Hedge Fund Crowding Update – Q1 2016

Analyses of hedge fund crowding typically focus on hedge funds’ individual positions (their residual, idiosyncratic, or stock-specific exposures). Yet, over 85% of the monthly return variance for the majority of hedge fund long equity portfolios is due to their factor (systematic) exposures. Stock-specific bets account for less than 15%. Factor – rather than residual – crowding has driven much of the industry’s past exuberance and its recent grief. In Q1 2016, nearly half of U.S. hedge funds’ relative long equity risk (tracking error) was due to a single factor, U.S. Market Exposure. This piece surveys the crowded factor and residual exposures at 3/31/2016 that are likely to drive long equity performance for hedge funds in coming quarters.

Identifying Hedge Fund Crowding

This piece follows the approach of our earlier articles on crowding: We processed regulatory filings of over 1,000 hedge funds and created a position-weighted portfolio (HF Aggregate) consisting of all tractable hedge fund long U.S. equity portfolios. We then analyzed HF Aggregate’s risk relative to the U.S. Market using the AlphaBetaWorks Statistical Equity Risk Model – a proven system for performance forecasting. The most crowded hedge fund bets are factors and, to a lesser extent, stocks that drive HF Aggregate’s relative risk and performance. Ironically, these are rarely the largest or the most common hedge fund positions.

Hedge Fund Aggregate’s Risk

The Q1 2016 HF Aggregate had 3.4% estimated future tracking error relative to the U.S. Market. Factor (systematic) exposures accounted for over two thirds of it:

Factor (systematic) and residual (idiosyncratic) components of U.S. hedge fund crowding and U.S. Hedge Fund Aggregate’s variance relative to U.S. Market on 3/31/2016

Components of the Relative Risk for U.S. Hedge Fund Aggregate in Q1 2016

Source Volatility (ann. %) Share of Variance (%)
Factor 2.78 68.23
Residual 1.89 31.77
Total 3.36 100.00

A simplistic analysis of hedge fund crowding that focuses on individual positions will overlook systematic exposures. Yet, they are responsible for over two thirds of the hedge fund industry’s risk. Since funds with no shared positions but similar factor exposures will correlate highly, a simplistic crowding analysis that lacks a predictive risk model will misidentify such similar funds as differentiated. This will misrepresent their risk and and can foster dangerous complacency.

Hedge Fund Factor (Systematic) Crowding

Below are the principal factor exposures (in red) relative to U.S. Market’s exposures (in gray) that are responsible for the factor crowding in the above table:

Chart of the factor exposures contributing most to the U.S. hedge fund crowding and factor variance of U.S. Hedge Fund Aggregate Portfolio relative to U.S. Market on 3/31/2016

Significant Absolute and Residual Factor Exposures of U.S. Hedge Fund Aggregate in Q1 2016

Of these exposures, Market (Beta) alone accounts for approximately two thirds of the relative and half of the total factor risk:

Chart of the main factors and their cumulative contribution to the factor variance of U.S. Hedge Fund Aggregate Portfolio relative to U.S. Market on 3/31/2016

Factors Contributing Most to Relative Factor Variance of U.S. Hedge Fund Aggregate in Q1 2016

Factor Relative Exposure Factor Volatility Share of Relative Factor Variance Share of Relative Total Variance
Market 15.46 12.67 64.54 44.04
Bond Index -21.46 3.32 18.23 12.44
Utilities -3.56 11.75 6.76 4.61
Consumer -9.07 3.97 4.74 3.23
Size -3.59 8.39 3.30 2.25
Health 3.44 7.29 2.48 1.69
Energy -2.50 13.17 -2.42 -1.65
Communications -1.17 12.02 1.82 1.24
Oil Price 0.18 30.91 0.99 0.68
Value -1.00 13.21 -0.74 -0.51

(Relative exposures and relative variance contribution. All values are in %. Volatility is annualized.)

The U.S. hedge fund industry’s most crowded bet is not a stock, or stocks, it is high systematic exposure to the U.S. Market. This makes the popular fascination with fund holdings and position overlap particularly dangerous. This factor crowding explains much of recent hedge fund misery. As large asset bases continue to diminish the importance of stock-specific risk, the survival of asset managers and allocators will increasingly rely on their analysis of systematic crowding with robust and predictive factor models.

Hedge Fund U.S. Market Factor Crowding

After working to refine our historical hedge fund portfolio database with particular attention to defunct firms and survivorship bias, we have an increasingly accurate picture of HF Aggregate’s historical factor exposures. Its current Market Factor Exposure is approximately 115% (i.e. the HF Aggregate’s Market Beta is approximately 1.15). The average hedge fund long equity portfolio now carries approximately 15% more Market Exposure than the Russell 3000 ETF and approximately 20% more Market Exposure than the S&P 500 ETF:

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

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

This Market crowding has been costly and disruptive during the recent volatility. It also partially explains the failures of simple performance and skill metrics: When portfolios carry different Market Exposure than S&P500, calculating security selection return as performance relative to S&P500 is perilous. High Market Exposure is a general risk to the industry and a source of turmoil. Since there is no relationship between Market Exposure of HF Aggregate and subsequent Market Factor return, Market Factor crowding is not a predictive indicator of future performance:

Chart of the correlation between the exposure of U.S. Hedge Fund Aggregate’s to the U.S. Market Factor and U.S. Market Factor’s return

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

Hedge Fund Bond Factor (Interest Rate) Crowding

We showed in an earlier piece that Bond (Interest Rate) Factor exposure is one of the top drivers of hedge fund long equity risk and performance. This bond risk is a natural consequence of hedge funds’ fondness for “cheap call options.” These are often levered companies with significant bond exposure: the companies’ creditors are long bonds; the companies (and their equity owners) are economically short them.

This short Bond Factor (long Interest Rate) exposure is now near record levels and is the second most important source of HF Aggregate’s Factor Crowding:

Chart of the historical exposure of U.S. Hedge Fund Aggregate’s to the U.S. Bond Factor

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

As with Market Factor, Bond Factor Exposure is a general risk to the industry. There is no relationship between Bond Exposure of HF Aggregate and subsequent Bond Factor return:

Chart of the correlation between the exposure of U.S. Hedge Fund Aggregate’s to the U.S. Bond Factor and U.S. Bond Factor’s return

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

Hedge Fund Residual (Idiosyncratic) Crowding

As of 3/31/2016, a  third of hedge fund crowding was due to residual (idiosyncratic, stock-specific) risk. Netflix (NFLX) is responsible for a quarter of it. The five most crowded stocks collectively account for half:

Chart of the main stock-specific bets and their cumulative contribution to the residual variance of U.S. Hedge Fund Aggregate Portfolio relative to U.S. Market on 3/31/2016

Stocks Contributing Most to Relative Residual Variance of U.S. Hedge Fund Aggregate in Q1 2016

These may be perfectly sound fundamental investments. However, they are sensitive to asset flows in and out of the industry. Given the sharp losses to residual hedge fund crowding in 2015-2016 and the tendency of liquidations to revert, crowding risk in these has diminished and the liquidation may even present long investment opportunities:

Symbol Name Relative Exposure Residual Volatility Share of Relative Residual Variance Share of Relative Total Variance
NFLX Netflix, Inc. 1.77 54.97 26.47 8.41
LNG Cheniere Energy, Inc. 1.60 32.94 7.73 2.46
CHTR Charter Communications 2.38 19.79 6.17 1.96
TWC Time Warner Cable Inc. 2.74 15.80 5.22 1.66
JD JD.com, Inc. Sponsored ADR 1.34 29.35 4.31 1.37
AGN Allergan plc 2.06 17.07 3.43 1.09
VRX Valeant Pharmaceuticals International 0.67 43.49 2.37 0.75
PCLN Priceline Group Inc 1.28 22.17 2.24 0.71
FLT FleetCor Technologies, Inc. 1.41 19.72 2.16 0.69
UAL United Continental Holdings, Inc. 0.92 28.15 1.86 0.59

(Relative exposures and relative variance contribution. All values are in %. Volatility is annualized.)

Though these stock-specific bets are important, they account for less than a third of the entire hedge fund crowding picture. Consequently, in the current environment of extreme systematic hedge fund crowding, allocators and fund followers should continue to pay more attention to factor risk. As hedge funds’ residual volatility continues to wane, allocators owning a broadly diversified portfolio of hedge funds are increasingly at risk of paying high active fees for a passive factor portfolio.

Summary

  • The main source of Q1 2016 hedge fund crowding, responsible for nearly half of relative long equity risk, was record U.S. Market exposure.
  • The second most important source of Q1 2016 hedge fund crowding was near-record short Bond (long interest rate) exposure.
  • Given high factor (systematic) hedge fund long equity crowding, analysis of crowding risks must focus on factor exposures, rather than individual positions.
The information herein is not represented or warranted to be accurate, correct, complete or timely. Past performance is no guarantee of future results. Copyright © 2012-2016, AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved. Content may not be republished without express written consent.
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Performance Persistence within International Style Boxes

We earlier discussed how nominal returns and related investment performance metrics revert: Since portfolio performance primarily comes from systematic (factor) exposures, such simplistic metrics merely promote the high-risk portfolios during the bullish regimes and the low-risk portfolios during the bearish regimes. As regimes change, the leaders flip. We also showed that, when security selection returns are distilled with a robust factor model, performance persists within all U.S. equity Style Boxes. Prompted by reader interest, we now investigate performance persistence within International Style Boxes.

Measuring the Persistence of International Portfolio Returns

As in our earlier work on return persistence, we examine all Form 13F filings for the past 10 years. This survivorship-free portfolio database covers all institutions that exercised investment discretion over at least $100 million and yields approximately 3,600 international portfolios with sufficiently long histories, low turnover, and broad positions to be suitable for the study.

We split the 10 years of history into two random 5-year samples and compared performance metrics of each portfolio over these two periods. The correlation between metrics over the sample periods measures the metrics’ persistence.

International Portfolios’ Performance Persistence

The Reversion of International Portfolios’ Nominal Returns

The following chart plots the rankings of each portfolio’s nominal returns during the two sample periods. The x-axis plots return percentile, or ranking, in the first sample period. The y-axis plots return percentile, or ranking, in the second sample period. The best-performing international portfolios of the first period have x-values near 100; the best-performing portfolios of the second period have y-values near 100:

Chart of the random relationship between nominal returns for two historical samples for all international equity 13F portfolios

13F Portfolios, International Positions: Correlation between the rankings of nominal returns for two historical samples

Whereas past performance of U.S. equity portfolios was a (negative) predictor of future results, there is no significant correlation between the two for international portfolios – best- and worst-performers tend to become average.

The Persistence of International Portfolios’ Security Selection Returns

Due to the domineering effects of Market and other systematic factors, top-performing managers during the bullish regimes are those that take the most risk, and top-performing managers during the bearish regimes are those that take the least risk. Since Market returns are approximately random, nominal returns do not persist. To eliminate this noise, the AlphaBetaWorks Performance Analytics Platform calculates each portfolio’s return from security selection net of factor effects. αReturn is the return a manager would have generated if all factor returns had been flat.

International portfolios with above-average αReturns in one period are likely to maintain them in the other. In the following chart, this relationship is represented by the concentration of portfolios in the bottom left (laggards that remained laggards) and top right (leaders that remained leaders):

Chart of the positive correlation between risk-adjusted returns from security selection (αReturns) for two historical samples for all international equity 13F portfolios

13F Portfolios, International Positions: Correlation between the rankings of αReturns for two historical samples

This test of persistence across two arbitrary 5-year samples is strict. Persistence of security selection skill is even higher over shorter periods.

Performance Persistence within International Style Boxes

Measures of investment style such as Size (portfolio market capitalization) and Value/Growth (portfolio valuation) are common approaches to grouping portfolios. Readers frequently ask whether the reversion of nominal returns and related metrics can be explained by Style Box membership. Perhaps we merely observed reversion in leadership that is eliminated by controlling for Style?

To test this, we compared performance persistence within each of the four popular Style Boxes.

International Large-Cap Value Portfolios’ Performance Persistence

The International Large-cap Value Style Box shows the highest persistence of long-term stock picking results, yet the relationship between nominal returns within it is still nearly random. Powerful performance analytics provide the biggest edge for this International Style Box:

Chart of the random relationship between nominal returns and positive correlation between risk-adjusted returns from security selection (αReturns) for two historical samples for equity 13F portfolios in the Large-Cap Value International Style Box

Large-Cap Value 13F Portfolios, International Positions: Correlation between the rankings of nominal returns αReturns for two historical samples

International equity portfolios differ from U.S. equity portfolios, where security selection persistence was highest for the Small-cap Value Style Box.

International Large-Cap Growth Portfolios’ Performance Persistence

International portfolios in the Large-cap Growth Style Box also show a nearly random relationship between the two periods’ returns. However, their αReturns persist strongly. Whereas large-cap growth stock picking is treacherous for U.S. equity portfolios, it is more rewarding internationally:

Chart of the random relationship between nominal returns and positive correlation between risk-adjusted returns from security selection (αReturns) for two historical samples for equity 13F portfolios in the Large-Cap Growth International Style Box

Large-Cap Growth 13F Portfolios, International Positions: Correlation between the rankings of nominal returns αReturns for two historical samples

International Small-Cap Value Portfolios’ Performance Persistence

International portfolios in the Small-cap Value Style Box have the least persistent αReturns, in contrast to the U.S. portfolios:

Chart of the random relationship between nominal returns and positive correlation between risk-adjusted returns from security selection (αReturns) for two historical samples for equity 13F portfolios in the Small-Cap Value International Style Box

Small-Cap Value 13F Portfolios, International Positions: Correlation between the rankings of nominal returns αReturns for two historical samples

International Small-Cap Growth Portfolios’ Performance Persistence

αReturns within the International Small-cap Growth Style Box persist almost as strongly as within the International Large-cap Style Boxes:

Chart of the random relationship between nominal returns and positive correlation between risk-adjusted returns from security selection (αReturns) for two historical samples for equity 13F portfolios in the Small-Cap Growth International Style Box

Small-Cap Growth 13F Portfolios, International Positions: Correlation between the rankings of nominal returns αReturns for two historical samples

Summary

  • Whereas nominal returns and related simplistic metrics of investment skill revert, security selection performance – once properly distilled with a capable factor model – persists.
  • The randomness and reversion of nominal returns and the persistence of security selection skill hold across all International Style Boxes.
  • Security selection performance persists most strongly for International Large-cap portfolios.
The information herein is not represented or warranted to be accurate, correct, complete or timely.
Past performance is no guarantee of future results.
Copyright © 2012-2016, AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved.
Content may not be republished without express written consent.
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The Top U.S. Stock Pickers’ Industrials Performance

And Their Consensus Industrials Ideas in 2016

The challenges of identifying good investors and distilling their skill obscure the top stock pickers’ consistently strong performance. For instance, contrary to popular wisdom 2015 was a good year for stock picking. These results also generally apply to large market sub-segments such as the Industrials sector. In this piece we use a robust risk model to identify the best U.S. stock pickers, distill their skills, and monitor their Industrials performance. We then track their consensus Industrials portfolio and reveal its top positions.

Identifying the Top U.S. Stock Pickers

Nominal returns and related simplistic metrics of investment skill are dominated by systematic factors and hence revert. Therefore, we must eliminate these systematic effects to get an accurate picture. The AlphaBetaWorks Performance Analytics Platform calculates each portfolio’s return from security selection, or αReturn. It is the residual performance net of factor effects and the performance a portfolio would have generated if all factor returns had been flat.

This study covers portfolios of all institutions that have filed Form 13F. Of these, approximately 5,000 filers had holdings histories suitable for skill evaluation. The AlphaBetaWorks Expert Aggregate (ABW Expert Aggregate) consists of the top five percent with the most consistently positive 36-month αReturns. This expert panel typically includes 100-150 firms. Manager fame and firm size are poor proxies for skill, so the panel is an eclectic collection light on celebrities but heavy on skill.

Industrials Performance of the Top U.S. Stock Pickers’

Since security selection skill persists, managers with above-average αReturns in the past are likely to maintain them in the future. This applies both to aggregate portfolios and to large portfolio subsets, such as sector holdings. To illustrate, we consider the top stock pickers’ Industrials performance.

A hedged portfolio that combines the top U.S. stock pickers’ net consensus Industrials longs (relative Industrials overweights), lagged 2 months to account for filing delay (the ABW Industrials Expert Aggregate), delivers consistent positive returns:

Chart of the cumulative return of the Industrials Benchmark (Vanguard Industrials ETF (VIS)) and the cumulative Industrials performance of the hedged portfolio that combines the to 5% U.S. long stock pickers’ net consensus industrials positions

Cumulative Hedged Portfolio Return: Top U.S. Stock Pickers’ Net Consensus Industrials Longs

For illustration, we include the performance of the Vanguard Industrials ETF (VIS) (Benchmark above).

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
ABW Industrials Expert Aggregate 4.13 -2.25 25.24 14.37 7.67 9.47 3.93 6.11 2.06 12.94 2.48 5.84
Vanguard Industrials ETF (VIS) 4.88 15.35 14.14 -39.37 23.15 27.74 -2.48 17.71 42.08 9.00 -3.73 -2.82

The top stock pickers’ consistently positive Industrials αReturns yield consistently positive returns, low volatility, and low drawdowns for the ABW Industrials Expert Aggregate:

ABW Industrials Expert Aggregate Vanguard Industrials ETF (VIS)
Annualized Return 8.56 7.75
Annualized Standard Deviation 7.38 19.24
Annualized Sharpe Ratio (Rf=0%) 1.16 0.40
ABW Industrials Expert Aggregate Vanguard Industrials ETF (VIS)
Semi Deviation 1.50 4.20
Gain Deviation 1.50 3.35
Loss Deviation 1.42 4.46
Downside Deviation (MAR=10%) 1.57 4.22
Downside Deviation (Rf=0%) 1.17 3.84
Downside Deviation (0%) 1.17 3.84
Maximum Drawdown 6.95 57.33
Historical VaR (95%) -2.52 -8.66
Historical ES (95%) -4.09 -13.08

Unfortunately, some highly skilled managers with strong Industrials books have fallen far short of the above results. Poor risk systems and losses from overlooked factor exposures often conceal stock-picking skill. The consistent absolute returns above are due in part to robust hedging that mitigates systematic noise.

Top U.S. Stock Pickers’ Consensus Industrials Positions

The top stock pickers are rarely the hottest funds and their consensus longs are rarely the hottest stocks. Below are the top 10 holdings of the ABW Industrials Expert Aggregate at year-end 2015:

Symbol Name Exposure (%)
MMM 3M Company 17.37
GE General Electric Company 5.47
ROP Roper Technologies, Inc. 3.64
UNP Union Pacific Corporation 3.61
DHR Danaher Corporation 3.26
UTX United Technologies Corporation 2.84
AGX Argan, Inc. 2.77
LMT Lockheed Martin Corporation 2.56
EMR Emerson Electric Co. 2.47
LUV Southwest Airlines Co. 2.37

It is worth noting that the above positions represent a consensus among stock-pickers who have proven their skill. This is not to be confused with crowding, which we have written about at length. Hedge fund crowding is a consensus among (often impatient and performance-sensitive) hedge funds, irrespective of their skill.

Top Stock Pickers’ Exposure to 3M (MMM)

The largest position within the ABW Industrials Expert Aggregate is 3M (MMM). The top panel on the following chart shows MMM’s cumulative nominal returns in black and cumulative residual returns (αReturns) in blue. Residual return or αReturn is the performance net of the systematic factors defined by the AlphaBetaWorks Statistical Equity Risk Model – the performance MMM would have generated if factor returns had been flat. The bottom panel shows exposure to MMM within the Aggregate:

Chart of the cumulative αReturn (residual return) of MMM and exposure to MMM within the hedged portfolio that combines the to 5% U.S. long stock pickers’ net consensus industrials positions

Cumulative αReturns of MMM and ABW Industrials Expert Aggregate’s MMM Exposure

Top Stock Pickers’ Exposure to General Electric (GE)

The second largest position within the ABW Industrials Expert Aggregate is General Electric (GE). The Expert Aggregate was mostly underweight (short) GE between 2008 and 2014 – a challenging period for GE. GE became experts’ consensus long in 2014 – about a year ahead of the 2015 turnaround in residual performance:

Chart of the cumulative αReturn (residual return) of GE and exposure to GE within the hedged portfolio that combines the to 5% U.S. long stock pickers’ net consensus industrials positions

Cumulative αReturns of GE and ABW Industrials Expert Aggregate’s GE Exposure

Top Stock Pickers’ Exposure to Roper Technologies (ROP)

The third largest position within the ABW Industrials Expert Aggregate is Roper Technologies (ROP). The Aggregate has had varied but mostly positive exposure to ROP over the past 10 years. Current exposure is at historic heights:

Chart of the cumulative αReturn (residual return) of ROP and exposure to ROP within the hedged portfolio that combines the to 5% U.S. long stock pickers’ net consensus industrials positions

Cumulative αReturns of ROP and ABW Industrials Expert Aggregate’s ROP Exposure

Conclusions

  • Robust analytics built on predictive risk models identify the top stock pickers in the sea of mediocrity.
  • When hedged, top stock pickers’ net consensus Industrials longs (relative overweights) tend to generate positive future absolute returns and net consensus industrials shorts (relative underweights) tend to generate negative future absolute returns.
  • The top stock pickers are often unglamorous firms and their consensus Industrials longs are often unglamorous stocks – both tend to outperform.
The information herein is not represented or warranted to be accurate, correct, complete or timely.
Past performance is no guarantee of future results.
Copyright © 2012-2016, AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved.
Content may not be republished without express written consent.
U.S. Patents Pending.
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How Did the Top U.S. Stock Pickers Do in 2015?

And What Did They Own at Year-end?

Contrary to popular wisdom, 2015 was a good year for stock picking. The problem is that few know who the good stock pickers are. Further, good stock pickers may be poor risk managers. In this article we use a robust risk model to track the top U.S. stock pickers and to distill their skill.

Since genuine investment skill persists, top U.S. stock pickers tend to generate persistently positive returns from security selection. Consequently, a hedged (market-neutral) portfolio of their net consensus longs (relative overweights) tends to generate positive returns, independent of the Market. Below we illustrate this portfolio’s performance and reveal its top positions.

Identifying the Top U.S. Stock Pickers

This study covers portfolios of all institutions that have filed Form 13F. This is the broadest and most representative survivorship-free portfolio database comprising thousands of firms: hedge funds, mutual fund companies, investment advisors, and all other institutions with over $100 million in U.S. long assets. Approximately 5,000 firms had sufficiently long histories, low turnover, and broad portfolios suitable for skill evaluation.

Nominal returns and related simplistic metrics of investment skill (Sharpe Ratio, Win/Loss Ratio, etc.) are dominated by Market and other systematic factors and hence revert. As market regimes change, top performers tend to become bottom performers. To eliminate these systematic effects and estimate residual performance due to stock picking skill, the AlphaBetaWorks Performance Analytics Platform calculates each portfolio’s return from security selection – αReturn. αReturn is the performance a portfolio would have generated if all factor returns had been flat.

Each month we identify the five percent of 13F-filers with the most consistently positive αReturns over the prior 36 months. This expert panel of the top stock pickers typically includes 100-150 firms. Since manager fame and firm size are poor proxies for skill, the panel is an eclectic bunch. It currently includes some hedge funds (Lumina Fund Management, Palo Alto Investors, and Chilton Investment Company), though few of the famed gurus. Many of the top long stock pickers are investment management firms (Eaton Vance Management, Fiduciary Management Inc., and Aristotle Capital Management), as well as banks, endowments, and trust companies.

Performance of the Top U.S. Stock Pickers

Since security selection skill persists, managers with above-average αReturns in the past are likely to maintain them in the future. Therefore, a hedged portfolio that combines the top U.S. stock pickers’ net consensus longs (relative overweights), lagged 2 months to account for filing delay (the ABW Expert Aggregate), delivers consistent positive returns as illustrated below:

Chart of the cumulative return of the Market (Russell 3000) and the cumulative return of the hedged portfolio that combines the to 5% U.S. long stock pickers net consensus exposures

Cumulative Hedged Portfolio Return: Top U.S. Stock Pickers’ Net Consensus Longs

  2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
ABW Expert Aggregate 4.56 14.43 12.74 5.95 -1.25 15.35 2.20 2.24 15.47 8.81 13.16 1.70
iShares Russell 3000 ETF 9.04 15.65 4.57 -37.16 28.21 16.81 0.78 16.43 32.97 12.41 0.34 -5.72

ABW Expert Aggregate achieved higher returns than those of the broad market with less than half of its volatility:

ABW Expert Aggregate iShares Russell 3000 ETF
Annualized Return 8.36 6.40
Annualized Standard Deviation 5.25 15.50
Annualized Sharpe Ratio (Rf=0%) 1.59 0.41

The consistency of top stock pickers’ αReturns manifests itself as low downside volatility and low losses of the ABW Expert Aggregate:

ABW Expert Aggregate iShares Russell 3000 ETF
Semi Deviation 1.00 3.33
Gain Deviation 1.19 2.40
Loss Deviation 0.70 3.45
Downside Deviation (MAR=10%) 1.09 3.43
Downside Deviation (Rf=0%) 0.64 3.04
Downside Deviation (0%) 0.64 3.04
Maximum Drawdown 5.06 51.24
Historical VaR (95%) -1.81 -7.58
Historical ES (95%) -2.21 -9.96

One often reads commentary on the challenges stock pickers faced in 2015. This analysis typically fails to identify skill and merely reveals the obvious: average managers do poorly in a low-return environment. In fact, 2015 was one of the strongest years for top stock pickers. It is the indiscriminate rallies such as that of 2009 that prove challenging.

Unfortunately, some skilled managers suffered from underdeveloped risk systems, and losses from hidden systematic risks concealed their stock-picking results. For instance, some highly skilled stock pickers with unintended small-cap (short Size) exposure experienced 5-10% systematic headwinds in 2015. A robust risk management program would have partially or wholly mitigated these.

Top U.S. Stock Pickers’ Consensus Positions

Since the top stock pickers are rarely the most celebrated firms, their top consensus longs are rarely the hottest stocks. Below are the top 10 holdings of the ABW Expert Aggregate at year-end 2015:

Symbol Name Exposure (%)
EA Electronic Arts Inc. 2.29
V Visa Inc. Class A 1.26
XOM Exxon Mobil Corporation 0.66
GTE Gran Tierra Energy Inc. 0.65
DIS Walt Disney Company 0.58
PEP PepsiCo, Inc. 0.57
MNST Monster Beverage Corporation 0.57
ORLY O’Reilly Automotive, Inc. 0.56
JKHY Jack Henry & Associates, Inc. 0.51
TTGT TechTarget, Inc. 0.50

Top Stock Pickers’ Exposure to Electronic Arts (EA)

Top stock pickers had negligible exposure to EA until 2015. In early-2015 EA became one of the largest exposures within our Expert Aggregate.

The top panel on the following chart shows cumulative nominal returns in black and cumulative residual returns (αReturns) in blue. Residual return or αReturn is the performance net of the systematic factors defined by the AlphaBetaWorks Statistical Equity Risk Model – the performance EA would have generated if systematic return had been flat. The bottom panel shows exposure to EA within the ABW Expert Aggregate:

Chart of the cumulative αReturn (residual return) of EA and exposure to EA within the hedged portfolio that combines the to 5% U.S. long stock pickers net consensus exposures

Cumulative αReturns of EA and Exposure to EA within the Hedged Portfolio of the Top U.S. Stock Pickers’ Net Consensus Longs

Top Stock Pickers’ Exposure to Visa Inc (V)

Our Expert Aggregate was underweight (short) V between 2011 and 2014 – a period of flat-to-negative αReturn when V lagged a passive portfolio with matching risk. V became an ABW Expert Aggregate consensus long by early 2014 – the start of a strong positive αReturn period:

Chart of the cumulative αReturn (residual return) of V and exposure to V within the hedged portfolio that combines the to 5% U.S. long stock pickers net consensus exposures

Cumulative αReturns of V and Exposure to V within the Hedged Portfolio of the Top U.S. Stock Pickers’ Net Consensus Longs

Top Stock Pickers’ Exposure to Exxon Mobil (XOM)

The Expert Aggregate was mostly underweight (short) XOM between 2008 and 2013, but grew exposure from 2012. By 2014 XOM was a top bet. This proved profitable during the 2014-2015 energy rout when XOM remained an island of stability, delivering positive αReturn:

Chart of the cumulative αReturn (residual return) of XOM and exposure to XOM within the hedged portfolio that combines the to 5% U.S. long stock pickers net consensus exposures

Cumulative αReturns of XOM and Exposure to XOM within the Hedged Portfolio of the Top U.S. Stock Pickers’ Net Consensus Longs

Conclusions

  • Robust analytics built on predictive risk models identify the top stock pickers in the sea of mediocrity.
  • Top stock pickers’ portfolios deliver consistently positive αReturns (residual returns), independent of the Market.
  • Top stock pickers’ net consensus longs (relative overweights) tend to generate positive future αReturns and net consensus shorts (relative underweights) tend to generate negative future αReturns .
  • Top stock pickers are rarely the most celebrated firms and their consensus longs are rarely the most celebrated stocks.
  • The most celebrated stocks frequently appear as top stock pickers’ consensus shorts.
The information herein is not represented or warranted to be accurate, correct, complete or timely.
Past performance is no guarantee of future results.
Copyright © 2012-2016, AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved.
Content may not be republished without express written consent.
U.S. Patents Pending.
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Hedge Fund Clustering in Q4 2015

Crowding consists of large capital pools chasing related 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 crowding. We illustrate the large-scale hedge fund clustering and crowded bets within the largest cluster. Allocators and fund followers without a handle on this phenomenon may be invested in an undifferentiated portfolio prone to liquidation, or paying high active fees for consensus factor exposures.

Hedge Fund Crowding and Hedge Fund Clustering

Our articles on hedge fund crowding analyze the factor (systematic) and residual (idiosyncratic) exposures of HF Aggregate, which consists of the long equity holdings of all U.S. hedge fund portfolios tractable from regulatory filings. Most analysis of crowding tends to overlook bets shared by fund groups within the aggregate. To explore this internal structure of hedge fun crowding, in 2014 AlphaBetaWorks pioneered research on hedge fund clustering. Here we update this analysis with Q4 2015 holdings data.

Hedge Fund Clusters

Note that simplistic analysis of holdings overlap fails to measure fund similarity. Since their variance is overwhelmingly systematic, two funds with no overlapping positions but similar factor exposures can track each other closely. To identify clusters of funds without these deficiencies, we analyze factor and residual exposures of every portfolio relative to every other portfolio 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 find funds with similar exposures and build clusters (related to phylogenic trees, or family trees) of funds. We use agglomerative hierarchical clustering with estimated future relative tracking error as the metric of differentiation or dissimilarity. The resulting clusters capture similarities of all analyzable U.S. hedge fund long equity portfolios:

Chart of hedge fund clustering for U.S. long equity portfolios in Q4 2015

Clusters of U.S. Hedge Funds’ Long Equity Portfolios in Q4 2015

The largest cluster contains approximately 40 funds. It and other large clusters warrant careful scrutiny by allocators: those invested in a portfolio of clustered funds may be paying high active fees for a handful of consensus factor and stock-specific bets.

The AQR-Adage Hedge Fund Cluster

The AQR-Adage Cluster, named after two of its large and similar members, has recently been the largest cluster of hedge funds’ long equity portfolios:

Chart of hedge fund clustering within the largest cluster of U.S. Hedge Funds’ Q4 2015 Long Equity Portfolios

The Largest Hedge Fund Long Equity Portfolio Cluster in Q4 2015

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

Chart of the flat view of clustering within the AQR-Adage cluster of U.S. Hedge Funds’ Q4 2015 Long Equity Portfolios

The AQR-Adage Long Equity Portfolio Cluster in Q4 2015

This cluster’s aggregate portfolio is similar to the U.S. equity market. We estimate only 1.8% tracking error of the AQR-Adage Cluster relative to the Russell 3000 Index.

Source Volatility (ann. %) Share of Variance (%)
Factor 1.26 48.23
Residual 1.31 51.77
Total 1.82 100.00

Put differently, we expect this cluster’s aggregate annual long portfolio return to differ from the market by more than 1.8% only about a third of the time.

AQR-Adage Cluster’s 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 Q4 2015 AQR-Adage hedge fund long equity portfolio cluster relative to the U.S. Market

Factor Exposures of the AQR-Adage Hedge Fund Cluster in Q4 2015

Market (high-beta) and Size (small-cap) are the primary sources of the relative factor risk:

Chart of contributions to the relative factor (systematic) variance of the risk factors contributing most to the risk of the Q4 2015 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 in Q4 2015

Factor Relative Exposure Factor Volatility Share of Relative Factor Variance Share of Relative Total Variance
Market 5.31 12.46 38.20 18.42
Size -8.23 8.09 22.14 10.68
Oil Price 1.42 29.43 18.84 9.09
Value -3.90 12.91 11.29 5.44
Finance -6.56 5.08 11.05 5.33
Utilities -2.21 11.28 6.05 2.92
Communications -1.18 11.98 2.79 1.35
Health -1.57 7.22 -2.97 -1.43
FX 1.50 7.28 -3.29 -1.59
Energy -2.07 11.77 -4.56 -2.20

(Relative exposures and relative variance contribution. All values are in %. Volatility is annualized.)

AQR-Adage Cluster’s Factor Crowding Stress Tests

AQR-Adage Cluster’s Maximum Outperformance

Given the AQR-Adage Cluster’s macroeconomic positioning (Long Market, Short Finance, Value, and Size), it would experience its highest outperformance in an environment similar to the 1999-2000 dot-com boom:

Chart of the cumulative factor (systematic) return for the historical scenario that would generate the larger relative outperformance for the AQR-Adage Hedge Fund Cluster in Q4 2015

Historical Scenario that Would Generate the Highest Relative Performance for the AQR-Adage Hedge Fund Cluster in Q4 2015

Factor Return Portfolio Exposure Benchmark Exposure Relative Exposure Portfolio Return Benchmark Return Relative Return
Market 31.52 107.31 102.00 5.31 34.06 32.21 1.85
Finance -19.62 12.76 19.32 -6.56 -2.62 -3.95 1.33
Oil Price 128.16 0.42 -1.00 1.42 0.39 -0.93 1.32
Size -10.49 -9.11 -0.88 -8.23 0.96 0.09 0.87
Value -21.96 -4.05 -0.15 -3.90 0.90 0.03 0.87

AQR-Adage Cluster’s Maximum Underperformance

These exposures would deliver the AQR-Adage Cluster its highest underperformance in an environment similar to the 2000-2001 .com crash:

Chart of the cumulative factor (systematic) return for the historical scenario that would generate the larger relative underperformance for the AQR-Adage Hedge Fund Cluster in Q4 2015

Historical Scenario that Would Generate the Lowest Relative Performance for the AQR-Adage Hedge Fund Cluster in Q4 2015

Factor Return Portfolio Exposure Benchmark Exposure Relative Exposure Portfolio Return Benchmark Return Relative Return
Finance 47.97 12.76 19.32 -6.56 5.39 8.24 -2.85
Value 86.46 -4.05 -0.15 -3.90 -2.67 -0.10 -2.57
Utilities 52.32 0.98 3.19 -2.21 0.45 1.46 -1.01
Market -14.21 107.31 102.00 5.31 -15.30 -14.51 -0.79
Energy 33.72 2.47 4.54 -2.07 0.77 1.43 -0.65

AQR-Adage Cluster Residual (Idiosyncratic) Crowding

The stock-specific bets of the AQR-Adage Cluster have grown more crowded as the idiosyncratic volatility of several crowded longs spiked recently. Four stocks account for most of its relative residual risk:

Chart of contributions to the relative residual (idiosyncratic) variance of the stocks contributing most to the risk of the Q4 2015 AQR-Adage hedge fund long equity portfolio cluster relative to U.S. Market

Stocks Contributing Most to Relative Residual Variance of the AQR-Adage Hedge Fund Cluster in Q4 2015

Symbol Name Relative Exposure Residual Volatility Share of Relative Residual Variance Share of Relative Total Variance
TPIV TapImmune Inc. 0.41 125.70 20.11 9.21
NHLD National Holdings Corporation 0.75 68.38 20.05 9.18
PTRC Petro River Oil Corp. 0.22 151.46 8.05 3.69
LRAD LRAD Corporation 0.76 38.81 6.69 3.06
VRX Valeant Pharmaceuticals International, Inc. 0.51 43.72 3.82 1.75
JD JD.com, Inc. Sponsored ADR Class A 0.49 31.91 1.86 0.85
CHTR Charter Communications, Inc. Class A 0.75 20.31 1.76 0.81
IBKR Interactive Brokers Group, Inc. Class A 0.71 19.64 1.47 0.67
AAPL Apple Inc. -0.81 16.25 1.33 0.61
GNUS Genius Brands International, Inc. 0.12 103.74 1.21 0.56

(Relative exposures and relative variance contribution. All values are in %. Volatility is annualized.)

Idiosyncratic crowding is not the main problem with the cluster, since the expected idiosyncratic tracking error is low (around 1.3%). However, it is vital for fund followers, as it helps explain unexpected volatility in the most crowded names. In fact, several of the crowded names have shown signs of mass liquidation. It is also worth noting that the crowded names’ from earlier in 2015 presaged subsequent disasters. Valeant Pharmaceuticals (VRX), Micron, Inc. (MU), and Cheniere Natural Gas (LNG) were all featured in our crowding work.

Passivity is a bigger problem still, since allocators to diversified portfolios of hedge funds within this cluster may be paying high fees for a few consensus bets.

Summary

  • An analysis of the underlying structure of hedge fund crowding reveals hedge fund clustering – groups of portfolios with similar bets.
  • The largest cluster’s factor herding is towards Market (high-beta), short Size (small-cap), and four stock-specific bets (TPIV, NHLD, PTRC, and LRAD).
  • Allocators and fund followers unaware of clustering may find themselves in a nearly passive factor portfolio and a handful of consensus stock-specific 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 © 2012-2016, 
AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved.
Content may not be republished without express written consent.

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Hedge Fund Crowding Update – Q4 2015

Most analyses of hedge fund crowding focus on their residual (idiosyncratic, stock-specific) bets. This is misguided, since over 85% of the monthly return variance for the majority of hedge fund long equity portfolios is due to factor (systematic) exposures, rather than individual stocks. Indeed, it is the exceptional factor crowding and the record market risk that have driven much of the industry’s recent misery (just as they have driven much of the earlier upswings). In Q4 2015, a single factor accounted for half of U.S. hedge funds’ relative long equity risk (tracking error). We survey all sources of hedge fund crowding at year-end 2015 and identify the market regimes that would generate the highest relative outperformance and underperformance for the crowded factor portfolio. These are the regimes that would most benefit or hurt hedge fund investors and followers.

Identifying Hedge Fund Crowding

This piece follows the approach of our earlier articles on crowding: We processed regulatory filings of over 1,000 hedge funds and created a position-weighted portfolio (HF Aggregate) consisting of all the tractable hedge fund long U.S. equity portfolios. We then analyzed HF Aggregate’s risk relative to U.S. Market using the AlphaBetaWorks Statistical Equity Risk Model – a proven system for performance forecasting. The top contributors to HF Aggregate’s relative risk are the most crowded hedge fund bets.

Hedge Fund Aggregate’s Risk

The Q4 2015 HF Aggregate had 3.7% estimated future tracking error relative to U.S. Market; over two thirds of this was due to factor (systematic) exposures:

Factor (systematic) and residual (idiosyncratic) components of hedge fund crowding, or U.S. Hedge Fund Aggregate’s variance relative to U.S. Market on 12/31/2015

Components of the Relative Risk for U.S. Hedge Fund Aggregate in Q4 2015

Source Volatility (ann. %) Share of Variance (%)
Factor 3.10 69.07
Residual 2.08 30.93
Total 3.73 100.00

Simplistic analysis of hedge fund crowding that lacks a capable risk model will miss these systematic exposures. Among its flows, this comparison of holdings will overlook funds with no position overlap but high future correlation due to similar factor exposures. Hence, this simplistic analysis of hedge fund crowding fosters dangerous complacency.

Hedge Fund Factor (Systematic) Crowding

Factor exposures drove nearly 70% of the relative risk of HF Aggregate at year-end 2015. Below are the principal factor exposures (in red) relative to U.S. Market’s exposures (in gray):

Chart of the factor exposures contributing most to hedge fund crowding, or the factor variance of U.S. Hedge Fund Aggregate Portfolio relative to U.S. Market on 12/31/2015

Significant Absolute and Residual Factor Exposures of U.S. Hedge Fund Aggregate in Q4 2015

Of these bets, Market (Beta) alone accounts for two thirds of the relative and half of the total factor risk, as illustrated below:

Chart of the main factors behind systematic hedge fund crowding and their cumulative contribution to the factor variance of U.S. Hedge Fund Aggregate Portfolio relative to U.S. Market on 12/31/2015

Factors Contributing Most to Relative Factor Variance of U.S. Hedge Fund Aggregate in Q4 2015

Factor Relative Exposure Factor Volatility Share of Relative Factor Variance Share of Relative Total Variance
Market 18.27 12.46 68.12 47.05
Oil Price 2.28 29.43 13.08 9.04
Bond Index -7.53 3.33 4.97 3.43
Utilities -3.10 11.28 4.77 3.30
Consumer -8.30 3.75 3.54 2.44
Energy -3.21 11.77 -2.96 -2.04
Health 4.79 7.22 2.54 1.75
Communications -1.67 11.98 1.91 1.32
Finance -6.89 5.08 1.68 1.16
Size -1.96 8.09 1.34 0.92

(Relative exposures and relative variance contribution. All values are in %. Volatility is annualized.)

Thus, the most important source of hedge fund crowding is not a stock or a group of stocks, but systematic exposure to the U.S. Market Factor. When nearly half of the industry’s risk comes from a single Factor, fixation on the individual crowded stocks is particularly dangerous.

The U.S. Market crowding alone explains much of the recent industry misery. In this era of systematic crowding, risk management with a robust and predictive factor model is particularly vital for managers’ and allocators’ survival.

Hedge Fund Factor Crowding Stress Tests

Hedge Fund Crowding Maximum Outperformance

Given Hedge Fund Aggregate’s bullish macroeconomic positioning (Long Market, Short Bonds/Long Interest Rates), it would experience its highest outperformance in an environment similar to the March-2009 rally. In this scenario, HF Aggregate’s factor portfolio would outperform by 20%:

Chart of the cumulative factor returns for the historical scenario that would generate the highest relative return for the 12/31/2015 U.S. Hedge Fund Aggregate Portfolio relative to U.S. Market

Historical Scenario that Would Generate the Highest Relative Performance for the Q4 2015 U.S. Hedge Fund Aggregate

The top contributors to this outperformance would be the following exposures:

Factor Return Portfolio Exposure Benchmark Exposure Relative Exposure Portfolio Return Benchmark Return Relative Return
Market 66.04 120.07 101.80 18.27 83.00 67.50 15.50
Oil Price 87.13 1.53 -0.75 2.28 1.05 -0.51 1.56
Bond Index -6.29 -4.92 2.61 -7.53 0.31 -0.17 0.48
Energy -12.54 1.61 4.82 -3.21 -0.20 -0.61 0.41
Communications -17.62 0.52 2.19 -1.67 -0.10 -0.41 0.31

Hedge Fund Crowding Maximum Underperformance

Given Hedge Fund Aggregate’s bullish macroeconomic positioning, combined with a long Technology and short Finance exposures, it would experience its highest underperformance in an environment similar to the 2000-2001 .com Crash. In this scenario, HF Aggregate’s factor portfolio would underperform by 8%:

Chart of the cumulative factor returns for the historical scenario that would generate the lowest relative return for the 12/31/2015 U.S. Hedge Fund Aggregate Portfolio relative to U.S. Market

Historical Scenario that Would Generate the Lowest Relative Performance for the Q4 2015 U.S. Hedge Fund Aggregate

The top contributors to this underperformance would be the following exposures:

Factor Return Portfolio Exposure Benchmark Exposure Relative Exposure Portfolio Return Benchmark Return Relative Return
Finance 47.97 12.48 19.36 -6.89 5.27 8.26 -2.99
Market -14.21 120.07 101.80 18.27 -17.22 -14.48 -2.74
Technology -36.73 23.75 20.14 3.62 -9.83 -8.38 -1.45
Utilities 52.32 0.22 3.31 -3.10 0.10 1.51 -1.42
Consumer 12.36 14.87 23.17 -8.30 1.82 2.85 -1.02

Hedge Fund Residual (Idiosyncratic) Crowding

A third of the year-end 2015 hedge fund crowding is due to residual (idiosyncratic, stock-specific) risk. Valeant Pharmaceuticals International (VRX) and Netflix (NFLX) are responsible for nearly half of it:

Chart of the main stock-specific sources of hedge fund crowding and their cumulative contribution to the residual variance of U.S. Hedge Fund Aggregate Portfolio relative to U.S. Market on 12/31/2015

Stocks Contributing Most to Relative Residual Variance of U.S. Hedge Fund Aggregate in Q4 2015

Though there may be sound individual reasons for these investments, they are vulnerable to brutal liquidation. Given the recent damage to hedge funds from herding, these crowded residual bets remain vulnerable:

Symbol Name Relative Exposure Residual Volatility Share of Relative Residual Variance Share of Relative Total Variance
VRX Valeant Pharmaceuticals International, Inc. 2.67 43.72 31.56 9.76
NFLX Netflix, Inc. 1.57 54.62 17.15 5.30
JD JD.com, Inc. Sponsored ADR Class A 1.60 31.91 6.05 1.87
LNG Cheniere Energy, Inc. 1.38 33.35 4.88 1.51
CHTR Charter Communications, Inc. Class A 1.79 20.31 3.08 0.95
TWC Time Warner Cable Inc. 1.85 16.14 2.06 0.64
AGN Allergan plc 1.83 14.62 1.66 0.51
FLT FleetCor Technologies, Inc. 1.18 19.61 1.23 0.38
PCLN Priceline Group Inc 1.12 20.10 1.18 0.36
MSFT Microsoft Corporation 1.54 14.13 1.10 0.34

(Relative exposures and relative variance contribution. All values are in %. Volatility is annualized.)

Though stock-specific bets remain important, allocators and fund followers should pay particular attention to their factor exposures in the current environment of extreme systematic hedge fund crowding. Many may be effectively invested in leveraged passive index fund portfolio, with the added insult of high fees. AlphaBetaWorks Analytics address all of these needs with the coverage of market-wide and sector-specific herding, plus aggregate factor exposures of funds and portfolios of funds.

Summary

  • The main source of Q4 2015 hedge fund crowding, responsible for nearly half of the relative long equity risk, was record U.S. Market exposure.
  • The main sources of Q4 2015 residual crowding were VRX and NFLX.
  • Given the high factor (systematic) crowding among hedge funds’ long equity portfolios, current analysis of crowding risks must focus on the factor exposures, rather than individual positions.
The information herein is not represented or warranted to be accurate, correct, complete or timely.
Past performance is no guarantee of future results.
Copyright © 2012-2016, AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved.
Content may not be republished without express written consent.

 

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Performance Persistence within Style Boxes

Common approaches to manager selection do a lousy job since nominal returns and similar simplistic metrics of investment performance revert: Most portfolio performance comes from systematic (factor) exposures, and such metrics merely identify the highest-risk portfolios during the bullish regimes and the lowest-risk portfolios during the bearish regimes. As regimes change, so do the leaders. In the past we demonstrated the reversion of mutual funds’ nominal returns, the reversion of hedge funds’ nominal returns, and the failures of popular statistics (Sharpe Ratio, Win/Loss Ratio, etc.) based on nominal returns. This article extends the study of performance persistence to the broadest universe of U.S. institutional portfolios and to the popular Size and Value/Growth style boxes within this universe.

Our earlier work also showed that, when security selection returns are properly calculated with a robust factor model, skill persists – portfolios of the top stock pickers of the past outperform market and peers in the future. We will now validate these findings across all major style boxes and note the particular effectiveness of predictive skill analytics for small-cap manager selection.

Measuring Persistence of Returns

We surveyed portfolios of over 5,000 institutions that have filed Form 13F in the past 10 years. This is the broadest and most representative survivorship-free portfolio database for all institutions that exercised investment discretion over at least $100 million. The collection includes hedge funds, mutual fund companies, and investment advisors. Approximately 3,000 institutions had sufficiently long histories, low turnover, and broad portfolios to be suitable for this study of performance persistence.

We split the 10 years of history into two random 5-year subsets and compared performance of each portfolio over these two periods. If performance persists over time, there will be a positive correlation between returns in one period and returns in the other.

Performance Persistence for all Institutional Portfolios

The Reversion of Nominal Returns

The chart below plots the ranking of nominal returns for each portfolio during the two periods. Each point corresponds to a single institution. The x-axis plots return percentile, or ranking, in the first historical sample. The y-axis plots return percentile, or ranking, in the second historical sample. For illustration, the best-performing filers of the first period have x-values near 100; the best-performing filers of the second period have y-values near 100:

Performance persistence for nominal returns: Chart of the negative correlation of nominal returns over two historical samples for all U.S. equity 13F portfolios

13F Equity Portfolios: Correlation between the rankings of nominal returns for two historical samples

Contrary to a popular slogan, past performance actually is an indication of future results: Managers with above-average nominal returns in one historical sample are likely to have below-average nominal returns in the other. In the above chart, this negative relationship between (reversion of) historical returns is visible as groupings in the bottom right (leaders that became laggards) and top left (laggards that became leaders).

The Persistence of Security Selection Returns

Nominal performance reverts because it is dominated by Market and other systematic factors. Top-performing managers during the bullish regimes are those who take the most risk; top-performing managers during the bearish regimes are those who take the least risk. As regimes change, leadership flips. To eliminate these disruptive factor effects, the AlphaBetaWorks Performance Analytics Platform calculates each portfolio’s return from security selection net of factor effects. αReturn is the return a manager would have generated if all factor returns had been flat.

Managers with above-average αReturns in one period are likely to maintain them in the other. In the following chart, this positive relationship between historical αReturns is visible as grouping in the bottom left (laggards that remained laggards) and top right (leaders that remained leaders):

Performance persistence for security selection skill: Chart of the positive correlation of risk-adjusted returns from security selection (αReturns) over two historical samples for all U.S. equity 13F portfolios

13F Equity Portfolios: Correlation between the rankings of αReturns for two historical samples

A test of performance persistence across two arbitrary 5-year samples of a 10-year span is especially strict. For most funds covered by the Platform, persistence of security selection skill is far higher over shorter periods. It is highest for approximately 3 years and begins to fade rapidly after 4 years. The chart above also illustrates that low stock picking returns persist and do so more strongly than high stock picking returns – the bottom left cluster of consistently weak stock pickers is the most dense.

Performance Persistence within Each Style Box

Measures of investment style such as Size (average portfolio market capitalization) and Value/Growth are a popular approach to grouping portfolios and analyzing risk. Though not the dominant drivers of portfolio risk and performance, they are often believed to be. Consequently, clients frequently ask whether the reversion of nominal returns and related metrics can be explained by Style Box membership and cycles of style leadership. To test this, we compared performance persistence within each of the four popular style boxes. It turns out style does not explain nominal return reversion and αReturns persist within each style box.

Large-Cap Value Portfolio Return Persistence

Portfolios in the Large-cap Value Style Box show especially high nominal return reversion (-0.23 Spearman’s rank correlation coefficient between samples). This is probably attributable to the high exposures of these portfolios to the cyclical industries that suffer from the most pronounced booms and busts:

Performance persistence for Large-Cap Value 13F Portfolios: Chart of the negative correlation of nominal returns and positive correlation of risk-adjusted returns from security selection (αReturns) over two historical samples for U.S. equity 13F portfolios in the Large-Cap Value Style Box

Large-Cap Value 13F Portfolios: Correlation between the rankings of nominal returns and αReturns for two historical samples

Large-Cap Growth Portfolio Return Persistence

Portfolios in the Large-cap Growth Style Box are the closest to random and show the lowest persistence of αReturns. Large-cap Growth stock picking is exceptionally treacherous over the long term. While it is possible to select skilled managers in this area, it is challenging even with the most powerful skill analytics:

Performance persistence for Large-Cap Growth 13F Portfolios: Chart of the negative correlation of nominal returns and positive correlation of risk-adjusted returns from security selection (αReturns) over two historical samples for U.S. equity 13F portfolios in the Large-Cap Growth Style Box

Large-Cap Growth 13F Portfolios: Correlation between the rankings of nominal returns and αReturns for two historical samples

Small-Cap Value Portfolio Return Persistence

Portfolios in the Small-cap Value Style Box show nearly random nominal returns. They also have the most persistent αReturns. Small-cap Value stock picking records are thus most consistent over the long term. This is the area where allocators and investors armed with powerful skill analytics should perform well, especially by staying away from the unskilled managers:

Performance persistence for Small-Cap Value 13F Portfolios: Chart of the negative correlation of nominal returns and positive correlation of risk-adjusted returns from security selection (αReturns) over two historical samples for U.S. equity 13F portfolios in the Small-Cap Value Style Box

Small-Cap Value 13F Portfolios: Correlation between the rankings of nominal returns and αReturns for two historical samples

Small-Cap Growth Portfolio Return Persistence

Portfolios in the Small-cap Growth Style Box have the second most persistent αReturns. This is also an area where allocators and investors armed with powerful skill analytics will have a strong edge:

Performance persistence for Small-Cap Growth 13F Portfolios: Chart of the negative correlation of nominal returns and positive correlation of risk-adjusted returns from security selection (αReturns) over two historical samples for U.S. equity 13F portfolios in the Small-Cap Growth Style Box

Small-Cap Growth 13F Portfolios: Correlation between the rankings of nominal returns and αReturns for two historical samples

Summary

  • Nominal returns and related simplistic metrics of investment skill revert: as market regimes change, the top performers tend to become the bottom performers.
  • Security selection returns, when properly calculated with a robust factor model, persist and yield portfolios that outperform.
  • Both skill and lack of skill persist, and the lack of skill persists most strongly; while it is important that investors correctly identify the talented managers, it is even more important to divest from their opposites.
  • The reversion of nominal returns and the persistence of security selection skill hold across all style boxes, but security selection skill is most persistent for small-cap portfolios.
The information herein is not represented or warranted to be accurate, correct, complete or timely.
Past performance is no guarantee of future results.
Copyright © 2012-2016, AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved.
Content may not be republished without express written consent.
U.S. Patents Pending.
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Are Momentum ETFs Delivering Momentum Returns?

There is a large difference between momentum strategies in theory and in practice. Given that much of its model performance derives from illiquid securities and high turnover, the academic momentum factor is a theoretical ideal that is not directly investable. Consequently, real-world momentum products, such as momentum ETFs, are restricted to investable liquid securities and usually reduce the approximately 200% annual turnover of theoretical momentum portfolios. After these modifications, their idiosyncratic momentum returns mostly vanish.

We consider a popular momentum ETF and illustrate that its historical performance is almost entirely attributable to passive exposures to simple non-momentum factors, such as Market and Sectors. Investors may thus be able to achieve and even surpass the performance of popular momentum ETFs with transparent, passive, and potentially lower-cost portfolios of simpler funds.

Attributing the Performance of Momentum ETFs to Simpler Factors

We analyzed iShares MSCI USA Momentum Factor ETF (MTUM) using the AlphaBetaWorks Statistical Equity Risk Model – a proven tool for forecasting portfolio risk and performance. We estimated monthly positions from regulatory filings, retrieved positions’ factor (systematic) exposures, and aggregated these. This produced a series of monthly portfolio exposures to simple investable risk factors such as Market, Sector, and Size. The factor exposures at the end of Month 1 and factor returns during Month 2 are used to calculate factor returns during Month 2 and any residual (security-selection, idiosyncratic, stock-specific) returns un-attributable to factors.

There are only two ways for a fund to deviate from a passive portfolio: residual returns un-attributable to factors and factor timing returns due to variation in factor exposures over time. We define and measure both components below.

iShares MSCI USA Momentum Factor (MTUM): Performance Attribution

We used iShares MSCI USA Momentum Factor (MTUM) as an example of a practical implementation of a theoretical momentum portfolio. MTUM is a $1.1bil ETF that seeks to track an index of U.S. large- and mid-cap stocks with high momentum. The fund’s turnover, around 100% annually, is about half that of the theoretical momentum factor.

iShares MSCI USA Momentum Factor (MTUM): Factor Exposures

The following factors are responsible for most of the historical returns and variance of MTUM:

Chart of exposures to the risk factors contributing most to the historical performance of MSCI USA Momentum Factor (MTUM)

MSCI USA Momentum Factor (MTUM): Significant Historical Factor Exposures

Latest Mean Min. Max.
Market 88.44 84.12 65.46 96.03
Health 23.73 30.28 23.73 34.94
Consumer 74.02 32.53 13.10 74.06
Industrial 1.69 9.71 1.13 24.51
Size -10.47 -1.04 -11.09 7.67
Oil Price -2.90 -2.45 -4.94 -0.04
Technology 17.72 16.56 1.50 32.29
Value -4.86 -2.13 -8.00 5.20
Energy 0.00 1.86 0.00 4.12
Bond Index 6.51 1.08 -22.90 23.64

iShares MSCI USA Momentum Factor (MTUM): Active Return

To replicate MTUM with simple non-momentum factors, one can use a passive portfolio of these simple non-momentum factors with MTUM’s mean exposures as weights. This portfolio defined the Passive Return in the following chart. Active return, or αβReturn, is the performance in excess of this passive replicating portfolio. It is the active return due to residual stock performance and factor timing:

Chart of the cumulative historical active return from security selection and factor timing of MSCI USA Momentum Factor (MTUM)

MSCI USA Momentum Factor (MTUM): Cumulative Passive and Active Returns

MTUM’s performance closely tracks the passive replicating portfolio. Pearson’s correlation between Total Return and Passive Return is 0.96. Consequently, 93% of the variance of monthly returns is attributable to passive factor exposures, primarily to Market and Sector factors.

Once passive exposures to simpler factors have been removed, MTUM’s active return is negligible. Since MTUM’s launch, the cumulative return difference from such passive replicating portfolio has been approximately 1%:

2013 2014 2015 Total
Total Return 16.73 14.62 8.50 45.18
  Passive Return 16.06 16.48 4.55 41.34
  αβReturn 1.11 -2.46 2.54 1.12
    αReturn 3.91 0.05 0.29 4.27
    βReturn -2.71 -2.52 2.23 -3.05

This active return can be further decomposed into security selection (αReturn) and factor timing (βReturn). These active return components generated low volatility, around 1% annually, mostly offsetting each other as illustrated below:

iShares MSCI USA Momentum Factor (MTUM): Active Return from Security Selection

AlphaBetaWorks’ measure of residual security selection performance is αReturn – performance relative to a factor portfolio that matches the funds’ historical factor exposures. αReturn is the return a fund would have generated if markets had been flat. MTUM has generated approximately 4% cumulative αReturn, primarily in 2013, compared to roughly 1.5% decline for the average U.S. equity ETF:

Chart of the cumulative historical active return from security selection of MSCI USA Momentum Factor (MTUM)

MSCI USA Momentum Factor (MTUM): Cumulative Active Return from Security Selection

iShares MSCI USA Momentum Factor (MTUM): Active Return from Factor Timing

AlphaBetaWorks’ measure of factor timing performance is βReturn – performance due to variation in factor exposures. βReturn is the fund’s outperformance relative to a portfolio with the same mean, but constant, factor exposures as the fund. MTUM generates approximately -3% cumulative βReturn, compared to a roughly 1% decline for the average U.S. equity ETF:

Chart of the cumulative historical active return from factor timing of MSCI USA Momentum Factor (MTUM)

MSCI USA Momentum Factor (MTUM): Cumulative Active Return from Factor Timing

These low active returns are consistent with our earlier findings that many “smart beta” funds are merely high-beta and offer no value over portfolios of conventional dumb-beta funds. It is thus vital to test any new resident of the Factor Zoo to determine whether they are merely exotic breeds of its more boring residents.

Conclusion

  • Theoretical, or academic, momentum portfolios are not directly investable.
  • A popular momentum ETF, MSCI USA Momentum Factor (MTUM), did not deviate significantly from a passive portfolio of simpler non-momentum factors.
  • Investors may be able to achieve and surpass the performance of the popular momentum ETFs with transparent, passive, and potentially lower-cost portfolios of simpler index funds and ETFs.
The information herein is not represented or warranted to be accurate, correct, complete or timely.
Past performance is no guarantee of future results.
Copyright © 2012-2016, AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved.
Content may not be republished without express written consent.
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Best and Worst Hedge Fund Long Stock Pickers

Who’s Been Naughty or Nice

The five top hedge fund long stock pickers with long U.S. equity AUM over $3 billion produced 10.7% average annual alpha in the past three years (through October 2015).  The five bottom hedge fund long U.S. equity stock pickers had negative alpha averaging -6.5% during the same period. Both lists contain well-known and well-followed managers with a combined long U.S. equity AUM over $100 billion. These rankings overcome the flaws of simplistic performance measures by using a persistent and hence predictive metric of alpha.

Ranking Hedge Fund Long Stock Pickers

Fund rankings usually focus on absolute or relative nominal returns. Reliance on such simplistic measures, frequently mislabeled as “alpha,” is hazardous for allocators and consultants and typically leads to picking yesterday’s winners, who tend to become tomorrow’s losers: high-beta funds in bull markets and low-beta funds in bear markets.  To overcome these flaws, AlphaBetaWorks calculates returns independent of risks taken – αReturn – the performance a fund would have generated if markets had been flat. A true measure of security selection skill, αReturn is strongly predictive of future stock picking performance.

To the extent a fund derives significant returns from long equity holdings, our approach helps allocators and consultants avoid losses and unhappy clients. Furthermore, absent this approach, mindless followers of famous funds are headed for disappointment.

Top and Bottom Five Hedge Funds by Stock Picking Return

A comparison of nominal long hedge fund portfolio performance to market indices is misleading, since a portfolio may be taking dramatically different risks. It follows that equating outperformance relative to S&P500 with alpha is wrong. Under this simplistic approach, leveraged market ETFs generate positive alpha in up years and negative alpha in down years – a flawed result and a dangerous criterion for investment decisions.

Adjusting returns for the factor (systematic) risks taken to generate them is required to identify stock picking return and, correspondingly, stock picking skill.

Below we list the top and bottom long U.S. equity stock pickers among the hedge funds that can be analyzed using regulatory filings:

Name αβReturn αβScore αReturn αScore βReturn βScore
Harbinger Capital Partners LLC 15.45 95.04 15.38 95.4 -0.01 49.92
Icahn Associates Holdings LLC 9.09 80.19 11.11 85.92 -2.01 23
PAR Capital Management, Inc. 8.32 95.56 10.93 98.85 -2.69 12.48
Longview Asset Management LLC 14.09 97.02 9.40 87.53 4.49 98.83
ValueAct Capital Management LP 3.29 74.01 6.73 90.06 -3.52 3.9

Hedge Funds with the Highest Long U.S. Equity Stock Picking Returns and > $3B AUM, 3-yr CAGR

Name αβReturn αβScore αReturn αScore βReturn βScore
Bridgewater Associates LP -9.05 1.86 -8.15 4.28 -0.99 28.03
Omega Advisors, Inc. -8.29 0.22 -7.19 1.25 -1.14 16.06
Pershing Square Capital Management LP -5.36 25.15 -5.83 25.01 0.28 56.73
Axiom International Investors LLC -2.62 16.93 -5.83 0.71 3.17 94.05
Paulson & Co., Inc. -5.05 14.65 -5.63 10.72 0.57 61.94

Hedge Funds with the Lowest Long U.S. Equity Stock Picking Returns and > $3B AUM, 3-yr CAGR

There are familiar fund names on both lists, with a combined $112 billion in long AUM.  While these funds’ reported returns will differ from those above due principally to factor (systematic) sources (market / sector betas, et cetera), international holdings, fixed income positions, derivatives, and shorts, αReturn is a clear and predictive measure of stock picking skill reflecting returns in excess of passive risks taken. Unlike nominal returns, Sharpe Ratios, and other returns-based measures (which revert), αReturn reflects what investors pay fees for: performance independent of risks taken. What’s more, both positive and negative αReturns tend to persist.

The tables above also show several related skill metrics. For example, βReturn is return from market timing, and αβReturn is total active return combining security selection and market timing. The Scores above, such as αScore, quantify return consistency using a scale of 0 to 100: 0 corresponds to consistently negative returns; 100 to consistently positive.

The following charts show cumulative αReturns of the best and worst 3-year long U.S. equity stock pickers from the above group. Portfolio αReturns in blue compare to the peer group of all hedge funds’ long U.S. equity portfolio in gray:

Chart of the historical cumulative return from security selection (stock picking) of the long U.S. equity portfolio of Harbinger Capital Partners LLC

Harbinger Capital Partners LLC: Risk-adjusted Return from Security Selection (αReturn), Long U.S. Equity Portfolio

Chart of the historical cumulative return from security selection (stock picking) of the long U.S. equity portfolio of Bridgewater Associates LP

Bridgewater Associates LP: Risk-adjusted Return from Security Selection (αReturn), Long U.S. Equity Portfolio

Conclusions

A comparison of nominal long hedge fund portfolio performance to market indices is misleading and can lead to allocators selecting high-return funds that are, in truth, merely high risk. Since systematic fund risk varies, the assumption that outperformance relative to S&P500 is alpha is wrong. In a rising market, allocators and consultants who make these mistakes are likely to allocate to the most aggressive managers, rather than the most skilled. In a flat or declining market, these mistakes are revealed, leading to losses, pain, and embarrassment.

The solution is using skill analytics that discriminate among the different levels of systematic risk, like AlphaBetaWorks’ αReturn (among others), which reveals managers with investment skill likely to generate future alpha.

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.
U.S. Patents Pending.
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Hedge Fund Crowding Update – Q3 2015

Since crowded stocks are prone to mass liquidation, investors are typically most concerned with residual (idiosyncratic, stock-specific) hedge fund crowding. This overlooks the exceptional factor (systematic) crowding and the record market risk that have been driving recent industry performance. In Q3 2015, when a single factor and a single stock accounted for over half of the aggregate U.S. hedge fund long equity portfolio’s relative risk, hedge fund crowding became unprecedented.

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 common U.S. equity holdings of all tractable long hedge fund portfolios. We then analyzed HF Aggregate’s risk relative to U.S. Market using the AlphaBetaWorks Statistical Equity Risk Model. The top sources of HF Aggregate’s relative risk are the top sources of hedge fund crowding.

Hedge Fund Aggregate’s Risk

The Q3 2015 HF Aggregate had 3.9% estimated future tracking error relative to U.S. Market; factor (systematic) bets were its primary sources. The components of HF Aggregate’s relative risk were as follows:

Factor (systematic) and residual (idiosyncratic) components of the U.S. Hedge Fund Aggregate’s variance relative to U.S. Market

Components of the Relative Risk for U.S. Hedge Fund Aggregate

Source Volatility (ann. %) Share of Variance (%)
Factor 2.91 55.17
Residual 2.62 44.83
Total 3.91 100.00

A simplistic crowding analysis that does not rely on an effective risk model ignores systematic exposures of positions. Since portfolios with very different holdings can have matching factor exposures and can track each other closely, crowding is common even for portfolios with little overlap. Such simplistic analyses thus overlooks factor (systematic) exposures that are responsible for the majority of covariance among hedge funds.

Hedge Fund Factor (Systematic) Crowding

Factor exposures drove over half of the relative risk of HF Aggregate in Q3. Below are its principal factor exposures (in red) relative to U.S. Market’s exposures (in gray):

Chart of the factor exposures contributing most to the factor variance of U.S. Hedge Fund Aggregate Portfolio relative to U.S. Market on 9/30/2015

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

Of these bets, Market (Beta) alone accounts for two thirds of the relative factor risk and over a third of the total risk. The top components of the 2.91% Factor Volatility in the first table are as follows:

Chart of the main factors and their cumulative contribution to the factor variance of U.S. Hedge Fund Aggregate Portfolio relative to U.S. Market on 9/30/2015

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

Factor Relative Exposure Factor Volatility Share of Relative Factor Variance Share of Relative Total Variance
Market 17.26 12.28 65.18 35.96
Oil Price 2.93 28.89 20.11 11.10
Industrial 9.30 5.41 7.80 4.30
Utilities -3.32 11.05 4.49 2.48
Finance -7.76 5.18 3.36 1.85
Consumer -4.85 4.27 2.68 1.48
Health 5.68 6.82 2.31 1.27
Communications -1.74 11.77 1.64 0.91
Energy -2.10 12.68 -4.19 -2.31
FX 4.78 7.59 -5.95 -3.28

(Relative exposures and relative variance contribution. All values are in %. Volatility is annualized.)

The most important source of hedge fund crowding is not a stock or a set of stocks, but systematic exposure to a risk factor. Currently, fixation on stock-specific hedge fund bets is at best misguided and at worst dangerous for allocators. Risk management using a robust and predictive system, such as AlphaBetaWorks Risk Analytics, is currently key to controlling systematic fund crowding.

Hedge Fund U.S. Market Factor Exposure History

HF Aggregate’s 9/30/2015 market exposure was approximately 120% (its Market Beta was approximately 1.2). The hedge fund industry is thus taking approximately 20% more market risk than U.S. equities and approximately 25% more market risk than S&P 500. This record exposure has been costly for the industry and many individual funds during the 2015 turmoil, exacerbating volatility due to stock-specific crowding:

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

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

HF Aggregate generally takes 10-20% more market risk than S&P500. Consequently, comparison of long hedge fund portfolio performance to market indices is misleading and assumption that outperformance relative to S&P500 is alpha is wrong. In a rising market, allocators who make these mistakes are likely to allocate to the most aggressive managers, rather than the most skilled. In flat or declining market, these mistakes become evident. Skill analytics that discriminate among the different levels of systematic risk are the solution.

Hedge Fund Residual (Idiosyncratic) Crowding

About 45% of recent hedge fund crowding is due to residual (idiosyncratic, stock-specific) risk. In part due to its spectacular volatility, a single position in Valeant Pharmaceuticals International (VRX) is now responsible for most of it:

Chart of the main stock-specific bets and their cumulative contribution to the residual variance of U.S. Hedge Fund Aggregate Portfolio relative to U.S. Market on 9/30/2015

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

Though individual crowded names may be wonderful investments, they have tended to underperform; they have seen consistent, and lately brutal, liquidation under the recent outflows:

Symbol Name Relative Exposure Residual Volatility Share of Relative Residual Variance Share of Relative Total Variance
VRX Valeant Pharmaceuticals International, Inc. 4.68 43.92 61.55 27.59
LNG Cheniere Energy, Inc. 1.80 40.12 7.57 3.39
NFLX Netflix, Inc. 1.10 56.17 5.59 2.51
CHTR Charter Communications, Inc. Class A 1.81 20.93 2.09 0.94
JD JD.com, Inc. Sponsored ADR Class A 1.31 28.91 2.08 0.93
TWC Time Warner Cable Inc. 1.86 16.57 1.39 0.62
AGN Allergan plc 1.67 15.39 0.96 0.43
FLT FleetCor Technologies, Inc. 1.20 19.53 0.80 0.36
PCLN Priceline Group Inc 1.11 20.74 0.78 0.35
PAGP Plains GP Holdings LP Class A 0.89 22.91 0.60 0.27

(Relative exposures and relative variance contribution. All values are in %. Volatility is annualized.)

Especially in the prevailing environment of portfolio liquidations, investors should not blindly follow star managers. Instead, any signs of crowding should trigger particularly thorough due-diligence. Allocators should be doubly concerned with crowding as they may be investing in a pool of undifferentiated bets and a leveraged factor portfolio. AlphaBetaWorks’ analytics address all of these needs with coverage of aggregate and sector-specific herding, predictive risk analytics, and detection of skills strongly predictive of future performance.

Summary

  • The main source of Q3 2015 hedge fund crowding is record Market (Beta) exposure, responsible for more than a third of the hedge fund industry’s relative risk.
  • The main source of Q3 2015 residual crowding is VRX.
  • In the current environment, analysis of hedge fund crowding must focus on the factor exposures driving systematic crowding, rather than individual positions.
The information herein is not represented or warranted to be accurate, correct, complete or timely.
Past performance is no guarantee of future results.
Copyright © 2012-2015, AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved.
Content may not be republished without express written consent.
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