Performance Persistence within Style Boxes

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

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

Measuring Persistence of Returns

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

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

Performance Persistence for all Institutional Portfolios

The Reversion of Nominal Returns

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

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

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

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

The Persistence of Security Selection Returns

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

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

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

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

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

Performance Persistence within Each Style Box

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

Large-Cap Value Portfolio Return Persistence

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

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

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

Large-Cap Growth Portfolio Return Persistence

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

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

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

Small-Cap Value Portfolio Return Persistence

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

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

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

Small-Cap Growth Portfolio Return Persistence

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

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

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

Summary

Nominal returns and related simplistic metrics of investment skill revert: as market regimes change, the top performers tend to become the bottom performers.
Security selection returns, when properly calculated with a robust factor model, persist and yield portfolios that outperform.
Both skill and lack of skill persist, and the lack of skill persists most strongly; while it is important that investors correctly identify the talented managers, it is even more important to divest from their opposites.
The reversion of nominal returns and the persistence of security selection skill hold across all style boxes, but security selection skill is most persistent for small-cap portfolios.

The information herein is not represented or warranted to be accurate, correct, complete or timely.
Past performance is no guarantee of future results.
Copyright © 2012-2016, AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved.
Content may not be republished without express written consent.
U.S. Patents Pending.

Are Momentum ETFs Delivering Momentum Returns?

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

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

Attributing the Performance of Momentum ETFs to Simpler Factors

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

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

iShares MSCI USA Momentum Factor (MTUM): Performance Attribution

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

iShares MSCI USA Momentum Factor (MTUM): Factor Exposures

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

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

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

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

iShares MSCI USA Momentum Factor (MTUM): Active Return

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Conclusion

Theoretical, or academic, momentum portfolios are not directly investable.
A popular momentum ETF, MSCI USA Momentum Factor (MTUM), did not deviate significantly from a passive portfolio of simpler non-momentum factors.
Investors may be able to achieve and surpass the performance of the popular momentum ETFs with transparent, passive, and potentially lower-cost portfolios of simpler index funds and ETFs.

The information herein is not represented or warranted to be accurate, correct, complete or timely.
Past performance is no guarantee of future results.
Copyright © 2012-2016, AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved.
Content may not be republished without express written consent.

Best and Worst Hedge Fund Long Stock Pickers

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Who’s Been Naughty or Nice

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

Ranking Hedge Fund Long Stock Pickers

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

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

Top and Bottom Five Hedge Funds by Stock Picking Return

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Conclusions

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

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

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

Hedge Fund Crowding Update – Q3 2015

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

Identifying Hedge Fund Crowding

This piece follows the approach of our earlier articles on crowding: We created a position-weighted portfolio (HF Aggregate) consisting of the common U.S. equity holdings of all tractable long hedge fund portfolios. We then analyzed HF Aggregate’s risk relative to U.S. Market using the AlphaBetaWorks Statistical Equity Risk Model. The top sources of HF Aggregate’s relative risk are the top sources of hedge fund crowding.

Hedge Fund Aggregate’s Risk

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

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

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

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

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

Hedge Fund Factor (Systematic) Crowding

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

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

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

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

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

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

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

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

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

Hedge Fund U.S. Market Factor Exposure History

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

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

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

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

Hedge Fund Residual (Idiosyncratic) Crowding

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

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

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

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

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

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

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

Summary

The main source of Q3 2015 hedge fund crowding is record Market (Beta) exposure, responsible for more than a third of the hedge fund industry’s relative risk.
The main source of Q3 2015 residual crowding is VRX.
In the current environment, analysis of hedge fund crowding must focus on the factor exposures driving systematic crowding, rather than individual positions.

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

Asset Flows and Hedge Fund Crowding

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The Virtuous and Vicious Cycles of Crowding

Hedge Fund Crowding has cost investors $12 billion in the first 10 months of 2015, and $9 billion in the August-October 2015 rout. That is to say, tractable hedge funds’ long U.S. equity portfolios have suffered severely negative active return from security selection (alpha, or αReturn) this year, and the liquidation has accelerated. Even ignoring fees, had hedge fund investors taken the same risks passively, they would have made $20 billion more since the winds turned in late 2014.

Crowding can be either a good thing or a bad thing: Net flows into crowded names can create positive alpha, typically gradually. However, liquidation of crowded longs is often rapid and painful. Indeed, the latter has been the case since 2014. In the face of these outflows, identifying and avoiding crowded bets is more vital than ever for fund managers, and choosing differentiated managers is more vital than ever for allocators.

Hedge Fund Alpha from Crowding

Below is a chart of the cumulative risk-adjusted return from security selection (alpha, or αReturn) of the AlphaBetaWorks’ Hedge Fund Aggregate, or HF Aggregate (a detailed discussion of crowding and our methodology is at the end of this piece):

Chart of the historical risk-adjusted return from security selection of the Hedge Fund Aggregate Portfolio

Cumulative Return from Security Selection of U.S. Hedge Fund Aggregate

Note the three distinct states of alpha generation for HF Aggregate: positive alpha from 2005 through late 2010, zero alpha (flat) from 2011 to mid-2014, and severely negative alpha from mid-2014 through late-2015. The decline from the 2009-2014 plateau is nearly $23 billion, which makes recent losses nearly 1.5 times greater than gains from the prior nine years.

Several more observations from the chart above are worth discussing:

First, HF Aggregate did not record severe negative alpha in 2008. Not surprisingly, negative nominal hedge fund long returns during this era were systematic, rather than idiosyncratic returns from security selection. However, numerous sectors within HF Aggregate experienced severe liquidations, some of which we have discussed in our prior work.

Second, the severity of the most-recent liquidation of crowded names is stunning. Accumulation is usually gradual. But liquidations tend to gather steam quickly when crowded stocks and hedge funds underperform. The severity of the most recent liquidation of crowded hedge fund names is historically unprecedented.

The key lesson here, for both hedge fund managers and allocators, is to know whether crowding is your friend or enemy. If crowding is your friend, enjoy the virtuous cycle but be wary. If it is your enemy, beware of the vicious cycle but be opportunistic. In individual sectors, forced liquidations do tend to end with mean-reversions: the biggest losers can eventually present attractive opportunities.

Hedge Fund Sector Alpha – Top Gains and Losses

Below are the four best-performing sectors within the HF Aggregate, ranked by dollar returns from security selection (alpha) since Q2 2014:

Chart of the historical risk-adjusted return from security selection of the Hedge Fund Aggregate Portfolio for the Top-Return Sectors

Cumulative Dollar Return from Security Selection of U.S. Hedge Fund Sector Aggregates: Top Sectors

The following table lists the top contributors to the above performance from of our database of thousands of positions spanning over 100 sectors:

Sector	Symbol	Name	αReturn ($ mil.)
Cable and Satellite TV	CHTR	Charter Communications, Inc. Class A	324
Packaged Software	ORCL	Oracle Corporation	262
Cable and Satellite TV	LBTYA	Liberty Global Plc Class A	206
Cable and Satellite TV	TWC	Time Warner Cable Inc.	169
Packaged Software	ADBE	Adobe Systems Incorporated	159
Casinos and Gaming	MGM	MGM Resorts International	98
Packaged Software	ADSK	Autodesk, Inc.	70
Casinos and Gaming	BYD	Boyd Gaming Corporation	66
Casinos and Gaming	LVS	Las Vegas Sands Corp.	57
Cable and Satellite TV	CVC	Cablevision Systems Corporation Class A	57

Below are the four worst-performing sectors within the HF Aggregate, ranked by dollar returns from security selection (alpha) since Q2 2014:

Chart of the historical risk-adjusted return from security selection of the Hedge Fund Aggregate Portfolio for the Bottom-Return Sectors

Cumulative Dollar Return from Security Selection of U.S. Hedge Fund Sector Aggregates: Bottom Sectors

The following are the top contributors to the above performance:

Sector	Symbol	Name	αReturn ($ mil.)
Semiconductors	SUNE	SunEdison, Inc.	(2,100)
Chemicals: Specialty	GB:PAH	Platform Specialty Products Corp.	(688)
Internet Software and Services	GOOGL	Alphabet Inc. Class A	(356)
Chemicals: Specialty	LYB	LyondellBasell Industries NV	(227)
Semiconductors	NXPI	NXP Semiconductors NV	(191)
Oil and Gas Production	WPZ	Williams Partners, L.P.	(176)
Semiconductors	INTC	Intel Corporation	(176)
Oil and Gas Production	CHK	Chesapeake Energy Corporation	(175)
Oil and Gas Production	OXY	Occidental Petroleum Corporation	(132)
Internet Software and Services	RAX	Rackspace Hosting, Inc.	(116)

The first chart above shows positive alpha in four sectors of the HF Aggregate: packaged software, regional banks, casinos & gaming, and cable & satellite TV. This is likely from a combination of manager skill, positive idiosyncratic events, and fund flows. The second chart shows severe negative alpha in oil & gas production, internet software & sales, specialty chemicals, and semiconductors. This also reflects a combination of poor investment skill, negative events, and liquidations in crowded names.

Identifying and Quantifying 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 the U.S. Market (Russell 3000) using AlphaBetaWorks’ Statistical Equity Risk Model. This proven tool for forecasting portfolio risk and performance identified aggregate and sector αReturn, as well as specific sources of crowding. αReturn is the residual portfolio performance and the return it would have generated if markets had been flat.

Without an effective risk model, simplistic crowding analyses ignore the systematic and idiosyncratic exposures of positions and typically merely identify as crowded companies with the largest market capitalizations. Further, since portfolios with no overlap in holdings can have matching factor exposures and can track each other closely, such simplistic analyses overlook factor (systematic) crowding.

For additional clarification of the benefits of robust crowding analyses, we published an article on crowding in the semiconductor industry in September, which highlighted the crowded state of the sector and profiled two particularly crowded stocks that have experienced severe liquidation – Micron (MU) and SunEdison (SUNE).

Conclusions

Recent risk-adjusted returns of crowded hedge fund bets have been horrific. Investors would be wise to step carefully around any crowded names or any funds that traffic in them. Our methodology provides insights into crowding at aggregate market, sector, and stock levels. The ability to identify the sources of crowding with a robust risk model provides fund managers and allocators with vital tools: managers can identify and avoid crowded situations; allocators can enhance their due-diligence by identifying differentiated managers and avoid, or divest from, the undifferentiated.

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.

The Risk Impact of Valeant on Sequoia Fund

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“This is your fund on drugs”

The Sequoia Fund’s (SEQUX) hefty sizing of Valeant Pharmaceuticals (VRX) dramatically changed the fund’s risk profile from historical norms. With the proper tools, allocators would have noticed this style drift back in Q2 2015 when Sequoia’s key factor exposures moved two to three times beyond historical averages. What’s more, allocators would have noticed a predicted volatility increase of 25% and a tracking-error increased 70%. Though this analysis would not have anticipated Valeant’s subsequent decline, it would have warned fund investors that Sequoia’s risk was out of the ordinary.

Sequoia Fund’s Risk Profile

Below is a chart of Sequoia’s major factor exposures, spanning a ten year history through June 2015:

Chart of the exposures of Sequoia Fund (SEQUX) to the risk factors contributing most to its risk

Sequoia Fund (SEQUX) – Historical Factor Exposures

(Note that this analysis and our model do not include Valeant’s recent heightened volatility: we are using the AlphaBetaWorks Statistical Equity Risk Model as of 8/31/15 and SEQUX’s positions as of 6/30/2015. In short, we are looking at the world prior to Valeant’s subsequent downside volatility.)

Sequoia’s stock selection and allocation decisions result in certain factor bets such as market beta (“US and Canada”, above), other factors (Value, Size), and sectors (Consumer, Health). The red dots above represent factor exposures in a particular month, the red boxes represent two quartile deviations, and the diamonds denote current (i.e. 6/30/15) exposures. Several sectors/factors are circled for emphasis: they are current exposures as well as outliers versus history. More importantly, these outlying factor bets are the direct result of Sequoia’s large percentage ownership of Valeant.

The Impact of Valeant on Sequoia Fund’s Factor Exposures

We examined Sequoia Fund’s factor exposures with and without Valeant. We assumed that the pro forma Sequoia Fund without Valeant would have increased all other positions proportionally to make up for the void. For example, we increase Sequoia’s next-largest position (TJX) from 7.3% to 10.9%, and so on for all longs for the pro forma non-Valeant Sequoia portfolio.

Below is a chart comparing the most salient factor exposures of Sequoia Fund, with and without Valeant:

Chart of the exposures of Sequoia Fund (SEQUX) to the risk factors contributing most to its risk including and excluding the position in Valeant (VRX)

Sequoia Fund (SEQUX) – Factor Exposures With and Without Valeant (VRX)

Valeant has had a significant impact on Sequoia’s factor exposures. The factors with the highest delta are the same as those highlighted as outliers on the first chart above.

This is significant in several ways. First, the large Valeant holding increases Sequoia Fund’s overall volatility by 25%. Second, Sequoia’s tracking error is increased by its Valeant holding by 70%. Sequoia Fund volatility estimates with and without Valeant are below:

The main components of Sequoia Fund’s (SEQUX’s) absolute and relative volatility and variance including the position in Valeant (VRX)

Sequoia Fund (SEQUX) with Valeant (VRX) – Absolute and Relative (to S&P 500) Estimated Risk

The main components of Sequoia Fund’s (SEQUX’s) absolute and relative volatility and variance excluding the position in Valeant (VRX)

Sequoia Fund (SEQUX) without Valeant (VRX) – Absolute and Relative (to S&P 500) Estimated Risk

Valeant increases Sequoia’s overall predicted volatility (tracking error) by 26% (from 9.73% to 12.31%, annualized – gold boxes). Likewise, Valeant increases Sequoia’s tracking error by 69% (from 5.19% to 8.76% – brown boxes). Increases in both Absolute and Relative volatility are due to the incremental Residual Risk contribution of Sequoia’s large Valeant holding (graphically shown by the larger blue boxes in the “with VRX” charts, in contrast to smaller blue boxes in the “without VRX” charts).

Conclusions

In the end, this analysis is not about Sequoia or VRX. It is a single example of decisions that could have been avoided by a portfolio manager or questions that would have arisen to an allocator with the proper risk toolkit. Sequoia’s decision to make Valeant an outsized position did not go unnoticed from a risk standpoint. Increases in factor exposures of two to three times outside historical bounds were an early warning. The impact of this was increased predicted volatility – both on an absolute basis and relative to the S&P 500. A framework that warns of a fund taking large factor and idiosyncratic bets aids greatly in avoiding negative surprises.

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

Hedge Fund Crowding Costs: Q3 2015

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Applying a robust risk model to hedge fund holdings data helps avoid losses and yields profitable opportunities. In this article, we highlight the sectors with the largest Hedge Fund losses due to crowding in Q3 2015, which sum to $4 billion. Our methodology provides an early-warning system for losses in crowded names. This analysis also identifies crowded stocks beaten up by hedge fund liquidations, which tend to mean-revert.

Analyzing Hedge Fund Sector Crowding

Our edge comes from a central thesis: the most crowded stocks are those that contribute the most to hedge fund stock-specific volatility (volatility of alpha). Furthermore, the direction of this alpha (positive or negative) is a leading indicator. A robust analysis of the AlphaBetaWorks Statistical Equity Risk Model allows us to identify stocks that are the highest contributors to stock-specific volatility for hedge funds in each sector. These are the most crowded stocks that stand to benefit the most from accumulation and stand to lose the most from liquidation.

While a static crowding analysis using our risk model provides valuable insights, we go further by identifying Hedge Fund Aggregate Sector Alpha – the alpha (stock-specific performance) of aggregated hedge fund portfolios by sector. This makes the analysis dynamic: If Hedge Fund Aggregate Sector Alpha is trending up, capital is flowing into crowded stocks. Conversely, if it is trending down, capital is flowing out of crowded stocks – often abruptly. Yes, crowding is good at some times and bad at others. Further, Hedge Fund Aggregate Sector Alpha trends persist for months and years, providing advanced notice of losses. Importantly, crowded stocks hit hard by liquidations tend to mean-revert: the worst risk-adjusted performers often become attractive long opportunities.

Hedge Fund Sector Aggregates

We create aggregate portfolios of hedge fund positions in each sector. Each such sector portfolio is a Hedge Fund Sector Aggregate within which we identify the highest contributors to security-specific (residual) volatility (the most crowded stocks). This follows the approach of our earlier articles on hedge fund crowding.

The Hedge Fund Sector Aggregate Alpha (αReturn, residual, or security-specific return) measures hedge fund security selection performance in a sector. It is the return HF Sector Aggregate would have generated if markets had been flat. αReturn can indicate accumulations and liquidations.

The AlphaBetaWorks Statistical Equity Risk Model, a proven tool for forecasting portfolio risk and performance, estimated factor exposures and residuals. Without an effective risk model, simplistic crowding analyses ignore the systematic and idiosyncratic exposures of positions and typically merely identify companies with the largest market capitalizations.

Sectors with the Largest Losses from Hedge Fund Crowding

During Q3 2015, hedge funds lost $4 billion to security selection in the five sectors below. Said another way: if hedge funds had simply invested passively with the same risk, their sector long equity portfolios would have made $4 billion more. The monthly losses are listed (in $millions) below:

	7/31/2015	8/31/2015	9/30/2015	Total
Other Consumer Services	-101.16	-113.93	-312.84	-426.77
Oil and Gas Pipelines	472.21	-465.63	-10.29	-475.93
Specialty Chemicals	-155.87	196.41	-730.73	-534.32
Oil Refining and Marketing	262.69	-167.15	-388.52	-555.67
Semiconductors	-240.71	-1,422.70	-660.95	-2,083.65

The Semiconductor Sector was particularly painful for hedge funds in Q3 2015, which we examined in a previous article.

Below we provide our data on three of the above sectors: historical Hedge Fund Sector Alpha and the most crowded names.

Specialty Chemicals – Hedge Fund Alpha and Crowding

Hedge Fund Specialty Chemicals Security Selection Performance

Hedge Fund Specialty Chemicals Sector Aggregate Historical Security Selection Performance

Historical Return from Security Selection of Hedge Fund Specialty Chemicals Sector Aggregate

Hedge Fund Specialty Chemicals Crowding

Chart of the stock-specific hedge fund crowding the cumulative contributors to the residual variance of Hedge Fund Specialty Chemicals Sector Aggregate Portfolio relative to Market

Crowded Hedge Fund Specialty Chemicals Sector Bets

The following table contains detailed data on these crowded holdings:

		Exposure (%)		Net Exposure			Share of Risk (%)
		HF Sector Aggr.	Sector Aggr.	%	$mil	Days of Trading
GB:PAH	Platform Specialty Products Corp.	17.59	2.52	15.07	1,351.8	14.3	44.62
APD	Air Products and Chemicals, Inc.	47.46	13.89	33.57	3,010.8	13.7	22.09
LYB	LyondellBasell Industries NV	3.36	23.03	-19.67	-1,764.2	-5.9	14.04
GRBK	Green Brick Partners, Inc.	2.99	0.25	2.74	245.7	79.7	10.58
GRA	W. R. Grace \& Co.	11.76	3.45	8.32	745.8	11.0	2.99
PX	Praxair, Inc.	0.31	16.29	-15.98	-1,433.5	-5.9	2.21
AXLL	Axiall Corporation	2.79	1.20	1.59	142.8	4.5	0.74
TROX	Tronox Ltd.	1.80	0.45	1.35	121.2	14.2	0.36
ARG	Airgas, Inc.	0.19	3.77	-3.59	-321.8	-4.1	0.33
SIAL	Sigma-Aldrich Corporation	3.32	7.88	-4.56	-408.6	-2.3	0.28
NEU	NewMarket Corporation	0.23	2.61	-2.38	-213.4	-6.0	0.26
VHI	Valhi, Inc.	0.02	0.91	-0.88	-79.2	-240.2	0.26
CYT	Cytec Industries Inc.	0.07	2.04	-1.97	-176.5	-2.0	0.18
ASH	Ashland Inc.	1.66	3.89	-2.23	-200.0	-2.4	0.18
POL	PolyOne Corporation	0.19	1.65	-1.46	-131.2	-4.3	0.10
TANH	Tantech Holdings Ltd.	0.00	0.19	-0.19	-17.3	-2.7	0.09
BCPC	Balchem Corporation	0.00	0.82	-0.82	-73.4	-8.8	0.07
CBM	Cambrex Corporation	0.06	0.65	-0.59	-53.2	-2.1	0.06
CMP	Compass Minerals International, Inc.	0.15	1.31	-1.16	-104.0	-4.8	0.06
…	Other Positions			0.29			0.51
Total							100.00

Oil Refining and Marketing – Hedge Fund Alpha and Crowding

Hedge Fund Oil Refining and Marketing Security Selection Performance

Hedge Fund Oil Refining and Marketing Sector Aggregate Historical Security Selection Performance

Historical Return from Security Selection of Hedge Fund Oil Refining and Marketing Sector Aggregate

Hedge Fund Oil Refining and Marketing Crowding

Chart of the stock-specific hedge fund crowding the cumulative contributors to the residual variance of Hedge Fund Oil Refining and Marketing Sector Aggregate Portfolio relative to Market

Crowded Hedge Fund Oil Refining and Marketing Sector Bets

The following table contains detailed data on these crowded holdings:

		Exposure (%)		Net Exposure			Share of Risk (%)
		HF Sector Aggr.	Sector Aggr.	%	$mil	Days of Trading
MWE	MarkWest Energy Partners, L.P.	18.23	5.31	12.92	848.9	6.1	31.86
VLO	Valero Energy Corporation	0.38	16.06	-15.68	-1,030.4	-2.7	23.34
TSO	Tesoro Corporation	14.32	5.36	8.96	589.0	1.4	12.74
TRGP	Targa Resources Corp.	8.99	2.52	6.47	425.3	8.7	7.76
PSX	Phillips 66	9.21	21.86	-12.66	-831.8	-2.8	6.03
PBF	PBF Energy, Inc. Class A	6.80	1.23	5.56	365.6	7.8	5.84
NGLS	Targa Resources Partners LP	8.74	3.52	5.21	342.7	6.2	2.84
WGP	Western Gas Equity Partners LP	3.58	6.63	-3.05	-200.5	-7.4	2.06
MPC	Marathon Petroleum Corporation	9.59	14.34	-4.75	-312.0	-1.1	1.81
TLLP	Tesoro Logistics LP	5.12	2.33	2.79	183.1	3.5	1.45
HFC	HollyFrontier Corporation	1.29	4.22	-2.93	-192.3	-1.4	1.11
WNR	Western Refining, Inc.	0.21	2.10	-1.89	-124.5	-1.4	0.61
IOC	Interoil Corporation	0.66	1.50	-0.84	-55.3	-6.9	0.49
GEL	Genesis Energy, L.P.	4.35	2.20	2.15	141.1	6.2	0.34
ENBL	Enable Midstream Partners LP	0.39	1.73	-1.34	-88.2	-31.6	0.33
EMES	Emerge Energy Services LP	0.01	0.43	-0.42	-27.6	-6.1	0.29
DK	Delek US Holdings, Inc.	0.00	1.07	-1.07	-70.0	-1.2	0.26
WNRL	Western Refining Logistics, LP	1.57	0.36	1.21	79.5	15.0	0.24
ALJ	Alon USA Energy, Inc.	0.00	0.67	-0.67	-44.1	-2.3	0.18
NS	NuStar Energy L.P.	3.50	2.33	1.17	76.9	1.4	0.15
…	Other Positions			0.07			0.28
Total

Semiconductors – Hedge Fund Alpha and Crowding

Hedge Fund Semiconductor Security Selection Performance

Hedge Fund Semiconductors Sector Aggregate Historical Security Selection Performance

Historical Return from Security Selection of Hedge Fund Semiconductors Sector Aggregate

Given the magnitude of recent semiconductor sector liquidations and the record of mean-reversions, the following crowded hedge fund semiconductor bets may now be especially attractive:

Hedge Fund Semiconductor Crowding

Chart of the stock-specific hedge fund crowding the cumulative contributors to the residual variance of Hedge Fund Semiconductors Sector Aggregate Portfolio relative to Market

Crowded Hedge Fund Semiconductors Sector Bets

The following table contains detailed data on these crowded holdings:

		Exposure (%)		Net Exposure			Share of Risk (%)
		HF Sector Aggr.	Sector Aggr.	%	$mil	Days of Trading
SUNE	SunEdison, Inc.	33.18	1.82	31.36	2,550.9	9.6	86.72
MU	Micron Technology, Inc.	18.87	3.95	14.93	1,214.1	2.9	8.85
INTC	Intel Corporation	3.72	27.94	-24.22	-1,970.2	-1.6	2.01
SEMI	SunEdison Semiconductor, Inc.	3.22	0.14	3.08	250.7	52.5	0.38
SWKS	Skyworks Solutions, Inc.	0.04	3.85	-3.82	-310.4	-0.9	0.38
TXN	Texas Instruments Incorporated	0.09	10.38	-10.28	-836.6	-1.9	0.32
NXPI	NXP Semiconductors NV	7.90	4.41	3.49	283.6	1.0	0.29
AVGO	Avago Technologies Limited	3.29	6.69	-3.40	-276.3	-0.5	0.18
FSL	Freescale Semiconductor Inc	0.02	2.40	-2.38	-193.5	-5.2	0.17
ON	ON Semiconductor Corporation	3.39	0.97	2.42	196.6	4.3	0.08
MLNX	Mellanox Technologies, Ltd.	1.89	0.43	1.45	118.3	0.7	0.08
BRCM	Broadcom Corporation Class A	7.81	5.51	2.30	187.2	0.5	0.07
MX	MagnaChip Semiconductor Corporation	0.92	0.05	0.87	70.9	31.2	0.07
ADI	Analog Devices, Inc.	0.05	3.90	-3.85	-312.9	-1.7	0.06
QRVO	Qorvo, Inc.	1.13	2.32	-1.19	-96.7	-1.1	0.06
NVDA	NVIDIA Corporation	0.58	2.10	-1.51	-123.1	-0.4	0.04
GB:0Q19	CEVA, Inc.	1.25	0.08	1.17	95.5	30.7	0.04
MRVL	Marvell Technology Group Ltd.	0.04	1.32	-1.28	-104.4	-0.9	0.03
MXIM	Maxim Integrated Products, Inc.	0.34	1.90	-1.56	-126.9	-1.7	0.02
MXL	MaxLinear, Inc. Class A	0.74	0.12	0.62	50.6	2.8	0.02
…	Other Positions			0.36			0.13
Total

Conclusions

Data on the crowded names and their alpha can reduce losses and provide profitable investment opportunities.
A robust and predictive equity risk model is necessary to accurately identify hedge fund crowding.
Allocators aware of crowding can gain new insights into portfolio risk, manager skill, and fund differentiation.
Crowded bets tend to mean-revert following liquidation: the worst risk-adjusted performers in a sector become the best.

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.

Mutual Fund Closet Indexing: 2015 Update

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

Closet Indexing Defined

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

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

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

Information Ratio – the Measure of Fund Activity

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

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

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

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

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

Historical Mutual Fund Closet Indexing

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

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

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

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

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

Current Mutual Fund Closet Indexing

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

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

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

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

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

Capital-Weighted Mutual Fund Closet Indexing

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

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

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

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

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

A Map of Mutual Fund Closet Indexing

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

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

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

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

Conclusions

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

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

Hedge Fund Closet Indexing: 2015 Update

1 Reply

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

Hedge Fund Closet Indexing Background

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

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

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

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

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

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

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

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

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

Historical Hedge Fund Closet Indexing

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

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

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

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

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

Estimated Future Hedge Fund Closet Indexing

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

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

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

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

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

Capital-Weighted Hedge Fund Closet Indexing

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

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

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

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

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

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

A Map of Hedge Fund Closet Indexing

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

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

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

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

Conclusions

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

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

Hedge Fund Energy Clustering: Q2 2015

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Fund crowding consists of investment bets shared by groups of funds. Long hedge fund portfolios crowd into clusters with similar systematic (factor) and idiosyncratic (residual) bets. This clustering exists for the aggregate market and for individual sectors; it is the internal structure of hedge fund crowding.

This piece surveys hedge fund clustering in the energy sector and examines the largest hedge fund energy cluster in which:

Factor crowding is due to high exposures to two factors;
Residual crowding is primarily due to six stock-specific bets.

Allocators who are unaware of hedge fund clustering and exposures within these clusters may be paying active fees for high passive risk. These investors will suffer in periods of stress.

Hedge Fund Crowding and Hedge Fund Clustering

Several of our earlier articles on hedge fund crowding analyzed the factor (systematic) and residual (idiosyncratic) bets of HF Aggregate, which consists of the equity holdings of long U.S. hedge fund portfolios tractable from regulatory filings.

Analysis of the overall hedge fund crowding does not address bets shared by fund groups within the aggregate, nor does it consider the crowding within market sectors. To explore this internal structure of hedge fun crowding we pioneered the study of hedge fund clustering. Our 2014 work proved predictive and invaluable to allocators. This piece dives deeper, focusing on hedge fund clustering in the energy sector in Q2 2015.

Hedge Fund Energy Clusters

To explore hedge fund energy clustering we analyze long energy sector portfolios of all hedge funds that are analyzable using regulatory filings. We then exclude funds with insignificant energy holdings. We use the AlphaBetaWorks Statistical Equity Risk Model, a proven tool for forecasting portfolio risk and performance. For each portfolio pair we estimate the future relative volatility (tracking error). The lower the expected relative tracking error between funds, the more similar they are to each other.

Once each hedge fund pair is analyzed we identify groups of funds with like exposures and build clusters (similar to phylogenic trees, or family trees) of the funds’ long portfolios. We use agglomerative hierarchical clustering with estimated future relative tracking error as the metric of differentiation or dissimilarity:

Chart of clustering of U.S. Hedge Funds’ Q2 2015 Long Equity Energy Sector Portfolios

Clusters of U.S. Hedge Funds’ Long Energy Sector Equity Portfolios: Q2 2015

The largest hedge fund energy sector cluster contains approximately 20 funds. Its members share similar systematic and idiosyncratic energy bets that we will now analyze in detail.

The Gateway-San Francisco Sentry Energy Cluster

The largest cluster is the Gateway-San Francisco Sentry Cluster, named after two of its large members with similar long energy bets: Gateway Investment Advisers LLC and San Francisco Sentry Investment Group:

Chart of clustering within the largest cluster of U.S. Hedge Funds’ Q2 2015 Long Equity Energy Sector Portfolios

The Largest Hedge Fund Long Energy Equity Portfolio Cluster: Q2 2015

A flat diagram of the cluster better illustrates the distances (estimated future tracking errors) among its members:

Chart of the flat view of the chart of clustering within the Gateway-San Francisco Sentry cluster of U.S. Hedge Funds’ Q2 2015 Long Energy Equity Portfolios

Flat View of the Gateway-San Francisco Sentry Long Energy Equity Portfolio Cluster: Q2 2015

About two thirds of this cluster’s risk relative to the (Market) Energy Aggregate (a capitalization-weighted portfolio of all U.S. energy stocks) comes from factor exposures:

Source	Volatility (%)	Share of Variance (%)
Factor	4.08	66.93
Residual	2.87	33.07
Total	4.99	100.00

We expect this cluster’s aggregate annual return to differ from the Energy Aggregate’s annual return by more than 5.0% only about a third of the time. We expect its factor (systematic) return to differ from the Energy Aggregate by more than 4.1% about a third of the time. In other words, this cluster will stand out from the Market Energy Portfolio little. When it does, this will be primarily due to high factor (systematic) risk that investors can purchase with cheap passive instruments. In fact, we show below that the cluster largely turns out to be a 1.2x levered version of the Market Energy Aggregate.

Gateway-San Francisco Sentry Energy Cluster’s Factor (Systematic) Crowding

Below are this cluster’s significant factor exposures (in red) relative to the Energy Aggregate (in gray):

Chart of exposures to the risk factors contributing most to the risk of the Gateway-San Francisco Sentry hedge fund long energy sector equity portfolio cluster relative to the U.S. Energy Sector Market Aggregate

Factor Exposures of the Gateway-San Francisco Sentry Energy Portfolio Cluster

Market (high market beta) and Energy (high energy sector beta) exposures are responsible for almost 90% of this cluster’s relative factor risk:

Chart of contributions to the relative factor (systematic) variance of the risk factors contributing most to the risk of the Gateway-San Francisco Sentry hedge fund long energy sector equity portfolio cluster relative to the U.S. Energy Sector Market Aggregate

Factors Contributing Most to Relative Variance of the Gateway-San Francisco Sentry Energy Portfolio Cluster

Factor	Portfolio Relative Exposure (%)	Factor Volatility (%)	Portfolio Relative Factor Variance (%²)	Share of Total Factor Variance (%)
Energy	18.56	12.63	7.83	47.01
Market	20.55	11.16	6.77	40.63
Oil Price	2.31	31.38	2.33	13.98
FX	-3.54	7.71	0.76	4.58
Value	5.97	13.45	0.70	4.19
Size	2.63	8.00	-0.18	-1.06
Bond Index	23.48	3.37	-1.55	-9.32
Other Factors			0.00	0.00
Total			16.66	100.00

Funds in the cluster are currently taking and have tended to take 10-20% more sector risk and market risk than the Energy Aggregate. This is significant crowding towards higher systematic risk: The cluster will outperform when market and energy sector returns are positive simply due to high factor exposures. It will suffer in periods of stress.

Gateway-San Francisco Sentry Energy Cluster’s Residual (Idiosyncratic) Crowding

Six stocks in the Gateway-San Francisco Sentry Portfolio Cluster are responsible for over half of the relative residual risk. This crowding away from the majors (XOM and CVX) with low systematic risk and towards higher-risk independents helps explain high factor exposures:

Chart of contributions to the relative residual (idiosyncratic) variance of the stocks contributing most to the risk of the Gateway-San Francisco Sentry hedge fund long energy sector equity portfolio cluster relative to the U.S. Energy Sector Market Aggregate

Stocks Contributing Most to Relative Residual Variance of the Gateway-San Francisco Sentry Energy Portfolio Cluster

Symbol	Name	Relative Exposure (%)	Residual Volatility (%)	Portfolio Relative Residual Variance (%²)	Share of Total Residual Variance (%)
XOM	Exxon Mobil Corporation	-14.52	14.45	1.31	15.93
WLB	Westmoreland Coal Company	2.21	48.07	0.72	8.78
CHK	Chesapeake Energy Corporation	3.50	37.10	0.67	8.10
TSO	Tesoro Corporation	2.92	36.16	0.61	7.41
NBL	Noble Energy Inc.	4.54	27.04	0.48	5.81
SXCP	SunCoke Energy Partners LP	2.86	25.72	0.37	4.47
SXC	SunCoke Energy Inc.	2.60	36.02	0.37	4.46
VLO	Valero Energy Corporation	-2.27	33.28	0.36	4.35
APC	Anadarko Petroleum Corporation	3.64	26.63	0.32	3.87
HFC	HollyFrontier Corporation	2.09	34.08	0.31	3.80
BBG	Bill Barrett Corporation	1.65	45.92	0.28	3.39
AR	Antero Resources Corporation	2.71	33.79	0.26	3.17
CVX	Chevron Corporation	-6.39	17.84	0.26	3.14
OGZPY	Public Joint-Stock Company Gazprom	1.97	33.89	0.20	2.44
WPZ	Williams Partners L.P.	-2.24	21.12	0.18	2.16
CVI	CVR Energy Inc.	1.14	41.06	0.15	1.84
AHGP	Alliance Holdings GP L.P.	2.07	21.94	0.15	1.76
EOG	EOG Resources Inc.	-2.09	25.87	0.14	1.72
COP	ConocoPhillips	-3.19	21.17	0.13	1.60
FANG	Diamondback Energy Inc.	1.45	35.29	0.09	1.07
…	Other Positions	-4.67		0.88	10.73
Total				8.23	100

Idiosyncratic crowding is not the main problem for investors in the cluster – systematic crowding into higher factor exposures is a bigger challenge: Allocators are at risk of paying high fees for mostly passive factor portfolios with high energy and market exposures.

Summary

An analysis of the underlying structure of hedge fund crowding reveals hedge fund clustering – groups of portfolios with similar bets.
Hedge fund clustering exists across aggregate and sector-specific portfolios.
The largest hedge fund energy cluster’s factor herding is towards high Market (high market beta) and high Energy (high energy sector beta) exposures.
This cluster’s residual herding is away from XOM and towards WLB, CHK, TSO, NBL, and SXCP.
Allocators unaware of their funds’ clustering may be exposed to unexpectedly high systematic risk due to factor crowding, costly in periods of stress.

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