Industry-Specific Security Selection – Paulson & Co

Managers Are Skilled in Specific Areas, Seldom Excellent at Everything

Investors typically treat all ideas of excellent managers with equal deference. This is a mistake. Most skilled managers achieve positive risk-adjusted performance in a few specific areas, and under-perform in others. This article continues the series surveying specific skills of widely-followed investment managers.

Paulson & Co – Long Equity Security Selection

John Paulson’s investment management firm, Paulson & Co., is best-known for the 2007 bet against the subprime mortgage market. The phenomenal success of this bet, generating a reported $15 billion in profit during 2007, earned the firm investor and media following. As a result, many continue to monitor the firm’s equity holdings and invest in its top positions. Putting the famed short-credit bet aside, how well has Paulson & Co selected long equity positions?

We estimate that over the past 10 years, Paulson & Co’s long equity holdings generated approximately 6% cumulative risk-adjusted return from security selection (stock picking). This is Paulson & Co’s 10-year αReturn, a metric of security selection performance – the estimated annual percentage return the fund would have generated in a flat market. αReturn is independent of the market and is a component of total return.

Chart of the Security Selection Return of Paulson & Co Long Equity Portfolio

Paulson & Co Security Selection Return – Long Equity Portfolio

  • Between 2007 and 2011 the firm generated over 35% αReturn. Insights into the subprime crisis benefitted risk-adjusted equity performance during this period.
  • In 2011 most of the firm’s well-publicized losses came from poor market timing. But stock picking also played a role: αReturn was
    -10%. An analysis of Paulson’s market timing is beyond the scope of this article.

Recently, Paulson’s stock-picking performance has been mixed and αReturn flat. While most of the firm’s investments are equities, the large gains that made the firm famous primarily came from fixed income bets. Paulson’s long equity risk-adjusted performance (blue line below) is only slightly above average:

Chart of the Security Selection Return Distribution of Paulson & Co  and Peer Long Equity Portfolios

Paulson & Co and Peers Security Selection Return Distribution – Long Equity Portfolios

Industrials Sector Performance

As we illustrated in our first post in the series, analyzing Greenlight Capital, most funds’ stock picking performance varies from sector to sector.While Paulson’s overall long stock picks have not generated positive risk-adjusted returns, industrials portfolio generated greater than 60% αReturn over the past three years. Said differently, if the market were flat for the past three years, Paulson’s long equity industrials sector picks would have gained over 60%.

Chart of Paulson & Co's Long Industrials Equity Portfolio Security Selection Return

Paulson & Co Security Selection Return – Long Industrials Equity Portfolio

Investors would be wise to pay attention to Paulson’s industrials picks.

Consumer Sector Performance

Investors may want to stay clear of Paulson & Co’s consumer picks. We estimate that if market were flat for the past three years, the consumer sector portfolio would have lost 20%:

Chart of Paulson & Co Long Consumer Equity Portfolio Security Selection Return

Paulson & Co Security Selection Return – Long Consumer Equity Portfolio

Recent Holdings

Paulson & Co’s risk-adjusted performance in the industrial and consumer sectors results from numerous investment decisions over many years and not from a few outliers. The top holdings in the industrial sector, an area of skill, are:

AAL American Airlines Group
DLPH Delphi Automotive
RKT Rock-Tenn Company

Based on Paulson & Co’s performance in this sector, these equities are likely to generate positive risk-adjusted returns in the future and positive returns in a flat market.

Paulson & Co’s top holdings in the consumer sector, an area of weakness, are:

STAY Extended Stay America
TWC Time Warner Cable
HMHC Houghton Mifflin Harcourt Company
MGM MGM Resorts International
RLGY Realogy Holdings

Based on Paulson & Co’s performance in this sector, these holdings are likely to generate negative risk-adjusted returns in the future and negative returns in a flat market.

Conclusions

  • Investors wrongly assume that outstanding investment managers are skilled in all areas.
  • Most managers have areas of skill and areas of weakness.
  • Paulson & Co’s recent industrial sector stock picking has been strong.
  • Paulson & Co’s recent consumer sector stock picking has been weak.
  • Investors following Paulson & Co should favor the firm’s industrial sector stock picks and should be skeptical of the firm’s consumer picks.

The insights above are primarily directed at those who indiscriminately follow the picks of prominent managers. A deep analysis of any company’s fundamentals may lead to different conclusions.

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

 

 

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The “Small-Cap Large-Cap Funds”

Many Large-Cap Funds in Theory Are Small-Cap in Practice

Using the market capitalization of holdings, common in rudimentary forms of style-box analysis, provides an incorrect picture of style and risk for as much as a fifth of large- and mega-cap funds. In practice, these funds have the risk and return profiles of small-cap funds. Misidentifying such “Small-Cap Large-Cap Funds” distorts the risk profile and performance of a fund portfolio. A robust risk model, such as the AlphaBetaWorks Statistical Equity Risk Model, estimates pure equity size risk.

Chart of the Weighted Average Market Cap and Size Risk for U.S. Mutual Funds

Weighted Average Market Cap and Size Risk – U.S. Mutual Funds

Size Risk Defined

The weighted average market cap of holdings is a common, simple, and convenient measurement of portfolio size risk. However, a factor model is preferable due to its stronger predictive power.

AlphaBetaWorks’ Size Factor (ABW Size Factor) is closely related to the Fama–French SMB Factor, but with enhancements: The ABW Size Factor strips out market and sector effects from security returns, revealing pure size risk. By contrast, SMB Factor captures size risk, but it also picks up market beta and sector effects within security returns, since these effects influence the relative performance of small and large cap stocks. This market and sector noise in the SMB Factor makes accurate risk estimation challenging and accurate performance attribution impossible.

The ABW Size Factor strips market and sector effects from security returns, revealing pure size risk. The ABW Size Factor is the difference in returns, net of market and sector effects, between the largest and the smallest stocks. The opposite of the ABW Size Factor is the ABW Small-Cap Factor – the outperformance, net of market and sector effects, of the smallest stocks:

Chart of the Cumulative Return History of U.S. Small-Cap Factor

U.S. Small-Cap Factor Return History

Size Factor and Small-Cap Factor are key drivers of portfolio returns in all markets. The Small-Cap Factor declined by approximately 6% from January through July of 2014. This volatility affected not only small-cap funds but also large-cap funds with small-cap risk (“Small-Cap Large-Cap Funds”).

Size Risk of Individual Stocks

Large-cap companies generally have positive exposure to Size Factor. For example, Abbott Laboratories (ABT), with a $64 billion market cap, has Size Factor exposure of +0.65. If large stocks outperform small stocks by 10% in a flat market, ABT will, on average, return 6.5%:

Chart of the Abbott Laboratories (ABT) Monthly Returns vs U.S. Size Factor Monthly Returns for the Past 5 Years

Abbott Laboratories (ABT) Monthly Returns vs U.S. Size Factor Monthly Returns (2009-2014)

Small-caps generally have negative exposure to Size Factor. For example, Isle of Capri Casinos, Inc. (ISLE), with a $300 million market cap, has Size Factor exposure of -2.47. If large stocks outperform small stocks by 10% in a flat market, ISLE will, on average, decline by 24.7%:

Chart of the Isle of Capri Casinos, Inc. (ISLE) Monthly Returns vs U.S. Size Factor Monthly Returns for the Past 5 Years

Isle of Capri Casinos, Inc. (ISLE) Monthly Returns vs U.S. Size Factor Monthly Returns (2009-2014)

Similar to large cap funds, not all large-cap companies have a positive relationship with Size Factor. If a large-cap company is owned primarily by small-cap investors, has a very small float, or was rapidly elevated to large-cap status, it may retain the risk and behavior of a small-cap. For instance, DexCom, Inc. (DXCM) having appreciated by approximately 500% in a few years, remains a favorite among small-cap traders, retaining its small-cap risk profile. Despite its current $3.5 billion market cap, DXCM has a Size Factor exposure of -2.24. If the largest stocks outperform the smallest stocks by 10% in a flat market, DXCM will, on average, decline by 22.4%:

Chart of the DexCom, Inc. (DXCM) Monthly Returns vs U.S. Size Factor Monthly Returns for the Past 5 Years

DexCom, Inc. (DXCM) Monthly Returns vs U.S. Size Factor Monthly Returns (2009-2014)

Size Risk Impact on Mutual Funds and Hedge Funds

How important is Size Factor in practice? Size Factor exposure accounts for approximately 1.0% of aggregate U.S. mutual fund variance – about the same as the Finance Sector – making Size Factor the third-most important driver of risk and return:

Chart of the Factors Contributing Most to U.S. Mutual Fund Performance

Factors Contributing Most to U.S. Mutual Fund Performance

Among U.S. hedge funds, Size Factor exposure is a more significant source of returns, explaining 1.7% of variance. Only the Market Factor (market beta) is more influential.

Chart of the Factors Contributing Most to U.S. Hedge Fund Performance

Factors Contributing Most to U.S. Hedge Fund Performance

The importance of size risk to hedge funds is one of the consequences of their fondness for “cheap call options,” often small and speculative issues. Mind you, this is aggregate data. For many individual funds size risk is even more important.

Size Risk and Market Cap

Since small-cap companies tend to outperform over the long-term, a large-cap fund can outperform its benchmark by owning large caps that act like small caps (i.e. being short Size). Therefore, we should expect to find large-cap funds trending towards short size exposures. Our observation of approximately 3,000 medium and lower turnover U.S. mutual funds confirms this trend: 20% (342) of 1759 large- and mega-cap funds have Size Factor exposure of small- and micro-cap funds. These funds will tend to act like small-cap funds in the future.

Small-Cap Large-Cap Funds

Below is a list of “large-cap” mutual funds having small-cap risk profiles. These are funds with the largest divergence between their weighted average market caps and their size risk (Size Factor exposure):

Symbol Name Weighted Average Market Cap ($bn) Size Factor Exposure
(% of Equity)
RYOIX Rydex Biotechnology Fund                                                                27.93                        -78.13
FBIOX Fidelity Select Biotechnology Portfolio                                                                30.59                        -66.80
FBDIX Franklin Biotechnology Discovery Fund                                                                35.90                        -64.88
ETNHX Eventide Healthcare & Life Sciences Fund                                                                   3.70                        -79.71
FBTTX Fidelity Advisor Biotechnology Fund                                                                36.02                        -59.77
SCATX RidgeWorth Aggressive Growth Stock Fund                                                                40.68                        -55.00
JAMFX Jacob Internet Fund                                                                68.97                        -49.35
HTECX Hennessy Technology Fund                                                                29.71                        -53.84
INPSX ProFunds Internet Ultrasector Fund                                                                56.34                        -45.69
PRGTX T Rowe Price Global Technology Fund                                                                65.29                        -43.59

For example, the size exposure of PRGTX above (-43.59%) is that of a typical mutual fund with holdings averaging $1 to $5 billion market capitalizations.

The large-cap funds above (and over 300 others we identified) benefit the most from small-cap returns. If misidentified or misunderstood, they and others may contribute to a risk profile that was not intended by the allocator or investor.

Conclusions

  • The market capitalization of holdings, used by the rudimentary forms of style analysis, mischaracterizes the risk of 20% of large-cap mutual funds.
  • Some large-cap funds and stocks have small-cap size risk.
  • The ABW Size Factor estimates pure size risk of securities and portfolios by stripping out all market and sector effects.
  • Since small-caps outperform over the long term, many large-cap funds seek small-cap risk exposures to enhance returns.
  • Investors must monitor the size risk of their funds, to ensure that their portfolios have the expected exposures to small-caps.
Copyright © 2012-2014, AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved.
Content may not be republished without express written consent.

 

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Hidden Bond Exposures in Equity Portfolios

For Many Equity Funds, Bond Risk is More Important than Industry and Style

This year, equity fund investors have been reading – and will soon read more – quarterly letters lamenting volatility and poor performance. The true reasons are rarely identified. Portfolio managers themselves may not fully understand the causes. The hidden bond exposure in equity portfolios is often the culprit.

Hidden Bond Risk in the Equity Market

An equity portfolio with no bond positions is still exposed to the bond market. The relationship between equity and fixed income markets evolves, depending on the macroeconomic environment; it has been significant lately. Over the past five years, 20% of U.S. Equity Market volatility can be explained by a negative correlation to the U.S. Bond Index:

Chart of the Correlation Between U.S. Market Monthly Returns and U.S. Bond Index Monthly Returns for 2009-2014

U.S. Market Monthly Returns vs U.S. Bond Index Monthly Returns (2009-2014)

For example: The 5.4% decline in bond prices explains over a third of the 31% Russell 3000 return in 2013.

Hidden Bond Risk in Small Caps

A less well-appreciated source of additional bond risk is the bond exposure of particular industries and stock types. For example, Size Factor (the difference in returns, net of market and sector effects, between the largest and the smallest stocks) has a significant positive relationship with bonds: Small caps tend to have a negative relationship to bond prices, while the opposite is true for large cap stocks.

Chart of the Correlation Between U.S. Size Factor Market Monthly Returns And U.S. Bond Index Monthly Returns For 2009-2014

U.S. Size Factor Monthly Returns vs U.S. Bond Index Monthly Returns (2009-2014)

In 2013, the smallest U.S. stocks outperformed the largest by 9%. The 5.4% decline in bonds explains over half of this outperformance. In short, if you’re making an allocation to small or large capitalization funds, you’re making an implicit bet on bonds.

This source of bond exposure is captured by AlphaBetaWorksSize Factor exposure (Size beta). This exposure is often overlooked, but a robust equity risk model will identify it.

Company-Specific Bond Risk

Financially levered companies – particularly those with fixed long-term liabilities – have negative exposure to bonds. Any change in interest rates will affect the value of their liabilities and thus their stock prices. For example, Valley National Bancorp (VLY), which is approximately 2.5 times levered, has significant short bond exposure. The statistically observed exposure is -1.4x – almost perfectly in-line with its 150% debt load:

Chart of the Correlation Between Valley National Bancorp (VLY) Monthly Returns And U.S. Bond Index Monthly Returns For 2009-2014

Valley National Bancorp (VLY) Monthly Returns vs U.S. Bond Index Monthly Returns (2009-2014)

Bond performance also captures a number of broad macroeconomic risks: deflation, credit crises, and recessions. Companies that are not financially levered, but are still heavily exposed to these risks, exhibit negative correlation with bonds. For instance, the earnings power of T. Rowe Price Group (TROW) is sensitive to the faith in capital markets, macroeconomic stability, and investor sentiment. TROW and other asset managers tend to have negative bond exposures. Approximately 20% of the volatility of TROW over the past five years, net of market and sector effects, is explained by bond returns:

Chart of the Correlation Between  T. Rowe Price Group (TROW) Monthly Returns And U.S. Bond Index Monthly Returns For 2009-2014

T. Rowe Price Group (TROW) Monthly Returns vs U.S. Bond Index Monthly Returns (2009-2014)

Some businesses own a relatively well-defined stream of long-duration cash flows and are structurally similar to bonds. Most REITs, Royalty Trusts, and MLPs have large and statistically significant long bond exposures. For instance, approximately 22% of the volatility of Education Realty Trust, Inc. (EDR), net of market and sector effects, is explained by bond price changes:

Chart of the Correlation Between Education Realty Trust, Inc. (EDR) Monthly Returns And U.S. Bond Index Monthly Returns For 2009-2014

Education Realty Trust, Inc. (EDR) Monthly Returns vs U.S. Bond Index Monthly Returns (2009-2014)

Mutual Fund and Hedge Fund Volatility Due to Bond Exposure

So how important is bond exposure in practice? The AlphaBetaWorks Performance Analytics Platform regularly analyzes 15 years of portfolio and performance history of approximately 3,000 medium and lower turnover U.S. mutual funds and 400 medium and lower turnover U.S. hedge funds to determine the main sources of risk and return. For hedge funds we analyze long equity portfolios, available from 13F filings, only.

For mutual funds, bond exposure accounted for approximately 0.5% of variance – about an equal contribution to the Value/Growth Factor and the Canadian Market.

Chart of the Contribution of Various Risk Factors to U.S. Mutual Fund Performance

Factors Contributing to U.S. Mutual Fund Performance

For Hedge Funds, bond exposure is a more significant return driver, explaining three times more variance. The Bond Factor is the fourth most important risk factor for long hedge fund portfolios, ahead of Value/Growth, Oil Price, and Technology Sector factors. Only the Market, Finance Sector, and Size factors are more influential to hedge funds.

Chart of the Contribution of Various Risk Factors to U.S. Hedge Fund Performance

Factors Contributing to U.S. Hedge Fund Performance

The importance of bond risk to hedge funds is a natural consequence of their fondness for indebted companies and other “cheap call options,” often levered bets with embedded short bond exposures. Mind you, this is aggregate data. For many hedge funds, bond exposure is the second most important risk, after market exposure.

Mutual Funds Most Exposed to Bonds

Some U.S. mutual funds with the largest bond bets are listed below. These are the bond exposures in addition to market, sector, and style risk – also sources of bond correlation. Many carry embedded bond bets on the same scale as their AUM:

Equity Mutual Funds – Short Bond Exposure

Symbol Name Bond Exposure (%)
LLSCX Longleaf Partners Small Cap Fund -101.41
RYPNX Royce Opportunity Fund -100.33
HRVIX Heartland Value Plus Fund -73.73
HRTVX Heartland Value Fund -70.98
LKSCX LKCM Small Cap Equity Fund -68.44
VTSIX Vanguard Tax Managed Small Cap Fund -60.56
MSSMX Morgan Stanley Instl. Fund-Small Company Growth Portfolio -60.54
WCSTX Waddell & Reed Advisors Science & Technology Fund -57.40
HIASX Hartford Small Company HLS Fund -57.27
RYVPX Royce Value Plus Fund -56.93

Equity Mutual Funds – Long Bond Exposure

Symbol Name Bond Exposure (%)
PRMTX T Rowe Price Media & Telecommunications Fund 26.69
TEDMX Templeton Developing Markets Trust 28.34
HCIEX Hirtle Callaghan International Equity Fund 31.16
WRVBX Waddell & Reed Advisors Vanguard Fund 33.82
MIEIX MFS Institutional International Equity Fund 34.27
OPGSX Oppenheimer Gold & Special Minerals Fund 83.63
CSEIX Cohen & Steers Realty Income Fund 139.67
CSRIX Cohen & Steers Institutional Realty Shares Fund 145.57
CSRSX Cohen & Steers Realty Shares Fund 146.59
TRREX T Rowe Price Real Estate Fund 154.04

Hedge Funds Most Exposed to Bonds

Some U.S. hedge funds with the largest bond bets are listed below. For many of these, bond returns will be the second most important driver of medium-term performance.

Long Equity Hedge Fund Portfolios – Short Bond Exposure

Name Bond Exposure (%)
ESL Investments, Inc. -98.81
Harbinger Capital Partners LLC -86.64
Starboard Value LP -82.37
Lakewood Capital Management LP -78.32
Paradigm Capital Management, Inc. -76.12
Basswood Capital Management LLC -68.92
Rima Senvest Management LLC -68.81
Fine Capital Partners LP -65.84
Palo Alto Investors LLC -52.35
Greenlight Capital, Inc. -48.67

Long Equity Hedge Fund Portfolios – Long Bond Exposure

Name Bond Exposure (%)
Cushing MLP Asset Management LP 42.71
Bridgewater Associates LP 42.99
Energy Income Partners LLC 45.24
Baker Bros. Advisors LP 47.29
Kayne Anderson Capital Advisors LP 48.13
SCS Capital Management LLC 49.55
Harvest Fund Advisors LLC 58.20
Center Coast Capital Advisors LP 58.92
H Partners Management LLC 80.39
Atlantic Investment Management, Inc. 81.61

Conclusions

  • Hidden bond exposures in equity portfolios are often overlooked by professional investors.
  • Market, style, and industry risk factors influence bond exposures in equity portfolios.
  • Small capitalization stocks and funds tend to have negative bond exposures.
  • Some equity securities are significantly exposed to bonds, even after accounting for market and sector risks.
  • For many equity funds, bond exposure is the second most important source of risk and return.
Copyright © 2012-2014, AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved.
Content may not be republished without express written consent.
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Industry-Specific Security Selection – Greenlight Capital

Managers Are Skilled in Specific Areas, Seldom Excellent at Everything

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. Skilled managers may derive all of their risk-adjusted performance from a few specific areas, and under-perform in others.

Greenlight Capital – Long Equity Security Selection

David Einhorn’s Greenlight Capital is one of the best-known value hedge funds. The long-term performance of the fund has attracted a media and investor following. Many analysts advocate buying a stock solely because it has been purchased by Greenlight Capital; many investors do.

The risk-adjusted return of Greenlight Capital’s long equity portfolio, estimated from the firm’s 13F filings, is indeed excellent. We estimate a 20% cumulative return from security selection (stock picking) over the past 10 years. This is Greenlight’s αReturn, a metric of security selection performance – the estimated annual percentage return a fund would have generated in a flat market:

Chart of the Security Selection Return of the Long Equity Portfolio of Greenlight Capital

Greenlight Capital Security Selection Return – Long Equity Portfolio

By contrast, the long portfolios of medium turnover U.S. hedge funds generated negative security selection return (αReturn) over the same period. If the market had been flat for the past 10 years, the average fund would have lost money. Greenlight Capital’s αReturn topped two thirds of its peers’:

Chart of the Distribution of Security Selection Returns of Medium Turnover U.S. Mutual Funds' and Greenlight Capital's Long Equity Portfolios

Greenlight Capital and Peers Security Selection Return Distribution – Long Equity Portfolio

Technology Sector Performance

Was Greenlight Capital equally effective selecting securities in all sectors? Or should investors be wary of some ideas, in spite of the outstanding overall security selection record?

It turns out that the αReturn of the overall fund came primarily from a single sector – Technology:

Chart of the Technology Sector Security Selection Return of Greenlight Capital's Long Equity Portfolio

Greenlight Capital Technology Sector Security Selection Return Contribution – Long Equity Portfolio

Viewed in isolation, the technology portfolio generated over 100% αReturn (risk-adjusted return from stock picking). If markets had been flat for the past 10 years, Greenlight Capital’s long technology portfolio would have generated over 100%:

Chart of the Security Selection Return of Greenlight Capital's  Long Technology Equity Portfolio

Greenlight Capital Security Selection Return – Long Technology Equity Portfolio

Non-Technology Sector Performance

Since the entire 10-year αReturn of Greenlight Capital came from its technology positions, other sectors contributed zero αReturn.

Therefore, investors should not treat all picks, positions, and ideas of the fund equally. For instance, investors may want to stay clear of the fund’s healthcare picks. We estimate that if markets had been flat for the past 10 years, Greenlight’s long healthcare equity portfolio would have lost over 40%:

Chart of the Security Selection Return of Greenlight Capital's Long Healthcare Equity Portfolio

Greenlight Capital Security Selection Return – Long Healthcare Equity Portfolio

Investors usually treat all ideas of excellent managers as equally excellent. This is a mistake. Even the most skilled stock pickers are rarely equally skilled in all industries. Often, their picks in some industries predictably underperform.

Current Holdings

Greenlight’s performance in these sectors results from a number of positions over time and not from a few outliers.  Their top current holdings in the technology sector, an area of exceptional skill, are:

MU Micron Technology, Inc.
MRVL Marvell Technology Group Ltd.
AAPL Apple Inc.
SUNE SunEdison, Inc.
EMC EMC Corporation

Based on Greenlight’s risk-adjusted performance in this sector, these equities are likely to generate positive risk-adjusted returns in the future.

Greenlight’s holdings in the health sector, an area of weakness, are:

CI Cigna Corpora
AET Aetna Inc.
XON Intrexon Corporation

Based on Greenlight’s risk-adjusted performance in this sector, these holdings are unlikely to match the risk-adjusted performance of the rest of the portfolio and may generate negative returns in a flat market.

Conclusions

  • Investors wrongly assume that outstanding investment managers are skilled in all areas.
  • Investment managers are rarely skilled in all areas.
  • Most skilled managers derive the bulk of their risk-adjusted returns from a few specific areas of skill.
  • Most skilled managers have areas of weakness.
  • Even highly skilled managers may have areas where they can be predicted to generate negative risk-adjusted returns.
Copyright © 2012-2014, 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 Closet Indexing

Fee Harvesting is a Problem for All Asset Classes

To generate active returns in excess of its fees, an active fund must take some active risk. However, some managers passively manage their funds but charge active fees. Others become less active as they accumulate assets. This problem of closet indexing is not confined to mutual funds. Over a third of the long capital of U.S. hedge funds is invested too passively to warrant a typical 1.5/15% fee structure, even if the funds’ managers are highly skilled. Investors could replace closet indexers with passive vehicles or truly active skilled managers and improve performance.

Closet Indexing Background

Two of our earlier articles explored past and current mutual fund closet indexing:

One article analyzed historical risk and performance of U.S. mutual funds.  It discovered that over a quarter (26%) of the funds have been so passive that, even after exceeding the information ratios of 90% of their peers, they would still not be worth the 1% mean management fee.

The other article addresses current risk and predicted volatility of U.S. mutual funds. It found that over two thirds (70%) of their capital is currently taking so little active risk that it will fail to merit the 1% mean management fee, even if the funds’ managers are highly skilled.

The current article surveys long portfolios of hedge funds. We analyze current and historical long positions of approximately 300 concentrated medium and lower turnover U.S. hedge funds, identifying those that are unlikely to earn their fees in the future given their current active risk. We then quantify the problem of closet indexing for a typical hedge fund investor.

How Much Active Risk is Needed 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 IR’s of 0.54 and higher; 90% achieve IR’s below 0.54:

Chart of the Distribution of Information Ratios of  Long Portfolios of U.S. Hedge Funds

U.S. Hedge Fund Information Ratio Distribution – Long Positions

If a fund’s long portfolio exceeds the performance of 90% of the peers and achieves the IR of 0.54, then it needs tracking error above 1.85% to generate active return above 1%.

What active return will cover a typical fee? We make conservative assumptions that funds’ long equity portfolios are burdened with 1.5% management fee and 15% incentive allocation. Assuming 7% expected market return, the mean fee is 2.55%.

If all funds were able to achieve the 90th percentile of IR, they will need annual tracking error above 4.7% to earn this estimated mean fee and generate a positive net active return.

Hedge Fund Active Risk

Tracking error is due to active risks a fund takes: security selection risk due to stock picking and market timing risk due to variation in factors bets. We applied the AlphaBetaWorks Statistical Equity Risk Model to funds’ historical and latest holdings and estimated their historical and future tracking errors. Tracking errors were calculated relative to the fund-specific benchmarks that represent each fund’s unique passive risk profile.

Over a tenth (33) of the funds have such low historical tracking errors that, even if they exceeded the performance of 90% of their peers, they would have failed to merit the 2.55% estimated mean fee:

Chart of the Distribution of Historical Tracking Errors of Long Portfolios of U.S. Hedge Funds

U.S. Hedge Fund Historical Tracking Error Distribution – Long Positions

Over a fifth (61) of the funds have such low estimated future tracking errors that, even if they exceed the performance of 90% of their peers, they will fail to merit the 2.55% estimated mean fee:

Chart of the Distribution of Estimated Future Tracking Errors of Long Portfolios of U.S. Hedge Funds

U.S. Hedge Fund Estimated Future Tracking Error Distribution – Long Positions

While there is less closet indexing among hedge funds than among mutual funds, the fees that hedge funds charge are significantly higher — to say nothing of the higher expectations that these higher fees warrant.  When practiced by hedge funds, closet indexing is all the more egregious.

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 4.7%, they represent nearly 40% of the assets ($207 billion out of the $391 billion total in our sample). Therefore, more than a third of hedge fund long capital will not earn the 2.55% estimated mean fee, even when the managers are skilled.

Chart of the Distribution of Capital Estimated Future Capital-Weighted Tracking Error of Long U.S. Hedge Fund Capital

U.S. Hedge Fund Capital Estimated Future Tracking Error Distribution – Long Positions

The assumption of all funds exceeding historical IR’s of 90% of their peers is unrealistic. In practice, a portfolio of large hedge funds, built without attention to closet indexing, may be doomed to generate negative active returns, regardless 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 more.

A Map of Hedge Fund Skill and Activity

Our previous article discussed the evolution of skilled managers’ utility curves as an explanation for their reluctance to take risk. As a manager accumulates assets, fee harvesting becomes increasingly attractive. The map of U.S. hedge fund active management skill and activity below, illustrates that large skilled funds tend to be relatively less active:

Chart Showing the Distribution of U.S. Hedge Fund Active Management Skill and Activity for Long Positions.

U.S. Hedge Fund Active Management Skill and Activity – Long Positions

Conclusions

  • 20% of long U.S. hedge fund portfolios surveyed are currently so passive that, even after exceeding the information ratios of 90% of their peers, they will still fail to merit a typical fee.
  • 39% of long U.S. hedge fund capital surveyed will fail to merit a typical fee, even if its managers are highly skilled.
  • Investors must monitor the evolution of their hedge fund managers towards closet indexing and mitigate fee harvesting.
  • A typical investor may be able to replace over a third of long hedge fund capital with passive vehicles or active skilled managers, improving performance.
Copyright © 2012-2014, AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved.
Content may not be republished without express written consent.
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Mutual Fund Closet Indexing – Part 3

Why Most Investors Lose, Even if Their Manager is Skilled

An actively managed fund must take risk sufficient to generate active returns in excess of the fees that it charges. However, as skilled managers accumulate assets, they tend to become less active. Skilled managers who took sufficient active risk to earn their fees in the past may be closet indexing today. Consequently, over two thirds of the capital invested in “active” U.S. mutual funds is allocated to managers who are unlikely to earn the average fee, even if highly skilled. Simply by identifying these managers, investors can eliminate most active management fees and improve portfolio performance. 

Closet Indexing Defined

Our first article in this series discussed closet indexing and proposed a new metric of fund activity: Active Share of Variance  the share of volatility due to active management (security selection and market timing). The second article analyzed historical performance of U.S. mutual funds and discovered that over a quarter (26%) of the funds surveyed have been so passive that, even after exceeding the information ratios of 90% of their peers, they would still not be worth the 1% mean management fee.

Too Little Current Risk to Earn Future Fees

Thus far, our analysis improved on the existing closet indexing metrics by evaluating past fund activity. The shortcoming of this analysis has been its failure to identify funds that have been active in the past but are closet indexing today. This article addresses the shortcoming: We analyze current and historical positions of approximately 1,700 non-index medium and lower turnover U.S. mutual funds, identifying those that are unlikely to earn their management fees in the future given their current active risk.

The Information Ratio (IR) is a measure of active return relative to active risk (tracking error). The top 10% of the funds achieve IR’s greater than or equal to 0.30; 90% achieve IR’s below 0.30:

Chart of the Distribution of Information Ratios for U.S. Mutual Funds

U.S. Mutual Fund Information Ratio Distribution

If a fund exceeds the performance of 90% of the peers and achieves IR of 0.30, then it needs tracking error above 3.3% to generate active return above 1%. The mean expense ratio for active U.S. mutual funds is approximately 1%. Therefore, if all funds were able to achieve the 90th percentile of IR, they will need annual tracking error above 3.3% to earn the mean fee and generate a positive net active return.

Tracking error is due to active risks a fund takes: security selection risk due to stock picking and market timing risk due to variation in factors bets. We applied the AlphaBetaWorks Statistical Equity Risk Model to funds’ historical and latest holdings and estimated their future tracking errors.

Over half (911) of the funds have such low estimated future tracking errors that, even if they exceeded the performance of 90% of their peers and achieved the IR of 0.30, they will fail to merit the 1% mean management fee:

Chart of the Distribution of Estimated Future Tracking Errors for U.S. Mutual Funds

U.S. Mutual Fund Estimated Future Tracking Error Distribution

Capital-Weighted Closet Indexing

Larger mutual funds are more likely to engage in closet indexing. While only 54% of mutual funds surveyed have estimated future tracking errors below 3.3%, they represent 70% of the assets ($2.4 trillion out of the $3.4 trillion total). Therefore, even if capital is invested with highly skilled managers, more than two thirds of it will not earn the 1% mean management fee:

Chart of the Distribution of Estimated Future Tracking Error of the Capital Invested in U.S. Mutual Funds

U.S. Mutual Fund Capital Estimated Future Tracking Error Distribution

A portfolio that primarily consists of large mutual funds may be doomed to generate negative active returns, regardless of the managers’ skills. The 1% management fee cited here is the mean. Plenty of closet indexers charge more and plenty of investors who remain with them stand to lose more.

Conclusions

  • Over half (54%) of active U.S. mutual funds surveyed are currently so passive that, even after exceeding the information ratios of 90% of their peers, they will still fail to merit a 1% management fee.
  • Over two thirds (70%) of active U.S. mutual fund capital surveyed will fail to merit a 1% management fee, even if its managers are highly skilled.
  • Skilled active managers do exist, but investors need to capture them early in their life cycles.
  • Investors must monitor the evolution of their skilled managers towards passivity.
  • By identifying closet indexers, a typical investor can eliminate most active fees and improve performance.
Copyright © 2012-2014, AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved.
Content may not be republished without express written consent.
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Mutual Fund Closet Indexing – Part 2

Can a Fund Earn Its Fees if It Does Not Try?

To be worth the fees it charges, an actively managed fund must take some active risk, rather than merely mirror passive market exposures. However, over a quarter of “active” medium and lower turnover US mutual funds take so little active risk, they are unlikely to earn their management fees. In this article, we build on our earlier work and estimate the risk an active fund must take in order to earn the 1% mean management fee. Simply by testing for funds that are taking too little risk to generate positive net active returns, investors can save billions in fees each year. 

Closet Indexing Defined

Our earlier article discussed closet indexing and proposed a new metric of fund activity: Active Share of Variance – theshare of volatility due to active management (security selection and market timing). This analytic relies on the factor analysis of historical holdings and is immune to the issues with holdings-based analysis and the issues with returns-based analysis that affect the popular closet indexing tests: Active Share and . This article uses the AlphaBetaWorks Performance Analytics Platform to objectively evaluate the level of fund activity necessary to earn a typical management fee.

Too Little Risk to Make a Difference

Is it possible for a highly skilled manager to take too little risk to earn management fees?

We surveyed 10 years of US filings history of approximately 1,700 non-index medium and lower turnover mutual funds with at least 5 years of filings. This group holds over $3.4 trillion in assets.

We applied the AlphaBetaWorks Statistical Equity Risk Model to funds’ historical holdings to estimate risk at the end of each month. We then attributed the following month’s returns to factor(market) and residual(security-specific) sources, estimated the appropriate factor benchmark, and calculated market timing returns due to variations in factor exposures.

The Information Ratio (IR) is a measure of active return relative to active risk (tracking error). The AlphaBetaWorks Performance Analytics Platform calculated historical (realized) IRs for all funds in the group. The 90th percentile of IR for the group is 0.30, which suggests that 90% of funds needed a tracking error above 3.3% to generate an active return above 1%:

US Mutual Fund Information Ratio Distribution

US Mutual Fund Information Ratio Distribution

Knowing that the mean expense ratio for active US mutual funds is approximately 1.0%, if all funds were able to achieve the 90th percentile of IR, they would need annual tracking error over 3.3% to generate a positive net active return. Over a quarter (445) of the funds in our survey realized tracking errors below this threshold; they have been so passive that, even assuming an IR of 0.30, they would have failed to generate 1% gross and 0% net active returns:

US Mutual Fund Tracking Error Distribution

US Mutual Fund Tracking Error Distribution

A Map of US Mutual Fund Skill and Activity

The evolution of skilled managers’ utility curves is one possible explanation for this reluctance to take risk. Perhaps, as a manager accumulates assets, fee harvesting becomes increasingly attractive. The map of fund active management skill and activity, included below, supports this hypothesis: Large skilled funds tend to be relatively less active. In fact, all the funds in the active and skilled (“Hungry”) group are relatively small:

US Mutual Fund Active Management Skill and Activity

US Mutual Fund Active Management Skill and Activity

Conclusions

  • Over a quarter (26%) of US mutual funds surveyed have been so passive that, even after exceeding the information ratios of 90% of their peers, they would still fail to merit a 1% management fee.
  • Large skilled funds tend to be relatively more passive.
  • Skilled active managers exist, but investors need to capture them early in their life cycles.
  • For this group alone, by identifying funds that take too little risk to generate positive active returns, investors could save between $4 and $10 billion in annual management fees.

Thus far, our work improves on the existing closet indexing metrics by evaluating past fund activity. In subsequent articles we will use the AlphaBetaWorks Performance Analytics Platform to analyze current risk and closet indexing, identifying those funds that are unlikely to earn their management fees in the future.

Copyright © 2012-2014, AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved.
Content may not be republished without express written consent.
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Sector Performance – First Half 2014

Separating the Signal from the Noise

Market noise obscures the true relationships among individual sectors and true industry-specific performance. By stripping away market and broad macroeconomic effects, we can derive the returns of pure sector factors. Without proper analysis of these factors, accurate risk management, performance attribution, and manager skill evaluation are impossible. Investors oblivious to trends in pure sector returns may be blindsided by secular trends.

Pure Sector Performance

Market performance is the most important factor influencing the returns of most securities, indices, and sectors. Hence, it is generally impossible to assess the true relative performance of two sectors without first removing market effects. For example, a simple analysis of US Healthcare and Industrial Sector index returns suggests that they are highly, and positively, correlated:

US Industrial and Healthcare Sector Return Correlation

US Industrial and Healthcare Sector Return Correlation

However, this positive relationship is entirely due to the shared market component of their returns. When these returns are stripped of market noise, a negative relationship emerges:

US Industrial and Healthcare Sector Return Correlation, Net of Market Effects

US Industrial and Healthcare Sector Return Correlation, Net of Market Effects

A simple analysis of sector index performance is often misleading: A portfolio manager may conclude that, given the high correlation between healthcare and industrials indices, one is a good hedge for the risk of the other. In reality, though their overall returns look similar, their industry-specific returns are negatively correlated. A risk model that cannot tell whether the relationship between sectors is positive or negative will produce erroneous estimates of risk and yield suboptimal portfolios.

The underlying differences in sector performance emerge once market and other macroeconomic effects are removed. Accordingly, pure sector returns provide insights into relative performance, trends, and fundamentals.

Best and Worst Performing Sectors: First Half of 2014

Below are two charts showing YTD performance in the best- and worst-performing sectors.  The first is the performance without removing market and macroeconomic noise:

Sector Index Returns, YTD

Sector Index Returns, YTD

Below are the best and worst performing sectors in the first half of 2014, after market and macroeconomic noise is removed:

Pure Sector Returns, Net of Market Effects, YTD

Pure Sector Returns, Net of Market Effects, YTD

Electronics Appliance Stores -31%
Internet Retail -24%
Forest Products -20%
Commercial Printing Forms -16%
Steel -16%
Pharmaceuticals Generic +18%
Airlines +19%
Beverages Alcoholic +21%
Pharmaceuticals Other +22%
Aluminum +23%

Best and Worst Performing Sectors: Trailing Five Years

The above differences in short-term performance between sector indices and pure sector factors may appear minor; they are far more dramatic over the long term. All but one sector index have generated positive returns over the past five years (including market performance):

Sector Index Returns, Trailing Five Years

Sector Index Returns, Trailing Five Years

The chart below, with market and macroeconomic noise removed, tells a different story. The worst-performing pure sector factors have lost over 50%. The industry-specific returns of the top performers are also substantially lower. Market tide lifted all industry boats, obscuring their intrinsic performance:

Pure Sector Returns, Net of Market Effects, Trailing Five Years

Pure Sector Returns, Net of Market Effects, Trailing Five Years

Steel -74%
Aluminum -70%
Coal -70%
Electronics Appliance Stores -70%
Consumer Sundries -69%
Airlines +100%
Movies Entertainment +102%
Food Specialty Candy +111%
Oil And Gas Pipelines +121%
Pharmaceuticals Generic +242%

Best Long-Term Performance: Generic Pharmaceuticals

The Generic Pharmaceuticals Sector had the best 5-year performance and remains one of the top performers in 2014. The uptrend started in 2008, continued through the market turmoil, and has persisted for over six years:

Generic Pharmaceuticals Pure Sector Return

Generic Pharmaceuticals Pure Sector Return

Worst Long-Term Performance: Steel

The Steel Producers Sector had the worst 5-year performance and remains among the worst performers in 2014. This time series tells a story of broad economic and geopolitical trends: industry overcapacity, the Chinese commodity boom, and uneconomic over-investment, precipitating a return to overcapacity:

Steel Producers Pure Sector Return

Steel Producers Pure Sector Return

This story is not visible in a simple chart of sector index performance. Obscured by market noise, the clean trends of pure sector returns disappear:

Chart of the Cumulative Return of the Steel Producers Sector Index

Steel Producers Sector Index Return

Conclusions

  • Industry-specific performance is clouded by market noise.
  • By stripping away the effects of market and macroeconomic variables, we can calculate the returns of pure sector factors.
  • Pure sector factors exhibit strong trends and are valuable indicators for tactical portfolio allocation.
  • Pure sector factors are required for robust risk management, performance attribution, and investment skill evaluation.
  • Failure to isolate pure sector factors leads to flawed risk analysis, performance attribution, and skill evaluation.

While we alluded to the trends in sector factor returns, we have not yet provided hard evidence that historically top-performing sector factors continue to outperform. Our upcoming articles on sector factors will address their return persistence.

Copyright © 2012-2014, AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved.
Content may not be republished without express written consent.

 

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Mutual Fund Closet Indexing – Part 1

Are you Paying Active Fees for Passive Management?

Closet indexing may be practiced by 20% to 50% of “active” medium and lower turnover US mutual funds. To make this case, we improve on traditional holdings- and returns-based closet indexing metrics. Simply by testing for closet indexing, investors can save billions in management fees each year.

Closet Indexing

A 2009 study introduced the concept of Active Share to measure the degree to which a fund is actively managed, showing that active stock pickers outperform closet indexers. Another notable active management metric is with respect to a multifactor model.  Both metrics have their drawbacks:  Active Share relies on vulnerable holdings-based analysis. For example, suppose a manager holds a position in a benchmark ETF; this position will increase Active Share without making the fund more active. R² relies on returns-based analysis, which does not distinguish between passive factor exposures and market timing, among other limitations. Hence, both metrics are susceptible to manipulation and may not properly identify passive funds.

A Robust Approach

The AlphaBetaWorks approach relies on risk and attribution capabilities free of the above issues. We surveyed ten years of historical holdings of approximately 1,700 non-index medium and lower turnover mutual funds with at least 5 years of position history. This group manages over $3.4 trillion in assets. We used our proprietary Statistical Equity Risk Model on historical holdings to estimate fund risk at the end of each month. We then attributed the following month’s returns to factor (market), and residual (security-specific) sources. We compared the variance of residual and factor returns of the group to NASDX, a NASDAQ 100 ETF. We selected this ETF as a passive reference strategy that is more concentrated than broad market benchmarks but less concentrated than granular sector indices. Using an S&P 100 ETF yields similar conclusions.

For NASDX, factor exposures at the end of a given month explain approximately 95% of return variance in the following month. The remaining 5% residual variance is due to security selection.

How active are funds in our group compared to this 100-position ETF? A quarter (425 funds) had a lower share of residual variance – they were less active stock pickers:

Security Selection Share of Historical US Mutual Fund Variance

Security Selection Share of Historical US Mutual Fund Variance

Security selection is not the only source of active performance. The variation in factor exposures, or factor (market) timing, is another. The NASDAX’s historical factor exposures were not constant over the last 10 years. This factor exposure variation is responsible for 4.1% of monthly return variance. Eighty percent (1,354 funds) had been less active market timers:

Market Timing Share of Historical US Mutual Fund Variance

Market Timing Share of Historical US Mutual Fund Variance

Active returns consist of both security selection and market timing. In the case of NASDX, 93.3% of its 10-year monthly return variance can be explained by passive factor bets, while 6.7% is active. We call the latter figure the Active Share of Variance.

Using the F test to evaluate whether historical Active Share of Variance of a given fund exceeded that of NASDX, we produced a confidence level that a fund has been more active than the ETF. The following chart illustrates the confidence that each fund in our group has been active and shows Historical Active Shares of Variance due to security selection and market timing:

Distribution of Historical Active Share of Variance and Confidence that a Fund is Active

Distribution of Historical Active Share of Variance and Confidence that a Fund is Active

At the 95% confidence level, fewer than half of the funds (737) have been more active than the ETF; more than a fifth (350) have been less active. The test is inconclusive for the remaining 36% (602 funds), suggesting that investors can’t be certain that over half the “active” mutual funds surveyed are in fact active:

Distribution of Confidence that a Fund is Active

Distribution of Confidence that a Fund is Active

Conclusions

  • Over half (56%) of active US mutual funds surveyed are not significantly more active than a 100-position NASDX ETF.
  • Less than half (44%) of the funds are significantly more active that the ETF.
  • More than a fifth (21%) of the funds are significantly less active than the ETF.
  • Investors must be sure that they are not being charged active fees for passive management.
  • By identifying closet indexers within this group alone, investors could save between $4 and $15 billion in annual management fees.

This work improves on traditional holdings-based and returns-based analyses of fund activity. Admittedly, we use a subjective benchmark for activity – a specific passive ETF. In later notes we will use the AlphaBetaWorks Performance Analytics Platform to evaluate activity levels using objective metrics.

Copyright © 2012-2014, AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved.
Content may not be republished without express written consent.

 

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Three Holdings-Based Style Analysis Tests

Or How to Tell if You Are Paying Top Dollar for a Flawed System

This article is part of an ongoing series exploring flaws in popular investment risk and skill evaluation techniques. We focus on the most common pitfalls that have been particularly costly for asset managers and fund investors over the years.

The two primary approaches to investment risk and skill evaluation are returns-based style analysis and holdings-based style analysis. These approaches are also the foundation of returns-based and holdings-based performance attribution. Our previous article discussed problems with returns-based analysis that can lead to flawed estimates of risk and skill – costly mistakes for fund investors and allocators. Here, we examine what it takes for holdings-based approaches to succeed.

Holdings-Based Analysis Can be Effective

In theory, holdings-based analysis can leverage all the available information on a portfolio and its constituents: past returns, composition, past holdings’ return and fundamental data. Since this information is a super-set of the data returns-based analysis uses, holdings-based analysis should be more effective.

In practice, the effectiveness of holdings-based style analysis and holdings-based performance attribution depends on the effectiveness of the underlying risk model. The risk model translates historical holdings data into portfolio style, or risk, data.

The Elements of Fund Style

A holdings-based analysis must capture the main Risk Factors that drive fund returns. We used the AlphaBetaWorks Performance Analytics Platform to analyze 15 years of portfolio and performance history of approximately 3,000 medium and lower turnover US mutual funds to determine the main sources of fund risk and return:

Factors Contributing Most to US Mutual Fund Performance

Factors Contributing Most to US Mutual Fund Performance

Approximately 76% of the variation in monthly performance of these funds is explained by their US Market Factor exposures, or US Market Betas. Sector factors, or sector betas, such as Finance and Technology, explain a further 4%. Traditional style factors, such as Value and Size explain approximately 1%.

The primacy of market and sector factors is consistent with existing research, including findings that in periods such as 1999-2001 the performance of Style Factors may be entirely due to sector effects. For holdings-based analysis to be effective, it must accurately identify market/region and sector exposures (betas).

Tests of Holdings-Based Analysis

Market factor exposure is the primary driver of fund risk and returns. Therefore, the first step in evaluating any model used for holdings-based style analysis and attribution is to establish how well it can discriminate between low-beta and high-beta securities.

We use two well-known financial stocks as examples. Bank of America Corporation (BAC) is more financially levered than M&T Bank Corporation (MTB). One would expect BAC to have correspondingly higher systemic risk. A chart of monthly returns confirms this hypothesis:

BAC Monthly Returns vs MTB Monthly Returns

BAC Monthly Returns vs MTB Monthly Returns

BAC is significantly correlated with, and levered to, the returns of MTB. Correlation among securities is due to shared systemic risk, primarily market and sector factors. Therefore, BAC’s risk factor exposures should be higher than MTB’s.

Holdings-Based Analysis Test 1 – Market Risk

As anticipated, BAC’s US Market exposure is approximately two times larger:

MTB and BAC Returns vs Market Factor Return

MTB and BAC Returns vs Market Factor Return

If a holdings based risk system suggests that $1 invested in BAC and $1 invested in MTB have similar US Market Risks, then it is flawed. It will fail to correctly measure market risk – the single most important driver of fund risk and return.

Holdings-Based Analysis Test 2 – Sector Risk

The next essential question is whether the higher risk of BAC is solely due to its higher US Market Beta. Intuitively, the higher leverage of BAC should affect its exposure to all risk factors, not just market risk. Therefore, one would also expect BAC’s Finance Sector Factor Exposure to be approximately twice as large. Indeed it is:

MTB and BAC Returns Net of Market Effects vs Finance Factor Return

MTB and BAC Returns Net of Market Effects vs Finance Factor Return

If a holdings-based risk system suggests that $1 invested in BAC and $1 invested in MTB have similar Finance Sector Risk, then it is flawed. It will fail to correctly measure sector risk – the second most important driver of fund risk and return.

Holdings-Based Analysis Test 3 – Levered ETFs

It can be time-consuming to test a holdings-based system with individual levered securities.  One must:

  • Identify two stocks with distinct market betas and correlation
  • Verify the difference in market exposures of these two stocks
  • Identify two stocks with distinct sector betas and correlation
  • Verify the difference in sector exposures of these two stocks

We propose a shortcut that will identify many, but not all, of the holdings-based systems that fail:

  • Take a levered and an un-levered ETF or index fund that track the same Index
  • Verify the difference in factor exposures of these securities
  • Verify that factor exposures are consistent with leverage

For example, consider Direxion Daily Small Cap Bull 3x Shares (TNA) and iShares Russell 3000 ETF (IWV):

Cumulative Returns for a Levered ETF (TNA) and an Un-Levered ETF (IWV)

Cumulative Returns for a Levered ETF (TNA) and an Un-Levered ETF (IWV)

Any competent risk system should assign TNA approximately 3x the exposures of IWV:

Returns for a Levered ETF (TNA) and an Un-levered ETF (IWV) vs Market Factor Return

Returns for a Levered ETF (TNA) and an Un-levered ETF (IWV) vs Market Factor Return

A performance attribution system should find that, due to time decay over the long term, the levered fund generates negative returns net of its factor exposures:

Cumulative Returns Net of Market Effects for a Levered ETF (TNA) and an Un-levered ETF (IWV)

Cumulative Returns Net of Market Effects for a Levered ETF (TNA) and an Un-levered ETF (IWV)

Unfortunately, some of the most highly-priced risk models, holdings-based style analysis tools, and performance attribution systems fail even this simple test.

Conclusions

A system that does not distinguish between low- and high- market and sector exposures (betas) will be fatally flawed. It will:

  • Underestimate the risk of high-risk portfolios
  • Overestimate the risk of low-risk portfolios
  • During bull markets, deem as skilled poor stock pickers passively taking high risk
  • During bull markets, deem as unskilled excellent stock pickers passively taking low risk

AlphaBetaWorks Statistical Equity Risk Model and Performance Analytics Platform are resilient against the issues identified above, having been refined on thousands of portfolios and tested over decades of history. Regrettably, some of the most commonly used commercial products exhibit these flaws. Even the highest-priced offerings are not immune. Investment Managers and Capital Allocators that are unaware of these flaws will be doubly-blindsided in periods of market turmoil. Their realized risk may be higher than estimated. Their “best” managers may turn out to be their worst.

Copyright © 2012-2014, AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved.
Content may not be republished without express written consent.

 

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