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


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

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.

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.


Over the next few years, I anticipate that a greater number of hedge fund investors will use liquidity tools to enhance portfolio efficiency and risk management.  Broadly speaking, these liquidity tools encompass investment strategies that seek to deliver hedge fund-like returns but with material structural advantages, like daily liquidity, real-time position-level transparency, no headline risk, scalability, and other features.  I’ve spent a lot of time looking at factor-based models that, in essence, seek to understand the current broad market exposures of the hedge fund industry or a portfolio of hedge funds, and to invest in those market factors directly through liquid futures and ETFs.  I’ve found that there are three key things to understand about these strategies:  the value proposition, why past returns are relevant, and why this approach is fundamentally different from passive indexing in traditional assets.


The goal of a well-designed liquidity program is to deliver similar returns to either a hedge fund index or portfolio.  It generally doesn’t work well for an individual manager – and I wouldn’t recommend trying.  That said, the strategy isn’t designed to outperform, but rather to deliver some or all of the returns of a target.  In practice, there will be periods of outperformance and underperformance, but a well-designed program should deliver the majority of the target returns over time.  Therefore, the principal “value” lies in structural benefits, not outperformance.  Put simply, a 10% return with daily liquidity is more valuable than a 10% return with a long lockup.[1]  Flexibility is valuable:  an investor who could exit the markets in late 2008 would have preserved capital and was far better positioned to re-invest in early 2009.

The question for each investor is how to think about this value proposition and how to deploy it in practice.  Each investor will ascribe a different value to benefits like liquidity, transparency, scalability, lack of headline risk, and other features.  An investor who demands a 300 bps (expected) return premium for investing in an illiquid asset will realize 300 bps of “value” if a liquid investment can deliver the same return.  Other investors may ascribe more value to intangible benefits like the absence of K1s or reduced internal monitoring/administrative costs.  A large institution may place value on the reduction in headline risk, real-time position-level transparency, and low all-in fees.  I personally place a high value on the information about how exposures shift over time — a window into where hedge fund managers see opportunities.


All predictive quantitative models are grounded in the premise that the past is representative of the future.  In factor-based programs, these models rely on an assumption that the composition of the industry and the key (market) drivers of returns are relatively constant over time, not that future returns will mimic past returns.  This is an important distinction and the evidence strongly supports this position.

What this means in practice is that it’s easier to approximate the returns of strategies with relatively stable exposures over time – equity long-short, credit, event-driven, distressed.  Others – CTAs and macro – are more variable over time and hence less predictable.

The factor models require a year or two of historical returns in order to estimate the current weights of the target portfolio/index.  In this sense, the models are always “backward looking.”  What we’ve seen over the past five years is that hedge funds, as a group, shift exposures slowly enough (they do change, but over months and quarters) that the factor models are able to detect material shifts on a timely basis.  While this may not be true for individual managers, it does hold true for diversified portfolios.

For more stable strategies, this “makes sense.”  Equity long-short, event-driven and credit managers generally build up positions over months and quarters based on incremental research and market information.   Consequently, today’s portfolio reflects a series of decisions over the past year or more.  Whether they make or lose money this month will largely be the result of accumulated decisions over the preceding quarters.  The factor models pick up on this.  Therefore, the positions today are, in fact, “forward-looking” in that they reflect the collective views of the underlying managers of where returns will be.

Consequently, an ancillary benefit of the liquidity programs is that they can provide a window into where hedge funds see better returns going forward. In early 2011 we saw a major shift in a typical portfolio away from emerging markets and into US equities – it’s quite clear today that this accurately reflected a reassessment of the relative attractiveness of US equities versus those in developing markets.


The principal difference between hedge fund liquidity tools and traditional passive index products is that you can easily and efficiently invest in the securities underlying the traditional index.  Not so with hedge funds.  A decade ago, some firms sought to create ultra-diversified portfolios of hedge funds – akin to buying 150 of the stocks in the S&P500 – as a means to generate an index-like product for hedge funds; Unfortunately, these “investable index” products suffered from data biases that caused persistent underperformance.

In general, any hedge fund liquidity program will have substantially larger tracking error than that of a passive index.  While we think they’ve proven to be highly effective over time, investors should be prepared for more month-to-month tracking error given that the models provide a (good) approximation of exposures over time, but that idiosyncratic factors can cause divergences.  Also, depending on the target, it may be prudent to expect that the liquidity program will provide only a portion of the target returns; however, given the structural benefits and cost of holding cash, this may still be compelling.

The second issue is that investors are more skeptical about the returns of hedge fund indices.  Many analysts have noted data biases that can skew the results upward or downward over time.  The result is a lack of unanimity as to which benchmark(s) provide the best representation of industry returns.

I’ve generally preferred to use actual portfolios as a target – this better aligns the liquidity program with the investor’s chosen hedge fund portfolio.  However, actual portfolios are more likely to change materially over time, and idiosyncratic manager risks can elevate tracking error.  That said, I’ve found that liquidity tools can effectively deliver the vast majority of the returns of a well-managed portfolio and provide a powerful means to improve risk management and flexibility.

[1]       Liquidity per se is difficult to value.  Endowments generally expect a 400 bps return premium for investing in illiquid assets.  We’ve looked at the value of being able to cut risk in portfolios below a certain loss threshold (i.e., down 5%); depending on the strategy, this can result in benefits of up to 300 bps per annum.

Many analysts have written about the scope and magnitude of estimated biases in hedge fund indices:  selection, survivorship and reporting bias.  As it relates to hedge fund indices, selection bias has three forms:  when only funds that are likely to do better than their peers are added, when strong performers are added retroactively, or when the fund sample is not representative of the overall population.  Survivorship bias is a problem when only good funds remain in the index and reporting bias arises when funds tend to report better numbers.  For reasons outlined below, as it relates to the HFRI Fund Weighted Index, the principal issue is a combination of survivorship and reporting bias.

As a starting point, funds that elect to report to the HFRIFWI have, on average, outperformed the index by around 7% per annum in the preceding three years.  Not surprisingly, a manager who has outperformed has more of an incentive to voluntarily report than one with poor performance.

That said, selection bias is not a significant issue in the HFRIFWI for two reasons.  First, those managers don’t do materially better than the index after they’ve joined; they revert to the mean almost immediately.  Second, HFR correctly does not adjust the prior index returns to reflect new additions.  (Unlike HFR, at least one other index provider — Eurekahedge — revises prior index returns as though each new addition had actually been in the index since inception.  This causes prior monthly numbers – e.g., December 2011 – to drift upward over time and effectively renders the data useless.  HFR hasn’t done this for as long as I’ve followed it, and told me they stopped doing this in 2005.)

HFR’s indices are, however, subject to two forms of data bias that are worth discussing.  First, HFR constructs its monthly indices as an equally weighted average of the monthly returns of the underlying constituents.  The HFRIFWI currently has around 2,200 funds that range in size from $19,000 to over $21 billion, with a median fund size of $45.7 million.  The smallest and the largest are given equal weighting in each month’s results.  This becomes a real issue, obviously, if smaller funds materially outperform over time and skew the results upward.  Interestingly, though, at least as it relates to the HFRIFWI, we don’t find that smaller funds do in fact outperform, so we tentatively conclude that this is not a material issue (at this time).

For the HFRIFWI, the more relevant bias is a combination of survivorship and reporting biases that is caused by the fact that HFR permits any fund in the index to wait up to 90 days to report its results for a given month.  Due to reporting delays, HFR publishes a “flash” estimate around seven days after the end of the month – generally, 20-25% of the index constituents — and will periodically revise that month’s return for until it’s “locked down” at the end of 90 days.  The practical effect is that managers who performed well in a given month generally report earlier than those who did poorly, and some who did really poorly never report at all.

To put this in perspective, since 2009 the flash number has been revised downward 63% of the time and by an average of 36bps, which equates to an annual downward revision of 4.41%.   The downward revisions tend to be much more pronounced during particularly bad months for hedge funds, which likely is due to a combination of intentional delays by certain managers and difficulty in settling on marks for some positions.  The following chart shows the month-by-month revisions since last summer and shows how much more pronounced the downward revisions are in down months (August and September 2011 and May 2012, which won’t be finalized until next week).  This should give you pause when you see the first reported “flash” results come across your Bloomberg screen.

What this doesn’t show us is the effect of managers who stop reporting altogether. Take the following example:  a manager is down 20% in August 2011 decides to delay reporting.  If the fund recovers in September and October, he/she will report August before the end of November and remain in the index.  However, if it doesn’t recover, the manager may never report August or later months and the index will be somewhat overstated for that period. Given that 15% of funds stop reporting during the course of any given year, this is a real issue.

We’ve found that the best way to estimate this effect is to compare the results of the HFRIFWI with those of the HFRI Fund of Funds index – an equally-weighted index of over 600 funds of hedge funds.  The reason is simple:  funds must report down months on a timely basis to their investors, and we see this flow through the fund of funds results.  We first assume that both the HFRIFWI and HFRIFOF are reasonably representative of the overall industry, then normalize the results for funds of funds level fees (which we ballpark at 125 bps on average).  The difference between the HFRIFWI and the fee-adjusted HFRIFOF should primarily reflect reporting bias.  In the chart below, the differential over the past five and ten years is between 100 bps and 200 bps per annum.

The next question is what this tells us about those funds that stop reporting.  The best that we can do is infer how much they underperform the rest of the index during the period immediately following their last reported result.  If we apply a 15% attrition rate, and assume that this bias accounts for around 150 bps per annum, then the average fund will have underperformed the index by around 10%.  Granted, we don’t know whether this underperformance occurred in a single month or over a longer period, but the magnitude is interesting.  This affirms the view that the industry is fairly ruthless about shooting the wounded.

The seminal work on discounted cash flow valuation methodologies arguably is Tom Copeland’s Valuation.   For anyone who wishes to understand how many private equity firms think about financial models and define free cash flow, this is the place to start.

While DCF methodologies are a critically important tool in the determination of value for any business, I’ve been puzzled recently about the way in which smart investors deal with a few of the assumptions that underlie the analysis:

  • Product and business lifecycles have shortened.  DCF models depend on predictability of cash flows out many years.  This calls into question the ability to make accurate predictions of business fundamentals out more than a year or two.  I was startled to hear a few months ago that the iPod was launched around a decade ago (and the iPhone half as recent).  Creative destruction is more active than ever.
  • Macroeconomic volatility compounds the issue by introducing large externalities – i.e., a breakup of the euro zone – that can have an indeterminate impact on forward cash flows.  My good friend, Jim Surowiecki, wrote a book called The Wisdom of Crowds that provides very good examples of how decision making by smaller groups (i.e., politicians) is subject to wider dispersion than those by very large groups (i.e., the invisible hand).   We’re seeing the same thing with Europe and the US fiscal cliff, where it becomes difficult to quantify the downside when we can’t be sure that the key players will reach an economically rational equilibrium.
  • DCFs are highly sensitive to estimates of weighted average cost of capital.  It is clear that there are massive distortions in global interest rates, and equity market premia have effectively been negative for some time.  Consequently, this must have a real effect on the ability to accurately estimate a given company’s WACC.  Given that cash flows implicitly extend for decades, minute changes in the WACC will have a profound impact on present value.

Take the simple example of a firm with $100 of free cash flow that’s growing 5% per annum and we decide that a reasonable weighted average cost of capital is 10% today.  Calculated as a growing perpetuity, the firm should be worth $2,000 today.  However, 70% of the net present value is due to cash flows in years 11 through infinity.

What DCFs don’t incorporate are valuable options embedded in a business.    This was the case with cable companies, where they were erroneously priced as growing perpetuities in the 1990s (even though they needed to continually reinvest in their businesses, and hence free cash flow was depressed).  However, the value of “owning the biggest pipe” into the house was soon appreciated and understood by the market.

When we speak to managers, we prefer professionals who are always willing to question the assumptions underlying the analysis and maintain a healthy skepticism about the results on any single methodology.

Any insights from investors in the investment business – private equity, hedge funds, etc. – on how best to address these limitations would be more than welcome.

[Many thanks to Matt Grayson for his help on this.]

Many investors focus on alpha when considering the returns of a particular hedge fund.  As shown below, while alpha at times is a compelling measure of risk-adjusted returns relative to a specified benchmark, investors should be aware of some of its limitations as an analytical tool.

In the examples below, I first show how a non-correlated return stream can have identically high alpha relative to two other returns streams even when one of those returns streams is definitively superior.

Consider this hypothetical returns series:

B has an Alpha of 1.3% per month over A. Indeed, B is a slightly noisy version of A with a constant added, as the plot below shows.

Series C is a slightly noisy constant, and is thus very uncorrelated to A. Its alpha over A is about 1.2% per month.

Since B is very correlated with A, it should be no surprise that C also has an Alpha of about 1.2% per month over B.

The Important point is that even though B consistently outperforms A by 1% per month, C has the identical alpha over both of A and B.

A more subtle example follows.  In the scatter plot below, we show two non-correlated return series.  Each return series has a positive mean return.  Statistically, Series D as an alpha of 0.1 per month over Series E.  Paradoxically, Series E also has an alpha of 0.1 per month over Series D.

The key point here is that alpha is a measure that explains the outperformance of one return stream against another, after adjusting for the amount of return that is explained by the other.  If the return streams are non-correlated, then the explanatory power is lost.

Typical hedge fund seed investing is subject to four disadvantages in the current investment climate.

  • First, it is generally well known that, in most circumstances, managers who need seed capital are those who are otherwise unable to raise it themselves.  This creates adverse selection bias of an indeterminate amount.   Post-crisis, those seed investors who were still offering to provide capital were only willing to do so at materially disadvantageous terms – such as 25% perpetual revenue shares – which further drove away better candidates.  A 25% revenue share is roughly equal to a 50% interest in the management company.  Therefore, if a seed investor was willing to provide $40 million – a fairly typical amount – the manager is starting with a management fee base of $600-800k.  This is insufficient today to support building out the infrastructure that most investors expect.
  • Second, the biggest issue today for smaller funds is how to grow from, say, $40 million AUMs to $250 million.  The revenue share makes it uneconomic to hire outside marketing support, especially third party marketers who will bear most of the costs of marketing in return for yet another revenue share.   Since investors generally want to speak to the manager, not marketers, any concerted effort to raise capital draws the manager away from investment decision-making and further reduces the likelihood of generating outsized performance.
  • Third, the pre-crisis investor base for smaller funds – family offices, European private banks, and smaller funds of funds – is still largely absent from the market.  The consolidation of the funds of funds industry means that most funds of funds must invest predominantly in larger funds.  Institutions and their advisors generally shy away from smaller funds due to perceived headline risk.
  • Fourth, a lower return environment means that all-in fees for funds are lower on average than they used to be.  A fund averaging 15% net might take in 5-6% on AUMs; a fund at 5% net two-thirds less.

In light of this, I would contend that providers of seed capital are inadequately compensated in most seed transactions.  To use some simple assumptions, if the seeded fund grows to $250 million in three years, the seeder is picking up an excess return of $1.5-2.0 million per annum (assuming 25% of management fees plus carry of $7.5 million).  While this represents a compelling 4-5% of excess annual return on the original $40 million investment, on a probability-adjusted basis the actual benefit is perhaps one-fifth of the amount, or around 1% per annum.

Most investors require an expected return premium of around 300 bps per annum for locking up money for multiple years.  Given that seeding vehicles require multi-year commitments, I would argue that this simple example shows that in most circumstances, on a probability and liquidity adjusted basis, seeding has negative net present value relative to direct investing.