George Soros and Julian Robertson have been the Tom Cruise and Mel Gibson of the hedge fund industry for the last two decades. These two well-known money managers have graced the covers of financial publications and investors around the world have closely watched where they invest their money. While both men have reduced their trading activity recently, their performances have been impressive. In 1986, Julian Robertson’s Tiger Fund was featured in Fortune Magazine for producing 43% annual returns, after fees, for its first six years of existence. A few years later, Soros made headlines for selling short the British pound and forcing Great Britain to eventually withdraw its currency from Europe’s ERM, a humiliating blow for that country.
 

In essence, the hedge fund industry grew up around these two men who helped establish an image that hedge fund managers can produce excess market returns with little or zero beta. The number of hedge funds ballooned from 37 in 1985 to 987 by 1997. Some estimates put the total assets under management at $1 trillion in 2000. Despite the mysterious and secretive world surrounding this area of high finance, this research project analyzes whether hedge funds, in fact, do generate significant returns after adjusting for risk.

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Four distinct features make hedge funds unique from other investment vehicles: trading strategy, leverage, regulation and compensation. Information is very difficult to obtain about hedge funds, but it is often easiest to compare them to mutual funds to illustrate these characteristics.

1.    Trading Strategy
Hedge funds normally adopt a dynamic trading strategy that can involve very short-term strategies, sometimes buying and selling in the market on an intraday basis, or taking a position before a scheduled government report and getting out of the position after the report is released. Traditional mutual funds tend to buy and hold stocks for an extended period.
 

2.     Leverage
Hedge funds typically leverage their bets by margining their positions and by using short sales. Long Term Capital Management, while an extreme example, shows the extent to which hedge funds lever their positions. In contrast, the use of leverage is often limited if not restricted for mutual funds.
 

3.    Regulatory Environment
This is probably the key differentiating factor for hedge funds and contributes to the “mystery” surrounding the industry. Hedge funds are considered private investment vehicles for wealthy individuals and institutional investors. They are formed as limited partnerships whereby the investors are considered limited partners and the money managers are general partners. Hedge funds are not allowed to advertise. Many funds are offshore for tax and regulatory reasons.  The U.S. Securities Act of 1933 requires certain disclosure reports for firms issuing publicly traded securities. But under rules of the Securities and Exchange Commission, which oversees publicly traded securities, hedge funds can claim the status of a “private placement,” so they are exempt from most registration and disclosure requirements. To qualify for the exemption, hedge funds must not have more than 35 “nonaccredited” investors. The SEC defines an "accredited investor" as someone with more than $1 million in wealth or earned more than $200,000 in the previous two years. Under another law, the Investment Company Act of 1940, the SEC can regulate mutual funds. But hedge funds get around this law by having no more than 99 investors and by not making public offerings. The law has been updated and hedge funds can now have up to 499 investors with more than $5 million each in assets.
 

4.    Compensation
Compensation is broken into two categories: a 1-2% management fee and a 15-20% “incentive” fee, based on the profits. The incentive fee, which is similar to those paid to venture capital firms, gives money managers a disproportionate incentive to shoot for much higher returns. The upside potential is similar to an option the manager gets for running the fund. It is likened to a quarterback throwing a “Hail Mary” pass to try and get a touchdown, even though odds of success aren’t as great. The managers might take greater risks because they are not as severely punished for poor returns. However, hedge fund managers typically invest a large percentage of their own money into the funds, so that may limit their aggressive investing.

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Data

CSFB/Tremont Hedge Fund Index

The primary source of data was the CSFB/Tremont Hedge Fund Index, currently the industry's only asset-weighted hedge fund index. Asset weighting, as opposed to equal weighting, provides a more accurate depiction of an investment in the asset class. The index uses the TASS+ database, which tracks over 2600 funds. The universe consists only of funds with a minimum of US $10 million under management and a current audited financial statement. Funds are separated into primary sub- categories based on their investment style. The Index in all cases represents at least 85% of the assets under management in the universe. CSFB/Tremont analyzes the percentage of assets invested in each sub-category and selects funds for the Index based on those percentages, matching the "shape" of the Index to the shape of the universe. The Index is re-balanced monthly. Funds are not removed from the Index until they are liquidated or fail to meet the financial reporting requirements. The objective is to minimize survivorship bias. Funds are re-selected quarterly

CSFB/Tremont Hedge Fund Index consists of a master index and series of sub-indices that represent the historical returns of this asset class. The series of sub-indices is designed to track the primary categories of investment styles used by hedge fund managers. Each sub-index is calculated using the same exacting methodology as the master Index. The sub-index categories are defined as follows:

Long/Short Equity
This directional strategy involves equity-oriented investing on both the long and short sides of the market. The objective is not to be market neutral. Managers have the ability to shift from value to growth, from small to medium to large capitalization stocks, and from a net long position to a net short position. Managers may use futures and options to hedge. The focus may be regional, such as long/short US or European equity, or sector specific, such as long and short technology or healthcare stocks. Long/short equity funds tend to build and hold portfolios that are substantially more concentrated than those of traditional stock funds.

Managed Futures
This strategy invests in listed financial and commodity futures markets and currency markets around the world. The managers are usually referred to as Commodity Trading Advisors, or CTAs. Trading disciplines are generally systematic or discretionary. Systematic traders tend to use price and market specific information (often technical) to make trading decisions, while discretionary managers use a judgmental approach.

Global Macro
Global macro managers carry long and short positions in any of the world's major capital or derivative markets. These positions reflect their views on overall market direction as influenced by major economic trends and/or events. The portfolios of these funds can include stocks, bonds, currencies, and commodities in the form of cash or derivatives instruments. Most funds invest globally in both developed and emerging markets.

Equity Market Neutral
This investment strategy is designed to exploit equity market inefficiencies and usually involves being simultaneously long and short matched equity portfolios of the same size within a country. Market neutral portfolios are designed to be either beta or currency neutral, or both. Well-designed portfolios typically control for industry, sector, market capitalization, and other exposures. Leverage is often applied to enhance returns.

Fixed Income Arbitrage
The fixed income arbitrageur aims to profit from price anomalies between related interest rate securities. Most managers trade globally with a goal of generating steady returns with low volatility. This category includes interest rate swap arbitrage, US and non-US government bond arbitrage, forward yield curve arbitrage, and mortgage-backed securities arbitrage. The mortgage-backed market is primarily US-based, over-the-counter and particularly complex.

Event-driven
This strategy is defined as equity-oriented investing designed to capture price movement generated by an anticipated corporate event. There are four popular sub-categories in event-driven strategies: risk arbitrage, distressed securities, Regulation D and high yield investing.
 

• Risk Arbitrage

Specialists invest simultaneously in long and short positions in both companies involved in a merger or acquisition. Risk arbitrageurs are typically long the stock of the company being acquired and short the stock of the two companies. The principal risk is deal risk, should the deal fail to close.

• Distressed Securities

Fund managers invest in the debt, equity or trade claims of companies is financial distress and generally bankruptcy. The securities of companies in need of legal action or restructuring to revive financial stability typically trade at substantial discounts to par value and thereby attract investments when managers perceive a turn-around will materialize.

• Regulation D

This sub-set refers to investments in micro and small capitalization public companies that are raising money in private capital markets. Investments usually take the form of a convertible security with an exercise price that floats or is subject to a look-back provision that insulates the investor from a decline in the price of the underlying stock.

• High Yield

Often called junk bonds, this sub-set refers to investing in low-graded fixed-income securities of companies that show significant upside potential. Managers generally buy and hold high yield debt.

Emerging Markets
This strategy involves equity or fixed income investing in emerging markets around the world. Because many emerging markets do not allow short selling, nor offer viable futures or other derivative products with which to hedge, emerging market investing often employs a long-only strategy.

Dedicated Short Bias
Dedicated short sellers were once a robust category of hedge funds before the long bull market rendered the strategy difficult to implement. A new category, short biased, has emerged. The strategy is to maintain net short as opposed to pure short exposure. Short biased managers take short positions in mostly equities and derivatives. The short bias of a manager's portfolio must be constantly greater than zero to be classified in this category.

Convertible Arbitrage
This strategy is identified by hedge investing in the convertible securities of a company. A typical investment is to be long the convertible bond and short the common stock of the same company. Positions are designed to generate profits from the fixed income security as well as the short sale of stock, while protecting principal from market moves.

Potential Biases

These performance results are flawed, however, due to several biases that crop up in the data.

• Selection Bias

Database vendors are not able to include the entire universe of hedge funds when calculating returns. Because hedge funds are not required to disclose their results, only funds with significant excess returns or funds that want to market their results to raise capital, are likely to report their results to the vendors, who require the consent of the managers to publish their returns. George Soros’ Quantum Funds are not included in some databases because the funds are closed and Soros does not need to raise money. While the exclusion of these top-performing funds may underestimate the industry’s returns, the bias is greater that exclusion of poor performing funds will overestimate returns.
 
• Survivorship Bias

This bias occurs because the database vendors only calculate returns for funds that are still “surviving,” and excludes those funds that have gone out of business due to poor results or other reasons. Academic research by David Hsieh at The Fuqua School of Business (Duke University) describes this bias as the difference between an “observable portfolio” or the funds at the beginning of a period and the “surviving portfolio” or the returns of the funds still in the database at the end of a period. According to Hsieh's estimate, the survivorship bias adds 3% per year to returns.
 
• Instant History Bias

Hedge funds normally go through an “incubation period” of 24-36 months where hedge fund managers trade with limited resources. After this period ends, the money manager markets the returns to the database vendor. A database vendors typically will “back fill” the results to the start of the fund, giving the fund a so-called “instant history.” Again, the fund is likely to report its results to the vendor only if they are significantly positive. Hsieh estimates this “instant history” bias inflates returns by about 1.4% per year.

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Our approach in this exercise is characterized by several key characteristics.  These include the selection of benchmarks and the method used to measure extreme volatility.  We also identified ways how to improve the methodology in future studies.

Benchmark Selection

Benchmark selection is an important aspect to this study, because the performance of a fund is measured relative to its benchmark.  Ideally, we would like to measure the fund's performance against a benchmark that intuitively and economically falls in a similar group of financial products.  Since our study deals with different groups of funds, we had to work with more than one benchmark.  After a careful evaluation, we matched our funds with the following benchmarks:
 

Fund	Benchmark
Long-Short	S&P500
Managed Futures	Goldman Sachs Commodities Index
Global Macro	MSCI World
Equity Market Neutral	S&P500
Fixed Income Arbitrage	Lehman Brothers Fixed Income
Event Driven	S&P 500
Emerging Markets	MSCI Emerging Markets Fund
Dedicated Short Bias	S&P 500
Convertible Arbitrage	Merrill Lynch Convertible Securities

Measuring extreme point volatility

The objective of our study is to capture the volatility of hedge funds in extreme market movements.  We could then compare this extreme volatility to average fund volatility, and evaluate whether funds are subject to increased risk exposure in periods of increased market uncertainty (both negative and positive).  The measure of volatility we use is hedge fund beta, as well as the slope of a line plotted

In order to achieve our objective, we isolated benchmark returns in four separate areas, and than studied hedge fund performance against benchmark performance for each of the areas.  The areas are:

1. Extremely negative returns (negative returns that fall one standard deviation or more away from the average benchmark return)
2. Negative returns (all returns below the average return)
3. All positive returns (all returns above the average return)
4. Extremely positive returns (positive returns that fall one standard deviation or more away from the average benchmark return)

We separated benchmark returns The following chart identifies the four groups of returns we focused on:

Once we isolated all of the months falling in a particular group of returns, we were able to relate them to respective hedge fund returns.  We calculated the dependency of the sub-set of data (influence of the benchmark sub-set on the fund sub-set) by evaluating the beta as well as the slope of the plot of respective returns.

Ways to Improve Methodology

Although our calculations gave us a good approximation of hedge fund volatility relative to the benchmark, we have identified ways to improve our methodology. In particular, two important aspects have to be taken into account while evaluating the results.

1. Sub-set bias
Betas in our analysis have been derived separately for each sub-sample.  Each sub-set was treated as a separate entity and the reference point of average return has been ignored.  A way to include the relative reference point would be to calculate correlation for beta by use the average return of the whole population, rather than using the average return of the sub-set only.

2. Inclusion of the risk free rate in slope calculation
While the slope of plotted returns in each sub-set of data is a good approximate of how returns interact with each other, these calculations ignore the impact of the risk-free rate on the relative market return and on the calculation of the real beta.  A better way to analyze our sub-sets would be to include monthly values for the risk free rate and then use the CAPM formula to derive hedge fund beta.

3. Funds-of-Hedge funds
One methodology to reduce the survivorship bias is to use a Fund-of-Hedge funds as a benchmark.  This method looks at the actual investment results of hedge fund investors themselves. The results will take into account those funds that have gone out of business.

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Using the methodology described above we came up with the betas for the hedge funds as functions of the magnitude of the benchmark returns. These results are showing the strength of the relationship between the hedge fund returns and the returns of the benchmark for each of the hedge fund type present in the Tremont index. The chart below shows the betas dynamics when the benchmark return is within one standard deviation from the mean and outside of the range.

The table lists the results for betas:
 

Fund Benchmark	Raw Return	Return Upper 50%	Return Lower 50%	ReturnHighest 15%	Return Lowest 15%
LS S&P500	0.55	0.20	0.61	(1.82)	0.72
Managed Futures GSCI	0.12	0.27	(0.06)	(0.10)	0.20
Global Macro MSCI World	0.44	0.00	0.48	(1.02)	0.26
Equity Market Neutral S&P500	0.40	(0.05)	0.49	(1.44)	0.42
Fixed Income Arbitrage LB FI	0.04	(0.24)	0.30	0.10	0.64
Event Driven S&P 500	0.31	0.03	0.54	(0.08)	0.95
Emerging Markets MSCI EMF	0.75	0.75	0.80	0.66	0.89
Dedicated Short Bias S&P 500	(1.13)	(0.94)	(1.40)	(0.75)	(1.74)
Convertible Arbitrage M.LYNCH CONVERT. SEC.	(0.01)	(0.00)	(0.06)	0.14	(0.12)

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We understand that the analysis was carried out based on “imperfect” data. The monthly return numbers for the Tremont hedge fund indices are only available starting 1994, the data is plagued with a number of biases that we discussed already and there were some approximations used in calculations and analysis addressed in the methodology part. However, even this rough analysis enables us to draw three important conclusions.

1. The hypothesis that hedge fund excess returns are uncorrelated with the benchmark excess return (beta is zero) is not true. It was widely believed that just because the hedge funds are basically constructing their portfolios by establishing short and long positions, they have betas close to zero. This argument might hold when we are talking about certain periods of time (which do not necessarily have to be short, rather, may be on the scale of few years) characterized by small market turbulence or prolonged bull makes. However, it is incorrect to assume that hedge funds are 100% immune from any kind of market volatility. Instead, we proved that for some types of hedge funds, beta is never small. For example, the Emerging Markets Tremont Index has beta from the (0.6 , 0.9) range and the Dedicated Short Biased Index has negative beta from (-1.75 , -0.75) interval. It is clear that during the periods with some modest positive benchmark returns the hedge fund betas are close to zero the most. The good news is that there are some funds (like Managed Futures or Convertible Arbitrage) whose beta stayed in (-0.25 , 0.25) range throughout the period. However, since we are drawing the conclusions from a small sample we can refute the hypothesis that high betas do not exist if we just observe them, but we cannot argue that high betas do not exist just because we did not see that in the sample in question.

2. Just saying that the hedge fund’s beta is not zero is not accurate either. Beta changes depending on market conditions, thus making the relationship between the hedge fund return and the return on the benchmark more complex (not linear). We can see from the analysis that we made that the of funds’ betas are growing large at the times of extreme negative market returns. And this is exactly the times when investors would seek shelters in low-market-correlation instruments! Thus, the hedge fund return could be very stable when the markets are either stable too or have some modest “positive” volatility. However, when the markets crash, the majority of hedge funds’ returns start significantly correlating with the benchmark. Another bad news is that this spike in positive correlation does not hold with the extreme upward swing in the benchmark.

3. Hedge funds do not always provide the benchmark-like returns with almost zero volatility. First, we have proved that the volatility of hedge fund returns is not uniformly distributed over time. It is highly concentrated around turmoil times, thus we can say that hedge fun redistributes the volatility of the benchmark without illuminating it. Second, there are other non-market “hidden” risks associated with the hedge fund that are not taken into account in this research. These risks might be rewarded by the excess returns produced by the hedge funds.

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"A Primer on Hedge Funds," by William Fung, Principal at Paradigm Financial Products, and David A. Hsieh, Professor of Finance at Fuqua. August 1999.
"Performance Characteristics of Hedge Funds and Commodity Funds: Natural versus Spurious Biases," by Fung and Hsieh. May 2000.
"Benchmarks of Hedge Fund Performance: Information Content and Measurement Biases," by Fung and Hsieh. June 2000.

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