May 2014

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Hedge Funds: Separating Myth From RealityBy Biagio Manieri, Ph.D., CFA, Director of Research at PFM Asset Management LLC

Executive SummaryAlthough hedge funds have been in existence for over 50 years, over the past decade many institutional investors have begun to consider whether these types of funds should be added to their portfolios. PFM Asset Management LLC (PFMAM) has developed a three-part discussion on hedge funds as part of our PFM Perspectives series; in the third and final installment of our discussion on hedge funds, Dr. Biagio Manieri, PFMAM’s Director of Research, looks at popular misconceptions about hedge funds and presents his views on how investors should look at an allocation to hedge funds within their overall portfolio, as well as what to look for when choosing hedge fund managers.1 PFMAM believes that the hedge fund universe can be utilized to search an expanded number of investment managers with the objective of finding the most compelling strategies, but does not view hedge funds as a distinct asset class. Investors should be aware of the distinct risk characteristics of many hedge funds and recognize that losses can often be amplified by these strategies’ use of leverage.

IntroductionIn 2013 alone, total hedge fund assets grew by more than $300 billion to more than $2.6 trillion overall, according to a report from Preqin.2 Of these inflows, 65% came from institutional investors. As institutions consider the decision to make an allocation to hedge funds or add to an existing position, it is important to see beyond many of the commonly held misconceptions about hedge funds to develop a well-informed strategy. For example, some investors too readily rely on standard statistical analysis and various models such as mean variance optimizers to understand the risk embedded in hedge fund strategies. They also too readily accept the allocations to hedge fund strategies that are produced by mean variance optimizers. The problem with this approach is that hedge fund strategies are not easily understood by these statistics and models. To understand the risk embedded in hedge fund strategies and how they can be incorporated in the portfolio, it is necessary to conduct qualitative analysis in order to understand the economics of the strategy. In this edition of PFM Perspectives, we will look at some of the myths surrounding hedge funds and provide an in-depth look at how hedge funds differ from traditional investments and what should be considered when deciding to add this type of investment to a portfolio.

Myth #1: If a strategy involves arbitrage, it will result in a risk-free profit.The term “arbitrage” is defined as buying and selling securities, currencies, or commodities in different markets or in derivative form in order to take advantage of pricing differences. While it is sometimes said that arbitrage can result in a risk-free profit, this is typically not the case when investing in hedge funds that employ arbitrage strategies. For instance, merger arbitrage funds can quickly lose money if a merger deal does not go through. Like other arbitrage-based funds, merger arbitrage strategies can make money slowly over time but can quickly lose a significant amount of money if the market environment for merger-and-acquisition deals deteriorates. Fixed-income arbitrage (that is, purchasing one bond and short-selling another) is also not risk-free, as was seen during the failure of Long Term Capital Management (LTCM) in the late 1990s.3 Arbitrage-based hedge fund strategies typically have lower volatility of returns since these funds hedge and try to eliminate exposure to market risks, but this approach does not always work. These funds can be thought of as being short volatility, in that they tend to make money during calm times and lose money in turbulent markets. These funds can experience small gains over long periods of time, but can also lose a large amount of money relatively quickly. For example, convertible bond

1 To read the entire series, please contact your relationship manager to request a copy of “Hedge Funds: Then and Now,” published by PFMAM in March 2013 and “An Intro-duction to Hedge Fund Strategies,” published by PFMAM in January 2014.2 Olsen, Kevin. “Preqin: Hedge fund assets climb to $2.6 trillion total in 2013.” Pensions & Investments. February 27, 2014.3 LTCM, a large hedge fund, experienced heavy losses due in large part to the Russian financial crisis and was ultimately bailed out by the federal government in order to mitigate the risk its failure posed to the broader capital markets.

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arbitrage strategies perform well in times of declining credit spreads, which occurs when investors are comfortable with assets that are perceived to be riskier. During times of rising credit spreads (such as during the 2007-2009 credit crisis), however, convertible bond arbitrage funds can suffer large losses. This happens because bond prices are falling and the liquidity of convertible bonds declines during a flight to quality. Other fixed-income arbitrage strategies also tend to benefit during tranquil market environments and declining credit spreads. These funds buy higher-yielding, lower-quality bonds (such as high-yield bonds) and sell short lower-yielding, higher-quality bonds (such as Treasuries). While they can earn a positive income spread over time, they can and do lose large amounts of money quickly during turbulent times, as a flight to quality can result in these strategies losing money on both sides of the trade as prices of the higher-quality bonds rally and prices of the lower-quality bonds fall. In addition, the liquidity of the lower-quality bonds declines, making it more expensive to exit those positions.

Myth #2: The risks of hedge funds are fully captured by traditional risk statistics.When they are applied to hedge funds, traditional risk statistics can understate some of the risks associated with this type of investment. For instance, as a result of the return pattern exhibited by arbitrage-based funds, these funds tend to have high Sharpe and information ratios (which both measure risk-adjusted return) since the volatility of the returns is low most of the time. Because of these characteristics, mean-variance optimizers will over-allocate to these strategies. Incorporating assets with “fat tails” (that is, more likelihood of an extreme event, whether positive or negative) can minimize volatility but at the expense of a significant increase in tail risks—that is, risks of incurring larger-than-expected losses since returns do not follow a normal distribution.

The issue with relying on traditional risk statistics can be seen in a simple example. A simple strategy that sells out-of-the-money put options4 on the S&P 500 Index will earn a steady return in most months. This strategy will have high Sharpe and information ratios. If a hedge fund is reporting returns similar to this simple strategy, an investor may see this as a low-risk strategy to generate alpha that is uncorrelated with the equity market. Of course, this strategy works until the S&P 500 Index suffers a serious decline. At that point, the hedge fund would suffer a serious loss, potentially wiping out years of returns. Profits that took years to generate could be wiped out in a short period of time. Since the volatility and standard deviation of selling out-of-the-money put options is low, the strategy would seem to generate consistent returns with low volatility. Therefore, mean-variance optimizer models and other quantitative approaches such as risk budgeting approaches would allocate a significant amount to this strategy, which could cause serious problems for the portfolio in the future should an extreme adverse event occur.

Hedge fund returns tend to exhibit significant skewness and kurtosis. In other words, while a normal probability distribution looks like a bell curve (as shown in Exhibit 1), a skewed distribution is asymmetrical, with a larger hump on the right or left (an example of which is shown in Exhibit 2 on the following page). A distribution with high kurtosis will have a thinner, peaked curve. Using mean-variance optimizer models to construct efficient frontiers (that is, optimal portfolios that offer the highest expected return for a defined level of risk) and determine how much to allocate to hedge funds, therefore, will not work since these models assume that the returns are normally distributed.

Exhibit 1: A Normal Distribution has a “Bell Curve” Shape

4 When an investor purchases a put option, they are purchasing the right to sell an underlying security at a certain price in the future, called a “strike price.” The investor will profit as the price of the underlying security falls. For example, an investor might buy a put for XYZ stock at a strike price $50, meaning that if the price of XYZ decreases to $35, the investor could sell the stock at $50 and earn a profit of $15, less the price of the put when it was purchased. In this case, an out-of-the-money put option would result from XYZ’s price being higher than the $50 strike price. In our example above, the strategy would sell put options to investors and be paid a premium. The strategy would profit when the price of the underlying security rises.

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Exhibit 2: Example of a Skewed Distribution

The returns exhibited by hedge funds tend to be non-normal in the wrong way. Investors want the highest expected return (mean), positive skewness, lower variance, and small kurtosis. However, hedge fund returns tend to have negative skewness and large excess kurtosis, similar to the distribution shown in Exhibit 3 below. We can see this if we analyze the expected return pattern from merger arbitrage funds. These funds sell insurance against the deal not going through. The fund will make a modest amount if the deal is completed. For a deal that is not completed, a large loss will be realized. This return pattern has negative skew and high kurtosis; therefore, the potential loss is higher than the potential gain. In other words, the loss from one deal not going through can wipe out the gains from many completed deals.

Exhibit 3: Example of a Distribution with Negative Skew and High Kurtosis

Using the return pattern from these strategies during calmer market environments is misleading because when turbulent times occur, losses are large and happen quickly. For example, LTCM lost more in a short period of time than it made over many years. Some have argued that risk management begins where Value at Risk (VaR) ends. We would extend that view by saying that portfolio construction begins where mean-variance optimizers end. Both of these statements are especially true when including hedge funds in a portfolio.

Myth #3: All hedge funds generate significant alpha.Whether hedge funds deliver alpha or provide exposure to exotic beta is debatable.5 Academic studies that have analyzed hedge fund returns generally arrive at one of two conclusions: (1) that either alpha is dependent on market conditions, or (2) what looks like alpha is exotic beta.6 A fund’s return can be decomposed into alpha and beta using linear regression analysis. Beta are the parts of the returns that are driven by market factors such as equity beta, credit spread, volatility, and other premia that are included in the model. What is left over from what these factors are able to explain is called alpha. For example, if we regress the returns of a fund on the S&P 500 Index, we will get some portion of the returns that varies in line with the S&P 500 Index; this is beta. Beta can be more or less than 1 depending on the investment’s sensitivity to movements in the broader equity market (represented in this example by the S&P 500 Index). What cannot be explained by the movement of the S&P 500 Index is called alpha. This is a very simple model using only one factor—the S&P 500 Index—but the returns of an investment may vary because of other factors that are not included in the regression model. In other words, the model may be misspecified, as it does not include all of the independent variables or factors. A simple improvement is to include the three risk factors that Professors French and Fama discovered: the stock market, capitalization or size, and value (represented by the book-to-market ratio).7 If we now regress the returns of the fund based on these three factors, we may find that some or all of what we were calling alpha is in fact beta, but beta that depends on size and/or value. So what was masquerading as alpha turned out to be what some have referred to as “beta in drag.”

Some academic studies examining hedge fund returns have identified several factors that explain most or all of the returns

5 Exotic beta are premia that are uncorrelated with equity market exposure (also known as beta).6 Fung, William. “Hedge Funds: Performance, Risk and Capital Formation.” March 2006.7 Fama, Eugene F. and Kenneth R. French. “Common risk factors in the returns on stocks and bonds”. Journal of Financial Economics, 1993.

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from the hedge fund universe.8 These factors include: (1) the S&P 500 Index, (2) the small-big factor (small-cap minus large-cap stocks), straddle options on (a) commodities, (b) currencies, and (c) bonds, (4) the yield spread of U.S. 10-year Treasury bonds over three-month T-bills, and (5) changes in the credit spread of the Moody’s Baa-rated bond over the 10-year Treasury. Using linear regression, a model incorporating these factors had an R-square of 74% (meaning that the statistical model explained 74% of the variation in the data points), which suggests that the average hedge fund has significant exposure to underlying risk factors. The study also found that the exposure to these risk factors changes over time, which may be one reason why replication strategies do a poor job of replicating the underlying index of hedge fund strategies. Changing the weights of each factor may or may not add value, but if the weights of the replication strategy product are fixed, then the replication product is not going to track the underlying index very closely.

Other studies have arrived at similar conclusions that most hedge funds retain significant equity and credit market exposures. For example, one study analyzed the performance of hedge funds from 1994 to 2009 and found that the average hedge fund did not deliver significant positive alpha when incorporating the factors listed above.9 These studies have typically found that traditional and exotic betas explain about 90% of the variance in the monthly returns of a diversified portfolio of hedge funds. This finding is similar to the results of the study by Brinson, Beebower, and Hood, which showed that about 90% of the variance in the returns of institutional portfolios can be explained by asset allocation, with market timing and security selection detracting from performance on average.10

In the case of hedge funds, what looked like 5% to 6% of alpha versus traditional betas declined to -1% after adding hedge fund beta factors.11 Therefore, on average, hedge funds as a group are not providing true alpha that cannot be explained by exposure to various risk factors. Instead, they are generally providing exposure to non-traditional betas, hedge fund betas, and exotic betas. These conclusions call into question the view that some investors have, which is that hedge funds produce absolute returns; this view assumes that these funds are factor neutral, meaning that they are not systematically exposed to various risk factors.

Myth #4: Hedge funds do not have as much equity market risk as traditional asset classes do. Unlike comparing some asset classes such as large-cap equity to a broad index like the Russell 1000 Index, hedge funds do not easily conform to a benchmark. Analyzing the return-risk profile of hedge funds is further complicated since the funds may hold illiquid assets that cannot easily be marked to market. In some cases, the prices used are either marked to model or are stale. This results in the smoothing of results and may lead to underestimating risk by as much as 40%.12 Performance statistics such as Sharpe and information ratios can be dramatically overstated, while the correlation with marketable securities is understated. This bias is more prevalent in fixed-income, convertible bonds, event-driven, and relative value strategies, as well as in other strategies where the investments are not as liquid.

The stale pricing of some of these strategies can be seen in the autocorrelation (also known as serial correlation) that is observed in their returns. One study found that 92% of convertible arbitrage hedge funds, 91% of event-driven funds, and 63% of long/short funds had significant positive serial correlation, while only 23% of global macro funds had significant positive serial correlation.13 These results make sense since global macro funds use exchange-traded futures, which have readily available prices. The same studies that have shown hedge fund returns to have serial correlation have also analyzed traditional managers and mutual fund returns and found that these returns have little serial correlation since these managers use highly liquid securities where market prices are readily available.

The serial correlation exhibited in hedge fund returns is also an indication of liquidity risk. These strategies should deliver returns that are higher than what is available in highly liquid, publicly traded securities based on illiquidity premium alone. According to Clifford Asness, founder of one of the largest hedge funds, AQR Capital Management, and a veteran of the hedge fund industry, investors who think that hedge funds do not smooth their returns through how they price their positions are naïve.14 In fact, he classified this practice as being frequent.

8 Fung, William. “Hedge Funds: Performance, Risk and Capital Formation.” March 2006.9 Xu, Xiaoqing Eleanor and Jiong Liu. “Hedge Fund Attrition, Survivorship Bias, and Performance: Perspectives from the Global Financial Crisis,” February 2010.10 Brinson, Gary P., L. Randolph Hood, and Gilbert L. Beebower. “Determinants of Portfolio Performance.” Financial Analysts Journal, July/August 1986.11 Fung, William. “Hedge Funds: Performance, Risk and Capital Formation.” March 2006.12 Black, Keith. “Investing in Hedge Funds: A Survey.” 2009, p. 8.13 Liew, Jimmy. “Neutralizing Betas without neutralizing Alphas in funds of hedge funds.” November 2004.14 Asness, Clifford. “The Future Role of Hedge Funds.” 2006.

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Asness compared the monthly and quarterly correlations between the CSFB/Tremont Hedge Fund Composite Index and the S&P 500 Index.15 Using monthly data, the correlation was 0.52, and when using quarterly data, the correlation increased to 0.64. Asness’ analysis—which used statistical techniques to remove the serial correlation in hedge fund returns—showed that hedge funds moved with the market but with a lag. For example, the beta of hedge fund returns regressed against the S&P 500 Index increased from 0.37 using monthly returns to 0.84; convertible arbitrage betas increased from 0.04 to 0.43. Event-driven beta increased from 0.28 to 0.61. Asness found that the beta increased for every hedge fund strategy except for managed futures funds, which use exchange-traded futures that are easily valued. The beta of long/short equity funds jumped from 0.55 to 0.99. This suggests that long/short equity funds have as much equity market risk as any other equity fund. One of the reasons that the beta of long/short equity funds increased may be that these funds invest in small-cap stocks, which are less liquid.

Asness also examined the beta after removing the serial correlation in up and down markets. The beta was 0.17 in up markets but 0.79 in down markets, which lends some credibility to the idea that hedge fund pricing is at least partially manipulated. Hedge funds have an incentive to delay the results in down markets but not in up markets where they want to show the increase in performance and do not want to underperform.

When using lagged results, strategies that had positive alpha now had negative alpha. For the CSFB/Tremont Hedge Fund Composite Index, the monthly results showed an alpha of 2.6% annually; when the serial correlation was removed and the monthly results were lagged, the alpha was -4.5% annually. The higher lagged beta turned what looked like alpha into beta. The Sharpe ratio for the hedge fund index dropped from 0.8 to -0.4. In fact, the Sharpe ratio dropped for every hedge fund strategy except for managed futures and dedicated short funds.

After analyzing the CSFB/Tremont Hedge Fund Composite Index, which is asset-weighted, Asness repeated his analysis using the Hedge Fund Research (HFR) index data, which is equal weighted, and found the same results. These results are interesting because they show that when hedge fund results are lagged to account for stale pricing and serial correlation, the diversification benefits from investing in a broad-based hedge fund index are no longer present.

Myth #5: The strong historical performance of hedge funds shows that these types of funds generally perform better than traditional investments.Much of what might look like consistent strong performance by a group of hedge funds over time is often reflecting only the continued performance of surviving hedge funds, which is also known as survivorship bias. While survivorship bias is an issue in analyzing traditional investment funds and mutual funds, it is particularly acute in analyzing hedge funds since the average life of a hedge fund is so short. One study found that less than 25% of hedge funds in existence in 1996 were still reporting results in 2004.16 Another study (Brooks and Kat, 2002) found that 30% of new hedge funds do not make it past three years due to poor performance.

Fabrice Rouah, a quantitative analyst and Director at Sapient Global Markets, examined mortality rates for hedge funds from 1994 to 2003.17 The general trend is that the annual mortality rate for hedge funds has been increasing: it was 3% in 1994; 7% in 1997; 11.4% in 2001; and 10.7% in 2003. Of course, part of the reason for this trend is that there are now more hedge funds, but these results also suggest that a larger percentage of existing hedge funds are managed by professionals with little talent and skill. In 2013, the mortality rate was 10%, which is significantly higher than it was in the early 1990s.18 Other studies have confirmed that, over a five-year period, about 40% to 50% of hedge funds close down.

While some studies have shown persistence of returns among hedge funds, one study (Brown, Goetzmann, 1999) analyzing hedge funds between 1989 and 1995 found that when both live and dead funds are analyzed, there is no evidence of persistence. This suggests that the persistence of returns observed by some may be due to survivorship bias in some of the databases that are used. It could also be due to market conditions. Of course, if outperformance or alpha is dependent on market conditions, persistence is period-dependent, which means there is no persistence. Some studies found persistence using quarterly data; however, when they moved beyond quarterly data, persistence weakened.19

15 Asness, Clifford. “Do Hedge Funds Hedge?” Fall 2011.16 Black, Keith. “Investing in Hedge Funds: A Survey.” 2009, p. 8.17 Rouah, Fabrice. “Survival and Mortality of Hedge Funds.” September 2005.18 “Ouch: Close to 10% of Hedge Funds Shut Down in 2013” Wall Street Journal, March 25, 2014.19 Agarwal, Vikas and Narayan Y. Naik. “Multi Period Performance Persistence Analysis of Hedge Funds.” 2000

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Another way to examine the issue of performance persistence and the impact of asset flows into hedge funds is to compare the time-weighted returns with dollar-weighted returns. Dollar-weighted returns are a better reflection of what the average investor receives from investing in hedge funds. Ilia Dichev, Chaired Professor of Accounting at the Goizueta Business School of Emory University, followed this approach, using dollar-weighted returns as a form of the internal rate of return, or IRR.20 Dichev showed that dollar-weighted returns were about 3% to 7% lower than time-weighted returns. This is closer to what the typical investor received by investing in hedge funds.

While one can argue that this is not fair to hedge fund managers since cash flow is controlled by investors and plan sponsors rather than the managers, it is instructive in that the assets in hedge funds have increased from about $40 billion in 1990 to about $2.5 trillion in 2007. Using dollar-weighted returns provides a closer approximation to what the typical investor in hedge funds has experienced. The lower return was found to be mostly due to investors chasing higher return hedge funds which then do not outperform. Given these findings, it suggests that investors in hedge funds are making the same mistake that retail investors are making: they are chasing performance. If performance was persistent, then this behavior would make sense. A fund that was outperforming would continue to outperform. But this is not the case; outperforming funds do not continue to outperform.

To further examine this issue, it is instructive to compare the hedge fund industry returns as provided by a hedge fund index with the returns from a fund of funds index. Fund of funds indices are less susceptible to survivorship bias since the dying funds will still be included in the returns of the funds of hedge funds, even if the hedge fund stops reporting results to the database. However, this does not completely eliminate the problem, because if a fund of hedge funds stops reporting results, then it is not included in the database. The funds of hedge funds provide a closer approximation to the performance experienced by investors in hedge funds. A comparison of a composite index of hedge funds and a fund of funds index can be found in Exhibit 4 below.

Exhibit 4: HFRI Composite Returns vs. HFRI Fund of Funds ReturnsYTD as of March 31,

2014

1 Year 3 Years 5 Years 7 Years 10 Years

HFRI Composite 1.1 % 6.5% 3.0% 8.0% 3.5% 5.4%HFRI Fund of Funds 0.4% 5.9% 2.3% 4.9% 1.0% 3.1%

Source: Hedge Fund Research, Inc.

Our View PFMAM believes that, just like traditional funds, an investor cannot rely on performance or performance statistics (such as the Sharpe ratio) to pick a talented hedge fund manager. Rather, it is necessary to understand the reasons for the performance, since few if any strategies consistently outperform or underperform. The only way that we know how to separate a few skillful managers from a group of mediocre managers is to perform in-depth due diligence in order to understand the people managing these hedge funds and the strategy they are pursuing.

This material is based on information obtained from sources generally believed to be reliable and available to the public, however PFMAM cannot guarantee its accuracy, completeness, or suitability. This material is for general information purposes only and is not intended to provide specific advice or a specific recommendation. All statements as to what will or may happen under certain circumstances are based on assumptions, some but not all of which are noted in the presentation. Assumptions may or may not be proven correct as actual events occur, and results may depend on events outside of your or our control. Changes in assumptions may have a material effect on results. Past performance does not necessarily reflect and is not a guaranty of future results. The information contained in this presentation is not an offer to purchase or sell any securities.

PFMAM is registered with the Securities and Exchange Commission under the Investment Advisers Act of 1940. PFMAM’s clients are state and local governments, non-profit corporations, pension funds, and similar institutional investors. www.pfm.com

20 Dichev, Ilia. “Higher risk, Lower returns: What hedge fund investors really earn.” May 2010.