Tag Archives: αReturn

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

And What They Owned at Year-end

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

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

Identifying the Top U.S. Stock Pickers

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

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

To eliminate the systematic noise that is the source of performance reversion, the AlphaBetaWorks Performance Analytics Platform calculates portfolio return from security selection – αReturn. αReturn is the performance a portfolio would have generated if all factor returns had been flat. This is the estimated residual performance due to stock picking skill, net of all factor effects. Each month we identify the top five percent among 13F-filers with the most consistently positive αReturns over the prior 36 months. This expert panel of the top stock pickers typically includes 100-150 firms. Data is lagged 2 months to account for the filing delay. We construct the aggregate expert portfolio (the ABW Expert Aggregate) by equal-weighting the expert portfolios and position-weighting stocks within the expert portfolios.

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

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

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

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

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

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

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

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

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

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

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

Market Performance of the Top U.S. Stock Pickers

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

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

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

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

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

 

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

Top U.S. Stock Pickers’ Consensus Positions

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

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

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

Top Stock Pickers’ Exposure to Electronic Arts (EA)

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

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

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

Top Stock Pickers’ Exposure to NetEase (NTES)

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

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

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

Top Stock Pickers’ Exposure to Occidental Petroleum (OXY)

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

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

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

Conclusions

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

We earlier discussed how nominal returns and related investment performance metrics revert: Since portfolio performance primarily comes from systematic (factor) exposures, such simplistic metrics merely promote the high-risk portfolios during the bullish regimes and the low-risk portfolios during the bearish regimes. As regimes change, the leaders flip. We also showed that, when security selection returns are distilled with a robust factor model, performance persists within all U.S. equity Style Boxes. Prompted by reader interest, we now investigate performance persistence within International Style Boxes.

Measuring the Persistence of International Portfolio Returns

As in our earlier work on return persistence, we examine all Form 13F filings for the past 10 years. This survivorship-free portfolio database covers all institutions that exercised investment discretion over at least $100 million and yields approximately 3,600 international portfolios with sufficiently long histories, low turnover, and broad positions to be suitable for the study.

We split the 10 years of history into two random 5-year samples and compared performance metrics of each portfolio over these two periods. The correlation between metrics over the sample periods measures the metrics’ persistence.

International Portfolios’ Performance Persistence

The Reversion of International Portfolios’ Nominal Returns

The following chart plots the rankings of each portfolio’s nominal returns during the two sample periods. The x-axis plots return percentile, or ranking, in the first sample period. The y-axis plots return percentile, or ranking, in the second sample period. The best-performing international portfolios of the first period have x-values near 100; the best-performing portfolios of the second period have y-values near 100:

Chart of the random relationship between nominal returns for two historical samples for all international equity 13F portfolios

13F Portfolios, International Positions: Correlation between the rankings of nominal returns for two historical samples

Whereas past performance of U.S. equity portfolios was a (negative) predictor of future results, there is no significant correlation between the two for international portfolios – best- and worst-performers tend to become average.

The Persistence of International Portfolios’ Security Selection Returns

Due to the domineering effects of Market and other systematic factors, top-performing managers during the bullish regimes are those that take the most risk, and top-performing managers during the bearish regimes are those that take the least risk. Since Market returns are approximately random, nominal returns do not persist. To eliminate this noise, the AlphaBetaWorks Performance Analytics Platform calculates each portfolio’s return from security selection net of factor effects. αReturn is the return a manager would have generated if all factor returns had been flat.

International portfolios with above-average αReturns in one period are likely to maintain them in the other. In the following chart, this relationship is represented by the concentration of portfolios in the bottom left (laggards that remained laggards) and top right (leaders that remained leaders):

Chart of the positive correlation between risk-adjusted returns from security selection (αReturns) for two historical samples for all international equity 13F portfolios

13F Portfolios, International Positions: Correlation between the rankings of αReturns for two historical samples

This test of persistence across two arbitrary 5-year samples is strict. Persistence of security selection skill is even higher over shorter periods.

Performance Persistence within International Style Boxes

Measures of investment style such as Size (portfolio market capitalization) and Value/Growth (portfolio valuation) are common approaches to grouping portfolios. Readers frequently ask whether the reversion of nominal returns and related metrics can be explained by Style Box membership. Perhaps we merely observed reversion in leadership that is eliminated by controlling for Style?

To test this, we compared performance persistence within each of the four popular Style Boxes.

International Large-Cap Value Portfolios’ Performance Persistence

The International Large-cap Value Style Box shows the highest persistence of long-term stock picking results, yet the relationship between nominal returns within it is still nearly random. Powerful performance analytics provide the biggest edge for this International Style Box:

Chart of the random relationship between nominal returns and positive correlation between risk-adjusted returns from security selection (αReturns) for two historical samples for equity 13F portfolios in the Large-Cap Value International Style Box

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

International equity portfolios differ from U.S. equity portfolios, where security selection persistence was highest for the Small-cap Value Style Box.

International Large-Cap Growth Portfolios’ Performance Persistence

International portfolios in the Large-cap Growth Style Box also show a nearly random relationship between the two periods’ returns. However, their αReturns persist strongly. Whereas large-cap growth stock picking is treacherous for U.S. equity portfolios, it is more rewarding internationally:

Chart of the random relationship between nominal returns and positive correlation between risk-adjusted returns from security selection (αReturns) for two historical samples for equity 13F portfolios in the Large-Cap Growth International Style Box

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

International Small-Cap Value Portfolios’ Performance Persistence

International portfolios in the Small-cap Value Style Box have the least persistent αReturns, in contrast to the U.S. portfolios:

Chart of the random relationship between nominal returns and positive correlation between risk-adjusted returns from security selection (αReturns) for two historical samples for equity 13F portfolios in the Small-Cap Value International Style Box

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

International Small-Cap Growth Portfolios’ Performance Persistence

αReturns within the International Small-cap Growth Style Box persist almost as strongly as within the International Large-cap Style Boxes:

Chart of the random relationship between nominal returns and positive correlation between risk-adjusted returns from security selection (αReturns) for two historical samples for equity 13F portfolios in the Small-Cap Growth International Style Box

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

Summary

  • Whereas nominal returns and related simplistic metrics of investment skill revert, security selection performance – once properly distilled with a capable factor model – persists.
  • The randomness and reversion of nominal returns and the persistence of security selection skill hold across all International Style Boxes.
  • Security selection performance persists most strongly for International Large-cap portfolios.
The information herein is not represented or warranted to be accurate, correct, complete or timely.
Past performance is no guarantee of future results.
Copyright © 2012-2016, AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved.
Content may not be republished without express written consent.
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The Top U.S. Stock Pickers’ Industrials Performance

And Their Consensus Industrials Ideas in 2016

The challenges of identifying good investors and distilling their skill obscure the top stock pickers’ consistently strong performance. For instance, contrary to popular wisdom 2015 was a good year for stock picking. These results also generally apply to large market sub-segments such as the Industrials sector. In this piece we use a robust risk model to identify the best U.S. stock pickers, distill their skills, and monitor their Industrials performance. We then track their consensus Industrials portfolio and reveal its top positions.

Identifying the Top U.S. Stock Pickers

Nominal returns and related simplistic metrics of investment skill are dominated by systematic factors and hence revert. Therefore, we must eliminate these systematic effects to get an accurate picture. The AlphaBetaWorks Performance Analytics Platform calculates each portfolio’s return from security selection, or αReturn. It is the residual performance net of factor effects and the performance a portfolio would have generated if all factor returns had been flat.

This study covers portfolios of all institutions that have filed Form 13F. Of these, approximately 5,000 filers had holdings histories suitable for skill evaluation. The AlphaBetaWorks Expert Aggregate (ABW Expert Aggregate) consists of the top five percent with the most consistently positive 36-month αReturns. This expert panel typically includes 100-150 firms. Manager fame and firm size are poor proxies for skill, so the panel is an eclectic collection light on celebrities but heavy on skill.

Industrials Performance of the Top U.S. Stock Pickers’

Since security selection skill persists, managers with above-average αReturns in the past are likely to maintain them in the future. This applies both to aggregate portfolios and to large portfolio subsets, such as sector holdings. To illustrate, we consider the top stock pickers’ Industrials performance.

A hedged portfolio that combines the top U.S. stock pickers’ net consensus Industrials longs (relative Industrials overweights), lagged 2 months to account for filing delay (the ABW Industrials Expert Aggregate), delivers consistent positive returns:

Chart of the cumulative return of the Industrials Benchmark (Vanguard Industrials ETF (VIS)) and the cumulative Industrials performance of the hedged portfolio that combines the to 5% U.S. long stock pickers’ net consensus industrials positions

Cumulative Hedged Portfolio Return: Top U.S. Stock Pickers’ Net Consensus Industrials Longs

For illustration, we include the performance of the Vanguard Industrials ETF (VIS) (Benchmark above).

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
ABW Industrials Expert Aggregate 4.13 -2.25 25.24 14.37 7.67 9.47 3.93 6.11 2.06 12.94 2.48 5.84
Vanguard Industrials ETF (VIS) 4.88 15.35 14.14 -39.37 23.15 27.74 -2.48 17.71 42.08 9.00 -3.73 -2.82

The top stock pickers’ consistently positive Industrials αReturns yield consistently positive returns, low volatility, and low drawdowns for the ABW Industrials Expert Aggregate:

ABW Industrials Expert Aggregate Vanguard Industrials ETF (VIS)
Annualized Return 8.56 7.75
Annualized Standard Deviation 7.38 19.24
Annualized Sharpe Ratio (Rf=0%) 1.16 0.40
ABW Industrials Expert Aggregate Vanguard Industrials ETF (VIS)
Semi Deviation 1.50 4.20
Gain Deviation 1.50 3.35
Loss Deviation 1.42 4.46
Downside Deviation (MAR=10%) 1.57 4.22
Downside Deviation (Rf=0%) 1.17 3.84
Downside Deviation (0%) 1.17 3.84
Maximum Drawdown 6.95 57.33
Historical VaR (95%) -2.52 -8.66
Historical ES (95%) -4.09 -13.08

Unfortunately, some highly skilled managers with strong Industrials books have fallen far short of the above results. Poor risk systems and losses from overlooked factor exposures often conceal stock-picking skill. The consistent absolute returns above are due in part to robust hedging that mitigates systematic noise.

Top U.S. Stock Pickers’ Consensus Industrials Positions

The top stock pickers are rarely the hottest funds and their consensus longs are rarely the hottest stocks. Below are the top 10 holdings of the ABW Industrials Expert Aggregate at year-end 2015:

Symbol Name Exposure (%)
MMM 3M Company 17.37
GE General Electric Company 5.47
ROP Roper Technologies, Inc. 3.64
UNP Union Pacific Corporation 3.61
DHR Danaher Corporation 3.26
UTX United Technologies Corporation 2.84
AGX Argan, Inc. 2.77
LMT Lockheed Martin Corporation 2.56
EMR Emerson Electric Co. 2.47
LUV Southwest Airlines Co. 2.37

It is worth noting that the above positions represent a consensus among stock-pickers who have proven their skill. This is not to be confused with crowding, which we have written about at length. Hedge fund crowding is a consensus among (often impatient and performance-sensitive) hedge funds, irrespective of their skill.

Top Stock Pickers’ Exposure to 3M (MMM)

The largest position within the ABW Industrials Expert Aggregate is 3M (MMM). The top panel on the following chart shows MMM’s cumulative nominal returns in black and cumulative residual returns (αReturns) in blue. Residual return or αReturn is the performance net of the systematic factors defined by the AlphaBetaWorks Statistical Equity Risk Model – the performance MMM would have generated if factor returns had been flat. The bottom panel shows exposure to MMM within the Aggregate:

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

Cumulative αReturns of MMM and ABW Industrials Expert Aggregate’s MMM Exposure

Top Stock Pickers’ Exposure to General Electric (GE)

The second largest position within the ABW Industrials Expert Aggregate is General Electric (GE). The Expert Aggregate was mostly underweight (short) GE between 2008 and 2014 – a challenging period for GE. GE became experts’ consensus long in 2014 – about a year ahead of the 2015 turnaround in residual performance:

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

Cumulative αReturns of GE and ABW Industrials Expert Aggregate’s GE Exposure

Top Stock Pickers’ Exposure to Roper Technologies (ROP)

The third largest position within the ABW Industrials Expert Aggregate is Roper Technologies (ROP). The Aggregate has had varied but mostly positive exposure to ROP over the past 10 years. Current exposure is at historic heights:

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

Cumulative αReturns of ROP and ABW Industrials Expert Aggregate’s ROP Exposure

Conclusions

  • Robust analytics built on predictive risk models identify the top stock pickers in the sea of mediocrity.
  • When hedged, top stock pickers’ net consensus Industrials longs (relative overweights) tend to generate positive future absolute returns and net consensus industrials shorts (relative underweights) tend to generate negative future absolute returns.
  • The top stock pickers are often unglamorous firms and their consensus Industrials longs are often unglamorous stocks – both tend to outperform.
The information herein is not represented or warranted to be accurate, correct, complete or timely.
Past performance is no guarantee of future results.
Copyright © 2012-2016, AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved.
Content may not be republished without express written consent.
U.S. Patents Pending.
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How Did the Top U.S. Stock Pickers Do in 2015?

And What Did They Own at Year-end?

Contrary to popular wisdom, 2015 was a good year for stock picking. The problem is that few know who the good stock pickers are. Further, good stock pickers may be poor risk managers. In this article we use a robust risk model to track the top U.S. stock pickers and to distill their skill.

Since genuine investment skill persists, top U.S. stock pickers tend to generate persistently positive returns from security selection. Consequently, a hedged (market-neutral) portfolio of their net consensus longs (relative overweights) tends to generate positive returns, independent of the Market. Below we illustrate this portfolio’s performance and reveal its top positions.

Identifying the Top U.S. Stock Pickers

This study covers portfolios of all institutions that have filed Form 13F. This is the broadest and most representative survivorship-free portfolio database comprising thousands of firms: hedge funds, mutual fund companies, investment advisors, and all other institutions with over $100 million in U.S. long assets. Approximately 5,000 firms had sufficiently long histories, low turnover, and broad portfolios suitable for skill evaluation.

Nominal returns and related simplistic metrics of investment skill (Sharpe Ratio, Win/Loss Ratio, etc.) are dominated by Market and other systematic factors and hence revert. As market regimes change, top performers tend to become bottom performers. To eliminate these systematic effects and estimate residual performance due to stock picking skill, the AlphaBetaWorks Performance Analytics Platform calculates each portfolio’s return from security selection – αReturn. αReturn is the performance a portfolio would have generated if all factor returns had been flat.

Each month we identify the five percent of 13F-filers with the most consistently positive αReturns over the prior 36 months. This expert panel of the top stock pickers typically includes 100-150 firms. Since manager fame and firm size are poor proxies for skill, the panel is an eclectic bunch. It currently includes some hedge funds (Lumina Fund Management, Palo Alto Investors, and Chilton Investment Company), though few of the famed gurus. Many of the top long stock pickers are investment management firms (Eaton Vance Management, Fiduciary Management Inc., and Aristotle Capital Management), as well as banks, endowments, and trust companies.

Performance of the Top U.S. Stock Pickers

Since security selection skill persists, managers with above-average αReturns in the past are likely to maintain them in the future. Therefore, a hedged portfolio that combines the top U.S. stock pickers’ net consensus longs (relative overweights), lagged 2 months to account for filing delay (the ABW Expert Aggregate), delivers consistent positive returns as illustrated below:

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

Cumulative Hedged Portfolio Return: Top U.S. Stock Pickers’ Net Consensus Longs

  2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
ABW Expert Aggregate 4.56 14.43 12.74 5.95 -1.25 15.35 2.20 2.24 15.47 8.81 13.16 1.70
iShares Russell 3000 ETF 9.04 15.65 4.57 -37.16 28.21 16.81 0.78 16.43 32.97 12.41 0.34 -5.72

ABW Expert Aggregate achieved higher returns than those of the broad market with less than half of its volatility:

ABW Expert Aggregate iShares Russell 3000 ETF
Annualized Return 8.36 6.40
Annualized Standard Deviation 5.25 15.50
Annualized Sharpe Ratio (Rf=0%) 1.59 0.41

The consistency of top stock pickers’ αReturns manifests itself as low downside volatility and low losses of the ABW Expert Aggregate:

ABW Expert Aggregate iShares Russell 3000 ETF
Semi Deviation 1.00 3.33
Gain Deviation 1.19 2.40
Loss Deviation 0.70 3.45
Downside Deviation (MAR=10%) 1.09 3.43
Downside Deviation (Rf=0%) 0.64 3.04
Downside Deviation (0%) 0.64 3.04
Maximum Drawdown 5.06 51.24
Historical VaR (95%) -1.81 -7.58
Historical ES (95%) -2.21 -9.96

One often reads commentary on the challenges stock pickers faced in 2015. This analysis typically fails to identify skill and merely reveals the obvious: average managers do poorly in a low-return environment. In fact, 2015 was one of the strongest years for top stock pickers. It is the indiscriminate rallies such as that of 2009 that prove challenging.

Unfortunately, some skilled managers suffered from underdeveloped risk systems, and losses from hidden systematic risks concealed their stock-picking results. For instance, some highly skilled stock pickers with unintended small-cap (short Size) exposure experienced 5-10% systematic headwinds in 2015. A robust risk management program would have partially or wholly mitigated these.

Top U.S. Stock Pickers’ Consensus Positions

Since the top stock pickers are rarely the most celebrated firms, their top consensus longs are rarely the hottest stocks. Below are the top 10 holdings of the ABW Expert Aggregate at year-end 2015:

Symbol Name Exposure (%)
EA Electronic Arts Inc. 2.29
V Visa Inc. Class A 1.26
XOM Exxon Mobil Corporation 0.66
GTE Gran Tierra Energy Inc. 0.65
DIS Walt Disney Company 0.58
PEP PepsiCo, Inc. 0.57
MNST Monster Beverage Corporation 0.57
ORLY O’Reilly Automotive, Inc. 0.56
JKHY Jack Henry & Associates, Inc. 0.51
TTGT TechTarget, Inc. 0.50

Top Stock Pickers’ Exposure to Electronic Arts (EA)

Top stock pickers had negligible exposure to EA until 2015. In early-2015 EA became one of the largest exposures within our Expert Aggregate.

The top panel on the following chart shows cumulative nominal returns in black and cumulative residual returns (αReturns) in blue. Residual return or αReturn is the performance net of the systematic factors defined by the AlphaBetaWorks Statistical Equity Risk Model – the performance EA would have generated if systematic return had been flat. The bottom panel shows exposure to EA within the ABW Expert Aggregate:

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

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

Top Stock Pickers’ Exposure to Visa Inc (V)

Our Expert Aggregate was underweight (short) V between 2011 and 2014 – a period of flat-to-negative αReturn when V lagged a passive portfolio with matching risk. V became an ABW Expert Aggregate consensus long by early 2014 – the start of a strong positive αReturn period:

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

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

Top Stock Pickers’ Exposure to Exxon Mobil (XOM)

The Expert Aggregate was mostly underweight (short) XOM between 2008 and 2013, but grew exposure from 2012. By 2014 XOM was a top bet. This proved profitable during the 2014-2015 energy rout when XOM remained an island of stability, delivering positive αReturn:

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

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

Conclusions

  • Robust analytics built on predictive risk models identify the top stock pickers in the sea of mediocrity.
  • Top stock pickers’ portfolios deliver consistently positive αReturns (residual returns), independent of the Market.
  • Top stock pickers’ net consensus longs (relative overweights) tend to generate positive future αReturns and net consensus shorts (relative underweights) tend to generate negative future αReturns .
  • Top stock pickers are rarely the most celebrated firms and their consensus longs are rarely the most celebrated stocks.
  • The most celebrated stocks frequently appear as top stock pickers’ consensus shorts.
The information herein is not represented or warranted to be accurate, correct, complete or timely.
Past performance is no guarantee of future results.
Copyright © 2012-2016, AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved.
Content may not be republished without express written consent.
U.S. Patents Pending.
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Performance Persistence within Style Boxes

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

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

Measuring Persistence of Returns

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

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

Performance Persistence for all Institutional Portfolios

The Reversion of Nominal Returns

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

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

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

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

The Persistence of Security Selection Returns

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

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

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

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

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

Performance Persistence within Each Style Box

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

Large-Cap Value Portfolio Return Persistence

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

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

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

Large-Cap Growth Portfolio Return Persistence

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

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

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

Small-Cap Value Portfolio Return Persistence

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

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

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

Small-Cap Growth Portfolio Return Persistence

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

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

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

Summary

  • Nominal returns and related simplistic metrics of investment skill revert: as market regimes change, the top performers tend to become the bottom performers.
  • Security selection returns, when properly calculated with a robust factor model, persist and yield portfolios that outperform.
  • Both skill and lack of skill persist, and the lack of skill persists most strongly; while it is important that investors correctly identify the talented managers, it is even more important to divest from their opposites.
  • The reversion of nominal returns and the persistence of security selection skill hold across all style boxes, but security selection skill is most persistent for small-cap portfolios.
The information herein is not represented or warranted to be accurate, correct, complete or timely.
Past performance is no guarantee of future results.
Copyright © 2012-2016, AlphaBetaWorks, a division of Alpha Beta Analytics, LLC. All rights reserved.
Content may not be republished without express written consent.
U.S. Patents Pending.
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The Impact of Fund Mean Reversion

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

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

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

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

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

Hedge Fund Selection Using Nominal Returns

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

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

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

Cumulative Return (%)

Annual Return (%)

High Historical Returns

99.54

6.74

Low Historical Returns

125.12

7.92

High – Low Returns

-25.57

-1.18

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

Hedge Fund Selection Using Sharpe Ratios

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

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

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

Cumulative Return (%)

Annual Return (%)

High Historical Sharpe Ratios

115.31

7.48

Low Historical Sharpe Ratios

115.52

7.49

High – Low Sharpe Ratios

-0.20

-0.01

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

Hedge Fund Selection Using Win/Loss Ratios

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

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

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

Cumulative Return (%)

Annual Return (%)

High Historical Win/Loss Ratios

112.41

7.35

Low Historical Win/Loss Ratios

136.86

8.41

High – Low Win/Loss Ratios

-24.45

-1.06

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

Hedge Fund Selection Using αReturns

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

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

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

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

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

Cumulative Return (%)

Annual Return (%)

High Historical αReturns

144.33

8.72

Low Historical αReturns

104.25

6.97

High – Low αReturns

40.08

1.75

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

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

Conclusions

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

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

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

Mean Reversion of Nominal Hedge Fund Returns

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

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

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

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

Hedge Fund Performance Persistence: High and Low Historical Returns

Prior 36 Months Return (%)

Subsequent 36 Months Return (%)

High Historical Returns

52.84

28.17

Low Historical Returns

-11.43

28.33

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

Sharpe Ratio and Mean Reversion of Returns

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

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

Hedge Fund Performance Persistence: High and Low Historical Sharpe Ratios

Prior 36 Months Return (%)

Subsequent 36 Months Return (%)

High Historical Sharpe Ratios

43.65

28.38

Low Historical Sharpe Ratios

-8.34

25.66

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

Win/Loss Ratio and Mean Reversion of Returns

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

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

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

Prior 36 Months Return (%)

Subsequent 36 Months Return (%)

High Historical Win/Loss Ratios

26.93

27.59

Low Historical Win/Loss Ratios

1.70

26.53

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

Persistence of Hedge Fund Security Selection Returns

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

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

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

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

Prior 36 Months αReturn (%)

Subsequent 36 Months αReturn (%)

High Historical αReturns

60.90

5.65

Low Historical αReturns

-35.66

-4.58

Security Selection and Persistent Nominal Outperformance

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

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

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

Hedge Fund Performance Persistence: High and Low Historical αReturns

Prior 36 Months Return (%)

Subsequent 36 Months Return (%)

High Historical αReturns

81.70

32.50

Low Historical αReturns

-28.93

21.41

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

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

Conclusions

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

Should Investors Follow Buffet out of XOM?

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

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

Berkshire Hathaway’s Security Selection

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

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

Berkshire Hathaway’s Security Selection Return

Berkshire Hathaway’s Energy Security Selection

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

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

Berkshire Hathaway’s Energy Security Selection Return

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

Return (%)

Symbol Name

Total

Factor

Residual

COP ConocoPhillips

94.54

88.12

6.42

PSX Phillips 66

40.33

13.03

27.29

PTR PetroChina Co. Ltd. Sponsored ADR

187.96

104.42

83.54

SU Suncor Energy Inc.

106.19

94.98

11.21

XOM Exxon Mobil Corporation

60.52

50.65

9.86

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

Berkshire Hathaway’s Energy Market Timing

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

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

Berkshire Hathaway’s Energy Market Timing

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

Summary

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

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

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

Analyzing Hedge Fund Performance and Crowding

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

Hedge Funds’ Worst Sector: Miscellaneous Metals and Mining

Historical Hedge Fund Performance: Miscellaneous Metals and Mining

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

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

Hedge Fund Miscellaneous Metals and Mining Sector Aggregate Historical Performance

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

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

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

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

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

Current Hedge Fund Bets: Miscellaneous Metals and Mining

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

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

Crowded Hedge Fund Miscellaneous Metals and Mining Sector Bets

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

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

Hedge Funds’ Best Sector: Real Estate Development

Historical Hedge Fund Performance: Real Estate Development

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

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

Hedge Fund Real Estate Development Sector Aggregate Historical Performance

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

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

Hedge Fund Real Estate Development Sector Aggregate Historical Security Selection Performance

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

Current Hedge Fund Real Estate Development Bets

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

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

Crowded Hedge Fund Real Estate Development Sector Bets

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

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

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

Conclusions

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