Category Archives: Risk

Mutual Fund Closet Indexing: 2015 Update

An index fund aims to track the market or its segment, with low fees. An actively managed fund aims to do better, but with higher fees. So in order to earn its fees, an active mutual fund must take risks. Much of the industry does not even try. Mutual fund closet indexing is the practice of charging active fees for passive management. Over a third of active mutual funds and half of active mutual fund capital appear to be investing passively: Funds tend to become less active as they accumulate assets. Skilled managers who were active in the past may be closet indexing today. Simply by identifying closet indexers, investors can eliminate half of their active management fees, increase allocation to skilled active managers, and improve performance.

Closet Indexing Defined

A common metric of fund activity is Active Share — the percentage difference between portfolio and benchmark holdings. This measure is flawed: If fund with S&P 500 benchmark buys SPXL (S&P 500 Bull 3x ETF), this passive position increases Active Share. If a fund with S&P 500 benchmark indexes Russell 2000, this passive strategy has 100% Active Share. Indeed, recent findings indicate that high Active Share funds that outperform merely track higher-risk benchmarks.

Factor-based analysis of positions can eliminate the above deficiencies. We applied the AlphaBetaWorks Statistical Equity Risk Model to funds’ holdings over time and estimated each fund’s unique factor benchmark. These passive factor benchmarks captured the representative systematic risks of each fund. We then estimated each fund’s past and future tracking errors relative to their factor benchmarks and identified those funds that are unlikely to earn their fees in the future given their current active risk. We also quantified mutual fund closet indexing costs for a typical investor.

This study covers 10-year portfolio history of approximately three thousand U.S. equity mutual funds that are analyzable from regulatory filings. It updates our earlier studies of mutual fund and closet indexing with 2015 data. Due to the larger fund dataset and higher recent market volatility, the mutual fund industry appears slightly more active now than in the 2014 study.

Information Ratio – the Measure of Fund Activity

The Information Ratio (IR) is the measure of active return a fund generates relative to its active risk, or tracking error. We estimated each fund’s IR relative to its factor benchmark. The top 10% of U.S. equity mutual funds achieved IRs above 0.36:

Chart of the historical information ratio for active returns of U.S. mutual funds’ equity portfolios

U.S. Equity Mutual Funds: Historical Information Ratio Distribution

 Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
-5.34   -0.49   -0.22   -0.23    0.06    3.26

If a fund outperforms 90% of the group and achieves 0.36 IR, then it needs tracking error above 1% / 0.36 = 2.79% to generate active return above 1%. So assuming a typical 1% fee, if a fund were able to consistently achieve IR in the 90th percentile, it would need annual tracking error above 2.79% to generate net active return. As we show, much of the industry is far less active. In fact, half of U.S. “active” equity mutual fund assets do not even appear to be trying to earn a 1% active management fee.

Historical Mutual Fund Closet Indexing

Tracking error comes from active exposures: systematic (factor) and idiosyncratic (stock-specific) bets. The AlphaBetaWorks Statistical Equity Risk Model used to estimate these exposures is highly accurate and predictive for a typical equity mutual fund.

Over 28% (746) of the funds have taken too little risk in the past. Even if they had exceeded the performance of 90% of their peers each year, they would still have failed to earn a typical fee. These funds have not even appeared to try to earn their fees:

Chart of the historical mutual fund closet indexing as measured by the tracking error of active returns of U.S. mutual funds’ equity portfolios

U.S. Equity Mutual Funds: Historical Tracking Error Distribution

Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
0.35    2.63    3.95    4.62    5.90   26.60

Current Mutual Fund Closet Indexing

Funds tend to become less active as they grow. To control for this, we estimated current tracking errors of all funds relative to their factor benchmarks.

Over a third (961) of the funds are taking too little risk currently. Even if they exceed the performance of 90% of their peers each year, they will still fail to merit a typical fee. These funds are not even appearing to try to earn their fees:

Chart of the predicted future mutual fund closet indexing as measured by the tracking error of active returns of U.S. mutual funds’ equity portfolios

U.S. Equity Mutual Funds: Predicted Future Tracking Error Distribution

Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
0.92    2.45    3.20    3.52    4.29   20.90

Capital-Weighted Mutual Fund Closet Indexing

Since funds become less active as they grow, larger mutual funds are more likely to closet index. The 36% of mutual funds that have estimated future tracking errors below 2.79% represent half of the assets ($2.25 trillion out of the $4.57 trillion total in our study). Hence, half of active equity mutual fund capital is unlikely to earn a typical free, even when its managers are highly skilled:

Chart of the capital-weighted predicted future mutual fund closet indexing as measured by the tracking error of active returns of U.S. mutual funds’ equity portfolios

U.S. Equity Mutual Funds: Capital-Weighted Predicted Future Tracking Error Distribution

Min. 1st Qu.  Mean 3rd Qu.    Max.
0.92    2.10  2.79    3.72   20.90

Even the most skilled managers will struggle to generate IRs in the 90th percentile each and every year. Therefore, portfolios of large funds, when built without robust analysis of manager activity, may be doomed to negative net active returns. Plenty of closet indexers charge more than the 1% fee we assume, and plenty of investors will lose even more.

A Map of Mutual Fund Closet Indexing

As a manager accumulates assets, fee harvesting becomes more attractive than risk taking. Managers’ utility curves may thus explain large funds’ passivity. The following map of U.S. mutual fund active management skill (defined by the αβScore of active return consistency) and current activity illustrates that large skilled funds are generally less active. Large skilled funds, represented by large purple circles on the right, cluster towards the bottom area of low tracking error:

Chart of the historical active management skill as represented by the consistency of active returns and predicted future tracking error of active returns of U.S. mutual funds’ equity portfolios

U.S. Equity Mutual Funds: Historical Active Management Skill and Predicted Future Activity

In spite of the widespread mutual fund closet indexing, numerous skilled and active funds remain. Many are young and, with a low asset base, have a long way to grow before fee harvesting becomes seductive for their managers.

Conclusions

Over a third of U.S. equity mutual funds are currently so passive that, even if they exceed the information ratios of 90% of their peers, they will still fail to merit a typical fee.
Half of U.S. equity mutual fund capital will fail to merit a typical fee, even when its managers are highly skilled.
As skilled managers accumulate assets, they are more likely to closet index.
A typical investor can re-allocate half of their active equity mutual fund capital to cheap passive vehicles or truly active skilled managers to improve performance.

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

Hedge Fund Closet Indexing: 2015 Update

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A fund must take active risk to generate active returns in excess of fees. However, some managers charge active fees but manage their funds passively. Managers also tend to become less active as they accumulate assets. This problem of hedge fund closet indexing is widespread. Over a third of capital invested in U.S. hedge funds’ long equity portfolios is too passive to warrant the common 1.5/15% fee structure, even if its managers are highly skilled. Investors can replace closet indexers with cheap passive vehicles or with truly active skilled managers and improve performance.

Hedge Fund Closet Indexing Background

This article updates our earlier pieces on mutual fund and hedge fund closet indexing with mid-2015 data. We examine current and historical long equity portfolios of approximately 500 U.S. hedge funds that are analyzable from regulatory filings and identify those that are unlikely to earn their fees in the future given their current active risk. We then quantify the cost of hedge fund closet indexing for a typical investor.

Recall from our earlier discussion that Active Share is a brittle metric of fund activity: If a fund buys a position in an index ETF, this passive position may increase Active Share while making the fund less active. If a fund with S&P 500 benchmark simply indexes Russell 2000, this passive fund will have 100% Active Share. These examples are consistent with recent findings that high Active Share funds appear to outperform merely due to miss-specified benchmarks. Our factor-based approach identifies the unique passive factor benchmark for each fund and is free from these deficiencies.

Information Ratio – the Measure of Active Risk Required to Earn a Fee

The Information Ratio (IR) is a measure of active return relative to active risk (tracking error). The best-performing 10% of U.S. hedge funds’ long portfolios achieve IRs above 0.59 relative to their passive factor benchmarks:

Chart of the historical information ratio for active returns of U.S. hedge funds’ long equity portfolios

U.S. Hedge Fund Long Equity Portfolios: Historical Information Ratio Distribution

 Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
-2.58   -0.34   -0.02   -0.04    0.28    2.17

If a fund’s long portfolio exceeds the performance of 90% of the peers and achieves a 0.59 IR, then it needs a tracking error above 1.00% / 0.59 = 1.69% to generate active return above 1%.

Let’s assume that hedge funds’ long equity portfolios are burdened with 1.5% management fee and 15% incentive allocation. Further assuming a 7% market return, the mean fee is 2.55%. If all funds were able to achieve IRs in the 90th percentile, they would need annual tracking error above 2.55% / 0.59 = 4.32% to earn the 2.55% estimated mean fee and a positive net active return. We show below that a significant fraction of the industry takes too little active risk to achieve this tracking error. In fact, much of the industry may not even be trying to earn its fees.

Historical Hedge Fund Closet Indexing

Tracking error comes from funds’ active exposures: systematic (factor) and idiosyncratic (stock-specific) bets. We applied the AlphaBetaWorks Statistical Equity Risk Model to funds’ historical holdings to estimate their unique factor benchmarks. These are passive factor portfolios that capture the representative systematic risks of each fund. We then estimated past and future tracking errors of each fund relative to these benchmarks.

Over 13% (67) of the funds have taken so little risk that, even if they had exceeded the performance of 90% of their peers each year, they would still have failed to earn a typical fee. In other words, these funds have not even appeared to try earning their fees:

Chart of the historical tracking error of active returns of U.S. hedge funds’ long equity portfolios

U.S. Hedge Fund Long Portfolios: Historical Tracking Error Distribution

Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
0.43    6.04   10.04   15.17   19.43  201.00

Estimated Future Hedge Fund Closet Indexing

Fund activity changes over time as managers accumulate assets. Many funds are more passive today than they have been historically. To control for this, we estimated current tracking errors.

Approximately a fifth (88) of the funds are currently taking so little risk that, even if they were to exceed the performance of 90% of their peers each year, they would still fail to merit a typical fee. In other words, these funds are not even appearing to try earning their fees:

Chart of the predicted future tracking error of active returns of U.S. hedge funds’ long equity portfolios

U.S. Hedge Fund Long Portfolios: Predicted Future Tracking Error Distribution

Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
0.76    4.96    7.67   11.01   12.48  148.30

Capital-Weighted Hedge Fund Closet Indexing

Larger hedge funds are more likely to engage in closet indexing. While approximately 20% of hedge funds surveyed have estimated future tracking errors below 4.30%, they represent a third of the assets ($240 billion out of the $720 billion total in our sample). Therefore, a third of hedge fund long equity capital is unlikely to exceed 4.32% tracking error and earn a typical fee, even when its managers are highly skilled:

Chart of the capital-weighted predicted future tracking error of active returns of U.S. hedge funds’ long equity portfolios

U.S. Hedge Fund Long Portfolios: Capital-Weighted Predicted Future Tracking Error Distribution

Min. 1st Qu.  Mean 3rd Qu.    Max.
0.76    3.70  5.49   8.21   116.47

The assumption that all funds will generate higher IRs than 90% of their peers have historically is unrealistic. Hence, a portfolio of large funds built without a robust analysis of hedge fund closet indexing may be doomed to generate negative net active returns, irrespective of the managers’ skills. The 2.55% fee cited here is the estimated mean. Plenty of closet indexers charge more on their long equity portfolios, and plenty of investors who remain with them stand to lose even more.

While there is less closet indexing among hedge funds than among mutual funds, the fees that hedge funds charge and the expectations they set are significantly higher. When practiced by hedge funds, closet indexing is all the more egregious.

A Map of Hedge Fund Closet Indexing

The evolution of managers’ utility curves may explain their reluctance to take risk. As a manager accumulates assets, fee harvesting becomes increasingly attractive. The following map of U.S. hedge fund active management skill and current activity illustrates that large skilled funds are generally less active (large purple circles on the right cluster towards the bottom):

Map of U.S. hedge fund closet indexing for long equity portfolios, charting historical active management skill as represented by the consistency of active returns and predicted future tracking error of active returns of U.S. hedge funds’ long equity portfolios

U.S. Hedge Fund Long Portfolios: Historical Active Management Skill and Predicted Future Activity

Yet, there are notable exceptions – several large, skilled, and active managers remain.

Conclusions

A fifth of U.S. hedge funds’ long equity portfolios are currently so passive that, even if they exceed the information ratios of 90% of their peers, they will still fail to merit a typical fee.
A third of U.S. hedge funds’ long equity capital will fail to merit a typical fee, even when its managers are highly skilled.
As skilled managers accumulate assets, they are more likely to closet index.
A typical hedge fund investor can replace a third of long hedge fund capital with cheap passive vehicles or truly active skilled managers and improve performance.

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

Hedge Fund Energy Clustering: Q2 2015

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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.

Hedge Fund Clustering: Q2 2015 Update

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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.

Hedge Fund Semiconductor Sector Crowding

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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:

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.

Hedge Fund Crowding Update – Q2 2015

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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.

Liquidation of Crowded Hedge Fund Energy Positions

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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

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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

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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

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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.