The Impact of Fund Mean Reversion

Real-world restrictions on hedge fund investing wreak havoc on common allocation strategies

Common return measures fail to predict future hedge fund performance. More important, under typical allocation and withdrawal constraints, these failures due to mean reversion become more severe:

  • Portfolios based on top nominal returns and win/loss ratios tend to under-perform.
  • Portfolios based on top Sharpe ratios don’t outperform.
  • Portfolios based on predictive skill analytics and robust factor models continue to consistently outperform.

To illustrate, we follow the approach of our earlier pieces on hedge funds: Our dataset spans the long portfolios of all U.S. hedge funds active over the past 15 years that are tractable using 13F filings. Top- and bottom-performing portfolios are selected based on 36 months of performance history.

But here we impose realistic allocation constraints: a 6-month delay between holdings reporting and fund investment, plus a bi-annual window for investments into, or withdrawals from, hedge funds. For example, an allocator who wishes to invest in a fund using 12/31/2013 data can only do so on 6/30/2014 and cannot redeem until 12/31/2014. These practical liquidity restrictions deepen the impact of hedge fund mean reversion.

Hedge Fund Selection Using Nominal Returns

The following chart tracks two simulated funds of hedge funds. One contains the top-performing 5% and the other the bottom-performing 5% of hedge fund U.S. equity long books. We use a 36-month trailing performance look-back; investments are made with a six-month delay (as above):

Chart of the cumulative returns of hedge fund portfolios constructed from funds with the highest 5% and the lowest 5% 36-month trailing returns

Performance of Portfolios of Hedge Funds Based on High and Low Historical Returns

Cumulative Return (%)

Annual Return (%)

High Historical Returns

99.54

6.74

Low Historical Returns

125.12

7.92

High – Low Returns

-25.57

-1.18

The chart reveals several regimes of hedge fund mean reversion: In a monotonically increasing market, such as 2005-2007, relative nominal performance persists; funds with the highest systematic risk outperform. When the regime changes, however, they under-perform. At the end of 2008, the top nominal performers are those taking the lowest systematic risk. In 2009, as the regime changes again, these funds under-perform.

Hedge Fund Selection Using Sharpe Ratios

The following chart tracks portfolios of funds with the top 5% and bottom 5% Sharpe ratios:

Chart of the cumulative returns of hedge fund portfolios constructed from funds with the highest 5% and the lowest 5% 36-month trailing Sharpe ratios

Performance of Portfolios of Hedge Funds Based on High and Low Historical Sharpe Ratios

Cumulative Return (%)

Annual Return (%)

High Historical Sharpe Ratios

115.31

7.48

Low Historical Sharpe Ratios

115.52

7.49

High – Low Sharpe Ratios

-0.20

-0.01

Since Sharpe ratio simply re-processes nominal returns, and only partially adjusts for systematic risk, it also fails when market regimes change. However, it is less costly. While Sharpe ratio may not be predictive under practical constraints of hedge fund investing, at least (unlike nominal returns) it does little damage.

Hedge Fund Selection Using Win/Loss Ratios

The following chart tracks portfolios of funds with the top 5% and the bottom 5% win/loss ratios, related to the batting average. These are examples of popular non-parametric approaches to skill evaluation:

Chart of the cumulative returns of hedge fund portfolios constructed from funds with the highest 5% and the lowest 5% 36-month trailing win/loss ratios

Performance of Portfolios of Hedge Funds Based on High and Low Historical Win/Loss Ratios

Cumulative Return (%)

Annual Return (%)

High Historical Win/Loss Ratios

112.41

7.35

Low Historical Win/Loss Ratios

136.86

8.41

High – Low Win/Loss Ratios

-24.45

-1.06

The win/loss ratio suffers from the same challenges as nominal returns: Win/loss ratio favors funds with the highest systematic risk in the bullish regimes and funds with the lowest systematic risk in the bearish regimes. As with nominal returns, this can be predictive while market trends continue. When trends change, the losses are especially severe under liquidity constraints.

Hedge Fund Selection Using αReturns

Systematic (factor) returns that make up the bulk of portfolio volatility are the primary source of mean reversion. Proper risk adjustment with a robust risk model controls for factor returns; it addresses mean reversion and identifies residual returns due to security selection.

AlphaBetaWorks’ measure of residual security selection performance is αReturn – outperformance relative to a replicating factor portfolio. αReturn is also the return a portfolio would have generated if markets had been flat.

The following chart tracks portfolios of funds with the top 5% and the bottom 5% αReturns. These portfolios have matching factor exposures:

Chart of the cumulative returns of hedge fund portfolios constructed from funds with the highest 5% and the lowest 5% 36-month trailing returns from security selection (αReturns)

Performance of Portfolios of Hedge Funds Based on High and Low Historical αReturns

Cumulative Return (%)

Annual Return (%)

High Historical αReturns

144.33

8.72

Low Historical αReturns

104.25

6.97

High – Low αReturns

40.08

1.75

Even with the same 6-month investment delay and bi-annual liquidity constraints, long portfolios of the top stock pickers outperformed long portfolios of the bottom stock pickers by 40% cumulatively over the past 10 years.

This outperformance has been consistent. Indeed, top stock pickers (high αReturn funds) have continued to do well in recent years. Security selection results of the industry’s top talent are strong. Widespread discussions of the difficulty of generating excess returns in 2014 reflect the sorry state of commonly used risk and skill analytics.

Conclusions

  • Due to hedge fund mean reversion, yesterday’s nominal winners tend to become tomorrow’s nominal losers.
  • Under typical hedge fund liquidity constraints, mean reversion is aggravated. Funds of top performing hedge funds under-perform.
  • Re-processing nominal returns does not eliminate mean reversion:
    • Funds with top and bottom Sharpe ratios perform similarly;
    • Funds with top win/loss ratios underperform funds with bottom win/loss ratios.
  • Risk-adjusted returns from security selection (stock picking) persist. Robust skill analytics, such as αReturn, identify strong future stock pickers.
The information herein is not represented or warranted to be accurate, correct, complete or timely.
Past performance is no guarantee of future results.
Copyright © 2012-2015, 
AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved.
Content may not be republished without express written consent.
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Hedge Fund Mean Reversion

Our earlier articles explored hedge fund survivor (survivorship) bias and large fund survivor bias. These artifacts can nearly double nominal returns and overstate security selection (stock picking) performance by 80%. Due to these biases, future performance of the largest funds disappoints. The survivors and the largest funds have excellent past nominal performance, yet it is not predictive of their future returns due to hedge fund mean reversion, a special case of reversion toward the mean. Here we explore this phenomenon and its mitigation.

We follow the approach of our earlier pieces that analyzed hedge funds’ long U.S. equity portfolios (HF Aggregate). This dataset spans the long portfolios of all U.S. hedge funds active over the past 15 years that are tractable using 13F filings.

Mean Reversion of Nominal Hedge Fund Returns

To illustrate the mean reversion of nominal hedge fund returns, we have assembled hedge fund portfolios with the highest and lowest trailing 36-month performance and track these groups over the subsequent 36 months. This covers the past 15 years and considers approximately 100 such group pairs.

If strong historical performance is predictive, we should see future (ex-post, realized) outperformance of the best historical performers relative to the worst. This would support the wisdom of chasing the largest funds or the top-performing gurus.

The following chart tracks past and future performance of each group. The average subsequent performance of the historically best- and worst-performing long U.S. equity hedge fund portfolios is practically identical and similar to the market return. There is some difference in the distributions, however: highest performers’ subsequent returns are skewed to the downside; lowest performers’ subsequent returns are skewed to the upside:

Chart of the past and future performance of hedge fund groups with high and low historical 36-month returns, assembled monthly over the past 15 years.

Hedge Fund Performance Persistence: High and Low Historical Returns

Prior 36 Months Return (%)

Subsequent 36 Months Return (%)

High Historical Returns

52.84

28.17

Low Historical Returns

-11.43

28.33

Thus, nominal historical returns are not predictive of future performance. We will try a few simple metrics of risk-adjusted performance next to see if they prove more effective.

Sharpe Ratio and Mean Reversion of Returns

Sharpe ratio is a popular measure of risk-adjusted performance that attempts to account for risk using return volatility. The following chart tracks past and future performance of portfolios with the highest and lowest historical Sharpe ratios. The average future performance of the best- and worst-performing portfolios begins to diverge, though we have not tested this difference for statistical significance:

Chart of the past and future performance of hedge fund groups with high and low historical 36-month Sharpe ratios, assembled monthly over the past 15 years.

Hedge Fund Performance Persistence: High and Low Historical Sharpe Ratios

Prior 36 Months Return (%)

Subsequent 36 Months Return (%)

High Historical Sharpe Ratios

43.65

28.38

Low Historical Sharpe Ratios

-8.34

25.66

Note that portfolios with the highest historical Sharpe ratios perform similarly to the best and worst nominal performers in the first chart. However, portfolios with the lowest historical Sharpe ratios underperform by 2.5%. Sharpe ratio does not appear to predict high future performance, yet it may help guard against poor results.

Win/Loss Ratio and Mean Reversion of Returns

Sharpe ratio and similar parametric approaches make strong assumptions, including normality of returns. We try a potentially more robust non-parametric measure of performance free of these assumptions – the win/loss ratio, closely related to the batting average. The following chart tracks past and future performance of portfolios with the highest and lowest historical win/loss ratios. The relative future performance of the two groups is similar:

Chart of the past and future performance of hedge fund groups with high and low historical 36-month win/loss ratios, assembled monthly over the past 15 years.

Hedge Fund Performance Persistence: High and Low Historical Win/Loss Ratios

Prior 36 Months Return (%)

Subsequent 36 Months Return (%)

High Historical Win/Loss Ratios

26.93

27.59

Low Historical Win/Loss Ratios

1.70

26.53

Win/loss ratio does not appear to improve on the predictive ability of Sharpe ratio. In fact, both groups slightly underperform the low performers from the first chart above.

Persistence of Hedge Fund Security Selection Returns

Nominal returns and simple metrics that rely on nominal returns both suffer from mean reversion, since systematic (factor) returns responsible for the bulk of portfolio volatility are themselves mean reverting. Proper risk adjustment with a robust risk model that eliminates systematic risk factors and purifies the residual addresses this problem.

AlphaBetaWorks’ measure of this residual security selection performance is αReturn – outperformance relative to a replicating factor portfolio. αReturn is also the return a portfolio would have generated if markets had been flat. The following chart tracks past and future security selection performance of portfolios with the highest and lowest historical αReturns. The future security selection performance of the best and worst stock pickers diverges by over 10%:

Charts of the past and future security selection (residual, αReturn) performance of hedge fund groups with high and low historical 36-month security selection (residua) returns, assembled monthly over the past 15 years.

Hedge Fund Security Selection Performance Persistence: High and Low Historical αReturns

Prior 36 Months αReturn (%)

Subsequent 36 Months αReturn (%)

High Historical αReturns

60.90

5.65

Low Historical αReturns

-35.66

-4.58

Security Selection and Persistent Nominal Outperformance

Strong security selection performance and strong αReturns can always be turned into nominal outperformance. In fact, a portfolio with positive αReturns can be hedged to outperform any broad benchmark. Nominal outperformance is convenient and easy to understand. These are the returns that investors “can eat.”

The following chart tracks past and future nominal performance of portfolios with the highest and lowest historical αReturns, hedged to match U.S. Equity Market’s risk (factor exposures). Hedging preserved security selection returns and compounded them with market performance: future performance of the two groups diverges by over 11%:

Charts of the past and future performance of hedge fund groups with high and low historical 36-month security selection (residua) returns, assembled monthly over the past 15 years and hedged to match U.S. Market.

Hedge Fund Performance Persistence: High and Low Historical αReturns

Prior 36 Months Return (%)

Subsequent 36 Months Return (%)

High Historical αReturns

81.70

32.50

Low Historical αReturns

-28.93

21.41

Note that, similarly to Sharpe ratio, αReturn is most effective in identifying future under-performers.

Thus, with predictive analytics and a robust model, investors can not only identify persistently strong stock pickets but also construct portfolios with predictably strong nominal performance.

Conclusions

  • Due to hedge fund mean reversion, future performance of the best and worst nominal performers of the past is similar.
  • Re-processing nominal returns does not eliminate mean reversion. However, Sharpe ratio begins to identify future under-performers.
  • Risk-adjusted returns from security selection (stock picking) persist. A robust risk model can isolate these returns and identify strong future stock pickers.
  • Hedging can turn persistent security selection returns into outperformance relative to any benchmark:
    • Hedged portfolio of the best stock pickers persistently outperforms.
    • Hedged portfolio of the worst stock pickers persistently underperforms.
The information herein is not represented or warranted to be accurate, correct, complete or timely.
Past performance is no guarantee of future results.
Copyright © 2012-2015, 
AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved.
Content may not be republished without express written consent.
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Hedge Fund Crowding – Q4 2014

Hedge funds share a few systematic and idiosyncratic bets. These crowded bets are the main sources of the industry’s relative performance and of many individual funds’ returns. We survey risk factors and stocks responsible for the majority of hedge fund long U.S. equity herding during Q4 2014.

Investors should treat crowded ideas with caution: Due to the congestion of their hedge fund investor base, crowded stocks tend to be more volatile and are vulnerable to mass liquidation. In addition, consensus hedge fund bets have underperformed in the past.

Identifying Hedge Fund Crowding

This piece follows the approach of our earlier articles on fund crowding: We created a position-weighted portfolio (HF Aggregate) consisting of popular long U.S. equity holdings of all hedge funds with medium to low turnover that are tractable from quarterly position filings. We then analyzed HF Aggregate’s risk relative to U.S. Market (Russell 3000) using AlphaBetaWorks’ Statistical Equity Risk Model to identify sources of crowding. More background information and explanations of the terms used below are available in those earlier articles.

Hedge Fund Aggregate’s Risk

The Q4 2014 HF Aggregate had 3.0% estimated future annual tracking error relative to U.S. Market. Risk was primarily due to factor (systematic) bets:

The components of HF Aggregate’s relative risk on 12/31/2014 were the following:

 Source

Volatility (%)

Share of Variance (%)

Factor

2.23

56.32

Residual

1.96

43.68

Total

2.97

100.00

Systematic risk increased by a tenth from the previous quarter. We will see the factors behind this increase below.

With an estimated future tracking error near 3%, HF Aggregate continues to be nearly passive. HF Aggregate will have a very hard time earning a typical fee. Investors in a broadly diversified portfolio of long-biased hedge funds will likely struggle also.

Hedge Fund Factor (Systematic) Crowding

Below are HF Aggregate’s principal factor exposures (in red) relative to U.S. Market’s (in gray) as of 12/31/2014:

Chart of the factor exposures contributing most to the relative factor (systematic) risk of U.S. Hedge Fund Aggregate

Factor Exposures Contributing Most to the Relative Risk of U.S. Hedge Fund Aggregate

Of these bets, Market (Beta) and Oil are responsible for over 80% of the factor risk relative to U.S. Market. These are the main components of the 2.23% Factor Volatility in the first table:

Chart of the factors contributing most to the relative factor (systematic) variance of U.S. Hedge Fund Aggregate

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

HF Aggregate has become more systematically crowded since Q3 2014. The following factors were the top contributors to the relative systematic risk on 12/31/2014:

Factor

Relative Exposure (%)

Portfoio Variance (%²)

Share of Systematic Variance (%)

Market

13.26

3.10

62.37

Oil Price

2.23

1.01

20.32

Finance

-7.49

0.43

8.65

Industrial

9.53

0.35

7.04

Utilities

-3.36

0.26

5.23

Other Factors -0.18

-3.62

Total 4.97

100.00

The increased factor risk during Q4 2014 was primarily due to a 2% increase in U.S. Market Exposure (Beta). After adding long oil exposure in Q3 2014 as the energy sector selloff intensified, hedge funds kept it steady through Q4.

Hedge Fund Residual (Idiosyncratic) Crowding

Turning to HF Aggregate’s residual variance relative to U.S. Market, eight stocks were responsible for over half of the relative residual risk:

Chart of the stocks contributing most to the relative residual (idiosyncratic) variance of U.S. Hedge Fund Aggregate

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

These stocks will be the primary drivers of HF Aggregate’s and of the most crowded firms’ returns. They will also be affected by the vagaries of capital flows into and out of hedge funds. Investors should be ready for seemingly inexplicable volatility in these names. They may be wonderful individual investments, but history is not on their side, since crowded bets have historically underperformed.

The list is mostly unchanged from the previous quarter:

Symbol

Name

Exposure (%)

Share of Idiosyncratic Variance (%)

LNG

Cheniere Energy, Inc.

1.70

15.73

AGN

Allergan, Inc.

3.53

9.51

VRX

Valeant Pharmaceuticals International, Inc.

2.35

9.18

CHTR

Charter Communications, Inc. Class A

1.80

3.88

HTZ

Hertz Global Holdings, Inc.

1.37

3.35

EBAY

eBay Inc.

1.91

3.27

MU

Micron Technology, Inc.

1.08

3.21

BIDU

Baidu, Inc. Sponsored ADR Class A

1.22

3.14

PCLN

Priceline Group Inc

1.29

2.43

SUNE

SunEdison, Inc.

0.63

2.29

When investing in these crowded names, investors should perform particularly thorough due-diligence, since any losses will be magnified when hedge funds rush for the exits.

Historically, consensus bets have done worse than a passive portfolio with the same risk. Consequently, fund allocators should thoroughly investigate hedge fund managers’ crowding to avoid investing in a pool of undifferentiated bets destined for disappointment.

AlphaBetaWorks’ analytics assist in both tasks: Our sector crowding reports identify hedge fund herding in each equity sector. Our fund reports measure hedge fund differentiation and skills that are strongly predictive of future performance.

Summary

  • There is both factor (systematic/market) and residual (idiosyncratic/security-specific) crowding of hedge funds’ long U.S. equity portfolios.
  • Hedge funds have become more systematically crowded during Q4 2014, primarily by increasing their Beta.
  • The main sources of idiosyncratic crowding are: LNG, AGN, VRX, CHTR, HTZ, EBAY, and MU.
  • The crowded hedge fund portfolio has historically underperformed its passive alternative. Investors would have made more by taking the same risk passively – hedge fund investors should pay close attention to crowding before allocating capital.
The information herein is not represented or warranted to be accurate, correct, complete or timely.
Past performance is no guarantee of future results.
Copyright © 2012-2015, AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved.
Content may not be republished without express written consent.
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Large Hedge Fund Survivor Bias

Why Size Isn’t Everything

Hedge fund survivor bias is especially insidious for the largest firms. Large hedge fund survivor bias overstates expected performance of the biggest firms by nearly half and their risk adjusted return from security selection (stock picking) by 80%. It is impossible to predict the largest funds of the future, but one doesn’t have to – robust skill analytics identify funds that will do even better in the future than tomorrow’s largest.

Past Performance of Today’s Largest Hedge Funds

We follow the approach of our earlier piece on hedge fund survivor (survivorship) bias, which analyzed firms’ long U.S. equity portfolios (HF Aggregate). This dataset spans the long portfolios of all hedge funds active over the past 10 years that are tractable using 13F filings.

We compare group returns to Factor Portfolio – a portfolio with matching factor (systematic) risk. Factor Portfolio captures the return of investing passively in ETFs and index futures with the same risk as the group. This comparison reveals security selection (stock picking) performance, or αReturn – outperformance relative to the Factor Portfolio and the return that would have been generated if markets had been flat.

The following chart compares the performance of the 20 largest U.S. equity hedge fund long portfolios (Large HFs, green) to the Factor Portfolio (black). The security selection performance, or αReturn (blue), is the difference between the two. This is the average past performance of the 20 largest funds of 2015:

Chart of the past total, factor, and residual returns of long U.S. equity portfolios of the 20 largest hedge funds of 2015

Current Largest Hedge Funds: Past Total, Factor, and Residual Long U.S. Equity Returns

Returns (%)

Annualized

10-year Cum.

Total

11.48

215.26

Factor

9.33

154.29

Total – Factor

2.15

60.97

Firms that have grown the largest over the past 10 years have performed exceptionally well: Including the effect of compounding, their long portfolios generated 61% higher return than their passive equivalents. If markets had been flat for the past 10 years, their long equity portfolios would have appreciated by nearly 25%.

The allure of this past performance arouses fund-following, guru-tracking, and billionaire portfolio strategies. But there is one problem: Today’s largest funds represent a top-performing sliver of the thousands of funds active in the past. Of the thousands of funds, some truly are skilled, but many simply got lucky on aggressive bets and became large as a result, irrespective of their skill. This constitutes large hedge fund survivor bias. This performance does not persist and tends to mean-revert.

Future Performance of Yesterday’s Largest Hedge Funds

Most billionaire and guru-following strategies make the assumption that the largest funds are likely to continue generating strong returns. To test this, we tracked the 20 largest long U.S. equity hedge fund portfolios of 2005. Below is the unappealing picture of their average performance:

Chart of the future total, factor, and residual returns of long U.S. equity portfolios of the 20 largest hedge funds of 2005

2005 Largest Hedge Funds: Future Total, Factor, and Residual Long U.S. Equity Returns

Returns (%)

Annualized

10-year Cum.

Total

7.70

116.05

Factor

8.68

138.11

Total – Factor

-0.97

-22.05

The 2005 Large HF Aggregate tracked Factor Portfolio closely until 2010 and has struggled since. Hence, including the effects of compounding, large hedge fund survivor bias overstated security selection returns by 80%.

Size does not always signal quality, nor does it guarantee future performance. Between 2005 and 2015, the forward-looking performance of the largest long hedge fund portfolios of 2005 was just over half the backward-looking performance of 2015’s largest. Why then would the largest hedge funds of 2015 perform differently than the poor showing of the 2005 vintage?

Predicting Top Future Hedge Funds: Stock Picking Skill

Absent a time machine, investors cannot know who will be the future stars. However, they need not despair. Instead of focusing on the largest or top-performing funds of the past, they can turn to those showing the highest evidence of skill. The following chart tracks the long U.S. equity portfolios of 20 hedge funds with the highest 3-year αReturn as of 12/31/2005:

Chart of the future total, factor, and residual returns of long U.S. equity portfolios of the 20 best stock picker hedge funds of 2005

Best 2005 Stock Picker Hedge Funds: Future Total, Factor, and Residual Long U.S. Equity Returns

Returns (%)

Annualized

10-year Cum.

Total

12.60

252.58

Factor

9.11

148.70

Total – Factor

3.49

103.88

The funds above were the best stock pickers of 2005, not the largest. If markets had been flat for the past 10 years, the top stock pickers of 2005 would have returned 40%. For a variety of reasons (scalability constraints, lifestyle preferences), many have not become the largest or best known, but their risk-adjusted returns are strong.

Since active management skills persist, skilled stock pickers of the past continue to generate strong nominal and risk-adjusted returns. The same analysis identifies today’s top stock pickers who will be tomorrow’s outperformers – and without the cost of a time machine!

Conclusions

  • Hedge fund survivor bias is larger for the largest hedge funds.
  • Between 2005 and 2015, large hedge fund survivor bias overstated expected nominal performance by nearly 100% and security selection performance by 80%.
  • Chasing large hedge funds is unnecessary and detrimental. Selecting a fund using robust skill analytics, as illustrated by αReturn, is superior to flawed results hampered by large hedge fund survivor bias.
The information herein is not represented or warranted to be accurate, correct, complete or timely.
Past performance is no guarantee of future results.
Copyright © 2012-2015, 
AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved.
Content may not be republished without express written consent.
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Hedge Fund Survivor Bias

And The Flaws of Blind Fund-Following Strategies

Numerous financial data and analytics vendors peddle hedge fund tracking strategies and content. Much of this data is hazardous to investors – Hedge fund survivor bias, a special case of the pervasive survivorship bias, is its key flaw. This artifact overstates nominal fund returns by a fifth and conceals mediocre risk-adjusted performance records.

This post is technical, but it illustrates an important phenomenon and sets up the foundation for upcoming articles. We analyze the long equity portfolios of approximately 1,000 medium and lower turnover non-quantitative hedge funds active over the past 10 years (HF Aggregate). This dataset spans the long portfolios of all non-quantitative hedge funds active over the past 10 years that are tractable using 13F filings.

HF Aggregate consists of two approximately equal sub-sets: HF Surviving Aggregate and HF Defunct Aggregate. HF Surviving Aggregate, similar to the datasets of many vendors, gives a deeply misleading picture of average hedge fund performance. Our HF Aggregate corrects this by including HF Defunct Aggregate – funds that stopped filing 13Fs as their U.S. assets dropped below $100 million.

All Hedge Fund Performance

We compare HF Aggregate to Factor Portfolio – a portfolio with matching factor (systematic) risk. Factor Portfolio captures the return investors would have realized if they had passively invested in ETFs and index futures with the same risk as HF Aggregate. We do this to calculate security selection (stock picking) returns of HF Aggregate.

With the exception of the 2009-2011 period, HF Aggregate generated negative returns from security selection. AlphaBetaWorks’ measure of security selection performance is αReturn – outperformance relative to the Factor Portfolio. αReturn is also the return HF Aggregate would have generated if markets were flat. Since 2011, HF Aggregate’s αReturn was -2%. If markets had been flat, the average medium-turnover long hedge fund portfolio would have lost 2% from its long portfolio. Including the effects of compounding with factor returns, αReturn was -3%.

Putting these elements together, the chart below compares HF Aggregate’s performance (green) to the Factor Portfolio (black). The security selection performance, or αReturn (blue), is the difference between the two. This is the true long performance of the average hedge fund:

Chart of the cumulative total, factor, and residual/security selection performance of all medium turnover hedge fund U.S. equity portfolios, free from hedge fund survivor bias

All Medium Turnover U.S. Hedge Fund Long Portfolios: Factor, Residual, and Total Returns

Performance (%)

Annualized

10-year
Total

8.48

133.57

Factor

8.60

136.39

Total – Factor

-0.12

-2.82

Survivor Hedge Fund Performance – Survivorship Bias in Action

The figures above contrast with those promoted by many data vendors and analytics providers. They typically consider (or provide data on) the survivors only – those funds that are still around, active, and reporting their holdings – HF Surviving Aggregate.

Indeed, the performance of surviving hedge funds is superior: their nominal return is 26% higher than HF Aggregate’s and their security selection performance is positive. Not surprisingly, surviving funds have consistently generated positive risk-adjusted returns from security selection, outperforming the replicating Factor Portfolio. This is the performance investors typically see:

Chart of the cumulative total, factor, and residual/security selection performance of surviving medium turnover hedge fund U.S. equity portfolios, affected by the hedge fund survivor bias

Surviving Medium Turnover U.S. Hedge Fund Long Portfolios: Factor, Residual, and Total Returns

Performance (%)

Annualized

10-year

Total

9.54

159.57

Factor

9.06

147.37

Total – Factor

0.48

12.20

Defunct Hedge Fund Performance

The disconnect between these two pictures of average hedge fund performance is due to survivor bias. Of the approximately 1,000 medium turnover hedge funds tractable using 13Fs that have been active filers over the past 10 years, only half remain. The defunct half dropped out of many databases and out of HF Surviving Aggregate. HF Defunct Aggregate struggled under low factor returns and poor security selection. This is the under-performance swept under the rug:

Chart of the cumulative total, factor, and residual/security selection performance of defunct medium turnover hedge fund U.S. equity portfolios, excluded to cause hedge fund survivor bias

Defunct Medium Turnover U.S. Hedge Fund Long Portfolios: Factor, Residual, and Total Returns

Performance (%)

Annualized

10-year

Total

6.07

83.52

Factor

7.14

104.12

Total – Factor

-1.06

-20.60

The difference in performance between surviving and defunct funds is especially dramatic post-2008:

  • Surviving and defunct hedge funds’ long portfolios show similar nominal returns through 2008. Surviving hedge funds are slightly ahead with a 5% higher αReturns.
  • The 2008 draw-down for surviving and defunct hedge funds is similar. Both groups generate negative αReturns: widespread portfolio liquidation devastates crowded hedge fund bets across both groups.
  • From 2009 the survivors decouple from the defunct funds: Defunct funds trim exposures. Surviving funds boost exposures.
  • Since 2009 HF Surviving Aggregate outperforms HF Defunct Aggregate by over 70%. Approximately half is due to higher systematic risk and half is due to security selection.
  • Survival is mostly a matter of exposure and stock picking.

Absent a time machine, investors and fund followers cannot know who will be the future survivors. HF Defunct Aggregate consists of survivors that did well enough to last until 2005, but subsequently perished. Unfortunately, many strategies are built on a swampy foundation – the assumption that the average hedge fund is the same as the average surviving hedge fund. Real fund performance is a fifth lower.

Consequently, robust skill analytics developed with the understanding of hedge fund survivor bias are critical to keep investors out of yesterday’s winners that tend to become tomorrow’s losers.

Conclusions

  • Historical performance of surviving hedge funds overstates actual average returns by a fifth.
  • Hedge fund survivor bias boosts 10-year nominal returns by 26%, primarily post-2008.
  • Hedge fund survivor bias boosts 10-year security selection returns by approximately 15%.
  • Fund performance and holdings studies that ignore survivor bias will deliver misleading conclusions and disappointing performance.
The information herein is not represented or warranted to be accurate, correct, complete or timely.
Past performance is no guarantee of future results.
Copyright © 2012-2015, 
AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved.
Content may not be republished without express written consent.
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Foreign Sectors Exposed to Strong USD

In an earlier article we discussed the U.S. sectors most affected by volatility in the U.S. Dollar. This analysis raised a number of questions from readers and clients:

  • For U.S. exporters hurt by strong USD: Do foreign competitors benefit, exhibiting the opposite (positive) USD exposure?
  • For U.S. retailers and distributors aided by strong USD: Do foreign suppliers benefit, exhibiting similar (positive) USD exposure?

Both intuitions are correct. Foreign transportation and technology companies turn out to be the top beneficiaries of appreciating USD.

U.S. Information Technology Sector USD FX Exposure

Recall from our earlier piece that U.S. Information Technology is one of the sectors with the highest negative correlation to USD:

Sector

USD FX
Correlation

USD FX
Correlation
p-value
USD FX
Beta

USD FX
Beta
p-value

Contract Drilling

-0.45

0.0002 -1.01

0.0006

Integrated Oil

-0.39

0.0011 -0.56

0.0011

Coal

-0.36

0.0021 -1.10

0.0004

Oilfield Services Equipment

-0.34

0.0042 -0.69

0.0059

Information Technology Services

-0.30

0.0109 -0.27

0.0373

Oil and Gas Production

-0.27

0.0174 -0.44

0.0131

Information Technology Services is an export industry that suffers when USD-denominated costs increase relative to foreign-currency-denominated revenues. USD appreciation squeezes margins and puts the sector at a disadvantage relative to foreign competitors. Consequently, we expect foreign technology companies to benefit from appreciating USD.

U.S. Retail Sector USD FX Exposure

U.S. Retail and Distribution are among the sectors with the highest positive correlation to USD:

Sector

USD FX Correlation

USD FX
Correlation
p-value
USD FX
Beta

USD FX
Beta
p-value

Real Estate Investment Trusts

0.29

0.0121 0.39

0.0101

Pulp and Paper

0.30

0.0102 0.52

0.0123

Aerospace and Defense

0.31

0.0084 0.32

0.0206

Beverages Alcoholic

0.33

0.0049 0.43

0.0025

Catalog Specialty Distribution

0.33

0.0045 0.41

0.0349

Department Stores

0.37

0.0020 0.70

0.0085

These businesses are sensitive to the price of imports and to the consumers’ purchasing power. When USD appreciates, U.S. retailers benefit from the drop in the price of imports and from the boost in U.S. consumers’ purchasing power. USD appreciation should also benefit foreign suppliers of U.S. retailers. Consequently, we expect foreign exporters and transportation companies to benefit from appreciating USD.

Foreign Sectors Most Positively Exposed to USD FX

There are two common techniques to quantify relationship between two variables: correlation and beta (leverage). Correlation between pure sector factor returns and USD returns quantifies the consistency of the relationship – how much of the sector variance is attributable to USD FX. Beta, or leverage, of pure sector factor returns relative to USD returns quantifies the magnitude of the relationship – how much sector changes given a change in USD FX.

Foreign Sectors with Highest USD Correlation

Foreign sectors most correlated to USD FX are dominated by transportation and technology companies. When USD appreciates, these businesses benefit the most from reduced competitiveness of U.S. Information Technology Industry, increased appetites of U.S. consumers, and decreased commodity prices:

Chart of international sector factors with market variance removed showing the highest correlation to USD FX

International Pure Sector Factors with Highest USD Correlation

Sector

USD FX
Correlation

USD FX Correlation
p-value
USD FX
Beta

USD FX Beta
p-value

China: Medical Distributors

0.28

0.0150 0.74

0.0223

Japan: Marine Shipping

0.31

0.0073 0.61

0.0206

Hong Kong: Wireless Telecommunications

0.31

0.0071 0.66

0.0061

Netherlands: Misc. Transportation

0.34

0.0043 0.93

0.0047

Germany: Semiconductors

0.42

0.0004 1.00

0.0033

Australia: Misc. Transportation

0.52

0.0000 1.24

0.0000

Foreign Sectors with Highest USD Beta

Likewise, foreign sectors with the highest beta (most leverage) to USD FX are dominated by transportation and technology companies. Chinese auto parts companies are another winner. Foreign Semiconductor and Auto Parts Sectors benefit from the reduced competitiveness of their U.S. competitors:

Chart of international sectors with market variance removed showing the highest beta to USD FX

International Pure Sector Factors with Highest USD Beta

Sector

USD FX
Beta

USD FX Beta
p-value

China: Wholesale Distributors

0.81

0.0082

China: Auto Parts OEM

0.83

0.0191

Netherlands: Misc. Transportation

0.93

0.0047

Germany: Semiconductors

1.00

0.0033

France: Semiconductors

1.02

0.0082

Australia: Misc. Transportation

1.24

0.0000

Conclusions

  • By stripping away the effects of broad markets, we reveal the performance of pure sector factors and their relationships with USD FX.
  • U.S. importers and retailers most consistently benefit from appreciating USD.
  • U.S. commodity producers and information technology exporters most consistently suffer from appreciating USD.
  • The top foreign beneficiaries of these trends are Transportation, Technology, and Auto Parts Sectors.
The information herein is not represented or warranted to be accurate, correct, complete or timely.
Past performance is no guarantee of future results.
Copyright © 2012-2015, 
AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved.
Content may not be republished without express written consent.
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Berkshire’s Energy Investment Skills

Should Investors Follow Buffet out of XOM?

Berkshire Hathaway’s year-end 2014 Form 13F showed the liquidation of the approximately $4 billion Exxon Mobil (XOM) position. This sale has generated considerable discussion. Absent data on Berkshire’s Energy Sector record, the sale is uninformative; we provide this data here.

Investors typically treat all ideas of excellent managers with equal deference. This is usually a mistake – even the most skilled managers are seldom equally skilled in all areas. However, Berkshire Hathaway has excellent track record of security selection in the energy sector. Investors should take note of this particular sale.

Berkshire Hathaway’s Security Selection

The risk-adjusted return of Berkshire’s long equity portfolio, estimated from the firm’s 13F filings, is spectacular. We estimate an approximately 60% cumulative return from security selection (stock picking) over the past 10 years. This is αReturn, a metric of security selection performance – the estimated annual percentage return the portfolio would have generated if markets were flat. Berkshire’s cumulative αReturn is shown in blue in the chart below. For comparison, the group of U.S. hedge funds generated slightly negative long security selection returns over the period (in gray):

Chart of the historical return from security selection (stock picking) of Berkshire Hathaway

Berkshire Hathaway’s Security Selection Return

Berkshire Hathaway’s Energy Security Selection

Berkshire’s risk-adjusted return in the Energy Sector is also excellent, though less consistent. If markets were flat over the past 10 years, the long energy portfolio would have returned over 125% compared to a greater than 10% loss for the average hedge fund:

Chart of the historical risk adjusted return from security selection (stock picking) of Berkshire Hathaway  in the Energy Sector

Berkshire Hathaway’s Energy Security Selection Return

All five energy investments over the past 10 years generated positive residual returns un-attributable to the market. These are the sources of the risk-adjusted returns from security selection:

Return (%)

Symbol Name

Total

Factor

Residual

COP ConocoPhillips

94.54

88.12

6.42

PSX Phillips 66

40.33

13.03

27.29

PTR PetroChina Co. Ltd. Sponsored ADR

187.96

104.42

83.54

SU Suncor Energy Inc.

106.19

94.98

11.21

XOM Exxon Mobil Corporation

60.52

50.65

9.86

Stock picking performance persists. Therefore, the sale of XOM by Berkshire is indeed a negative indicator.

Berkshire Hathaway’s Energy Market Timing

While Berkshire shows significant skill in selecting energy stocks, it does not appear skilled in timing the overall energy market. There is no statistically significant relationship between Berkshire’s exposure to the Energy Factor and the factor’s subsequent return:

Chart of Berkshire Hathaway 's Energy Factor timing: the relationship between energy factor exposure and return

Berkshire Hathaway’s Energy Market Timing

Therefore, Berkshire’s sale of XOM is not a bearish indicator for the overall energy sector.

Summary

  • Managers’ trades are predictive only in areas where the managers display statistically significant investment skills (or lack thereof).
  • Berkshire Hathaway has a strong record of energy security selection (stock picking). Consequently, the sale of XOM is a bearish indicator for this particular stock.
  • Berkshire Hathaway does not have a consistent record of energy market timing. Consequently, the sale of XOM is not an indicator for the sector in general.
The information herein is not represented or warranted to be accurate, correct, complete or timely.
Past performance is no guarantee of future results.
Copyright © 2012-2015, AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved.
Content may not be republished without express written consent.
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Sectors Most Exposed to USD FX

Currencies are major drivers of other assets. In periods of Foreign Exchange (FX) volatility, there is much discussion of its impact on specific equity sectors. Regrettably, market noise obscures true industry-specific performance, so FX impact is impossible to judge from simple index returns. But, by stripping away market effects, we observe relationships between pure sector returns and exchange rates:

  • Oil Drillers have the largest negative correlation with USD and one of the largest negative exposures.
  • Retailers have the highest positive correlation and one of the highest positive exposures.

Below we identify sectors most exposed to USD FX volatility and quantify these relationships.

Pure Sector Performance

As we illustrated earlier, market noise obscures relationships among individual sectors; it also conceals industry-specific performance. Without separating pure industry-specific returns from the market, robust risk management, performance attribution, and investment skill evaluation are impossible. When stripped of market effects, pure sector factors capture sector-specific trends and risks, including sector-specific USD exposures.

Equity Market’s USD FX Exposure

In addition to industry-specific foreign currency exposures, the equity market is significantly correlated with the currency market. Broad macroeconomic risks affect both exchange rates and the equity market. Below we plot U.S. Market returns against USD returns:

Chart of the correlation between USD FX and U.S. Equity Market

USD FX and U.S. Market Return Correlation

The beta of U.S. Equity Market to USD FX is approximately -1.1: Over the past five years, when USD appreciated by 1% relative to a basket of foreign currencies, U.S. Equity Market decreased by approximately 1.1%. USD FX variance explains approximately 38% of U.S. market variance. Perhaps more accurately, 38% of U.S. market variance is due to shared macroeconomic variables that drive both equities and currencies.

The exposure of an individual stock to USD FX is a combination of market, sector, and idiosyncratic effects.

Sectors Most Negatively Exposed to USD FX

Sectors with the highest negative correlation to USD are not surprising:

Chart of the correlation between pure sector factors and USD FX for the sectors most negatively correlated with USD FX

Pure Sector Factors Most Negatively Correlated with USD FX

Sector USD FX Correlation USD FX Correlation
p-value
USD FX Beta USD FX Beta
p-value
Contract Drilling -0.45 0.0002 -1.01 0.0006
Integrated Oil -0.39 0.0011 -0.56 0.0011
Coal -0.36 0.0021 -1.10 0.0004
Oilfield Services Equipment -0.34 0.0042 -0.69 0.0059
Information Technology Services -0.30 0.0109 -0.27 0.0373
Oil and Gas Production -0.27 0.0174 -0.44 0.0131

(Note that we use the Spearman’s rank correlation coefficient to evaluate correlations. Spearman’s correlation is robust against outliers, unlike the commonly used Pearson’s correlation. All correlations are significant; most at a 1% level or better.)

Oil Price USD FX Exposure

Commodity industries’ (oil, coal, etc) exposure to USD FX is due to their macroeconomic sensitivity, inflation sensitivity, and the global nature of the commodity markets. When USD strengthens, USD-denominated commodity prices have to decline in order for broad currency-weighted prices to remain unchanged. Consequently, commodity prices tend to be strongly negatively correlated with USD FX:

Chart of the correlation between historical USD FX returns and Oil Price returns

USD FX and Oil Price Return Correlation

The Oil Price’s beta to USD FX is -1.9: Over the past five years, when USD appreciated by 1% relative to a basket of foreign currencies, the Oil Price decreased by approximately 1.9%. 30% of Oil Price variance is explained by the shared macroeconomic variables that drive both commodity and currency markets.

Information Technology Sector USD FX Exposure

Information Technology Services is a typical export industry that suffers margin compression when USD-denominated costs increase relative to foreign-currency-denominated revenues. However, our analysis indicates this exposure is barely statistically significant with the beta’s p-value of 0.04. This exposure is also low: a 1% increase in USD FX is associated with approximately 0.3% decrease in the value of the sector.

Sectors Most Positively Exposed to USD FX

The list of sectors with the highest positive correlation to USD FX is less intuitive:

Chart of the correlation between USD FX returns and the returns of pure sectors factors most positively correlated with it

Pure Sector Factors Most Positively Correlated with USD FX

Sector USD FX Correlation USD FX Correlation
p-value
USD FX Beta USD FX Beta
p-value
Real Estate Investment Trusts 0.29 0.0121 0.39 0.0101
Pulp and Paper 0.30 0.0102 0.52 0.0123
Aerospace and Defense 0.31 0.0084 0.32 0.0206
Beverages Alcoholic 0.33 0.0049 0.43 0.0025
Catalog Specialty Distribution 0.33 0.0045 0.41 0.0349
Department Stores 0.37 0.0020 0.70 0.0085

The list is dominated by import-sensitive sectors that benefit from a boost in U.S. consumer purchasing power from an appreciating USD.  Also, when the USD appreciates, the associated drop in import prices boosts aerospace and defense companies, likely due to depreciating foreign inputs.

The presence of REITs on the list appears unexpected. Yet, it is due to the same shared variables as the negative correlation between REITs and oil prices: inflation, growth rates, and macroeconomic uncertainty.

Conclusion

  • Industry-specific performance is clouded by market noise.
  • By stripping away the effects of market and macroeconomic variables, we reveal the performance of Pure Sector Factors and their relationships with USD FX.
  • Commodity producers and information technology exporters most consistently suffer from appreciating USD.
  • Importers and retailers most consistently benefit from appreciating USD.
The information herein is not represented or warranted to be accurate, correct, complete or timely.
Past performance is no guarantee of future results.
Copyright © 2012-2015, 
AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved.
Content may not be republished without express written consent.
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Hedge Funds’ Best and Worst Sectors

Due to the congestion of their investor base, crowded hedge fund stocks are volatile and vulnerable to mass selling. The risk-adjusted performance of consensus bets tends to disappoint. In two past pieces we illustrated the toll of crowding on exploration and production as well as internet companies. We also reviewed two specific crowded bets: SanDisk and eHealth.

While crowded hedge fund ideas do poorly most of the time, they don’t always. Market efficiency varies across sectors, and some industries are more analytically tractable than others. In this article we survey the sectors with the best and worst hedge fund performance records. We will illustrate when investors should stay clear of crowded ideas and when they can embrace them.

Analyzing Hedge Fund Performance and Crowding

To explore performance and crowding we analyze hedge fund sector holdings (HF Sector Aggregate) relative to the Sector Market Portfolio (Sector Aggregate). HF Sector Aggregate is position-weighted, and Sector Aggregate is capitalization-weighted. This follows the approach of our earlier articles on aggregate and sector-specific hedge fund crowding.

Hedge Funds’ Worst Sector: Miscellaneous Metals and Mining

Historical Hedge Fund Performance: Miscellaneous Metals and Mining

Hedge funds’ worst security selection performance for the past ten years has been in the Miscellaneous Metals and Mining sector. The figure below plots historical HF Miscellaneous Metals and Mining Aggregate’s return. Factor return is due to systematic (market) risk. It is the return of a portfolio that replicates HF Sector Aggregate’s market risk. The blue area represents positive and the gray area represents negative risk-adjusted returns from security selection (αReturn).

Chart of the historical total, factor, and security selection performance of the Hedge Fund Miscellaneous Metals and Mining Sector Aggregate

Hedge Fund Miscellaneous Metals and Mining Sector Aggregate Historical Performance

Even without adjusting for risk, crowded bets have done poorly. They consistently underperformed the factor portfolio, missing out on over 300% in gains.

The HF Sector Aggregate’s risk-adjusted return from security selection (αReturn) is the return it would have generated if markets were flat – all market effects on performance have been eliminated. This idiosyncratic performance of the crowded portfolio is a decline of 87%. Crowded bets in this sector are especially dangerous, given their persistently poor performance:

Chart of the historical security selection performance of the Hedge Fund Miscellaneous Metals and Mining Sector Aggregate

Hedge Fund Miscellaneous Metals and Mining Sector Aggregate Historical Security Selection Performance

In this sector, hedge funds lost $900 million to other market participants. In commodity industries, the recipients of this value transfer are usually private investors and insiders.

Current Hedge Fund Bets: Miscellaneous Metals and Mining

The following stocks contributed most to the relative residual (security-specific) risk of the HF Miscellaneous Metals and Mining Sector Aggregate as of Q3 2014. Blue bars represent long (overweight) exposures relative to the Sector Aggregate. White bars represent short (underweight) exposures. Bar height represents contribution to relative stock-specific risk:

Chart of the top contributors' contribution to the Hedge Fund Miscellaneous Metals and Mining Sector Aggregate's risk

Crowded Hedge Fund Miscellaneous Metals and Mining Sector Bets

The following table contains detailed data on these crowded bets. Large and illiquid long (overweight) bets are most at risk of volatility, mass liquidation, and underperformance:

Exposure (%) Net Exposure Share of Risk (%)
HF Sector Aggr. Sector Aggr. % $mil Days of Trading
ZINC Horsehead Holding Corp. 72.74 2.41 70.33 148.5 15.6 80.55
SLCA U.S. Silica Holdings, Inc. 0.30 9.68 -9.39 -19.8 -0.2 6.45
LEU Centrus Energy Corp. Class A 4.54 0.22 4.32 9.1 17.2 4.85
SCCO Southern Copper Corporation 7.69 70.19 -62.51 -132.0 -2.3 4.18
CSTE CaesarStone Sdot-Yam Ltd. 0.00 5.18 -5.18 -10.9 -0.8 1.14
MCP Molycorp, Inc. 3.84 0.84 3.01 6.3 1.7 0.92
MTRN Materion Corporation 7.15 1.82 5.33 11.3 2.1 0.69
HCLP Hi-Crush Partners LP 0.49 2.90 -2.41 -5.1 -0.2 0.35
CA:URZ Uranerz Energy Corporation 2.00 0.27 1.72 3.6 11.7 0.29
IPI Intrepid Potash, Inc. 0.36 3.38 -3.02 -6.4 -0.5 0.22
OROE Oro East Mining, Inc. 0.00 0.52 -0.52 -1.1 -39.9 0.05
CANK Cannabis Kinetics Corp. 0.00 0.10 -0.10 -0.2 -2.7 0.05
UEC Uranium Energy Corp. 0.00 0.33 -0.33 -0.7 -0.4 0.02
FCGD First Colombia Gold Corp. 0.00 0.09 -0.09 -0.2 -19.0 0.02
MDMN Medinah Minerals, Inc. 0.00 0.16 -0.16 -0.3 -4.8 0.01
QTMM Quantum Materials Corp. 0.00 0.13 -0.13 -0.3 -6.3 0.00
ENZR Energizer Resources Inc. 0.00 0.12 -0.12 -0.3 -11.7 0.00
AMNL Applied Minerals, Inc. 0.00 0.20 -0.20 -0.4 -18.5 0.00
LBSR Liberty Star Uranium and Metals Corp. 0.00 0.03 -0.03 -0.1 -4.9 0.00
Other Positions 0.61 0.21
Total 100.00

Hedge Funds’ Best Sector: Real Estate Development

Historical Hedge Fund Performance: Real Estate Development

Hedge funds’ best security selection performance has been in the Real Estate Development Sector. The figure below plots the historical return of HF Real Estate Development Aggregate. Factor return and αReturn are defined as above:

Chart of the historical total, factor, and security selection returns of the Hedge Fund Real Estate Development Sector Aggregate

Hedge Fund Real Estate Development Sector Aggregate Historical Performance

Since 2004, the HF Sector Aggregate outperformed the portfolio with equivalent market risk by approximately 200%. In a flat market, HF Sector Aggregate would have gained approximately 180%:

Chart of the historical security selection (residual) return of the Hedge Fund Real Estate Development Sector Aggregate

Hedge Fund Real Estate Development Sector Aggregate Historical Security Selection Performance

In this sector, hedge funds gained $1 billion at the expense of other market participants. The Real Estate Development Sector appears less efficient but tractable, providing hedge funds with consistent stock picking gains.

Current Hedge Fund Real Estate Development Bets

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

Chart of the contribution to the residual (stock-specific) risk of the various hedge fund Crowded Hedge Fund Real Estate Development Sector bets

Crowded Hedge Fund Real Estate Development Sector Bets

The following table contains detailed data on these crowded bets. Since in this sector hedge funds are “smart money,” large long (overweight) bets are most likely to outperform and large short (underweight) bets at most likely to do poorly:

Exposure (%) Net Exposure Share of Risk (%)
HF Sector Aggr. Sector Aggr. % $mil Days of Trading
HHC Howard Hughes Corporation 28.47 15.98 12.49 326.5 17.5 36.73
CBG CBRE Group, Inc. Class A 52.28 26.54 25.74 672.7 10.8 27.58
JLL Jones Lang LaSalle Incorporated 0.14 15.21 -15.07 -393.9 -8.5 12.86
JOE St. Joe Company 0.04 4.94 -4.91 -128.2 -13.5 8.82
ALEX Alexander & Baldwin, Inc. 0.00 4.71 -4.71 -123.2 -13.5 5.38
HTH Hilltop Holdings Inc. 1.35 4.86 -3.51 -91.8 -18.3 4.29
KW Kennedy-Wilson Holdings, Inc. 3.60 6.11 -2.51 -65.6 -7.6 1.19
TRC Tejon Ranch Co. 3.36 1.55 1.81 47.2 37.9 0.77
EACO EACO Corporation 0.00 0.22 -0.22 -5.7 -436.1 0.65
FOR Forestar Group Inc. 0.62 1.66 -1.05 -27.3 -5.3 0.42
FCE.A Forest City Enterprises, Inc. Class A 8.78 10.56 -1.78 -46.5 -1.9 0.35
SBY Silver Bay Realty Trust Corp. 0.07 1.68 -1.61 -42.0 -8.4 0.23
AVHI A V Homes Inc 0.26 0.87 -0.61 -15.8 -28.7 0.20
MLP Maui Land & Pineapple Company, Inc. 0.00 0.29 -0.29 -7.5 -132.0 0.10
CTO Consolidated-Tomoka Land Co. 0.16 0.77 -0.61 -15.9 -24.5 0.09
RDI Reading International, Inc. Class A 0.02 0.54 -0.52 -13.7 -14.2 0.08
ABCP AmBase Corporation 0.00 0.15 -0.15 -3.8 -130.1 0.06
AHH Armada Hoffler Properties, Inc. 0.00 0.59 -0.59 -15.5 -9.4 0.06
OMAG Omagine, Inc. 0.00 0.07 -0.07 -1.9 -24.7 0.05
FVE Five Star Quality Care, Inc. 0.26 0.49 -0.23 -6.1 -5.1 0.04
Other Positions 0.01 0.07
Total 100.00

Real Estate Development is not the only sector where hedge funds excel. Crowded Coal, Hotels, and Forest Product sector ideas have also done well. Skills vary within each sector: The most skilled funds persistently generate returns in excess of the crowd, while the least skilled funds persistently fall short. Performance analytics built on robust risk models help investors and allocators reliably identify each.

Conclusions

  • With proper data, attention to hedge fund crowding prevents “unexpected” volatility and losses.
  • Market efficiency and tractability vary across sectors – crowded hedge fund bets do poorly in most sectors, but do well in some.
  • Investors should avoid crowded ideas in sectors of persistent hedge fund underperformance, such as Miscellaneous Metals and Mining.
  • Investors can embrace crowded ideas in sectors of persistent hedge fund outperformance, such as Real Estate Development.
  • Funds with significant and persistent stock picking skills exist in most sectors, even those with generally poor hedge fund performance. AlphaBetaWorks’ Skill Analytics identify best overall and sector-specific stock pickers.
The information herein is not represented or warranted to be accurate, correct, complete or timely.
Past performance is no guarantee of future results.
Copyright © 2012-2015, AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved.
Content may not be republished without express written consent.
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Hedge Fund Crowding – Q3 2014

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

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

Identifying Crowding

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

Hedge Fund Aggregate Risk

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

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

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

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

Hedge Fund Factor (Systematic) Crowding

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

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

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

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

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

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

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

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

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

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

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

Hedge Fund Residual (Idiosyncratic) Crowding

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

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

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

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

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

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

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

Summary

  • There is both factor (systematic/market) and residual (idiosyncratic/security-specific) crowding of long hedge fund portfolios.
  • Hedge funds have become more crowded and more passive in Q3 2014.
  • The main sources of factor crowding are: Market (higher beta) and Oil.
  • The main sources of residual crowding are: LNG, AGN, VRX, MU, BIDU, and AIG.
  • Our research reveals that, collectively, hedge funds’ long U.S. equity portfolios tend to generate negative risk-adjusted returns. Crowded bets tend to disappoint and hedge fund investors should pay close attention to crowding before allocating capital.
The information herein is not represented or warranted to be accurate, correct, complete or timely.
Past performance is no guarantee of future results.
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