lehman brothers alternative investment management 2008

L e h m a n B r o t h e r s a L t e r n a t i v e i n v e s t m e n t m a n a g e m e n t

2008 Strategy Outlook

Lehman Brothers Alternative Investment Management Team

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table of Contentsintroduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2

Chapter 1: Updates to hedge Fund strategy research . . . . . . . . . . . . . . . . . . . . .5

Chapter 2: assessing the opportunity for Distressed securities Funds . . . . . . . . 17

Chapter 3: the growth Prospects for asian hedge Funds . . . . . . . . . . . . . . . . . 24

Chapter 4: a new regime for Fixed income arbitrage? . . . . . . . . . . . . . . . . . . .38

Chapter 5: Comparison of equity Portfolios in small and Large Funds . . . . . . . .44

introductionWe continue to view the identification and vetting of individual managers as central to successful hedge fund investing . nevertheless, strategy-oriented research is helpful in ascertaining cyclical and structural opportunities in specific sectors, as well as general trends that may impact funds across the industry . in this document, we discuss two cyclical opportunities — in distressed securities and fixed income arbitrage investing — and one structural opportunity, asian hedge fund investing, in detail . We also update research on several industry trends, including rising strategy correlations, illiquidity of hedge fund holdings and sensitivity to equity markets . a key feature from the 2007 landscape is the illiquidity shock and re-pricing of risk triggered by the subprime mortgage fallout; several of our research topics address the continuing impact of this event .

our strategy outlook focus remains on empirically-driven conclusions obtained using our granular hedge fund database and an array of market and macro data, such as hedge fund holdings and liquidity terms, yield curve and credit default swap data, equity analyst coverage and short interest ratios . these results are leavened with our market experience to provide context and translate them into constructive insights regarding hedge fund investing .

Lehman Brothers alternative investment

management 2008 strategy outlook

the forecasts, views or opinions expressed in the outlook may not reflect those of the firm as a whole and may not actually come to pass . economic or market estimates discussed herein may or may not be realized and no opinion or representation is being given regarding such estimates . this material is presented solely for informational purposes and nothing herein constitutes investment, legal, accounting or tax advice, or a recommendation or solicitation to buy, sell or hold a security . no recommendation or advice is being given as to whether any investment or strategy is suitable for a particular investor . it should not be assumed that any investments in securities, companies, sectors or markets identified and described were or will be profitable . information is obtained from sources deemed reliable, but there is no representation or warranty as to its accuracy, completeness or reliability . all information is current as of the date of this material and is subject to change without notice . the information shown in this material is not representative of any Lehman Brothers investment product or service .

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Chapter 1: Updates to Hedge Fund Strategy ResearchGrowth in the market share of hedge fund equity holdings among small- and mid-cap stocks continued in 2007. Consistent with this trend, hedge funds have retained a high degree of sensitivity to market-wide illiquidity shocks, resulting in widespread incremental negative performance of holdings in August and November 2007.

Last year, we studied four credit/distressed sub-strategies in spread widening and tightening environments, showing that conservative distressed and neutral biased high yield outperformed aggressive distressed and long-biased high yield, respectively, in widening episodes. In line with our analysis, during the June – November 2007 spread widening, aggressive distressed lost 4.9%, while conservative distressed lost just 0.8%; similarly, long-biased high yield lost 4.7% compared with a gain of 1.6% for neutral-bias high yield.

Quantitative equity hedge funds experienced a turbulent August 2007. Using the reported holdings of a set of quant equity funds from June 30, we showed how overlapping positions drove cross-sectional returns during a mid-August drawdown and subsequent rebound. In this section, we update our analysis with the September 2007 holdings, finding the aggregate market value of holdings fell by 25%, to $154bn over the quarter.

The sensitivity of multi-strategy hedge funds to equity markets continued to rise in 2007, with average excess returns falling, as well. We augment this factor analysis with our recent research on liquidity premia of hedge funds (the incremental returns required for each added year of lock up), finding that top-quartile multi-strategy funds delivered substantial alpha after adjusting for illiquidity. However, even the most-liquid bottom-quartile funds delivered virtually no alpha after accounting for liquidity premia.

Correlations among historically uncorrelated hedge fund strategies remained high in 2007. At the same time, dispersion among managers in relative value strategies rose significantly. This combination of high correlation among strategy average returns on the one hand, and high dispersion of intra-strategy returns on the other resulted in manager selection dominating strategy weighting as a driver of hedge fund portfolio returns in 2007, continuing a trend begun in 2003 where manager selection has become increasingly important.

Chapter 2: Assessing the Opportunity for Distressed Securities FundsGiven the potential for significantly higher corporate default rates in 2008, we analyze the likely timing and magnitude of the opportunity for distressed securities hedge funds. Additionally, we address the question of whether rising default rates are a tide that lifts all funds in the strategy, or instead whether there is above-average dispersion among managers. Our analysis indicates that returns of distressed hedge funds are initially negatively impacted by a rise in default rates, but that future performance benefits from this increase in distressed supply. Additionally, our data shows that manager selection within the distressed universe becomes increasingly important (i.e. dispersion of fund returns increases) during and, particularly, following a period of elevated default rates. Consequently, unless the pace of defaults increases dramatically in early 2008, it may be 2009 before most distressed hedge fund investors realize substantial gains from this turn in the credit cycle.

Chapter 3: The Growth Prospects for Asian Hedge FundsThe pan-Asian hedge fund universe has experienced a period of rapid expansion and development in recent years. This growth has been driven by a number of market related developments, which have increased the scope for hedge fund activity in the region. A key question for investors is whether these developments represent a permanent change in opportunity set, as opposed to a temporary or cyclical improvement in circumstances. Our analysis suggests that, despite the pace of recent growth and positive market tailwinds, Asian markets are under-penetrated by hedge funds relative to other markets globally and that many structural factors currently in place will likely encourage further growth in the hedge fund sector in Asia in 2008 and beyond.

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Market risk and issues over the sustainability of liquidity in a severe market decline remain an inherent component of hedge fund risk. Even in a severe market decline, however, the capital-protecting attributes of hedge fund strategies, combined with an ongoing desire for Asian exposure, may contribute to hedge fund growth by leading capital into, rather than away from, hedge funds in the region. This was the case for US hedge funds in the post-bubble years early in this decade. Meanwhile, thanks to growth in the number and types of participants in the market, as well as in the array of products offered, the opportunity set for Asian hedge funds in 2008 looks robust.

Chapter 4: A New Regime for Fixed Income Arbitrage?An important aspect of fixed income arbitrage (FI arb) funds’ strategy is capitalizing on anomalies along the yield curve. We analyze how the US yield curve evolves using principal components analysis. Since 1992, we identify two regimes, “High”, through 2000; and “Low”, 2001 – 2007, based on yield curve behavior. In the High regime, median FI arb fund returns for the year ahead (2.7% per quarter) are significantly higher than in the Low regime (1.7% per quarter). The past few quarters, particularly Q4 2007, have seen the yield curve return to the High regime, indicating expected returns of about 11% for 2008. Combined with the fact that correlations between FI arb and other strategies have fallen in the last year, this gives us a favorable outlook on the strategy.

Chapter 5: Comparison of Equity Portfolios in Small and Large FundsHedge fund capital is becoming increasingly concentrated in the largest funds. We study how characteristics of equity hedge fund portfolios differ depending on the total market value (TMV) of equity holdings. We found that the number of stocks increased slowly with TMV, requiring a 16x increase in TMV to double the number of stocks. As TMV increased, funds do not shift to larger or more liquid stocks. Instead, funds scale up their positions as a percent of shares outstanding or days trading volume. With the growth in assets of the largest funds, this scaling of positions means an increasing fraction of assets are in less liquid portfolios. Should this trend persist, investors in larger funds may face more onerous liquidity terms or risk a liquidity mismatch between funds’ holdings and terms.

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Chapter 1: Updates to hedge Fund strategy researchIn this section, we update topics that appeared in our 2007 Strategy Outlook and in subsequent articles published in 2007. We begin with an update on the illiquidity of hedge fund equity holdings, showing that hedge funds have continued to grow their presence in small- and mid-cap stocks, as measured by market float. In aggregate, funds also retained a high sensitivity (beta) to market-wide illiquidity shocks, contributing to underperformance of equity hedge fund holdings in August and November 2007.

We reprise our analysis of various credit and distressed securities sub-strategies in environments where spreads widen. As expected, when spreads widened in mid-2007, hedged credit outperformed long-biased credit and conservative distressed beat aggressive distressed. Unlike 2007, however, the risk of further spread widening in 2008 no longer appears decidedly larger than that of spread tightening.

August 2007 witnessed historic turbulence in quantitative equity strategies. We demonstrated that this crisis was caused by a rapid delevering across funds,1 exacerbated by funds’ substantial growth in assets and leverage. This study was based on the June 2007 holdings of 34 quant equity funds with a total market value (i.e., including assets and leverage on the funds’ longs, but not their shorts) of $205bn. Updating our analysis with September holdings, we find that for the typical quant equity fund, total market value of holdings fell by 20% over the ensuing quarter, due to a combination of capital outflows and reduced leverage. We also identify stocks that are the most popular (longs) and unpopular (shorts) among quant funds, relative to their market cap rankings, and hence most at-risk in a similar liquidation event.

Last year, we documented the rising equity betas and diminishing excess returns (alphas) generated by multi-strategy hedge funds. Overall equity beta continued to rise in 2007, with somewhat lower alpha, as well. Furthermore, multi-strategy funds can be subject to long lock ups, and we adjust funds’ excess returns based on their liquidity terms and the strategy liquidity premia we computed in a 2007 research article.2 While top-quartile funds continued to produce alpha at their historic levels, alpha has fallen in median- and bottom-quartile funds; these latter funds now produce anemic liquidity-adjusted alpha.

We noted last year that strategy correlations were historically high while dispersion among funds within strategies was low. During 2007, strategy correlations remained high, with only fixed income arbitrage showing a modest reduction. Dispersion among managers within strategies rose dramatically last year, however. This rise in dispersion was not due to a few outlier funds, as we measure it using the spread between the top- and bottom- quartile returns, rather than from a standard deviation. These developments further tilt the balance to manager selection (versus strategy weighting) in hedge fund portfolio construction.

1 B. Hayes, “August 2007 Quantitative Equity Turbulence: An Unknown Unknown Becomes a Known Unknown”, Lehman Brothers preprint, 2007. We showed that during August 2007, the daily returns and trading volumes of US equities were largely explained by their popularity within the portfolios of a set of quant equity funds.

2 B. Hayes and K. Kharas, “Measuring the Liquidity Premia in Hedge Fund Strategies”, Lehman Brothers preprint, 2007.

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As of September 2007, hedge

funds owned over 10% of the

float for U.S. stocks under $2bn

market capitalization

Despite their growing concentration

in the small- and mid-cap sectors,

hedge funds, in aggregate, may be

less susceptible than quantitative

funds to an industry-wide sell-off

hedge Fund holdings LiquidityThe growing presence of hedge funds in equity markets, along with their continued bias towards small- and mid-cap stocks, has led hedge funds to hold a disproportionate percentage of the float in small- and mid-cap stocks. To quantify this effect, we aggregated the holdings of hedge funds in a broad universe of US equities using their 13-F filings. In Chart 1.1, we show the percentage of total f loat held by hedge funds in 12 market cap intervals — $0 – 100mm, $100 – 200mm, $200 – 500mm, … and $200 – 500bn — at three points in time. In the most recent, September 2007 reading, hedge funds held 10% or more of the float for stocks $2bn and under. Except for the smallest market-cap range, 2007 percentage of floats held increased uniformly across market cap ranges over those of 2006, and are 30-50% more than those of September 2005. While hedge funds are also short equities, positive equity betas of composite hedge fund indices highlight a net long bias in aggregate. Also, anecdotal evidence on funds’ short positions (no analogue to the 13-F filings exists on the short side) indicates that many use index futures or more liquid, large-cap stocks to hedge market exposure. The result is a net exposure to small- and mid-cap stocks.

Sources: FactSet and LBAIM analysis

Implications of the growing presence of hedge funds in small- and mid-cap stocks include: the prospect of diminishing excess returns (alpha) as more funds seek to capitalize on these less efficient market segments; the potential for an industry-wide sell-off in small- and mid-cap stocks; and the month-to-month impact on returns when liquidity ebbs and flows. Of the three, the first two prospects are unknown. Since the exposure of many funds to small- and mid-cap stocks is driven by a bottom-up, stock-picking investment model (either fundamental or quantitative), rather than a top-down view, the continuing small- and mid-cap bias may be indicative of difficulty in generating alpha from large-cap stocks.

Despite their growing concentration in the small- and mid-cap sectors, hedge funds, in aggregate, may be less susceptible than quantitative funds to an industry-wide sell-off. In August, quantitative equity funds delevered almost simultaneously, causing a severe disruption among funds in many strategies — we discuss this event in more detail later. A key contributor to the August quant meltdown was the high degree of leverage used by many funds, often exceeding $4 long and short for each $1 of equity. Most of the aggregate hedge fund equity holdings, however, are due to fundamental, long-biased managers who use less leverage than quant funds. This lower leverage and an arguably more diverse set of signals may allow fundamental long/short managers to avoid such a meltdown, even as they continue to grow.

Chart 1.1 Percentage of Float Held by Hedge Funds, 2005–2007

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The month-to-month impact of changes in market liquidity on equity hedge fund portfolios was already apparent to investors in 2007. To monitor sensitivity of funds to market illiquidity shocks, we performed an analysis using a factor incorporating daily trading volumes and absolute returns. A negative sensitivity (beta) to this factor indicates that — typically — funds suffer losses when illiquidity spikes upwards. In a setting where we account for equity market sensitivity, small-cap bias, value/growth bias and sensitivity to illiquidity shocks, a negative beta on the latter variable indicates that even controlling for equity and small-cap exposure, a fund would be expected to suffer incremental losses when illiquidity spikes. In Chart 1.2, we show this multi-factor beta of hedge fund holdings to illiquidity shocks. Starting in 2005, this sensitivity turned negative, where it has remained. The economic impact of this illiquidity beta is indicated by bars in Chart 1.2 that correspond to incremental monthly returns from this exposure: hedge fund holdings lost an added 1.35% in November (returning -6.0% vs. -4.5% for the Wilshire 5000) and lost an added 0.9% in August (returning -0.1% instead of 1.3%). Some hedge funds have illiquidity-shock betas many times that of the aggregate holdings portfolio, resulting in incremental losses of several percent in these months.

Sources: FactSet and LBAIM Analysis

Past performance is not indicative of future results

Credit spread Widening/tighteningIn our 2007 Outlook, we investigated which class of credit managers was likely to perform best during a period of sustained credit spread volatility and rising default rates. We argued last year that a sustained period of heightened credit spread movement should lead to greater dispersion of sub-strategy (Long-biased Conservative Distressed, Long-biased Aggressive Distressed, Long/Short High Yield — Long Bias, and Long/Short High Yield — Neutral Bias) performance within the broader credit hedge fund universe, suggesting that manager selection (or at least sub-strategy allocations) would become increasingly important.3

3 Based on our understanding of the underlying portfolio exposures of the hedge funds we track in the credit/distressed space, we sub-divided this universe into four separate categories. We then analyzed the returns of these four categories of funds over both periods of pronounced credit spread widening (5 distinct periods) as well as periods characterized credit spread tightening or range-bound credit markets (3 distinct periods)

Chart 1.2 Sensitivity of Hedge Fund Holdings to Market Wide Illiquidity Shocks and Incremental Impact on Monthly Returns

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The more hedged a given fund’s

portfolio and the more senior

its investments are in a capital

structure, the more immune

it will be to periods of credit

spread widening

Based on our statistical analysis, we posited that empirical evidence confirms intuition: the more hedged a given fund’s portfolio (i.e. Long/Short High Yield-Neutral Bias) and the more senior its investments are in a capital structure (Long-biased Conservative Distressed), the more immune it will be to periods of credit spread widening. If we incorporate the last several months of credit spread widening into the analysis (the most extreme widening in recent history), the conclusion reached last year is reinforced, as can be seen from Table 1.1. It is important to acknowledge here that while senior instruments in distressed companies’ capital structures outperformed more junior instruments over this period, the senior instruments of non-distressed companies (i.e., par bank loans) declined as much as these companies unsecured bonds in many cases. While thus far our predictions for relative sub-strategy performance were accurate, we should caution that it is unclear that the current period (June – November 2007) represents the full extent of the credit spread widening episode.

Statistical analysis of the data shows that only the returns for Long/Short High Yield-Neutral Bias and Long/Short High Yield-Long Bias over these five periods are statistically different than zero at a 95% confidence interval. As was the case last year, the analysis also shows that return differences between Long/Short High Yield-Neutral Bias and Long/Short High Yield-Long Bias, between Long/Short High Yield-Neutral Bias and Long-biased Aggressive Distressed, and between Long-biased Conservative Distressed and Long/Short High Yield-Long Bias, are statistically significant.

Sources: TASS, Altvest and LBAIM Analysis


Credit Sub-strategy Definitions

Long-biased Aggressive Distressed1. — includes funds that have little exposure to shorts, but differ from conservative distressed managers in the respect that they will make use of leverage and have the flexibility to invest throughout a company’s capital structure (bank debt down to equity)

Long-biased Conservative Distressed2. — includes funds that employ little or no leverage, invest a majority of their assets in the senior most portion of a distressed company’s capital structure (bank debt and senior secured bonds), and make limited use of shorts

Long/Short High Yield-Long Bias3. — includes funds that focus primarily in the non distressed high yield bond universe, typically have a net exposure of greater than +75%, and are capital structure agnostic (i .e ., loans, bonds, equity)

Long/Short High Yield-Neutral Bias4. — includes funds that invest in high yield debt on a more hedged basis . net exposure tends to be south of +50% and more typically is closer to neutral . these managers invest in capital structure arbitrage and pair trades in addition to outright long and short positions

Table 1.1 Performance of Credit Sub-Strategies in Recent Spread-Widening Periods

PERIOD CREDIT SPREAD EvENT

LONG-bIASED AGGRESSIvE DISTRESSED

LONG-bIASED CONSERvATIvE

DISTRESSED

LONG/SHORT HIGH YIELD - LONG bIAS

LONG/SHORT HIGH YIELD -

NEUTRAL bIAS

Aug 2000 - Dec 2000 Widened 265 bps -2.7% 5.9% -4.8% 6.2%

Sep-01 Widened 189 bps -1.6% -1.6% -3.2% 0.9%

June 2002 - July 2002 Widened 218 bps -8.3% -2.6% -3.2% 0.8%

March 2005 - May 2005 Widened 124 bps -0.3% 1.9% -2.7% 1.0%

June 2007 - Nov 2007 Widened 302 bps -4.9% -0.8% -4.7% 1.6%

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Widespread liquidation,

exacerbated by rapid growth

in assets and leverage, caused

the quant equity turbulence of

August 2007

When we performed this analysis one year ago, it appeared unambiguous that with spreads at historically tight levels, the risks were asymmetrically skewed towards spreads widening as opposed to further spread tightening. At present, with spreads essentially double where they were this time last year, it is worth acknowledging that should the forecasted economic/corporate earnings weakness fail to materialize, there is now greater room for credit spread tightening than there was at the outset of 2007. In an environment of tightening or unchanged credit spreads, as we showed last year, long-biased aggressive distressed and long-biased high yield managers tend to outperform.

Quantitative equity UpdateIn August 2007, a number of funds that use mathematical models to invest in equities (quant equity funds) suffered large losses, and even many funds that ended the month flat or positive experienced large intra-month drawdowns. We analyzed the equity holdings of 34 quant equity funds and determined that widespread liquidation, exacerbated by rapid growth in assets and leverage, caused the quant equity turbulence. Between December 2004 and June 2007, the market value of these 34 funds grew from $49bn to $205bn; this market value incorporates both assets under management and leverage of funds’ longs, but not their (nearly equal-valued) shorts. Subsequently, we analyzed the September 2007 holdings for 31 of 34 funds from our prior (June holdings) study and found that their aggregate market value fell from $203.7bn to $153.5bn, a drop of about 25%. Reductions in aggregate market value of fund holdings reflect a combination of net capital outflows and lower leverage. For each of the 31 funds, we computed its ratio of September market value to June market value; this distribution is shown in Chart 1.3. The median fund’s market value fell by 20%, while nine funds lost over half of their market value and six funds actually increased their market value in the aftermath of August.


Despite the observed reduction in market value, it may be premature to conclude that the risk of a repeat of August has abated. For one thing, this decrease in market value only returns our sample aggregate to its December 2006 value. In addition, key sources of uncertainty remain. Our sample does not include proprietary trading desks and funds that mix fundamental and quantitative strategies, since their holdings cannot be disaggregated: are these omitted funds similar to those in our sample? Although quant equity funds are generally liquid, some investors may not have been able to redeem capital until year-end 2007; this may result in further, near-term decreases in strategy market value. Also, to the extent the reduction in market value is leverage-driven, it is unclear whether funds will return to higher leverage once volatility ebbs or with the passage of time (in the case

Chart 1.3 Quant Equity Fund Holdings: Ratio of September 30 to June 30, 2007 Market values for 31 Funds

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of models with exponentially decaying volatility measures). In fact, some funds that cut leverage during August have already returned to higher leverage, in some instances nearly to pre-August levels. The long-term prospects for a repeat of August depend heavily on capital f lows to the strategy, for which there is a prisoner’s dilemma: individual funds have incentives to manage ever-greater amounts of capital, resulting in potentially greater collective risk in a delevering episode.

For the 31 funds in our June and September samples, we examined how the market-cap breakdown of their holdings changed. Using equal-weights among all funds (so as not to give inordinate weights to a few large funds, given that we’re likely missing much of the universe), between June and September, mega-cap ($50+bn) rose from 9.8% to 11.6%; large-cap ($10-50bn) rose from 24% to 24.7%; mid-cap ($2-10bn) fell from 36.3% to 35.7%; small-cap ($250mm-2bn) fell from 27.9% to 25.5% and micro-cap (<$250mm) rose from 2% to 2.5%. While the holdings are still more mid/small-cap tilted than the overall equity market, there was a slight move towards larger-cap stocks. This rebalancing — albeit slight — is consistent with funds moving towards more liquid stocks, possibly as a result of an increasing penalty for illiquidity in their models. Alternatively, it may be a directional bet on larger stocks specified by their models, or even noise brought on by changes in funds’ market values. Further monitoring is needed on both the overall market value and the characteristics of quant equity holdings.

From 13-F filings for September 2007, we identified 45 quant equity funds (14 new funds from June), with an aggregate market value of $165.8bn. Based on our scenario analysis of the August 7 – 9 drawdown experienced by most funds, we analyzed how certain stocks could perform if another similar scenario occurred. We performed an analysis to gauge how certain widely held stock might perform should another similar scenario occur. We computed potential return impacts for stocks based on the number of funds holding each stock, its short interest ratio and its market cap ranking. On the long side, a stock’s scenario return is proportional to the number of holders in excess of that expected for the stock, based on the stock’s market-cap. On the short side, stock returns are proportional to short interest, among those stocks underrepresented in the holdings of quant funds. In Table 1.2, we list large-cap stocks with the most-negative scenario outcomes (long portfolio watch list) and most-positive scenario outcomes (short portfolio watch list). The range is -7% to -14% on the long side and 7% to 15% on the short side.

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This information is shown for illustrative purposes only and does not constitute a recommendation to buy, sell or hold a security. The scenario analysis is based largely upon historic data. There are numerous factors that impact the performance of an indi-vidual security and actual results in a delevering scenario may be significantly different than the analysis results shown.

Note: Number of holders is the number of quant equity funds (out of 45) with holdings in the stock

Expected number of holders is computed from a Poisson regression of number of holders on market capitalization rank (1=largest)

Unexpected count is the difference between number of holders and expected number of holders

Sources: FactSet and LBAIM analysis. Past performance is not indicative of future results

Table 1.2 Large-Cap Stocks with Greatest Risk in an August 2007 Delevering Scenario Alphabetically by Name Based on September 30, 2007 Holdings of 45 Quant Equity Funds

Long Portfolio Watch List Short Portfolio Watch List

TICkER NAMENUMbER OFHOLDERS TICkER NAME

NUMbER OFHOLDERS

BIG Big Lots Inc 20 AMD Advanced Micro Devices 8CTL Centurytel Inc 25 AMG Affiliated Managers Grp Inc 7C Citigroup Inc 26 ALXN Alexion Pharmaceuticals Inc 6DLTR Dollar Tree Stores Inc 22 BSC Bear Stearns Companies Inc 12EDS Electronic Data Systems Corp 23 BMRN Biomarin Pharmaceutical Inc 7FTO Frontier Oil Corp 22 BUCY Bucyrus International Inc 9INTC Intel Corp 26 KMX Carmax Inc 10JPM Jpmorgan Chase & Co 28 CTX Centex Corp 9KG King Pharmaceuticals Inc 21 CERN Cerner Corp 4KR Kroger Co 27 CMG Chipotle Mexican Grill Inc 9WFR Memc Electronic Matrials Inc 24 EXBD Corporate Executive Brd Co 7MRK Merck & Co 27 CROX Crocs Inc 9MER Merrill Lynch & Co Inc 26 DPL DPL Inc 7MS Morgan Stanley 26 EK Eastman Kodak Co 11OMC Omnicom Group 26 EXH Exterran Holdings Inc 3ORCL Oracle Corp 26 FMD First Marblehead Corp 8PFE Pfizer Inc 26 ITRI Itron Inc 7RSH Radioshack Corp 24 MLM Martin Marietta Materials 10RRI Reliant Energy Inc 23 NUAN Nuance Communications Inc 9BID Sotheby's 20 NVR NVR Inc 0TRA Terra Industries Inc 20 JOE St Joe Co 7TDW Tidewater Inc 23 UA Under Armour Inc 7VLO Valero Energy Corp 25 URBN Urban Outfitters Inc 5VSEA Varian Semiconductor Equipmt 21 USG USG Corp 6WHR Whirlpool Corp 24 VMC Vulcan Materials Co 11

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During the past year, aggregate

equity beta for multi-strategy

funds has continued to rise

Typical multi-strategy funds have

economically meaningful equity

market sensitivity, with funds

in the top-quintile of beta being

rather equity-like

multi-strategy equity market sensitivityIn the 2007 Outlook, we documented rising equity market sensitivity in relative value multi-strategy funds, a group that formerly exhibited little equity sensitivity (i.e., beta). During the past year, aggregate equity beta for this group has continued to rise. Chart 1.4 shows the graph of rolling 36-month S&P 500 beta of the (equal-weight) average fund return in LBAIM’s Multi-Strategy peer group. In November 2007, this beta reached a historic high of 0.36, comparable to that of many long/short equity hedge funds. Simultaneously, annualized excess returns (i.e., alpha) generated by multi-strategy funds continued to fall — also shown in Chart 1.4 — to a low of 6.2%; by way of comparison, this group averaged 3.6% per year from equity market exposure over this period. While the current alpha still appears healthy, investors should consider how alpha is distributed among funds and how much illiquidity they can tolerate to capture this alpha; we therefore turn to analysis of funds within the group.

Sources: TASS, Altvest and LBAIM analysis


To see the prevalence of equity beta among multi-strategy funds and confirm that the results of Chart 1.4 are not due to just a few high-beta funds, we computed the distribution of equity betas for funds in our multi-strategy peer group over 36-month periods. For the period ended November 2007, Table 1.3 shows that the median beta among 54 funds with at least 18 months of return data is 0.2, while the top-quintile beta is 0.42 and the bottom-quintile value is 0.1. Thus, typical multi-strategy funds have economically meaningful equity market sensitivity, with funds in the top-quintile of beta being rather equity-like. Although successive periods in Table 1.3 share 24 of 36 months, and hence it would be unfair to expect dramatic shifts in beta by year, there is no sign of a drop-off in beta within the group. Median beta in 2007 is down only slightly from 2006 , while bottom-quintile beta is up from last year.


Chart 1.4 Relative value Global Multi-Strategy Hedge Funds: Rolling 36-Month Aggregate Alpha and beta to S&P 500

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November 2007 beta is 0.36Beta to S&P 500Annualized Alpha to S&P 500

Table 1.3 Relative value Multi-Strategy Hedge Funds—36-Month Rolling beta to S&P 500

DEC 04 – NOv 07 β JAN 04 – DEC 06 β JAN 03 – DEC 05 β JAN 02 – DEC 04 βTop Quintile 0.42 Top Quintile 0.42 Top Quintile 0.17 Top Quintile 0.10

Median 0.20 Median 0.23 Median 0.08 Median 0.03

Bottom Quintile 0.10 Bottom Quintile 0.06 Bottom Quintile 0.03 Bottom Quintile -0.02Sample Size 54 Sample Size 55 Sample Size 53 Sample Size 48

13

In our cross-sectional analysis of multi-strategy fund alpha, we use two factors, equity and credit, which capture key exposures of many funds. The resulting quartiles of alpha are shown in the left-hand column of Table 1.4. A long-run study of multi-strategy fund two-factor alpha, conducted by us in 2007 and using funds’ entire return histories through 2006, found that the median fund generated 8% annual alpha, with 6% and 10% for the bottom and top quartiles, respectively. Thus, over the last 36 months, median- and bottom-quartile fund alpha is down by 1% per year, while top quartile alpha is steady. Although these differences are not statistically significant, they are consistent with anecdotal evidence of declining typical hedge fund quality accompanying recent proliferation.

Another dimension to the alpha generated by multi-strategy hedge funds is the sometimes onerous liquidity terms set by funds. Due to opportunity costs and greater uncertainty of alpha generation associated with longer lock-ups, investors generally require a premium for added illiquidity. We measured this premium by strategy based on cross-sectional regressions of funds’ excess returns on their liquidity durations. This latter variable is an asset weighted time until investor capital is returned, depending on lock-ups, notice periods, post-lock liquidity intervals, and gates. We define a fund’s net liquid alpha as its alpha minus its strategy liquidity premium times the fund’s liquidity duration. While some strategies require illiquidity to execute their trades, net liquid alpha allows comparison of alpha between funds within strategies and, when combined with strategy weight targets or portfolio liquidity constraints, can also be used to compare funds across strategies.

In Table 1.4, we display net liquid alphas of multi-strategy funds, divided into quartiles of alpha and liquidity duration — both within lock-up and post-lock. Funds in the top quartile of alpha generation (top shaded row) produced strong net liquid alpha regardless of liquidity-duration quartile. Thus, investors who identified top-quartile managers were generally well-compensated for illiquidity. Funds with median alphas over the last 36 months also provided solid, 4-5% net liquid alphas, as long as they were not in the highest quartile of liquidity duration. On the other hand, funds in the bottom quartile of alpha produced anemic net liquid alpha, between 0 and 3%, unless they were also among the most liquid quartile of funds. Thus, even a very-illiquid, top-quartile alpha fund fared better than all but the most-liquid median alpha funds and much better than even the most liquid bottom-quartile alpha funds, when alpha is adjusted for liquidity.

Fund alphas are from two-factor regressions of fund net returns on S&P 500 and Lehman High Yield Index returns

Uses 54 funds with 18-36 months of returns within this period

Lock up durations are asset weighted times from initial investment until capital is returned to investors

Durations include initial lock ups, notice periods, post-lock liquidity intervals, cross-sectional and individual gates

Post-lock durations measured from mid-point of fund’s post-lock liquidity interval

Net liquid alpha is alpha minus liquidity duration times strategy liquidity premium

Multi-strategy lock up liquidity premium is 1.6% per year of lock-up; post-lock is 3.2% per year of duration



Rising equity betas, falling alphas and often illiquid terms combine to make us less sanguine on multi-strategy hedge funds as a strategy. While top-quartile funds continue to generate substantial

Table 1.4 Net-Liquid Annual Excess Returns of Multi-Strategy Funds, Dec 2004 – Nov 2007

Percentile of Fund’s

Alpha

LOCk UP DURATIONS, GATED (YRS) POST-LOCk DURATIONS, GATED (YRS)

Two-Factor Alpha

25%ile 1.11

Median 1.82

75%ile 3.16

25%ile 0.53

Median 0.75

75%ile 1.25

75%ile 10.1% 8.4% 7.2% 5.1% 8.5% 7.7% 6.1%Median 7.1% 5.3% 4.2% 2.0% 5.4% 4.7% 3.1%25%ile 5.1% 3.3% 2.1% 0.0% 3.4% 2.7% 1.1%

Even a very-illiquid, top-quartile

alpha fund fared better than all but

the most-liquid median alpha funds

and much better than even the most

liquid bottom-quartile alpha funds,

when alpha is adjusted for liquidity

14

During 2007, manager dispersion

increased dramatically from what it

had been over the prior few years

alpha, many multi-strategy funds are opaque regarding their portfolios, failing to provide even high-level information on leverage, strategy weights, exposures and position concentration. In theory, multi-strategy hedge funds should be able to take advantage of changing strategy environments by quickly redeploying their capital. In practice, however, illiquid holdings and a reluctance to fire or pull capital from portfolio managers can limit this benefit.

strategy Correlation and manager DispersionCorrelations between hedge fund strategies remained at historically high levels during 2007. In Chart 1.5, we show the number of strategy-pair correlations (out of 10) that are statistically significant (at the 1% level) for five HFRI indices: convertible arbitrage, merger arbitrage, distressed, fixed income arbitrage and equity market neutral. Except in the wake of Long Term Capital Management (LTCM), few strategy pairs have had high correlations. Since 2004, however, correlations between strategies have risen markedly.4 Chart 1.5 indicates a decrease, from 8 to 6, in the number of significant correlation pairs over the last year. Just one strategy — fixed income arbitrage — is responsible for this decrease, however, as three of its correlation pairs fell below the cutoff. Generally, correlations remain quite high, with 5 of 10 pairs actually being greater than they were one year ago.

*Analysis considered 10 rolling 36-month correlation pairs of HFRI indices of the following strategies: Convertible Arb, Merger Arb, Distressed, Fixed Income Arb, and Equity Market Neutral.

Sources: HFR and LBAIM analysis

During 2007, manager dispersion increased dramatically from what it had been over the prior few years. In Chart 1.6, we show, by calendar year, the spread between the top-quartile fund and bottom-quartile fund in seven of LBAIM’s peer groups. In four strategies — statistical arbitrage, credit arbitrage, fixed income arbitrage and equity market neutral — the quartile spread was higher than at any point since 2000. In merger arbitrage and convert arbitrage, dispersion ticked up slightly from 2006, while dispersion in distressed securities was flat year-over-year. By using quartile spread, rather than standard deviation as our measure of strategy dispersion, we reduce the impact of outlier funds. In fact, two strategies where managers generated outlier returns through short positions in subprime mortgages, distressed and merger arbitrage, do not register increased manager dispersion based on our metric.

4 In the absence of significant market dislocations prior to Summer 2007, such high strategy correlations are unprecedented. Possible sources of higher correlation include the rise of multi-strategy funds, particularly very large AuM funds, that may trade different strategies using similar risk-control of buy/sell procedures; and the overall growth of hedge funds, with consequent strategy crowding leading to reduced differentiation and greater impact of illiquidity shocks across strategies.

Chart 1.5 Number of Significant Correlations in 5 Arbitrage Strategies* (Dec 92 – Nov 07)

0

1

2

3

4

5

6

7

8

9

10

Dec-92

Dec-93

Dec-94

Dec-95

Dec-96

Dec-97

Dec-98

Dec-99

Dec-00

Dec-01

Dec-02

Dec-03

Dec-04

Dec-05

Dec-06

LTCM

15

Manager dispersion was actually

greatest among the relative value

strategies, where severe liquidity

shocks in the summer of 2007

affected equity and credit markets

*Strategy quartile returns for 2007 are computed by annualizing returns for funds with returns through at least October 2007

Sources: TASS, Altvest and LBAIM analysis. Based on LBAIM peer groups.


Dispersion was actually greatest among the relative value strategies, where severe liquidity shocks in the summer of 2007 affected equity and credit markets. Some funds with extreme leverage suffered massive mark-to-market losses; in some cases, the funds were forced to liquidate and capitalize losses by their prime brokers; in other cases, funds voluntarily cut risk in order to avoid further losses and perhaps save the fund. As a result of these events, many funds and investors are revisiting their approach towards the use of leverage.

In Table 1.5, we use a simulation approach to evaluate the relative importance of manager selection vis-à-vis strategy weighting in (relative value/event-driven) hedge fund portfolio construction. To quantify the relative benefits from strategy weighting versus manager selection, we conducted a simulation where we allowed a broad range of weights for each strategy and then drew weights for each strategy randomly (uniformly) from their respective ranges. We also required that the seven strategy weights sum to one. To quantify the impact of strategy weighting, we used the median annual fund return for each strategy, obtained from our database. Median strategy returns are multiplied by randomly selected strategy weights and summed to obtain simulated portfolio returns. Using about 15,000 admissible sets of weights, we computed the mean and standard deviation of this portfolio return distribution; the return distribution is essentially normal, being the sum of seven uniform variables with similar variance.

The opportunity set from strategy weighting is measured by the standard deviation of the simulated return distribution: a skilled tactical allocation might be able to achieve — with the same set of typical (median) funds — a one-standard deviation increase, above the average return across all sets of weights. On the other hand, the manager selection opportunity set is derived using the top and bottom quartile of strategy returns each year — again obtained from our database. We re-ran simulations using the top-quartile strategy returns and computed the mean portfolio return across simulations. This approach assumed the investor has no skill in tactical allocation, but can pick funds across all strategies whose average returns match the top-quartile. Similarly, poor fund-selection ability is modeled using the mean return over all strategy-weight draws assuming bottom-quartile strategy returns. Manager selection opportunity is thus defined as the spread between the mean-simulated portfolio returns using top-quartile strategy returns and bottom-quartile strategy returns. In Table 1.5, the first three columns show that in 2007, the average portfolio based on median funds in each strategy returned 5%, with a 0.5% standard deviation due to varying strategy weights.

Chart 1.6 Difference in Annual Return between Top-Quartile Fund and bottom-Quartile Fund

0%

5%

10%

15%

20%

25%

2000 2001 2002 2003 2004 2005 2006 2007*

Distressed SecuritiesCredit ArbMerger Arb

Convert ArbStat Arb

Equity Mkt NeutralFixed Income Arb

16

*2007 fund returns computed by annualizing those of funds that have reported returns through at least October


Simulated results are shown for illustrative purposes only. Simulated performance results are subject to the fact that they are generally designed with the benefit of hindsight. Such results do not represent actual trading, and thus may not reflect material economic and market factors, such as liquidity constraints or the impact of financial risk and the ability to withstand losses, that may have an impact on actual decision making. No representation is being made that any client will or is likely to achieve the simulated results represented above. The simulated results are not representative of any investment product or service and do not reflect the fees and expenses associated with managing a portfolio.

In the next three columns of Table 1.5, we contrast this with the returns obtained using either the top-quartile or bottom-quartile fund in each strategy — corresponding to investors with strong or weak fund-picking skills. We compute portfolio returns in both cases using expected strategy weights (i.e., the weights in the 5%-return portfolio for 2007 with median funds). Here, the manager dispersion is evident: portfolio “quartile” spread rose to 12.1% in 2007, the highest since 2000. Unfortunately for investors, this dispersion was driven more by unusually low bottom-quartile performance, as top-quartile performance was typical (or low) compared with other years. By taking the ratio of the quartile spread to twice the standard deviation obtained from varying strategy weights, we get a measure of the relative impact of manager selection versus strategy weighting. On this score, manager selection dominated in 2007; the ratio of 12.2 was over 50% larger than the next highest year (2006). This also continues a trend, started in 2003, in which manager selection takes on a relatively larger role each year in portfolio performance.

Table 1.5 Relative Impact of Manager Selection vs. Strategy Weighting in Hedge Fund Portfolios

PORTFOLIO SIMULATED RETURNS

PORTFOLIO STATISTICS bASED ON MEDIAN STRATEGY RETURNS

PORTFOLIO STATISTICS bASED ON TOP-bOTTOM-QUARTILE RETURNS

QUARTILE SPREAD/ MEDIAN

QUARTILE SPREAD/

(2*STD DEv)YEAR

MEDIAN FUND 2*STD DEv/ TOP 25%ILEbOTTOM 25%ILE QUARTILE

MEAN STDEv MEDIAN MEAN MEAN SPREAD

2000 13.3% 0.9% 0.14 19.7% 7.5% 12.1% 0.91 6.62001 9.5% 0.8% 0.17 15.1% 5.7% 9.4% 1.00 5.72002 5.4% 0.6% 0.22 9.8% 0.8% 9.0% 1.67 7.52003 12.0% 1.5% 0.25 18.4% 7.3% 11.0% 0.92 3.72004 8.0% 1.0% 0.25 11.6% 3.9% 7.7% 0.97 3.92005 6.6% 0.5% 0.17 9.8% 3.2% 6.6% 1.00 6.02006 12.5% 0.4% 0.07 16.7% 9.4% 7.3% 0.58 8.32007* 5.0% 0.5% 0.20 10.6% -1.5% 12.1% 2.44 12.2

17

While distressed hedge fund returns

are initially negatively impacted

by a rise in default rates, future

performance has historically been

positively impacted by this increase

in distressed supply

Chapter 2: assessing the opportunity for Distressed securities FundsGiven the potential for significantly higher corporate default rates in 2008, we seek to analyze the likely timing and magnitude of the opportunity for distressed securities hedge funds. Additionally, we attempt to address the question of whether rising default rates are a tide that lifts all funds in the strategy, or instead whether there will be substantial (above average) dispersion among managers. Our analysis indicates that while distressed hedge fund returns are initially negatively impacted by a rise in default rates, future performance has historically been positively impacted by this increase in distressed supply. Additionally, our data shows that manager selection within the distressed universe becomes increasingly important (i.e., dispersion of hedge fund performance increases) both during and immediately following a period of elevated default rates.

After nearly 4H years of essentially uninterrupted spread tightening and gradually declining default rates, the credit markets seized abruptly in the summer of 2007. At roughly 575 bps over Treasuries as of the end of November, high yield credit spreads are the widest they have been since Q3 of 2003 and approximately 170 bps wider than the median credit spread over the last 5 years. This corporate credit market volatility which began in June was initially viewed by the majority of market participants as a temporary technical, liquidity driven, phenomenon stemming from weakness in the subprime mortgage market and a supply/demand imbalance in the leveraged loan market owing to the massive pipeline of Q3 and Q4 LBO financings. However, the macro and microeconomic data released over the intervening period has called into question the fundamental strength of the corporate credit market as well, raising the specter of increasing corporate defaults and the onset of a new distressed cycle.

On the macroeconomic front, Lehman Brothers Global Economics projects U.S. growth to be 1.4% during the first half of 2008, down from 2.2% in 2007, and puts the probability of recession at 35%.5 In a similar vein, the Fed recently revised lower its forecast for full year 2008 growth to 1.8-2.5% from 2.5-2.75%.6 At the company specific level, 3Q 2007 profits dropped for the first time in five years. Many postulate that this trend of slower earnings growth, if not earnings recession, coupled with the high proportion of single B and CCC rated debt issued over the last 3 years+, will result in high yield default rates moving off their recent two-decade low of 0.9% reached at the end of 2007.7 In fact, Moody’s Investor Services recently predicted that the high yield default rate will more than quintuple to 4.8% by the end of 2008 and rise to 5% in 2009. In Q4 of 2007 alone, Moody’s lowered ratings on 445 corporate issues while upgrading only 182, the highest downgrade/upgrade ratio since Q2 2003.

Conventional wisdom argues (as do many market participants) that should rising default rates materialize, the investable universe for hedge funds investing in distressed and stressed credit would expand significantly, translating into a period of elevated returns. While this seems fairly intuitive, what is less obvious in our view is the size and timing of such an opportunity. With this as a backdrop, we sought to analyze empirically the impact rising default rates have had historically on distressed/stressed credit hedge fund returns. We chose first to calculate our own estimate of the future default rate in order to quantify the magnitude of the potential opportunity. We then proceeded to analyze the impact of this potentially increased distressed opportunity set on the returns of distressed/stressed credit hedge funds.

We began our analysis by determining a range of implied default rates based on two separate approaches: 1) a ratings-based approach, using the current distribution of firms’ bond ratings and the historical 12-month ahead default rates by bond rating, and 2) a credit default swap (CDS) implied default rate, based on current CDS spreads on a universe of

5 Lehman Brothers Fixed Income Research, Outlook 2008: When Will Liquidity Return, p. 46

6 Bloomberg, Defaults poised to Quadruple as Companies Sinking to Junk Surge, 12/18/07, p. 2

7 Bloomberg, Junk Bonds to rise Fivefold from 26-year low, 01/08/08, p. 1

18

firms.8 Our analysis produced a 2008 default rate range of 2.35% – 3.16% for the corporate credit universe as a whole (investment grade and high yield) and 3.24 – 3.97% for the high yield market. While this estimate is below that predicted by the ratings agencies, it represents a material increase from current corporate default levels and suggests, at least superficially, a broadened investing opportunity set.

The next step in our analysis was to determine the sensitivity of a universe of distressed hedge funds’ performance to the level of both current year as well as one-year lagged default rates. Our motivation for including the latter variable was to account for possible delays in a hedge fund’s ability to capitalize on defaults. Our analysis involved regressing the returns of three different universes of distressed hedge funds (HFRI Distressed, CS Tremont Distressed, and LBAIM’s Distressed Global peer group). The HFRI Distressed data set used is an equal-weighted, non-investable index of self-classified managers (covering the time period of 1990-2007 YTD). The CS Tremont data set, while also based on manager self-classification, is AUM-weighted and non-investable (covering the time period of 1994-2007 YTD). The final data set used is the LBAIM Distressed peer group as classified by Lehman fund of funds analysts based on knowledge of the underlying managers’ portfolios. This series is equal-weighted and began in 1991 at a point when there were return histories for at least 5 funds available. For our analyses of strategy average returns, we compounded monthly returns to give calendar year annual returns. For 2007, we annualized the year-to-date compound returns through October. Our corresponding series of calendar year default rates were obtained from Dr. Edward Altman of New York University’s Stern School of Business.9 The resulting model contains between 14 and 18 annual data points, depending on the Distressed series used. As can be seen from the below table, the HFRI and CS Tremont universes produced less statistically significant results than did LBAIM’s distressed peer group, where we have consequently chosen to focus the remainder of our discussion.

1Index Returns from 1990-2007 (18 observations); Source: Hedge Fund Research, Inc. 2Index Returns from 1994 -2007 (14 observations); Source: Credit Suisse Tremont Index LLC3Universe reflects all funds in LBAIM’s distressed peer group since 1991 (ranging from 5 - 51 observations); when we (1) exclude a fund with outlier returns in 2007 the results are unchanged, and (2) when we start from 1996 (when there was more data available), the results are qualitatively similar.

Italics denotes a significance at the 10% level

Bold denotes a significance at the 5% level

Bold italics denotes a significance at the 1% level


Source: Default rates from Dr. Edward Altman “Current Conditions in Global Credit Markets”, (seminar” 11/28/07; LBAIM’s Distressed Global Peer Group

As can be seen from the above, while only one coefficient on default rates was statistically significant at the 5% confidence level [LBAIM Peer Group average returns for the lagged default rate is significant at the 5% level], the coefficient on the lagged default rate was significant at the 10% level for the HFRI Distressed series. Though not significant, the coefficient on the lagged default rate is also positive (i.e., higher levels of default in the prior

8 See Appendix: Estimating the 2008 Default Rate for U.S. Corporate Bonds for a more detailed discussion of our calculation methodology

9 “Current Conditions in Global Credit Markets”, (seminar) 11/28/07

Table 2.1 Default Rate Regression Analysis on the HFRI Distressed Index, CS Tremont Distressed Index and LbAIM’s Global Distressed Peer Group

REGRESSION COEFFICIENTS

INTERCEPTCURRENT YEAR DEFAULT RATE

ONE-YEAR LAGGED DEFAULT RATE

HFRI Distressed Index1 0.12 -0.75 1.37CS Tremont Distressed Index2 0.15 -1.09 0.80LBAIM Global Distressed Peer Group3 0.14 -1.27 1.96

19

Dispersion of individual

hedge fund returns (a measure

indicating the potential returns

due to manager selection), was

greater during periods of higher

default rates than in more benign

default environments

year positively impacts returns in the current year) and of a similar magnitude for CS/Tremont. While none of the current year default rate coefficients are significant, all three are negative (i.e., higher levels of default adversely impact returns in the current year) and have similar magnitudes. For the LBAIM data series, we obtained very similar results when we (1) started in 1996 instead of 1991 and (2) excluded an outlier fund with extremely high returns for 2007.


Source: Default rates from Dr. Edward Altman “Current Conditions in Global Credit Markets”, (seminar) 11/28/07; LBAIM’s Distressed Global Peer Group

2008 Expected Return = Regression intercept + (coefficient to current year default rate)(2008 default rate) + (coefficient to one-year lagged default rate)(lagged default rate) = 14% + (-1.27)(2.35% or 3.16% as the high and low range) + (1.96)(0.45%) = 9.8% to 10.9%

Please see Disclosures at the end of this document for important information regarding the target return data and analyses contained in this report.

One fundamental explanation for the negative coefficient associated with current year defaults would be that at the inflection point of default rates (i.e., transition from a period of declining/stable default rates to a period of increasing defaults), prices of distressed assets tend to widen across the board, adversely impacting the current holdings of distressed/credit hedge funds from a mark-to-market perspective. This was certainly the case in the summer of 2002, the last period of sharply rising default rates, as high yield debt spreads widened dramatically in response to the WorldCom and Adelphia frauds. As time passes and selling pressure abates, bonds/loans begin to trade at prices more reflective of the idiosyncrasies of the individual bankruptcy, explaining why the lagged year’s default rate would have a significantly positive sign.

These results seem to challenge the notion that a spike in defaults in 2008 will translate into a strong year of returns for distressed/stressed managers. Instead, they suggest the upside from rising defaults will not be realized until 2009. Having said this, one must take into account several other factors, including: 1) the trajectory of the rise in default rates (i.e. does the rise occur early in the year, later in the year, or spread out across the year). In theory, a spike in defaults in January, while adversely impacting returns in the first portion of the year, could begin to have positive implications for returns during the tail end of 2008; 2) the significant amount of distressed capital sitting on the sidelines that potentially limits the magnitude of the sell-off in distressed securities (i.e., duration of drawdown in 2008 may be short-lived owing to large demand); 3) the default rate itself may understate the true level of distressed in the credit markets. More specifically, the rise in the frequency of “rescue financings” — the providing of capital to struggling companies that would otherwise file for bankruptcy — may artificially reduce the officially reported default rate which only captures those companies which formally file for bankruptcy. If this is in fact the case, the true increase in corporate distress may have already begun; and, 4) previous default cycles, and this analysis, have centered around corporate distressed assets. Given the massive sell-off in RMBS securities over the last year, it is reasonable to speculate that many distressed hedge funds will allocate capital to these distressed instruments. Again, this suggests that broad market default rates may already be higher, and the current supply of distressed assets greater, than the headline default number implies.

Table 2.2 Factors Used to Calculate Expected 2008 LbAIM Distressed Hedge Fund Peer Group Returns

REGRESSION INTERCEPT

COEFFICIENT TO CURRENT YEAR DEFAULT RATE

ESTIMATED 2008 CURRENT DEFAULT RATE

COEFFICIENT TO ONE-YEAR LAGGED

DEFAULT RATE2007 LAGGED DEFAULT RATE

LBAIM Global Distressed Peer Group 14% -1.27 2.35% -

3.16% 1.96 0.45%

20

Fund managers have generally

performed more homogeneously

prior to the outset of a rise in default

rates and then began to differentiate

themselves from one another as the

distressed cycle progresses

Another significant finding of this analysis was that the dispersion of individual hedge fund returns (a measure indicating the potential returns due to manager selection), was greater during periods of higher default rates than in more benign default environments. Further, manager dispersion in the one-year lagged case was generally higher than it was in the current year.10 To calculate dispersion of returns, we used the LBAIM Distressed Peer Group, taking only those funds with a full 12 months of returns each year (it was not possible to conduct such a cross-sectional analysis with HFR or CS/Tremont indexes). We measured the dispersion in two ways: (1) using the standard deviation of annual fund returns each year, and (2) using the spread between the top- and bottom-quartile funds’ returns each year. The latter measure is less sensitive to outlier returns. Note that while neither standard deviation nor quartile spread is normally distributed, it is still quite common for people to conduct ordinary least squares regressions using them as dependent variables.

12007 standard deviation calculated excluding the performance of 2 outlier funds in order to diminish dispersion between the quartile spread and standard deviation.


Source: Default rates from Dr. Edward Altman “Current Conditions in Global Credit Markets”, (seminar) 11/28/07; LBAIM’s Distressed Global Peer Group

The result of this dispersion analysis suggests that hedge fund managers have generally performed more homogeneously prior to the outset of a rise in default rates and then began to differentiate themselves from one another as the distressed cycle progresses. The 1991/1992 period is a prime example of the lagged effect on hedge fund performance: annual default rates spiked to a high of 10.27%, resulting in a ballooned quartile spread of 27.89% the following year. When viewed in concert with this data showing a greater impact of defaults on a lagged vs. current basis, one conclusion would be that entering a distressed cycle, distressed/stressed hedge funds’ exposure levels are more similar to one another than they are after a rise in defaults. That is, they do not have significantly different hedge or outright short exposure, and their longs — either because there is high overlap at the underlying position level given the finite supply of distressed securities or because these securities perform similarly in a sell-off — are not measurably distinct. As the opportunity set (i.e., universe of investable distressed credits) grows, however, there is greater room for differentiation. Further, another explanation for this pattern of manager dispersion is that the timing of events that results in the realization of profits from distressed investments (e.g., emergence from bankruptcy, asset sales, refinancings, etc.) are highly company specific and should be unrelated to the broader distressed market. As the supply of new distressed securities increase and hedge funds select different situations in which to invest, the pattern of fund returns is increasingly driven by

10 The coefficient on the lagged default rate was statistically significant at the 1% level, in contrast to that of the current year default rate which was not even significant at the 10% level.

Table 2.3 Dispersion of LbAIM’s Distressed Peer Group Returns (Selected Periods around Default Rate Increase)

ANNUAL DEFAULT RATE QUARTILE SPREAD

1991 10.27% 15.22%

1992 3.40% 27.89%

1993 1.11% 10.44%

1997 1.25% 6.28%

1998 1.60% 13.09%

1999 4.15% 13.93%

2001 9.80% 10.11%

2002 12.80% 16.35%

2003 4.66% 15.28%

2004 1.25% 12.03%

20071 0.45% 7.12%

21

Given the illiquid terms common

to distressed hedge fund investing,

manager due diligence is crucial

even in times of great opportunity

for the strategy as a whole

the uncorrelated timing of events specific to the individual restructurings in their respective portfolios. These results seem to back-up our contention that given the illiquid terms (i.e., long lock-ups) common to distressed hedge fund investing, manager due diligence is crucial even in times of great opportunity for the strategy as a whole.

While one might conclude from this analysis that an investor is better served by waiting to make an allocation to distressed debt hedge funds in the period following a rise in defaults rather than in the period preceding it (i.e., now), there are mitigating factors that should be considered. One fairly obvious consideration is the return expectations one has for other hedge fund strategies as well as the broader markets overall. While the outlook for returns for distressed debt hedge funds might be less sanguine for 2008 than it is for 2009, the opportunity cost of allocating to distressed managers today might be minimal or non-existent if other hedge fund strategies or traditional asset classes are not deemed to have materially greater upside. The issue of scarce capacity should also be taken under advisement as many high quality hedge funds with long, successful track records that are selectively open for capital commitments today might not accept subscriptions at the “bottom” of the market. As seen from the dispersion data, the benefit of being with top-quartile managers is significant in the one-year lagged case, and this future potential outperformance might be worth the cost of more pedestrian returns in the year coinciding with a default increase. Even if a decision is made to increase distressed allocations for 2008, it is helpful to approach the year with realistic performance expectations.

A real-life manifestation of this recognition of the potential challenges of investing aggressively in distressed prior to a marked rise in defaults is the increasing number of high caliber distressed funds that are raising assets with a drawdown structure (i.e., private equity-like committed capital arrangement where an investor commits a pre-specified dollar amount and the hedge fund only draws the capital when/if it sees opportunities to invest productively in the asset class). While this seems to be a more efficient way of investing capital in the absence of perfect foresight with respect to the timing of the distressed opportunity, the illiquidity and uncertainty of cash outflows makes this a challenging structure for most hedge fund investors. A related alternative is allocating capital to event-driven multi-strategy managers who can move capital into distressed investments after the opportunity presents itself and, in the meantime, allocate that capital to more productive strategies. Though in theory this is a viable option, we are skeptical of the ability of large multi-strategy firms to reallocate both capital and resources effectively in periods of major market moves (e.g., convertible bond sell-off of 2005).

appendix: estimating the 2008 Default rate for U .s . Corporate BondsWe used two approaches to estimate default rates for U.S. corporate bonds: (1) a ratings-based approach, using the current distribution of firms’ bond ratings and the historical 12-month ahead default rates by bond rating, and (2) a credit default swap (CDS)11 implied default rate, based-on current CDS spreads on a universe of firms. Since CDS are increasingly liquid and there is evidence that CDS spreads lead cash bond yields,12 a CDS-based default rate may provide incremental (and potentially superior) information to a ratings-based default rate.

In relating CDS spreads to default rates, we use Duffie’s simplified model,13 where the annualized spread, S , is the product of the risk-neutral default rate, D̃ , and the loss given default, expressed in terms of the recovery rate, R : S = D (1 - R)˜ . Since we want the market-wide default rate, our spread is an effective value across a universe of N corporate bonds; i.e., i

N

i i SwS1

. The weights, wi , for firm i, having spread S

i , are discussed later. Two

11 In a credit default swap (CDS) on a bond, the “long” party makes fixed quarterly payments for the term of the swap (often five years) to the “short” party and receives par value in the event of a bond default; payments are quoted as an annualized spread over Treasury yields.

12 See R. Blanco, S. Brennan and I. W. Marsh, “An Empirical Analysis of the Dynamic Relationship between Investment Grade Bonds and Credit Default Swaps”, Bank of England Working Paper 211, 2004.

13 D. Duffie, “Credit Swap Valuation”, Financial Analysts Journal, Jan-Feb 1999, 73-87.

22

technical issues remain: (1) we want real-world default rates, D; these embed a time-varying risk premium relative to risk-neutral default rates, and (2) recovery rates have historically depended on default rates, generally decreasing as default rates rise.

To map risk-neutral default rates onto real-world default rates, we use historical studies of CDS risk-neutral default rates and actual default intensities;14 these studies express the risk premium as a multiple, λ , of the risk-neutral rate: DD

~, with 3/23/1 . The

resulting formula for real-world default rates is )1(/ RSD . Rather than stipulating a long-term recovery rate, we use the historical relation between default and recovery rates compiled by Altman, et al:15

%45%20%,5For

%65%25%,5%3For

%75%35%,30For

RD

RD

RD

For each value of risk-premium scaled spread, λS, we then compute the minimum and maximum default rates, D

min and D

max , respectively, consistent with the above default/

recovery relationship.16 Curves for these default rate bounds are shown in Chart 2.1. When combined with our range on risk premium, λ , this results in a range of default rates for a measured effective CDS spread. For such an effective spread, the ranges in risk premium and recovery rates give a 2 x 2 grid of possible default rates. Interpolating within this grid allows us to potentially refine our default-rate range by layering on fundamental views for the risk premium and recovery rates.

Source: LBAIM analysis; E. Altman, et. al. , “The Link Between Default and Recovery Rates, “ NYU Salomon Center, S-03-4; D. Duffie “Credit Swap Valuation,” Financial Analysts Journal, Jan – Feb 1999, 73 – 87

The final element in the default-rate calculation is the effective spread computation. We obtained data on CDS spreads from Markit, as of November 20, 2007, and used 5-year CDS spreads under the “MM” restructuring convention. This data set contained 508 firms for which we could identify current S&P bond ratings. The frequency of ratings across our CDS universe was skewed towards higher-grade firms, compared with the corresponding frequency for a larger set of 1,271 firms with credit ratings, obtained through FactSet; see Chart 2.2. Also, we did not obtain CDS spread data for any firms with CCC+ or lower ratings — about 1% of the larger universe.

14 D. Duffie, “The Price for Bearing Default Risk”, IAFE presentation, June 2004, Figure 18, and Berndt, A. and R. Douglas, “Estimating Default Risk Premia from Default Swap Rates and EDFs”, preprint 2004.

15 E. Altman, et al.,”The Link between Default and Recovery Rates,” NYU Salomon Center, S-03-4.

16 To compute minD , we start in the left-most default-rate region and use the default rate corresponding to the lowest recovery rate when possible: for %3)35.01(0 S , )35.01/(min SD ; for %3)25.01(%3)35.01( S , %3minD ; for

%5)25.01(%3)25.01( S , )25.01/(min SD ; etc. A similar calculation yields maxD , except starting from the right-most region.

Chart 2.1 Minimum and Maximum Real-World Default Rates vs. Risk Premium * Weighted CDS Spread

Minimum Default Rate

Risk Premium * Weighted CDS Spread

Real

Wor

ld D

efau

lt R

ate Maximum Default Rate

6.0%5.5%5.0%4.5%4.0%3.5%3.0%2.5%2.0%1.5%1.0%0.5%0.0%0%

2%

4%

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23

Sources: Markit, FactSet, Bloomberg and LBAIM analysis.

To compensate for the observed biases in our CDS data set, we computed average spreads by bond rating, equal-weighting each firm within a rating, then weighted these spreads by the frequency distribution of ratings in the larger data set. This yields an effective spread for 99.3% of the universe; from this spread we used the above model to compute a range of default rates. For the remaining 0.7% of the universe (CCC+ and CCC) without spread data, we used historical real-world default rates by rating, with weights based on the frequency of each rating in the larger data set. The ultimate range of default rates was obtained by adding the low-rated historical piece, scaled by 0.7%, to the CDS-based implied range, scaled by 99.3%.

For bonds rated above CCC+, the effective spread was 2.4%, resulting in the range of default rates between 1.5% and 5.0%, shown in Table 2.4 — constructed by interpolating between the min/max values. We refined this range based on our judgment that (1) recovery rates will be average to below average, due to the weakened covenants recently in place, and (2) the risk premium will be average to below average, since economic conditions and market sentiment have not deteriorated to levels historically consistent with high risk premia (e.g., mid-2002). This narrows the range of default rates to 2.3-3.1% for the boxed area of Table 2.4. For the CCC+ and CCC portion of the universe, we compute a 7.9% weighted probability of default using historical year-ahead default rates.17 Combining these two values in a 993:7 ratio, we obtain an aggregate default rate range of 2.35-3.16% for 2008.

Sources: Markit, FactSet, Bloomberg and LBAIM analysis.

17 From http://www.firstknow.it/rating_mean.aspx This web site had granular year-ahead default rates by rating. For those ratings where other sources of historical default-rate data were available, the default rates were quite close.

Chart 2.2 Distribution of Firms by Ratings: Overall and Subset with 5-Year CDS Spread Data

0%

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18%

DCCC CCC+B-BB+BB-BBBB+BBB-BBBBBB+A-AA+AA-AAAA+AAA

Universe of 1,271 Firms with RatingsUniverse of 508 Firms with CDS Spreads and Ratings

Current S&P Bond Rating (November 2007)

Perc

enta

ge o

f Fir

ms

Table 2.4 CDS-Implied Default Rates For 99% of bonds rated above CCC+

RECOvERY RATES

MIN LOW MEDIAN HIGH MAx

RISk PREMIUM

MAx 1.22% 1.66% 2.11% 2.55% 3.00%HIGH 1.52% 1.99% 2.45% 2.92% 3.38%AvERAGE 1.83% 2.31% 2.80% 3.28% 3.77%LOW 2.13% 2.64% 3.14% 3.64% 4.15%

MIN 2.44% 2.96% 3.49% 4.01% 4.53%

24

Despite the pace of recent growth

and the “helping hand” of a positive

market, the Asian markets are

under-penetrated by hedge funds

relative to other markets globally

Chapter 3: the growth Prospects for asian hedge FundsThe pan-Asian hedge fund universe has experienced a period of rapid expansion and development in recent years. This growth has been driven by a number of market related developments, which have increased the scope for hedge fund activity in the region. A key question for investors has been how these developments represent a permanent change in opportunity set, as opposed to a temporary or cyclical improvement in circumstances. Our analysis suggests that, despite the pace of recent growth and the “helping hand” of a positive market, the Asian markets are under-penetrated by hedge funds relative to other markets globally and that many factors currently in place should lead to further growth in the hedge fund sector in Asia in 2008 and the foreseeable future. These are discussed below.

Source: Eurekahedge

Before entering into this discussion, we acknowledge that while we expect overall f lows and the opportunity set to remain positive into 2008, it would be foolhardy merely to extrapolate recent strong performance unquestioningly into the future. The strong performance of all asset categories and investment styles has had two key drivers:

1. Developing structural characteristics which make the Asian markets a compelling investment universe for foreign institutional investors, including hedge funds; and

2. Strong growth by the regional economies and stock markets which have benefited investors with a long bias

In this analysis, we do not attempt to predict the future direction of the Asian markets themselves, which inevitably will be a partial determinant of hedge fund performance, given that few funds in the region are wholly market neutral. We focus instead on the current characteristics of the Asian capital markets and how these lead to hedge fund trading opportunities, as well as the levels of penetration of Asian markets by hedge funds relative to other global markets and the implications of both for the development of hedge fund activity in the region.

Clearly, it is the case that some of the structural developments discussed below are not isolated from market direction, which creates certain “grey” areas. An obvious example would be market liquidity, which is driven both by certain long-term changes in market structures and should have a permanent effect, as well as by shorter-term market performance and asset in-flows, which are easily reversible. However, even with a reduction in marketing liquidity, there is a case to be made that Asian markets are inefficient and under-penetrated relative to their size and therefore represent an interesting playing field for hedge fund investors.

Chart 3.1 Asian Hedge Fund AUM

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25

Progress towards the opening up

of capital markets has been a key

factor in improving accessibility,

overall liquidity, and transparency

of regional Asian markets and of

emerging markets in general

1. Capital markets have liberalized and this has created greater liquidity for hedge funds to tradeOngoing progress towards the opening up of capital markets has been a key factor in improving accessibility, overall liquidity, and transparency of regional Asian markets and of emerging markets in general. This, in turn, creates a broader and more stable trading environment for hedge funds, which creates a positive outlook for future growth and performance into 2008 and beyond.

A prime example of recent developments would be the QFII and QDII systems introduced by the Chinese government to begin to allow onshore Chinese investors to invest outside of mainland China and to allow non-Chinese investors to invest into onshore Chinese equities (“A” and “B” shares) respectively. Hedge funds have benefited from these quota systems, as they have both increased the number of stocks available for investment and also introduced additional liquidity into the Hong Kong market in particular, which has been the beneficiary of f lows from onshore Chinese investors via the QDII scheme. This improvement in liquidity is evidenced by looking at trends in market volume in Hong Kong (denominated in HKD):


Source: Bloomberg

The turnover as a proportion of the market has also increased — turnover was over 40% higher relative to market size in both Q2 and Q3 of 2007 compared to 2006. However, the key point here is that regulatory developments have increased market volumes and size and these represent structural developments, rather than being simply driven by market returns.

Similarly, in the China local market, although shorts are still not permitted, the long side turnover has been very substantial. For example, the A-share market in Shanghai has varied in daily turnover in USD from $7bn to $35bn in 2007.

The ability to short, often raised as an issue for Asian hedge fund investors, has also improved in Hong Kong as a consequence of recent market developments. There has been something of a virtuous cycle, where the positive conditions have created liquidity for hedge funds who have in turn created more demand for short access, leading the banks to work harder to source, borrow and create innovative product. This is illustrated by the very significant increase in short-selling turnover, or the total amount of transactions concluded on the Hong Kong exchange in the Chart 3.3 (expressed in USD):

Chart 3.2 Hong kong Market Turnover

02000400060008000

100001200014000160001800020000

$HK

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26


Source: Bloomberg

Again, this doesn’t guarantee permanency, but positive steps have been made. The indication is that the direction of these policy changes is positive for hedge funds and other investors.

Further measures have been proposed by the Chinese government in 2007, such as the “Through-train” system — a proposal to allow retail investors access to offshore markets. While this has run into problems in the planning stages, it is indicative of a desire to improve transferability of assets and the openness of capital markets. Progress with this in 2008 could create trading opportunities for hedge funds and further improve market liquidity (just as it has in 2007). Other developments in China, such as the ongoing trend of privatizing State Owned Enterprises (“SOEs”) and the implementation of a newer, more Chapter 11 style, bankruptcy code enhances this impression.

Elsewhere, we can evidence other positive trends developing from policy. The broad trend in improved liquidity is shown in the chart below. Singapore, for example, has been notably open in welcoming hedge funds to the region by easing the initial and ongoing regulatory burden for alternative investment managers operating in the country. As a result, the number of hedge funds in Singapore has increased substantially over recent periods, with Eurekahedge estimating 122 funds as of 1 July of 2007. This has increased activity in South East Asia and trading in that region.


Source: Bloomberg

Chart 3.3 Hong kong Short-Selling Turnover

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Chart 3.4 Asian Market Trading volumes

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27

More broadly, we can see that access to Asian markets is improving with time. 2007 saw the soft launch of securities lending and regulated short selling in Malaysia and the Philippines. In addition, regulators in India, Indonesia and Pakistan have announced plans to introduce securities lending. The table below shows the current status of a number of regional markets in terms of ability to trade cash and derivatives, as well as the local market rules regarding trading and share ownership. As can be seen, access is available in many markets on both the long and the short side; whether this is via cash or synthetic structures, there is very often a solution.

Source: Lehman Brothers & Morgan Stanley1Direct Market Access

In India, there have been two interesting developments this year. The proposed overhaul of the P-note system, through which many overseas investors accessed Indian equities, was initially seen as a hostile move towards hedge funds. However, the requirement for registration in India being suggested serves to increase the transparency and, arguably, also the stability of f lows into the region, which ultimately should be a positive influence on money management in the region. Second, as mentioned above, the SEBI (India’s market regulatory body) made comments early in the year to the effect that they planned to allow cash shorting in the equity markets. Progress has now been made here and indications point to an imminent launch. Meanwhile, the rapidly growing single stock futures market has provided participants to the ability to short. The openness of SEBI to the concept of cash shorts again augurs well for hedge funds in the territory.

Table 3.1 Share Ownership and Short-Selling Status of Asian Regional Markets

CASH SYNTHETICS LOCAL MARkET RULES

CASH LONGS

CASH SHORTS

SYNTHETIC LONGS

SYNTHETIC SHORTS

SUbJECT TO FOREIGN OWNERSHIP LIMITS DMA¹ AvAILAbLE

China (A & B Shares) No No Yes No Yes No (TBD)

India Yes No Yes Yes Yes TBDIndonesia Yes No Yes Yes Yes NoKorea Yes No Yes Yes Yes YesMalaysia Yes No Yes Yes No NoPakistan Yes No Yes Yes Yes NoPhilippines Yes Yes Yes Yes No NoTaiwan Yes No Yes Yes Yes YesThailand Yes Yes Yes Yes No NoAustralia Yes Yes Yes Yes No YesHong Kong Yes Yes Yes Yes No YesJapan Yes Yes Yes Yes No YesSingapore Yes Yes Yes Yes No Yes

28

Source: Bloomberg


Another revealing development in the past year was the implementation and subsequent reversal of capital controls by the military coup in Thailand early in 2007. Initially, the new military government addressed the concerns of external investors over the implementation of capital controls (only to reverse their policy almost immediately). These efforts are in stark contrast to the disregard that has been shown to the interests of investors by certain governments or central banks in the past. We are still far from seeing a very liquid Thai market, but again, this more liberal, economically friendly attitude widens the playing field for hedge funds and reduces political risk, which has always been a key concern for investors in the region. This is evidenced by compressed sovereign-risk premium across the region.

The table below gives some indication of the size of the equity universe available to hedge funds in the region, listing the number of stocks in the markets at various levels of daily turnover, split by region. It can be seen that at the lower end of the spectrum, i.e., $1m to $5m turnover per day, there are more stocks available in Asia than either the U.S. or the European markets. Given the lower average size of hedge funds in the region ($137m in Asia in June 2007, per Eurekahedge), this is material. Even at higher turnover levels, the Asian market offers considerable variety for stock-pickers.

Source: Bloomberg

¹Developed Asia includes Australia, Hong Kong, Japan, New Zealand, Singapore, South Korea, Taiwan.

²Emerging Asia includes Bangladesh, Cambodia, China, Cook Islands, Fiji, French Polynesia, India, Indonesia, Kazakhstan, Kyrgyzstan, Macau, Malaysia, Mongolia, Pakistan, Papua New Guinea, the Philippines, Samoa, Sri Lanka, Thailand, Turkmenistan and Vietnam.

With many markets continuing to open up and developed markets such as Japan, Korea and Australia already trading very significant volumes, the stage is set for equity based

Chart 3.5 India Total Futures and Options Open Interest volume

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Table 3.2 Equity Trading volume by Region

vOLUME (USD DAILY 3M AvERAGE) DEvELOPED ASIA¹ EMERGING ASIA² U.S. WESTERN EUROPE

$1m per day 3078 1922 3479 1850

$5m per day 1432 948 2304 1040

$10m per day 957 505 1762 767

$20m per day 585 226 1251 550

$50m per day 250 54 713 319

$100m per day 107 13 410 187

29

With many markets continuing

to open up, the stage is set for

equity based hedge fund strategies

to benefit from growing liquidity

in a broadening and generally

under-covered stock universe

hedge fund strategies to benefit from growing liquidity in a broadening and generally under-covered stock universe.

A final comment here would be that the capital market developments described will continue to create shorter-term trading opportunities for hedge funds as new initiatives experience teething problems. For example, the Indian market was limit down on the day of the announcement of P-note reform, but then bounced substantially as participants began to weigh up the actual implications. The Hong Kong market moved very substantially in mid-August when the Through-train system was announced, with certain stocks favored by onshore Chinese investors moving harder than others. We would expect these types of opportunities to continue into 2008 and hedge funds — especially those with flexible equity-oriented mandates — to benefit from similar trading opportunities.

2. Growth has led to sizeable inefficiencies which hedge funds can exploitWhile governments, regulators and exchanges have been adopting helpful policy, other market developments have also increased the opportunity set for hedge funds moving into 2008. The number of investable stocks in the market has expanded substantially, aided by IPO volume, growth in corporate earnings and the expansion in market capitalization of many companies. A key feature of this expansion has been the speed at which the growth in equities has outpaced sell-side coverage. The chart below gives some indication of the lack of research coverage in the Asian equity markets relative to the U.S. and the Western European markets. The chart shows the ratio of stocks with no analyst coverage at different levels of market capitalization, as a percentage of the total stocks in that size bracket, split between Emerging Asia, Developed Asia, Europe and the U.S. The implication is clearly that there is a higher ratio of companies with no analyst coverage in Asia than elsewhere.

Source: Bloomberg

At the same time, the sheer number of stocks underlying this chart is also substantial. The chart that follows indicates the total number of stocks at each size bracket in each region. It can be seen that there is a broad Asian equity universe for hedge funds to access.

Chart 3.6 Stocks With Analyst Coverage vs. Total Universe

5%

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95%

Less thanor = 100M

100M-250M

250M-500M 500M-1B 1B-5B 5B-10B 10B +

% o

f Com

pani

es w

ith

Ana

lyst

Cov

erag

e

Market Capitalization (USD MM)

Developed AsiaEmerging AsiaNorth AmericaEurope

30

Evident inefficiency in Asia, relative

to other regions, suggests a fertile

arena for managers with the

ability to value companies and take

advantage of low broker coverage

to gain an informational edge in

their company analysis

Source: Bloomberg

Finally, the table below combines the two aspects described above to show the scale in terms of numbers of companies and levels of inefficiency in Asia relative to the other regions. The clear conclusion is that the level of analyst coverage at different market capitalization brackets for companies is consistently lower in Asia than elsewhere in the world. This lack of research coverage creates inefficient pricing in the equity markets and therefore an opportunity for hedge funds to exploit this inefficiency from both the long and the short side. This evident inefficiency suggests a fertile arena for managers with the ability to value companies and take advantage of low broker coverage to gain an informational edge in their company analysis.

Source: Bloomberg

Another effect of the development of the equity universe has been more variety at index level. A basic example would be the declining concentration of indices across the region. As markets become more mature, index diversification increases, as evident from the charts on the following page.

Chart 3.7 Market Capitalization Distribution by Region

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Table 3.3 Distribution of Companies by Market Cap and Analyst Coverage in Asian and Western Markets

MARkET CAP

DEvELOPED ASIA EMERGING ASIA NORTH AMERICA EUROPE

# OF COMPANIES

ANALYST COvERAGE

<=3# OF

COMPANIES

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<=3# OF

COMPANIES

ANALYST COvERAGE

<=3# OF

COMPANIES

ANALYST COvERAGE

<=3

Less than or = 100M 6366 744 5383 448 9846 982 4880 957

100M-250M 1871 113 1028 292 1606 543 1568 492

250M-500M 988 384 930 299 1109 270 986 291

500M-1B 683 236 752 238 902 146 835 184

1B-5B 921 135 864 229 1612 149 1311 157

5B-10B 197 8 131 17 426 58 335 24

10B + 238 3 130 5 769 177 642 35

Total 11264 1623 9218 1528 16270 2325 10557 2140

31

Source: Bloomberg

Source: Bloomberg

Source: Bloomberg

Chart 3.8a Index Concentration Trends — Hang Seng (Uk)

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Chart 3.8b Index Concentration Trends — kOSPI (korea)

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Chart 3.8c Index Concentration Trends — Straits Times (Singapore)

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32

While mainstream indices in Western markets such as the S&P500 or the FTSE 100 are clearly considerably less concentrated than this, the moves make the indices more useful for many investors.

Source: Bloomberg

Another interesting effect is that in addition to the greater potential diversification across hedge fund holdings of a broader universe, indices can become more effective hedges for portfolios as they become less concentrated and therefore more representative of their respective markets. While the major caveat here remains the substantial basis risk of relying on an index hedge — especially where indices remain reasonably concentrated by some standards (compared to the S&P 500, for example), the efficacy of the use of futures or index options as hedging possibilities is potentially enhanced by these developments. Prime brokerage houses have also been active in developing bespoke and general basket hedging products, which have further contributed to hedging options. The growth of ETFs as a global presence has complemented these hedging tools. For long-short or event driven funds looking for hedging options, these are becoming more diverse into 2008.

This has also been a factor in the increase in the size and volume of the options market. More diversified indices offer a more varied underlying for options traders. Structured product issuance by the banks on single stocks and indices has seen rising volumes over the past few years. Although much of the option volume is OTC, and thus difficult to track, an example would be the increase in the Asian Structured Retail Product Market from $44.9bn in 2003 to $63.5bn in 2006 (Source Arete Consulting, 2007 Review), with significantly higher estimates for 2007. The retail buyers of this product drive inefficiencies in the options markets for traders adept at price discovery and with a view on the direction of the underlying (the delta) or the value of the implied volatility (the vega). Much of this product is driven by market views and volumes will undoubtedly decline if markets become less attractive. However, in the near-term, the ongoing product pipeline looks robust and much of the existing product in place has duration into and beyond 2008, requiring ongoing hedging by the banks and prop desks responsible for issuance. Therefore, the supply of volatility product should continue to create opportunities for hedge funds with the expertise and capability to trade options and derivatives in the near-term.

Moving back to the fundamental side, corporate activity in the region has been rising and a number of factors suggest the possibility that this may continue into 2008:

1. Many cash-rich companies with under-levered balance sheets — average debt-equity for Asian corporates in December 2006 was 1.3 (per data collected by the Asian Development Bank) at the end of 2006 and 2007 numbers, although not yet released, are not expected to be significantly higher.

Chart 3.9 Index Concentration as of 12/31/2007

-15.00%

5.00%

25.00%

45.00%

65.00%

85.00%

SENSEX KOSPI HANG SENG STRAITS TIMES S&P 500 MSCI EURO NIKKEI

Perc

enta

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f Ind

ex

Top 5 CompaniesTop 10 CompaniesTop 15 Companies

33

The Asian markets are

under-penetrated by hedge funds

compared to Europe or the US,

which means that opportunities

should be greater and relative

value more compelling in Asia

2. Unconsolidated industries where there are strong potential synergies

3. A large amount of private equity capital has been raised in Asia and remains unspent (estimates are that there is around $50bn of private equity cash available to deploy in Asia at the end of 2007, before leverage — if leverage is taken into account, this could constitute some very material buying power).

The implication would be that event driven funds could potentially benefit from a rise in corporate activity. Deal spreads in Asia can be more attractive at times than elsewhere globally, but the greater opportunity set is probably more for those event driven funds able to take views on merger synergies, consolidation activity and the impact of transactions on corporate value. Share buybacks and balance sheet restructurings are also becoming increasingly common, generating opportunities to identify value creation at an early stage. Event driven managers with investment banking and corporate finance skills should be able to capitalize on any pick up in corporate activity.

3. The Asian capital markets are under-penetrated by hedge funds relative to Western MarketsPerhaps the most stark positive for Asian hedge funds is the fact that, even with the structural improvements, increasing liquidity and compelling inefficiencies described, the market is under-penetrated by hedge funds relative to Western markets.

One way of looking at this is to compare the level of hedge fund assets to the market capitalization of the markets in which these assets are employed. The chart below shows the growth in hedge fund assets in Asia, Europe and the U.S. relative to the growth in equity market cap in those regions.

The key points are as follows. First, the absolute growth in equity markets in Asia has far out-paced the growth in hedge fund assets. Although equity market volume is only a proxy for opportunity set, this would suggest that hedge fund trades in Asia have not become more crowded over time, contrary to popular wisdom. Second and crucially, the Asian markets are under-penetrated by hedge funds compared to Europe or the U.S. This means that opportunities should be greater and relative value more compelling in Asia, attracting further assets into Asian hedge funds.

Source: Eurekahedge, Bloomberg and LBAIM analysis.

Another way of looking at this would be to consider that Asian hedge funds, according to Eurekahedge, account for around 8% of global hedge fund assets, while the markets in which they invest account for 25.27% of global equity markets. Again, this suggests that the balance should change over time. While the chart above may exclude some allocations to Asia by several of the large Global Multi-strategy funds, the pattern and level of under-penetration seems clear.

Chart 3.10 Hedge Fund AUM as a Percentage of Total Market Cap

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34

5. The Asian hedge fund universe will likely retain elements of market sensitivityWhile we do not attempt to anticipate market direction, it is possible to make a number of observations about the types of market-related risks we do see in the Asian hedge fund universe which could play a role in impacting performance in 2008.

Although a number of sustainable structural developments should create a positive environment for Asian hedge funds in 2008, it is worth highlighting the fact that growth clearly is not isolated from market risk. To remind ourselves of this fact we need only look at Japan, whose share of the Asian hedge fund market had shrunk from 25% to 20% in the first six months of the year (Source: Eurekahedge) and plummeted further in the latter half of 2007. The demise of Japanese hedge funds since 2005 offers a parallel to Asia ex-Japan’s current euphoria. The darlings of the hedge fund world in 2005, Japanese hedge funds saw substantial asset f lows in that year, much of which were invested in long positions in small-cap companies. The exposure was difficult to hedge due to the lack of borrow on smaller-cap stocks, leading to outright long exposure, or a mismatch between long books dominated by small-cap names and short books with large-cap or index exposure. Japanese small-cap stocks became very illiquid in the subsequent two years as the reflationary story faded and investor interest waned leading to heavy redemptions. Losses were incurred by many Japanese managers over this period.

We do see some of the same trends emerging in Asia ex-Japan with, unsurprisingly, some of the less efficient small- and mid-cap stocks finding their way into the long books of hedge fund portfolios. Below is the rolling correlation of our proprietary Asia ex-Japan Long/Short hedge fund peer group to a very basic small-cap factor — the performance of small-cap minus large-cap in Asia ex-Japan. The chart evidences an increased sensitivity to small-cap returns versus large-cap.


Source: LBAIM database and Bloomberg

Similar to Japan, these smaller-cap stocks are difficult to hedge, so are generally held as outright long positions or represent basis risk versus a larger-cap hedge. Although current liquidity is substantial, there is a clear risk that this same liquidity would be absent if players wished to exit simultaneously, as was the case in Japan in 2006 and 2007. This could result in fat tail risk for Asian hedge funds.

However, a couple of factors may mitigate this risk. First, large-cap indices have appreciated strongly in Asia this year — leading, rather than lagging the market, as f lows have been oriented to index stocks — and there is little evidence in terms of valuations to suggest that

Chart 3.11 36-Month Rolling Correlation of Asia ex-Japan L/S Peer Group Returns to Asia Small Minus Large Factor

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35

small-cap valuations are excessive comparative to larger-cap stocks, in contrast to Japan in late 2005. Second, the small-cap bias is less clear in Asia ex-Japan looking at actual hedge fund portfolios, as many hedge funds continue to find value in the larger-cap end of the market. Finally, much of the flow into Asian hedge funds has come into products with redemption structures which are more robust than has been the case historically. This is partly due to the fact that many in-flows have come through large multi-strategy funds with long lock-ups on investor capital. Funds are more cautious in setting redemption terms than before, so the possibility of very rapid redemptions leading to selling pressure is potentially lower than in the Japanese example. With all this said, however, sensitivity to small-cap looks to be on the increase and so a cyclical crunch impacting small cap could put the squeeze on some of the long/short universe.

A criticism often leveled at many Asian hedge funds is that their gains have stemmed primarily from market-driven gains, rather than from a more sustainable “alpha”. What would happen to hedge fund returns in 2008 if global growth failed, regional demand declined, liquidity dried up and the markets fell over the course of the year? We show below the rolling 36-month correlations between our Asian long/short strategy peer groups and the equity market indices in the regions in which they invest.

Source: Bloomberg, TASS, Alvest and LBAIM analysis1 Peer Groups used for this analysis include LS-MC Asia, LS-MC Asia ex-Japan, LS-MC China, LS-MC India, LS-MC Japan, LS-MC Korea, LS-Activist Japan


The indication is that, especially for long/short, returns have been becoming increasingly market correlated. Market sensitivity will likely be an inherent element of many Asian focused hedge funds into 2008 due to:

An ongoing desire to have exposure to regional economies with high single and double •digit GDP growth through long positions

Despite much improved short liquidity, shorting can still pose issues — not just from an •access perspective, but also against the often momentum-driven indiscriminate flows into the market which do not distinguish by valuation

At the same time, we note exposures increasing to Asia ex-Japan and away from Japan. For pan-regional long-short managers, as well as for multi-strategy managers, this is a recurrent pattern. Look, for example, at the increased sensitivity of the long-short hedge fund universe to the Indian, Chinese, Korean and Singaporean markets versus the decline in sensitivity to Japan:

Chart 3.12 Median Correlation of Asia Long/Short Peer Groups to their Respective benchmarks1

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Hedge fund capital is moving into

Asia ex-Japan and away from

Japan, which reflects the relative

increases in market size within the

region, but it also implies a chasing

of growth and momentum


Source: LBAIM Database and Bloomberg

The clear implication is that hedge fund capital is moving into Asia ex-Japan and away from Japan. In part, this reflects the relative increases in market size within the region, but it also implies a chasing of growth and momentum, which could lead to suffering in a sudden reversal. Greater emerging market sensitivity is therefore a characteristic of the Asian hedge fund universe, possibly leading to higher volatility in 2008.

However, it is unfair to suggest that correlations with the market will be stable. Just because hedge funds have been predominantly long in many cases does not mean exposures will not be reversed in a down cycle — certainly we have seen exposures reduced in the past. Managers have generally been bullish on market prospects through 2006 and 2007 and this has generally been the right call. Despite this, we have noticed managers dropping exposures towards the end of 2007 — reflected by lower average net market exposures and gross exposures across our peer groups. The long-short universe in particular appears to be running less directional risk on the long side than has been the case for some time.

In many cases, there is also meaningful directional risk on the credit side. The lack of a developed high yield bond market, coupled with the preference of banks to lend only to very established and sizeable companies, has led to a number of “special situations” hedge funds being established in Asia ex-Japan. The premise has been to step into the banking role and lend to companies to finance growth, generally strapping some equity participation onto the deal. This means that many hedge funds formerly labeled “credit,” “event driven” or even “distressed debt” often have some interesting characteristics. First, they have increasing market sensitivity as the market moves up and their warrants come into the money. Second, they are reliant upon the creditworthiness of the companies themselves, the robustness of their deal structures and their ability to recover capital in the event of default. Third, they have an illiquid portfolio of loans with warrants which would be difficult to unwind under stress. The returns of these funds could vary substantially, dependent on the path of economic growth and clearly the greater level of uncertainty revolves around the downside in a less optimistic scenario. As such, even taking into account some of the potential (though often untested) downside protection, market sensitivity of this sector could be higher than anticipated into 2008.

Chart 3.14 illustrates the growth of the overall local currency debt market, but the relatively low growth in the local currency corporate debt market over the past few years, which have given rise to some of the lending activities described above.

Chart 3.13 Asia Multi-Strategy PG Median Correlation to various Asian Indices

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Asian capital markets have made

some very positive structural steps

forward in terms of liquidity which

should facilitate hedge fund activity

in 2008 and in future years

Source Asian Development Bank 2008

ConclusionThe Asian capital markets have made some very positive structural steps forward in terms of liquidity which should facilitate hedge fund activity in 2008 and in future years. These combine with ongoing market inefficiencies to create a productive environment for hedge funds with the skill and experience to take advantage of these factors. Despite these attractive characteristics, the Asian markets are still under-penetrated by hedge funds, relative to Western markets, suggesting hedge fund assets will continue to grow into 2008. Market risk and issues over the sustainability of liquidity in a severe market decline will remain an inherent component of hedge fund risk going into 2008. Even in a severe market decline, however, elements of hedging in hedge fund strategies combined with an ongoing desire for Asian exposure may contribute to hedge fund growth by leading capital into, rather than away from, hedge funds in the region, as was the case for the U.S. in the post-bubble years early in this decade. Meanwhile, thanks to the growth in the depth and sophistication of the market, the opportunity set for Asian hedge funds in 2008 looks robust.

Chart 3.14 Local Currency Debt Market

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The past few quarters have seen

the yield curve move back into

a regime that has historically

been favorable for fixed income

arbitrage funds

Chapter 4: a new regime for Fixed income arbitrage?

SummaryAn important aspect of fixed income arbitrage (FI arb) funds’ strategy is capitalizing on anomalies along the yield curve. We analyze how the U.S. yield curve evolves using principal components analysis. Since 1992, we identified two regimes, “High”, through 2000; and “Low”, 2001-2007, based on yield curve behavior. In the High regime, median FI arb fund returns for the upcoming year (2.7% per quarter) are significantly higher than in the Low regime (1.7% per quarter). The past few quarters, particularly Q4 2007, have seen the yield curve move back into the High regime, indicating performance of about 11% for 2008. Combined with the fact that correlations between FI arb and other strategies have fallen in the last year, this gives us a favorable outlook on the strategy.

While a number of hedge fund strategies invest in fixed-income instruments (bonds, futures, swaps and options), we take a somewhat narrower view of fixed income arbitrage (FI arb), placing macro funds, which take directional bets on yields, credit relative-value funds, and mortgage-backed securities (MBS) funds into distinct peer groups. By our definition, funds in the FI arb peer group should be hedged with respect to directional moves in yields and avoid credit exposure. Consequently, a core strategy for such funds is to capitalize on anomalies in prices of sovereign bonds and swaps. These anomalies may be with respect to the prices of bonds having different maturities. An example of such a trade — one among many employed by FI arb funds — is a “butterfly” spread on 2-, 5- and 10-year maturity bonds. Suppose a fund determines that the 5-year bond is expensive relative to the 2-year and 10-year bonds. It shorts 5-year bonds and buys a combination of 2-year and 10-year bonds, and holds until prices converge.

In this chapter, we show how information contained in the recent dynamics of bond yields can be used to estimate returns of FI arb funds. This analysis begins with our proprietary dataset of FI arb funds. Throughout the section, readers may refer to the Appendix for details on data, methodology and additional tests performed. Starting in 1996, when we have at least five funds available, we build our quarterly strategy return series by compounding individual funds’ monthly returns. By taking the median (50th percentile) fund return each quarter, we obtain the dashed curve shown in Chart 4.1. Alternatively, by equal-weight averaging funds’ quarterly returns, we obtain the solid curve in Chart 4.1.

A significant feature of FI arb returns in Chart 4.1 is the negative spike — particularly for the solid, average curve — that occurred in Q3 of 1998. This was due to the collapse of the hedge fund Long Term Capital Management (LTCM).18 The fact that the mean is so much lower than the median during LTCM indicates that some funds suffered extreme losses — generally those funds with higher leverage, a fact that makes us circumspect about high-leverage funds in FI arb. In order to avoid having outlier returns results drive our conclusions, we focus our analysis on estimating median (dashed) returns.

Please see Disclosures at the end of this document for important information regarding the target return data and analyses contained in this report .18 For more details, see P. Jorion, Risk Management Lessons from Long-Term Capital Management”, European Financial Management, 6

(September 2000), 277-300. Past performance is not indicative of future results.

39



Despite the presence of LTCM, the pre-2002 period had both a higher mean and median return than the 2002-2006 period, as shown by the horizontal bars in Chart 4.1. While the early, high return period was more volatile, the transition between periods is still fairly sharp. Our key insight is that these high and low return periods for FI arb match-up well with a single variable describing how yields evolve at different maturities. In fact, the dashed vertical line in Chart 4.1 is not drawn arbitrarily; rather, it is placed at a transition point for this yield variable. Recent performance of FI arb funds has a positive trend in Chart 4.1; consistent with our analysis, this coincides with another transition (back to high-return) for our yield variable.

The evolution of yields of sovereign bonds (e.g., U.S. Treasuries) with different maturities — the yield curve — is important for identifying opportunities and producing returns in FI arb. Describing this evolution can be complicated, however, given that between 6 and 12 different maturities, ranging from a few months to 30 years, are used in constructing the yield curve. Fortunately, changes in yields are highly correlated among bonds, especially for those with nearby maturities. This means that the yield curve can be analyzed with a technique called principal components analysis (PCA) that reduces the degrees of freedom in how the yield curve evolves, enabling us to describe it with three or fewer factors, called principal components (PC).

Based on our analysis, about 98% of all yield curve dynamics is described by three PC,19 with the bulk of it contained in just the 1st PC, which measures changes in the level of yields across all maturities; i.e., parallel shifts in the yield curve.20 While most of the variation in the yield curve is due to the 1st PC, the percentage of variation explained (PVE) by the 1st PC fluctuated between 75% and 93% over the last 12 years, averaging 83.6%. This is shown in Chart 4.2, where PVE was high from 1996 to 2001, then moved sharply lower, where it remained until 2007. Recent quarters show an increased PVE, moving more definitively into the high-PVE region in Q4 2007.

19 R. Litterman and J. Scheinkman, “Common Factors Affecting Bond Returns”, J. Fixed Income 1:54-61.

20 The 2nd PC pertains to steepening or flattening of the yield curve (i.e., increasing or decreasing yield spreads between short- and long-maturity bonds), while the 3rd PC pertains to curvature (concavity) in the yield curve. Higher PC do not have straightforward interpretations

Chart 4.1 Mean and Median Quarterly Returns of Fixed Income Arbitrage Funds

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Sources: U.S. Federal Reserve Bank and LBAIM analysis

The transition in yield curve dynamics shown in Chart 4.2 is clearly reflected in the average returns of FI arb funds in Chart 4.1. This motivated us to look for a predictive relationship between PVE and future FI arb performance. From a fundamental standpoint, there is a case for why high-PVE should be associated with increased FI arb returns as well: Traditional fixed-income risk management centers on duration matching;21 i.e., protecting portfolios from losses in the event of a parallel shift in the yield curve. In a high-PVE regime, where yield curve dynamics are dominated by parallel (i.e., 1st PC) moves, investors are focused on hedging 1st PC risk and may have to update their hedges as conditions change. FI arb funds can take advantage of this situation in two ways: (1) by anticipating the near-term hedging demands of traditional investors, they can trade ahead of them and capture potential profits; and (2) the incomplete hedging by some market participants creates anomalies across the yield curve that can be exploited.

We focused our analysis on how well the lagged PVE forecasts quarterly median fund returns — a trade-off between data availability and the desire for a longer-horizon forecast than just monthly — for lags of 1-4 quarters. Combining these results gives a year-ahead forecast. Chart 4.3 shows a scatter plot of median FI arb quarterly return against PVE. The left-hand, low-PVE region has a much lower average FI arb return than the right-hand, high-PVE region. We divided our 44-quarter sample into low- and high-PVE sub-samples using the mean PVE. While this is natural from a statistical perspective, there is no economic theory to guide us in distinguishing between low- and high-PVE. Fortunately, our results are insensitive to where we make the cut-off between the two regions.

21 See D. J. Bolder, G. Johnson and A. Metzler, “An Empirical Analysis of the Canadian Term Structure of Zero-Coupon Interest Rates”, Bank of Canada Working Paper 2004-48. They study the Canadian yield curve over a similar time period and also observe a transition in PVE.

Chart 4.2 Percent variance Explained by First Principal Component Using a 6-Year Moving Window

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Sources: TASS, Altvest, U.S. Federal Reserve Bank and LBAIM analysis


In the low-PVE region, the average quarterly FI arb return is 1.71%, compared with a 2.71% average in the high-PVE region. Using a t-test that takes into account the apparently higher volatility of the high-PVE region, we find that the difference in average returns between regions is very significant, with a p-value well below 0.01. We repeated our analysis using a range of look-back periods to compute the PC, from 36–72 months, and obtained quite similar results. We also tested to see if the higher FI arb returns following periods of high PVE were due to “closet” macro bets by managers on the direction of rates and did not find evidence of market timing (either positive or negative) among the funds. Thus, the effect we observe is not due to directionality of rates per se, but instead due to the opportunities the high-PVE environment creates for FI arb managers.

In addition to studying the impact of the one-quarter lagged PVE on FI arb returns, we looked at the relationship between lags of 2–4 quarters. These differences were also statistically significant and are shown in Table 4.1, along with their 95% confidence intervals. Year-ahead expected results, obtained by summing quarterly returns and variances, result in the average returns and confidence intervals shown in the last column. Based on the fact that the Q4 2007 PVE is in the high region, our forecast expected return for FI arb in 2008 is 11%. This conclusion is only based on current and historical yield curve dynamics; if, in fact, the yield curve is moving into the high-PVE region, as recent data suggests in Chart 4.2, this would be incrementally positive for FI arb.

Sources: TASS, Altvest, U.S. Federal Reserve Bank and LBAIM analysis

Chart 4.3 Plot of Percentage variance Explained by First Principal Component vs. Median Quarterly Return One Quarter Lagged Q1 1996 – Q4 2007

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Table 4.1 Average Fixed Income Arbitrage Returns Over Next Four Quarters Depending on Yield Curve Regime

1ST QUARTER 2ND QUARTER 3RD QUARTER 4TH QUARTER YEAR (COMPOUNDED)

REGIME Mean 95% c.i. Mean 95% c.i. Mean 95% c.i. Mean 95% c.i. Total 95% c.i.

LOW PvE 1.71% (1.5%, 1.9%) 1.71% (1.4%, 2.0%) 1.66% (1.4%, 1.9%) 1.66% (1.4%, 1.9%) 6.73% (6.56%, 6.90%)

HIGH PvE 2.71% (2.2%, 3.2%) 2.71% (2.2%, 3.2%) 2.76% (2.3%, 3.2%) 2.75% (2.3%, 3.2%) 11.01% (10.76%, 11.10%)

42

Manager selection was crucial

in 2007, with top-quartile fixed

income arb funds outperforming

bottom-quartile fixed income arb

funds by 15% through November

Our analysis focused on one driver of FI arb return, namely U.S. yield curve dynamics, but there are other potentially significant return drivers for 2008, as well; these include Japanese and Euro yield curve arbitrage, swap curve spreads over sovereign yield curves, new issue dynamics, fixed income option volatility and flight to safety. Furthermore, while we emphasize strategy-wide performance, manager selection was crucial in 2007, with top-quartile FI arb funds outperforming bottom-quartile FI arb funds by 15% through November. Risk management practices of funds, including leverage and holdings liquidity, are also important considerations. However, we noted in the Updates section that correlations between FI arb and other strategies have fallen in the last year. This suggests potential diversification benefits to FI arb as a strategy, in addition to the increased return expectations we presented here, warranting an improved outlook on the strategy.

appendix: Data, methods and Detailed resultsWe examined the relationship between lagged characteristics of the U.S. Treasury yield curve and average returns of Fixed Income arbitrage funds. A significant relationship was found between the percent of variance explained by the first principal component of the yield curve and the median quarterly Fixed Income arbitrage fund return.

To isolate these high/low potential periods, we utilize regression analyses with percent of variance explained (PVE) by the principal components, and the rolling volatility of the principal components as predictor/independent variables with median quarterly returns as the response/dependent variables. Here we find statistically significant results and are able to utilize the regression models to predict the future profitability of fixed income arbitrage strategies in general. Explorations were also made using the volatility/spread characteristics of the quarterly returns as the response/dependent variable, however the results for this case were not as compelling.

Data ConsiderationsThe data sets were constructed from publicly available data on the federalreserve.gov and treasury.gov web sites. We have used the constant maturity treasury 3-month, 6-month, 1-year, 2-year, 3-year, 5-year, 7-year, and 10-year monthly yields to construct the quarterly yield curve dataset which is then first differenced to obtain the dataset used in the analysis. The three monthly returns in a given quarter are compounded to give a single return for each quarter.

The dates used for yield curve data are from Q1 1991 through Q3 2007. This allows us to consider a lag of up to four quarters back from the first observed quarterly returns data with moving time horizon windows of 3 years, 4 years, and 5 years (i.e., 12, 16, and 20 quarters, respectively). The first quarterly returns data we will use is from Q4 1996. This gives us 44 data points to work with for the analysis. We work with the returns data aggregated over the given quarter (i.e., mean and/or median quarterly return for all return observations for a given quarter).

Due to the fact that each quarter will potentially have a different number of observations for returns data, we have used weighted versions of all regression analyses (e.g., weighted linear regressions using the number of return observations per quarter as the weight variable).

Methodology We quantify the shape of the yield curve using principal components analysis (PCA) methodologies. PCA is a multivariate statistical method that is used for dimension reduction. That is, in situations where there are a large number of variables (in our case, eight: one for each maturity), PCA can be used to reduce the large number of variables to a more manageable one (say two or three).

One approach used in the literature is to apply PCA to (first differenced) yield curve data to extract the first three principal components. It is common to interpret the first, second,

43

and third principal components as the level, slope, and curvature, respectively, of the yield curve. One of the important outputs from PCA is the percentage of the variance in the data explained by each principal component. Another is the set of principal components themselves. This reduced-dimension data set can often be used as a surrogate for the full-dimension data set.

Within this general framework, we have made two different explorations. We first consider using the percent of the variance explained by the different principal components as the predictor/independent variable for predicting 1-, 2-, 3-, and 4-quarter-ahead median quarterly returns. To achieve this, we compute the principal components repeatedly across a moving window (of 72-, 60-, 48-, and 36-month horizons) on the first differenced yield data set. In this way we get a time series of percentages for each principal component. We utilize a weighted linear regression approach and utilize the resulting r2 values as a screen to identify statistically significant relationships.

A second approach within the PCA framework is to look at the PCA across the entire first differenced yield data set. Then we compute the principal components (i.e., the original data multiplied by the eigenvectors) and calculate the rolling volatility, square root of the rolling median absolute deviation (MAD) of this time series. All rolling calculations were computed using a 72- month, 60- month, 48- month, and 36-month moving window. These quantities are explored as possible predictor/independent variables for predicting median quarterly returns as above. We again utilize weighted linear regression as in the previous approach.

ResultsWe will use r2 values as our main measure of predictive ability for the regressions. Recall that the statistical significance of the difference from zero of the correlation, r, of two variables can be determined using the test statistic,

212r

nrT

where n is the sample size. T follows a t-distribution with n-2 degrees of freedom. The regressions we consider have 44 observations (from the 44 quarterly return observations). Hence a value of r that is less than -0.2973 or greater than 0.2973 (or equivalently, an r2 value of 0.0884 or greater) is needed to achieve a correlation that has a statistically significant difference from zero.

For observations with percent of variance explained by the 1st PC less than or equal to 83.64%, the mean quarterly return for fixed income arbitrage funds during the next quarter was 1.71%. For observations with percent of variance explained by the 1st PC greater than 83.64%, the mean quarterly return for fixed income arbitrage funds was 2.71%. The p-value for a t-test comparing these two means is 0.0009546, a very significant result. Similar results are observed at other lagged-ahead quarters.

These results cover observed percent variance explained by the 1st PC from Q4 1995 through Q2 2007. The value of the percent of variance explained by the 1st PC for Q3 and Q4 2007 are 82.1% and 87.1%, respectively.

the forecasting methodology has the limitation that it is not possible to invest in the strategy, because it is based on a statistical analysis of funds from the LBaim database, some of which may be closed to new investments while others may no longer exist .

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A trend within the trend of

hedge fund proliferation is for a

greater share of hedge fund

assets to be managed by the

largest hedge funds

Chapter 5: Comparison of equity Portfolios in small and Large FundsHedge fund capital is becoming increasingly concentrated in the largest funds. We study how characteristics of equity hedge fund portfolios change depending on the total market value (TMV) of long equity holdings. We find that the number of stocks increases slowly with TMV, requiring a 16x increase in TMV to double the number of stocks. As TMV increases, funds generally do not shift to larger or more-liquid stocks. Instead, funds generally scale their positions with market value: the average percentage of shares outstanding held by funds more than doubles when TMV triples. With the rapid growth in assets of the largest funds, this scaling of positions means an increasing fraction of assets are in less-liquid portfolios. Should this growth trend persist, investors in larger funds may face more onerous liquidity terms or risk a liquidity mismatch between funds’ holdings and terms.

A trend within the trend of hedge fund proliferation is for a greater share of hedge fund assets to be managed by the largest hedge funds. While hedge fund assets grew substantially over the last several years, assets managed by the largest 100 hedge funds grew even faster. As shown in Chart 5.1, the largest 100 funds managed about 49% of total hedge fund assets22 in December 2001; this fraction grew to over 69% of overall assets by December 2006. The popularity of large funds has been driven, in part, by the rise in institutional investment in hedge funds. Institutional investors often require substantial capacity and, rather than disperse investments across many small funds (incurring higher monitoring costs and increased headline risk), they often select a more tractable number of large funds.

Sources: HFR, Alpha Magazine and LBAIM analysis

Recent strong relative performance of large hedge funds may lead to a continuation or acceleration of capital f lows to large funds. As shown in Chart 5.2, two proxies for the relative performance of large funds were positive in recent months.23 the spread between the asset-weighted CS/Tremont Composite Index and the equal-weighted HFRI Composite Index was 2.8% in November 2007, the highest since 2003. Also in 2007, the spread between the HFRI Fund of Funds (FoF) Composite Index and HFRI Composite Index turned positive for the first time since 2003. Due to capacity requirements of some Fund of Funds, the HFRI FoF index may exhibit a tilt towards larger hedge funds relative to the HFRI Composite

22 Alpha Magazine, June 2007, and HFRI Industry Report, September 2007.

23 Investors should be careful to distinguish returns due to market exposure from excess returns (i.e., alpha).

Chart 5.1: Largest 100 Hedge Funds as a Fraction of Total Hedge Fund Assets

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45

Index. While the two spread curves in Chart 5.2 are highly correlated, indicating they are capturing a similar effect, we caution that hedge fund indexes are subject to data biases that may influence these results. Academic studies have examined the relative performance of large and small hedge funds;24 while this is an interesting topic, an analysis of differences between the portfolios of large and small funds also has important implications for performance and liquidity.

Sources: HFR, CS/Tremont and LBAIM analysis


We study how characteristics of hedge fund equity portfolios change with TMV.25 Equities are a significant portion of hedge fund allocations, with the aggregate U.S. equity TMV of hedge funds exceeding half of all hedge fund assets in September 2007. Compared with that of a small fund, a large fund’s portfolio can have more stocks, larger average stocks and/or a larger average percentage of shares outstanding in its stocks. In fact, it is approximately true that TMV can be decomposed into three terms: the number of stocks, the average market cap of stocks held and the average percentage of shares outstanding held.26 Consistent with this decomposition, we run cross-sectional regressions of number of holdings, average market cap and average percentage of shares outstanding on TMV. If illiquidity of positions and transaction costs are not limiting factors in equity hedge fund portfolios,27 positions of larger funds may simply be scaled up (in dollar value) relative to those of smaller funds. On the other hand, if funds adjust their position sizes to limit illiquidity or control transaction costs, we would expect to see more holdings or a shift toward larger, more-liquid stocks in larger funds.

We use a universe of equities held by hedge funds to study the relationship between equity portfolio characteristics and TMV. Our sample is based on the 13-F filings of funds as of September 30, 2007. We isolated 721 hedge funds with at least 5 holdings each (a minimal

24 See, M. Getmansky, “What Drives Hedge Fund Returns? Models of Flows, Autocorrelation, Optimal Size, Limits to Arbitrage and Fund Failures”, MIT working paper, May 2004, and references therein. Chart 5.2 suggests that the relative performance of large versus small funds may by cyclical in nature, perhaps influenced by the number of opportunities for large-AuM funds to participate in large deals.

25 In “How Does Size Affect Mutual Fund Behavior?”, J. M. Pollet and M. Wilson, preprint (2007), the authors study how mutual funds change their investment behavior as assets under management (AuM) rise. We use TMV, instead of AuM to study hedge fund portfolios, as TMV includes funds’ leverage decisions.

26 This holds if each term is expressed as a logarithm. The total market value of a fund is the sum of the market values held in each of its N stocks. Each market value is, in turn, the product of the stock’s market cap and the percentage of shares outstanding held by the fund. If covariance between market cap and percentage held is typically small, we can approximate the average of their product as the product of their averages: )Pct_OS()Cap()Pct_OSCap(Pct_OSCapMVTMV

11avgavgNavgNi

N

i i

N

i i . Taking logarithms yields: )Pct_OS(log)Cap(loglog)TMVlog( avgavgN . When we run regressions of each term on the right-hand side of the log equation against log of TMV, the sum of coefficients on log of TMV is, in fact, approximately one.

27 Such a finding may also be due to a rapid decrease in expected returns (or alpha) outside of an investment universe that grows slowly with TMV.

Chart 5.2: Two Proxies for Relative Performance of Large Hedge Funds Spread between CS/Tremont and HFRI Composite HF Indexes and Spread between HFRI FoF and HFRI HF Composite Indexes

-25%12/94 12/95 12/96 12/97 12/98 12/99 12/00 12/01 12/02 12/03 12/04 12/05 12/0712/06

-20%

-15%

-10%

-5%

0%

5%

10%

15%

CS/Tremont HF – HFRI HFHFRI FoF – HFRI HFSp

read

(Rol

ling

12-m

onth

)

46

diversification requirement) in a broad universe of U.S. equities (the largest 5,000 stocks). Table 5.1 shows some characteristics of our data set. Portfolio market values range from $0.4mm to nearly $55bn, but most funds’ TMV is between $100mm and $1bn. While the median fund holds 39 stocks, some funds hold over 2,000 stocks and others hold just five (our lower bound). There is also a wide range in average market cap held by funds, from $134mm to $116bn. Across all funds, typical positions are not particularly illiquid, however, with the median fund having an average percentage of shares outstanding of 0.5% and an average trading volume of 0.68 days.

Notes: Based on 721 hedge funds with five or more U.S. equity holdings (4,093 stock universe) as of September 30, 2007

Market-cap cutoffs: Large-cap , over $10bn; mid-cap, $2-10bn; small-cap, under $2bn (as of Sept. 30, 2007)

Average daily volume computed between June 30 and September 30, 2007 (one quarter)

Average Size/Liquidity and Average Position Size are geometric averages; i.e., exponentials of averages of logs of stock and position sizes within each fund

Holdings information obtained through Form 13-F filings as of September 30, 2007

Sources: FactSet and Lehman Brothers analysis

Within our fund universe, portfolios of some funds may differ from others due to their market-cap style or because they use mathematical models to select stocks (i.e., quant equity funds). Acknowledging these potential differences, we isolate four cap-styles: small-cap, mid-cap, SMID and large-cap, based on allocations to different market-cap ranges;28 we also distinguish 44 quant funds from the other 677 funds that use mainly fundamental means to select stocks. In Table 5.2, we show the number of funds within each cap-style, depending on the minimum allocation required for classification into that style. We tested three cutoffs for inclusion in a cap-style: at the most-restrictive, we required at least 90% of a fund’s weight to be in stocks of that market-cap range; we also tested less-restrictive 70% and 80% cutoffs. Our results are reported for the middle, 80% cutoff; the higher and lower thresholds gave similar results.29

28 We divided equities into large-cap (> $10bn), mid-cap ($2-10bn) and small-cap (< $2bn), then computed the total market weight of each fund by category. To be classified as large-cap, mid-cap or small-cap, a fund needed a minimum total weight in that cap-range (70%, 80% or 90%). We added a fourth cap-style, SMID, for funds with a large combined percentage of small- and mid-cap stocks, but that were neither small-cap nor mid-cap. Quant funds were identified based on either familiarity with the fund, or a combination of the manager’s reported style and a minimum number of holdings.

29 For some firms in our sample, the reported holdings represent combined investments across multiple funds. It is generally not possible to disaggregate these firms’ holdings by fund; furthermore, it is often unclear which firms have multiple funds — making it difficult to include a “fund family” category.

Table 5.1 Summary Characteristics of Equity Hedge Fund Portfolios

MARkET vALUE ($MM)

NUMbER OF

HOLDINGS

PERCENTAGE OF STOCkS bY MARkET CAP

AvERAGE SIzE/LIQUIDITY OF HOLDINGS ($MM) AvERAGE POSITION SIzE

LARGE CAP

MID CAP

SMALL CAP

MARkET CAP

MARkET FLOAT

DAILY vOLUME

% SHARES HELD

% FLOAT HELD

# DAYS’ vOLUME

MAxIMUM 54518 2414 100% 97% 100% 116314 111516 808.4 11.8% 17.6% 176.7

75%ILE 1042 80 54% 40% 48% 26413 24798 238.5 1.2% 1.4% 2.0

MEDIAN 323 39 37% 29% 25% 14996 14257 149.9 0.5% 0.6% 0.7

25%ILE 117 21 18% 19% 12% 7871 7204 83.3 0.1% 0.2% 0.2

MINIMUM 0.4 5 0.0% 0.0% 0.0% 133.8 101.7 0.9 0.0% 0.0% 0.0

47

Notes: Based on 721 hedge funds with five or more U.S. equity holdings (4,093 stock universe) as of September 30, 2007

Market-cap cutoffs: Large-cap , over $10bn; mid-cap, $2-10bn; small-cap, under $2bn (as of Sept. 30, 2007)

Funds must have at least the cutoff percentage in their cap group to be classified as that cap style

SMID managers are predominately small- and mid-cap, but do not fall into either category exclusively

Holdings information obtained through Form 13-F filings as of September 30, 2007


Our models for the number of stocks, average market cap and average percentage outstanding use 11 potential explanatory variables: log of TMV, five indicator variables for each cap-style and for quant funds, and the aforementioned indicator variables scaled by log of TMV. For robustness, we tested three measures of stock size: market capitalization (with percentage of shares outstanding used to measure corresponding position sizes), market float (percentage of float) and average daily trading volume over the past quarter (days trading volume held). There are seven models displayed in Table 5.3: one model for expected number of holdings and three models each for average stock size and average position size.

ResultsOur main results are contained in Table 5.3. It shows how the average number of stocks, average position size and average market-cap or liquidity of holdings change with portfolio market value. There are five sub-tables, one for number of stocks, three for different measures of position size (shares outstanding, f loat and days trading volume) and one for average size/liquidity of holdings. Each sub-table has five columns, showing possibly different behavior for quant funds or cap-style groups depending on portfolio TMV.30 The first column, “Non-Style Specific Fund”, refers to funds that do not fall into a particular cap-style; these multi-cap, non-quant funds are the bulk of our universe. Some columns are identical within a sub-table (e.g., three of five columns coincide with “Number of Stocks”), indicating that there was no significant difference in behavior among those styles of funds. Portfolio TMV increases by factors of ten from one row to the next, except in the bottom sub-table, which shows average stock size/liquidity, where we did not find any significant relationship between TMV and average stock characteristics.

30 All factors included in the seven models were significant at the 5% level, and often significant at the 1% level. R-squared (percent of variance explained) was typically 50-60% across the set of 721 funds. Mid-cap does not appear as a separate column because we did not find any significant differences between mid-cap funds and other fund types.

Table 5.2 Number of Funds in Each Market-Cap Style Category

CUTOFF FOR INCLUSION IN CAP-STYLE (MINIMUM % OF WEIGHT IN CAP RANGE)

NUMbER OF FUNDS bY MARkET-CAP STYLE

LARGE CAP MID CAP SMID SMALL CAP

90% or more 19 2 78 38

80% or more 45 3 135 55

70% or more 83 12 191 81

48

Notes: Results based on cross-sectional regressions of the equity holdings for 721 hedge funds

Holdings obtained from 13-F filings as of Sept 30, 2007 in a universe of 4,089 U.S. equities

Models are for log of {number of holdings, average size of stock and average position size} against log of TMV and cap-style and quant indicator variables

To compute average number of holdings, average stock size and average position size in table, we used the expected value of a log-normal variable

Mean of log-normal variable from regression model and assumed TMV; variance from residual variance of the model


The number of holdings in hedge funds’ equity portfolios increases slowly with TMV: for multi-cap funds, large-cap funds and SMID funds, TMV must increase by a factor of 16 before the expected number of stocks doubles. Small-cap funds also grow their holdings count at this rate, but have about 40% fewer stocks for a given TMV. Said another way, the expected number of stocks for a small-cap fund of a given TMV is the same as that of a multi-cap fund with ten times the TMV. Quant funds increase their number of stocks faster than other fund types, doubling when TMV grows only 7.3-fold.

Most of the difference in TMV between small funds and large funds is taken-up by larger position sizes; i.e., higher average percentages of shares outstanding. This is true for each of the three size metrics tested. For each metric (percentage of shares outstanding, percent of float and days trading volume), just a 3x increase in TMV results in a more than doubling of position sizes for multi-cap, quant and large-cap funds. Quant and large-cap funds have smaller position sizes, however, with positions averaging only 11% and 22%, respectively, of those in multi-cap funds of the same TMV. Small-cap funds’ position sizes are larger and grow faster than those of multi-cap funds, with only a 2.4-fold increase in TMV needed to double position sizes. SMID funds’ position sizes grow faster, as well, with just a 2.6-fold increase in TMV needed to double position sizes.

The average market-cap (or market float or daily trading volume) of stocks in hedge fund portfolios does not change with TMV. While large-cap funds have higher average market caps than multi-cap and quant funds, there is no trend within large-cap funds to have even higher average market-cap stocks in funds with higher TMV. Likewise, among small-cap

Table 5.3: Average Equity Portfolio Characteristics Depending on Size and Style of Fund

AvERAGE NUMbER OF STOCkS AvERAGE PERCENTAGE OF SHARES OUTSTANDING HELD

Portfolio Market Value

($ mm)

Non-Style Specific

Fund

Specific Fund Styles Portfolio Market Value

($ mm)

Non-StyleSpecificFund

Specific Fund Styles

Quant Fund

Large-Cap Fund

Small-Cap Fund

SMID Fund

QuantFund

Large-CapFund

Small-CapFund

SMIDFund

1 15 62 15 8 15 1 0.01% 0.0% 0.0% 0.1% 0.02%10 26 138 26 15 26 10 0.05% 0.01% 0.01% 0.6% 0.1%

100 46 307 46 26 46 100 0.2% 0.03% 0.05% 3.5% 0.7%1000 81 685 81 46 81 1000 1.0% 0.1% 0.2% 21.3% 3.6%

10000 143 1530 143 143 10000 4.5% 0.5% 1.0% 18.6%100000 251 3417 251 100000 20.6% 2.4% 4.7%

AvERAGE PERCENTAGE OF FLOAT HELD AvERAGE DAYS TRADING vOLUME HELD

PortfolioMarket Value

($ mm)


Specific Fund Styles PortfolioMarket Value

($ mm)



QuantFund

Large-CapFund

Small-CapFund

SMIDFund

QuantFund

Large-CapFund

Small-CapFund

SMIDFund

1 0.01% 0.0% 0.0% 0.1% 0.03% 1 0.02 0.0 0.0 0.3 0.110 0.06% 0.01% 0.01% 0.7% 0.2% 10 0.1 0.01 0.02 1.7 0.3

100 0.3% 0.03% 0.06% 4.6% 0.9% 100 0.4 0.05 0.1 11.6 1.71000 1.2% 0.1% 0.3% 29.1% 4.6% 1000 2.0 0.2 0.4 76.9 9.2

10000 5.7% 0.7% 1.2% 24.2% 10000 9.1 1.0 1.8 50.2100000 25.9% 3.0% 5.6% 100000 41.8 4.8 8.3

AvERAGE STOCk SIzE/LIQUIDITY

Measure ofSize/

Liquidity($ mm)



QuantFund

Large-CapFund

Small-CapFund

SMIDFund

Market Cap 23,527 23,527 56,046 1,414 7,353 Market Float 22,442 22,442 53,915 1,215 6,831

Daily Volume 217.0 217.0 234.9 15.7 78.4

49

The average market-cap (or market

float or daily trading volume) of

stocks in hedge fund portfolios does

not change with the total market

value of long equity holdings

and SMID funds, there is no measurable tendency of funds to gravitate to larger (or more liquid) small-cap or mid-cap stocks as TMV increases. Thus, of the three components of hedge fund TMV growth — more stocks, larger average stock size/liquidity and larger position size — about 25% of the growth comes from more stocks (33% for quant funds and 20% for small-cap funds), 75% comes from scaling-up position sizes (67% for quant funds and 80% for small-cap funds) and none derives from larger average stock sizes.

Based on our results in Table 5.3, the average positions in large multi-cap funds are relatively illiquid. For example, at a $10bn multi-cap fund, the average position size is expected to be 4.5% of shares outstanding. Quant funds and large-cap funds of this size are more liquid, with 0.5% and 1% of shares outstanding on average, respectively. For small-cap funds, even $1bn TMV is associated with illiquid average position sizes; e.g., 21.3% of shares outstanding. A $10bn SMID fund is similarly illiquid. Also from Table 5.3, it does not appear that hedge funds can grow to $100bn TMV without either (1) using a quant strategy, with 2.4% of shares outstanding on average; (2) being a dedicated large-cap fund, with 4.7% of shares outstanding on average; or (3) accepting substantial illiquidity, with 20.6% of shares outstanding in an average position. Similar pictures emerge when position sizes are measured in percentage of float or days trading volume.

Using average position sizes understates funds’ illiquidity, since funds tend to scale their largest positions even faster with TMV. In addition to the regressions of average days trading volume reported in Table 5.3, we also conducted regressions of percentiles of days trading volume (e.g., regressing the median position in days trading volume for each fund against its TMV, cap-style and/or quant variables). By combining the results for different percentiles31 at the same TMV, we obtained expected distributions of days trading volume in funds of a given style. In Chart 5.3, we display these distributions for multi-cap funds at several values of TMV. From left to right, the curves represent increasing TMV (by a factor of 10 per curve). The curves become increasingly S-shaped with TMV, indicating that funds scale their least-liquid (largest) positions faster than typical positions and much faster than the most-liquid (smallest) positions. For a $10bn TMV multi-cap fund, about 20% of its positions are expected to represent over 10 days trading volume. Similar profiles are also observed for small-cap and quant funds.


The trend towards ever larger hedge funds, combined with the tendency of funds to scale up their positions, as measured by shares outstanding or days trading volume, has the potential to increase the illiquidity of hedge fund equity holdings. One possible response by hedge funds is to make their liquidity terms more onerous. In such a case, investors should consider the additional compensation they may require in terms of excess returns (alpha) in compensation for this reduced liquidity.32 Absent increased liquidity terms, investors should be cognizant of a potential mismatch between holdings liquidity and redemption terms.

31 We conducted regressions for the minimum, 10%ile, 20%ile, …, 90%ile, 95%ile and maximum position size, as measured by days trading volume.

32 See B. Hayes and K. Kharas, “Measuring the Liquidity Premium in Hedge Fund Strategies”, Lehman Brothers preprint (2007), for a discussion of strategy-specific liquidity premia.

Chart 5.3: Distribution of Days Trading volume for Funds of varying Total Market value: Multi-Cap, Non-Quant Hedge Funds

0%10%20%30%40%50%60%70%80%90%

100%

0.001 0.01 0.1 1 10 100 1000Days Trading Volume

Perc

enti

le fo

r Fu

nd

$10mm TMV$100mm TMV$1bn TMV$10bn TMV

50

DefinitionsAlpha is a measure of value added. The estimated alpha represents how much of the rate of return on the portfolio is attributable to the manager’s ability to derive above-average risk adjusted returns.

beta is a measure of systematic (or market) risk. Beta measures the risk of a particular investment relative to the market as a whole (the “market” can be any index or investment). Beta describes the sensitivity of the investment to broad market movements.

Credit Suisse/Tremont Composite Index is a broadly diversified index encompassing over 387 funds (August 2004) across ten style-based sectors. It is the largest asset weighted hedge fund index.

Credit Suisse/Tremont Distressed Index is an asset-weighted benchmark of hedge fund managers which includes corporate strategies focused on distressed securities, high-yield debt, Regulation D, and risk arbitrage.

FTSE 100 is an index of the share prices of the 100 largest companies (by market capitalization) in the U.K.

Hang Sang Index is a market-value weighted index of the stock prices of the 33 largest companies on the Hong Kong market.

HFRI Composite Index is an equally weighted composite of constituent funds, as reported by the hedge fund managers listed within HFR Database. The composite accounts for over 1,600 hedge funds from all the strategy-specific indices.

HFRI Convertible Arbitrage Index is an equal-weighted hedge fund index which includes funds which: report monthly returns, report net of all fees returns, and report assets in USD. Includes managers who purchase portfolios of convertible securities, and hedging a portion of the equity risk by selling short the underlying common stock.

HFR Distressed Index is an equal-weighted hedge fund index which includes funds which: report monthly returns, report net of all fees returns, and report assets in USD. Includes managers employing an investment process focused on corporate fixed income instruments, primarily on corporate credit instruments of companies trading at significant discounts to their value at issuance or obliged (par value) at maturity as a result of either formal bankruptcy proceeding or financial market perception of near term proceedings.

HFR Equity Market Neutral Index is an equal-weighted hedge fund index which includes funds which: report monthly returns, report net of all fees returns, and report assets in USD. Include managers who employ sophisticated quantitative techniques of analyzing price data to ascertain information about future price movement and relationships between securities, select securities for purchase and sale.

HFRI Fixed Income Arbitrage Index is an equal-weighted hedge fund index which includes funds which: report monthly returns, report net of all fees returns, and report assets in USD. Includes managers employing a variety of strategies involving investment in fixed income instruments, and weighted in an attempt to eliminate or reduce exposure to changes in the yield curve.

HFRI Fund of Funds Composite Index is an equally weighted composite of managers who invest with multiple managers through funds or managed accounts. The Fund of Funds Composite Index includes over 800 constituent funds, both domestic and offshore.

HFR Merger Arbitrage Index is an equal-weighted hedge fund index which includes funds which: report monthly returns, report net of all fees returns, and report assets in USD. Includes managers utilizing an investment process primarily focused on opportunities in equity and equity related instruments of companies which are currently engaged in a corporate transaction.

korea Composite Stock Price Index (kOSPI) is an index of all companies traded on the Korea Stock Exchange. The index is a market capitalization-based index introduced in 1983.

LbAIM Peer Group Universe—LBAIM maintains a database of approximately 3,000 hedge funds, drawn from both publicly available databases, such as TASS, Altvest and Eurekahedge, and private communications. There is no survivorship bias, as funds’ historical returns are retained even for funds that have ceased reporting. Funds are divided into approximately 80 peer groups, based on strategy and geography; these peer group classifications are determined by LBAIM analysts. Some estimates place the current number of hedge funds at around 10,000.

MSCI Europe Index is a market capitalization-weighted benchmark index made up of equities from 15 European countries. France, Germany, and the United Kingdom represent about two-thirds of the index.

Nikkei 225 is comprised of the largest 225 stocks of the Tokyo Stock Exchange. The index is a simple average, unweighted.

Principal Components Analysis (PCA) is a technique used to reduce multidimensional data sets to lower dimensions for analysis.

SENEx, the bombay Stock Exchange Sensitive Index is a cap-weighted index. The selection of the index members has been made on the basis of liquidity, depth, and floating-stock-adjustment depth and industry representation. Sensex has a base date and value of 100 in 1978-1979. The index uses free float.

Straits Times Index (STI) is a market value-weighted stock market index based on the stocks of 50 representative companies listed on the Singapore Exchange.

Standard Deviation measures the dispersal or uncertainty in a random variable (in this case, investment returns). It measures the degree of variation of returns around the mean (average) return. The higher the volatility of the investment returns, the higher the standard deviation will be. For this reason, standard deviation is often used as a measure of investment risk.

S&P 500 Index is a composite of 500 U.S. Companies representing an industry-wide sample of the entire actively traded U.S. Stock Market.

51

risk Considerationsall hedge fund performance is reported net of fees .

While hedge funds offer you the potential for attractive returns and diversification for your portfolio, they also pose greater risks than more traditional investments . an investment in hedge funds is only intended for sophisticated investors . You should consider the risks inherent with investing in hedge funds .

Leveraged and speculative investments: an investment in hedge funds is speculative and involves a high degree of risk . hedge funds commonly engage -in swaps, futures, forwards, options and other derivative transactions that can result in volatile fund performance . Leveraging may increase risk .

Limited Liquidity: there is no secondary market for investors’ interests in hedge funds (and none is expected to develop), there may be restrictions on -transferring interests in hedge funds, and hedge funds may suspend or limit the right of redemption under certain circumstances . thus, an investment in hedge funds should generally be regarded as illiquid .

absence of regulatory oversight: hedge funds are not required to be registered under the U .s . investment Company act of 1940; therefore hedge funds -are not subject to the same regulatory requirements as mutual funds .

Dependence upon investment manager: the general Partner or manager of a hedge fund normally has total trading authority over its respective fund . -the use of a single advisor applying generally similar trading programs could mean the lack of diversification and, consequently, higher risk .

Foreign exchanges: select hedge funds may execute a portion of their trades on foreign exchanges . material economic conditions and/or events involving -those exchanges may affect future results .

Fees and expenses: hedge funds often charge high fees; such fees and expenses may offset trading profits . -

Complex tax structures: hedge funds may involve complex tax structures and delays in distributing important tax information . -

Limited reporting: While hedge funds generally may provide periodic performance reports and annual audited financial statements, they are not -otherwise required to provide periodic pricing or valuation information to investors .

Business and regulatory risks of hedge Funds: Legal, tax and regulatory changes could occur during the term of a hedge fund that may adversely affect -the fund or its managers .

specific risks Particular to each hedge Funds: in addition to these risk considerations, specific risks will apply to each hedge fund based on its particular -investment strategy . any investment decision with respect to an investment in a hedge fund should be based upon the information contained in the confidential offering memorandum of that fund .

no part of this document may be reproduced in any manner without the written permission of Lehman Brothers inc . and/or one of its affiliates which includes LBam europe and neuberger Berman LLC . references herein to “Lehman Brothers” shall include LBam europe and its affiliates .

this paper is furnished on a confidential basis only for the use of the intended recipient and only for discussion purposes, may be amended and/or supplemented without notice .

Certain products and services may not be available in all jurisdictions or to all client types . investing entails risks, including possible loss of principal . Past performance is no guarantee of future results . indexes are unmanaged and are not available for direct investment .

Lehman Brothers inc . and/or its affiliated companies may make a market or deal as principal in the securities mentioned in this document or in options or other derivatives based thereon . in addition, Lehman Brothers inc ., its affiliated companies, shareholders, directors, officers and/or employees, including persons involved in preparation or issuance of this material, may from time to time have long or short positions in such securities or in options, futures, or other derivative instruments based thereon . one or more directors, officers, and/or employees of Lehman Brothers inc . or its affiliated companies may be a director of the issuer of the securities mentioned in this document . Lehman Brothers inc . or its affiliated companies may have managed or comanaged a public offering of securities for any issuer mentioned in this document within the last three years .

Hedge Fund Data and Analyses: the hedge fund data contained in this material is based upon internal analyses of information obtained from public and third-party sources . any returns shown were constructed for illustrative purposes only . there are numerous limitations inherent in the data presented, including incompleteness and unavailability of hedge fund holdings, activity and performance data (i .e ., unavailability of short activity and intra-quarter activity), and the reliance upon assumptions . no representation or warranty is made as to the accuracy of the information shown, the reasonableness of the assumptions used, or that all assumptions and limitations inherent in such analysis have been fully stated or considered . Changes in assumptions may have a material impact on the data and results presented . the simulated, estimated and expected returns and characteristics constructed for any hedge fund strategies are shown for illustrative purposes only, and actual returns and characteristics of any fund or group of funds may differ significantly from any simulated, estimated and expected returns shown . all return data is shown net of fees and other expenses and reflect reinvestment of any dividend and distributions .

Expected Return Data: the expected return data contained herein is based upon internal analyses using the assumptions and methodologies described herein . expected returns are shown for illustrative purposes only and should not be viewed as a prediction of returns . no representation or warranty is made as to the reasonableness of the assumptions made or that all assumptions used in achieving the expected returns have been stated or fully considered . Changes in assumptions, such as default rates, interest rates and other economic or market factors, could have a material impact on the target returns shown . there is no guarantee that the conditions on which such assumptions are based will materialize or not deteriorate . there can be no assurance that any estimated or expected returns will be achieved or are realistic in any given market conditions . actual purchase price, returns, investment hold periods, cost of leverage, default rates of fund investments, interest rates and other factors may differ from those underlying the assumptions and estimates utilized to calculate the expected returns shown and such differences could be material . actual results for any fund or groups of funds may differ significantly from any expected returns shown . accordingly, readers should not expect investments in any fund or groups of funds to produce returns similar to any expected returns shown . investing entails risks, including possible loss of principal .

This document has been approved by LBAM Europe which is authorised and regulated by the UK Financial Services Authority (“FSA”) and is registered in England and Wales, 25 Bank Street, London, E14 5LE. Lehman Brothers Asset Management is a registered trademark.

H0222 02/08 LB17936 All rights reserved © 2008 Lehman Brothers Inc. Member SIPC. Lehman Brothers Alternative Investment Management LLC, a registered investment advisor and an affiliate of Lehman Brothers Inc.

gLoBaL CLient DeveLoPment team

This report was prepared by the LBAIM global investment team.

[email protected]

north americaBrian Sears212-526-4537

Christian Robinson212-526-9020

Nicola Halsall Idehen415-274-5292

Mona Girotra 212-526-6038

Dan Parant212-526-4305

europe and the middle east Hobson Barnes44-20-7102-7611

Francesca Guagnini 44-20-7102-5463

asia-PacificRia Nova852-2252-1309

Lehman Brothers aLternative investment management (LBaim) investment team memBers

eric Weinstein, Brian hayes, Ph .D ., Fred ingham, aCa, CFa, Jeff majit, CFa, ian haas, CFa, Jim mcDermott, Ph .D ., ernest odinec, Paresh shah, Brooke Borner, CtFa, David Cohn, supriya menon, avery Kiser, Laura hawkins, Caitlin horn, Khushnum Kharas, andrew Liebowitz, Jennifer neff, gurcag Poyraz, sophie steele, and Kelly Chapman

lehman brothers alternative investment management 2008

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