Tag Archives: risk

Smart Beta and Market Timing

Why Returns-Based Style Analysis Breaks for Smart Beta Strategies

Smart beta (SB) strategies tend to vary market beta and other factor exposures (systematic risk) over time. Consequently, market timing is an important source of their risk-adjusted returns, at times more significant than security selection. We have previously discussed that returns-based style analysis (RBSA) and similar methods fail for portfolios that vary exposures. Errors are most pronounced for the most active funds:

  • Estimates of a fund’s historical and current systematic risks may be flawed.
  • Excellent low-risk funds may be incorrectly deemed poor.
  • Poor high-risk funds may be incorrectly deemed excellent.

Due to the variation in Smart Beta strategies’ exposures over time, returns-based methods tend to fail for these strategies as well.

Three Smart Beta Strategies

We analyze the historical risk of three SB strategies as implemented by the following ETFs:

SPLV indexes 100 stocks from the S&P 500 with the lowest realized volatility over the past 12 months. PRF indexes the largest US equities based on book value, cash flow, sales, and dividends. SPHQ indexes the constituents of the S&P 500 with stable earnings and dividend growth.

All three smart beta strategies varied their factor exposures including their market exposures.

Low Volatility ETF (SPLV) – Market Timing

The low-volatility smart beta strategy has varied its market exposure significantly, increasing it by half since 2011. As stocks with the lowest volatility – and their risk – changed over time, the fund implicitly timed the broad equity market.  The chart below depicts the market exposure of SPLV over time:

Chart of this historical U.S. market exposure of the low volatility smart bet (SB) strategy as implemented by PowerShares S&P 500 Low Volatility Portfolio ETF (SPLV)

PowerShares S&P 500 Low Volatility Portfolio ETF (SPLV) – Historical U.S. Market Exposure

Low Volatility ETF (SPLV) – Historical Factor Exposures

SPLV’s market exposure fluctuates due to changes in its sector bets. Since the market betas of sectors differ from one another, as sector exposures vary so does the fund’s market exposure:

Chart of the historical exposures to significant risk factors of the low volatility smart bet (SB) strategy as implemented by PowerShares S&P 500 Low Volatility Portfolio ETF (SPLV)

PowerShares S&P 500 Low Volatility Portfolio ETF (SPLV) – Significant Historical Factor Exposures

Low Volatility ETF (SPLV) – Returns-Based Analysis

The chart below illustrates a returns-based analysis (RBSA) of SPLV. A regression of SPLV’s monthly returns against U.S. Market’s monthly returns estimates the fund’s U.S. Market factor exposure (beta) at 0.50 – significantly different from the historical risk observed above:

Chart of the regression of the historical returns of PowerShares S&P 500 Low Volatility Portfolio ETF (SPLV) against the Market

PowerShares S&P 500 Low Volatility Portfolio ETF (SPLV) – Historical Returns vs. the Market

This estimate of beta understates SPLV’s historical market beta (0.55) by a tenth and understates current market beta (0.70) by more than a third. RBSA thus fails to evaluate the current and historical risk of this low volatility smart beta strategy. Performance attribution and all other analyses that rely on estimates of historical factor exposures will also fail.

Fundamental ETF (PRF) – Market Timing

The market risk of the Fundamental ETF has been remarkably constant, except from 2009 to 2010. Back in 2009 PRF increased exposure to high-beta (mostly financial) stocks in a spectacularly prescient act of market timing:

Chart of the historical exposures of the fundamental smart beta (SB) strategy as implemented by the PowerShares FTSE RAFI US 1000 Portfolio ETF (PRF) to U.S. and Canadian Markets

PowerShares FTSE RAFI US 1000 Portfolio ETF (PRF) – Historical Market Exposure

Fundamental ETF (PRF) – Historical Factor Exposures

The historical factor exposure chart for PRF illustrates this spike in Finance Factor exposure from the typical 20-30% range to over 50% and the associated increase in U.S. Market exposure:

Chart of the exposures of the fundamental smart beta (SB) strategy as implemented by the PowerShares FTSE RAFI US 1000 Portfolio ETF (PRF) to significant risk factors

PowerShares FTSE RAFI US 1000 Portfolio ETF (PRF) – Significant Historical Factor Exposures

This 2009-2010 exposure spike generated a significant performance gain for the fund. PRF made approximately 20% more than it would have with constant factor exposures, as illustrated below:

Chart of the historical return from market timing (variation in factor exposures) of the PowerShares FTSE RAFI US 1000 Portfolio ETF (PRF)

PowerShares FTSE RAFI US 1000 Portfolio ETF (PRF) – Historical Risk-Adjusted Return from Market Timing

By contrast, PRF’s long-term risk-adjusted return from security selection is insignificant:

Chart of the historical returns from security selection (stock picking) of the PowerShares FTSE RAFI US 1000 Portfolio ETF (PRF)

PowerShares FTSE RAFI US 1000 Portfolio ETF (PRF) – Historical Risk-Adjusted Return from Security Selection

Factor timing turns out to be more important for the performance of some smart beta strategies than security selection.

Fundamental ETF (PRF) – Returns-Based Analysis

A returns-based analysis of PRF estimates historical U.S. market beta around 1.05:

Chart of the regression of the returns of PowerShares FTSE RAFI US 1000 Portfolio ETF (PRF) against the U.S. Market

PowerShares FTSE RAFI US 1000 Portfolio ETF (PRF) – Historical Returns vs. the Market

This 1.05 beta estimate only slightly overstates the fund’s current and historical betas, but misses the 2009-2010 exposure spike. Returns-based analysis thus does a decent job evaluating the average risk of a fundamental indexing smart beta strategy, but fails in historical attribution.

Quality ETF (SPHQ) – Market Timing

The market exposure of the quality smart beta strategy (SPHQ) swung wildly before 2011. It has been stable since:

Chart of the U.S. and Canadian Market exposures of the quality smart beta (SB) strategy as implemented by the PowerShares S&P 500 High Quality Portfolio ETF (SPHQ)

PowerShares S&P 500 High Quality Portfolio ETF (SPHQ) – Historical Market Exposure

Quality ETF (SPHQ) – Historical Factor Exposures

As with the other smart beta strategies, market timing by SPHQ comes from significant variations in sector bets:

Chart of the historical exposures of the quality smart beta (SB) strategy as implemented by the PowerShares S&P 500 High Quality Portfolio ETF (SPHQ) to significant risk factors

PowerShares S&P 500 High Quality Portfolio ETF (SPHQ) – Significant Historical Factor Exposures

Quality ETF (SPHQ) – Returns-Based Analysis

A returns-based analysis of SPHQ estimates historical U.S. market beta around 0.86:

Chart of the regression of the historical returns of PowerShares S&P 500 High Quality Portfolio ETF (SPHQ) against the U.S. Market

PowerShares S&P 500 High Quality Portfolio ETF (SPHQ) – Historical Returns vs. the Market

Given the large variation in SPHQ’s risk over time, this 0.86 beta estimate understates the average historical beta but slightly overstates the current one. While the current risk estimate is close, RBSA fails for historical risk estimation and performance attribution.

Conclusions

  • Low volatility indexing, fundamental indexing, and quality indexing smart beta strategies vary market and other factor exposures (systematic risk) over time.
  • Due to exposure variations over time, returns-based style analysis and similar methods tend to fail for smart beta strategies:
    • Funds’ historical systematic risk estimates are flawed.
    • Funds’ current systematic risk estimates are flawed.
    • Performance attribution and risk-adjusted performance estimates are flawed.
  • Analysis and aggregation of factor exposures of individual holdings throughout a portfolio’s history with a capable multi-factor risk model produces superior risk estimates and performance attribution.
The information herein is not represented or warranted to be accurate, correct, complete or timely.
Past performance is no guarantee of future results.
Copyright © 2012-2015, AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved.
Content may not be republished without express written consent.
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Returns-Based Style Analysis – Overfitting and Collinearity

Plagued by overfitting and collinearity, returns-based style analysis frequently fails, confusing noise with portfolio risk.

Returns-based style analysis (RBSA) is a common approach to investment risk analysis, performance attribution, and skill evaluation. Returns-based techniques perform regressions of returns over one or more historical periods to compute portfolio betas (exposures to systematic risk factors) and alphas (residual returns unexplained by systematic risk factors). The simplicity of the returns-based approach has made it popular, but it comes at a cost – RBSA fails for active portfolios. In addition, this approach is plagued by the statistical problems of overfitting and collinearity, frequently confusing noise with systematic portfolio risk. 

Returns-Based Style Analysis – Failures for Active Portfolios

In an earlier article we illustrated the flaws of returns-based style analysis when factor exposures vary, as is common for active funds:

  • Returns-based analysis typically yields flawed estimates of portfolio risk.
  • Returns-based analysis may not even accurately estimate average portfolio risk.
  • Errors will be most pronounced for the most active funds:
    • Skilled funds may be deemed unskilled.
    • Unskilled funds may be deemed skilled.

These are not the only flaws. We now turn to the subtler and equally critical issues – failures in the underlying regression analysis itself. We use a recent Morningstar article as an example.

iShares Core High Dividend ETF (HDV) – Returns-Based Style Analysis

A recent Seeking Alpha article provides an excellent illustration of problems created by overfitting and collinearity. In this article, Morningstar performed a returns-based style analysis of iShares Core High Dividend ETF (HDV).

Morningstar estimated the following factor exposures for HDV using the Carhart model:

Morningstar: Returns-Based Analysis of the iShares Core High Dividend ETF (HDV) Using the Carhart Model

iShares Core High Dividend ETF (HDV) – Estimated Factor Exposures Using the Carhart Model – Source: Morningstar

The Mkt-RF coefficient, or loading, is HDV’s estimated market beta. A beta value of 0.67 means that given a +1% change in the market HDV is expected to move by +0.67%, everything else held constant.

The article then performs RBSA using an enhanced Carhart + Quality Minus Junk (QMJ) model:

Morningstar: Returns-Based Analysis of iShares Core High Dividend ETF (HDV) Using the Carhart + Quality Minus Junk (QMJ) Model

iShares Core High Dividend ETF (HDV) – Estimated Factor Exposures Using the Carhart + Quality Minus Junk (QMJ) Model – Source: Morningstar

With the addition of the QMJ factor, the market beta estimate increased by a third from 0.67 to 0.90. Both estimates cannot be right. Perhaps the simplicity of the Carhart model is to blame and the more complex 5-factor RBSA is more accurate?

iShares Core High Dividend ETF (HDV) – Historical Factor Exposures

Instead of Morningstar’s RBSA approach, we analyzed HDV’s historical holdings using the AlphaBetaWorks’ U.S. Equity Risk Model. For each month, we estimated the U.S. Market exposures (betas) of individual positions and aggregated these into monthly estimates of portfolio beta:

Chart of the historical market exposure (beta) of iShares Core High Dividend ETF (HDV)

iShares Core High Dividend ETF (HDV) – Historical Market Exposure (Beta)

Over the past 4 years, HDV’s market beta varied in a narrow range between 0.50 and 0.62.

Both of the above returns-based analyses were off, but the simpler Carhart model did best. It turns out the simpler and a less sophisticated returns-based model is less vulnerable to the statistical problems of multicollinearity and overfitting. Notably, the only way to find out that returns-based style analysis failed was to perform the more advanced holdings-based analysis using a multi-factor risk model.

Statistical Problems with Returns-Based Analysis

Multicollinearity

Collinearity (Multicollinearity) occurs when risk factors used in returns-based analysis are highly correlated with each other. For instance, small-cap stocks tend to have higher beta than large-cap stocks, so the performance of small-cap stocks relative to large-cap stocks is correlated to the market.

Erratic changes in the factor exposures for various time periods, or when new risk factors are added, are signs of collinearity. These erratic changes make it difficult to pin down factor exposures and are signs of deeper problems:

A principal danger of such data redundancy is that of overfitting in regression analysis models.
-Wikipedia

Overfitting

Overfitting is a consequence of redundant data or model over-complexity. These are common for returns-based analyses which usually attempt to explain a limited number of return observations with a larger number of correlated variable observations.

An overfitted returns-based model may appear to describe data very well. But the fit is misleading – the exposures may be describing noise and will change dramatically under minor changes to data or factors. A high R squared from returns-based models may be a sign of trouble, rather than a reassurance.

As we have seen with the HDV example above, exposures estimated by RBSA may bear little relationship to portfolio risk. Therefore, all dependent risk and skill data will be flawed.

Conclusions

  • When a manager does not vary exposures to the market, sector, and macroeconomic factors, returns-based style analysis (RBSA) using a parsimonious model can be effective.
  • When a manager varies bets, RBSA typically yields flawed estimates of portfolio risk.
  • Even when exposures do not vary, returns-based style analysis is vulnerable to multicollinearity and overfitting:
    • The model may capture noise, rather than the underlying factor exposures.
    • Factor exposures may vary erratically among estimates.
    • Estimates of portfolio risk will be flawed.
    • Skilled funds may be deemed unskilled.
    • Unskilled funds may be deemed skilled.
  • Holdings-based analysis using a robust multi-factor risk model is superior for quantifying fund risk and performance.
The information herein is not represented or warranted to be accurate, correct, complete or timely.
Past performance is no guarantee of future results.
Copyright © 2012-2015, AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved.
Content may not be republished without express written consent.
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When “Smart Beta” is Simply High Beta

WisdomTree Mid Cap Earnings Fund (EZM) vs. PowerShares Dynamic Large Cap Value Portfolio (PWV)

Many “smart beta” funds are merely high-beta, delivering no value over traditional index funds. On the other hand, some smart beta strategies are indeed exceptional and worth their fees.

Most analyses of enhanced index funds and smart beta strategies lack a rigorous approach to risk evaluation and performance attribution. Consequently, risky and mediocre funds are mislabeled as excellent, while conservative and exceptional funds are wrongly considered mediocre. Investors relying on simplistic analyses may end up with mediocre funds, hidden risks, and subpar performance.

The Not-So-Smart Beta

Some smart beta funds deliver consistent outperformance with high liquidity and low tracking error. Others merely deliver high market beta or high exposures to other common risk factors. Analyses of these funds’ performance are usually simplistic, failing to differentiate between the two groups.

Enhanced indexing and smart beta strategies are usually more active than the underlying indices. This can cause their risk to vary dramatically over time. For instance, a fund’s market beta can vary by 40-50% over a few years. This variation makes it difficult to determine whether a particular strategy is smart or merely risky. When a market correction arrives, risky funds suffer outsized losses.

Many estimate the beta of a fund by fitting its returns to the market or a benchmark using a regression, a technique known as returns-based style analysis. This is a flawed approach, which fails to accurately estimate the risk of active strategies. We discussed the flaws of returns-based style analysis in earlier articles.

A robust approach to estimating a fund’s historical risk and risk-adjusted performance is to evaluate its holdings over time. At each period, the risk of individual holdings is aggregated to estimate the risk of the fund. This is AlphaBetaWorks’ approach, implemented in our Performance Analytics Platform. Our analysis reveals that many “smart beta” funds are merely high-beta. These funds deliver no value over traditional index funds. On the other hand, some smart beta strategies are indeed exceptional and worth the fees they charge.

WisdomTree Mid Cap Earnings Fund (EZM) – Historical Risk

On the surface, the returns of the WisdomTree Mid Cap Earnings Fund (EZM) appear strong. The fund has dramatically outperformed its broad benchmark, the Russell Midcap Index (IWR):

Chart of the Cumulative Return of WisdomTree Mid Cap Earnings Fund (EZM) and of the Benchmark (IWR)

Cumulative Return of WisdomTree Mid Cap Earnings Fund (EZM) vs the Benchmark (IWR)

However, this is nominal outperformance, not risk-adjusted outperformance.

The main source of security risk and return is market risk, or beta. With this in mind, we analyzed the holdings of EZM and IWR during each historical period, calculated their holdings’ risk, and calculated the total risk of each fund. Not surprisingly, IWR’s beta has been stable, averaging 1.09 (109% of the risk of U.S. Market). Meanwhile, EZM’s beta has varied in a wide range, averaging 1.18 (118% of the risk of U.S. Market):

Chart of the historical beta of the WisdomTree Mid Cap Earnings Fund (EZM) compared to the historical beta of the Benchmark (IWR)

Historical Beta of WisdomTree Mid Cap Earnings Fund (EZM) vs the Benchmark (IWR)

EZM had higher returns, but it also consistently took more market risk. With greater risk comes greater volatility, and a down cycle will affect EZM more.

To determine its risk-adjusted return, we must compare the performance of EZM to the performance of a passive portfolio with the same factor exposures.  Below are EZM’s current and historical factor exposures:

Chart of the historical and current factor exposures of the WisdomTree Mid Cap Earnings Fund (EZM)

Historical Factor Exposures of WisdomTree Mid Cap Earnings Fund (EZM)

WisdomTree Mid Cap Earnings Fund (EZM) – Risk-Adjusted Performance

Instead of owning EZM, investors could have owned a passive portfolio with similar risk (a passive replicating portfolio). If EZM had profitably timed the market (varied its risk) or selected securities, it should have outperformed.

EZM’s risk-adjusted performance closely matches a passive replicating portfolio. Relative to its passive equivalent, EZM has generated negligible active return (abReturn in the following chart):

Chart of the historical passive and active returns of the WisdomTree Mid Cap Earnings Fund (EZM)

Historical Passive and Active Return of WisdomTree Mid Cap Earnings Fund (EZM)

PowerShares Dynamic Large Cap Value Portfolio (PWV) – Risk-Adjusted Performance

Let’s contrast the performance of EZM with the results of another smart beta option: PowerShares Dynamic Large Cap Value Portfolio (PWV).

PWV has also varied U.S. Market exposure by approximately 40%:

Chart of the historical and current factor exposures of the PowerShares Dynamic Large Cap Value Portfolio (PWV)

Historical Factor Exposures of PowerShares Dynamic Large Cap Value Portfolio (PWV)

PWV has consistently outperformed its passive replicating portfolio and produced strong active returns due to market timing and security selection:

Chart of the historical passive and active returns of the PowerShares Dynamic Large Cap Value Portfolio (PWV)

Historical Passive and Active Return of PowerShares Dynamic Large Cap Value Portfolio (PWV)

Conclusion

Performance evaluation tools lacking accurate insights into risk may rank the better-performing but riskier EZM ahead of PWV, which produced superior active returns. The accurate picture of their relative active performance emerges once both funds’ historical holdings are examined with a multi-factor risk model and their excess returns distilled.

Unsuspecting investors relying on simplistic analysis may conclude that a risky and mediocre fund is excellent while a conservative and exceptional fund is mediocre. At best, they will face higher-than-anticipated risks. At worst, they will get a nasty surprise when a correction comes.

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

And Not Missing Out

Maybe your fund took extra risk to keep up with its benchmark. Maybe your fund should have made more – much more – given the risks it took. By the time market volatility reveals underlying exposures, it may be too late to avoid severe losses. There is a better way: Investors can continuously monitor a fund’s risk, the returns it should be generating, and the value it creates. This value should matter most to investors and allocators. Regrettably, most fund analysis tools and services pay no attention to it.

To illustrate, we analyze two funds: one that did much worse than it should have, and one that did better.

PRSCX – Negative Active Returns

The T. Rowe Price Science & Technology Fund (PRSCX) manages approximately $3 billion. This fund generally tracks its benchmark and it gets 3 star rating from a popular service. Notwithstanding this, PRSCX has produced persistently negative active returns. Given its historical risk, PRSCX should have made investors far more money: Over the past ten years, an investor would have made 50-80% more owning a passive portfolio with PRSCX’s risk profile.

Chart of the historical cumulative passive and active returns of T. Rowe Price Science & Technology Fund (PRSCX)

T. Rowe Price Science & Technology Fund (PRSCX) – Passive and Active Return History

While we seem to bolster arguments for passive investing, reality is more complex: Active returns (both positive and negative) persist over time. Thus, upgrading from PRSCX to a fund with persistently positive active returns is a superior move. We will provide one candidate.

PRSCX – Historical Risk

The chart below shows PRSCX’s historical risk (exposures to significant risk factors). The red dots indicate monthly exposures (as a percentage of assets) over the past 10 years; the black diamonds indicate latest exposures:

Chart of the historical exposures of T. Rowe Price Science & Technology Fund (PRSCX) to significant risk factors

T. Rowe Price Science & Technology Fund (PRSCX) – Exposure to Significant Risk Factors

PRSCX varied its exposures over time. U.S. Market is the most important exposure, reaching 200% (market beta of 2) at times. As expected for a technology fund, its U.S. Technology exposure has been near 100%. Also note PRSCX’s occasional short bond exposure. Many equity funds carry large hidden bond bets due to the risk profile of their equity holdings. Most investors and portfolio managers are not aware of these bets. Yet for these funds, bond risk is a key driver of portfolio returns and volatility.

PRSCX – Historical Active Returns

The above exposures define a passive replicating portfolio matching PRSCX’s risk. The fund manager’s job is to outperform this passive alternative by generating active returns.

To isolate active returns, we quantify passive factor exposures, estimate the passive return, and then calculate the remaining active return – αβReturn. We further break down αβReturn into risk-adjusted return from security selection, or stock picking (αReturn), and market timing (βReturn):

Component 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Total 1.6 2.46 7.1 11.88 -43.8 67.83 21.25 -4.51 6.25 43.7 9.39
Passive -0.69 1.4 5.45 19.26 -46.13 77.52 23.21 -3.42 20.36 51.54 13.61
αβReturn 0.98 -0.69 -2.2 -7.45 2.71 -11.61 -2.22 -8.24 -13.76 -14.14 -5.84
αReturn -1.95 -3.29 -6.45 -2.79 7.92 0.78 4.17 -12.31 -11.47 -4.54 1.11
βReturn 2.94 2.6 4.25 -4.66 -5.2 -12.39 -6.39 4.07 -2.29 -9.6 -6.95
Undefined 1.3 1.75 3.85 0.07 -0.38 1.92 0.26 7.15 -0.35 6.3 1.61

Note that we are unable to account for trades behind some of the returns – the “Undefined” component. It may be due to private securities or intra-period trading; it may be passive or active. Yet, even if we assume that all undefined returns above are active, PRSCX still delivered persistently negative αβReturn over the past ten years. Furthermore, the compounding of negative αβReturn leaves investors missing out on 50-80% in gains.

FSCSX – An Upgrade Option with Similar Historical Risk

While a passive portfolio would have been superior to PRSCX, it is not the best upgrade. Allocators and investors can do better owning a fund with consistently positive αβReturns, since αβReturns persist. One candidate is Fidelity Select Software & Computer Services Portfolio (FSCSX):

Chart of the historical exposures of Fidelity Select Software & Computer Services Portfolio (FSCSX) to significant risk factors

Fidelity Select Software & Computer Services Portfolio (FSCSX) – Exposure to Significant Risk Factors

Currently, FSCSX and PRCSX have similar exposures. AlphaBetaWorks’ risk analytics estimate the current annualized tracking error between the two funds at a 5.29% (about the same volatility as bonds, and less than one half of market volatility).

FSCSX – Historical Active Returns

FSCSX’s 3-year trailing average annual return of 23% is slightly ahead of PRSCX’s 20%. But most importantly, given its lower historical risk, FSCSX has delivered positive αβReturns versus PRSCX’s significantly negative ones. The chart below shows FSCSX’s ten-year performance. The purple area is the positive αβReturn. The gray area is FSCSX’s passive return:

Chart of the historical cumulative passive and active returns of Fidelity Select Software & Computer Services Portfolio (FSCSX)

Fidelity Select Software & Computer Services Portfolio (FSCSX) – Passive and Active Return History

FSCSX is superior to a passive portfolio with similar risk and to PRSCX. Mind you, this is not a sales pitch for FSCSX but merely a consequence of its positive αβReturn and αβReturn persistence.

Few fund investors and allocators possess the tools to quantify active returns. Yet, this knowledge is an essential competitive advantage, leading to improved client returns, client retention, and asset growth. Unfortunately, many are content to pick funds based on past nominal returns and to suffer the consequences: picking yesterday’s winners tends to pick tomorrow’s losers. AlphaBetaWorks spares clients from the data processing headaches, financial modeling, and statistical analysis of thousands of portfolios, delivering predictive risk and skill analytics on thousands of funds.

Conclusions

  • Analyzing a fund’s performance relative to a benchmark ignores the most important question: What should you have made given its risk?
  • Some mutual funds produce persistently negative active returns; others produce persistently positive active returns.
  • Upgrading from a fund with persistently negative active return (αβReturn) to a replicating passive portfolio tends to improve performance.
  • Upgrading from a passive portfolio to a fund with persistently positive αβReturn also tends to improve performance.
  • Tools that accurately estimate fund risk and active returns provide enduring competitive advantages for investors and professional allocators, leading to improved client returns, client retention, and asset growth.
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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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 addressed 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.

This 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 its peers and achieves an 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 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 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.
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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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 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 its 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.
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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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 – the share 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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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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