Tag Archives: hedge fund crowding

Hedge Fund Crowding Costs: Q3 2015

Applying a robust risk model to hedge fund holdings data helps avoid losses and yields profitable opportunities. In this article, we highlight the sectors with the largest Hedge Fund losses due to crowding in Q3 2015, which sum to $4 billion. Our methodology provides an early-warning system for losses in crowded names. This analysis also identifies crowded stocks beaten up by hedge fund liquidations, which tend to mean-revert.

Analyzing Hedge Fund Sector Crowding

Our edge comes from a central thesis: the most crowded stocks are those that contribute the most to hedge fund stock-specific volatility (volatility of alpha). Furthermore, the direction of this alpha (positive or negative) is a leading indicator. A robust analysis of the AlphaBetaWorks Statistical Equity Risk Model allows us to identify stocks that are the highest contributors to stock-specific volatility for hedge funds in each sector.  These are the most crowded stocks that stand to benefit the most from accumulation and stand to lose the most from liquidation.

While a static crowding analysis using our risk model provides valuable insights, we go further by identifying Hedge Fund Aggregate Sector Alpha – the alpha (stock-specific performance) of aggregated hedge fund portfolios by sector.  This makes the analysis dynamic: If Hedge Fund Aggregate Sector Alpha is trending up, capital is flowing into crowded stocks. Conversely, if it is trending down, capital is flowing out of crowded stocks – often abruptly. Yes, crowding is good at some times and bad at others.  Further, Hedge Fund Aggregate Sector Alpha trends persist for months and years, providing advanced notice of losses. Importantly, crowded stocks hit hard by liquidations tend to mean-revert: the worst risk-adjusted performers often become attractive long opportunities.

Hedge Fund Sector Aggregates

We create aggregate portfolios of hedge fund positions in each sector. Each such sector portfolio is a Hedge Fund Sector Aggregate within which we identify the highest contributors to security-specific (residual) volatility (the most crowded stocks). This follows the approach of our earlier articles on hedge fund crowding.

The Hedge Fund Sector Aggregate Alpha (αReturn, residual, or security-specific return) measures hedge fund security selection performance in a sector. It is the return HF Sector Aggregate would have generated if markets had been flat. αReturn can indicate accumulations and liquidations.

The AlphaBetaWorks Statistical Equity Risk Model, a proven tool for forecasting portfolio risk and performance, estimated factor exposures and residuals. Without an effective risk model, simplistic crowding analyses ignore the systematic and idiosyncratic exposures of positions and typically merely identify companies with the largest market capitalizations.

Sectors with the Largest Losses from Hedge Fund Crowding

During Q3 2015, hedge funds lost $4 billion to security selection in the five sectors below. Said another way: if hedge funds had simply invested passively with the same risk, their sector long equity portfolios would have made $4 billion more. The monthly losses are listed (in $millions) below:

7/31/2015 8/31/2015 9/30/2015 Total
Other Consumer Services -101.16 -113.93 -312.84 -426.77
Oil and Gas Pipelines 472.21 -465.63 -10.29 -475.93
Specialty Chemicals -155.87 196.41 -730.73 -534.32
Oil Refining and Marketing 262.69 -167.15 -388.52 -555.67
Semiconductors -240.71 -1,422.70 -660.95 -2,083.65

The Semiconductor Sector was particularly painful for hedge funds in Q3 2015, which we examined in a previous article.

Below we provide our data on three of the above sectors: historical Hedge Fund Sector Alpha and the most crowded names.

Specialty Chemicals – Hedge Fund Alpha and Crowding

Hedge Fund Specialty Chemicals Security Selection Performance

Hedge Fund Specialty Chemicals Sector Aggregate Historical Security Selection Performance

Historical Return from Security Selection of Hedge Fund Specialty Chemicals Sector Aggregate

Hedge Fund Specialty Chemicals Crowding

Chart of the stock-specific hedge fund crowding the cumulative contributors to the residual variance of Hedge Fund Specialty Chemicals Sector Aggregate Portfolio relative to Market

Crowded Hedge Fund Specialty Chemicals Sector Bets

The following table contains detailed data on these crowded holdings:

Exposure (%) Net Exposure Share of Risk (%)
HF Sector Aggr. Sector Aggr. % $mil Days of Trading
GB:PAH Platform Specialty Products Corp. 17.59 2.52 15.07 1,351.8 14.3 44.62
APD Air Products and Chemicals, Inc. 47.46 13.89 33.57 3,010.8 13.7 22.09
LYB LyondellBasell Industries NV 3.36 23.03 -19.67 -1,764.2 -5.9 14.04
GRBK Green Brick Partners, Inc. 2.99 0.25 2.74 245.7 79.7 10.58
GRA W. R. Grace \& Co. 11.76 3.45 8.32 745.8 11.0 2.99
PX Praxair, Inc. 0.31 16.29 -15.98 -1,433.5 -5.9 2.21
AXLL Axiall Corporation 2.79 1.20 1.59 142.8 4.5 0.74
TROX Tronox Ltd. 1.80 0.45 1.35 121.2 14.2 0.36
ARG Airgas, Inc. 0.19 3.77 -3.59 -321.8 -4.1 0.33
SIAL Sigma-Aldrich Corporation 3.32 7.88 -4.56 -408.6 -2.3 0.28
NEU NewMarket Corporation 0.23 2.61 -2.38 -213.4 -6.0 0.26
VHI Valhi, Inc. 0.02 0.91 -0.88 -79.2 -240.2 0.26
CYT Cytec Industries Inc. 0.07 2.04 -1.97 -176.5 -2.0 0.18
ASH Ashland Inc. 1.66 3.89 -2.23 -200.0 -2.4 0.18
POL PolyOne Corporation 0.19 1.65 -1.46 -131.2 -4.3 0.10
TANH Tantech Holdings Ltd. 0.00 0.19 -0.19 -17.3 -2.7 0.09
BCPC Balchem Corporation 0.00 0.82 -0.82 -73.4 -8.8 0.07
CBM Cambrex Corporation 0.06 0.65 -0.59 -53.2 -2.1 0.06
CMP Compass Minerals International, Inc. 0.15 1.31 -1.16 -104.0 -4.8 0.06
Other Positions 0.29 0.51
Total 100.00

Oil Refining and Marketing – Hedge Fund Alpha and Crowding

Hedge Fund Oil Refining and Marketing Security Selection Performance

Hedge Fund Oil Refining and Marketing Sector Aggregate Historical Security Selection Performance

Historical Return from Security Selection of Hedge Fund Oil Refining and Marketing Sector Aggregate

Hedge Fund Oil Refining and Marketing Crowding

Chart of the stock-specific hedge fund crowding the cumulative contributors to the residual variance of Hedge Fund Oil Refining and Marketing Sector Aggregate Portfolio relative to Market

Crowded Hedge Fund Oil Refining and Marketing Sector Bets

The following table contains detailed data on these crowded holdings:

Exposure (%) Net Exposure Share of Risk (%)
HF Sector Aggr. Sector Aggr. % $mil Days of Trading
MWE MarkWest Energy Partners, L.P. 18.23 5.31 12.92 848.9 6.1 31.86
VLO Valero Energy Corporation 0.38 16.06 -15.68 -1,030.4 -2.7 23.34
TSO Tesoro Corporation 14.32 5.36 8.96 589.0 1.4 12.74
TRGP Targa Resources Corp. 8.99 2.52 6.47 425.3 8.7 7.76
PSX Phillips 66 9.21 21.86 -12.66 -831.8 -2.8 6.03
PBF PBF Energy, Inc. Class A 6.80 1.23 5.56 365.6 7.8 5.84
NGLS Targa Resources Partners LP 8.74 3.52 5.21 342.7 6.2 2.84
WGP Western Gas Equity Partners LP 3.58 6.63 -3.05 -200.5 -7.4 2.06
MPC Marathon Petroleum Corporation 9.59 14.34 -4.75 -312.0 -1.1 1.81
TLLP Tesoro Logistics LP 5.12 2.33 2.79 183.1 3.5 1.45
HFC HollyFrontier Corporation 1.29 4.22 -2.93 -192.3 -1.4 1.11
WNR Western Refining, Inc. 0.21 2.10 -1.89 -124.5 -1.4 0.61
IOC Interoil Corporation 0.66 1.50 -0.84 -55.3 -6.9 0.49
GEL Genesis Energy, L.P. 4.35 2.20 2.15 141.1 6.2 0.34
ENBL Enable Midstream Partners LP 0.39 1.73 -1.34 -88.2 -31.6 0.33
EMES Emerge Energy Services LP 0.01 0.43 -0.42 -27.6 -6.1 0.29
DK Delek US Holdings, Inc. 0.00 1.07 -1.07 -70.0 -1.2 0.26
WNRL Western Refining Logistics, LP 1.57 0.36 1.21 79.5 15.0 0.24
ALJ Alon USA Energy, Inc. 0.00 0.67 -0.67 -44.1 -2.3 0.18
NS NuStar Energy L.P. 3.50 2.33 1.17 76.9 1.4 0.15
Other Positions 0.07 0.28
Total

Semiconductors – Hedge Fund Alpha and Crowding

Hedge Fund Semiconductor Security Selection Performance

Hedge Fund Semiconductors Sector Aggregate Historical Security Selection Performance

Historical Return from Security Selection of Hedge Fund Semiconductors Sector Aggregate

Given the magnitude of recent semiconductor sector liquidations and the record of mean-reversions, the following crowded hedge fund semiconductor bets may now be especially attractive:

Hedge Fund Semiconductor Crowding

Chart of the stock-specific hedge fund crowding the cumulative contributors to the residual variance of Hedge Fund Semiconductors Sector Aggregate Portfolio relative to Market

Crowded Hedge Fund Semiconductors Sector Bets

The following table contains detailed data on these crowded holdings:

Exposure (%) Net Exposure Share of Risk (%)
HF Sector Aggr. Sector Aggr. % $mil Days of Trading
SUNE SunEdison, Inc. 33.18 1.82 31.36 2,550.9 9.6 86.72
MU Micron Technology, Inc. 18.87 3.95 14.93 1,214.1 2.9 8.85
INTC Intel Corporation 3.72 27.94 -24.22 -1,970.2 -1.6 2.01
SEMI SunEdison Semiconductor, Inc. 3.22 0.14 3.08 250.7 52.5 0.38
SWKS Skyworks Solutions, Inc. 0.04 3.85 -3.82 -310.4 -0.9 0.38
TXN Texas Instruments Incorporated 0.09 10.38 -10.28 -836.6 -1.9 0.32
NXPI NXP Semiconductors NV 7.90 4.41 3.49 283.6 1.0 0.29
AVGO Avago Technologies Limited 3.29 6.69 -3.40 -276.3 -0.5 0.18
FSL Freescale Semiconductor Inc 0.02 2.40 -2.38 -193.5 -5.2 0.17
ON ON Semiconductor Corporation 3.39 0.97 2.42 196.6 4.3 0.08
MLNX Mellanox Technologies, Ltd. 1.89 0.43 1.45 118.3 0.7 0.08
BRCM Broadcom Corporation Class A 7.81 5.51 2.30 187.2 0.5 0.07
MX MagnaChip Semiconductor Corporation 0.92 0.05 0.87 70.9 31.2 0.07
ADI Analog Devices, Inc. 0.05 3.90 -3.85 -312.9 -1.7 0.06
QRVO Qorvo, Inc. 1.13 2.32 -1.19 -96.7 -1.1 0.06
NVDA NVIDIA Corporation 0.58 2.10 -1.51 -123.1 -0.4 0.04
GB:0Q19 CEVA, Inc. 1.25 0.08 1.17 95.5 30.7 0.04
MRVL Marvell Technology Group Ltd. 0.04 1.32 -1.28 -104.4 -0.9 0.03
MXIM Maxim Integrated Products, Inc. 0.34 1.90 -1.56 -126.9 -1.7 0.02
MXL MaxLinear, Inc. Class A 0.74 0.12 0.62 50.6 2.8 0.02
Other Positions 0.36 0.13
Total

Conclusions

  • Data on the crowded names and their alpha can reduce losses and provide profitable investment opportunities.
  • A robust and predictive equity risk model is necessary to accurately identify hedge fund crowding.
  • Allocators aware of crowding can gain new insights into portfolio risk, manager skill, and fund differentiation.
  • Crowded bets tend to mean-revert following liquidation: the worst risk-adjusted performers in a sector become the best.
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 Energy Clustering: Q2 2015

Fund crowding consists of investment bets shared by groups of funds. Long hedge fund portfolios crowd into clusters with similar systematic (factor) and idiosyncratic (residual) bets. This clustering exists for the aggregate market and for individual sectors; it is the internal structure of hedge fund crowding.

This piece surveys hedge fund clustering in the energy sector and examines the largest hedge fund energy cluster in which:

  • Factor crowding is due to high exposures to two factors;
  • Residual crowding is primarily due to six stock-specific bets.

Allocators who are unaware of hedge fund clustering and exposures within these clusters may be paying active fees for high passive risk. These investors will suffer in periods of stress.

Hedge Fund Crowding and Hedge Fund Clustering

Several of our earlier articles on hedge fund crowding analyzed the factor (systematic) and residual (idiosyncratic) bets of HF Aggregate, which consists of the equity holdings of long U.S. hedge fund portfolios tractable from regulatory filings.

Analysis of the overall hedge fund crowding does not address bets shared by fund groups within the aggregate, nor does it consider the crowding within market sectors. To explore this internal structure of hedge fun crowding we pioneered the study of hedge fund clustering. Our 2014 work proved predictive and invaluable to allocators. This piece dives deeper, focusing on hedge fund clustering in the energy sector in Q2 2015.

Hedge Fund Energy Clusters

To explore hedge fund energy clustering we analyze long energy sector portfolios of all hedge funds that are analyzable using regulatory filings. We then exclude funds with insignificant energy holdings. We use the AlphaBetaWorks Statistical Equity Risk Model, a proven tool for forecasting portfolio risk and performance. For each portfolio pair we estimate the future relative volatility (tracking error). The lower the expected relative tracking error between funds, the more similar they are to each other.

Once each hedge fund pair is analyzed we identify groups of funds with like exposures and build clusters (similar to phylogenic trees, or family trees) of the funds’ long portfolios. We use agglomerative hierarchical clustering with estimated future relative tracking error as the metric of differentiation or dissimilarity:

Chart of clustering of U.S. Hedge Funds’ Q2 2015 Long Equity Energy Sector Portfolios

Clusters of U.S. Hedge Funds’ Long Energy Sector Equity Portfolios: Q2 2015

The largest hedge fund energy sector cluster contains approximately 20 funds. Its members share similar systematic and idiosyncratic energy bets that we will now analyze in detail.

The Gateway-San Francisco Sentry Energy Cluster

The largest cluster is the Gateway-San Francisco Sentry Cluster, named after two of its large members with similar long energy bets: Gateway Investment Advisers LLC and San Francisco Sentry Investment Group:

Chart of clustering within the largest cluster of U.S. Hedge Funds’ Q2 2015 Long Equity Energy Sector Portfolios

The Largest Hedge Fund Long Energy Equity Portfolio Cluster: Q2 2015

A flat diagram of the cluster better illustrates the distances (estimated future tracking errors) among its members:

Chart of the flat view of the chart of clustering within the Gateway-San Francisco Sentry cluster of U.S. Hedge Funds’ Q2 2015 Long Energy Equity Portfolios

Flat View of the Gateway-San Francisco Sentry Long Energy Equity Portfolio Cluster: Q2 2015

About two thirds of this cluster’s risk relative to the (Market) Energy Aggregate (a capitalization-weighted portfolio of all U.S. energy stocks) comes from factor exposures:

Source Volatility (%) Share of Variance (%)
Factor 4.08 66.93
Residual 2.87 33.07
Total 4.99 100.00

We expect this cluster’s aggregate annual return to differ from the Energy Aggregate’s annual return by more than 5.0% only about a third of the time. We expect its factor (systematic) return to differ from the Energy Aggregate by more than 4.1% about a third of the time. In other words, this cluster will stand out from the Market Energy Portfolio little. When it does, this will be primarily due to high factor (systematic) risk that investors can purchase with cheap passive instruments. In fact, we show below that the cluster largely turns out to be a 1.2x levered version of the Market Energy Aggregate.

Gateway-San Francisco Sentry Energy Cluster’s Factor (Systematic) Crowding

Below are this cluster’s significant factor exposures (in red) relative to the Energy Aggregate (in gray):

Chart of exposures to the risk factors contributing most to the risk of the Gateway-San Francisco Sentry hedge fund long energy sector equity portfolio cluster relative to the U.S. Energy Sector Market Aggregate

Factor Exposures of the Gateway-San Francisco Sentry Energy Portfolio Cluster

Market (high market beta) and Energy (high energy sector beta) exposures are responsible for almost 90% of this cluster’s relative factor risk:

Chart of contributions to the relative factor (systematic) variance of the risk factors contributing most to the risk of the Gateway-San Francisco Sentry hedge fund long energy sector equity portfolio cluster relative to the U.S. Energy Sector Market Aggregate

Factors Contributing Most to Relative Variance of the Gateway-San Francisco Sentry Energy Portfolio Cluster

Factor Portfolio Relative Exposure (%) Factor Volatility (%) Portfolio Relative Factor Variance (%²) Share of Total Factor Variance (%)
Energy 18.56 12.63 7.83 47.01
Market 20.55 11.16 6.77 40.63
Oil Price 2.31 31.38 2.33 13.98
FX -3.54 7.71 0.76 4.58
Value 5.97 13.45 0.70 4.19
Size 2.63 8.00 -0.18 -1.06
Bond Index 23.48 3.37 -1.55 -9.32
Other Factors 0.00 0.00
Total 16.66 100.00

Funds in the cluster are currently taking and have tended to take 10-20% more sector risk and market risk than the Energy Aggregate. This is significant crowding towards higher systematic risk: The cluster will outperform when market and energy sector returns are positive simply due to high factor exposures. It will suffer in periods of stress.

Gateway-San Francisco Sentry Energy Cluster’s Residual (Idiosyncratic) Crowding

Six stocks in the Gateway-San Francisco Sentry Portfolio Cluster are responsible for over half of the relative residual risk. This crowding away from the majors (XOM and CVX) with low systematic risk and towards higher-risk independents helps explain high factor exposures:

Chart of contributions to the relative residual (idiosyncratic) variance of the stocks contributing most to the risk of the Gateway-San Francisco Sentry hedge fund long energy sector equity portfolio cluster relative to the U.S. Energy Sector Market Aggregate

Stocks Contributing Most to Relative Residual Variance of the Gateway-San Francisco Sentry Energy Portfolio Cluster

Symbol Name Relative Exposure (%) Residual Volatility (%) Portfolio Relative Residual Variance (%²) Share of Total Residual Variance (%)
XOM  Exxon Mobil Corporation -14.52 14.45 1.31 15.93
WLB  Westmoreland Coal Company 2.21 48.07 0.72 8.78
CHK  Chesapeake Energy Corporation 3.50 37.10 0.67 8.10
TSO  Tesoro Corporation 2.92 36.16 0.61 7.41
NBL  Noble Energy Inc. 4.54 27.04 0.48 5.81
SXCP  SunCoke Energy Partners LP 2.86 25.72 0.37 4.47
SXC  SunCoke Energy Inc. 2.60 36.02 0.37 4.46
VLO  Valero Energy Corporation -2.27 33.28 0.36 4.35
APC  Anadarko Petroleum Corporation 3.64 26.63 0.32 3.87
HFC  HollyFrontier Corporation 2.09 34.08 0.31 3.80
BBG  Bill Barrett Corporation 1.65 45.92 0.28 3.39
AR  Antero Resources Corporation 2.71 33.79 0.26 3.17
CVX  Chevron Corporation -6.39 17.84 0.26 3.14
OGZPY  Public Joint-Stock Company Gazprom 1.97 33.89 0.20 2.44
WPZ  Williams Partners L.P. -2.24 21.12 0.18 2.16
CVI  CVR Energy Inc. 1.14 41.06 0.15 1.84
AHGP  Alliance Holdings GP L.P. 2.07 21.94 0.15 1.76
EOG  EOG Resources Inc. -2.09 25.87 0.14 1.72
COP  ConocoPhillips -3.19 21.17 0.13 1.60
FANG  Diamondback Energy Inc. 1.45 35.29 0.09 1.07
 Other Positions -4.67 0.88 10.73
Total 8.23 100

Idiosyncratic crowding is not the main problem for investors in the cluster – systematic crowding into higher factor exposures is a bigger challenge: Allocators are at risk of paying high fees for mostly passive factor portfolios with high energy and market exposures.

Summary

  • An analysis of the underlying structure of hedge fund crowding reveals hedge fund clustering – groups of portfolios with similar bets.
  • Hedge fund clustering exists across aggregate and sector-specific portfolios.
  • The largest hedge fund energy cluster’s factor herding is towards high Market (high market beta) and high Energy (high energy sector beta) exposures.
  • This cluster’s residual herding is away from XOM and towards WLB, CHK, TSO, NBL, and SXCP.
  • Allocators unaware of their funds’ clustering may be exposed to unexpectedly high systematic risk due to factor crowding, costly in periods of stress.
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 Clustering: Q2 2015 Update

Fund crowding consists of investment bets shared by groups of funds – large pools of capital chasing similar strategies. Within the hedge fund industry, long equity portfolios crowd into several clusters with similar systematic (factor) and idiosyncratic (residual) bets. This hedge fund clustering is the internal structure of hedge fund crowding.

This piece illustrates the large-scale hedge fund clustering and examines the largest hedge fund cluster in which:

  • Factor crowding is due to two factors;
  • Residual crowding is moderate and four stock-specific bets stand out.

Allocators who are unaware of hedge fund clustering and hedge fund crowding may be invested in an undifferentiated portfolio, paying active fees for passive factor exposure.

Hedge Fund Crowding and Hedge Fund Clustering

Several of our earlier articles on hedge fund crowding analyzed the factor (systematic) and residual (idiosyncratic) bets of HF Aggregate, which consists of the popular equity holdings of all long U.S. hedge fund portfolios tractable from regulatory filings.

Analysis of overall industry crowding does not address bets shared by fund groups within the aggregate. To explore this internal structure of hedge fun crowding – clusters of funds with shared systematic (factor) and idiosyncratic (residual) bets – in 2014 we released pioneering research on hedge fund clustering. The 2014 work proved predictive and invaluable to allocators. This piece updates the analysis of hedge fund clustering with Q2 2015 holdings data.

Hedge Fund Clusters

To explore hedge fund clustering we analyze long portfolios of every pair of hedge funds analyzable using regulatory filings using the AlphaBetaWorks’ Statistical Equity Risk Model, a proven tool for forecasting portfolio risk and future performance. For each portfolio pair we estimate the future relative volatility (tracking error). The lower the expected relative tracking error between two funds, the more similar they are to each other.

Once each hedge fund pair is analyzed – hundreds of thousands of factor-based risk analyses – we identify groups of funds with similar factor and residual exposures and build clusters (similar to phylogenic trees, or family trees) of the funds’ long portfolios. We use agglomerative hierarchical clustering with estimated future relative tracking error as the metric of differentiation or dissimilarity. The result is a picture of clustering among all analyzable U.S. hedge funds’ long portfolios:

Chart of clustering of U.S. Hedge Funds’ Q2 2015 Long Equity Portfolios

Clusters of U.S. Hedge Funds’ Long Equity Portfolios: Q2 2015

The largest cluster contains approximately 50 funds. A number of portfolios had exposures that were so similar, we expect their relative annual volatility to be under 3% – their annual returns should differ from one another by less than 3% about two thirds of the time.

This is critical for allocators: if they are invested in clustered funds, they may be paying high active fees for a handful of passive factor bets and consensus stock picks.

The AQR-Adage Hedge Fund Cluster

The largest cluster is currently the AQR-Adage Cluster, named after two of its large members with similar long exposures:

Chart of clustering within the largest cluster of U.S. Hedge Funds’ Q2 2015 Long Equity Portfolios

The Largest Hedge Fund Long Equity Portfolio Cluster: Q2 2015

A flat diagram of the cluster better illustrates the distances (estimated future tracking errors) between its members:

Chart of the flat view of the chart of clustering within the AQR-Adage cluster of U.S. Hedge Funds’ Q2 2015 Long Equity Portfolios

Flat View of the AQR-Adage Long Equity Portfolio Cluster: Q2 2015

In aggregate, this cluster’s risk is very close to that of the U.S. equity market. We estimate the AQR-Adage Cluster’s expected tracking error relative to the Russell 3000 Index at 1.4%.

Source

Volatility (%)

Share of Variance (%)

Factor

0.97

48.18

Residual

1.01

51.82

Total

1.40

100.00

Put differently, we expect this cluster’s aggregate long portfolio return to differ from the market by more than 1.4% only about a third of the time.

AQR-Adage Cluster Factor (Systematic) Crowding

Below are this cluster’s significant factor exposures (in red) relative to the Russell 3000’s exposures (in gray):

Chart of exposures to the risk factors contributing most to the risk of the AQR-Adage hedge fund long equity portfolio cluster relative to the U.S. Market

Factor Exposures of the AQR-Adage Hedge Fund Cluster: Q2 2015

Market (high-beta) and Size (small-cap) exposures are responsible for most of this cluster’s relative factor risk:

Chart of contributions to the relative factor (systematic) variance of the risk factors contributing most to the risk of the AQR-Adage hedge fund long equity portfolio cluster relative to the U.S. Market

Factors Contributing Most to Relative Variance of the AQR-Adage Hedge Fund Cluster: Q2 2015

Factor

Relative Exposure (%)

Portfolio Variance (%²)

Share of Systematic Variance (%)

Market

5.18

0.44

46.48

Size

-4.65

0.16

16.46

Oil Price

0.90

0.15

15.38

Finance

-5.61

0.12

12.19

Utilities

-2.58

0.10

10.29

Other Factors

-0.03

-0.80

Total

0.94

100.00

AQR-Adage Cluster Residual (Idiosyncratic) Crowding

There is less residual crowding in the AQR-Adage Cluster than in HF Aggregate. For HF Aggregate, just three stocks were responsible for over half of the relative residual risk in Q2 2015. By contrast, in the AQR-Adage Cluster, four stocks are responsible for approximately a quarter of the relative residual risk:

Chart of contributions to the relative residual (idiosyncratic) variance of the stocks contributing most to the risk of the AQR-Adage hedge fund long equity portfolio cluster relative to the U.S. Market

Stocks Contributing Most to Relative Residual Variance of the AQR-Adage Hedge Fund Cluster: Q2 2015

Symbol Name

Exposure (%)

Share of Idiosyncratic Variance (%)

AAPL Apple Inc.

-1.67

8.34

CHTR Charter Communications, Inc. Class A

1.09

5.97

FOLD Amicus Therapeutics, Inc.

0.35

5.60

SBAC SBA Communications Corporation

1.40

3.57

PCRX Pacira Pharmaceuticals, Inc.

0.37

2.56

LBTYK Liberty Global Plc Class C

1.08

2.47

SQBG Sequential Brands Group, Inc.

0.09

2.16

BAC Bank of America Corporation

-0.73

1.71

VRX Valeant Pharmaceuticals International, Inc.

0.50

1.66

LVLT Level 3 Communications, Inc.

0.41

1.45

Crowding within the AQR-Adage Cluster may not affect AAPL, CHTR, FOLD, and SBAC. However, these consensus bets will be the key contributors to the active returns of the AQR-Adage Cluster and many members. These stocks will also be the key drivers of some allocators’ idiosyncratic performance.

Idiosyncratic crowding is not the main problem with the cluster, since the expected idiosyncratic tracking error is low. Passivity is a bigger problem: Allocators to diversified portfolios of hedge funds within this cluster may be paying high fees for what’s effectively an index fund of passive factor bets. Closet indexing may be practiced by 70% of “active” U.S. mutual fund capital, but the high fees charged by hedge funds make fund differentiation especially important.

Summary

  • An analysis of the underlying structure of hedge fund crowding reveals hedge fund clustering – groups of portfolios with similar bets.
  • The largest hedge fund cluster consists of approximately 50 funds with shared factor and residual exposures.
  • The largest cluster’s factor herding is towards Market (high-beta) and short Size (small-cap) exposures.
  • This cluster’s residual herding is away from AAPL and towards CHTR, FOLD, and SBAC.
  • Allocators unaware of the clustering of their funds may be paying active fees for an effectively passive factor portfolio.
The information herein is not represented or warranted to be accurate, correct, complete or timely.
Past performance is no guarantee of future results.
Copyright © 2012-2015, 
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 Semiconductor Sector Crowding

Our June 2015 piece listed SunEdison (SUNE) and Micron (MU) among the top ten stocks driving hedge fund risk and alpha. In the semiconductor sector, they were virtually the sole drivers. In addition, since mid-2014 semiconductor sector alpha for hedge funds has been sharply negative. Extreme semiconductor sector crowding and threat of liquidation were ominous and actionable. Investors armed with capable analytics could have avoided the bulk of their losses (by liquidating), or profited (by shorting); allocators could have asked undifferentiated managers probing questions.

This situation is not unique – liquidations devastated crowded bets across several sectors in 2015. For example, our July analysis highlighted the liquidation of crowded energy stocks. These lessons for investors and allocators apply across sectors and market cycles.

Hedge Fund Crowding in SunEdison (SUNE) and Micron (MU)

SunEdison and Micron were the two major sources of idiosyncratic (stock-specific) risk for hedge funds in the semiconductor sector for some time. For example, MU and SUNE contributed over 90% of stock-specific hedge fund risk in the semiconductor sector in Q3 2014:

Chart of the main stock-specific bets and their cumulative contribution to the residual variance of Hedge Fund Semiconductor Sector Aggregate Portfolio relative to Market on 9/30/2014

Stocks Contributing Most to U.S. Hedge Fund Semiconductor Aggregate Relative Residual Risk in Q3 2014

This continued into the new year, and by Q2 2015, MU and SUNE contributed almost 95% of stock-specific hedge fund risk in the semiconductor sector:

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

Stocks Contributing Most to U.S. Hedge Fund Semiconductor Aggregate Relative Residual Risk in Q2 2015

The following table contains detailed data on hedge fund semiconductor crowding as of Q2 2015:

Exposure (%) Net Exposure Share of Risk (%)
HF Sector Aggregate Sector Aggregate % $mil Days of Trading
SUNE SunEdison, Inc. 28.03 1.24 26.79 2,177.1 9.2 65.40
MU Micron Technology, Inc. 31.10 5.56 25.54 2,075.8 3.9 28.23
INTC Intel Corporation 5.71 28.19 -22.49 -1,827.7 -2.0 2.48
NXPI NXP Semiconductors NV 8.55 4.46 4.10 333.0 1.1 0.55
TXN Texas Instruments Incorporated 0.13 11.40 -11.27 -915.6 -2.7 0.50
SEMI SunEdison Semiconductor, Inc. 3.86 0.20 3.65 296.8 44.9 0.45
AVGO Avago Technologies Limited 1.61 6.20 -4.59 -373.5 -0.8 0.44
SWKS Skyworks Solutions, Inc. 0.05 3.57 -3.52 -286.1 -0.8 0.44
BRCM Broadcom Corporation Class A 0.33 4.53 -4.20 -341.2 -0.7 0.33
MLNX Mellanox Technologies, Ltd. 2.39 0.39 1.99 161.8 5.7 0.21
FSL Freescale Semiconductor Inc 0.07 2.38 -2.31 -187.8 -2.7 0.21
QRVO Qorvo, Inc. 0.19 2.25 -2.06 -167.7 -0.9 0.18
ON ON Semiconductor Corporation 3.60 0.99 2.60 211.5 3.5 0.12
NVDA NVIDIA Corporation 0.10 2.19 -2.09 -170.2 -0.9 0.08
GB:0Q19 CEVA, Inc. 1.39 0.08 1.30 106.0 42.2 0.07
ADI Analog Devices, Inc. 0.02 3.74 -3.72 -302.3 -2.1 0.07
MX MagnaChip Semiconductor Corporation 0.57 0.04 0.54 43.5 9.5 0.03
MXIM Maxim Integrated Products, Inc. 0.38 1.87 -1.50 -121.6 -1.3 0.02
LLTC Linear Technology Corporation 0.00 2.13 -2.13 -173.2 -1.8 0.02
MCHP Microchip Technology Incorporated 0.11 1.88 -1.77 -143.7 -1.5 0.02
Other Positions 0.34 0.15
Total 100.00

Hedge Fund Security Selection in the Semiconductor Sector

The above data is informative and actionable on its own – it points to massive concentration of risk. However, the data becomes more threatening when combined with hedge funds’ semiconductor security selection performance:

Hedge Fund Semiconductor Sector Aggregate Historical Security Selection Performance

Historical Return from Security Selection of Hedge Fund Semiconductor Sector Aggregate

AlphaBetaWorks’s metric of security selection is αReturn – the performance a portfolio would have generated if markets had been flat. Hedge funds enjoyed positive αReturn in the semiconductor sector over ten years, albeit with up and down cycles. The latest surge in αReturn started in 2012 and peaked in 2014. Since then, hedge funds’ long semiconductor picks underperformed by over 30%, on a risk-adjusted basis. Had hedge funds taken the same risk passively (say by owning a cap-weighted semiconductor index) they would have made over 30% more.

Negative αReturn is often a sign of liquidation and hedge fund semiconductor bets have a history of booms and busts. As illustrated in the charts above, most of the stock-specific hedge fund risk came from two stocks: SunEdison and Micron. When liquidation became evident in late-2014, these stocks became vulnerable.

Hedge Fund Liquidation of SUNE and MU

Analytics built on a robust risk model, such as the AlphaBetaWorks Statistical Equity Risk Model used here, identify crowding and leading indicators of liquidations. Portfolio managers and investors armed with these analytics see early warning signs and avoid losses, or even profit from herding. Allocators have the data on undifferentiated managers.

The above pattern is not unique: crowded names typically underperform on a risk-adjusted basis. Liquidations are routine.

Conclusions

  • Holdings-based analytics built on robust risk models identify crowding and detect early signs of portfolio liquidations.
  • Investors with the tools to identify hedge fund crowding and liquidations can reduce losses or profit from opportunities.
  • Allocators aware of crowding can gain new insights into portfolio risk, manager skills, and fund differentiation.
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 – 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 diversified 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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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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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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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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