ESG constraints and overlays can create significant systematic exposures within equity portfolios. Whereas some of these exposures may be intentional long and short industry bets, others are unintentional bets on the overall equity market or other macroeconomic factors. In order to deploy ESG strategies successfully, it is vital to identify, quantify, and manage the effect of ESG constraints on sy[…]

Factor analysis is a popular and effective
technique that explains and forecasts security returns. The factor models prevalent
in academic circles (Fama-French, Carhart) tend to
rely heavily on the size and value style
factors. Meanwhile, effective industry models often attribute
risk to sector and industry factors before style. Which approach is more
effective? Though claims that style
explains […]

Active Share is a popular metric that purports to measure portfolio activity. Though Active Share’s fragility and ease of manipulation are increasingly well-understood, there has been no research on its predictive power. This paper quantifies the predictive power of Active Share and finds that, though Active Share is a statistically significant predictor of the performance difference between port[…]

Rapid asset flows into smart beta strategies have led to concerns about froth and a vigorous debate among systematic portfolio vendors. At the same time, few discussions of smart beta crowding are burdened by data on the aggregate risk of smart beta strategies. This article attempts to remedy this data vacuum. We survey the risk factors and the stocks responsible for U.S. smart beta crowding. In d[…]

Whereas hedge fund crowding primarily consists of systematic factor bets, most analysis of hedge fund crowding focuses solely on popular positions. Moreover, such analysis usually assumes that hedge fund crowding in individual stocks is a bullish indicator. This article illustrates the flaws of these common assumptions, identifies the principal sources of hedge fund crowding, and discusses the opp[…]

The proliferation of smart beta strategies has raised questions about the relationship between the core risk factors that have formed the foundation of quantitative investment analysis for decades and the growing factor zoo of strategies. Whereas some state that “smart beta is the vehicle to deliver factor investing” others argue that “factor tilts are not smart ‘smart beta’”. A central question i[…]

A typical analysis of hedge fund crowding considers large, popular, and concentrated hedge fund long equity holdings. Such analysis usually assumes that crowding comes from stock-specific bets and that it is a bullish indicator. These assumptions are incorrect and have cost investors dearly:

Residual, idiosyncratic, or stock-specific bets now account for less than a third of hedge fund crow[…]

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 dem[…]

A typical analysis of hedge fund crowding surveys popular equity holdings. Yet, such residual, idiosyncratic, or stock-specific bets account for only 31% of current hedge fund crowding. Factor (systematic) risk, rather than a few specific stocks, is driving absolute and relative returns. Consequently, most analysis of hedge fund crowding focuses on a small fraction of crowding, missing its bulk.
[…]

Whereas most analysis of hedge fund crowding focuses on individual stocks, over 85% of hedge funds’ recent long equity variance has been due to their factor (systematic) risk. Residual, idiosyncratic, or stock-specific bets accounted for less than 15%. Thus, factor crowding has dominated hedge fund industry’s absolute and relative returns. This article reviews the most crowded hedge fund long equi[…]

Though many smart beta ETFs do provide valuable exposures, others mainly re-shuffle familiar dumb beta factors. Our earlier article showed that traditional, or dumb, Market and Sector Betas account for over 92% of monthly return variance for most U.S. equity smart beta ETFs. This article extends the analysis to international smart beta ETFs.

It turns out that international smart beta ETFs are e[…]

Our earlier articles discussed how some smart beta strategies turn out to be merely high beta strategies, and how others actively time the market, requiring careful monitoring. We also showed that the returns of popular factor ETFs such as Momentum and Quality are mostly attributable to exposures to traditional Market and Sector Factors. We now quantify the influence of traditional factors, or dum[…]

Typical analysis of hedge fund crowding focuses on individual stocks. This is misguided since over 85% of hedge funds’ monthly return variance is due to factor (systematic) exposures. Their residual, (idiosyncratic, or stock-specific) bets account for less than 15% of it. Likewise, factor crowding has driven much of the hedge fund industry’s performance and volatility. In Q2 2016, half of U.S. hed[…]

Asset outflows and portfolio liquidations have devastated crowded hedge fund bets since 2015. Losses have been especially severe in the Finance Sector. We survey hedge fund finance sector crowding and identify the stocks driving it. Investors and allocators must be vigilant: when capital flows out, these bets tend to suffer sharp losses. When capital flows in, they tend to benefit. We also provide[…]

Analyses of hedge fund crowding typically focus on hedge funds’ individual positions (their residual, idiosyncratic, or stock-specific exposures). Yet, over 85% of the monthly return variance for the majority of hedge fund long equity portfolios is due to their factor (systematic) exposures. Stock-specific bets account for less than 15%. Factor – rather than residual – crowding has driven much of […]

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[…]

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[…]

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 t[…]

Crowding consists of large capital pools chasing related strategies. Within the hedge fund industry, long equity portfolios crowd into several clusters with similar systematic (factor) and idiosyncratic (residual) bets. This hedge fund clustering is the internal structure of crowding. We illustrate the large-scale hedge fund clustering and crowded bets within the largest cluster. Allocators and fu[…]

Most analyses of hedge fund crowding focus on their residual (idiosyncratic, stock-specific) bets. This is misguided, since over 85% of the monthly return variance for the majority of hedge fund long equity portfolios is due to factor (systematic) exposures, rather than individual stocks. Indeed, it is the exceptional factor crowding and the record market risk that have driven much of the industry[…]

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 th[…]

There is a large difference between momentum strategies in theory and in practice. Given that much of its model performance derives from illiquid securities and high turnover, the academic momentum factor is a theoretical ideal that is not directly investable. Consequently, real-world momentum products, such as momentum ETFs, are restricted to investable liquid securities and usually reduce the ap[…]

Who’s Been Naughty or Nice
The five top hedge fund long stock pickers with long U.S. equity AUM over $3 billion produced 10.7% average annual alpha in the past three years (through October 2015).  The five bottom hedge fund long U.S. equity stock pickers had negative alpha averaging -6.5% during the same period. Both lists contain well-known and well-followed managers with a combined long U.S. eq[…]

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

The Virtuous and Vicious Cycles of Crowding
Hedge Fund Crowding has cost investors $12 billion in the first 10 months of 2015, and $9 billion in the August-October 2015 rout. That is to say, tractable hedge funds’ long U.S. equity portfolios have suffered severely negative active return from security selection (alpha, or αReturn) this year, and the liquidation has accelerated. Even ignoring fees,[…]

“This is your fund on drugs”
The Sequoia Fund’s (SEQUX) hefty sizing of Valeant Pharmaceuticals (VRX) dramatically changed the fund’s risk profile from historical norms. With the proper tools, allocators would have noticed this style drift back in Q2 2015 when Sequoia’s key factor exposures moved two to three times beyond historical averages. What’s more, allocators would have noticed a predicted[…]

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

An index fund aims to track the market or its segment, with low fees. An actively managed fund aims to do better, but with higher fees. So in order to earn its fees, an active mutual fund must take risks. Much of the industry does not even try. Mutual fund closet indexing is the practice of charging active fees for passive management. Over a third of active mutual funds and half of active mutual f[…]

A fund must take active risk to generate active returns in excess of fees. However, some managers charge active fees but manage their funds passively. Managers also tend to become less active as they accumulate assets. This problem of hedge fund closet indexing is widespread. Over a third of capital invested in U.S. hedge funds’ long equity portfolios is too passive to warrant the common 1.5/15% f[…]

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

This piece surveys hedge fund clustering in the energy sector and examines the large[…]

Fund crowding consists of investment bets shared by groups of funds – large pools of capital chasing similar strategies. Within the hedge fund industry, long equity portfolios crowd into several clusters with similar systematic (factor) and idiosyncratic (residual) bets. This hedge fund clustering is the internal structure of hedge fund crowding.

This piece illustrates the large-scale hedge fun[…]

Our June 2015 piece listed SunEdison (SUNE) and Micron (MU) among the top ten stocks driving hedge fund risk and alpha. In the semiconductor sector, they were virtually the sole drivers. In addition, since mid-2014 semiconductor sector alpha for hedge funds has been sharply negative. Extreme semiconductor sector crowding and threat of liquidation were ominous and actionable. Investors armed with c[…]

Hedge funds share a few bets. These crowded systematic and idiosyncratic exposures are the main sources of the industry’s relative performance and of many firms’ returns. Two factors and three stocks were behind most herding of hedge fund long U.S. equity positions in Q2 2015.

Investors should treat consensus ideas with caution: Crowded stocks are prone to mass liquidation. Crowded hedge fund b[…]

The 2014-2015 energy carnage has been worse for crowded hedge fund energy positions than the global financial crisis. Past liquidations of crowded hedge fund bets were followed by rapid recoveries. Consequently, energy investors should survey the wreckage for opportunities.

Crowded hedge fund oil and gas producers underperformed their sector peers by over 20% since 2013 as fund energy books wer[…]

Property and casualty insurance company portfolios share a few systematic bets. These crowded bets are the main sources of the industry’s and many individual companies’ relative investment performance. Since the end of 2013, these exposures have cost the industry billions.
Identifying Property and Casualty Industry Crowding
This analysis of property and casualty (P&C) insurance industry portfo[…]

Hedge funds share a few systematic and idiosyncratic bets. These crowded bets are the main sources of the industry’s relative performance and of many individual funds’ returns. Three factors and four stocks were behind the majority of hedge fund long U.S. equity herding during Q1 2015.

Investors should treat crowded ideas with caution: Crowded stocks are more volatile and vulnerable to mass liq[…]

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 to[…]

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 th[…]

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

Investors should treat crowded ideas with caution: Due to the congestion of their hedge fund investor […]

Why Size Isn’t Everything
Hedge fund survivor bias is especially insidious for the largest firms. Large hedge fund survivor bias overstates expected performance of the biggest firms by nearly half and their risk adjusted return from security selection (stock picking) by 80%. It is impossible to predict the largest funds of the future, but one doesn’t have to – robust skill analytics identify fund[…]

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

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

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

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 defer[…]

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

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[…]

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

Investors should treat crowded ideas with caution: Due to the congestion of their hedge fund investor base, crowded sto[…]

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 f[…]

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 system[…]

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 pe[…]

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. […]

U.S. hedge funds share a few systematic and idiosyncratic long bets – a phenomenon called “crowding.” Hedge fund crowding within specific sectors can be heavy; bets on exploration and production (E&P) companies are particularly crowded. Hedge fund E&P bets are the subject of this article. Eight stocks are responsible for three quarters of the herding.

Crowding is costly to investors, fu[…]

A Survey of Pershing Square’s Security Selection and Market Timing
Not all outperformance is true outperformance. There are many funds whose performance looks spectacular on the surface, but whose risk-adjusted performance is poor. This article takes a closer look at Pershing Square Capital Management, nominally one of the top performing funds throughout 2014 and over the long-term. We show that […]

U.S. hedge funds share a few systematic and idiosyncratic long bets – a phenomenon called “crowding.” Crowding exists within aggregate portfolios and within specific sectors. Energy bets are particularly crowded and are the subject of this article. Crowded bets are the main sources of hedge funds’ collective and many individual funds’ energy sector returns. Four risk factors (systematic bets) and […]

In periods of high oil price volatility, there is much discussion of its impact on various industries. Market noise obscures true industry-specific performance, so Oil’s impact is impossible to judge from simple index returns. But, by stripping away market effects, we observe the relationships between pure sector and oil returns. Airlines have the largest negative exposure to Oil, yet REITs provid[…]

Allocators who are unaware of hedge fund clustering and hedge fund crowding may be investing in an undifferentiated pool of consensus bets and paying high fees for closet indexing.

Hedge fund crowding has internal structure – clusters of funds with shared systematic (factor) and idiosyncratic (residual) bets. We examine the largest hedge fund cluster in which:

two risk factors cause most o[…]

The March to Uniformity – Illustrated and Quantified
We examined the evolution of systematic, idiosyncratic, and total risk of long equity hedge fund portfolios relative to each other.  We found decreasing differentiation and increasing herding over time. In summary, over the past 10 years total differentiation declined by 30% while systematic (factor) differentiation declined by 39%. As capital […]

Extraordinary Popular Delusions and the Madness of Crowding
U.S. hedge funds share a few systematic and stock-specific long bets. These crowded bets are the main sources of aggregate long hedge fund relative performance as well as many individual funds’ returns. Two risk factors and six stocks are behind most of this herding. The crowded stocks may experience elevated volatility due to the conges[…]

Managers Are Skilled in Specific Areas, Seldom Excellent at Everything
Investors typically treat all ideas of excellent managers with equal deference. This is a mistake. Most skilled managers achieve positive risk-adjusted performance in a few specific areas, and under-perform in others. This article continues the series surveying specific skills of widely-followed investment managers.
Paulson &[…]

Many Large-Cap Funds in Theory Are Small-Cap in Practice
Using the market capitalization of holdings, common in rudimentary forms of style-box analysis, provides an incorrect picture of style and risk for as much as a fifth of large- and mega-cap funds. In practice, these funds have the risk and return profiles of small-cap funds. Misidentifying such “Small-Cap Large-Cap Funds” distorts the risk […]

For Many Equity Funds, Bond Risk is More Important than Industry and Style
This year, equity fund investors have been reading – and will soon read more – quarterly letters lamenting volatility and poor performance. The true reasons are rarely identified. Portfolio managers themselves may not fully understand the causes. The hidden bond exposure in equity portfolios is often the culprit.
Hidden B[…]

Managers Are Skilled in Specific Areas, Seldom Excellent at Everything
Investors typically treat all ideas of excellent managers with equal deference. This is usually a mistake – even the most skilled managers are seldom equally skilled in all areas. Skilled managers may derive all of their risk-adjusted performance from a few specific areas, and under-perform in others.
Greenlight Capital – Lon[…]

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 invest[…]

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 […]

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 th[…]

Separating the Signal from the Noise
Market noise obscures the true relationships among individual sectors and true industry-specific performance. By stripping away market and broad macroeconomic effects, we can derive the returns of pure sector factors. Without proper analysis of these factors, accurate risk management, performance attribution, and manager skill evaluation are impossible. Invest[…]

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 conce[…]

Or How to Tell if You Are Paying Top Dollar for a Flawed System
This article is part of an ongoing series exploring flaws in popular investment risk and skill evaluation techniques. We focus on the most common pitfalls that have been particularly costly for asset managers and fund investors over the years.

The two primary approaches to investment risk and skill evaluation are returns-based sty[…]

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

Investment risk and skill evaluation frequently relies on returns-based style analysis, and returns-based performance attribution. These techniques perform regressions […]

If a fund posts returns that beat the indices, with moderate volatility and low benchmark correlation, there is no guarantee that such performance will continue. Comparable results might have been achievable with a passive portfolio. The fund could have taken hidden systematic risks or it may have been lucky. This happens often. Among Medium Turnover U.S. Mutual Funds, the relative ranking of a fu[…]