The old adage was that equity long/short managers would capture 80% of the upside in the S&P 500 but only half the downside.  Frustratingly, in the post-crisis period, the typical equity long/short manager has returned roughly half the upside and half the downside – not a very compelling proposition given the fee structure, illiquidity and headline risk.

Why, in recent years, has this proven strategy failed to outperform some traditional equity markets on a risk-adjusted basis?  I conclude that there are two interrelated factors.  First, a broad migration of capital out of the equity markets has undermined a key element of traditional value investing.  Second, a change in the structure and oversight of many funds has intensified pressure on near-term performance and position-level liquidity.  Both these factors have narrowed the typical opportunity set and, I believe, hindered returns.

The typical equity long/short fund was approximately flat over the five years to June 2012.  The driving factor, of course, is that equity returns have been poor – most indices are flat to down over the period.  The chart below tells the first part of the story.  Over the five years to June 2012, global equities, emerging markets and commodities were all roughly flat to negative 22%.  Clearly, this is bad for equity long/short managers who, as a group, run consistently positive betas (generally 0.3-0.5).

Figure 1 – Compounded performance by investment over the past 5 years

Given that equity long/short managers delivered around 400 bps per annum of alpha in the decade leading up to the crisis, one might have expected several hundred basis points of positive net returns even during a flat period in the markets.  What changed?  Most observers blame a combination of macroeconomic volatility, political uncertainty, high correlations, and too much capital in the industry.  I contend that a more subtle structural change in the equity markets has created an additional impediment to returns.

In the chart above, what’s striking is that fixed income returns were staggeringly high during this period of low to no returns in risk assets.  The Ten-Year Treasury future – an asset with a fraction of the risk of the other asset classes – appreciated around 60% with de minimus volatility.  The divergence between bonds and other assets is important since empirical evidence clearly demonstrates that capital chases returns.  This period is no exception:  the following chart (from ICI data) shows how almost $1.1 trillion of capital flowed into fixed income mutual funds over five years while $600 billion was withdrawn from equity mutual funds.  The scale of the withdrawal from equity mutual funds is notable given that all US equity mutual funds had perhaps $4.0-4.5 trillion in AUMs at the beginning of this period.

Figure 2 – Cumulative capital flows in mutual funds by underlying asset class

Interestingly, ETF fund flows were not nearly as significant a factor.  Despite rapid growth, ETFs still account for less than 10% of the domestic mutual fund industry; inflows to both bond and equity ETFs were less than $150 billion each over the same period.  Aggregate information on direct investment by institutional investors shows the same trend: allocations to equities have come down by around one-third over the past decade.

The narrow question is why this has been so problematic for equity long/short managers.  Equity long/short managers are predominantly fundamental value buyers.  Buy good/cheap stocks, short expensive/bad ones.  Consequently, most value investors rely on two assumptions:  first, the market will occasionally underprice the fair market value of a business and, second, other market participants over time will recognize the discount, bid up the price and close the valuation gap.  At Baupost, I’d sponsor investor dinners:  invite ten mutual fund managers and pitch one of our positions – and hope that Fidelity or Wellington would load up on it.  What we could find at 60 cents on the dollar, they might buy at 65, 70 or 80 cents.  This latter part of the process seems to have broken down.  Anecdotally, I repeatedly hear stories of managers who’ve bought “cheap” stocks only to see them drift downward on no material news.  I get the strong sense that this pattern has been repeated over hundreds of individual stocks over the past few years, and you can hear the fatigue in managers’ voices.   Overall, the data strongly suggests that the natural pool of buyers for off the run value stocks has been dwindling.

Historically, there have been two other potential buyers of undervalued stocks:  corporations themselves and private equity buyers.  In the chart below, we see that, while the number of announced buybacks has recovered to pre-crisis levels, the dollar amount is still 30% lower.  This is despite the fact that, as shown in the chart on the right, corporations are sitting on record levels of cash and can borrow at low rates (and, yes, there is still a huge tax benefit to leverage).  Another “buyer at a cheap price” appears to have pulled back from the market.

Figure 3 & 4- Quarterly share repurchase (left) and cash to debt (right) for S&P 500 companies

The third pool of potential buyers consists of private equity funds.  Pre-crisis, if a company’s stock was sufficiently cheap, a leveraged buyout firm or other private buyer might try to buy it.  Today, however, despite historically low interest rates and moderate equity valuations, public investments in US public companies remain at a small fraction of pre-crisis levels.  The following chart gives a sense of the magnitude of the decline:

Figure 5 – Private equity investments in US publicly-traded companies 

Put together, the data suggests that equity long/short managers have been caught on the wrong side of a major structural shift in the investment landscape.  Two generations of value investors have been trained that “other” buyers will recognize a mispricing, and correct it.  Over the past few years, the “other” buyers simply haven’t been there.  The change hasn’t been as sudden and dramatic as the seizing up of credit markets in 2007-08, but it’s arguably been just as important.

What will cause capital to return to the equity markets?  Several factors are at work here.  First, investors simply cannot afford to keep chasing fixed income returns when central banks have made it clear that they want to see more inflation in the system.  The scale of the distortion between the fixed income and equity markets over the past few years is shown below:  for fifteen years, the earnings yield of the Russell 2000 Value index was approximately equal to yield on the Two-Year Treasury note.  This 1:1 relationship was remarkably stable until early 2010.  Now, the forward earnings yield on the index is above 7% while the Two-Year yield is hovering close to zero.  Something is seriously askew here and it will correct itself over time.

Figure 6 – Yield comparison between Russell 2000 Value Index companies and 2Yr Notes

As the global deleveraging process continues, excess cash on corporate balance sheets will be used for acquisitions and stock buybacks; corporations worldwide have $3.4 trillion in cash today.  Private equity firms, sitting on $1 trillion of commitments that are due to expire in several years if undrawn, will resume investing in US public equities.  In the mutual fund world, capital chases returns, so this year’s performance will help.  Retiring Baby Boomers and institutions simply cannot afford to remain over-allocated to fixed income investments with negative real yields.  As shown below, the dividend yield on the S&P 500 now exceeds the yields on both the Two-Year Treasury Note and Ten-Year Treasury Bond.  For Baby Boomers, given that most dividends (still) qualify for special tax treatment, the after-tax current income of investing in equities is now materially greater than holding Treasurys.

Figure 7 – 2Yr and 10Yr US Treasury Notes yields vs. S&P dividend yield

For the reasons above, beta looks better today than at any time in the past ten years.  Just how good is a matter of debate.  Earlier this year, Goldman calculated an ex ante risk premium as high as 8-9%, and a well-known valuation expert came up with 6% — both well above the long-term average of 3-4%.  (Note that both of the calculations do rely on current negative real interest rate benchmarks.)  Either way, equities are a lot more attractive than fixed income today.  Ironically, my research shows that hedge fund managers correctly recognized this early in 2011, but remained underinvested due to macroeconomic uncertainties.

So, how has this affected the typical equity long/short manager?  The waning confidence in traditional value investing has led to a greater focus on positions with catalysts and, consequently, competition across a narrower opportunity set:

  • With less patience and tolerance for simply buying cheap stocks, I see more focus on stocks with defined catalysts.  2008 showed that the “valuation floor” simply wasn’t there for many stocks, especially illiquid ones.  Instead of “I can lose $10 but make $30,” managers have trouble quantifying the downside.  2008 also taught managers that to stubbornly hold a losing position can be fatal to the fund.  The subsequent years have reinforced the idea that cheap stocks can get cheaper – much cheaper – without much news.  However, by avoiding cheap stocks without catalysts, managers narrow the opportunity set and may limit returns.
  • Managers have gravitated to large cap stocks, where liquidity is seen as a potential buffer against severe drawdowns.  Among investors, the fear of gating remains irrationally high; a highly liquid portfolio is a precondition for retaining and raising assets these days.  Managers seek to demonstrate that they can liquidate positions in a matter of days; the decline in overall trading volume, as shown to the right, limits many funds to large cap stocks.  This raises a legitimate question as to whether managers can maintain an information advantage relative to the rest of the market.
        Figure 8 – NYSE 12 month average daily value traded

Both shifts have the effect of narrowing the opportunity set; consequently, competition has increased and position crowding is prevalent.  The Tiger and Lone Pine progeny employ similar analytical techniques, information, etc.  A talented pool of managers has been fighting for alpha in a narrower opportunity set.  A secondary constraint has created an additional impediment to returns:  managers have faced intense pressure to post near-term results. More lenient redemption terms plus greater institutional oversight has shortened the typical evaluation cycle from six months to one or two.  Patience is rarely rewarded and managers are often driven to cut risk quickly in down markets and chase returns in up markets.  My research clearly shows that hedge funds are very good at gradually adjusting exposures across asset classes over months and quarters, but that near-term market timing is difficult, if not impossible, to get right consistently.  This represents another clear constraint that may impact long-term returns.

The past five years have demonstrated quite clearly that even highly talented managers will struggle in the face of wrenching structural shifts in the markets and creeping changes in the fund management investment model.  Going forward, I conclude that the principal underlying issue – the flight of capital out of equities – will reverse itself.  Stronger returns will draw capital flows, institutions and individuals will rebalance overweight fixed income allocations accordingly, and external buyers like corporations and private equity firms will resume historical levels of investment.  This process will create ample opportunities for talented managers, broaden the opportunity set, and enable many equity long/short funds to deliver higher risk-adjusted returns going forward.

However, not all managers will benefit equally.  When evaluating an individual fund or strategy, it has become increasingly important to understand the precise constraints that can hinder returns – for instance, a cyclically underwhelming opportunity set, a mismatch between manager and investor expectations, reactive deleveraging and re-leveraging, an overemphasis on near-term results, a focus on crowded large cap stocks, the competitive landscape among investors, insufficient resources to identify off the run opportunities, and many other factors.

I look forward to discussing these topics with you in the future.  Please do not hesitate to send me any questions or comments you may have.



The Hedge Fund Mirage, a book by Simon Lack, has attracted considerable attention in the press over the past few months.  It was reviewed favorably in several well-respected financial newspapers and has been cited as an authority in several recent articles on hedge fund performance.  Lack’s headline-grabbing conclusion is that hedge fund managers were paid 84% of every dollar the industry made during 1998-2010.  The actual number is somewhere between 35% and 40%.  Lack’s analysis is somewhat convoluted and confusing, but it appears that his estimate is badly overstated for two simple reasons:  first, he relies on a distorted estimate of “hedge fund industry” returns that skews his analysis and, second, he deducts the risk-free rate from “net investor profits.”

Inexplicably, Lack uses a hedge fund index – the HFRX Global Investable Hedge Fund Index (HFRXGL) – that simply didn’t exist until March 2003, or almost halfway though the period studied.  The pro forma numbers preceding inception are replete with “backfill” bias, and hence not reflective of actual industry returns.  From January 1998 through March 2003, returns are overstated by over 6% per annum, or roughly double actual industry returns.  In the chart below, the performance of the HFRXGL during the “pro forma” period to that of the more reliable HFRI Fund of Funds index.  (See Jan 2012 blog post The Least-Biased Way to Estimate “Hedge Fund Industry” Returns for a more detailed explanation.)

By contrast, from 2003 to 2010, the index had a design flaw that created a statistical problem called “adverse selection.”  Better managers simply refused to join this particular index given requirements for managed accounts, transparency and liquidity.  Consequently, during the “live” period, the HFRXGL underperformed more reliable measures of hedge fund industry returns by almost 4% per annum.  (Lack argues that the HFRXGL numbers should have an additional “haircut” of around 3% per annum since, as he notes, some indices have an upward statistical bias.  This assertion should be dismissed and is further evidence of a failure to understand the HFRXGL.)

Accidentally, it seems, the compound returns for the HFRXGL for 1998-2010 are similar to actual industry returns – around 7% per annum.  The issue arises when that Lack insists on the use of “dollar-weighted” returns or, more precisely, weights each year’s percentage returns by average assets under management.  Since the HFRXGL returns are badly understated during the high AUM period, the latter years of “poor” performance are given far too much weight.  Correcting for this, the results are around 35-40% to hedge fund managers, not the 84% he cites.

The second issue is that Lack deducts Treasury bill yields from net investor returns.  In rough terms, the average Treasury bill yield over 1998-2010 was approximately 3.4%, or roughly half industry returns of 7% per annum.  This statistic shocks most investors; it’s a good argument for industry-wide hurdle rates and one of the most often overlooked issues in the industry.  Unfortunately, today there are many asset classes that failed to even match the returns of short-dated Treasurys over the same period.

The problem is with the distorted HFRXGL numbers – too high early on, too low later.  During 1998-2002, the pro forma returns were over 13% per annum, or 800 bps over the average risk-free rate of 5.2%.  For 2003-2010, by contrast, the index outperformed the risk-free rate by only 100 bps per annum (3.4% to 2.4%).  Using dollar weighted returns, the latter years are overemphasized and result in the risk free rate accounting for over 80% of net investor dollar profits.  The end result is that the numbers provide a compelling case for how industry returns have declined relative to the risk free rate in recent years as AUMs have grown.

Ironically, using this metric and a more accurate gauge of industry returns, the latter years actually look better.  (I question whether it is in fact a useful metric, but that’s for another time.)  During 1998-2002, when short-term rates were higher, the industry returned approximately 200 bps over the risk free rate; over 2003-2010, the industry returned 350 bps per annum over the risk-free rate.  In any case, overall the risk-free rate was equivalent to approximately 50% of net investor dollar profits, not the 80% he calculates.

To conclude, serious debate requires rigorous analysis.  As someone who has spent the past six years spearheading practical ways to enable investors to get “hedge fund returns” with lower fees, more transparency, better liquidity and other features, I find myself in agreement with Lack’s general conclusions:  indeed, there has been a big drop off in industry performance since 2007 and many managers certainly are overpaid.  However, I’m disappointed by the lack of careful analysis.  AIMA’s thirty page paper in response to Lack’s book missed the critical issue – that the HFRXGL is simply the wrong starting point.  The most compelling argument in Lack’s book is that investors would benefit from a hurdle rate on incentive fees – whether based on a risk free rate or market beta — but this is lost in his confusing and misguided analysis.

One of the most vexing issues in the hedge fund industry is the relationship between growth in assets under management (AUMs) and future returns. An abundance of anecdotal evidence suggests that smaller managers with great performance attract capital quickly, which dilutes future returns. Further, the recent concentration of capital among large hedge funds has raised questions as to whether this has contributed to the decline in industry-wide alpha.

In a recent report, we analyzed nearly 3,000 equity long/short funds in order to get to the heart of this issue. Prior studies have had limited usefulness due to three issues:  (a) the inclusion of backfilled database returns, which seriously distorts the overall results (Pertrac and Barclays); (b) a population of funds across the industry as a whole which includes differing strategies with divergent capacity issues; and (c) a failure to exclude managers or funds below a reasonable AUM threshold (most studies), which results in an impractically small average fund size. By contrast, we focused narrowly on the equity long/short space since these managers are likely to face similar capacity constraints (as opposed to, for instance, macro investors or CTAs). Backfill issues were addressed by including fund returns only from the date when the funds began to report to the database; our studies show that “pre-reporting” returns are 6-8% above “post-reporting” results, which shows the magnitude of the issue. Finally, we grouped funds according to manager since many funds have multiple share classes and since some growth-related issues are firm-specific as much as they are fund-specific.

In our study, we divided the universe into firms that managed $50 million to $500 million in equity long/short AUMs (“Small”) and those that managed more (“Big”). The $50 million lower bound was selected to make the sample more representative of an actual emerging manager investment program. The result of the study clearly demonstrates that smaller hedge fund managers tend to outperform larger ones – a conclusion that will resonate with many hedge fund investors:

  • Small firms outperformed Big by 254 bps and 220 bps per annum over five and ten years, respectively;
  • Outperformance was most pronounced preceding and following the crisis, especially 2009, while drawdowns were in line during the crisis;
  • Virtually all of the outperformance was due to alpha, not beta; and
  • The dispersion of returns among smaller funds was greater than those of larger firms.

The fund population in the Beachhead study consisted of 2,827 equity long/short hedge funds drawn from the HFR database of live and dead funds. Funds were included based on strategy classifications for fundamental value, fundamental growth and sector specialists in technology, healthcare, energy and basic materials; quantitative-driven, market neutral and short biased funds were excluded.  For each year, the funds were grouped into Small and Big portfolios that were equally weighted and rebalanced monthly. To mitigate backfill bias, the returns for a given fund were only included as of the date it began reporting to the database.

In the sample, the number of Small and Big firms was roughly equal. In 2012, a Small firm managed $193 million on average while a Big firm managed $3.7 billion.  Interestingly, average Big firm assets have tripled over the past decade while Small firm assets have remained roughly constant, which reinforces the view that Big firms have had a much easier time attracting capital.

Over the past ten years, Small equity long/short managers returned 7.56% per annum and outperformed their larger peers by 220 bps per annum (over the past five years, outperformance was 254 bps per annum). Small managers showed a moderately higher annualized standard deviation, but drawdowns were in line and the Sharpe ratio was significantly higher. The chart below shows the returns of Small and Big managers over the past ten years.

As shown below, Small managers materially outperformed both before and after the crisis, especially during the post-crisis recovery. Somewhat surprisingly, despite somewhat higher beta, smaller firms did not underperform larger ones during the crisis, which may reflect superior risk management or a more manageable capital base.

The remarkable consistency of the outperformance is evident in the following chart. The date on the top row reflects the inception of an investment that terminates in the year on the vertical column; the percentage in the corresponding cell reflects the compound annual return differential of Small less Big over the specified period. For instance, the cell on the upper left shows 2.20% of compound annual outperformance of Small vs. Big over 2003 to 2012. Green boxes show periods of outperformance of Small.

It’s notable that outperformance does not appear to be attributable to rapid or regular changes in market exposure; rather, the beta of Small to Big is quite consistently around 1.1 with stable correlations above 0.95. Consequently, comparable drawdowns during the crisis itself seem to reflect somewhat larger market-driven losses offset by persistent positive alpha.

The following chart examines the alpha of the Small portfolio relative to the Big portfolio over the ten year period.  Note that alpha is annualized in the box. The vast majority of outperformance is due to alpha, not beta, which confirms that higher risk taking does not explain the differential.

We also studied the dispersion of returns in order to test the hypothesis that smaller managers are motivated to outperform, while larger firms are likely to be more risk averse. The chart below shows the annual returns of the funds in the Small and Big portfolios with the mean, minimum and maximum fund returns. In virtually every year since the late 1990s, the top performing Small funds have materially outperformed the comparable Big funds.  Interestingly, the worst performing Small funds matched or outperformed the comparable Big funds in the vast majority of years, which supports the argument that Small fund outperformance is not driven by higher symmetric risk taking.

As a final test, we analyzed what would happen if the “threshold” between Small and Big managers was lowered or raised. The chart below shows the differential in returns over the past five and ten years and clearly shows that outperformance of “smaller” managers declines as AUMs increase. For instance, when “small” is defined as less than $1.0 billion, the outperformance relative to larger managers is 1.1% over ten years and 0.6% over five years.

The key question for investors is what factors drive the differential in returns. Three potential factors include:

  • The most talented managers self-select to start firms rather than work at larger firms. Many talented managers build experience in larger firms before launching their own firms.
  • Smaller managers have a better opportunity set:  based on trading volume, the number of potential long and short side investments declines by up to 80% between $100 million and $1 billion in AUMs. Off the run and less efficiently priced stocks can have a meaningful impact on returns. Anecdotally, capacity issues materialize sooner on short positions.
  • Pressure to perform:  performance fees are a higher percentage of overall compensation and drive asset growth. Our analysis suggests that up to 80% of the enterprise value of larger firms is due to capitalized management fee EBITDA as opposed to performance fees.

The study is part of a series of papers on the equity long/short space and is available at


Short selling is an integral component of the hedge fund business model.  In addition to mitigating market risk, hedge funds can generate excess returns by identifying overpriced securities.  With the ability to both buy and short stocks, hedge funds have roughly double the opportunity set of long only investors, a critical competitive advantage.

Shorting has always been more difficult than buying stocks.  Equity markets tend to appreciate over time, so even highly talented short sellers are likely to lose money on an absolute basis in most years.  Heavily shorted stocks are often prone to significant price volatility and stock prices can run up before rationality sets in. Theoretical upside is capped at 100% while downside is unlimited. In extreme cases, regulatory changes can materially disrupt the market, such as the temporary short selling ban on financial stocks during the height of the financial crisis.

Based on internal studies, hedge funds had difficulty making money by shorting stocks during 2010 and 2011.  In this note, we briefly explore four factors that have created headwinds for short sellers post-crisis:

  • Lower interest rates have reduced the short rebate and made shorting more costly on an absolute basis.
  • Stock lenders have become more proactive about increasing borrow costs for difficult to borrow securities which cuts into fund profits.
  • A concentration of capital among larger funds has narrowed the opportunity set for large funds.
  • Regulatory changes have increased disclosure and created other challenges.

It’s important to note that the short side of the market in many ways is antiquated and much more opaque than the long side.  Funds do not file 13Fs or other easily accessible reports (with the exception to the recently introduced disclosure requirements in Europe), there is no central repository or clearinghouse for stock borrow rates, and rebate and other costs are negotiated between lenders and borrowers on a confidential basis.  The market is largely an overnight market since most stock lenders are unwilling to lend stock for term in case the portfolio manager elects to sell it.

Four Factors

1.            Decline in interest rates.  When a hedge fund borrows a stock and sells it short, the hedge fund provides the cash proceeds (plus a little extra) as collateral to the lender. The lender typically invests this cash and generates a return, a portion of which is shared with the hedge fund. This “short rebate” is typically tied to the Fed Funds rate.  As shown in the adjacent chart, the Fed Funds rate is at historically low levels and is not expected to increase materially anytime soon.

Short Selling 1

Figure 1 – Fed Funds Rate 2000-12

The decline in interest rates has a material impact on the economics of shorting. To use a simple example, when short-term rates are 4%, the hedge fund might expect a short rebate of, say, 3%. If the hedge fund runs an average gross short exposure of 70%, it can expect to earn around 2% from the short rebate (assuming that the dividend yield on the stock is zero, a reasonable assumption for companies with deteriorating business models). This 2% gross return offsets the higher fee structure of hedge funds relative to long only managers. Conversely, when short-term rates are close to zero, the short rebate essentially is zero.

A related factor is that lenders are much more sensitive to the risks of how the collateral is invested.  During the crisis, many beneficial owners experienced large and unexpected losses on collateral pools that were exposed to Lehman Brothers, SIVs, RMBS and other yield enhancement products.

2.            More proactive stock lenders.  JP Morgan recently published a highly informative report which demonstrates that the rising cost of borrowing heavily shorted stocks is not due to excess demand, but rather that lenders have become much more active participants in the market. Pre-crisis, many pension funds and other institutional investors were relatively passive lenders, content to earn a portion of the return on cash collateral to add a premium to stock returns.  Today, the combination of the decline in short term rates and the unexpected losses on collateral pools led many beneficial owners to focus more on how to maximize profits and mitigate risk on securities lending.

As more beneficial owners and their agents view stock lending as a profit center, wholesale stock inventories have increased materially.  Wholesale inventories have more than tripled over the past five years (Figure 2), which corresponds with a rapid growth in the shares held in wholesale inventories by agent lenders (Figure 3), who have the infrastructure and knowledge base to help beneficial owners maximize value on securities lending.

 Short Selling 2a

Figure 2 – Shares of equity securities available for lending in agent lender inventories [1]

 Short Selling 2b

Figure 3 – Shares of equity securities on loan by agent lenders [2]

Notably, higher inventories have not translated into lower borrowing costs:  in North America and Asia, the cost of borrowing equities has risen by 41 bps and 61 bps, respectively, since 2007.

By far the greatest impact is in the small subset of stocks where demand to borrow is high – those with annual lending rates of greater than 250 bps per annum. With interest rates near zero and aversion to return-enhanced short term instruments increasing, stock lenders have become particularly aggressive in raising rates on 6% of securities that fall into this category – arguably, the stocks with the most demand from hedge funds.  While the average borrowing cost on very hard-to-borrow securities (>250 bps per annum) has trended upward (Figure 4), these securities now constitute the majority of the stock lending revenue (Figure 5).

Short Selling 3a

Figure 4 – Equity borrowing costs for specials [3]

Short Selling 3b

Figure 5 – Securities lenders revenue attribution [4]

The net result is that borrow rates appear to rise faster today in response to greater demand and that successful fund managers must short stocks earlier than others. Timing and uniqueness of ideas have become substantially more important.

3.            Concentration of capital.  The concentration of capital among larger funds is a deterrent for the hedge fund industry as a whole to make money on the short side. Anecdotally, larger managers run into capacity constraints much earlier on the short than long side and are forced to rely more heavily on sector or market indices as beta hedges. In the chart, we show break points in the opportunity set of US equities assuming that a manager limits investable positions to 3% of the portfolio and 20% of the average daily trading volume over five days.

Short Selling 4

Figure 6 – Constraints as AUMs Increase

A diversified short portfolio typically includes 30 to 40 positions.  In theory, a smaller average target position size should lead to a larger opportunity set.  Somewhat paradoxically, though, larger managers run into capacity constraints much earlier on the short side.  The reasons are both structural and behavioral.  Short positions, especially those with higher short interest ratios, are subject to violent price movements; liquidity evaporates quickly as a succession of managers hit loss limits and rush to cover.  Funds can be forced to buy-in positions if stock loans are pulled – generally, at precisely the wrong time.  Due to this, liquidity constraints are far more onerous on the short side.  Loss aversion and risk control criteria lead managers to limit short-side positions sizes to avoid catastrophic, franchise-threatening losses.

4.            Regulatory changes.  Due to a widespread view that naked short sales exacerbated price declines during the crisis, US regulators have sought to tighten regulatory compliance and oversight in the stock lending market. Most significantly, in October 2008 the SEC issued temporary Rule 204T to curb naked short selling by shortening the delivery window and expanding the rule to cover all equity securities. The rule successfully reduced fails to deliver and the SEC finalized it in July 2009.  The practical consequence is that prime brokers today have far less time to obtain shares, as short positions in equity securities must be closed out on the settlement date. This development has made it more difficult for hedge funds to short as aggressively as they once did. Additionally, to ensure that they are able to locate shares pursuant to Rule 204, prime brokers have become increasingly willing to borrow securities irrespective of their rates. Brokers’ willingness to borrow at higher rates has, in turn, added to the rising costs for hedge funds to sell short equity securities.

In Europe, the EU passed new short selling regulations on November 1, 2012.  The new rules have similar provisions to Regulation SHO to limit naked short sales.  In addition, the regulations mandate public disclosure of large short positions (greater than 0.5% of the shares outstanding) for securities whose primary trading venue is in the EU.


The response from managers to these questions has been varied.  A few consider the rising cost of specials to be insignificant relative to the opportunity set available on the short side:  with so many industries in flux, they argue that a few hundred basis points of incremental borrowing costs are irrelevant when ailing businesses can disappear within a few years.  Others argue that they tend to avoid crowded, and hence expensive, shorts by virtue of focusing on off the run opportunities.  A third group argues that there are ample opportunities in large capitalization equities with de minimus borrowing costs.

Clearly, the most important factor in the profitability of a short book will be the ability of a given manager to identify securities that materially underperform the market, and hence add alpha.  In a prior note, we noted that the most heavily shorted US equity securities outperformed (hence short sellers underperformed) the S&P 500 in both 2010 and 2011, in sharp contrast to the several hundred basis points of annual underperformance during the mid-2000s.  We later saw some evidence that this trend abated in 2012, when heavily shorted equities performed in line with the S&P.  Given the combination of macroeconomic uncertainty but strong equity markets, it will be interesting to see how this trend develops over the coming year or two.

[1] Source: JP Morgan Prime Brokerage Perspectives December 2012

[2] Source: JP Morgan Prime Brokerage Perspectives December 2012

[3] Source: JP Morgan Prime Brokerage Perspectives December 2012

[4] Source: JP Morgan Prime Brokerage Perspectives December 2012

This note re-examines two frequently cited studies on factor-based hedge fund replication:  Hasanhodzic and Lo’s seminal paper, “Can Hedge Fund Returns be Replicated?:  The Linear Case” (“Lo”) and Amenc et al., “Performance of Passive Hedge Fund Replication Strategies” (“Amenc”). Lo was the first to articulate that a linear, factor-based model could successfully replicate the returns of various hedge fund strategies. Amenc, on the other hand, was highly critical of the approach and sought to disprove its effectiveness.

As outlined below, the most important finding of the Lo paper is often overlooked:  that the simple five factor model appears to have done an even better job of replicating the returns of the sample than the authors articulate. The Amenc paper, on the other hand, was highly critical of the approach and concluded that the replication results were consistently inferior to those of actual hedge funds.  However, the study’s conclusions were severely undermined by poor factor specifications which distorted the results.

Hasanhodzic and Lo, “Can Hedge Fund Returns be Replicated?:  The Linear Case” (2007)

This important paper, first released in 2006, introduced the concept of using a 24 month rolling-window linear regression to replicate hedge fund returns out of sample.  In many ways, this seminal paper launched the factor-based hedge fund replication business.  Remarkably, though, the authors overlooked the most important conclusion:

  • Using a simple five factor model, the replication of an equally-weighted portfolio of 1,610 funds appears to capture all or virtually all of the returns over almost 20 years, adjusted for survivorship bias.

In other words, the simple clone’s performance exceeded all expectations and is consistent with the performance of actual hedge fund replication indices since 2007. Remarkably, this pro forma performance of the clone was approximately equal to the performance of the S&P 500 over the same period, but with materially lower volatility and drawdowns. This is a startling result that is lost in the paper’s forty pages of formulas, text and tables. Here’s why:

The data set used was based entirely on “live” funds in the TASS database as of September 2005 – 1,610 funds.  Invariably, “live” funds have outperformed “dead” peers by a wide margin:  in the HFR database, for instance, by more than 400 bps per annum. Inexplicably, Hasanhodzic and Lo assert that “any survivorship bias should impact both funds and clones identically,” and therefore can be ignored. This simply is incorrect. We know today that these kinds of data bias, by definition, are “non-replicable.”  Therefore, the clone should be compared to actual realized performance – i.e. adjusted for survivorship bias. This is why replicators are often benchmarked against indices the like HFRI Fund of Funds index that are more representative of actual investor returns.

From Figure 5 in the paper, we can infer that the equally-weighted portfolio of sample funds returned between 13% and 14% on a compound annual basis over almost twenty years. This clearly is unrealistically high: hedge funds as a group simply did not outperform the S&P by 200-300 bps per annum on a net basis during a twenty year bull market in which stocks returned 10% per annum. Assuming several hundred bps of survivorship bias, the hedge fund portfolio would have slightly underperformed the S&P 500, but with materially lower drawdowns and volatility.  And, in fact, this is precisely how the simple clone performed. See Figure 5 reproduced below with commentary added.

Factor based photo 1

In this context, the performance of the linear clone (around 10% per annum) is remarkable and should have been highlighted more prominently.

A secondary issue is the use of a factor set that is missing important market exposures. The study employs only five market factors: the S&P 500 total return, the Lehman AA index, the spread between the Lehman BAA index and Lehman Treasury index, the GSCI total return, and the USD index total return. More recent studies, including our own, have demonstrated that emerging markets, short term Treasury notes and small capitalization equities are important factors since they enable the models to incorporate, respectively, volatility expectations, yield curve trades and market capitalization bias. Conversely, while the inclusion of the GSCI has intrinsic appeal, it does not appear to be additive over time to out of sample results. Consequently, the overall results arguably would have been even more compelling with a slightly more robust factor set.

Amenc et al., The Performance of Passive Hedge Fund Replication Strategies (2009)

In response to the paper by Hasandhozic and Lo and the launch of several factor-based indices, EDHEC released several papers that were highly critical of the concept during 2007-09.  In the first paper, “The Myths and Limits of Passive Hedge Fund Replication:  An Attractive Concept… Still a Work-in-Progress,” the authors seek to redo the rolling linear model employed by Hasandhozic and Lo, but apply it to the EDHEC hedge fund database. Since there is very little explanation of the underlying data, it is impossible to estimate the effect of survivorship bias or other sampling issues.

The more relevant paper was published in 2009, “The Performance of Passive Hedge Fund Replication Strategies.” It is difficult to read this paper without the sense that the authors, who are closely tied to the fund of hedge fund industry (and funded by Newedge), had a predetermined agenda.  The end result is a paper that includes some very helpful analysis – for instance, that Kalman filters and non-linear factors don’t improve out of sample results – but whose conclusions are undermined by selective omission.  For instance:

  • Even though there was over two years of live data from replication indices that showed strong results with high correlation through the crisis, the authors neglect to include this and focus instead on re-doing the Lo analysis with the admittedly incomplete five factor set.
  • When the authors do in fact acknowledge that Lo’s factor base should be expanded to include emerging markets, small cap stocks and other factors, they test each strategy with an unreasonably narrow subset of factors even though it was well established by this time that a more robust factor set was critical.  This is discussed in detail below.

In Section 3.2, the authors “test whether selecting specific sets of factors for each strategy leads to an improvement in the replication performance. Based on an economic analysis and in accordance with Fung and Hsieh (2007), who provide a comprehensive summary of factor based risk analyses over the past decade, we select potentially significant risk factors for each strategy.” The factors identified are quite reasonable, such as the spread between small and large capitalization stocks, emerging markets, and other fixed income spreads.

In the table below, the five factors on the left side represent the original Lo portfolio, while the five on the right represent the Fung & Hsieh additions.

Factor based photo 2

The logical next step would be to test whether the results of the Lo five factor set is improved by the addition of one or more of the factors.  Instead, the authors only use 1-4 factors for each strategy and throw out most of the original factors. Remember that at this time it was well established that a narrow factor base was insufficient to replicate most hedge fund returns.  This is why Merrill, Goldman Sachs and others all used 6-8 factors, not 1-4. To use one example, in order to seek to replicate the macro space, the authors used only the Lehman AA Intermediate Bond index – a single factor – with a 24 month rolling window. For distressed, the one factor is the spread between a BAA index and Treasurys. For risk arbitrage, it’s only the S&P 500. For long/short equity and funds of funds, it’s the S&P 500 and the small cap-large cap spread.

To underscore the point, the debate at the time was not whether one or two factors could reasonably replicate sector returns, but whether a diversified portfolio of market factors could do so. By starkly reducing the factor set, the authors essentially designed an experiment that was bound to fail.  Consequently, investors should seriously question the validity of the authors’ conclusion that “the performance of the replicating strategies is systematically inferior to that of the actual hedge funds.”

As institutional investors search for ways to reduce fees in hedge fund portfolios, attention has turned to the relative merits of investing directly in hedge funds vs. seeking to replicate the performance of a given strategy by investing directly in the underlying market exposures, or risk premia.  The idea has intrinsic appeal:  why pay hedge funds 2/20 if returns can be delivered cheaply and efficiently by investing directly in the underlying exposures?

Factor-based replication is highly effective at delivering the results of most hedge fund strategies – especially equity long/short and more directional strategies – but is arguably less effective with strategies with low exposure to traditional asset classes.  For merger arbitrage specifically, it may make more sense to try to replicate the underlying trading strategy itself:  that is, by acquiring a representative sample of corporate acquisition targets and, for stock-based deals, shorting the acquirer.  In theory, this should enable a sophisticated investor to derive similar returns but with greater liquidity and transparency, lower fees and other benefits.

In this note, we examine three indices that have been constructed to deliver a liquid, investable alternative to investing in merger arbitrage hedge funds.  We compare the returns of the live results against the performance of actual merger arbitrage hedge funds and conclude that the approach indeed can be effective at delivering comparable returns, with a few important caveats:

  • First, index design is extremely important – one of the three indices clearly fails at its stated objective and it’s not clear how to anticipate which index will perform well going forward.
  • Second, the indices do not appear to improve returns – that is, eliminating the 2/20 does not result in higher returns.
  • Third, and surprisingly, the ability to short appears to have very little impact on returns.

For comparative purposes, we also explore two alternatives to rules-based trading:  an established merger focused mutual fund and factor based replication.

Merger Arbitrage Index Construction and Results

The S&P Long Only Merger Arbitrage (SPARBM) and the IndexIQ Merger Arbitrage (IQMNAT) indices were launched prior to the financial crisis, while the Credit Suisse Liquid Alternative Beta (CSLABMN) index was launched in January 2010.  Each index seeks to provide a broad representation of the merger arbitrage space by investing in companies that are subject to takeover offers. A review of the index construction methodology of each index provides a window into the complexity of designing a set of coherent and consistent “rules”:

  • which markets to include, especially whether to include emerging market targets;
  • minimum transaction or target size;
  • when to initiate the position, how to size it and when/how to rebalance;
  • which types of transactions to exclude, such as CVRs or non-control tenders;
  • whether/when to short and which instruments to use;
  • whether the target needs to be at a discount to the announced price or not; and
  • how to treat new offers or stale deals.

The following chart provides a comparison of key parameters and recent top five holdings:

1 (2)

Given similar objectives, it’s a bit surprising that no top five position is shared by all three indices.  In fact, two of the top five CSLABMN positions do not appear at all in the IQMNAT holdings (unfortunately, the full CSLABMN position list is unavailable, so it’s impossible to undertake a full portfolio comparison).  The S&P index appears to have more of a midcap bias, with a median daily trading volume of roughly one fourth those of the other two indices.

The differences in construction can have a material impact on returns.  (Note:  Since we are skeptical of back-filled index data, we use only the results from when the sponsor began to publish live results.   Consequently, we examine the two former indices from the beginning of 2008, and include all three after January 2010.)  Surprisingly, IndexIQ’s long/short product had materially greater drawdowns during the crisis relative to the (long only) S&P index and has performed relatively poorly since.  By contrast, CS’s product performed similarly to the S&P index until 2012, when it underperformed by approximately 600 bps.

1 (2)

As noted, the S&P index above is a long only index.  In late 2012, S&P introduced a long/short version of the index, which has had very erratic performance since inception.   The following chart shows the performance of both the long only and long/short indices since September 2012:

S&P Long Only Merger Arb

The variability in returns between the different indices (IndexIQ vs the others) and even among providers (S&P Long Only vs S&P Long/Short) highlights the fact that the specification of rules for an “alternative beta” strategy like  merger arbitrage is far more complicated than that for traditional indices.

Index Performance vs. Hedge Fund Performance

Due to the relatively erratic performance of the IndexIQ index and the recent (and very poor) performance of the S&P Long/Short index, we have excluded them from the following analysis under the assumption that few investors would opt to use them as merger arbitrage proxies at this point.  Instead, for simplicity and clarity, we compare the results of just the S&P (the live period) over the past five years to the performance of the HFRI Event-Driven:  Merger Arbitrage index[1]:


Since January 2010, when the CSLABMN was introduced, both the SPARBMN and CSLABMN have outperformed the hedge fund indices. That said, year to year differences can be significant:  the indices outperformed materially during 2010 when many hedge funds deleveraged during the inception of the European fiscal crisis, while the CS index underperformed by approximately 600 bps during 2012.

S&P - HFR - CS

It’s important to note that the indices do not include management fees (although the CSLABMN does include a 50 bps index calculation fee), while the hedge fund indices are reported after hedge fund level fees.  In rough terms, the indices returned around 4% per annum gross over the past three years, while actual hedge funds returned around 3% net.  A more accurate comparison would be to look at the net returns to investors of each approach.  If we assume 100 bps of management fees for the index products, the compound returns over the past three years are comparable.  Based on this, it seems reasonable to conclude that the majority of the hedge fund returns are driven by an underlying risk premium. While an investor might not have realized a material increase in returns over the past three or five years, the rules-based approach may still provide materially better liquidity and transparency.

Alternative Mutual Fund Approach

The recent growth of the alternative mutual fund industry raises the question of whether investors can realize similar returns to hedge funds but in a more highly-regulated, potentially lower cost structure.  Most alternative mutual fund strategies do not have sufficiently long track records to allow for effective comparison; in the merger arbitrage space, we fortunately can analyze the returns of the Merger Fund, a mutual fund that was launched in 1990 with a mandate to focus exclusively on takeovers.  With a large asset base ($4.5 billion), highly diversified portfolio (78 longs and 16 shorts), and a narrow focus, MERFX serves as an interesting proxy for the merger arbitrage sector.  The chart below shows performance from January 2008 to the present vs. the HFR index.

The Merger Fund

The outperformance of the Merger Fund during the crisis may have been attributable to an outsized weighting in the BoA-Merrill transaction, which had a material impact on merger fund returns.

MERFX charges no incentive fee, but the all-in management fees and expenses are similar to those of a typical merger arbitrage fund (1.33% per annum excluding trading and other investment related expenses).  The absence of incentive fees, which for the merger arbitrage hedge fund averaged less than 1% per annum over the past five years, did not appear to translate into higher returns, although some investors may draw comfort from investing in a mutual fund structure.

Factor-based replication

Another approach is to use a factor model to seek to replicate the returns of the merger arbitrage hedge fund indices.  In the following charts, we examine the results of a replication of the HFR index over the past five (left) and three (right) years.[2]

HFR index over the past five (left) and three (right) yearsThese results are based on simulated or hypothetical performance results that have certain inherent limitations. Unlike the results shown in an actual performance record, these results do not represent actual trading. Also, because these trades have not actually been executed, these results may have under-or over-compensated for the impact, if any, of certain market factors, such as lack of liquidity. Simulated or hypothetical trading programs in general are also subject to the fact that they are designed with the benefit of hindsight. No representation is being made that any account will or is likely to achieve profits or losses similar to these being shown.


We find that a factor-based approach was not effective during the financial crisis; however, since 2010 the factor model approach would have materially outperformed actual hedge funds with comparable volatility but relatively low monthly correlations.  Despite the recent results, as practitioners, we would be disinclined to use factor based replication for a portfolio that consists exclusively of merger arbitrage funds due to the consistently low market exposure.


Any conclusions from the analysis above are constrained by the lack of a robust pool of data.  We have only two merger arbitrage indices that extend back through the crisis; a single large diversified mutual fund; and the hedge fund indices themselves are replete with data biases.  With those caveats in mind, there are several interesting conclusions and questions that follow.

The most important conclusion appears to be that the construction of merger arbitrage indices is complicated and introduces its own form of idiosyncratic risk.  This likely is true for any rules-based “alternative beta” index.  While the results above show that comparable returns potentially can be achieved (with greater liquidity, lower all in fees, etc.), it is by no means obvious how to determine – in advance – which complex set of rules will provide the most effective means to capture the merger arbitrage risk premium.  An investor in IndexIQ’s product in 2008 would be sorely disappointed today, as would an investor in the recently launched S&P Long/Short index, or even an investor in the CS product in early 2012.  Consequently, investors who expect this approach to materially reduce tracking error, monitoring costs or other risks are likely to be disappointed.  Further, the complications involved in developing and running such a program will limit fee savings, as highlighted by the difficulty of making net of fee comparisons between indices and actual investments.

If the rules-based indices, on average, underperform actual hedge funds, then the question is why.  One possible explanation is that by focusing on larger, simpler deals the indices over-allocate to more efficiently priced transactions – where the merger arb risk premium is low.  Discussions with actual managers suggest that traditional sellers – long only mutual funds – are now more willing to make their own assessment of deal risk, which undoubtedly has improved with experience and better dissemination of information.  This should naturally compress the premium over time and force managers to seek excess returns from more complicated transactions – those with variable consideration, the likelihood of a topping bid, capital structure opportunities, etc.

A broader question that arises is whether “alternative betas” are stable over time. Investors generally believe that certain risk premia – for equities, for credit, for illiquidity – should persist indefinitely; however, even traditional risk premia dramatically compress (equities during the 2000s) and widen (illiquidity post-crisis) in different market environments.   Since more complicated risk premia – such as those for benchmark strategies like value, momentum and “insuring” takeovers – are based on the principle that less sophisticated/more constrained investors will consistently transfer value to more sophisticated/less constrained investors, the key question is whether more widespread knowledge of these strategies – and how to efficiently implement them – will inexorably lead to capital inflows, more educated sellers and, by definition, a compression of excess returns over time.

[1] We also compared the results to the Dow Jones Credit Suisse Risk Arbitrage hedge fund index.  The results are very similar to those of the HFRI Merger Arbitrage index; therefore, for ease of comparison we have limited the analysis in this note to the latter.

[2] Note that the HFR Merger Arb replication performance presented here is gross of replication fees in order to make the returns comparable to the rules-based merger indices.

The Newedge CTA index (“NEIXCTA”) has returned a disappointing 0.82% per annum over the past five years.[1] Many explanations have been given, including the lack of a trending market.  This explanation implies that, as markets normalize, CTA performance should revert to higher historical levels – the 6%+ per annum realized in the five years preceding the crisis.


However, this analysis ignores the role of LIBOR in managed futures portfolios: CTAs earn a return on investor cash posted as margin for futures positions. Consequently, an evaluation of CTA returns should account for LIBOR.  In this light, the historical performance of the index excluding LIBOR over even the preceding ten years is distressingly low: approximately 1.5% per annum, or less than half the reported figures, and slightly negative over the past five years.


Viewed on an annual basis, the impact of LIBOR on pre-crisis returns is strikingly clear:


This analysis suggests that the performance issue for CTAs may be systemic.  Despite this, CTA assets under management has increased five-fold over the past ten years.[2]


The question is why. The most compelling explanation is that investors place a premium on assets with a low correlation to equity markets. What’s often overlooked is that a key assumption underlying modern portfolio theory is that each asset has the same expected risk-adjusted return. From the above, it is difficult to conclude that the expected returns for CTAs should be comparable to those of assets with clearly defined risk premia – like equities or credit.

A secondary explanation is that CTAs are often sold as a “hedge” with the expectation that they will perform well in a material drawdown in the equity markets, as they did during 2008.  The return standard for insurance-like investments may be somewhat lower than that for others:  it may be given a “pass” when it loses money as long as other assets are rising.  A third possible explanation is that CTAs generally have lenient liquidity terms, a valuable feature post-crisis.

The primary beneficiary of the increase in assets under management appears to be the managers themselves.  Using an assumed fee structure of 2/20, slightly less than half of gross returns were paid away to managers over the past ten years.  Adjusted for LIBOR, the numbers are more pronounced:  fees were roughly double what the managers made above collateral interest.  Over the past five years, virtually all of gross performance was paid away.


The figures raise a fundamental issue of fairness in the managed futures space and argue for a material change in the industry fee structure.  Simply put, investors should question whether managers should be rewarded for warehousing cash in interest bearing accounts.  At a minimum, a LIBOR based hurdle is warranted.

The analysis raises a serious question about whether the pre-crisis returns of managed futures funds were an anomaly or are likely to recur.  Given performance over the past ten years, it appears that investors should expect CTAs to earn 1-3% over time in a low interest rate environment.  Moreover, the performance of systematic strategies is dependent on the ability to capitalize on market anomalies – such as evidence of excess returns from momentum or mean reversion.  Recent poor performance implies that many anomalies are subject to being arbitraged away over time and that the recent underperformance of the industry is likely to persist.

[1] Source: Bloomberg, net returns

[2] Source: BarclayHedge

The recent launch of several mutual funds of hedge funds (or, more accurately, funds of managed accounts) geared to the US retirement market has turned attention to the question of how much investors should expect to sacrifice in returns for daily liquidity and pricing.  Since these products mostly have very short track records, the answer to this question is best found by looking at past efforts to introduce greater liquidity into the hedge fund investment model.

The historical record is not particularly promising.  Investable indices were first launched over a decade ago but initially suffered from adverse selection bias that caused one index to underperform its non-investable counterparts by 300-600 bps during its first four years of live performance.[1]  More recent innovations like managed account platforms and UCITS structures appear to have a lower performance drag, but making apples to apples comparisons is notoriously difficult.  In a recent study, Cliffwater sought to calculate the annual return difference between 148 hedge funds and their direct liquid counterparts, such as managed account platforms, 40 Act funds, bank platforms and UCITS structures.  They concluded that investors in the liquid alternative sacrificed approximately 1% per annum due to a combination of fees and lost alpha.  They noted that the annual cost was greatest for Event Driven managers (2.3% per annum) and lowest for Managed Futures (0.5%) and Macro (0.2%); Equity Long/Short was close to the mean at 1.1%.   However, the performance drag in the Cliffwater study – conducted in March 2013 and based on liquid alternatives that were still in operation at that time – likely is understated since it excludes liquid alternatives that were launched over the past decade but subsequently shut down due to poor tracking.

In this note we seek to estimate the performance drag associated with liquid alternatives to investing directly in hedge funds during the post-crisis period.  Rather than analyze individual managers, we look at the recent performance of several indices that track liquid alternatives.  As shown in the chart below, a composite of liquid alternatives underperformed the HFRI Fund Weighted and Fund of Funds indices by 3.08% per annum and 1.29% per annum, respectively, over the three and a half year period from January 2010 through June 2013.  Factoring in some of the data biases discussed below and variance due to fee structures and strategies, we estimate that it is reasonable for a typical investor in hedge funds to expect a performance drag of 150-250 bps per annum when electing to invest in highly liquid hedge fund strategies.

Performance Drag 1 (2)

Data Considerations

At the hedge fund index level there are two principal issues with the data.  The HFRI Fund Weighted index is slightly overstated due to reporting bias; that is, funds have a window in which to decide whether to report a given month’s returns.  Second, the HFRI Fund of Funds index includes a second layer of fees, which accounts for some of the recent performance differential.  In general, we assume that the HFRIFWI is a reasonable proxy for the direct hedge fund portfolio of an institutional investor, while the HFRIFOF is more representative of the returns expected by smaller investors.

With respect to the liquid products, we ideally would analyze the full universe of managed account and UCITS products that are (and have been) designed to track the performance of a given hedge fund so that we can make apples to apples comparisons on a fee equivalent basis – an expanded version of the Cliffwater study.  However, proprietary platforms generally restrict access to data and hence make such comparisons impossible or, alternatively, present the data in a self-serving manner.  Further, institutions that have negotiated special managed accounts typically do not publish comparative results.

Given these limitations, there are three principal hurdles in the data available to us:

  • Fee Equivalency.  In general, there is very little transparency into whether certain indices (e.g. Lyxor) include platform level fees.  Some products, such as the Credit Suisse AllHedge Solutions, are actual investment products, while others merely aggregate reported data.  UCITS and registered funds may have materially different fee structures than the actual hedge funds.
  • Selection Bias.  When first introduced, indices are often backfilled and historical results are replete with survivorship bias.  Hence, we focus only on live performance.  Further, indices that perform poorly can be shut down, as was the case with the Dow Jones Credit Suisse Core Hedge Fund index in May 2013.
  • Strategy Bias.  A liquid index may overweight certain strategies that are inherently more liquid and therefore more conducive to being offered in a liquid structure.  Certain strategies like managed futures have underperformed others (e.g. credit) and therefore may depress results.  To address this we look at a group of indices rather than focus on a specific provider.

We hope to overcome these limitations by looking at a broad enough sample of indices so that any fund level or platform specific bias has a limited impact on overall results.  In light of this, the data shows that there is a consistent and significant performance drag associated with liquid alternatives.

What Causes the Performance Drag?

There are several likely causes of the persistent performance drag.  Daily (or near daily) liquidity should limit the investment universe to positions that, by definition, lack an illiquidity premium.  If illiquid assets command a 3-4% return premium over time, we might expect a performance differential of perhaps 1% over time if we assume that a typical hedge fund with restrictive liquidity terms might hold a quarter to a third of its portfolio in less liquid instruments.  Note that certain liquid hedge fund proxies – such as the Lyxor managed account platform and factor based replication models – outperformed materially during the crisis, which supports the conclusion that at least a portion of the differential is due to a mismatch in the liquidity of the underlying portfolios.  However, this clearly does not explain the entire differential.

As noted, adverse selection among managers was very pronounced in the formative years of the investable index business.  More recently, established and highly regarded hedge fund managers appear much more willing to run liquid alternative products and the performance differential has narrowed.  The persistent performance drag even in recent years suggests that investment restrictions in more liquid products cause a portfolio-level form of adverse selection.

Another cause, as noted by Callan in a recent research report on alternative mutual funds, is that registered funds and similar vehicles restrict leverage, which can be a key contributor to returns in certain strategies.  A fourth likely cause is that managers need to retain a cash buffer to manage more frequent inflows and outflows.

The mix of factors clearly will vary from product to product and strategy to strategy.  Cliffwater’s conclusion that the performance differential is lowest for managed futures is consistent with the ease of running such strategies in multiple vehicles and the leverage available through the futures markets.


The initial appeal of the hedge fund model was that it enabled talented and motivated managers to pursue investment opportunities outside the constraints of registered investment products like mutual funds.  Managers were relatively unfettered to pursue compelling investment opportunities as market conditions warranted.  A talented merger arbitrage specialist in the late 1980s might have evolved into a distressed investor by 1990 and a buyer of nonperforming real estate portfolios within a few years thereafter.

The institutionalization of the business over the past decade has gradually introduced a series of new constraints into the investment model.  Institutional investors are wary of style drift and value consistency in the investment process, sometimes even in the face of an inferior opportunity set.  The concentration of capital among large firms post-crisis naturally has narrowed much of the investment universe to situations where managers can deploy hundreds of millions of dollars in a single position.  Post-crisis aversion to gating/suspension risk has led to intense scrutiny of the liquidity of underlying portfolios.

A rational question for investors is whether the structural constraints of liquid vehicles cause the underlying managers to deviate from the core investment strategy to such a degree that it undermines the original investment thesis – that is, to invest in strategies with a higher expected risk adjusted return than, or low correlation to, traditional investments.  Based on a cursory analysis, a recently launched mutual fund of managed accounts product appears to have underperformed the flagship hedge fund counterparts run by the same managers by 600 bps on a fee equivalent basis over just the first half of 2013.  If anything, this suggests that investors will be sorely disappointed if they expect the mutual fund to approximate the performance of a portfolio of those hedge funds over time.

[1]  The adverse selection issue was most acute with the HFRX Global Investable index versus either the HFRI Fund of Funds index or HFRI Fund Weighted index.  Credit Suisse’s Blue Chip investable index did not suffer from nearly as much of a performance drag; however, this product was never designed to offer daily or weekly liquidity.

[2]  The Dow Jones Credit Suisse Index, designed to track the universe of managed account and UCITS products, was launched in early 2011 but ceased reporting in May 2013.  Consequently, comparative statistics are for the live period only.

“After the wheel, God’s greatest invention was the carry.”
Private Equity Titan

In this note, we examine the relationship between the hedge fund fee structure and how it impacts alpha.

In the early days of the industry, higher management fees were designed to cover costs of a deep and rigorous research and investment process; performance fees were meant to reward the manager for alpha generation.  The standard 2/20 fee structure made sense when hedge funds were smaller and either truly “hedged” –offsetting long and short positions and hence little market exposure – or focused on markets like commodities where beta alternatives were not obvious.

Over the past decade, several changes in the industry have drawn attention to the issue of whether the standard hedge fund fee structure is equitable.  Today, a good portion of the industry – event driven, equity long/short  – has consistent and identifiable exposure to equity market beta; likewise, as we’ve gained a more comprehensive understanding of hedge fund performance, it has become clear that more diverse forms of beta explain the majority of returns.  This raises the question of whether investors are overpaying for sources of return that can be obtained more cheaply and efficiently elsewhere. Finally, the concentration of capital among larger funds has created windfall profits for managers as management fees no longer just cover costs but have become a valuable profit center.

As shown below, we argue that high management fees can be a direct transfer of valuable alpha from investors to the managers.  In fact, a reduction in management fees results in a dollar for dollar increase in expected alpha.  In this way, fee reduction is the purest form of alpha.

Less intuitively, we also argue that performance fees can be equally problematic.  As beta returns rise, fund returns generally increase as well.  However, the absence of a hurdle rate means that investors often pay incentive fees on beta.  Consequently, as markets rise, alpha received by investors can actually decline.

Breakdown of Hedge Fund Returns

Investors today are much more knowledgeable about the composition of hedge fund returns.  A framework that includes multiple forms of beta has supplanted the simple model of equity beta/alpha.  The very definition of beta has broadened to include benchmark strategies and other investment programs designed to efficiently deliver returns from more exotic risk premia.  The net effect of this is that over time, betas have come to explain a greater and greater portion of returns, which leaves less and less in the “pure alpha” category.

The current thinking is that there are four primary sources of returns:  static beta, dynamic beta, alternative beta and alpha.  A brief description of each is included in the box below.

In practice, when looking at an individual fund, it can be difficult to cleanly distinguish between different categories. Fee Paper 1Should we consider the decision to cut risk prior to a market drawdown alpha, dynamic beta, or simply luck?  As the firm evolves and markets change, at one point does a shift in static betas represent a form of dynamic beta?  Into which category should we place dynamic allocations to alternative betas?  More broadly, as investor sophistication grows, will we continue to move more and more sources of alpha into defined beta categories?

In the chart below, we order the different sources of returns according to expected correlation to traditional assets and Sharpe ratio.  Static beta clearly has the highest correlation to traditional assets and a low expected Sharpe ratio.  Alternative betas have a higher expected Sharpe ratio and much lower correlation to traditional assets, which is precisely why they used to be categorized as alpha.  Dynamic beta – shifts in exposures and asset allocation weights – is much more variable.  Alpha stands on its own with both a very low correlation and very high expected Sharpe ratio.

Fee Paper 2

Clearly, the most valuable portion of the return stream is alpha.  This is what investors seek when they invest in hedge funds:  a reliable source of returns that is noncorrelated to the rest of their portfolio.  The justification of a high fee structure is grounded in the belief that a talented manager can generate excess returns over time, and that these returns will be utterly uncorrelated to overall market movements.  In fact, the very statistical definition of alpha is most easily visualized as the return that the fund should generate when the market return is precisely zero.

In this light, the expected alpha of a portfolio should be highly stable and noncorrelated.  This can seem counterintuitive at first.  After all, every manager has good and bad years; market conditions at times are better and worse for a given strategy.  The point is that investors expect alpha to be noncorrelated and that there is no logical reason why the excess returns should be driven directly by market conditions.  After all, if alpha predictably increased in rising markets, then by definition we would classify a portion of it as beta.  Therefore, alpha generation must be truly independent of the various forms of beta.

Alpha, Hedge Fund Returns and Fees

With this framework in mind, assume we have a simple hedge fund that has a net exposure to the S&P of precisely 0.50 and delivers 600 bps per annum of alpha before fees.  The manager does not employ alternative beta strategies and market exposure does not change over time. As noted, alpha generation does not vary with market returns.  Due to the fund’s remarkable consistency of outperformance, the manager is able to charge a 2% management fee and 20% carry.

Given the stability of alpha, all variation in fund returns will be driven by moves in the market.  In the chart on the left, we show net fund performance at market returns of 0% to 20%.  In the chart on the right, we show how total fees paid decline as returns increase, which is what investors expect.

Fee Paper 3

For instance, with the equity market up 10%, the fund returns 7.2%, 220 bps of which is alpha.  Certainly, almost 35% of gross returns were paid to the manager, but roughly half of this consisted of performance fees which are paid only when the fund performs well.  Most investors would be content with this.

But what is the effect on alpha received by investors?  When the market returns zero, the manager earns 6% before fees.  Even though we pay away 2.8%, we’ve earned 3.2% in a difficult year for equities and our manager has delivered 320 bps of alpha.  Simply, the manager generated 600 bps of alpha and we were willing to pay away 47%.  Expensive for sure, but alpha is highly valuable, and not many funds can consistently deliver it.

But what happens when the market returns 10%?  As noted, the fund returns 7.2% and investors received 220 bps of alpha – again, a very respectable performance.  Performance has increased, yet alpha has declined.  At a market return of 20%, the fund gains 11.2% net while alpha has declined to 120 bps.  What’s going on here?

The issue is that performance fees are paid on both alpha and beta.  As beta returns increase, the investor pays a higher performance fee without a commensurate increase in alpha.  The chart on the left below shows how the percentage of alpha paid away rises from 47% to 80% as market returns rise from 0% to 20%.  All of this higher payout is due to higher performance fees.  The chart on the right looks at the same question from a different angle:  how much alpha (before fees) does the manager need to generate to deliver 250 bps of alpha (after fees)?  The more the market rises, the higher alpha needs to be. In an up 20% market, alpha must be over 750 bps in order for the investor to net 250 bps; here, over two thirds is paid away due to performance fees on beta.

Fee Paper 4

Implications:  Why Fee Reduction is the Purest Form of Alpha

At multibillion dollar hedge funds, management fees have become a profit center – in many cases, a more important contributor to firm profits (and firm value) than performance fees.  This represents an enormous transfer of wealth to the managers.  The markets understand this.  When analysts (or strategic investors) seek to value an alternative asset manager, profits derived from management fees are valued at roughly twice those of performance fees.  Why?  Because management fees are stable and are paid irrespective of whether the market and/or fund is up or down.  Management fees are the pure “alpha” of the hedge fund management company.

Performance fees, while intuitively appealing to many investors, often do not result in a more equitable sharing of risk and reward.  In the example above, we might be comfortable paying 80 bps of performance fees when the market returns zero and the fund has returned 6% gross, but it should give us pause that we pay away another 200 bps, now 80% of alpha, simply because the market rose 20%.[1] As investors, we bore that risk and its benefit should inure to us.


There are two obvious ways to make the hedge fund fee structure more equitable over time:

  • Management fees should scale downward as fund AUMs increase.  When management fees become a profit center at large funds, this results in a direct transfer of the most valuable portion of the return stream from investors to managers.
  • Incentive fees should have a hurdle based on the appropriate measure of fund beta (or betas).

We have repeatedly made the point that “fee reduction is the purest form of alpha.”  In practice this means that investors need to consider the idiosyncratic nature of a given hedge fund when deciding which type of fee reduction is likely to be most valuable over time.  For a fund with high beta strategy (e.g., activist, event driven), the net benefit of a hurdle rate on performance fees might far exceed that of a modest reduction in management fees.  For smaller funds, a higher management fee might be necessary to support operational stability and depth of research, but early investors might insist that this scales down as AUMs increase.

This begs the question, what is an equitable split between managers and investors for pure alpha generation?  One extremely sophisticated family office recently offered that they were content paying away 40%, provided that it truly was for alpha and not a disguised form of beta.  This seems like a rational starting point for an ongoing debate among investors and managers, and certainly is preferable to some of the adverse outcomes outlined above.


[1]              From a technical perspective, the hedge fund manager has been given a free call option on the market that is equivalent to a one year European call option struck 4% in the money on notional equal to 10% of our investment in the fund.  The present value of this one year option is between 80-100 bps.  Investors hand this to the manager each January 1.