Hedge Fund Crowding Update – Q2 2015

Hedge funds share a few bets. These crowded systematic and idiosyncratic exposures are the main sources of the industry’s relative performance and of many firms’ returns. Two factors and three stocks were behind most herding of hedge fund long U.S. equity positions in Q2 2015.

Investors should treat consensus ideas with caution: Crowded stocks are prone to mass liquidation. Crowded hedge fund bets tend to do poorly in most sectors, though there are some exceptions.

Identifying Hedge Fund Crowding

This piece follows the approach of our earlier articles on crowding: We created a position-weighted portfolio (HF Aggregate) consisting of the popular U.S. equity holdings of all long hedge fund portfolios tractable from regulatory filings. We then analyzed HF Aggregate’s risk relative to U.S. Market Aggregate (similar to the Russell 3000 index) using AlphaBetaWorks’ Statistical Equity Risk Model to identify sources of crowding.

Hedge Fund Aggregate’s Risk

The Q2 2015 HF Aggregate had 3.2% estimated future tracking error relative to U.S. Market. Factor (systematic) bets were the primary source of risk and systematic crowding increased slightly from prior quarters:

The components of HF Aggregate’s relative risk on 6/30/2015 were the following:

 Source Volatility (%) Share of Variance (%)
Factor 2.46 60.01
Residual 2.01 39.99
Total 3.17 100.00

Because of the close relationship between active risk and active performance, the low estimated future volatility (tracking error) indicates that the long book of a divercified portfolio of hedge funds will behave similarly to a passive factor portfolio. Even if its active bets pay off, HF Aggregate will have a hard time earning a typical fee. Consequently, the long portion of highly diversified hedge fund portfolios will struggle to outperform a passive alternative.

Hedge Fund Factor (Systematic) Crowding

Below are HF Aggregate’s principal factor exposures (in red) relative to U.S. Market’s (in gray) as of 6/30/2015:

Chart of the factor exposures contributing most to the factor variance of Hedge Fund Aggregate Portfolio relative to Market on 6/30/2015

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

Of these bets, Market (Beta) and Oil are responsible for 90% of the relative factor risk. These are the components of the 2.46% Factor Volatility in the first table:

Chart of the main factors and their cumulative contribution to the factor variance of Hedge Fund Aggregate Portfolio relative to Market on 6/30/2015

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

Factor Relative Exposure (%) Portfolio Variance (%²) Share of Systematic Variance (%)
Market 15.76 3.68 60.91
Oil Price 2.93 1.75 28.94
Industrial 9.72 0.53 8.72
Finance -8.36 0.46 7.58
Utilities -2.78 0.25 4.13
Other Factors -0.62 -10.28
Total 6.04 100.00

Exposures to the three main factor bets are near 10-year highs.

Hedge Fund U.S. Market Factor Exposure History

HF Aggregate’s market exposure is approximately 115% (its Market Beta is approximately 1.15). Hedge fund’s long books are taking approximately 15% more market risk than U.S. equities and approximately 20% more market risk than S&P 500. This bet has proven costly in August of 2015:

Chart of the historical exposure of Hedge Fund Aggregate Portfolio to the U.S. Market Factor

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

Also note that long hedge fund portfolios consistently take 5-15% more market risk than S&P500 and other broad benchmarks. This is why simple comparison of long hedge fund portfolio performance to market indices is misleading.

Hedge Fund Oil Price Exposure History

HF Aggregate’s oil exposure, near 3%, is also close to the 10-year highs last reached in 2009:

Chart of the historical exposure of Hedge Fund Aggregate Portfolio to the Oil Price Factor

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

As oil prices collapsed in 2014, hedge funds rapidly boosted oil exposure. This contrarian bet is a weak bullish indicator for the commodity.

Hedge Fund Industrial Factor Exposure History

HF Aggregate’s industrials factor exposure remained near the all-time high:

Chart of the historical exposure of Hedge Fund Aggregate Portfolio to the Industrial Factor

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

This has been a losing contrarian bet since 2014 and it is a weak bearish indicator for the sector.

Hedge Fund Residual (Idiosyncratic) Crowding

About 40% of hedge fund crowding is due to residual (idiosyncratic, stock-specific) risk. Just three names are responsible for over half of it:

Chart of the main stock-specific bets and their cumulative contribution to the residual variance of Hedge Fund Aggregate Portfolio relative to Market on 6/30/2015

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

These stocks will be primary drivers of HF Aggregate’s and of the most crowded firms’ stock-specific performance. Investors should be ready for seemingly inexplicable volatility due to portfolio liquidation and rebalancing. Though individual crowded names may be wonderful investments, they have tended to underperform:

Symbol Name Exposure (%) Share of Idiosyncratic Variance (%)
VRX Valeant Pharmaceuticals International, Inc. 4.78 36.25
LNG Cheniere Energy, Inc. 1.58 10.53
JD JD.com, Inc. Sponsored ADR Class A 1.59 4.60
NFLX Netflix, Inc. 0.74 4.55
SUNE SunEdison, Inc. 0.92 4.03
CHTR Charter Communications, Inc. Class A 1.55 3.04
PCLN Priceline Group Inc 1.36 2.37
EBAY eBay Inc. 1.47 1.58
FLT FleetCor Technologies, Inc. 1.10 1.17
TWC Time Warner Cable Inc. 1.27 1.17

Investors drawn to these names should not use hedge fund ownership as a plus. Instead, this ownership should trigger particularly thorough due-diligence. Any company slip-ups will be magnified as impatient investors stampede out of positions.

Fund allocators should also pay attention to crowding: Historically, consensus bets have done worse than a passive portfolio with the same risk. Investing in crowded books is investing in a pool of undifferentiated bets destined to disappoint.

AlphaBetaWorks’ analytics identify hedge fund herding in each equity sector. Our fund analytics measure hedge fund differentiation and identify specific skills in each sector that are strongly predictive of future performance.

Summary

  • There is both factor (systematic/market) and residual (idiosyncratic/security-specific) crowding of hedge funds’ long U.S. equity portfolios.
  • Hedge fund crowding is approximately 60% systematic and 40% idiosyncratic.
  • The main sources of systematic crowding are Market (Beta) and Oil.
  • The main sources of idiosyncratic crowding are VRX, LNG, JD, NFLX, and SUNE.
  • The crowded hedge fund portfolio has historically underperformed its passive alternative – allocators and fund followers should pay close attention to these consensus bets.
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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Liquidation of Crowded Hedge Fund Energy Positions

The 2014-2015 energy carnage has been worse for crowded hedge fund energy positions than the global financial crisis. Past liquidations of crowded hedge fund bets were followed by rapid recoveries. Consequently, energy investors should survey the wreckage for opportunities.

Crowded hedge fund oil and gas producers underperformed their sector peers by over 20% since 2013 as fund energy books were liquidated. Crowded oilfield service bets underperformed by over 15%. This is worse than 10-15% underperformance during the 2008-2009 global financial crisis.

Forced hedge fund portfolio liquidations are usually followed by rapid recoveries in the affected names – liquidations during the global financial crisis reversed in under a year. Since the energy market in 2015 faces unique challenges, history may not repeat itself. Still, some of the crowded positions should present opportunities.

Performance of Crowded Hedge Fund Oil and Gas Producer Bets

To explore crowding we analyze hedge fund Oil and Gas Producer Sector holdings (HF Sector Aggregate) relative to the Sector Market Portfolio (Sector Aggregate). HF Sector Aggregate is position-weighted; Sector Aggregate is capitalization-weighted. This follows the approach of our earlier articles on hedge fund crowding.

The figure below plots historical return of HF Oil and Gas Producer Aggregate. Factor return is due to systematic (market) risk. Blue area represents positive and gray area represents negative risk-adjusted returns from security selection (αReturn). Crowded bets underperformed the portfolio with the same systematic risk (factor portfolio) by over 50% during the past 10 years, largely since 2014:

Chart of the passive and security selection performance of the aggregate portfolio of Hedge Fund Oil and Gas Producer Sector holdings

Hedge Fund Oil and Gas Producer Sector Aggregate Historical Performance

The risk-adjusted return from security selection (αReturn) of HF Sector Aggregate is the return it would have generated if markets had been flat – all market effects on performance have been eliminated. This is the idiosyncratic performance of HF Sector Aggregate:

Chart of the security selection performance of the aggregate portfolio of Hedge Fund Oil and Gas Producer Sector holdings

Hedge Fund Oil and Gas Producer Sector Aggregate Historical Security Selection Performance

The above chart reveals that by Q2 2009 the crowded hedge fund energy producers erased underperformance due to 2008 liquidation. The liquidation since 2013 has been even larger than in 2008. Since they may be posed for a steep recovery, crowded hedge fund oil and gas producer bets are worth watching in the coming months.

Performance of Crowded Hedge Fund Oilfield Service Bets

The figure below plots historical return of HF Oilfield Service Aggregate. It follows the approach of HF Oil and Gas Producer Aggregate above:

Chart of the passive and security selection performance of the aggregate portfolio of Hedge Fund Oilfield Service Sector holdings

Hedge Fund Oilfield Service Sector Aggregate Historical Performance

Since 2013, the crowded oilfield service portfolio has underperformed, similarly to the crowded oil and gas portfolio:

Chart of the security selection performance of the aggregate portfolio of Hedge Fund Oilfield Service Sector holdings

Hedge Fund Oilfield Service Sector Aggregate Historical Security Selection Performance

Crowded energy producers and service companies have underperformed sector peers by 15-25% in the latest liquidation. Many may now be attractive, given the recovery that typically follows. Below are the hedge fund energy bets that may present these opportunities:

Crowded Hedge Fund Oil and Gas Producer Bets

The following stocks contributed most to the relative residual (idiosyncratic, security-specific) risk of the HF Oil and Gas Aggregate as of Q1 2015. Blue bars represent long (overweight) exposures relative to Sector Aggregate. White bars represent short (underweight) exposures. Bar height represents contribution to relative stock-specific risk:

Chart of the contribution to relative risk of the most crowded hedge fund oil and gas production bets

Crowded Hedge Fund Oil and Gas Producer Bets

The following table contains detailed data on these crowded hedge fund oil and gas producer bets:

Exposure (%)

Net Exposure

Share of Risk (%)
HF Sector Aggr. Sector Aggr. % $mil Days of Trading
WPZ Williams Partners, L.P. 17.93 4.75 13.18 1,812.2 15.0 23.04
PXD Pioneer Natural Resources Company 14.42 4.01 10.41 1,432.0 4.9 17.91
CRC California Resources Corp 3.42 0.48 2.93 403.2 8.2 10.79
CHK Chesapeake Energy Corporation 8.31 1.55 6.76 930.1 2.8 9.95
COP ConocoPhillips 0.99 12.62 -11.63 -1,599.0 -3.7 7.00
OXY Occidental Petroleum Corporation 0.69 9.25 -8.56 -1,176.6 -3.3 5.45
EOG EOG Resources, Inc. 2.13 8.28 -6.14 -844.7 -2.4 4.40
RRC Range Resources Corporation 5.33 1.45 3.88 533.8 3.4 3.68
CIE Cobalt International Energy, Inc. 3.10 0.64 2.46 338.2 11.2 2.93
OAS Oasis Petroleum Inc. 3.15 0.33 2.82 387.9 2.7 2.39
CMLP Crestwood Midstream Partners LP 3.83 0.45 3.38 465.2 47.0 1.99
AR Antero Resources Corporation 3.97 1.60 2.37 325.5 4.5 1.39
WLL Whiting Petroleum Corporation 3.57 1.04 2.53 347.5 1.2 1.06
NBL Noble Energy, Inc. 0.28 3.12 -2.84 -390.1 -2.2 0.80
CLR Continental Resources, Inc. 0.18 2.68 -2.50 -344.1 -2.2 0.76
COG Cabot Oil \& Gas Corporation 0.49 2.01 -1.52 -209.5 -1.1 0.71
DVN Devon Energy Corporation 0.55 4.06 -3.51 -483.0 -2.2 0.62
EQT EQT Corporation 0.16 2.07 -1.91 -262.3 -2.5 0.59
APA Apache Corporation 1.15 3.74 -2.59 -356.6 -1.7 0.47
APC Anadarko Petroleum Corporation 4.99 7.02 -2.04 -280.2 -0.8 0.43
Other Positions 0.80 3.65
Total 100.00

Crowded Hedge Fund Oilfield Service Bets

The following stocks contributed most to the relative residual risk of the HF Sector Aggregate as of Q1 2015:

Chart of the contribution to relative risk of the most crowded hedge fund oilfield service bets

Crowded Hedge Fund Oilfield Service Bets

The following table contains detailed data on these crowded hedge fund oilfield service bets:

Exposure (%) Net Exposure Share of Risk (%)
HF Sector Aggr. Sector Aggr. % $mil Days of Trading
BHI Baker Hughes Incorporated 32.63 9.95 22.68 1,258.9 6.1 50.38
SLB Schlumberger NV 3.31 38.39 -35.07 -1,946.7 -2.8 22.65
HAL Halliburton Company 28.87 13.42 15.45 857.4 1.4 12.44
DAKP Dakota Plains Holdings, Inc. 0.31 0.04 0.27 15.1 78.3 3.86
HOS Hornbeck Offshore Services, Inc. 3.21 0.24 2.97 164.9 6.8 1.89
NOV National Oilwell Varco, Inc. 2.88 7.38 -4.49 -249.4 -0.9 1.45
FTI FMC Technologies, Inc. 0.02 3.08 -3.06 -169.9 -1.2 1.06
FTK Flotek Industries, Inc. 1.51 0.29 1.22 67.9 5.8 0.85
WFT Weatherford International plc 1.25 3.43 -2.18 -121.1 -1.0 0.71
CLB Core Laboratories NV 0.00 1.62 -1.62 -90.0 -1.1 0.57
SDRL Seadrill Ltd. 0.00 1.66 -1.66 -92.1 -0.6 0.49
OIS Oil States International, Inc. 2.71 0.74 1.97 109.5 2.7 0.39
EXH Exterran Holdings, Inc. 1.98 0.83 1.14 63.4 2.6 0.36
USAC USA Compression Partners LP 1.80 0.24 1.56 86.6 45.7 0.31
OII Oceaneering International, Inc. 0.13 1.93 -1.81 -100.3 -1.5 0.27
FI Frank’s International NV 0.00 1.04 -1.04 -57.7 -4.2 0.26
KNOP KNOT Offshore Partners LP 2.31 0.12 2.19 121.4 47.1 0.25
RES RPC, Inc. 0.05 1.00 -0.96 -53.0 -2.0 0.23
WG Willbros Group, Inc. 0.46 0.07 0.39 21.5 11.3 0.19
MDR McDermott International, Inc. 1.04 0.33 0.71 39.5 1.4 0.17
Other Positions 0.34 1.22
Total 100.00

Summary

  • The 2014-2015 carnage has been worse for crowded hedge fund oil and gas producer and oilfield service bets than the global financial crisis.
  • Past liquidations of crowded positions were followed by rapid recoveries.
  • Energy investors should survey the wreckage of crowded hedge fund energy bets for opportunities.
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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Property and Casualty Industry Crowding

Property and casualty insurance company portfolios share a few systematic bets. These crowded bets are the main sources of the industry’s and many individual companies’ relative investment performance. Since the end of 2013, these exposures have cost the industry billions.

Identifying Property and Casualty Industry Crowding

This analysis of property and casualty (P&C) insurance industry portfolios resulted from collaboration with Peer Analytics, the only provider of accurate peer universe comparisons to the insurance industry.

In analyzing property and casualty industry portfolios, we follow the approach of our earlier articles on crowding: We created a position-weighted portfolio (P&C Aggregate) consisting of all property and casualty insurance portfolios reported in regulatory filings. P&C Aggregate covers over 1,300 companies with total portfolio value over $300 billion. We analyzed P&C Aggregate’s risk relative to Russell 3000 index (a close proxy for the U.S. Market) using AlphaBetaWorks’ Statistical Equity Risk Model to identify sources of crowding.

Property and Casualty Industry 2014-2015 Underperformance

P&C Aggregate systematic (factor) performance lagged the market by over 4%, or over $12 billion, since the end of 2013. This is largely due to low (short, underweight) exposures to Market (Beta), Health, and Technology factors:

Chart of the factor returns of the Property and Casualty Industry’s Aggregate Portfolio relative to Market during 2014-2015

2014-2015 Underperformance due to Property and Casualty Industry’s Portfolio Factor Exposures

Below are the main contributing exposures, in percent:

Factor

Return

Portfolio Exposure Benchmark Exposure Relative Exposure Portfolio Return Benchmark Return

Relative Return

Market

16.64

91.90 99.97 -8.07 15.25 16.63

-1.39

Health

21.12

6.59 13.09 -6.50 1.30 2.58

-1.29

Technology

5.93

8.93 19.10 -10.17 0.53 1.13

-0.60

FX

21.94

-3.72 -1.19 -2.53 -0.75 -0.24

-0.51

Energy

-25.18

7.26 5.67 1.59 -1.99 -1.56

-0.43

For some companies, these exposures may be due to conscious portfolio and risk management processes. For others, they may have been unintended. For industry as a whole, robust risk and portfolio management would have generated billions in additional returns.

Property and Casualty Industry Year-end 2013 Crowding

Property and casualty industry’s recent crowding has been costly in practice. P&C Aggregate’s relative factor bets have cost it over 4% since year-end 2013. The industry made $12 billion less than it would have if it had simply matched market factor exposures.

Year-end 2013 Systematic (Factor) Exposures

Below are P&C Aggregate’s most significant factor exposures (Portfolio in red) relative to Russell 3000 (Benchmark in gray) as of 12/31/2013:

Chart of the factor exposures contributing most to the factor variance of Property and Casualty Industry’s Aggregate Portfolio relative to Market on 12/31/2013

Factors Contributing Most to the Relative Portfolio Risk for Property and Casualty Industry Aggregate on 12/31/2013

P&C Aggregate’s factor exposures drive its systematic returns in various scenarios. The exposures above (underweight Market and Technology factors) suggest the P&C industry is preparing for technology crash akin to 2001. This and other historical regimes provide the stress tests below, similar to those now required of numerous managers.

Property and Casualty Industry Year-end 2014 Crowding

Year-end 2014 Systematic (Factor) Exposures

Property and casualty industry portfolio turnover is low. Consequently, industry factor exposures at year-end 2014 were close to those at year-end 2013. Below are P&C Aggregate’s most significant factor exposures (Portfolio in red) relative to Russell 3000 (Benchmark in gray) as of 12/31/2014:

Chart of the factor exposures contributing most to the factor variance of Property and Casualty Industry’s Aggregate Portfolio relative to Market on 12/31/2014

Factors Contributing Most to the Relative Portfolio Risk for Property and Casualty Industry Aggregate on 12/31/2014

The main exposures of the property and casualty industry were: short/underweight Market (Beta), long/overweight Size (large companies), short Health, and short Technology. The industry crowds towards large and low-beta Consumer and Financials stocks:

Factor

Portfolio Exposure

Benchmark Exposure Relative Exposure Factor Volatility Share of Absolute Factor Variance Share of Absolute Total Variance Share of Relative Factor Variance

Share of Relative Total Variance

Market

90.39

99.97 -9.58 13.44 98.18 96.21 55.19

26.60

Size

13.32

-1.01 14.33 8.03 -0.91 -0.90 46.71

22.51

Health

7.68

13.09 -5.41 6.91 0.29 0.28 6.19

2.98

Technology

9.31

19.10 -9.79 5.80 -0.06 -0.06 4.16

2.00

Mining

1.54

0.63 0.91 15.61 -0.20 -0.19 1.76

0.85

Energy

3.93

5.67 -1.74 10.47 1.04 1.02 1.62

0.78

Consumer

27.11

23.04 4.08 3.91 -0.68 -0.66 1.53

0.74

Finance

21.48

18.92 2.56 5.48 -1.93 -1.89 1.49

0.72

Value

1.52

0.78 0.73 13.45 -0.04 -0.04 0.61

0.29

Scenario Analysis: 2000-2001 Outperformance

Given property and casualty industry’s under-weighting of Market and Technology, it would experience its highest outperformance in an environment similar to the 2001 technology crash. In this environment, industry’s systematic exposures would generate 2% outperformance:

Chart of the factor returns of the Property and Casualty Industry’s Aggregate Portfolio relative to Market during 2000-2001

2000-2001: Stress test of outperformance due to Property and Casualty Industry’s Portfolio Factor Exposures

Below are the main contributors to this outperformance, in percent:

Factor Return Portfolio Exposure Benchmark Exposure Relative Exposure Portfolio Return Benchmark Return Relative Return
Technology

-36.83

9.31 19.10 -9.79 -3.96 -7.99

4.04

Market

-29.28

90.39 99.97 -9.58 -26.75 -29.27

2.52

Consumer

19.60

27.11 23.04 4.08 5.03 4.26

0.77

Finance

27.27

21.48 18.92 2.56 5.48 4.81

0.66

Value

42.82

1.52 0.78 0.73 0.58 0.30

0.28

Mining

32.25

1.54 0.63 0.91 0.47 0.20

0.28

Scenario Analysis: 1999-2000 Underperformance

Given property and casualty industry’s under-weighting of Market and Technology, it would experience its highest underperformance in an environment similar to the 1999 technology boom.  In this environment, industry’s systematic exposures would underperform the market by more than 10%:

Chart of the factor returns of the Property and Casualty Industry’s Aggregate Portfolio relative to Market during 1999-2000

1999-2000: Stress test of underperformance due to Property and Casualty Industry’s Portfolio Factor Exposures

Below are the main contributors to this underperformance, in percent:

Factor

Return

Portfolio Exposure Benchmark Exposure Relative Exposure Portfolio Return Benchmark Return

Relative Return

Technology

53.04

9.31 19.10 -9.79 4.30 8.95

-4.66

Market

29.23

90.39 99.97 -9.58 26.22 29.22

-3.00

Size

-18.83

13.32 -1.01 14.33 -2.63 0.20

-2.83

Consumer

-16.57

27.11 23.04 4.08 -4.72 -4.02

-0.70

Finance

-20.59

21.48 18.92 2.56 -4.54 -4.01

-0.54

Energy

14.38

3.93 5.67 -1.74 0.62 0.90

-0.27

FX

6.84

-3.74 -1.19 -2.55 -0.25 -0.08

-0.17

Value

-14.04

1.52 0.78 0.73 -0.17 -0.09

-0.08

Mining

-8.54

1.54 0.63 0.91 -0.08 -0.03

-0.05

Communications

0.52

1.30 2.06 -0.76 0.02 0.04

-0.01

Conclusions

  • There is factor (systematic/market) crowding of property and casualty insurance companies’ long U.S. equity portfolios.
  • The main sources of systematic crowding are short (underweight) exposures to Market (Beta), Technology, and Health.
  • Since year-end 2013, factor exposures have cost the property and casualty industry over 4%, more than $12 billion, in underperformance.
  • For some portfolios, this may be a conscious risk management decision; for others, it is a costly oversight.
  • By managing its exposures in recent quarters, the industry would have generated billions in additional returns.
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 Update – Q1 2015

Hedge funds share a few systematic and idiosyncratic bets. These crowded bets are the main sources of the industry’s relative performance and of many individual funds’ returns. Three factors and four stocks were behind the majority of hedge fund long U.S. equity herding during Q1 2015.

Investors should treat crowded ideas with caution: Crowded stocks are more volatile and vulnerable to mass liquidation. Crowded hedge fund bets generally fare poorly in most sectors, though they do well in a few.

Identifying Hedge Fund Crowding

This piece follows the approach of our earlier articles on crowding: We created a position-weighted portfolio (HF Aggregate) consisting of popular long U.S. equity holdings of all hedge funds tractable from quarterly filings. We then analyzed HF Aggregate’s risk relative to U.S. Market Aggregate (similar to the Russell 3000 index) using AlphaBetaWorks’ Statistical Equity Risk Model to identify sources of crowding.

Hedge Fund Aggregate’s Risk

The Q1 2015 HF Aggregate had 3.1% estimated future tracking error relative to U.S. Market. Factor (systematic) bets were the primary source of risk and systematic crowding increased slightly from Q4 2014:

The components of HF Aggregate’s relative risk on 3/31/2015 were the following:

 Source

Volatility (%)

Share of Variance (%)

Factor

2.42

61.21

Residual

1.92

38.79

Total

3.09

100.00

The low estimated future tracking error indicates that, even if its active bets pay off, HF Aggregate will have a hard time earning a typical fee. Consequently, the long portion of highly diversified hedge fund portfolios will struggle to outperform a passive alternative after factoring in the higher fees.

Hedge Fund Factor (Systematic) Crowding

Below are HF Aggregate’s principal factor exposures (in red) relative to U.S. Market’s (in gray) as of 3/31/2015:

Chart of the current and historical exposures to the most significant risk factors of U.S. Hedge Fund Aggregate

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

Of these bets, Market (Beta) and Oil are responsible for almost 90% of the relative factor risk and 50% of the total. These are the components of the 2.42% Factor Volatility in the first table:

Chart of the cumulative contribution to relative factor variance of the most significant risk factors of U.S. Hedge Fund Aggregate

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

Factor

Relative Exposure (%)

Portfolio Variance (%²)

Share of Systematic Variance (%)

Market

14.91

3.83

65.58

Oil Price

2.48

1.37

23.46

Industrial

9.38

0.46

7.88

Finance

-6.10

0.29

4.97

Utilities

-2.80

0.28

4.79

Other Factors

-0.39

-6.68

Total

5.84

100.00

Absolute exposures to all three primary sources of factor crowding are at or near 10-year highs.

Hedge Fund U.S. Market Factor Exposure History

HF Aggregate’s market exposure is near 115% (Beta is near 1.15) – the level last reached in mid-2006:

Chart of the historical U.S. Market Factor exposure of U.S. Hedge Fund Aggregate

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

We will discuss the predictive value of this indicator in later posts. Note that long hedge fund portfolios consistently take 5-15% more market risk than S&P500 and other broad market benchmarks. Therefore, simple comparison of long hedge fund portfolio performance to market indices is generally misleading.

Hedge Fund Oil Price Exposure History

HF Aggregate’s oil exposure of 2.5% is similarly near 10-year highs and near the levels last seen in 2009:

Chart of the historical Oil Price factor exposure of U.S. Hedge Fund Aggregate

U.S. Hedge Fund Aggregate’s Oil Price Exposure History

As oil prices collapsed in 2014, hedge funds rapidly boosted oil exposure. This contrarian bet began to pay off in 2015. A comprehensive discussion of HF Aggregate’s historical oil factor timing performance is beyond the scope of this piece.

Hedge Fund Industrial Factor Exposure History

HF Aggregate’s industrials factor exposure over 25% is now at the all-time height:

Chart of the historical Industrial Factor exposure of U.S. Hedge Fund Aggregate

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

This has been a losing contrarian bet since 2014.

Hedge Fund Residual (Idiosyncratic) Crowding

About a third of hedge fund crowding is due to residual (idiosyncratic, stock-specific) risk. Only four stocks were responsible for over half of the relative residual variance:

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

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

These stocks will be primary drivers of HF Aggregate’s and of the most crowded firms’ stock-specific performance. Investors should be ready for seemingly inexplicable volatility in these names. Some may be wonderful individual investments, but most have historically underperformed:

Symbol

Name

Exposure (%)

Share of Idiosyncratic Variance (%)

VRX

Valeant Pharmaceuticals International, Inc.

4.13

29.75

LNG

Cheniere Energy, Inc.

1.72

15.06

SUNE

SunEdison, Inc.

0.80

3.51

CHTR

Charter Communications, Inc. Class A

1.54

2.84

PCLN

Priceline Group Inc

1.26

2.27

MU

Micron Technology, Inc.

0.86

1.99

ACT

Actavis Plc

1.68

1.94

EBAY

eBay Inc.

1.46

1.70

BIDU

Baidu, Inc. Sponsored ADR Class A

0.86

1.52

PAGP

Plains GP Holdings LP Class A

1.40

1.35

When investing in these crowded names, investors should perform particularly thorough due-diligence, since any losses will be magnified if hedge funds rush for the exits.

Historically, consensus bets have done worse than a passive portfolio with the same risk. Consequently, fund allocators should thoroughly investigate hedge fund managers’ crowding to avoid investing in a pool of undifferentiated bets destined to disappoint.

AlphaBetaWorks’ analytics assist in both tasks: Our sector crowding reports identify hedge fund herding in each equity sector. Our fund analytics measure hedge fund differentiation and identify skills that are strongly predictive of future performance.

Summary

  • There is both factor (systematic/market) and residual (idiosyncratic/security-specific) crowding of hedge funds’ long U.S. equity portfolios.
  • Hedge fund crowding is approximately 60% systematic and 40% stock-specific.
  • The main sources of systematic crowding are Market (Beta), Oil, and Industrials.
  • The main sources of idiosyncratic crowding are VRX, LNG, SUNE, and CHTR.
  • Allocators and fund followers should pay close attention to crowding: The crowded hedge fund portfolio has historically underperformed its passive alternative – investors would have made more by taking the same risks passively.
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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The Impact of Fund Mean Reversion

Real-world restrictions on hedge fund investing wreak havoc on common allocation strategies

Common return measures fail to predict future hedge fund performance. More important, under typical allocation and withdrawal constraints, these failures due to mean reversion become more severe:

  • Portfolios based on top nominal returns and win/loss ratios tend to under-perform.
  • Portfolios based on top Sharpe ratios don’t outperform.
  • Portfolios based on predictive skill analytics and robust factor models continue to consistently outperform.

To illustrate, we follow the approach of our earlier pieces on hedge funds: Our dataset spans the long portfolios of all U.S. hedge funds active over the past 15 years that are tractable using 13F filings. Top- and bottom-performing portfolios are selected based on 36 months of performance history.

But here we impose realistic allocation constraints: a 6-month delay between holdings reporting and fund investment, plus a bi-annual window for investments into, or withdrawals from, hedge funds. For example, an allocator who wishes to invest in a fund using 12/31/2013 data can only do so on 6/30/2014 and cannot redeem until 12/31/2014. These practical liquidity restrictions deepen the impact of hedge fund mean reversion.

Hedge Fund Selection Using Nominal Returns

The following chart tracks two simulated funds of hedge funds. One contains the top-performing 5% and the other the bottom-performing 5% of hedge fund U.S. equity long books. We use a 36-month trailing performance look-back; investments are made with a six-month delay (as above):

Chart of the cumulative returns of hedge fund portfolios constructed from funds with the highest 5% and the lowest 5% 36-month trailing returns

Performance of Portfolios of Hedge Funds Based on High and Low Historical Returns

Cumulative Return (%)

Annual Return (%)

High Historical Returns

99.54

6.74

Low Historical Returns

125.12

7.92

High – Low Returns

-25.57

-1.18

The chart reveals several regimes of hedge fund mean reversion: In a monotonically increasing market, such as 2005-2007, relative nominal performance persists; funds with the highest systematic risk outperform. When the regime changes, however, they under-perform. At the end of 2008, the top nominal performers are those taking the lowest systematic risk. In 2009, as the regime changes again, these funds under-perform.

Hedge Fund Selection Using Sharpe Ratios

The following chart tracks portfolios of funds with the top 5% and bottom 5% Sharpe ratios:

Chart of the cumulative returns of hedge fund portfolios constructed from funds with the highest 5% and the lowest 5% 36-month trailing Sharpe ratios

Performance of Portfolios of Hedge Funds Based on High and Low Historical Sharpe Ratios

Cumulative Return (%)

Annual Return (%)

High Historical Sharpe Ratios

115.31

7.48

Low Historical Sharpe Ratios

115.52

7.49

High – Low Sharpe Ratios

-0.20

-0.01

Since Sharpe ratio simply re-processes nominal returns, and only partially adjusts for systematic risk, it also fails when market regimes change. However, it is less costly. While Sharpe ratio may not be predictive under practical constraints of hedge fund investing, at least (unlike nominal returns) it does little damage.

Hedge Fund Selection Using Win/Loss Ratios

The following chart tracks portfolios of funds with the top 5% and the bottom 5% win/loss ratios, related to the batting average. These are examples of popular non-parametric approaches to skill evaluation:

Chart of the cumulative returns of hedge fund portfolios constructed from funds with the highest 5% and the lowest 5% 36-month trailing win/loss ratios

Performance of Portfolios of Hedge Funds Based on High and Low Historical Win/Loss Ratios

Cumulative Return (%)

Annual Return (%)

High Historical Win/Loss Ratios

112.41

7.35

Low Historical Win/Loss Ratios

136.86

8.41

High – Low Win/Loss Ratios

-24.45

-1.06

The win/loss ratio suffers from the same challenges as nominal returns: Win/loss ratio favors funds with the highest systematic risk in the bullish regimes and funds with the lowest systematic risk in the bearish regimes. As with nominal returns, this can be predictive while market trends continue. When trends change, the losses are especially severe under liquidity constraints.

Hedge Fund Selection Using αReturns

Systematic (factor) returns that make up the bulk of portfolio volatility are the primary source of mean reversion. Proper risk adjustment with a robust risk model controls for factor returns; it addresses mean reversion and identifies residual returns due to security selection.

AlphaBetaWorks’ measure of residual security selection performance is αReturn – outperformance relative to a replicating factor portfolio. αReturn is also the return a portfolio would have generated if markets had been flat.

The following chart tracks portfolios of funds with the top 5% and the bottom 5% αReturns. These portfolios have matching factor exposures:

Chart of the cumulative returns of hedge fund portfolios constructed from funds with the highest 5% and the lowest 5% 36-month trailing returns from security selection (αReturns)

Performance of Portfolios of Hedge Funds Based on High and Low Historical αReturns

Cumulative Return (%)

Annual Return (%)

High Historical αReturns

144.33

8.72

Low Historical αReturns

104.25

6.97

High – Low αReturns

40.08

1.75

Even with the same 6-month investment delay and bi-annual liquidity constraints, long portfolios of the top stock pickers outperformed long portfolios of the bottom stock pickers by 40% cumulatively over the past 10 years.

This outperformance has been consistent. Indeed, top stock pickers (high αReturn funds) have continued to do well in recent years. Security selection results of the industry’s top talent are strong. Widespread discussions of the difficulty of generating excess returns in 2014 reflect the sorry state of commonly used risk and skill analytics.

Conclusions

  • Due to hedge fund mean reversion, yesterday’s nominal winners tend to become tomorrow’s nominal losers.
  • Under typical hedge fund liquidity constraints, mean reversion is aggravated. Funds of top performing hedge funds under-perform.
  • Re-processing nominal returns does not eliminate mean reversion:
    • Funds with top and bottom Sharpe ratios perform similarly;
    • Funds with top win/loss ratios underperform funds with bottom win/loss ratios.
  • Risk-adjusted returns from security selection (stock picking) persist. Robust skill analytics, such as αReturn, identify strong future 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 Mean Reversion

Our earlier articles explored hedge fund survivor (survivorship) bias and large fund survivor bias. These artifacts can nearly double nominal returns and overstate security selection (stock picking) performance by 80%. Due to these biases, future performance of the largest funds disappoints. The survivors and the largest funds have excellent past nominal performance, yet it is not predictive of their future returns due to hedge fund mean reversion, a special case of reversion toward the mean. Here we explore this phenomenon and its mitigation.

We follow the approach of our earlier pieces that analyzed hedge funds’ long U.S. equity portfolios (HF Aggregate). This dataset spans the long portfolios of all U.S. hedge funds active over the past 15 years that are tractable using 13F filings.

Mean Reversion of Nominal Hedge Fund Returns

To illustrate the mean reversion of nominal hedge fund returns, we have assembled hedge fund portfolios with the highest and lowest trailing 36-month performance and track these groups over the subsequent 36 months. This covers the past 15 years and considers approximately 100 such group pairs.

If strong historical performance is predictive, we should see future (ex-post, realized) outperformance of the best historical performers relative to the worst. This would support the wisdom of chasing the largest funds or the top-performing gurus.

The following chart tracks past and future performance of each group. The average subsequent performance of the historically best- and worst-performing long U.S. equity hedge fund portfolios is practically identical and similar to the market return. There is some difference in the distributions, however: highest performers’ subsequent returns are skewed to the downside; lowest performers’ subsequent returns are skewed to the upside:

Chart of the past and future performance of hedge fund groups with high and low historical 36-month returns, assembled monthly over the past 15 years.

Hedge Fund Performance Persistence: High and Low Historical Returns

Prior 36 Months Return (%)

Subsequent 36 Months Return (%)

High Historical Returns

52.84

28.17

Low Historical Returns

-11.43

28.33

Thus, nominal historical returns are not predictive of future performance. We will try a few simple metrics of risk-adjusted performance next to see if they prove more effective.

Sharpe Ratio and Mean Reversion of Returns

Sharpe ratio is a popular measure of risk-adjusted performance that attempts to account for risk using return volatility. The following chart tracks past and future performance of portfolios with the highest and lowest historical Sharpe ratios. The average future performance of the best- and worst-performing portfolios begins to diverge, though we have not tested this difference for statistical significance:

Chart of the past and future performance of hedge fund groups with high and low historical 36-month Sharpe ratios, assembled monthly over the past 15 years.

Hedge Fund Performance Persistence: High and Low Historical Sharpe Ratios

Prior 36 Months Return (%)

Subsequent 36 Months Return (%)

High Historical Sharpe Ratios

43.65

28.38

Low Historical Sharpe Ratios

-8.34

25.66

Note that portfolios with the highest historical Sharpe ratios perform similarly to the best and worst nominal performers in the first chart. However, portfolios with the lowest historical Sharpe ratios underperform by 2.5%. Sharpe ratio does not appear to predict high future performance, yet it may help guard against poor results.

Win/Loss Ratio and Mean Reversion of Returns

Sharpe ratio and similar parametric approaches make strong assumptions, including normality of returns. We try a potentially more robust non-parametric measure of performance free of these assumptions – the win/loss ratio, closely related to the batting average. The following chart tracks past and future performance of portfolios with the highest and lowest historical win/loss ratios. The relative future performance of the two groups is similar:

Chart of the past and future performance of hedge fund groups with high and low historical 36-month win/loss ratios, assembled monthly over the past 15 years.

Hedge Fund Performance Persistence: High and Low Historical Win/Loss Ratios

Prior 36 Months Return (%)

Subsequent 36 Months Return (%)

High Historical Win/Loss Ratios

26.93

27.59

Low Historical Win/Loss Ratios

1.70

26.53

Win/loss ratio does not appear to improve on the predictive ability of Sharpe ratio. In fact, both groups slightly underperform the low performers from the first chart above.

Persistence of Hedge Fund Security Selection Returns

Nominal returns and simple metrics that rely on nominal returns both suffer from mean reversion, since systematic (factor) returns responsible for the bulk of portfolio volatility are themselves mean reverting. Proper risk adjustment with a robust risk model that eliminates systematic risk factors and purifies the residual addresses this problem.

AlphaBetaWorks’ measure of this residual security selection performance is αReturn – outperformance relative to a replicating factor portfolio. αReturn is also the return a portfolio would have generated if markets had been flat. The following chart tracks past and future security selection performance of portfolios with the highest and lowest historical αReturns. The future security selection performance of the best and worst stock pickers diverges by over 10%:

Charts of the past and future security selection (residual, αReturn) performance of hedge fund groups with high and low historical 36-month security selection (residua) returns, assembled monthly over the past 15 years.

Hedge Fund Security Selection Performance Persistence: High and Low Historical αReturns

Prior 36 Months αReturn (%)

Subsequent 36 Months αReturn (%)

High Historical αReturns

60.90

5.65

Low Historical αReturns

-35.66

-4.58

Security Selection and Persistent Nominal Outperformance

Strong security selection performance and strong αReturns can always be turned into nominal outperformance. In fact, a portfolio with positive αReturns can be hedged to outperform any broad benchmark. Nominal outperformance is convenient and easy to understand. These are the returns that investors “can eat.”

The following chart tracks past and future nominal performance of portfolios with the highest and lowest historical αReturns, hedged to match U.S. Equity Market’s risk (factor exposures). Hedging preserved security selection returns and compounded them with market performance: future performance of the two groups diverges by over 11%:

Charts of the past and future performance of hedge fund groups with high and low historical 36-month security selection (residua) returns, assembled monthly over the past 15 years and hedged to match U.S. Market.

Hedge Fund Performance Persistence: High and Low Historical αReturns

Prior 36 Months Return (%)

Subsequent 36 Months Return (%)

High Historical αReturns

81.70

32.50

Low Historical αReturns

-28.93

21.41

Note that, similarly to Sharpe ratio, αReturn is most effective in identifying future under-performers.

Thus, with predictive analytics and a robust model, investors can not only identify persistently strong stock pickets but also construct portfolios with predictably strong nominal performance.

Conclusions

  • Due to hedge fund mean reversion, future performance of the best and worst nominal performers of the past is similar.
  • Re-processing nominal returns does not eliminate mean reversion. However, Sharpe ratio begins to identify future under-performers.
  • Risk-adjusted returns from security selection (stock picking) persist. A robust risk model can isolate these returns and identify strong future stock pickers.
  • Hedging can turn persistent security selection returns into outperformance relative to any benchmark:
    • Hedged portfolio of the best stock pickers persistently outperforms.
    • Hedged portfolio of the worst stock pickers persistently underperforms.
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 – Q4 2014

Hedge funds share a few systematic and idiosyncratic bets. These crowded bets are the main sources of the industry’s relative performance and of many individual funds’ returns. We survey risk factors and stocks responsible for the majority of hedge fund long U.S. equity herding during Q4 2014.

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 liquidation. In addition, consensus hedge fund bets have underperformed in the past.

Identifying Hedge Fund Crowding

This piece follows the approach of our earlier articles on fund crowding: We created a position-weighted portfolio (HF Aggregate) consisting of popular long U.S. equity holdings of all hedge funds with medium to low turnover that are tractable from quarterly position filings. We then analyzed HF Aggregate’s risk relative to U.S. Market (Russell 3000) using AlphaBetaWorks’ Statistical Equity Risk Model to identify sources of crowding. More background information and explanations of the terms used below are available in those earlier articles.

Hedge Fund Aggregate’s Risk

The Q4 2014 HF Aggregate had 3.0% estimated future annual tracking error relative to U.S. Market. Risk was primarily due to factor (systematic) bets:

The components of HF Aggregate’s relative risk on 12/31/2014 were the following:

 Source

Volatility (%)

Share of Variance (%)

Factor

2.23

56.32

Residual

1.96

43.68

Total

2.97

100.00

Systematic risk increased by a tenth from the previous quarter. We will see the factors behind this increase below.

With an estimated future tracking error near 3%, HF Aggregate continues to be nearly passive. HF Aggregate will have a very hard time earning a typical fee. Investors in a broadly diversified portfolio of long-biased hedge funds will likely struggle also.

Hedge Fund Factor (Systematic) Crowding

Below are HF Aggregate’s principal factor exposures (in red) relative to U.S. Market’s (in gray) as of 12/31/2014:

Chart of the factor exposures contributing most to the relative factor (systematic) risk of U.S. Hedge Fund Aggregate

Factor Exposures Contributing Most to the Relative Risk of U.S. Hedge Fund Aggregate

Of these bets, Market (Beta) and Oil are responsible for over 80% of the factor risk relative to U.S. Market. These are the main components of the 2.23% Factor Volatility in the first table:

Chart of the factors contributing most to the relative factor (systematic) variance of U.S. Hedge Fund Aggregate

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

HF Aggregate has become more systematically crowded since Q3 2014. The following factors were the top contributors to the relative systematic risk on 12/31/2014:

Factor

Relative Exposure (%)

Portfoio Variance (%²)

Share of Systematic Variance (%)

Market

13.26

3.10

62.37

Oil Price

2.23

1.01

20.32

Finance

-7.49

0.43

8.65

Industrial

9.53

0.35

7.04

Utilities

-3.36

0.26

5.23

Other Factors -0.18

-3.62

Total 4.97

100.00

The increased factor risk during Q4 2014 was primarily due to a 2% increase in U.S. Market Exposure (Beta). After adding long oil exposure in Q3 2014 as the energy sector selloff intensified, hedge funds kept it steady through Q4.

Hedge Fund Residual (Idiosyncratic) Crowding

Turning to HF Aggregate’s residual variance relative to U.S. Market, eight stocks were responsible for over half of the relative residual risk:

Chart of the stocks contributing most to the relative residual (idiosyncratic) variance of U.S. Hedge Fund Aggregate

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

These stocks will be the primary drivers of HF Aggregate’s and of the most crowded firms’ returns. They will also be affected by the vagaries of capital flows into and out of hedge funds. Investors should be ready for seemingly inexplicable volatility in these names. They may be wonderful individual investments, but history is not on their side, since crowded bets have historically underperformed.

The list is mostly unchanged from the previous quarter:

Symbol

Name

Exposure (%)

Share of Idiosyncratic Variance (%)

LNG

Cheniere Energy, Inc.

1.70

15.73

AGN

Allergan, Inc.

3.53

9.51

VRX

Valeant Pharmaceuticals International, Inc.

2.35

9.18

CHTR

Charter Communications, Inc. Class A

1.80

3.88

HTZ

Hertz Global Holdings, Inc.

1.37

3.35

EBAY

eBay Inc.

1.91

3.27

MU

Micron Technology, Inc.

1.08

3.21

BIDU

Baidu, Inc. Sponsored ADR Class A

1.22

3.14

PCLN

Priceline Group Inc

1.29

2.43

SUNE

SunEdison, Inc.

0.63

2.29

When investing in these crowded names, investors should perform particularly thorough due-diligence, since any losses will be magnified when hedge funds rush for the exits.

Historically, consensus bets have done worse than a passive portfolio with the same risk. Consequently, fund allocators should thoroughly investigate hedge fund managers’ crowding to avoid investing in a pool of undifferentiated bets destined for disappointment.

AlphaBetaWorks’ analytics assist in both tasks: Our sector crowding reports identify hedge fund herding in each equity sector. Our fund reports measure hedge fund differentiation and skills that are strongly predictive of future performance.

Summary

  • There is both factor (systematic/market) and residual (idiosyncratic/security-specific) crowding of hedge funds’ long U.S. equity portfolios.
  • Hedge funds have become more systematically crowded during Q4 2014, primarily by increasing their Beta.
  • The main sources of idiosyncratic crowding are: LNG, AGN, VRX, CHTR, HTZ, EBAY, and MU.
  • The crowded hedge fund portfolio has historically underperformed its passive alternative. Investors would have made more by taking the same risk passively – 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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Large Hedge Fund Survivor Bias

Why Size Isn’t Everything

Hedge fund survivor bias is especially insidious for the largest firms. Large hedge fund survivor bias overstates expected performance of the biggest firms by nearly half and their risk adjusted return from security selection (stock picking) by 80%. It is impossible to predict the largest funds of the future, but one doesn’t have to – robust skill analytics identify funds that will do even better in the future than tomorrow’s largest.

Past Performance of Today’s Largest Hedge Funds

We follow the approach of our earlier piece on hedge fund survivor (survivorship) bias, which analyzed firms’ long U.S. equity portfolios (HF Aggregate). This dataset spans the long portfolios of all hedge funds active over the past 10 years that are tractable using 13F filings.

We compare group returns to Factor Portfolio – a portfolio with matching factor (systematic) risk. Factor Portfolio captures the return of investing passively in ETFs and index futures with the same risk as the group. This comparison reveals security selection (stock picking) performance, or αReturn – outperformance relative to the Factor Portfolio and the return that would have been generated if markets had been flat.

The following chart compares the performance of the 20 largest U.S. equity hedge fund long portfolios (Large HFs, green) to the Factor Portfolio (black). The security selection performance, or αReturn (blue), is the difference between the two. This is the average past performance of the 20 largest funds of 2015:

Chart of the past total, factor, and residual returns of long U.S. equity portfolios of the 20 largest hedge funds of 2015

Current Largest Hedge Funds: Past Total, Factor, and Residual Long U.S. Equity Returns

Returns (%)

Annualized

10-year Cum.

Total

11.48

215.26

Factor

9.33

154.29

Total – Factor

2.15

60.97

Firms that have grown the largest over the past 10 years have performed exceptionally well: Including the effect of compounding, their long portfolios generated 61% higher return than their passive equivalents. If markets had been flat for the past 10 years, their long equity portfolios would have appreciated by nearly 25%.

The allure of this past performance arouses fund-following, guru-tracking, and billionaire portfolio strategies. But there is one problem: Today’s largest funds represent a top-performing sliver of the thousands of funds active in the past. Of the thousands of funds, some truly are skilled, but many simply got lucky on aggressive bets and became large as a result, irrespective of their skill. This constitutes large hedge fund survivor bias. This performance does not persist and tends to mean-revert.

Future Performance of Yesterday’s Largest Hedge Funds

Most billionaire and guru-following strategies make the assumption that the largest funds are likely to continue generating strong returns. To test this, we tracked the 20 largest long U.S. equity hedge fund portfolios of 2005. Below is the unappealing picture of their average performance:

Chart of the future total, factor, and residual returns of long U.S. equity portfolios of the 20 largest hedge funds of 2005

2005 Largest Hedge Funds: Future Total, Factor, and Residual Long U.S. Equity Returns

Returns (%)

Annualized

10-year Cum.

Total

7.70

116.05

Factor

8.68

138.11

Total – Factor

-0.97

-22.05

The 2005 Large HF Aggregate tracked Factor Portfolio closely until 2010 and has struggled since. Hence, including the effects of compounding, large hedge fund survivor bias overstated security selection returns by 80%.

Size does not always signal quality, nor does it guarantee future performance. Between 2005 and 2015, the forward-looking performance of the largest long hedge fund portfolios of 2005 was just over half the backward-looking performance of 2015’s largest. Why then would the largest hedge funds of 2015 perform differently than the poor showing of the 2005 vintage?

Predicting Top Future Hedge Funds: Stock Picking Skill

Absent a time machine, investors cannot know who will be the future stars. However, they need not despair. Instead of focusing on the largest or top-performing funds of the past, they can turn to those showing the highest evidence of skill. The following chart tracks the long U.S. equity portfolios of 20 hedge funds with the highest 3-year αReturn as of 12/31/2005:

Chart of the future total, factor, and residual returns of long U.S. equity portfolios of the 20 best stock picker hedge funds of 2005

Best 2005 Stock Picker Hedge Funds: Future Total, Factor, and Residual Long U.S. Equity Returns

Returns (%)

Annualized

10-year Cum.

Total

12.60

252.58

Factor

9.11

148.70

Total – Factor

3.49

103.88

The funds above were the best stock pickers of 2005, not the largest. If markets had been flat for the past 10 years, the top stock pickers of 2005 would have returned 40%. For a variety of reasons (scalability constraints, lifestyle preferences), many have not become the largest or best known, but their risk-adjusted returns are strong.

Since active management skills persist, skilled stock pickers of the past continue to generate strong nominal and risk-adjusted returns. The same analysis identifies today’s top stock pickers who will be tomorrow’s outperformers – and without the cost of a time machine!

Conclusions

  • Hedge fund survivor bias is larger for the largest hedge funds.
  • Between 2005 and 2015, large hedge fund survivor bias overstated expected nominal performance by nearly 100% and security selection performance by 80%.
  • Chasing large hedge funds is unnecessary and detrimental. Selecting a fund using robust skill analytics, as illustrated by αReturn, is superior to flawed results hampered by large hedge fund survivor bias.
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 Survivor Bias

And The Flaws of Blind Fund-Following Strategies

Numerous financial data and analytics vendors peddle hedge fund tracking strategies and content. Much of this data is hazardous to investors – Hedge fund survivor bias, a special case of the pervasive survivorship bias, is its key flaw. This artifact overstates nominal fund returns by a fifth and conceals mediocre risk-adjusted performance records.

This post is technical, but it illustrates an important phenomenon and sets up the foundation for upcoming articles. We analyze the long equity portfolios of approximately 1,000 medium and lower turnover non-quantitative hedge funds active over the past 10 years (HF Aggregate). This dataset spans the long portfolios of all non-quantitative hedge funds active over the past 10 years that are tractable using 13F filings.

HF Aggregate consists of two approximately equal sub-sets: HF Surviving Aggregate and HF Defunct Aggregate. HF Surviving Aggregate, similar to the datasets of many vendors, gives a deeply misleading picture of average hedge fund performance. Our HF Aggregate corrects this by including HF Defunct Aggregate – funds that stopped filing 13Fs as their U.S. assets dropped below $100 million.

All Hedge Fund Performance

We compare HF Aggregate to Factor Portfolio – a portfolio with matching factor (systematic) risk. Factor Portfolio captures the return investors would have realized if they had passively invested in ETFs and index futures with the same risk as HF Aggregate. We do this to calculate security selection (stock picking) returns of HF Aggregate.

With the exception of the 2009-2011 period, HF Aggregate generated negative returns from security selection. AlphaBetaWorks’ measure of security selection performance is αReturn – outperformance relative to the Factor Portfolio. αReturn is also the return HF Aggregate would have generated if markets were flat. Since 2011, HF Aggregate’s αReturn was -2%. If markets had been flat, the average medium-turnover long hedge fund portfolio would have lost 2% from its long portfolio. Including the effects of compounding with factor returns, αReturn was -3%.

Putting these elements together, the chart below compares HF Aggregate’s performance (green) to the Factor Portfolio (black). The security selection performance, or αReturn (blue), is the difference between the two. This is the true long performance of the average hedge fund:

Chart of the cumulative total, factor, and residual/security selection performance of all medium turnover hedge fund U.S. equity portfolios, free from hedge fund survivor bias

All Medium Turnover U.S. Hedge Fund Long Portfolios: Factor, Residual, and Total Returns

Performance (%)

Annualized

10-year
Total

8.48

133.57

Factor

8.60

136.39

Total – Factor

-0.12

-2.82

Survivor Hedge Fund Performance – Survivorship Bias in Action

The figures above contrast with those promoted by many data vendors and analytics providers. They typically consider (or provide data on) the survivors only – those funds that are still around, active, and reporting their holdings – HF Surviving Aggregate.

Indeed, the performance of surviving hedge funds is superior: their nominal return is 26% higher than HF Aggregate’s and their security selection performance is positive. Not surprisingly, surviving funds have consistently generated positive risk-adjusted returns from security selection, outperforming the replicating Factor Portfolio. This is the performance investors typically see:

Chart of the cumulative total, factor, and residual/security selection performance of surviving medium turnover hedge fund U.S. equity portfolios, affected by the hedge fund survivor bias

Surviving Medium Turnover U.S. Hedge Fund Long Portfolios: Factor, Residual, and Total Returns

Performance (%)

Annualized

10-year

Total

9.54

159.57

Factor

9.06

147.37

Total – Factor

0.48

12.20

Defunct Hedge Fund Performance

The disconnect between these two pictures of average hedge fund performance is due to survivor bias. Of the approximately 1,000 medium turnover hedge funds tractable using 13Fs that have been active filers over the past 10 years, only half remain. The defunct half dropped out of many databases and out of HF Surviving Aggregate. HF Defunct Aggregate struggled under low factor returns and poor security selection. This is the under-performance swept under the rug:

Chart of the cumulative total, factor, and residual/security selection performance of defunct medium turnover hedge fund U.S. equity portfolios, excluded to cause hedge fund survivor bias

Defunct Medium Turnover U.S. Hedge Fund Long Portfolios: Factor, Residual, and Total Returns

Performance (%)

Annualized

10-year

Total

6.07

83.52

Factor

7.14

104.12

Total – Factor

-1.06

-20.60

The difference in performance between surviving and defunct funds is especially dramatic post-2008:

  • Surviving and defunct hedge funds’ long portfolios show similar nominal returns through 2008. Surviving hedge funds are slightly ahead with a 5% higher αReturns.
  • The 2008 draw-down for surviving and defunct hedge funds is similar. Both groups generate negative αReturns: widespread portfolio liquidation devastates crowded hedge fund bets across both groups.
  • From 2009 the survivors decouple from the defunct funds: Defunct funds trim exposures. Surviving funds boost exposures.
  • Since 2009 HF Surviving Aggregate outperforms HF Defunct Aggregate by over 70%. Approximately half is due to higher systematic risk and half is due to security selection.
  • Survival is mostly a matter of exposure and stock picking.

Absent a time machine, investors and fund followers cannot know who will be the future survivors. HF Defunct Aggregate consists of survivors that did well enough to last until 2005, but subsequently perished. Unfortunately, many strategies are built on a swampy foundation – the assumption that the average hedge fund is the same as the average surviving hedge fund. Real fund performance is a fifth lower.

Consequently, robust skill analytics developed with the understanding of hedge fund survivor bias are critical to keep investors out of yesterday’s winners that tend to become tomorrow’s losers.

Conclusions

  • Historical performance of surviving hedge funds overstates actual average returns by a fifth.
  • Hedge fund survivor bias boosts 10-year nominal returns by 26%, primarily post-2008.
  • Hedge fund survivor bias boosts 10-year security selection returns by approximately 15%.
  • Fund performance and holdings studies that ignore survivor bias will deliver misleading conclusions and disappointing 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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Foreign Sectors Exposed to Strong USD

In an earlier article we discussed the U.S. sectors most affected by volatility in the U.S. Dollar. This analysis raised a number of questions from readers and clients:

  • For U.S. exporters hurt by strong USD: Do foreign competitors benefit, exhibiting the opposite (positive) USD exposure?
  • For U.S. retailers and distributors aided by strong USD: Do foreign suppliers benefit, exhibiting similar (positive) USD exposure?

Both intuitions are correct. Foreign transportation and technology companies turn out to be the top beneficiaries of appreciating USD.

U.S. Information Technology Sector USD FX Exposure

Recall from our earlier piece that U.S. Information Technology is one of the sectors with the highest negative correlation to USD:

Sector

USD FX
Correlation

USD FX
Correlation
p-value
USD FX
Beta

USD FX
Beta
p-value

Contract Drilling

-0.45

0.0002 -1.01

0.0006

Integrated Oil

-0.39

0.0011 -0.56

0.0011

Coal

-0.36

0.0021 -1.10

0.0004

Oilfield Services Equipment

-0.34

0.0042 -0.69

0.0059

Information Technology Services

-0.30

0.0109 -0.27

0.0373

Oil and Gas Production

-0.27

0.0174 -0.44

0.0131

Information Technology Services is an export industry that suffers when USD-denominated costs increase relative to foreign-currency-denominated revenues. USD appreciation squeezes margins and puts the sector at a disadvantage relative to foreign competitors. Consequently, we expect foreign technology companies to benefit from appreciating USD.

U.S. Retail Sector USD FX Exposure

U.S. Retail and Distribution are among the sectors with the highest positive correlation to USD:

Sector

USD FX Correlation

USD FX
Correlation
p-value
USD FX
Beta

USD FX
Beta
p-value

Real Estate Investment Trusts

0.29

0.0121 0.39

0.0101

Pulp and Paper

0.30

0.0102 0.52

0.0123

Aerospace and Defense

0.31

0.0084 0.32

0.0206

Beverages Alcoholic

0.33

0.0049 0.43

0.0025

Catalog Specialty Distribution

0.33

0.0045 0.41

0.0349

Department Stores

0.37

0.0020 0.70

0.0085

These businesses are sensitive to the price of imports and to the consumers’ purchasing power. When USD appreciates, U.S. retailers benefit from the drop in the price of imports and from the boost in U.S. consumers’ purchasing power. USD appreciation should also benefit foreign suppliers of U.S. retailers. Consequently, we expect foreign exporters and transportation companies to benefit from appreciating USD.

Foreign Sectors Most Positively Exposed to USD FX

There are two common techniques to quantify relationship between two variables: correlation and beta (leverage). Correlation between pure sector factor returns and USD returns quantifies the consistency of the relationship – how much of the sector variance is attributable to USD FX. Beta, or leverage, of pure sector factor returns relative to USD returns quantifies the magnitude of the relationship – how much sector changes given a change in USD FX.

Foreign Sectors with Highest USD Correlation

Foreign sectors most correlated to USD FX are dominated by transportation and technology companies. When USD appreciates, these businesses benefit the most from reduced competitiveness of U.S. Information Technology Industry, increased appetites of U.S. consumers, and decreased commodity prices:

Chart of international sector factors with market variance removed showing the highest correlation to USD FX

International Pure Sector Factors with Highest USD Correlation

Sector

USD FX
Correlation

USD FX Correlation
p-value
USD FX
Beta

USD FX Beta
p-value

China: Medical Distributors

0.28

0.0150 0.74

0.0223

Japan: Marine Shipping

0.31

0.0073 0.61

0.0206

Hong Kong: Wireless Telecommunications

0.31

0.0071 0.66

0.0061

Netherlands: Misc. Transportation

0.34

0.0043 0.93

0.0047

Germany: Semiconductors

0.42

0.0004 1.00

0.0033

Australia: Misc. Transportation

0.52

0.0000 1.24

0.0000

Foreign Sectors with Highest USD Beta

Likewise, foreign sectors with the highest beta (most leverage) to USD FX are dominated by transportation and technology companies. Chinese auto parts companies are another winner. Foreign Semiconductor and Auto Parts Sectors benefit from the reduced competitiveness of their U.S. competitors:

Chart of international sectors with market variance removed showing the highest beta to USD FX

International Pure Sector Factors with Highest USD Beta

Sector

USD FX
Beta

USD FX Beta
p-value

China: Wholesale Distributors

0.81

0.0082

China: Auto Parts OEM

0.83

0.0191

Netherlands: Misc. Transportation

0.93

0.0047

Germany: Semiconductors

1.00

0.0033

France: Semiconductors

1.02

0.0082

Australia: Misc. Transportation

1.24

0.0000

Conclusions

  • By stripping away the effects of broad markets, we reveal the performance of pure sector factors and their relationships with USD FX.
  • U.S. importers and retailers most consistently benefit from appreciating USD.
  • U.S. commodity producers and information technology exporters most consistently suffer from appreciating USD.
  • The top foreign beneficiaries of these trends are Transportation, Technology, and Auto Parts 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 © 2012-2015, 
AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved.
Content may not be republished without express written consent.
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