Tag Archives: crowding

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:

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.

Property and Casualty Industry Crowding

2 Replies

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:

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.

Hedge Fund Crowding Update – Q1 2015

Leave a reply

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.

Hedge Fund Crowding – Q4 2014

Leave a reply

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.

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.

Hedge Funds’ Best and Worst Sectors

Leave a reply

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:

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.

Hedge Fund Crowding – Q3 2014

Leave a reply

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

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

Identifying Crowding

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

Hedge Fund Aggregate Risk

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

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

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

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

Hedge Fund Factor (Systematic) Crowding

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

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

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

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

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

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

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

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

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

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

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

Hedge Fund Residual (Idiosyncratic) Crowding

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

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

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

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

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

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

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

Summary

There is both factor (systematic/market) and residual (idiosyncratic/security-specific) crowding of long hedge fund portfolios.
Hedge funds have become more crowded and more passive in Q3 2014.
The main sources of factor crowding are: Market (higher beta) and Oil.
The main sources of residual crowding are: LNG, AGN, VRX, MU, BIDU, and AIG.
Our research reveals that, collectively, hedge funds’ long U.S. equity portfolios tend to generate negative risk-adjusted returns. Crowded bets tend to disappoint and hedge fund investors should pay close attention to crowding before allocating capital.

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