration and production (E&P) herding, we followed the approach of our earlier work on aggregate and sector-specific hedge fund crowding: We created an aggregate position-weighted portfolio (HF E&P Aggregate) consisting of all exploration and production long equity positions reported by over 400 U.S. hedge funds with medium to low turnover. We then evaluated HF E&P Aggregate’s risk relative to the capitalization-weighted portfolio of E&P equities (Market E&P Aggregate) using AlphaBetaWorks’ Statistical Equity Risk Model.

Crowded E&P Stocks Underperform

Crowding hurts performance. HF E&P Aggregate had poor returns following the peak of the last energy cycle in 2008. Consequently, understanding Hedge Fund E&P crowding is vital to investors, fund managers, and allocators.

When the broad market and the E&P sector are doing well, crowded hedge fund E&P stocks generally outperform. However, these stocks generally underperform in the down cycle. This is a tell-tale sign of flocking to higher-risk stocks. This crowding towards higher market and sector betas is consistent with the aggregate systematic crowding of hedge funds:

Chart of the historical returns of Hedge Fund Exporation and Production (E&P) Aggregate and Market E&P Aggregate

Historical Return for Hedge Fund E&P Aggregate vs. Market E&P Aggregate

Indeed, we will see later that hedge fund E&P aggregate has both higher market exposure (market beta) and higher E&P sector exposure (E&P sector beta) than the market E&P Aggregate.

When we adjust for the systematic (factor) risk, we discover that the residual return of HF E&P aggregate due to security selection is even worse. Investors would have made 52% more over the past 10 years if they had invested in an ETF portfolio with similar factor risk:

Chart of the historical nominal, factor, and residual (risk-adjusted returns due to security selection ) of Hedge Fund Exploration and Production (E&P) Aggregate

Historical Hedge Fund E&P Aggregate, Factor, and Residual Returns

Hedge Fund E&P Risk

HF E&P Aggregate has a 5.0% estimated future tracking error relative to Market E&P Aggregate, primarily due to stock-specific bets:

Chart of the factor and residual contribution to the hedge fund E&P crowding, measured as the relative variance of Hedge Fund Exploration and Production (E&P) Aggregate

Sources of Relative Risk for Hedge Fund E&P Aggregate

Source Volatility (%) Share of Variance (%)
Factor 2.77 24.67
Residual 4.31 75.33
Total 4.96 100.00

The 5.0% tracking error means that HF E&P Aggregate’s annual return is forecasted to differ from Market E&P Aggregate’s by less than 5.0% two thirds of the time.

Hedge Fund Factor (Systematic) E&P Crowding

On the chart below, HF E&P Aggregate’s factor exposures (red) are similar to Market E&P Aggregate’s (gray), but tend to be higher. Hedge Funds tend to invest in names with higher market and sector betas, perhaps as a logical consequence of their compensation structure:

Exposure of Hedge Fund Exploraton and Production (E&P) Aggregate to factors contributing most to hedge fund E&P crowding, or risk relative to Market E&P Aggregate

Hedge Fund E&P Aggregate’s Exposure to Significant Risk Factors

Hedge Fund Residual (Idiosyncratic) E&P Crowding

Over 75% of the relative risk of HF E&P Aggregate is due to stock-specific (residual) bets. Below are the sources of HF E&P Aggregate’s relative residual variance. Three quarters of the estimated relative residual risk is due to only eight stocks:

Stocks contributing most to hedge fund E&P crowding: their contribution to the relative residual variance of Hedge Fund E&P Aggregate

Stocks Contributing Most to Relative Residual Variance of Hedge Fund E&P Aggregate

As with HF Energy Aggregate, some of the largest bets are not the stocks hedge funds own, but the stocks they don’t own. (For example, hedge funds are underweight COP, OXY, and EOG.) The crowded bets are likely to deliver negative risk-adjusted returns in flat or declining oil and gas producer cycle.

Position (%)
Symbol Name HF E&P
Aggregate
Market E&P
Aggregate
Relative Share of
R
isk (%)
ATHL Athlon Energy, Inc. 7.95 0.54 7.41 16.64
CHK Chesapeake Energy Corporation 9.63 2.41 7.23 14.96
COP ConocoPhillips 0.51 12.30 -11.79 12.21
OXY Occidental Petroleum Corporation 0.36 9.33 -8.97 9.18
EOG EOG Resources, Inc. 0.87 7.46 -6.60 6.65
CIE Cobalt International Energy, Inc. 3.31 0.87 2.44 5.11
CNQ Canadian Natural Resources Limited 6.06 0.00 6.06 4.58
EPE EP Energy Corp. Class A 11.31 0.66 10.65 4.55
TLM Talisman Energy Inc. 3.96 0.00 3.96 2.69
SD SandRidge Energy, Inc. 2.67 0.41 2.26 2.68
CLR Continental Resources, Inc. 0.09 3.43 -3.34 2.09
CA:CTA Crocotta Energy Inc. 0.18 0.00 0.18 1.93
WLL Whiting Petroleum Corporation 4.08 1.12 2.95 1.92
NBL Noble Energy, Inc. 0.33 3.29 -2.96 1.09
PXD Pioneer Natural Resources Company 6.12 3.83 2.28 1.02
MRO Marathon Oil Corporation 0.08 3.13 -3.05 0.95
OAS Oasis Petroleum Inc. 2.48 0.66 1.83 0.84
PRMRF Paramount Resources Ltd. Class A 1.26 0.00 1.26 0.81
APA Apache Corporation 1.63 4.49 -2.86 0.78
ATHOF Athabasca Oil Corporation 1.13 0.00 1.13 0.77

Of course, the poor long-term performance of the HF E&P aggregate does not mean that some individual equities will not do well. In fact, in the third quarter of 2014, ATHL was acquired. With half of the shares owned by hedge funds, ATHL was one of the largest positions of HF E&P Aggregate and the top idiosyncratic bet.

Summary

  • Hedge Fund Exploration and Production (E&P) Aggregate tends to have higher risk than Market E&P Aggregate.
  • Crowded Hedge Fund E&P stocks tend to underperform market aggregate over the long-term, in spite of higher risk.
  • Crowded Hedge Fund E&P stocks tend to generate negative risk-adjusted returns.
  • There is both factor (systematic/market) and residual (idiosyncratic/security-specific) crowding of long hedge fund E&P portfolios.
  • Over 80% of recent crowding is attributable to eight stocks: ATHL, CHK, COP, OXY, EOG, CIE, CNQ, and EPE.
The information herein is not represented or warranted to be accurate, correct, complete or timely.
Past performance is no guarantee of future results.
Copyright © 2012-2014, 
AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved.
Content may not be republished without express written consent.
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Top-Performing Hedge Fund Profile – Pershing Square

A Survey of Pershing Square’s Security Selection and Market Timing

Not all outperformance is true outperformance. There are many funds whose performance looks spectacular on the surface, but whose risk-adjusted performance is poor. This article takes a closer look at Pershing Square Capital Management, nominally one of the top performing funds throughout 2014 and over the long-term. We show that the risk-adjusted performance of Pershing’s reported long equity positions is, in fact, impressive. We also delve into sectors and factors behind these outstanding risk-adjusted results.

Risk-Adjusted Performance Defined

The AlphaBetaWorks (ABW) Performance Analytics Platform identifies risk-adjusted performance as αβReturn – performance relative to a passive replicating portfolio. αβReturn consists of performance due to security selection (αReturn) and market timing (βReturn). αReturn is the return a fund would have generated if markets were flat. βReturn is the return a fund generated by varying its factor exposures.

Pershing Square – Risk-Adjusted Returns

The fund’s 10-year long αβReturn is around 300%, while passive long return is over 500%. Below we illustrate Pershing’s overall active return (αβReturn) and passive return over the past ten years. The purple area highlights cumulative positive active return; cumulative passive return is the gray area underneath:

Chart of the historical active returns (market timing and security selection) of Pershing Square's reported long equity portfolio

Pershing Square Capital Management Historical Passive and Active Returns – Long Equity Portfolio

Below we provide the components of Pershing’s estimated annual returns. Active returns (αβReturns) are broken down into security selection, or stock picking, (αReturn) and market timing (βReturn). Most of the 2014 return is active: roughly two thirds from stock picking and one third from market timing.

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Total 130.78 31.65 29.23 -0.61 -33.27 52.18 32.09 2.74 21.18 35.03 31.21
Passive 25.06 5.20 15.92 4.62 -35.87 41.23 24.38 5.68 19.00 34.16 7.95
αβReturn 105.72 26.45 13.31 -5.23 2.60 10.94 7.71 -2.94 2.17 0.87 23.26
αReturn 124.96 39.02 10.13 -0.81 2.69 11.09 12.22 -5.25 -1.85 -0.36 14.62
βReturn -19.24 -12.57 3.19 -4.42 -0.09 -0.15 -4.51 2.31 4.02 1.24 8.63

We will now delve deeper into the sources of Pershing’s high risk-adjusted returns – the sectors and the factors that contributed most to αβReturn.

Pershing Square – Long Equity Security Selection

αReturn is the risk-adjusted return from security selection – the return a fund would have generated if markets were flat. Pershing’s recent and 10-year results are stellar:

Chart of the historical security selection return of Pershing Square's reported long equity portfolio

Pershing Square Capital Management Historical Security Selection Return – Long Equity Portfolio

Furthermore, Pershing’s trailing three-year annualized αReturn of 5% exceeds αReturns of 81% of its peers:

Chart of the distribution of security selection returns of U.S. medium turnover hedge funds' long equity portfolios relative to Pershing Square

Pershing Square Capital Management 3-year Sector Security Selection Return vs. Peers

Sector Security Selection

Digging deeper, most of the 2014 αReturn came from the Industrial and Consumer sectors. Over history, there have been a few years of especially large positive (and negative) sector-specific αReturns:

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Communications 1.20 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
Consumer 60.30 0.21 7.63 -0.18 0.75 2.75 8.74 1.01 -14.08 -3.97 3.11
Finance 63.47 38.81 2.50 -0.46 0.64 3.88 3.51 -10.26 2.67 -3.43 1.35
Health 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.80
Industrial 0.00 0.00 0.00 0.00 0.00 0.00 0.00 3.96 9.55 7.03 8.37
Miscellaneous 0.00 0.00 0.00 -0.17 0.00 0.00 0.00 0.00 0.00 0.00 0.00
Technology 0.00 0.00 0.00 0.00 1.30 4.47 -0.03 0.03 0.00 0.00 0.00

The Industrial sector is an area of consistent strength:

Chart of the security selection return of Pershing Square's  reported long equity positions in the industrial sector

Pershing Square Capital Management Industrial Sector Security Selection Return – Long Equity Portfolio

Pershing’s recent long positions in the industrial sector are:

APD Air Products and Chemicals, Inc.;
CA:CP Canadian Pacific Railway;
GB:PAH Platform Specialty Products Corp.

Pershing’s Consumer sector αReturn has been mixed over the past 3 years; it has been excellent over the long-term and in 2014:

Chart of the security selection  return of Pershing Square's reported long equity positions in the industrial sector

Pershing Square Capital Management Consumer Sector Security Selection Return – Long Equity Portfolio

Pershing’s recent long position in the consumer sector is:

BKW Burger King Worldwide, Inc.

Additional security-selection insights, such as position sizing skill and the analysis of portfolio scalability/overcapitalization, are available but beyond the scope of this article.

Pershing Square – Long Equity Market Timing

βReturn is the risk-adjusted return from market timing – the return due to variation in factor exposures over time. Positive βReturns occur when a manager takes on larger exposures to factors that subsequently generate larger-than-typical returns.

Pershing Square has varied factor and sector exposures dramatically over time. In the chart below, the red dots depict historical long portfolio factor exposures, while the diamonds show current exposures. Critically for skill evaluation, ABW’s non-market risk factors exclude all market effects, which enables sound performance attribution:

Chart of the historical exposures to systematic risk factor of  Pershing Square's reported long equity portfolio

Pershing Square Capital Management Historical Factor Exposures – Long Equity Portfolio

Pershing Square’s factor timing performance (βReturn) was negative from 2004 to 2006, largely flat through 2012, and positive since 2012:

Chart of the historical return due to market timing (factor exposure variation) of Pershing Square's reported long equity portfolio

Pershing Square Capital Management Historical Market Timing Return – Long Equity Portfolio

Similar to analyzing αReturns by sector, AlphaBetaWorks analyzes specific sources of βReturn to identify which factors the fund has timed successfully, or unsuccessfully. For example, In 2014 Pershing’s positive market-timing returns were due to:

  • Increased exposure to the U.S. Health, Canada Market, and Canada Industrial factors;
  • Decreased exposure to U.S. Consumer.

Pershing Square’s significant βReturn components, by year:

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
U.S. Consumer Sector -2.33 3.61 -0.57 -3.48 1.25 0.13 1.56 2.88 -0.47 -1.69 1.70
U.S. Health Sector 0.03 -0.07 0.05 -0.06 -0.02 -0.04 0.08 -0.14 -0.09 -0.23 2.11
USD FX 8.30 -8.77 -1.21 -1.41 -0.01 0.40 -1.23 -0.40 0.11 0.41 -1.05
Canada Market (Beta) -4.19 -3.70 -2.07 0.53 0.55 0.46 -2.37 0.98 0.61 4.18 2.40
Canada Industrial Sector -1.18 0.59 0.14 -1.28 -0.10 -0.42 -0.93 -0.80 1.27 4.67 3.68

Conclusion

  • Risk-adjusted performance is return above a passive portfolio replicating a fund’s typical risk profile.
  • Pershing Square exhibited strong risk-adjusted return from overall security selection (αReturn) and, recently, strong market/factor timing (βReturn).
  • Pershing Square recently exhibited especially strong risk-adjusted security selection returns (αReturns) in Consumer and Industrial sectors.
  • Pershing Square recently exhibited strong risk adjusted market timing returns (βReturns) due to increased exposures to U.S. Health, Canada Market, and Canada Industrial factors, and reduced exposure to U.S. Consumer factor.
  • Among its many advantages, our analysis provides allocators insights into manager core competencies. Portfolio managers, too, benefit from deeper understanding of their own – and their teams’ – strengths and weakness.
The information herein is not represented or warranted to be accurate, correct, complete or timely.
Past performance is no guarantee of future results.
Copyright © 2012-2014, 
AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved.
Content may not be republished without express written consent.
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Hedge Fund Energy Crowding – Q2 2014

U.S. hedge funds share a few systematic and idiosyncratic long bets – a phenomenon called “crowding.” Crowding exists within aggregate portfolios and within specific sectors. Energy bets are particularly crowded and are the subject of this article. Crowded bets are the main sources of hedge funds’ collective and many individual funds’ energy sector returns. Four risk factors (systematic bets) and six stocks (idiosyncratic bets) are behind three quarters of the herding.

Systematic (factor) hedge fund energy crowding primarily consists of:

  1. A bet against integrated energy companies;
  2. A bet on oil refiners;
  3. A bet on cheap international and against expensive U.S. producers;
  4. A bet on independent oil and gas producers.

Idiosyncratic (residual) hedge fund energy sector crowding primarily consist of exposures to CVI, XOM, CHK, ATHL, CVX, and YPF.

Combined, these bets account for three quarters of aggregate hedge fund long energy sector risk (tracking error) relative to the overall energy equity market.

Crowding has two vital implications: First, hedge fund impatience contributes to greater volatility in crowded bets. Second, hedge fund long energy equity portfolios tend to generate negative risk-adjusted returns.  Thus, consensus stock and factor picks are likely to disappoint.

Identifying Hedge Fund Energy Crowding

We created an aggregate position-weighted portfolio (HF Energy Aggregate) consisting of all energy sector equities held by over 400 U.S. hedge funds with medium to low turnover. The size of each position is the dollar value of its ownership by hedge funds. This process is similar to our earlier analysis of aggregate long hedge fund crowding.

We then evaluated HF Energy Aggregate’s risk relative to the capitalization-weighted portfolio of U.S. energy equities (Market Energy Aggregate) using AlphaBetaWorks’ Statistical Equity Risk Model. Finally, we analyzed HF Energy Aggregate’s systematic and idiosyncratic bets and looked for evidence of crowding.

Hedge Fund Energy Risk

HF Energy Aggregate has a 7.8% estimated future tracking error relative to Market Energy Aggregate, mostly due to factor bets:

Chart of the sources of relative variance of aggregate portfolio of hedge funds' long energy holdings

Sources of Relative Risk for Hedge Fund Energy Aggregate

Source Volatility (%) Share of Variance (%)
Factor 6.55 70.10
Residual 4.28 29.90
Total 7.83 100.00

HF Energy Aggregate’s annual return will differ from Market Energy Aggregate’s by more than 7.8% about one third of the time. Combined, hedge funds’ energy portfolios are active, and closet indexing is not a concern.

Hedge Fund Factor (Systematic) Energy Crowding

Below are HF Energy Aggregate’s most significant relative factor exposures (red). The benchmark (gray) is Market Energy Aggregate:

Chart of the factor exposures of the aggregate portfolio of hedge funds' long energy holdings to most influential risk factors

Factors Contributing Most to the Relative Risk for Hedge Fund Energy Aggregate

Below is the contribution of various factors to HF Energy Aggregate’s relative systematic risk. These are the components of the red “Factor Variance” in the first chart:

Chart of the contribution to systematic (factor) hedge funds energy crowding from the most significant risk factors

Factors Contributing Most to Relative Factor Variance of Hedge Fund Energy Aggregate

Four factors are responsible for 75% of the relative factor risk of Hedge Fund Energy Aggregate:

Exposure (%)
Factor HF Energy Aggregate Market Energy Aggregate Relative Share of Risk (%)
Integrated Oil 2.62 38.75 -36.13 23.01
Oil Refining and Marketing 30.87 13.18 17.68 17.79
Stat Factor 1 11.86 0.26 11.59 17.08
Oil and Gas Production 65.53 47.23 18.30 16.48

These bets have the following meanings:

  1. Integrated Oil – short bet on integrated energy companies;
  2. Oil Refining and Marketing – long bet on oil refiners;
  3. Stat Factor 1 – systematic risk not captured by the standard market risk factors;
  4. Oil and Gas Production – long bet on non-integrated oil and gas producers.

AlphaBetaWorks’ Statistical Factors (Stat Factors) capture systematic risks overlooked by traditional risk models. Statistical factors and exposures to them are estimated using factor analysis. Stat Factor 1 turns out to be a combination of bets on cheap international, and against expensive U.S. producers. Securities with the largest positive exposure to Stat Factor 1 are LukOil ADR (LUKOY), Rosneft GDR (GB:ROSN), and BP ADR (BP). Securities with the largest negative exposure to it are Range Resources (RRC) and Cabot Oil & Gas (COG):

Chart of exposure to statistical factors of various positions of hedge funds' aggregate long energy  portfolio

Hedge Fund Energy Positions’ Exposures to Stat Factors

Hedge Fund Residual (Idiosyncratic) Energy Crowding

Below are the sources of HF Energy Aggregate’s relative residual variance. These are the components of the blue “Residual Variance” in the first chart. Six stocks are responsible for over three quarters of the relative residual (idiosyncratic) risk of HF Energy Aggregate:

Chart of the contribution to total residual risk of the most significant positions of the aggregate hedge fund energy portfolio

Stocks Contributing Most to Relative Residual Variance of Hedge Fund Energy Aggregate

These stocks will have the most sway on HF Energy Aggregate, and many individual funds. Conversely, the funds’ impatience will also affect these stocks the most. Investors should be ready for seemingly inexplicable volatility, especially among smaller companies:

Position (%)
Symbol Name HF Energy Aggregate Market Energy Aggregate Relative Share of Risk (%)
CVI CVR Energy, Inc. 8.93 0.23 8.70 46.67
XOM Exxon Mobil Corporation 0.50 23.90 -23.39 16.98
CHK Chesapeake Energy Corporation 5.78 0.91 4.87 5.49
CVX Chevron Corporation 0.22 13.50 -13.28 4.64
ATHL Athlon Energy, Inc. 4.77 0.34 4.43 4.37
YPF YPF SA Sponsored ADR Class D 1.89 0.00 1.89 3.05
EPE EP Energy Corp. Class A 6.78 0.26 6.53 2.29
OXY Occidental Petroleum Corporation 0.22 4.47 -4.25 1.70
CIE Cobalt International Energy, Inc. 1.99 0.33 1.66 1.64
COP ConocoPhillips 0.31 5.61 -5.30 1.64
CNQ Canadian Natural Resources Limited 3.64 0.00 3.64 1.18
MWE MarkWest Energy Partners, L.P. 3.65 0.85 2.80 1.03
EOG EOG Resources, Inc. 0.52 3.23 -2.71 1.01
SD SandRidge Energy, Inc. 1.60 0.13 1.48 0.86
TLM Talisman Energy Inc. 2.38 0.00 2.38 0.78
RGP Regency Energy Partners LP 3.25 0.74 2.52 0.71
PXD Pioneer Natural Resources Company 3.67 1.68 1.99 0.62
WLL Whiting Petroleum Corporation 2.44 0.55 1.89 0.56
WPZ Williams Partners L.P. 3.21 1.39 1.83 0.35
ATLS Atlas Energy, L.P. 0.62 0.14 0.48 0.31

As with factor crowding, there is significant stock-specific crowding into a handful of popular names.

Summary

  • There is both factor (systematic/market) and residual (idiosyncratic/security-specific) crowding of long hedge fund energy portfolios.
  • The main sources of systematic crowding are three bets on energy sub-sectors and a relative value bet on international vs. domestic producers.
  • The main sources of stock-specific crowding are CVI, XOM, CHK, ATHL, CVX, and YPF.
  • Investors in crowded stocks may experience elevated volatility.
  • Collectively, hedge funds’ long U.S. energy equity portfolios tend to generate negative risk-adjusted returns. Consequently, their crowded bets in this sector tend to disappoint.
The information herein is not represented or warranted to be accurate, correct, complete or timely.
Past performance is no guarantee of future results.
Copyright © 2012-2014, 
AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved.
Content may not be republished without express written consent.
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Sectors Most Exposed to Oil Price

In periods of oil price volatility, there is much discussion of its impact on various industries. Market noise obscures true industry-specific performance, so Oil’s impact is impossible to judge from simple index returns. But, by stripping away market effects, we observe the relationships between pure sector and oil returns. Airlines have the largest negative exposure to Oil, yet REITs provide the most consistent negative exposure. In addition, sectors that benefit from increased discretionary spending are significantly negatively levered to Oil. Here we discuss the sectors most exposed to oil price, and quantify the relationships.

Pure Sector Performance

As we illustrated earlier, market noise obscures relationships among individual sectors, concealing industry-specific performance. Without separating pure industry-specific returns from the market, robust risk management, performance attribution, and investment skill evaluation are impossible.

For example, the oilfield services sector generally follows the market – industry trends are indiscernible:

Chart of the 10-year cumulative return of the oilfield services sector

Oilfield Services Sector Index Return

By removing market and macroeconomic effects from security returns and calculating the performance of the pure sector factor, we reveal sector-specific trends:

Chart of the 10-year cumulative return of the oilfield services pure sector factor

Oilfield Services Pure Sector Return

Within this pure sector performance, we can now identify:

Since pure sector factors capture sector-specific trends and risks, they also capture sector-specific oil exposure.

Equity Market’s Oil Exposure

In addition to industry-specific oil exposure, the broad equity market is significantly correlated with Oil. Broad macroeconomic risks affect both commodity prices and the equity market:

Chart of the historical correlation of monthly returns of oil price and U.S. equity market

Oil Price and U.S. Market Return Correlation

Over the past five years, when oil prices increased by 1%, U.S. equity market increased by approximately 0.4%. Oil price variance explains approximately 28% of U.S. market variance. Perhaps more accurately, 28% of the market variance is explained by shared macroeconomic variables.

The exposure of an individual stock to Oil is a combination of exposures due to its market and pure sector risks.

Sectors Most Positively Correlated to Oil

The list of sectors with the highest positive correlation to Oil is hardly surprising:

Chart of the correlation of monthly returns of oil price and U.S. pure sector factors most positively correlated with oil price

Pure Sector Factors Most Positively Correlated with Oil Price

Sector Oil Correlation p-value
Industrial Machinery 0.29 0.0117
Oil and Gas Pipelines 0.31 0.0075
Contract Drilling 0.37 0.0018
Oilfield Services Equipment 0.38 0.0016
Integrated Oil 0.41 0.0006
Oil and Gas Production 0.43 0.0003

(We use the Spearman’s rank correlation coefficient to evaluate correlation. Spearman’s correlation is robust against outliers, unlike the commonly used Pearson’s correlation. All correlations are significant; most at 1% level or better.)

Some may find the oil price influence over the industrial machinery sector unexpected. This exposure reflects this sector’s dependence on the energy value chain.

Sectors Most Negatively Correlated to Oil

The list of sectors with the highest negative correlation to Oil is unexpected:

Chart of the monthly return correlation of oil price and U.S. pure sector factors most negatively correlated with oil price

Pure Sector Factors Most Negatively Correlated with Oil Price

Once again, all correlations are statistically significant, generally at 1% level or better:

Sector Oil Correlation p-value
Real Estate Investment Trusts -0.43 0.0003
Airlines -0.36 0.0021
Aerospace and Defense -0.34 0.0044
Multi Line Insurance -0.33 0.0053
Hotels Resorts Cruiselines -0.32 0.0067
Casinos Gaming -0.28 0.0142

A maxim among investors is that Airlines are the best way to get exposure to falling oil prices. Economic reality is more complex.

In fact, over the past five years, Real Estate Investment Trusts (REITs) have been most consistently negatively related to oil prices:

Chart of correlation between oil price and U.S. REIT pure sector factor

Real Estate Investment Trusts Pure Sector and Oil Return Correlation

Oil price variance explains approximately 19% of increases and decreases in REIT share prices.

Shared variables drive the performance or REITs and the performance of Oil: inflation, growth, macroeconomic uncertainty.

Sectors Most Negatively Exposed to Oil

Correlation captures the strength of a relationship, or how well changes in one variable explain changes in the other, but it does not capture the magnitude of relative changes.

The magnitude of changes is captured by a regression: exposure, beta, or regression term measures the magnitude of pure sector changes due to oil price changes. Sectors with the largest oil exposure (beta) are:

Chart of the correlation between the oil price and U.S. pure sector factors for sectors most negatively exposed to the oil price

Pure Sector Factors Most Negatively Exposed to Oil Price

Airlines do indeed benefit the most when oil prices decline:

Sector Oil Exposure (Beta) p-value
Airlines -0.44 0.0007
Casinos Gaming -0.21 0.0531
Aluminum -0.20 0.0413
Hotels Resorts Cruiselines -0.18 0.0056
Motor Vehicles -0.18 0.0944
Real Estate Investment Trusts -0.18 0.0005

Over the past five years, when oil prices declined by 1%, the sector-specific performance of airlines has been approximately +0.4%:

Chart of the correlation between oil price and U.S. airlines pure sector factor

Airlines Pure Sector and Oil Return Correlation

The relationship between Oil and Airlines has not been as consistent as the relationship between Oil and REITs. However, the magnitude of changes in Airlines has been more than twice as large.

  • If investors seek short oil exposure with the most consistency, they should go long REITs.
  • If investors seek short oil exposure with the most “bang for the buck,” they should go long Airlines.

Hotels, Cruiselines, and Gaming are also featured on both lists. They offer a combination of consistency and “bang for the buck.” Presumably, they benefit from the increases in consumer disposable incomes due to declines in energy prices.

Conclusion

  • Industry-specific performance is clouded by market noise.
  • By stripping away the effects of market and macroeconomic variables, we reveal the performance of pure sector factors and their relationships with oil prices.
  • Airlines are not the only, and not always the best, way to benefit from falling oil prices.
  • REITs benefit from falling oil prices most consistently.
  • Airlines benefit from falling oil prices less consistently than REITs, but with more 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 © 2012-2014, AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved.
Content may not be republished without express written consent.
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