Category Archives: Risk

Testing Global Equity Risk Models

Due to differences in financial reporting and transparency across international markets, fundamental company data is often unsuitable for building global risk models. Consequently, global equity risk models can be even more complex, brittle, and hard to interpret than their U.S. counterparts. Global statistical equity risk models are immune to these deficiencies in fundamental data and, when properly constructed to robustly capture the key risk factors, are highly predictive. An intuitive Global Statistical Equity Risk Model using Regional and Sector/Industry factors delivers over 0.96 correlation between predicted and reported portfolio returns for a median U.S. Equity Mutual Fund.

Predictive Power of Global Statistical Equity Risk Models

We analyze 10 years of historical positions and returns for over 3,000 non-index U.S. Equity Mutual Funds. The dataset spans domestic and international portfolios, extending our earlier test of U.S. equity risk models on domestic funds. We calculate factor exposures using estimated holdings at the end of each month and predict the next month’s performance using these ex-ante factor exposures and ex-post factor returns.

The correlation between an equity risk model’s predictions and subsequent performance illustrates the model’s power. High correlation indicates effectiveness at hedging, attributing returns to systematic sources, and evaluating manager skill. Global statistical equity risk models turn out to be even more effective than their U.S. counterparts.

Testing Predictions of Single-Factor Global Statistical Equity Risk Models

Our simplest global risk model uses a single systematic risk factor for each security – Region Beta. This factor is simply Market Beta for each of the 10 global regions such as North America, Developed Europe, and China. Since Market Beta is the dominant factor behind portfolio performance, even this simple model, when built with robust methods, delivers 0.94 mean and 0.95 median correlation between predicted and actual monthly returns:

Chart of the correlations between predicted returns constructed using a single-factor global statistical equity risk model and actual historical returns for U.S.-domiciled Global Equity Mutual Funds

Global U.S. Equity Mutual Funds: Correlation between a single-factor global statistical equity risk model’s predictions and actual monthly returns

   Min.    1st Qu. Median  Mean    3rd Qu. Max. 
   0.3881  0.9214  0.9540  0.9386  0.9758  0.9968

Testing Predictions of Two-Factor Global Statistical Equity Risk Models

We now consider a two-factor model that adds a Sector Risk Factor. Each security belongs to one of 10 sectors such as Technology, Energy, or Utilities. Market and Sector Betas, estimated with robust methods, deliver 0.95 mean and over 0.96 median correlation between predicted and actual monthly returns:

Chart of the correlations between predicted returns constructed using a two-factor global statistical equity risk model and actual historical returns for U.S.-domiciled Global Equity Mutual Funds

Global U.S. Equity Mutual Funds: Correlation between a two-factor global statistical equity risk model’s predictions and actual monthly returns

  Min.    1st Qu. Median  Mean    3rd Qu. Max. 
  0.7030  0.9380  0.9647  0.9534  0.9809  0.9976

We picked sector as the second factor since research indicates that sector/industry performance captures more systematic portfolio risk than style factors do. Performance of common style factors can generally be explained by difference in sector composition of style portfolios. In contrast, performance of sectors cannot typically be attributed to differences in style of sector portfolios.

Even for the 25% funds the two-factor model handles the worst, the correlation between predicted and actual returns is 0.70-0.94. The lower accuracy of predictions is primarily caused by hybrid and fixed-income securities that are poorly described by an equity risk model.

Summary

Differences in financial reporting and transparency among countries make global equity risk model construction using fundamental data challenging and the resulting models fragile.
For a typical global U.S. mutual fund, even a minimalist statistical equity risk model with intuitive and investable factors delivers over 0.96 correlation between predicted and actual monthly returns.
An equity risk model with perfect prediction would, at most, improve correlation between predicted and actual returns by 0.035 and explain 6.9% more ex-post variance.

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.

Testing Predictions of Equity Risk Models

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Equity risk models can be complex and hard to interpret. Yet, when properly constructed, robust statistical equity risk models capturing just the most salient factors are highly predictive. For instance, Market and Sector/Industry factors alone deliver 0.96 median correlation between predictions of equity risk models and reported portfolio returns for U.S. Equity Mutual Funds.

Predictive Power of Statistical Equity Risk Models

We analyze historical positions and returns of approximately 3,000 non-index U.S. Equity Mutual Funds over 10 years. We calculate factor exposures using estimated holdings at the end of each month and predict next month’s performance using these ex-ante factor exposures and ex-post factor returns.

The correlation between an equity risk model’s predictions and subsequently reported fund returns illustrates the model’s power. The higher the correlation, the more effective a model is at hedging, attributing returns to systematic sources, and evaluating manager skill.

Testing Predictions of Single-Factor Statistical Equity Risk Models

The simplest statistical equity risk model uses a single systematic risk factor – Market Beta. Since Market Beta is the dominant factor behind portfolio performance, even a very simple model built with robust methods delivers 0.92 mean and 0.94 median correlation between predicted and actual monthly returns:

Chart of the correlations between predicted returns constructed using a single-factor statistical equity risk model and actual historical returns for U.S. Equity Mutual Funds

U.S. Equity Mutual Funds: Correlation between a single-factor statistical equity risk model’s predictions and actual monthly returns

  Min.    1st Qu. Median  Mean    3rd Qu. Max. 
  0.1360  0.9010  0.9401  0.9157  0.9650  0.9981

Testing Predictions of Two-Factor Statistical Equity Risk Models

Research indicates that sector/industry risk factors capture more systematic portfolio risk than style factors do. For instance, in periods such as 1999-2001 the performance of common style factors is due to difference in sector composition of style portfolios.

Thus, we consider a two-factor model that adds a Sector Risk Factor. Each security belongs to one of 10 sectors. Market and Sector Betas, estimated with robust methods delivers 0.94 mean and 0.96 median correlation between predicted and actual monthly returns:

Chart of the correlations between predicted returns constructed using a two-factor statistical equity risk model and actual historical returns for U.S. Equity Mutual Funds

U.S. Equity Mutual Funds: Correlation between a two-factor statistical equity risk model’s predictions and actual monthly returns

  Min.    1st Qu. Median  Mean    3rd Qu. Max. 
  0.6639  0.9254  0.9562  0.9420  0.9753  0.9984

Testing Predictions of Multi-Factor Statistical Equity Risk Models

With correlation between predicted and actual returns very close to 1, the benefit of increased model complexity is rapidly diminishing. Even a perfect model would, at most, provide 0.0438 higher correlation, or explain 0.0857 higher fraction of ex-post variance for most funds than the above two-factor model.

Extending the two-factor model with Style Factors (Value/Growth and Size) as well as Macroeconomic Factors (Bonds, Oil, Currency, etc.), we arrive at the AlphaBetaWorks’ U.S. Equity Statistical Risk Model. It delivers 0.95 mean and 0.96 median correlation between predicted and actual monthly returns for U.S. Equity Mutual Funds:

Chart of the correlations between predicted returns constructed using a multi-factor statistical equity risk model and actual historical returns for U.S. Equity Mutual Funds

U.S. Equity Mutual Funds: Correlation between a multi-factor statistical equity risk model’s predictions and actual monthly returns

  Min.    1st Qu. Median  Mean    3rd Qu. Max. 
  0.6661  0.9420  0.9629  0.9503  0.9766  0.9987

Even for the 25% funds it handles the worst, the model delivers 0.67-0.94 correlation between predicted and actual returns.

Summary

Complex equity risk models with non-intuitive factors may offer no better predictions than robust models with a few intuitive factors.
Even a perfect equity risk model would, at most, explain 8.6% more ex-post variance than a simple two-factor model.
For a typical U.S. mutual fund, a statistical equity risk model with intuitive and investable factors delivers over 0.96 correlation between predicted and actual monthly 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 Industrials Factor Timing

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In an earlier post, we discussed the largest bets hedge fund long portfolios were making in Q1 2015. The third largest was on the Industrials Factor. This is the risk specific to the industrials sector after controlling for market exposure. It captures capital allocation to industrials and sensitivity (beta) to the sector. There is weak statistical evidence of poor industrials factor timing by hedge funds – investors who follow hedge funds should either ignore this bet or treat it as a negative indicator.

At the end of Q1 2015, high industrials sector factor exposure was the third largest source of U.S. hedge funds’ long portfolio crowding. HF Aggregate, a portfolio consisting of popular long U.S. equity holdings of all hedge funds tractable from quarterly filings, had over 25% industrials factor exposure – a 9% overweight relative to Market. This exposure was at an all-time high:

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

U.S. Hedge Fund Aggregate’s Industrials Sector Factor Exposure History

This industrials factor exposure captures sector risk after controlling for market exposure. For example, a fund with 10% allocated to a broad industrials index will have approximately 10% industrials factor exposure. A fund with 10% allocated to a 2x-levered broad industrials ETF will have approximately 20% industrials factor exposure.

Here we analyze the hedge fund industry’s skill in timing the U.S. Industrials Factor by varying this exposure. The AlphaBetaWorks Performance Analytics Platform evaluates market timing skills and performance using two related tests:

Statistical test for the relationship between factor exposure and subsequent factor returns,
Statistical test for the size and consistency of returns generated by varying factor exposures.

Hedge Fund Industrials Factor Exposure and Industrials Factor Return

We calculated the Spearman’s rank correlation coefficient of HF Aggregate’s industrials factor exposure and subsequent industrials factor return for the past 10 years and tested it for significance. The chart below illustrates the correlation between the two series and the test results:

Chart of the correlation between historical industrials factor exposure of U.S. Hedge Fund Aggregate and subsequent factor return

U.S. Hedge Fund Aggregate’s Industrials Factor Exposure and Return

There is a statistically weak negative relationship between HF Aggregate’s industrials factor exposure and subsequent factor performance. Hedge fund industrials factor exposure is a weak predictor of future industrials returns.

Hedge Fund Industrials Factor Timing Returns

Over the past 10 years, HF Aggregate (USHFS in red) made approximately 0.9% less than it would have with constant industrials factor exposure, as illustrated below. The performance of HF Aggregate is compared to all tractable 13F filers (Group in gray). The AlphaBetaWorks Performance Analytics Platform identifies this performance due to industrials factor timing as industrials βReturn:

Chart of the cumulative historical contribution of variation in industrials factor exposure of U.S. Hedge Fund Aggregate to the Aggregate’s performance

U.S. Hedge Fund Aggregate’s Industrials Factor Timing Return

The weak evidence of poor industrials factor timing by the industry, combined with the high recent industrials factor exposure, is a weak bearish indicator for the sector.

Summary

The industrials factor exposure of U.S. hedge funds’ long portfolios is weakly predictive of subsequent sector performance.
Current hedge fund industrials factor exposure, at 10-year highs, is a weak bearish indicator for the Industrials Sector.
Investors who track hedge fund holdings should either ignore this bet or treat is as a negative indicator.

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 Oil Factor Timing

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In an earlier post, we discussed the largest bets hedge fund long portfolios were making in Q1 2015. The second largest was on the oil price. This exposure is the residual oil price risk after controlling for market and sector exposures. It captures overweighting within various sectors of companies that out- or underperform under rising oil price. There is weak statistical evidence of skilled oil factor timing by hedge funds – the current bet is a weak bullish indicator for oil prices.

At the end of Q1 2015, high oil price factor exposure was the second largest source of U.S. hedge funds’ long portfolio crowding. HF Aggregate, a portfolio consisting of popular long U.S. equity holdings of all hedge funds tractable from quarterly filings, had approximately 2.5% oil price factor exposure. This exposure was approaching the 10-year highs reached in 2007-2009:

Chart of the exposure of oil price factor of the U.S. Hedge Fund Aggregate

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

This oil price exposure captures residual oil risk after controlling for sector exposures. For examples, airlines with higher operational or financial leverage than peers have negative oil price factor exposure – they will underperform peers when oil price increases; airlines with lower operational or financial leverage than peers have positive oil price factor exposure –they will outperform peers when oil price increases.

Here we analyze the hedge fund industry’s skill in timing the oil price by varying this intra-sector oil price risk. The AlphaBetaWorks Performance Analytics Platform evaluates market timing skills and performance using two related tests:

Statistical test for the relationship between factor exposure and subsequent factor returns,
Statistical test for the size and consistency of returns generated by varying factor exposures.

Hedge Fund Oil Factor Exposure and Oil Return

We calculated the Spearman’s rank correlation coefficient of HF Aggregate’s oil price factor exposure and subsequent oil price return and tested it for significance. The chart below illustrates the correlation between the two series and the test results:

Chart of the correlation between oil price factor exposure of the U.S. Hedge Fund Aggregate and subsequent oil price return

U.S. Hedge Fund Aggregate’s Oil Factor Exposure and Return

There is a statistically weak positive relationship between HF Aggregate’s oil factor exposure and subsequent oil performance. Hedge fund oil factor exposure is a weak indicator of future oil price direction.

Hedge Fund Oil Factor Timing Returns

Over the past 10 years, HF Aggregate (USHFS in red) made approximately 1.5% more than it would have with constant oil factor exposure, as illustrated below. The performance of HF Aggregate is compared to all tractable 13F filers (Group in gray). The AlphaBetaWorks Performance Analytics Platform identifies this performance due to oil price factor timing as oil price βReturn:

Chart of the return due to variation in oil price factor exposure of the U.S. Hedge Fund Aggregate

U.S. Hedge Fund Aggregate’s Oil Factor Timing Return

The weak evidence of oil factor timing skill by the industry, combined with the high recent oil factor exposure, is a weak bullish indicator for oil prices.

Summary

The oil price factor exposure of U.S. hedge funds’ long portfolios is weakly predictive of subsequent oil performance.
Current hedge fund oil factor exposure, near 10-year highs, is a weak bullish indicator for oil prices.

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 U.S. Market Timing

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The single largest bet hedge fund long portfolios were making in Q1 2015 was on the U.S. market (high beta). While the bet is exceptionally large, it is not predictive of market direction. Allocators should pay attention to this risk aggregation.

At the end of Q1 2015, high market factor exposure (high beta) was the primary source of U.S. hedge funds’ long portfolio crowding. HF Aggregate, a portfolio consisting of popular long U.S. equity holdings of all hedge funds tractable from quarterly filings, had approximately 115% U.S. market factor exposure (1.15 U.S. market beta). This exposure was at 10-year highs – the level last seen 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

Our work on hedge fund crowding has so far not addressed hedge fund factor timing and hedge fund U.S. market timing specifically. We dive into this performance here.

The AlphaBetaWorks Performance Analytics Platform evaluates market timing skills and performance using two related tests:

Statistical test for the relationship between factor exposure and subsequent factor returns,
Statistical test for the size and consistency of returns generated by varying factor exposures.

Hedge Fund Market Exposure and Market Return

We calculated the Spearman’s rank correlation coefficient of HF Aggregate’s market exposure and subsequent market return and tested it for significance. The chart below illustrates the correlation between the two series and the test results:

Chart of the correlation between the U.S. market exposure of long U.S. hedge fund equity portfolios and U.S. market return

U.S. Hedge Fund Aggregate’s U.S. Market Exposure and Return

The chart shows that there is a positive relationship between HF Aggregate’s U.S. market exposure and subsequent U.S. market return, but it is weak and statistically insignificant. In other words, hedge funds’ variation in U.S. market exposure has done little to help their performance.

Hedge Fund U.S. Market Timing Returns

Over the past 10 years, HF Aggregate (USHFS in red) made approximately 1% more than it would have with constant factor exposures, as illustrated below. The performance of HF Aggregate is compared to all tractable 13F filers (Group in gray). The AlphaBetaWorks Performance Analytics Platform identifies this performance due to U.S. market factor timing as U.S. market βReturn:

Chart of the historical return due to variation in U.S. market exposure of long U.S. hedge fund equity portfolios

U.S. Hedge Fund Aggregate’s U.S. Market Timing Return

The performance impact of this variation in beta within U.S. hedge fund long portfolios is also minor. However, the group of all 13F filers was a poor market timer, particularly during the volatility of 2008-2009. During the crisis, non-filers were the smart money and took advantage of U.S. market volatility, at the expense of 13F filers. 13F filers were a contrarian indicator.

Summary

Hedge fund long equity portfolios consistently take 5-15% more market risk than S&P500 and other broad market benchmarks.
There has been no statistically significant relationship between U.S. market exposure (market beta) of U.S. hedge funds’ long portfolios and subsequent market return.
The high market factor exposure of U.S. hedge funds’ long portfolios is not predictive of subsequent market returns.
The broader group of all 13F filers generated significant negative returns by varying market exposure, particularly during the 2008-2009 volatility.

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 Toll: January 2015

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Several of our articles discussed the vulnerability of crowded hedge fund positions to volatility, mass liquidation, and losses. We illustrated specific blow-ups (SanDisk (SNDK) and eHealth (EHTH)), as well as sectors with persistently poor hedge fund performance (Oil and Gas Production and Miscellaneous Mining). However, we have not showed how representative these examples are. This piece illustrates the performance toll of hedge fund crowding in a single (especially damaging) month – January 2015.

Identifying Crowding

We analyze hedge fund holdings (HF Aggregate) relative to the market portfolio (Market Aggregate). HF Aggregate is position-weighted while Market Aggregate is capitalization-weighted. We follow the approach of our earlier articles on aggregate and sector-specific hedge fund crowding.

January 2015 Hedge Fund Crowding Toll

January 2015 is a convenient month to consider: Due to intra-month volatility, the market was approximately flat at several dates. Hence, relative performance at these points gives a good picture of risk-adjusted performance. Whereas Russell 3000 was down about 2.7%, the portfolio of equal-weighted crowded hedge fund longs (in red on the chart below) declined twice as much:

Chart of the January 2015 performance of crowded long hedge fund bets

January 2015 Performance of Crowded Hedge Fund Long Bets

The crowded hedge fund longs in the above chart consist of HF Aggregate’s illiquid long bets relative to the Market Aggregate. Illiquid longs are overweight exposures of HF Aggregate valued at over 10 days of trading volume. We have discussed earlier that illiquidity is a key source of crowding risk since funds have difficulty exiting these positions. The specific 10 day limit was chosen arbitrarily. Below we show that results are consistent across a broad range of liquidity limits. The portfolio was constructed using AlphaBetaWorks’ Q3 2014 hedge fund crowding dataset, available to subscribers in late November.

Crowded hedge fund shorts are defined similarly: They consist of illiquid underweights relative to the Market Aggregate valued at over 10 days of trading volume. Crowded shorts (in green on the chart below) slightly outperformed the market in January:

Chart of he January 2015 performance of the crowded hedge fund long and short bets

January 2015 Performance of Crowded Hedge Fund Long and Short Bets

The performance of crowded hedge fund bets was roughly proportional to their crowding and liquidity. In the following chart, each line represents the performance of crowded hedge fund bets with a given level of crowding. Crowded and illiquid longs sized at 100 days of volume (red), underperformed crowded longs sized at 50 days (orange), etc. Meanwhile, illiquid shors (in shades of green) outperformed dramatically:

Chart of the January 2015 performance of crowded hedge fund bets as a function of liquidity

January 2015 Crowded Hedge Fund Bet Performance and Liquidity

In general, the larger and more illiquid a crowded hedge fund long bet was, the worse it fared. Even when a positive catalyst ought to lift a crowded long, impatient hedge fund holders may sell on the news, muting any upside. On the other hand, even when a negative catalyst ought to sink a crowded short, managers may buy on the news, muting any downside.

The damage covered a variety of industries and company sizes. Some of the notable losses in the crowded and illiquid long group were the following:

		HF Aggregate Exposure
Symbol	Name	Value ($ mil)	Liquidity (days)	Performance (%)
NOR	Noranda Aluminum Holding Corporation	103.80	40.19	-16.68
NMIH	NMI Holdings, Inc. Class A	127.93	43.41	-16.71
AGYS	Agilysys, Inc.	100.22	114.63	-18.42
RP	RealPage, Inc.	112.04	10.96	-18.73
LPG	Dorian LPG Ltd.	107.44	28.71	-19.01
TWI	Titan International, Inc.	125.13	16.29	-19.09
HTZ	Hertz Global Holdings, Inc.	2778.20	10.77	-20.46
DPM	DCP Midstream Partners, LP	377.28	15.00	-20.85
HLF	Herbalife Ltd.	982.00	10.42	-22.16
THRX	Theravance, Inc.	321.32	19.18	-24.92
SALT	Scorpio Bulkers, Inc.	139.66	32.25	-25.35
SXC	SunCoke Energy, Inc.	161.15	16.09	-25.73
CACQ	Caesars Acquisition Co. Class A	110.62	83.10	-29.25
SD	SandRidge Energy, Inc.	317.85	11.38	-29.83
YRCW	YRC Worldwide Inc.	280.23	15.99	-36.12
CZR	Caesars Entertainment Corporation	245.84	10.74	-39.47
LE	Lands’ End, Inc.	321.92	12.10	-46.47
ASPS	Altisource Portfolio Solutions S.A.	245.55	10.77	-52.17
ADES	Advanced Emissions Solutions, Inc.	107.92	41.21	-77.55
OCN	Ocwen Financial Corporation	910.00	12.37	-87.76

Some of the notable gains in the crowded short group were the following:

		HF Aggregate Exposure
Symbol	Name	Value ($ mil)	Liquidity (days)	Performance (%)
SBUX	Starbucks Corporation	-766.74	-2.02	6.78
KR	Kroger Co.	-390.36	-1.81	6.80
SPG	Simon Property Group, Inc.	-508.85	-1.90	6.83
LUV	Southwest Airlines Co.	-767.17	-1.88	6.89
NS	NuStar Energy L.P.	-141.06	-3.79	6.97
PRXL	PAREXEL International Corporation	-277.74	-6.47	7.09
BAH	Booz Allen Hamilton Holding Corporation Class A	-140.18	-7.13	7.60
ICLR	ICON Plc	-281.30	-9.46	8.28
GILD	Gilead Sciences, Inc.	-2122.35	-1.29	9.56
BA	Boeing Company	-2372.83	-4.29	9.78
NEU	NewMarket Corporation	-178.11	-10.24	10.35
PBYI	Puma Biotechnology, Inc.	-109.17	-1.57	10.75
ATK	Alliant Techsystems Inc.	-108.27	-2.90	11.02
TMUS	T-Mobile US, Inc.	-149.48	-1.11	11.13
BIIB	Biogen Idec Inc.	-1221.11	-2.01	12.86
SLXP	Salix Pharmaceuticals, Ltd.	-695.73	-2.19	14.75
PCRX	Pacira Pharmaceuticals, Inc.	-249.52	-4.43	16.82
CLR	Continental Resources, Inc.	-535.76	-3.54	17.56
HAR	Harman International Industries, Incorporated	-111.76	-1.31	19.27
ICPT	Intercept Pharmaceuticals, Inc.	-378.15	-4.27	25.29

January 2015 was an especially costly month for crowded hedge fund ideas, but it illustrated a broad and consistent effect. Crowding takes a heavy toll on performance and warrants close scrutiny from portfolio managers, analysts, and allocators.

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 Costs – eHealth

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EHTH (eHealth, Inc.), an Internet health insurance agency, was down over 50% following disappointing revenue and EPS guidance. The events were yet another example of the mass sell-offs in crowded hedge fund bets after disappointing news. This once again demonstrates that investors must monitor their portfolios for crowding.

EHTH was not the largest stock-specific hedge fund insurance sector bet, but it was one of the least liquid. The aggregate crowded position constituted about nine days’ trading volume. As funds struggled to exit, more shares traded in one day than in the prior week, knocking the price down 54%. We illustrate how investors aware of this crowding could have avoided the losses.

To explore crowding we analyze hedge fund Life and Health Insurance Sector holdings (HF Sector Aggregate) relative to the Sector Market Portfolio (Sector Aggregate). HF Sector Aggregate is position-weighted while Sector Aggregate is capitalization-weighted. This follows the approach of our earlier articles on aggregate and sector-specific hedge fund crowding. Crowded positions are vulnerable to volatility, mass liquidation, and losses; they persistently underperform in some sectors.

Hedge Fund Life and Health Insurance Performance

The figure below plots historical return of HF Life and Health Insurance 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 factor portfolio by over 35%:

Chart of the historical factor and idiosyncratic performance of the Hedge Fund Life and Health Insurance Sector Aggregate

Hedge Fund Life and Health Insurance Sector Aggregate Historical Performance

Hedge Fund Life and Health Insurance Risk-Adjusted Performance

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

Chart of the historical security selection (stock picking) performance of the Hedge Fund Life and Health Insurance Sector Aggregate

Hedge Fund Life and Health Insurance Sector Aggregate Historical Security Selection Performance

Crowded Hedge Fund Life and Health Insurance Ideas

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

Crowded Hedge Fund Life and Health Insurance Sector Bets

The following table contains detailed data on these crowded ideas:

		Exposure (%)		Net Exposure			Share of Risk (%)
		HF Sector Aggr.	Sector Aggr.	%	$mil	Days of Trading

VOYA	Voya Financial, Inc.	29.79	5.83	23.96	375.4	4.5	39.08
UAM	Universal American Corp.	8.73	0.39	8.34	130.7	123.5	18.76
LNC	Lincoln National Corporation	23.20	8.45	14.75	231.2	1.9	11.48
EHTH	eHealth, Inc.	3.55	0.26	3.29	51.6	8.9	8.98
AFL	Aflac Incorporated	1.05	15.94	-14.88	-233.2	-1.6	8.52
MET	MetLife, Inc.	12.56	36.31	-23.75	-372.1	-1.1	8.15
GNW	Genworth Financial, Inc. Class A	5.55	3.93	1.62	25.4	0.3	1.04
UNM	Unum Group	0.92	5.29	-4.37	-68.5	-1.0	0.97
PNX	Phoenix Companies, Inc.	1.59	0.19	1.39	21.9	9.5	0.87
SYA	Symetra Financial Corporation	3.75	1.63	2.12	33.1	3.6	0.42
RGA	Reinsurance Group of America, Incorporat	0.55	3.32	-2.76	-43.3	-1.4	0.28
TMK	Torchmark Corporation	0.00	4.13	-4.13	-64.7	-1.5	0.27
ANAT	American National Insurance Company	0.00	1.82	-1.82	-28.6	-17.4	0.27
SFG	StanCorp Financial Group, Inc.	0.00	1.64	-1.64	-25.6	-1.8	0.24
PRI	Primerica, Inc.	0.00	1.58	-1.58	-24.8	-1.7	0.16
EIG	Employers Holdings, Inc.	1.06	0.37	0.69	10.8	4.1	0.10
AEL	American Equity Investment Life Holding	0.13	1.03	-0.91	-14.2	-0.7	0.09
PL	Protective Life Corporation	2.41	3.32	-0.91	-14.2	-0.3	0.08
CNO	CNO Financial Group, Inc.	3.11	2.13	0.98	15.4	0.6	0.07
IHC	Independence Holding Company	0.60	0.14	0.46	7.2	42.1	0.05
…	Other Positions			0.03			0.11
Total							100.00

The table above shows that, while EHTH was neither the highest contributor to risk nor the most illiquid, it was one of the top bets in each category. When the bad news came, its value halved. VOYA, UAM, and LNC, the other large and illiquid bets, are also at risk. Even when a positive catalyst arrives, impatient hedge fund holders may sell on the news, muting any upside. Whether or not investors choose to steer clear of crowded names, they must monitor them. With proper data, this attention to crowding can prevent “unexpected” volatility.

Conclusion

eHealth illustrates the vulnerability of crowded and illiquid positions to disorderly liquidation. eHealth was especially dangerous, given the tendency of crowded hedge fund life and health insurance ideas to disappoint. Investors armed with hedge fund crowding insights could have avoided these losses.

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 Costs – SanDisk

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SanDisk (SNDK) was down 14% following a disappointing pre-announcement. This is a common occurrence for crowded ideas: SanDisk is the most crowded hedge fund bet in its sector, and crowded hedge fund Electronic Components picks tend to do poorly. These events illustrate crowding costs, particularly in the areas where hedge funds display a persistent lack of skill.

This piece analyzes hedge fund Electronic Components sector holdings (HF Sector Aggregate) relative to the sector market portfolio (Sector Aggregate). HF Sector Aggregate is position-weighted while Sector Aggregate is capitalization-weighted. We follow the approach of our earlier articles on aggregate and sector-specific hedge fund crowding. Crowded positions are vulnerable to volatility, mass liquidation, and losses. In some sectors crowded positions persistently underperform.

Hedge Fund Electronic Components Performance

The figure below plots historical return of HF Sector 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):

Hedge Fund Electronic Components Sector Aggregate Historical Performance

Hedge Fund Electronic Components Risk-Adjusted Performance

The risk-adjusted return from security selection (αReturn) of HF Sector Aggregate is the return it would have generated if markets were flat. This is the idiosyncratic performance of the crowded portfolio. Adjusted for market returns, crowded bets have lost 24% since 2004:

Chart of the risk-adjusted return from security selection of the Hedge Fund Electronic Components Sector Aggregate

Hedge Fund Electronic Components Sector Aggregate Historical Risk-Adjusted Performance

Crowded Hedge Fund Electronic Components Bets

The following stocks contributed most to the relative residual (security-specific) risk of the HF Sector Aggregate as of 2014-09-30. 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 recent crowded stock-specific (idiosyncratic) Hedge Fund Electronic Components Sector bets

Crowded Hedge Fund Electronic Components Sector Bets

The following table contains detailed data on these crowded bets:

		Exposure (%)		Net Exposure			Share of Risk (%)
		HF Sector Aggr.	Sector Aggr.	%	$mil	Days of Trading
SNDK	SanDisk Corporation	40.46	16.60	23.87	584.0	1.7	43.86
FLEX	Flextronics International Ltd.	35.31	4.59	30.73	751.9	13.7	37.92
GLW	Corning Incorporated	2.68	18.87	-16.19	-396.1	-2.0	5.73
TEL	TE Connectivity Ltd.	0.02	17.10	-17.07	-417.9	-3.0	4.84
FSLR	First Solar, Inc.	1.38	4.98	-3.60	-88.1	-0.7	3.00
APH	Amphenol Corporation Class A	1.31	11.86	-10.55	-258.3	-3.4	1.30
CREE	Cree, Inc.	0.82	3.70	-2.88	-70.5	-0.8	1.02
KN	Knowles Corp.	3.51	1.70	1.81	44.3	1.1	0.58
PLUG	Plug Power Inc.	0.03	0.58	-0.55	-13.5	-0.6	0.47
SANM	Sanmina-SCI Corporation	0.00	1.30	-1.30	-31.9	-1.9	0.23
OLED	Universal Display Corporation	0.27	1.15	-0.88	-21.5	-1.3	0.17
JBL	Jabil Circuit, Inc.	1.67	3.05	-1.38	-33.7	-0.5	0.12
RSYS	RadiSys Corporation	0.75	0.07	0.67	16.5	60.7	0.11
IMI	Intermolecular, Inc.	0.91	0.08	0.82	20.1	145.2	0.10
RELL	Richardson Electronics, Ltd.	1.62	0.09	1.53	37.5	162.9	0.08
CODE	Spansion Inc. Class A	0.21	1.05	-0.84	-20.7	-0.3	0.06
PLXS	Plexus Corp.	0.00	0.94	-0.94	-23.0	-2.9	0.05
AVX	AVX Corporation	0.00	1.69	-1.69	-41.3	-13.2	0.04
VICR	Vicor Corporation	0.70	0.19	0.51	12.5	9.9	0.04
FN	Fabrinet	0.00	0.39	-0.39	-9.5	-2.8	0.04
…	Other Positions			0.19			0.23
Total							100.00

Conclusion

SanDisk illustrates the vulnerability to crowded names to mass liquidation by impatient investors. In general, crowded Electronic Component stocks tend to disappoint and hedge funds do even worse in other sectors.

Instead of blindly following hedge funds into popular technology names, investors should be wary of these ideas. Even excellent managers are seldom skilled in all areas and tend to generate the bulk of their active returns from a few specific skills.

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 Internet Software Crowding

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Hedge funds tend to pile into the same few stocks. In most sectors these crowded bets underperform. In previous articles we discussed the crowding of hedge fund energy as well as exploration and production bets. Internet software stocks show similar underperformance.

Hedge Fund Internet Software Aggregate

If markets were flat since 2004, the aggregate position-weighted hedge fund internet software portfolio (HF Aggregate) would have lost 25%. This is HF Aggregate’s risk adjust return from security selection (αReturn):

Chart of the cumulative risk-adjusted returns from security selection of the HF Internet Software Aggregate

Hedge Fund Internet Software Aggregate’s Residual Returns (αReturns)

Given the compounding of αReturns and internet software sector returns, the cumulative underperformance is even larger: Since 2004 HF Aggregate returned 200%. A portfolio with the same systematic risk as HF Aggregate (Factor Portfolio) returned 300%. Investors in crowded bets missed out on 100% in gains:

Chart of the cumulative total and factor (systematic) returns of the HF Internet Software Aggregate

Hedge Fund Internet Software Aggregate’s Total and Factor Returns

Hedge Fund Internet Software Crowding History

The following video shows the history of crowded hedge fund internet software bets. These are the stocks behind the record:

Current Hedge Fund Internet Software Crowding

The following are the currently crowded hedge fund internet software bets. Just two (long EQIX, short/underweight GOOGL) are responsible for over three quarters of the risk of HF Internet Software Aggregate:

		Position (%)
Symbol	Name	HF Aggregate	Market Aggregate	Relative	Share of Risk (%)
EQIX	Equinix, Inc.	23.77	1.63	22.14	53.73
GOOGL	Google Inc. Class A	10.51	44.39	-33.88	25.65
TWTR	Twitter, Inc.	0.49	3.46	-2.97	4.65
ZNGA	Zynga Inc. Class A	2.89	0.26	2.63	3.52
RBDC	RBID.com, Inc.	0.00	0.17	-0.17	2.07
P	Pandora Media, Inc.	2.41	0.53	1.88	1.87
RAX	Rackspace Hosting, Inc.	3.13	0.86	2.27	1.76
SFLY	Shutterfly, Inc.	2.32	0.22	2.10	1.38
WBMD	WebMD Health Corp.	1.84	0.18	1.66	0.93
NERO	NeuroMama Ltd.	0.00	0.74	-0.74	0.75
AKAM	Akamai Technologies, Inc.	3.04	1.50	1.54	0.61
TRLA	Trulia, Inc.	1.01	0.25	0.76	0.36
IACI	IAC/InterActiveCorp.	2.83	0.66	2.16	0.34
YNDX	Yandex NV Class A	2.32	0.81	1.51	0.33
MSTR	MicroStrategy Incorporated Class A	1.54	0.20	1.34	0.31
LNKD	LinkedIn Corporation Class A	2.20	3.20	-1.00	0.27
FB	Facebook, Inc. Class A	21.76	22.54	-0.78	0.21
MELI	MercadoLibre SA	0.00	0.81	-0.81	0.15
KING	King Digital Entertainment Plc	1.05	0.63	0.42	0.13
ECOM	Channeladvisor Corporation	0.42	0.06	0.37	0.11

The information herein is not represented or warranted to be accurate, correct, complete or timely.
Past performance is no guarantee of future results.
Copyright © 2012-2014, AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved.
Content may not be republished without express written consent.

Hedge Fund E&P Crowding History

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As we have discussed in earlier articles on hedge fund energy as well as exploration and production crowding, funds pile into the same few stocks. Over time these crowded stocks tend to underperform.

The following video shows the most crowded hedge fund E&P stocks over history, and their historical risk-adjusted performance: