Category Archives: Hedge Funds

Hedge Fund Crowding Update – Q2 2017

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Whereas hedge fund crowding primarily consists of systematic factor bets, most analysis of hedge fund crowding focuses solely on popular positions. Moreover, such analysis usually assumes that hedge fund crowding in individual stocks is a bullish indicator. This article illustrates the flaws of these common assumptions, identifies the principal sources of hedge fund crowding, and discusses the opportunities that this crowding presents:

  • Factor (systematic) exposures, rather than individual stocks, account for 70% of the crowding.
  • Residual (idiosyncratic, or stock-specific) bets account for just 30% of hedge fund crowding.
  • Simplistic analysis of crowding in individual stocks overlooks the majority of crowding risk.
  • Crowded hedge fund factor bets have been experiencing steep losses since 2015 and have been attractive shorts.
  • Crowded hedge fund stock-specific bets have been attractive longs following the 2014-2015 liquidations and the subsequent recovery, but the trend appears to have ended.

Identifying Hedge Fund Crowding

The analysis of hedge fund crowding in this article follows the approach of our earlier studies: We started with a decade of hedge fund Form 13F filings. Form 13F discloses positions of firms with long U.S. assets over $100 million. We only considered funds with a sufficiently low turnover to be analyzable from filings, and our database is free of survivorship bias. This sample included approximately 1,000 firms. We combined all portfolios into a single position-weighted portfolio – HF Aggregate. We then used the AlphaBetaWorks (ABW) Statistical Equity Risk Model – an effective predictor of future risk – to analyze HF Aggregate’s risk relative to the U.S. Market (represented by the iShares Russell 3000 ETF (IWV) benchmark), identify the crowded exposures, and analyze their performance trends.

Factor and Residual Components of Hedge Fund Crowding

Virtually all of HF Aggregate’s absolute risk is systematic. Thus, the aggregate long U.S. equity holdings of hedge funds will very nearly track a passive factor portfolio with similar risk:

Chart of the factor (systematic) and residual (idiosyncratic) components of absolute U.S. long equity hedge fund crowding on 6/30/2017

Components of U.S. Hedge Fund Aggregate’s Absolute Risk in Q2 2017

Source Volatility (ann. %) Share of Variance (%)
Factor 11.80 97.78
Residual 1.78 2.22
Total 11.93 100.00

HF Aggregate has 2.8% estimated future volatility (tracking error) relative to the Market. Approximately 70% of this relative risk is due to factor crowding:

Chart of the factor (systematic) and residual (idiosyncratic) components of U.S. long equity hedge fund crowding relative to the Market on 6/30/2017

Components of U.S. Hedge Fund Aggregate’s Relative Risk in Q2 2017

Source Volatility (ann. %) Share of Variance (%)
Factor 2.37 69.90
Residual 1.55 30.10
Total 2.83 100.00

Consequently, simplistic analysis of hedge fund crowding that focuses on the popular holdings and position overlap is fatally flawed. It will capture less than a third of the crowding risk that is stock-specific and will overlook the bulk that is systematic.

Stock Picking and Market Timing Returns from Crowding

Crowded factor and residual bets go through cycles of outperformance and underperformance, depending on capital flows. These trends can provide attractive investment opportunities: short during liquidation, and long during expansion.

The following chart shows HF Aggregate’s cumulative βReturn (risk-adjusted returns from factor timing). Crowded hedge fund factor bets have experienced steep and accelerating losses since 2015. We identified these factors in earlier research as attractive short candidates, and the downtrend has persisted:

Chart of the cumulative risk-adjusted return from factor timing (variation in factor exposures) of the U.S. Hedge Fund Aggregate portfolio

Historical Risk-Adjusted Return from Factor Timing of U.S. Hedge Fund Aggregate

The following chart shows HF Aggregate’s cumulative αReturn (risk-adjusted returns from security selection). Following the unprecedented losses on crowded residual bets during 2011-2015, we advised long exposures to these beaten-down stocks in late-2015. The subsequent recovery has been spectacular but now appears over. Thus, the crowded hedge fund residual bets no longer seem to offer clear long or short opportunities:

Chart of the cumulative risk-adjusted return from security selection (stock picking) of the U.S. Hedge Fund Aggregate portfolio

Historical Risk-Adjusted Return from Security Selection of U.S. Hedge Fund Aggregate

The rest of this article considers the crowded factor and residual bets responsible for the above trends.

Hedge Fund Factor (Systematic) Crowding

The following chart shows the main sources of hedge fund factor crowding as of 6/30/2017 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 6/30/2017

Significant Absolute and Relative Factor Exposures of U.S. Hedge Fund Aggregate in Q2 2017

The dominant hedge fund long equity bet is Market (high Beta). Thus, the most crowded bet is high overall market risk, rather than a specific stock. Like a leveraged ETF, HF Aggregate outperforms when the Market is up and underperforms when it is down.

Chart of the main contributions to the factor variance of U.S. Hedge Fund Aggregate Portfolio relative to U.S. Market on 6/30/2017

Factors Contributing Most to Relative Factor Variance of U.S. Hedge Fund Aggregate in Q2 2017

Factor Relative Exposure Factor Volatility Share of Relative Factor Variance Share of Relative Total Variance
Market 14.05 9.80 47.63 33.29
Health Care 8.72 7.75 13.41 9.37
FX -8.49 6.77 11.35 7.93
Real Estate -2.54 12.40 6.11 4.27
Oil Price 1.11 29.50 5.30 3.71
Utilities -3.17 12.72 4.89 3.42
Bond Index -8.12 3.41 3.86 2.70
Financials -5.29 7.90 2.23 1.56
Industrials -4.15 4.72 2.17 1.51
Consumer Discretionary 8.11 4.31 1.51 1.06

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

In fact, crowding in the simple Market Factor alone accounts for more risk than all the stock-specific crowding combined.

Despite recent losses, HF Aggregate’s Health Care Factor exposure remains near the recent record levels.

Hedge Fund Residual (Idiosyncratic) Crowding

The remaining 30% of hedge fund crowding as of 6/30/2017 was due to residual (idiosyncratic, stock-specific) risk:

Chart of the main contributors to the residual variance of U.S. Hedge Fund Aggregate Portfolio relative to U.S. Market on 6/30/2017

Stocks Contributing Most to Relative Residual Variance of U.S. Hedge Fund Aggregate in Q2 2017

Symbol Name Relative Exposure Residual Volatility Share of Relative Residual Variance Share of Relative Total Variance
BABA Alibaba Group Holding ADR 2.23 23.93 11.82 3.56
LNG Cheniere Energy, Inc. 1.44 27.51 6.54 1.97
HLF Herbalife Ltd. 0.92 36.18 4.59 1.38
CHTR Charter Communications, Inc. 1.66 19.10 4.15 1.25
AVGO Broadcom Limited 1.32 21.20 3.24 0.98
AAPL Apple Inc. -1.96 13.37 2.84 0.86
ALXN Alexion Pharmaceuticals, Inc. 0.85 29.14 2.52 0.76
FB Facebook, Inc. Class A 1.01 24.17 2.47 0.74
EXPE Expedia, Inc. 1.08 21.31 2.21 0.66
FWONK Liberty Media Corporation Formula One 0.91 24.90 2.11 0.64

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

Following the 2011-2015 losses and the subsequent gains, these crowded bets do not offer clear long or short opportunities. Moreover, residual crowding accounts for a small fraction of the industry’s risk. While systematic hedge fund crowding continues to dominate, investors and allocators should focus on managing the crowded factor exposures. Without a firm grasp of factor crowding, investors and fund followers may blindly follow losing bets.

Summary

  • Factor (systematic) exposures that capture risks shared by many stocks, rather than individual stocks, are responsible for the majority of hedge fund crowding.
  • The main sources of Q2 2017 hedge fund crowding were long U.S. Market (high Beta), long Health Care, and short USD (preference for exporters over importers).
  • The crowded factor bets have been in a multi-year bearish trend.
  • The crowded residual bets have recovered from steep losses and no longer offer clear opportunities.
  • Without a robust analysis of the factor and residual components of crowding, a hedge fund investor, follower, or allocator may be missing the bulk of crowding risk and investing in a generic passive factor portfolio.
The information herein is not represented or warranted to be accurate, correct, complete or timely.
Past performance is no guarantee of future results.
Copyright © 2012-2017, AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved.
Content may not be republished without express written consent.

 

Hedge Fund Crowding Update – Q1 2017

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A typical analysis of hedge fund crowding considers large, popular, and concentrated hedge fund long equity holdings. Such analysis usually assumes that crowding comes from stock-specific bets and that it is a bullish indicator. These assumptions are incorrect and have cost investors dearly:

  • Residual, idiosyncratic, or stock-specific bets now account for less than a third of hedge fund crowding. Factor (systematic) risk, rather than the risk from individual stocks, is driving hedge funds’ active returns. Consequently, simplistic analysis of hedge fund crowding that focuses on specific stocks misses the bulk of funds’ active risk and return.
  • The returns of crowded hedge fund factor and residual bets vary over time as the funds go through cycles of capital inflows and outflows. Consequently, generic analysis of hedge fund crowding can herd investors into losing bets on the wrong side of a cycle. For instance, depending on the trend, investors may desire long exposure to the crowded factor exposures in one year and short exposure in another.

This article reviews hedge fund long equity crowding at the end of Q1 2017. We identify the dominant systematic exposures and the top residual bets that will have the largest impact on investor performance. We also explore the current trends in returns from crowding that indicate profitable positioning.

Identifying Hedge Fund Crowding

This article follows the approach of our earlier studies of hedge fund crowding: We start with a survivorship-free database of SEC filings by over 1,000 U.S. hedge funds spanning over a decade. This database contains all funds that had ever filed 13F Reports (which disclose long U.S. assets over $100 million). We only consider funds with a sufficiently low turnover to be analyzable from filings. We combine all fund portfolios into a single position-weighted portfolio (HF Aggregate). The analysis of HF Aggregate’s risk relative to the U.S. Market reveals its active bets and the industry’s crowding. The AlphaBetaWorks (ABW) Statistical Equity Risk Model – an effective predictor of future risk – identifies and quantifies the crowded exposures driving HF Aggregate’s performance.

Factor and Residual Components of Hedge Fund Crowding

The 3/31/2017 HF Aggregate had 2.6% estimated future volatility (tracking error) relative to the U.S. Market (represented by the iShares Russell 3000 ETF (IWV) benchmark). Approximately 30% of this was due to residual crowding, and approximately 70% was due to factor crowding:

Chart of the factor (systematic) and residual (idiosyncratic) components of US hedge fund crowding on 03/31/2017

Components of the Relative Risk for U.S. Hedge Fund Aggregate in Q1 2017

Source Volatility (ann. %) Share of Variance (%)
Factor 2.19 69.01
Residual 1.47 30.99
Total 2.64 100.00

Hedge fund crowding analysis that focuses on the popular holdings and position overlap thus captures less than a third of the total risk and overlooks over two-thirds of crowding that is due to factors – a fatal flaw. Since similar factor exposures can cause funds with no shared positions to correlate closely, a simplistic analysis of holdings and position overlap fosters dangerous complacency.

Stock Picking and Market Timing Returns from Crowding

A precise understanding of crowding is critical to investors and allocators since, depending on the capital flows, crowded bets can generate large and unexpected gains or losses.

The following chart shows cumulative βReturn (risk-adjusted returns from factor timing, or from the variation of factor exposures) of HF Aggregate. Crowded hedge fund factor bets have underperformed since 2011, and losses from hedge fund factor crowding have accelerated since 2015. The crowded factor bets below could have been attractive short candidates. In aggregate, hedge funds’ long equity portfolios would have made approximately 10% more since 2015 had they kept their factor exposures constant:

Chart of the cumulative risk-adjusted return from factor timing (variation in systematic exposures) due to U.S. long equity hedge fund crowding

Historical Risk-Adjusted Return from Factor Timing of U.S. Hedge Fund Aggregate

Crowded hedge fund residual bets have also underperformed since 2011. The following chart shows cumulative αReturn (risk-adjusted returns from security selection) of HF Aggregate. HF Aggregate experienced massive losses from security selection during 2011-2015. Given the unprecedented losses, we advised long exposures to the crowded residual bets in late-2015, and these have indeed recovered:

Chart of the cumulative risk-adjusted return from security selection (stock picking) due to U.S. long equity hedge fund crowding

Historical Risk-Adjusted Return from Security Selection of U.S. Hedge Fund Aggregate

We now turn to the specific crowded factor and residual bets behind the trends above.

Hedge Fund Factor (Systematic) Crowding

The following chart illustrates the main sources of factor crowding. HF Aggregate’s factor exposures are in red. The U.S. Market’s (defined as the iShares Russell 3000 ETF (IWV) benchmark) is 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 03/31/2017

Significant Absolute and Residual Factor Exposures of U.S. Hedge Fund Aggregate in Q1 2017

The dominant bet of hedge funds’ long equity portfolios is Market (high Beta). The most crowded hedge fund bet is thus not a particular stock, but high overall market risk. HF Aggregate partially behaves like a leveraged market ETF, outperforming during bullish regimes and underperforming during bearish ones.

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 03/31/2017

Factors Contributing Most to Relative Factor Variance of U.S. Hedge Fund Aggregate in Q1 2017

Factor Relative Exposure Factor Volatility Share of Relative Factor Variance Share of Relative Total Variance
Market 13.25 10.67 54.00 37.26
Health Care 8.55 7.62 15.93 10.99
Utilities -3.05 12.86 8.16 5.63
Real Estate -2.69 12.87 7.91 5.46
Bond Index -7.33 3.59 6.02 4.15
Consumer Staples -5.04 8.04 4.64 3.20
Size -2.02 9.35 2.45 1.69
Oil Price 0.53 30.36 2.38 1.64
Industrials -4.28 4.96 1.85 1.27
FX 2.59 6.77 -1.83 -1.26

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

Crowding into a single factor (Market) accounts for more hedge fund risk than all their stock-specific and other factor bets combined. The three top sector bets are long Health Care, short Utilities, and short Real Estate.

HF Aggregate’s exposures to Market, Health Care, and Bond Factors remained near record levels reached recently.

Hedge Fund Residual (Idiosyncratic) Crowding

The remaining third of hedge fund crowding as of 3/31/2017 was due to residual (idiosyncratic, stock-specific) risk:

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 03/31/2017

Stocks Contributing Most to Relative Residual Variance of U.S. Hedge Fund Aggregate in Q1 2017

Symbol Name Relative Exposure Residual Volatility Share of Relative Residual Variance Share of Relative Total Variance
CHTR Charter Communications, Inc. Class A 2.53 18.64 10.31 3.19
LNG Cheniere Energy, Inc. 1.41 29.37 7.96 2.47
BABA Alibaba Group Holding Ltd. Sponsored ADR 1.17 26.26 4.40 1.36
FB Facebook, Inc. Class A 1.02 28.05 3.76 1.17
FLT FleetCor Technologies, Inc. 1.19 22.02 3.19 0.99
HCA HCA Holdings, Inc. 1.12 21.36 2.66 0.82
AAPL Apple Inc. -1.72 13.87 2.63 0.81
NXPI NXP Semiconductors NV 0.78 28.62 2.31 0.71
PYPL PayPal Holdings Inc 1.26 17.48 2.26 0.70
ATVI Activision Blizzard, Inc. 0.98 21.53 2.05 0.64

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

While systematic hedge fund crowding continues to dominate, investors and allocators should focus on the factor exposures. Without a firm grasp of factor crowding, allocators to a supposedly diversified hedge fund portfolio may be paying high active management fees for what is effectively a leveraged ETF book. Also, investors and fund followers may blindly follow losing factor bets.

Nevertheless, residual hedge fund crowding can be a profitable long and short indicator. The 25% decline in 2010-2015 was followed by a 15% gain.

Summary

  • Factor (systematic) exposures and risks shared across stocks, rather than individual positions, are the primary drivers of hedge fund industry’s long equity risk.
  • The main sources of Q1 2017 hedge fund crowding were long U.S. Market (high Beta), long Health Care, short Utilities, and short Real Estate Factor exposures.
  • Without a robust analysis of factor and residual crowding, a hedge fund investor, follower, or allocator may be investing in a generic passive factor portfolio, likely with leverage.
  • The crowded factor bets have been in a bearish trend and may represent attractive short candidates.
  • The crowded residual bets have been recovering from steep losses and may continue to represent attractive long candidates, though less so than in 2016.
The information herein is not represented or warranted to be accurate, correct, complete or timely.
Past performance is no guarantee of future results.
Copyright © 2012-2017, AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved.
Content may not be republished without express written consent.

Performance of the Top U.S. Stock Pickers in 2016

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And What They Owned at Year-end

Though 2016 was a poor year for most institutional portfolio managers, it was a satisfactory year for the most skilled ones. Security selection returns of the top U.S. stock pickers in 2016 were positive. When hedged to match market risk, a consensus portfolio of the top intuitional U.S. stock pickers outperformed the Market by approximately 2%.

This article demonstrates how a robust equity risk model and predictive performance analytics identify the top stock pickers – the hard part of measuring investment skill. Since genuine investment skill persists, the top stock pickers of the past tend to generate positive stock picking returns in the future. We illustrate this performance and share the top consensus positions driving it. These consensus positions of the top U.S. stock pickers are a profitable resource for investors searching for ideas. The method for tracking the top active managers and this method’s performance are benchmarks against which capital allocators can evaluate qualitative and quantitative manager selection processes.

Identifying the Top U.S. Stock Pickers

This study updates our analysis for 2015 and follows a similar method:  We analyzed long U.S. equity portfolios of all institutions that have filed Forms 13F. This survivorship-free portfolio database comprises thousands of firms. The database covers all institutions that have managed over $100 million in long U.S. assets. Some of these firms were not suitable for skill evaluation, for instance due to short filings histories or high turnover. Approximately 4,000 firms were evaluated.

During bullish market regimes, the top-performing portfolios are those that take the most factor (systematic) risk. During bearish market regimes, the top-performing portfolios are those that take the least risk. Hence, when the regimes change the leaders revert. This is the main reason nominal returns and related simplistic metrics of investment skill (Sharpe Ratio, Win/Loss Ratio, etc.) revert and fail. This is also the evidence behind most purported proofs of the futility of active manager selection. These arguments assume that, since the flawed performance metrics are non-predictive, all performance metrics are non-predictive, and it is impossible to identify future outperformers.

To eliminate the systematic noise that is the source of performance reversion, the AlphaBetaWorks Performance Analytics Platform calculates portfolio return from security selection – αReturn. αReturn is the performance a portfolio would have generated if all factor returns had been flat. This is the estimated residual performance due to stock picking skill, net of all factor effects. Each month we identify the top five percent among 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. Data is lagged 2 months to account for the filing delay. We construct the aggregate expert portfolio (the ABW Expert Aggregate) by equal-weighting the expert portfolios and position-weighting stocks within the expert portfolios.

Manager fame and firm size are poor proxies for skill. Consequently, the ABW Expert Aggregate is an eclectic collection that includes hedge funds and asset management firms, banks, endowments, trust companies, and other institutions.

Market-Neutral Performance of the Top U.S. Stock Pickers

Since security selection skill persists, portfolios that have generated positive αReturns in the past are likely to generate them in the future. Consequently, a hedged aggregate of such portfolios (the Market-Neutral ABW Expert Aggregate) delivers consistent positive returns:

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

Cumulative Market-Neutral Portfolio Return: Top U.S. Stock Pickers’ Net Consensus Longs

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
Market-Neutral
ABW Expert Aggregate		 4.15 14.43 12.74 5.95 -1.25 15.35 2.20 2.24 15.47 8.81 13.16 1.72
iShares Russell
3000 ETF		 6.08 15.65 4.57 -37.16 28.21 16.81 0.78 16.43 32.97 12.41 0.34 12.61

ABW Expert Aggregate outperformed the broad market with less than half the volatility:

Market-Neutral
ABW Expert Aggregate		 iShares Russell 3000 ETF
Annualized Return 7.69 7.49
Annualized Standard Deviation 5.29 14.71
Annualized Sharpe Ratio (Rf=0%) 1.45 0.51

There are several ways to reconcile the positive stock picking performance above with the apparently challenging environment for fundamental stock picking in 2016:

  • Performance of an average manager is a poor proxy for the performance of a top manager.
  • Some skilled managers may suffer from underdeveloped risk systems, and losses from hidden systematic risks conceal their stock-picking results.
  • Many skilled stock pickers are poor market timers, or they may have experienced a challenging market-timing environment.

ABW Expert Aggregate is different than the crowded portfolios, which we have written about at length. Whereas crowded bets are shared by the entire universe of investors, ABW Expert Aggregate is a small subset covering the consistently best stock pickers. It is common for crowded hedge fund longs (overweights) to be shorts (underweights) of ABW Expert Aggregate, and vice versa.

Market Performance of the Top U.S. Stock Pickers

The Market-Neutral ABW Expert Aggregate is fully hedged. Accordingly, it has insignificant market exposure and will, by definition, underperform the Market during the bullish regimes. Therefore, the Market-Neutral ABW Expert Aggregate is not suitable as a core holding and is not directly comparable to long portfolios.

The aggregate portfolio of the top stock pickers can be hedged to match the market risk. This portfolio (the Market-Risk ABW Expert Aggregate) delivers consistent outperformance, instead of the consistent absolute returns of the Market-Neutral ABW Expert Aggregate:

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

Cumulative Market-Risk Portfolio Return: Top U.S. Stock Pickers’ Net Consensus Longs

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
Market-Risk
ABW Expert Aggregate		 10.43 32.20 17.32 -33.79 26.43 34.52 2.78 19.15 52.93 22.07 13.10 14.77
iShares Russell
3000 ETF		 6.08 15.65 4.57 -37.16 28.21 16.81 0.78 16.43 32.97 12.41 0.34 12.61

 

Market-Risk
ABW Expert Aggregate		 iShares Russell 3000 ETF
Annualized Return 15.53 7.49
Annualized Standard Deviation 16.57 14.71
Annualized Sharpe Ratio (Rf=0%) 0.94 0.51

Top U.S. Stock Pickers’ Consensus Positions

Just as few celebrated firms were the top U.S. stock pickers in 2016, few celebrated stocks were their top ideas. Below are the top 10 consensus overweights of the ABW Expert Aggregate at year-end 2016:

Symbol Name Exposure (%)
EA Electronic Arts Inc. 1.41
NTES NetEase, Inc. 1.00
OXY Occidental Petroleum Corporation 0.69
PXD Pioneer Natural Resources 0.68
PEP PepsiCo, Inc. 0.63
SCHW Charles Schwab Corporation 0.55
ACN Accenture Plc 0.52
JNJ Johnson & Johnson 0.48
NKE NIKE, Inc. 0.46
V Visa Inc. 0.44

Many of these positions remained since year-end 2015, illustrating the stability of the ABW Expert Aggregate.

Top Stock Pickers’ Exposure to Electronic Arts (EA)

The top panel on the following chart shows EA’s cumulative nominal return in black and cumulative residual return (αReturn) in blue. Recall that residual return or αReturn is the performance EA would have generated if all factor returns had been zero. The bottom panel shows exposure to EA within the ABW Expert Aggregate. Top stock pickers had negligible exposure to EA until 2015. In early-2015 EA became one of the largest exposures within the Expert Aggregate, and it remained a top position through 2016:

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 NetEase (NTES)

ABW Expert Aggregate had negligible exposure to NTES until early 2016. NTES became a consensus long by early-2016. The strong positive αReturn of NTES continued through 2016:

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

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

Top Stock Pickers’ Exposure to Occidental Petroleum (OXY)

The Aggregate was mostly underweight (short) OXY between 2010 and 2016. This means that the top U.S. stock pickers were underweight the stock. Their exposure to OXY grew through 2016 and by year-end it was a top bet. The smart money has added to OXY in 2016 even as it underperformed:

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

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

Conclusions

  • Robust equity risk models and predictive performance analytics can identify the top stock pickers in the sea of mediocrity.
  • The market-neutral aggregate of the top stock pickers’ portfolios delivers consistent absolute performance.
  • The aggregate of the top stock pickers’ portfolios matching market risk delivers consistent outperformance relative to the Market.
  • Consensus portfolio of the top stock pickers is a profitable source of investment ideas.
  • Provided they control properly for systematic (factor) effects, simple rules for manager selection tend to select future outperformers.
The information herein is not represented or warranted to be accurate, correct, complete or timely.
Past performance is no guarantee of future results.
Copyright © 2012-2017, AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved.
Content may not be republished without express written consent.
U.S. Patents Pending.

Hedge Fund Crowding Update – Q4 2016

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A typical analysis of hedge fund crowding surveys popular equity holdings. Yet, such residual, idiosyncratic, or stock-specific bets account for only 31% of current hedge fund crowding. Factor (systematic) risk, rather than a few specific stocks, is driving absolute and relative returns. Consequently, most analysis of hedge fund crowding focuses on a small fraction of crowding, missing its bulk.

Nearly 70% of the hedge fund industry’s long equity risk comes from factor crowding. Market exposure (high Beta) constitutes half of that – more than all the remaining factor bets and more than all the stock-specific bets combined. Since the consensus factor exposures can be obtained cheaply via ETFs and do not warrant the same compensation as idiosyncratic insights, it is vital for investors and allocators to understand and manage these crowded exposures. In addition, crowded factor bets are vulnerable to damaging liquidations.  This article reviews hedge fund long equity bets at the end of 2016 and focuses on the dominant systematic exposures that will have the largest impact on investor performance.

Identifying Hedge Fund Crowding

This article follows the approach of our earlier studies of hedge fund crowding: We started with a 10-year survivorship-free database of SEC filings by over 1,000 U.S. hedge funds. This database contains all funds that had long U.S. assets in excess of $100 million and sufficiently low turnover to be analyzable from their filings. We then combined all fund portfolios into a single position-weighted portfolio (HF Aggregate). The analysis of HF Aggregate’s risk relative to the U.S. Market revealed its main active bets. The AlphaBetaWorks (ABW) Statistical Equity Risk Model – an effective predictor of future risk – identified and quantified these crowded exposures driving HF Aggregate’s performance.

Hedge Fund Industry’s Risk

The 12/31/2016 HF Aggregate had 2.7% estimated future volatility (tracking error) relative to the U.S. Market. Approximately a third of this tracking error was due to residual crowding, and the remaining two thirds  was due to factor crowding:

Chart of the factor (systematic) and residual (idiosyncratic) components of the U.S. Hedge Fund Aggregate’s variance relative to U.S. Market on 12/31/2016

Components of the Relative Risk for U.S. Hedge Fund Aggregate in Q4 2016

Source

Volatility (ann. %)

Share of Variance (%)
Factor

2.25

68.89

Residual

 1.51

31.11
Total

2.71

100.00

The low 1.5% residual volatility, less than a third of the total, illustrates the challenges of hedge fund crowding analysis that focuses on the popular holdings and position overlap. Such stock-specific view overlooks the two thirds of crowding that is due to factors – a fatal flaw. As a result, simplistic analysis of popular holdings and of position overlap fosters dangerous complacency when funds with no shared positions correlate highly due to similar factor exposures.

Hedge Fund Factor (Systematic) Crowding

The following chart illustrates the main sources of factor risk. HF Aggregate’s factor exposures are in red. The U.S. Market’s (defined as the iShares Russell 3000 ETF (IWV) Benchmark) is 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 12/31/2016

Significant Absolute and Residual Factor Exposures of U.S. Hedge Fund Aggregate in Q4 2016

The dominant bet of hedge funds’ long equity portfolios is Market (high Beta). The most crowded hedge fund bet is thus not a particular stock, but high overall market risk. HF Aggregate thus partially behaves like a leveraged market ETF, outperforming during bullish regimes and underperforming during bearish ones.

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 12/31/2016

Factors Contributing Most to Relative Factor Variance of U.S. Hedge Fund Aggregate in Q4 2016

Factor Relative Exposure Factor Volatility Share of Relative Factor Variance Share of Relative Total Variance
Market 12.92 10.52 50.26 34.62
Health Care 10.14 7.61 19.26 13.26
Bond Index -9.71 3.59 7.32 5.04
Utilities -3.19 12.53 7.14 4.92
Real Estate -2.51 12.88 7.07 4.87
Industrials -5.00 4.96 3.09 2.13
Consumer Staples -4.97 7.75 2.27 1.56
Oil Price 0.56 30.34 2.27 1.56
FX -1.12 6.87 1.02 0.70
Financials -2.38 7.71 -0.83 -0.57

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

Hedge funds’ Market Factor crowding accounts for more risk than all their stock-specific bets combined. This dominance of a single systematic risk illustrates how asset managers’ and allocators’ endurance increasingly depends on their grasp of systematic crowding. It also illustrates the dangers of fixation on individual holdings.

HF Aggregate’s exposures to Market, Health Care, and Bond Factors remained near record levels reached recently. We analyze these in their order of importance below.

Hedge Fund U.S. Market Factor (Beta) Crowding

Hedge Fund Aggregates’ U.S. Market exposure decreased slightly from the mid-2016 record level:

Chart of the historical exposure of U.S. Hedge Fund Aggregate’s to the U.S. Equity Market Factor through 12/31/2016

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

Even following this decrease in risk, the average dollar of hedge fund long U.S. equity capital carries approximately 20% more market risk than S&P 500. Thus, hedge fund portfolios move in concert with the market, but with heightened sensitivity to it. Consequently, simple comparison of hedge fund returns to broad equity benchmarks and identification of nominal outperformance with alpha remain dangerous. Further, simple equation of capital invested in (dollar exposures to) a market or sector with actual portfolio risk remains flawed.

Hedge Fund Health Care Crowding

Hedge Fund Aggregates’ Health Care exposure also decreased sharply from its 2016 record:

Chart of the historical exposure of U.S. Hedge Fund Aggregate’s to the U.S. Health Care Sector Factor through 12/31/2016

U.S. Hedge Fund Aggregate’s U.S. Health Care Factor Exposure History

Even after this decrease, HF Aggregate continues to carry almost twice the Health Care exposure of the Market. The Health Care Factor remains the second most significant hedge fund long equity bet.

Hedge Fund Short Bonds/Long Interest Rates Factor Crowding
HF Aggregate’s Short Bonds/Long Interest Rates Factor exposure was profitable in late-2016. This exposure decreased by half in the second half of the year, following the election catalyst:

Chart of the historical exposure of U.S. Hedge Fund Aggregate’s to the U.S. Long Bonds/Short Interest Rates Factor through 12/31/2016

U.S. Hedge Fund Aggregate’s U.S. Long Bonds/Short Interest Rates Factor Exposure History

Short bond exposure is a natural consequence of hedge funds’ interest in “cheap call options”, often highly financially leveraged companies with asymmetric profit and loss potential. We discussed the fundamental sources of this Bonds/Interest Rates Factor exposure in more detail in a prior article.

Hedge Fund Residual (Idiosyncratic) Crowding

The remaining third of hedge fund crowding on 12/31/2016 was due to residual (idiosyncratic, stock-specific) risk. Though this is a minor component of the total crowding, we survey it for completeness and to facilitate comparisons with the basic surveys of crowding found elsewhere:

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 12/31/2016

Stocks Contributing Most to Relative Residual Variance of U.S. Hedge Fund Aggregate in Q4 2016

Symbol Name Relative Exposure Residual Volatility Share of Relative Residual Variance Share of Relative Total Variance
CHTR Charter Communications, Inc. 2.62 19.03 10.87 3.38
LNG Cheniere Energy, Inc. 1.40 29.27 7.29 2.27
FLT FleetCor Technologies, Inc. 1.27 23.17 3.80 1.18
AGN Allergan plc 1.44 18.31 3.05 0.95
AAPL Apple Inc. -1.85 13.81 2.86 0.89
FB Facebook, Inc. Class A 0.91 27.00 2.63 0.82
BABA Alibaba Group Holding Ltd. ADR 1.02 23.23 2.47 0.77
HCA HCA Holdings, Inc. 1.09 21.27 2.34 0.73
HUM Humana Inc. 1.05 21.88 2.29 0.71
WMB Williams Companies, Inc. 0.83 25.52 1.94 0.60

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

Though these exposures are sensitive to asset flows, they generally constitute minor risks within the crowded portfolios. While systematic hedge fund crowding continues to dominate, investors and allocators should focus on the factor exposures. Without a firm grasp of factor crowding, a supposedly diversified hedge fund portfolio may be charging high active management fees for what is effectively a leveraged ETF book.

Summary

  • Factor (systematic) exposures and risks shared across stocks, rather than individual positions, are driving hedge fund industry’s long equity risk. Exposure to these crowded bets can be obtained much more cheaply via ETFs and other passive products.
  • The main sources of Q4 2016 hedge fund crowding were long U.S. Market (high Beta), long Health Care, and short Bonds/long Interest Rates Factor exposures.
  • Without a robust analysis of factor and residual crowding, a hedge fund investor, follower, or allocator may be investing in a generic passive factor portfolio, likely with leverage.
The information herein is not represented or warranted to be accurate, correct, complete or timely.
Past performance is no guarantee of future results.
Copyright © 2012-2017, AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved.
Content may not be republished without express written consent.

Hedge Fund Crowding Update – Q3 2016

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Whereas most analysis of hedge fund crowding focuses on individual stocks, over 85% of hedge funds’ recent long equity variance has been due to their factor (systematic) risk. Residual, idiosyncratic, or stock-specific bets accounted for less than 15%. Thus, factor crowding has dominated hedge fund industry’s absolute and relative returns. This article reviews the most crowded hedge fund long equity bets at 9/30/2016.

Understanding and quantifying this factor crowding is vital for hedge fund investors and allocators: Factor exposures that are shared by the entire hedge fund industry and that can be obtained cheaply with passive funds do not warrant the same compensation as the distinctive insights of gifted managers. Even worse, crowded bets expose investors to damaging stampedes during liquidations.

Identifying Hedge Fund Crowding

This article’s approach follows our earlier studies of hedge fund crowding: We started with a 10-year survivorship-free dataset of SEC filings by over 1,000 hedge funds. We then created a position-weighted portfolio (HF Aggregate) comprising all hedge fund long U.S. equity portfolios that can examined using the filings. We analyzed HF Aggregate’s risk and its historical exposures relative to the U.S. Market. The top contributors to hedge fund industry’s relative risk are the industry’s most crowded bets. Factor exposures were analyzed using the AlphaBetaWorks (ABW) Statistical Equity Risk Model – an effective predictor of future risk.

Hedge Fund Industry’s Risk

The 9/30/2016 HF Aggregate had 3.9% estimated future volatility (tracking error) relative to the U.S. Market. Less than 20% of this risk came from individual stocks, or from stock-specific crowding; the remainder – more than 80% – came from factor (systematic) crowding:

Factor (systematic) and residual (idiosyncratic) components of the U.S. Hedge Fund Aggregate’s variance relative to U.S. Market on 9/30/2016

Components of the Relative Risk for U.S. Hedge Fund Aggregate in Q3 2016

Source Volatility (ann. %) Share of Variance (%)
Factor 3.50 82.19
Residual 1.63 17.81
Total 3.86 100.00

Since residual risk accounts for just 18% of the total, basic analysis of hedge fund crowding that examines popular holdings and position overlap is misguided. Such stock-specific analysis of crowding covers less than 20% of the industry’s risk, overlooking the dominant 80% of hedge fund crowding that is due to factors – a fatal flaw. Even funds with no shared positions correlate highly when they have similar factor exposures, so simplistic analysis of popular holdings and of position overlap understates portfolio risk and fosters complacency.

Hedge Fund Factor (Systematic) Crowding

Below are HF Aggregate’s principal factor exposures (in red). The U.S. Market, defined as the iShares Russell 3000 ETF (IWV) is the Benchmark (in gray). These factors are the primary sources of risk in the table above:

Chart of the factor exposures contributing most to the factor variance of U.S. Hedge Fund Aggregate Portfolio relative to U.S. Market on 9/30/2016

Significant Absolute and Residual Factor Exposures of U.S. Hedge Fund Aggregate in Q3 2016

The dominant bet of hedge funds’ long equity portfolios is Market (high Beta):

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 9/30/2016

Factors Contributing Most to Relative Factor Variance of U.S. Hedge Fund Aggregate in Q3 2016

Factor Relative Exposure Factor Volatility Share of Relative Factor Variance Share of Relative Total Variance
Market 20.65 10.59 46.19 37.97
Size -14.67 8.62 19.06 15.66
Health Care 12.68 7.54 16.22 13.33
Bond Index -19.34 3.37 8.94 7.35
Consumer Staples -7.87 7.24 5.28 4.34
Utilities -3.40 12.46 5.00 4.11
FX 10.70 6.80 -4.22 -3.47
Real Estate -1.86 12.80 2.97 2.44
Oil Price 1.03 30.15 2.75 2.26
Financials -4.84 7.05 -2.09 -1.72

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

This high U.S. Market exposure alone is twice as influential as all the stock-specific bets combined. Given this importance of factor crowding compared to residual crowding, popular fascination with fund holdings and position overlap is especially dangerous. Asset managers’ and allocators’ endurance thus depends increasingly on their edge in assessing systematic crowding.

Hedge Fund Short Bonds/Long Interest Rates Factor Crowding

HF Aggregate’s exposures to Market, Size, and Health Factors were near their peak levels seen in recent quarters. In addition to these, their Short Bonds/Long Interest Rates Factor exposure has also recently reached historic extremes:

Chart of the historical exposure of U.S. Hedge Fund Aggregate’s to the U.S. Long Bonds/Short Interest Rates Factor

U.S. Hedge Fund Aggregate’s U.S. Long Bonds/Short Interest Rates Factor Exposure History

We discussed the fundamental sources of this Bonds/Interest Rates Factor exposure in a prior article. Short bond risk is a natural consequence of hedge funds’ fondness for financially leveraged companies, often viewed as “cheap call options.” A company’s indebtedness creates economic and statistically observable short bond exposure. Given the Q4 2016 moves in yields, this bet should prove profitable for the hedge fund industry.

Hedge Fund Residual (Idiosyncratic) Crowding

A fifth of hedge fund crowding on 9/30/2016 was due to residual (idiosyncratic, stock-specific) risk. The following stocks were the main contributors to residual hedge fund crowding:

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 9/30/2016

Stocks Contributing Most to Relative Residual Variance of U.S. Hedge Fund Aggregate in Q3 2016

Symbol Name Relative Exposure Residual Volatility Share of Relative Residual Variance Share of Relative Total Variance
LNG Cheniere Energy, Inc. 1.63 28.94 8.43 1.50
VRX Valeant Pharmaceuticals International Inc 0.89 43.63 5.76 1.02
AGN Allergan plc 1.86 18.17 4.32 0.77
WMB Williams Companies, Inc. 1.28 25.87 4.12 0.73
CHTR Charter Communications, Inc. Class A 1.69 19.27 4.01 0.71
FLT FleetCor Technologies, Inc. 1.62 17.40 3.02 0.54
AAPL Apple Inc. -1.90 14.29 2.78 0.50
EXPE Expedia, Inc. 1.04 24.57 2.47 0.44
BABA Alibaba Group Holding Ltd. Sponsored ADR 1.07 23.61 2.43 0.43
HCA HCA Holdings, Inc. 1.10 21.96 2.22 0.40

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

The importance of residual crowding diminished in recent quarters as factor crowding increased. Consequently, hedge fund stock-picking has faded in importance relative to market timing. The most crowded stocks are sensitive to asset flows in and out of the industry, but they are not the main threat to crowded portfolios. In the current environment of extreme systematic hedge fund crowding, investors and allocators should focus on the factor exposures. Without an accurate view of factor crowding, investors in a supposedly diversified hedge fund portfolio often end up paying high active fees for a passive factor portfolio.

Summary

  • At Q3 2016, over 80% of hedge fund industry’s relative long equity risk was due to factor, or systematic, crowding.
  • The main sources of Q3 2016 hedge fund crowding were high U.S. Market, short Size, long Health, and short Bonds/long Interest Rates Factor exposures.
  • Short Bonds/Long Interest Rates Factor exposure reached historic extremes.
  • Systematic exposures and risks shared across stocks, rather than individual positions, are driving 80% of the hedge fund industry’s long equity risk.
  • Only robust analysis of factor and residual crowding can determine whether a hedge fund investor, follower, or allocator is investing in exceptional insights or in a generic passive factor portfolio.
The information herein is not represented or warranted to be accurate, correct, complete or timely.
Past performance is no guarantee of future results.
Copyright © 2012-2016, AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved.
Content may not be republished without express written consent.

Hedge Fund Crowding Update – Q2 2016

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Typical analysis of hedge fund crowding focuses on individual stocks. This is misguided since over 85% of hedge funds’ monthly return variance is due to factor (systematic) exposures. Their residual, (idiosyncratic, or stock-specific) bets account for less than 15% of it. Likewise, factor crowding has driven much of the hedge fund industry’s performance and volatility. In Q2 2016, half of U.S. hedge funds’ long equity risk (tracking error) relative to the U.S. Market was due to a single crowded factor and two thirds was due to three crowded factors. This article reviews the most crowded bets at 6/30/2016 that have been driving hedge funds’ long equity performance.

Note that active risk is required to generate active returns and warrant management fees. Yet, not all exposures are created equal. Systematic exposures that are shared by the entire hedge fund industry and that can be obtained cheaply via index funds and ETFs do not warrant the same compensation as the distinctive insights of gifted managers. Even worse, these crowded bets expose investors to the damaging stampede of impatient capital.

Identifying Hedge Fund Crowding

We followed the approach of our earlier studies of hedge fund crowding: We processed regulatory filings of over 1,000 hedge funds and created a position-weighted portfolio (HF Aggregate) comprising all tractable hedge fund long U.S. equity portfolios. We then analyzed HF Aggregate’s risk relative to the U.S. Market. The most crowded bets are driving the hedge fund industry’s risk and performance. We identified these bets using the AlphaBetaWorks (ABW) Statistical Equity Risk Model – an effective system of forecasting future risk and performance.

Hedge Fund Industry’s Risk

HF Aggregate had 3.5% estimated future volatility (tracking error) relative to the U.S. Market in Q2 2016. Nearly 80% of this was due to its factor (systematic) exposures, rather than individual stocks:

Factor (systematic) and residual (idiosyncratic) components of the U.S. Hedge Fund Aggregate’s variance relative to U.S. Market on 6/30/2016

Components of the Relative Risk for U.S. Hedge Fund Aggregate in Q2 2016

Source Volatility (ann. %) Share of Variance (%)
Factor 3.12 77.84
Residual 1.67 22.16
Total 3.54 100.00

A typical analysis of hedge fund crowding that focuses on individual stocks and popular holdings is thus misguided. It dwells on only 20% of the industry’s risk, overlooking the other 80%. Funds with no shared positions can still correlate highly when they have similar factor exposures. Consequently, such a simplistic analysis of position overlap and holdings misrepresents fund risk risk and fosters dangerous complacency.

Hedge Fund Factor (Systematic) Crowding

Below are HF Aggregate’s principal factor exposures (in red) relative to the U.S. Market’s (in gray). These are the primary bets behind factor risk and crowding in the table above:

Chart of the factor exposures contributing most to the factor variance of U.S. Hedge Fund Aggregate Portfolio relative to U.S. Market on 6/30/2016

Significant Absolute and Residual Factor Exposures of U.S. Hedge Fund Aggregate in Q2 2016

Market (Beta) is the dominant long equity bet within the hedge fund industry. It accounts for approximately two thirds of relative factor risk and half of relative total 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 6/30/2016

Factors Contributing Most to Relative Factor Variance of U.S. Hedge Fund Aggregate in Q2 2016

Factor Relative Exposure Factor Volatility Share of Relative Factor Variance Share of Relative Total Variance
Market 17.62 12.58 62.96 49.01
Size -9.39 8.46 10.00 7.78
Health 9.45 6.95 9.19 7.15
Oil Price 1.89 31.29 9.12 7.10
Utilities -3.74 12.42 5.71 4.44
Bond Index -7.90 3.55 5.01 3.90
Consumer -5.40 3.96 2.30 1.79
FX 2.18 7.30 -1.99 -1.55
Energy -2.30 13.44 -1.62 -1.26
Value -1.81 13.33 -0.53 -0.41

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

The most crowded long equity bet is high systematic exposure to the U.S. Market – not any particular stock. In fact, high systematic market risk is more important to U.S. hedge fund long portfolios than all of their stock-specific bets combined. This makes the popular fascination with fund holdings and position overlap particularly dangerous. As factor crowding continues to dominate stock-specific risk and stock picking skill, the survival of asset managers and allocators increasingly relies on their grasp of systematic crowding and the predictive power of their risk management systems.

Hedge Fund U.S. Market Factor Crowding

The current Market Factor Exposure of HF Aggregate is approximately 115% (its Market Beta is approximately 1.15). This exposure has remained above 100% since 2012:

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

The average hedge fund long equity portfolio now carries approximately 15% more market risk than the Russell 3000 Index and approximately 20% more than the slightly less risky S&P 500 Index. This higher exposure illustrates the danger of evaluating them relative to broad benchmarks. In a year when S&P 500 returns 10%, the average hedge fund would need to return approximately 12% to match what investors would have earned by taking the same risk passively.

Hedge Fund U.S. Size Factor Crowding

The ABW Size Factor is the difference in returns, net of market and sector effects, between the largest and the smallest stocks. It is closely related to the Fama–French SMB Factor, but includes critical fixes: The ABW Size Factor strips out market and sector effects from security returns, revealing pure size risk. By contrast, SMB Factor captures size risk together with market beta and sector effects, since market exposure and sector composition differ between small- and large-cap stocks. This market and sector noise in the SMB Factor makes accurate risk estimation challenging and accurate performance attribution impossible.

Negative Size exposure corresponds to small-cap risk.  Hedge fund long equity portfolios currently have near-record small-cap exposure, equivalent to an approximately 10% bet on small company outperformance:

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

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

Hedge Fund U.S. Health Factor Crowding

Current hedge fund Heath Factor exposure remains near an all-time high:

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

U.S. Hedge Fund Aggregate’s U.S. Size Health Factor Exposure History

Hedge Fund Residual (Idiosyncratic) Crowding

As of 6/30/2016, a quarter of hedge fund crowding was due to residual (idiosyncratic, stock-specific) risk. As factor crowding increased, residual crowding has diminished. Thus, stock-specific risk and stock-picking still have faded in importance:

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 6/30/2016

Stocks Contributing Most to Relative Residual Variance of U.S. Hedge Fund Aggregate in Q2 2016

Symbol Name Relative Exposure Residual Volatility Share of Relative Residual Variance Share of Relative Total Variance
LNG Cheniere Energy 1.65 34.66 11.77 2.61
AGN Allergan plc 2.79 14.71 6.06 1.34
CHTR Charter Communications 1.84 20.58 5.16 1.14
PCLN Priceline Group 1.61 22.25 4.64 1.03
FLT FleetCor Technologies 1.74 19.87 4.29 0.95
VRX Valeant Pharmaceuticals 0.82 39.76 3.85 0.85
FB Facebook, Inc. Class A 0.89 31.39 2.84 0.63
HCA HCA Holdings 1.21 22.78 2.76 0.61
AAPL Apple Inc. -1.68 16.41 2.74 0.61
PYPL PayPal Holdings Inc 1.66 15.88 2.49 0.55

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

The most crowded stocks continue to be sensitive to asset flows in and out of the industry. Yet, in the current environment of extreme systematic hedge fund crowding, allocators and fund followers should continue to pay more attention to factor risk. Indeed, allocators invested in a seemingly diversified portfolio of hedge funds may, in fact, be paying high active fees for a passive factor portfolio.

Summary

  • At Q2 2016, nearly 80% of hedge funds’ relative long equity risk was due to factor, or systematic, exposures.
  • The main source of Q2 2016 hedge fund crowding, responsible for half of long equity tracking error, was record U.S. Market exposure.
  • Short Size Factor (small-cap bias) and long Health Factor exposures were the next most crowded bets, both near their historic extremes.
  • Given high current hedge fund factor crowding, an analysis of aggregate and individual hedge funds must focus on systematic exposures and risk shared across positions, and not solely on individual positions.
  • Fund investors, followers, and allocators must monitor whether they are investing in exceptional insights or generic factor exposures otherwise available via cheap passive instruments.
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.

Hedge Fund Finance Sector Crowding

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Asset outflows and portfolio liquidations have devastated crowded hedge fund bets since 2015. Losses have been especially severe in the Finance Sector. We survey hedge fund finance sector crowding and identify the stocks driving it. Investors and allocators must be vigilant: when capital flows out, these bets tend to suffer sharp losses. When capital flows in, they tend to benefit. We also provide an early indicator of this underperformance and outperformance.

Identifying Hedge Fund Finance Crowding

We created an aggregate position-weighted portfolio (Hedge Fund Finance Aggregate, or HF Finance Aggregate) consisting of finance sector equities held by all hedge fund long equity portfolios that are tractable from regulatory filings. The size of each position is the dollar value of its ownership by hedge funds. This process is similar to our earlier analyses of hedge fund crowding. We then evaluated HF Finance Aggregate’s risk relative to the capitalization-weighted portfolio of U.S. finance equities (Market Finance Aggregate) using an AlphaBetaWorks’ Statistical Equity Risk Model. Finally, we analyzed HF Finance Aggregate’s idiosyncratic bets and identified the most crowded ones.

Hedge Fund Finance Sector Performance

Over the past 10 years HF Finance Aggregate generated approximately the same return as a portfolio of index funds and ETFs with the same systematic (market) risk (Factor Portfolio):

Historical cumulative factor, security selection, and total returns of the Hedge Fund Finance Sector Aggregate through Q2 2016

Historical Factor and Total Return of the Hedge Fund Finance Sector Aggregate

Blue area represents positive and gray area represents negative risk-adjusted returns from security selection, net of factor effects. HF Finance Aggregate outperformed the Factor Portfolio between 2006 and 2013 and has underperformed since. A look at the security selection performance below illustrates the underlying cycles of performance.

Hedge Fund Finance Sector Security Selection

AlphaBetaWorks’ metric of security selection is αReturn – the performance a portfolio would have generated if markets had been flat. It is also the performance of a portfolio with its factor exposures hedged:

Historical cumulative security selection return of the Hedge Fund Finance Sector Aggregate through Q2 2016

Historical Return from Security Selection of Hedge Fund Finance Sector Aggregate

Hedge funds have enjoyed positive αReturn in the finance sector during calm market regimes. Throughout our test period, the only episode of security selection losses prior to 2014 was Q3 2008. It was followed by a sharp reversal starting in late-2008. The 2008-2010 security selection gains of HF Finance Aggregate illustrate how forced liquidation of 2008 ended with a mean-reversion: the biggest losers became attractive opportunities.

The cycles of asset inflows and liquidations are common to HF Sector Aggregates. Illustrations can be found in our previous pieces on hedge fund semiconductor crowding and hedge fund exploration and production crowding.

HF Finance Aggregate has been showing signs of liquidation since mid-2014. This was also the time when the overall HF Aggregate began to generate negative αReturns that eventually turned into a rout. Since 2014, hedge funds’ long finance picks underperformed by 15% on a risk-adjusted basis. Had the industry taken the same risks passively with ETFs, its long financials portfolio would have generated approximately 15% higher return.

Hedge Fund Residual (Idiosyncratic) Finance Sector Crowding

Hedge fund sector portfolios have a history of booms and busts. Their sharply negative αReturns usually signal liquidations. Consequently, identifying and avoiding crowded bets is vital during these periods. When a cycle eventually turns, the biggest losers can present attractive opportunities. The following stocks were recent top contributors to idiosyncratic (stock-specific) risk of HF Finance Aggregate – its most crowded stocks. Blue bars represent long (overweight) exposures relative to Market Finance Aggregate. White bars represent short (underweight) exposures. Bar height represents contribution to relative stock-specific risk:

Chart of the residual Hedge Fund Finance Sector Crowding: the main stock-specific bets and their cumulative contribution to the residual variance of Hedge Fund Finance Sector Aggregate Portfolio relative to Market on 6/30/2016

Stocks Contributing Most to U.S. Hedge Fund Finance Aggregate Relative Residual Risk in Q2 2014

The following table contains detailed data on the residual hedge fund finance sector crowding:

Exposure (%) Net Exposure Share of Risk (%)
HF Sector Aggr. Sector Aggr. % $mil Days of Trading
AIG American International Group, Inc. 7.98 1.49 6.49 3,651.8 6.6 13.33
HTZ Hertz Global Holdings, Inc. 2.51 0.11 2.40 1,350.1 10.3 13.32
EQIX Equinix, Inc. 4.20 0.56 3.64 2,048.5 10.0 10.95
JPM JPMorgan Chase \& Co. 0.80 5.31 -4.51 -2,536.9 -1.9 5.85
AER AerCap Holdings NV 2.24 0.19 2.05 1,154.8 8.4 5.71
CAR Avis Budget Group, Inc. 1.39 0.06 1.33 746.5 8.2 5.34
BAC Bank of America Corporation 0.66 3.40 -2.74 -1,543.8 -0.9 5.11
LPLA LPL Financial Holdings Inc. 1.62 0.05 1.56 879.1 38.4 3.60
CACC Credit Acceptance Corporation 1.32 0.09 1.23 693.5 22.3 2.84
CBG CBRE Group, Inc. Class A 1.76 0.24 1.52 856.0 8.9 2.00
WLTW Willis Towers Watson Public Limited Comp 2.46 0.40 2.06 1,161.3 8.0 1.73
IBKR Interactive Brokers Group, Inc. Class A 1.27 0.06 1.21 680.8 20.9 1.65
BK Bank of New York Mellon Corporation 3.60 0.97 2.63 1,481.9 5.1 1.58
MA MasterCard Incorporated Class A 4.29 2.51 1.78 1,002.7 0.9 1.56
ALLY Ally Financial Inc 1.84 0.22 1.62 911.9 4.1 1.41
WFC Wells Fargo & Company 2.43 5.97 -3.54 -1,994.8 -1.9 1.33
GLPI Gaming and Leisure Properties, Inc. 1.28 0.09 1.19 672.0 9.1 1.33
NSAM NorthStar Asset Management Corp 0.95 0.05 0.90 506.3 19.3 1.29
FNMA Federal National Mortgage Association 0.29 0.04 0.25 139.5 42.7 1.10
SPG Simon Property Group, Inc. 0.01 1.60 -1.59 -894.9 -2.0 0.89
… Other Positions 0.64 18.06
Total 100.00

Long (overweight) exposures to AIG, HTZ, EQIX, and AER as well as short (underweight) exposure to JPM account for half of the stock-specific risk and volatility of hedge funds’ long financials books. The stock-specific losses of the crowded financials bets in 2015-2016 have been more severe than those in the 2008 crisis. Given this severity, when the cycle turns positive the crowded books are likely to outperform.

Analytics built on a robust risk model, such as the AlphaBetaWorks Statistical Equity Risk Model used here, offer leading indicators of portfolio liquidations and losses to crowding. These analytics provided portfolio managers and investors with warning signs as early as 2014, helping avoid losses, or even profit from herding. Since liquidations and crowding losses are routine, it is also vital that allocators identify undifferentiated managers.

Conclusions

  • Analysis of hedge fund crowding using robust risk models provides early signs of portfolio liquidations and opportunities.
  • Half of hedge fund residual (idiosyncratic, stock-specific) finance sector crowding comes from only five stocks.
  • Investors with robust data on hedge fund crowding and cycles of capital flow can reduce losses and profit from opportunities.
  • Allocators with robust data on hedge fund crowding can monitor manager differentiation and reduce 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 © 2012-2016, AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved.
Content may not be republished without express written consent.

Hedge Fund Crowding Update – Q1 2016

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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 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 Relative 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.

Performance Persistence within International Style Boxes

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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.

Hedge Fund Clustering in Q4 2015

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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 investing 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 analyses of crowding 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 this 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 above 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.