This report analyzes an asset-weighted portfolio of the 30 largest reporting hedge funds to determine what market exposures, risk factors, and performance expectations this institutional portfolio would create for an investor. Understanding the current market exposures, risk factors, and performance expectations of these funds, especially if a crisis were to occur during the following month, offers a way to gauge the positioning of the broader hedge fund industry.

2016 Hedge Fund Letters

The same analysis is produced for portfolios constructed of the 10 largest macro, long short equity, and fixed income/credit hedge funds.

Highlights

  • In the event of another Brexit-like event – in which the GBP depreciated 11.3% versus the USD from Thursday, June 23rd to Monday, June 27th – all four portfolios appear well insulated.
  • Exposure positioning for the 30 largest hedge fund portfolio is likely to act as a hedge against a steepening yield curve and rising inflation following US Presidential election results.
  • Large macro funds’ aggregate, asset-weighted positioning favors European equities, (in general) rising inflation in the US, and a depreciating EUR versus USD

Introduction

The 30 largest reporting hedge funds represent 15.45% of our estimated total industry assets at the end of Q3 2016. Understanding the current market exposures, risk factors, and performance expectations of these funds, especially if a crisis were to occur, offers a way to gauge the positioning of the broader hedge fund industry.

We utilize eVestment’s RiskPlus product (developed in partnership with BISAM) which incorporates returns-based analysis and a fat-tailed methodology, to create a forward-looking, assetweighted, pro forma portfolio of these 30 hedge funds. To put it simply, if an institutional investor were to purchase a basket of the largest hedge funds, the output of this analysis would quantify their market exposures and risks. The 30 hedge funds in our analysis reported $467.49 billion in assets under management (AUM) at the end of Q3 2016; individual fund AUM ranged from $6.56 billion to $66.00 billion; average fund AUM was $15.58 billion and the median $9.90 billion.

This report examines the 30-fund asset-weighted portfolio’s market exposures, anticipated volatility from these exposures, expected risk and return characteristics, and performance expectations if historical crises were to reoccur, along with outlooks during customized market panic settings.

We also perform the analysis on three portfolios comprised of the 10 largest reporting hedge funds within their respective strategy segments:

  • Top 10 Macro $187.69 billion combined AUM
  • Top 10 Long Short Equity (LSEQ) $80.79 billion combined AUM
  • Top 10 Fixed Income/Credit (FIC) $51.39 billion combined AUM

Several multivariate factor models, constructed specifically for each group, were used in the analysis. Itemized descriptions of these factors are found at the end of the report.

Top-Level Portfolio Risk and Return Characteristics

We begin with broad overviews of expected portfolio characteristics for the following month, noting that each asset-weighted portfolios’ results are shaped by custom factor models. Itemized descriptions of the factors are found at the end of the report.

  • Expected volatilities for the current portfolios are lower relative to the portfolios in our prior report.
  • Based on VaR and ETL statistics, the current portfolios show less risk of extreme losses versus our prior portfolios.
  • Correlation to markets was generally higher with our new portfolios. The long short equity segment’s factor model beta declined however; since our prior report, two of the ten funds in the LSEQ portfolio have been replaced, due to either AUM changes or lack of performance reporting.

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Historical Stress Tests

The four hedge fund portfolios, each using different factor models, are put under historical stress test scenarios. Expectations of portfolio gains/losses are determined by weighted betas – the funds’ relationships to market factors (beta) and our chosen fund allocations (weights based on AUM).

A repeat of market shocks similar to those during the 2008 Lehman Bankruptcy period is forecast to have the largest negative impact across all four portfolios. The Top 10 FIC portfolio is susceptible to the largest portfolio decline, with an expected loss of -18.30% that hints at large credit funds’ exposure to instruments with similar characteristics to those hard hit during the crisis.

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Of the 10 funds in the Top 10 FIC portfolio, the stress test results show only one fund expecting to fare well during a repeat of Q4 2008 market conditions. This sole fund is also slated to do well under the four scenarios in figure 4 in which the overall FIC portfolio is forecast to produce positive results. Nevertheless, the FIC portfolio is the most robust to financial shocks, ex-Lehman Bankruptcy, showing only one other crisis period in which it is slated to post losses below -1.67%.

Large declines in oil prices should be of little concern to all four portfolios, and quite constructive for the Top 10 LSEQ portfolio. A repeat of the market conditions in 4Q14, in which the price of WTI dropped 44% in roughly 3 months, is projected to give this LSEQ portfolio a 5.95% boost.

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In the event of another Brexit-like event – in which the GBP depreciated 11.3% versus the USD from Thursday, June 23rd to Monday, June 27th – all four portfolios also appear well insulated. A repeat of similar market conditions which occurred during the 2015 Chinese market crash and subsequent CNY devaluation bodes most poorly for the Top 10 Macro portfolio. An expected loss of -4.59% is heavily influenced by the asset-weighted setup of our analysis (i.e. the two largest funds are projecting 2 of the 3 lowest return expectations in the scenario).

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Top 30 Largest – Factor Exposures and Risks

  • Equity exposure for the 30 largest hedge funds shows a clear preference for developed market securities. This exposure however, also acts as the leading driver of systematic risk (market beta) within the portfolio.
  • Since our last report, exposure to developed market equities has flipped from more US-heavy to greater emphasis on EAFE.
  • In the context of the overall factor model, the portfolio is positioned to benefit from depreciations of AUD, GBP, EUR, and JPY currencies relative to USD. However, under stress test scenarios significant AUD depreciation (two standard deviations below L10Y mean) is undesirable (see figure 7). Calculated stress tests results for each individual fund vary based upon the scenario, and the impact of such a large AUD depreciation on the two largest funds in our asset-weighted portfolio is notably more harmful for this factor.

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  • The portfolio’s two largest volatility contributing fixed income factors—corporate convertibles and inflation-linked US debt—were also its main areas of exposure within this asset class. This positioning will likely act as a hedge against a steepening yield curve and rising inflation following US Presidential election results. Since 31-Oct-2016 through 15-Dec-2016, yields have risen across all UST maturities (between 15 bps and 72 bps, inclusive) and in Nov-2016 the all-items CPI index increased 1.7% YoY, the largest rise since Sept-2014. The hybrid feature of convertible securities – option of exchanging into equity, which tends to rise with rising rates – paired with inflation-protected US debt, should be advantageous for the portfolio going forward.
  • Positive exposure to the CBOE Volatility Index as Difference factor is currently the most volatility diversifying element within the context of this factor model. If the factor were to decline by two standard deviations from its L10Y monthly mean over the course of the following month, the portfolio is set to reap 1.16% in gains. Conversely, if volatility were to increase to the same degree the portfolio is expected to lose -0.70%.

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Top 10 Macro – Factor Exposures and Risks

  • Large macro funds’ aggregate, asset-weighted positioning favors European equities, (in general) rising inflation in the US, and a depreciating EUR versus USD.
  • Simulating a two-standard deviation depreciation of the EUR/USD relationship results in meaningful portfolio gains of 1.27% during the following month. Positive exposure to FX factors indicates that the portfolio is positioned to reap benefits when a currency depreciates relative to the USD in the context of the overall model. In Nov-2016 the EUR depreciated ~3.60%, or roughly half of its two-standard deviation figure over L10Y.

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  • In a complete break from our prior analysis, this portfolio’s overall position in below investment grade global corporate debt reversed, swinging from bearish to bullish (-16.28% to 13.95%); our second largest-weighted macro fund was the primary driver of this shift. While exposure to global high yield corporates may offer benefits in a rising rate environment, any significant declines in the value of these instruments would not bode well for the portfolio based on stress test results, which shock the Global High Yield factor separately and then calculate the values of the remaining factors conditionally on this individual factor.
  • At the end of Oct-2016 the macro portfolio was positioned to benefit from falling 2-year and 10-year US interest rates. YTD through this period, there were four month-over-month declines by each maturity, but at the end of Nov-2016 both rates increased substantially: 2-year from 0.86% to 1.11% and 10-year from 1.84% to 2.37%.
  • The lack of commodity market risk is notable and suggests the portfolio may have been insulated from commodities’ volatility in the wake of the US Presidential election, which included heavy fiscal stimulus rhetoric. The portfolio’s relatively large bullish posturing towards industrial metals was likely beneficial, as agriculture and precious metals declined, while energy and especially industrial metals rose.

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Top 10 Long Short Equity – Factor Exposures and Risks

  • Based on the same factor model, but with two different constituents from our last report, the Top 10 LSEQ portfolio’s direct exposure to US equities via the Russell 3000 factor has dropped considerably. Indirectly, exposure to the US remains via its link to the developed market MSCI World sectors.
  • Following the US Presidential election, the equity sectors listed in figure 10 ended Nov-2016 with their highest performance dispersion since Nov-2014. The difference between the top and bottom sectors’ gross returns was 13.93 for the month; the top 3 performers were financials (portfolio positioned long), energy (short), and industrials (long), while the bottom three were utilities (slight long), consumer staples (long), and telecom services (slight short).

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  • In addition to our two deviation stress tests listed in figure 11, we also ran portfolio stress tests with similar values of actual MSCI World sector returns for the month of Nov-2016. Results indicate mixed outcomes based on the conditionally calculated stress results, with the portfolio faring best under the rise in industrials in global developed markets:

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  •  The portfolio adjusted its preference for size, switching positions to favor smaller caps instead of large in developed global markets; partiality towards growth stocks continued and increased.

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Top 10 Fixed Income/Credit – Factor Exposures and Risks

  • A “risk-on” mentality in certain market segments became manifest in the Top 10 FIC portfolio. Highly bullish posturing in lower-graded US CMBS was paired with large bearishness in the investment grade segment; a similar scenario played out between the lower and highergraded global corporate fixed income securities. Positive exposures to these lower-rated products were by far the largest volatility contributing elements within the portfolio.
  • Positive exposure towards other factors shows the portfolio may be hedging the risk associated with lower-rated instruments to a certain extent. Exposure to investmentgrade ABS and the US Dollar (via Trade Weighted Exchange Index: Broad) were the portfolio’s third and fourth largest bullish positions. The former stance produced barely any portfolio volatility while the latter led to an -8.91% reduction.

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  • While the portfolio assumed more volatility from exposure to lower graded global corporates, the negative posturing towards the middle-tiered BBB/BB US corporates lessened volatility by -10.18%.
  • If the CMBS Fixed Rate BBB or Global Broad Mrkt Corp BBB factors were to experience declines of two standard deviations below their L10Y monthly means over the course of the following month, the expectations are for positive gains. When a specific factor is shocked downward, the returns of other factors are calculated conditionally on this factor, given historical correlations. In this case, falling US CMBS and global corporates are correlated with positive returns for interest rates, volatility, and USD factors. The result is that the positive returns of these factors plus our asset-weighted portfolio setup (the two largest funds are slated for positive gains, as are 6 of the 10 under both shock scenarios), work to offset the direct impact of declining returns for these two factors.

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