Hedge funds


Hedge funds have gained a lot of popularity in the last decade and are one of the fastest growing industries. The main aim of most hedge funds is to reduce volatility and risk. It also attempts to preserve capital and deliver positive returns under all market conditions. Not all hedge funds are same therefore it is important to know the difference between them. It differs in terms of its risks,investment returns and volatility among the different hedge fund strategies. The strategies which are correlated to equity markets deliver consistent returns and have low risk while the ones that are not will be more volatile. Main objective of hedge funds is to provide consistency in its returns for investor, lower portfolio volatility and preserve their capital investments, which is the reason why investors such as pension funds, insurance companies, institutional investors and high net worth individuals and families invest in hedge funds.

This paper reviews various issues relating to the investment in hedge funds, which have become popular with high net-worth individuals and institutional investors, as well as discuss their empirical risk and return profiles. The concerns regarding the empirical measurements are highlighted, and meaningful analytical methods are proposed to provide greater risk transparency in performance reporting. It also discusses the development of the hedge fund industry in Asia.

Asian hedge funds have grown vastly in past few years. It is said to have grown nearly six times as many funds while managing ten times are much in assets since 2000 according to Eurekahedge. The industry is estimated to consist over 1100 funds, and managing roughly $175 billion in assets (www.terrapinn.com). International managers are starting up their own Asia-focused funds too. Allocators are increasingly eyeing investment opportunities in Asia. Funds with a global mandate are increasing their allocation to Asia.

The paper presents an overview of hedge funds, describing their development and characteristics. It also discusses the various issues related to the measurement of hedge fund performance, as well as examined alternative performance measures. This thesis ends with some remarks on the development of the hedge fund industry in Asia.


1.1 What Are Hedge Funds?

There has several definition of hedge funds throughout the history. There isn't one particular sentence that defines what hedge funds really means. However, according to Chicago Board Options Exchange, hedge funds can be defined as: “A conservative strategy used to limit investment loss by effecting a transaction that offsets an existing position.”

Alfred Winslow Jones was the first person to create hedge fund structure more than 50 years ago. The fund established had following feature:

* He created “hedges” by investing in securities that was said to be undervalued and funded these positions by taking short positions in overvalued securities hence creating “market-neutral” position.

* He designed an incentive fee compensation arrangement for fund mangers. They were paid a percentage of profit from the clients capital assets; and

* He so invested his own investment capital in the fund, to make sure that his capital and that of his investors were coordinated and in line so that it is not just an individual investment but a partnership

Almost all modern hedge funds have above listed features in them, and are set up as limited partnerships with a lucrative incentive-fee structure. In most hedge funds, managers also have a significant portion of their own capital invested in the partnerships. The term “hedge fund” has been generalized to describe investment strategies that range from the original “market-neutral” style of Jones to many other strategies and opportunistic situations, including global/macro investing.

1.2 Catagories of Hedge Funds

There is a large variety of hedge fund investing strategies present today and therefore no standard way to classify hedge funds separately. Many data vendors and fund advisors set up their own major hedge fund styles according to their popularity. Under the classification by Credit Suisse, the categories of hedge funds with 10 differentiated styles and a fund-of-funds category:

(a) Event driven funds are the funds that take positions on corporate events when companies are undergoing re-structuring or mergers. For example, fund managers would purchase bank debt or high yield corporate bonds of companies undergoing the re-organization which is often referred to as 'distressed securities'. Another event-driven strategy is merger arbitrage where the funds seize the opportunity to invest just after a takeover has been announced. They purchase the shares of the target companies and then short these shares of the acquiring companies.

(b) Global funds are categories of funds that invest in non-US stocks and bonds with no specific strategy reference. This fund has the largest number of hedge funds and it includes funds that specialize on the emerging markets.

(c) Global/Macro funds are the funds that rely on macroeconomic analysis and invest in long and short position in order to capitalise on major risk factors and unforeseen markets such as currencies, interest rates, stock indices and commodities.

(d) Market neutral funds refer to hedge fund strategy that involves utilizing strategies such as long-short equity, stock index arbitrage, convertible bond arbitrage and fixed income arbitrage. Long-short equity funds use the strategy of Jones by taking long positions in selective stocks and going short on other stocks to limit their exposure to the stock market. Stock index arbitrage funds trade on the spread between index futures contracts and the underlying basket of equities.

(e)Dedicated Short Biasfunds are strategies that take more short positions than long positions and earn returns by maintaining net short exposure in long and short equities. To affect the short sale, the manager borrows the stock from a counter-party and sells it in the market. Short positions are sometimes implemented by selling forward. Risk management consists of offsetting long positions and stop-loss strategies.

(f)Convertible bond arbitrage funds typically capitalize on the embedded option in these bonds by purchasing them and shorting the equities.

(g)Fixed income arbitrage is a strategy that bets on the convergence of prices of bonds from the same issuer but with different maturities over time. This is the second largest grouping of hedge funds after the Global category.

(h) Short/long fund-, shorts focus on engineering short positions in stocks with or without matching long positions. They play on markets that have raised too fast and on mean reversion strategies. Long funds take long equity positions with leverage. Emerging market funds that do not have short-selling opportunities also fall under this category.

(i)Emerging Marketsfunds invest in currencies, debt instruments, equities and other instruments of countries with “emerging” or developing markets (typically measured by GDP per capita). Such countries are considered to be in a transitional phase between developing and developed status. Examples of emerging markets include China, India, Latin America, much of Southeast Asia, parts of Eastern Europe, and parts of Africa. There are a number of sub-sectors, including arbitrage, credit and event driven, fixed income bias, and equity bias.

(j) Multi Strategy fund refers to a combination of even driven stratgegies, which invest in illiquid stocks and that are raising private equity and high yield investment.

(j) Fund of funds refer to funds that invests in a pool of hedge funds. They specialize in identifying fund managers with good performance and rely on their good industry relationships to gain entry into hedge funds with good track records.

1.3 Research Question

By using quantitative study, I will try to answer the following question:

* What are the issues relating the investment in hedge funds in terms of risk, return and performance measurement?

To answer this question I will be investigating the reasons why investors invest in hedge fund by looking at the annualised return of Credit Suisse /Tremont hedge funds within a specific period of time and compare its performance against other market index like S&P 500 .I would also be investigating risk and various measures of performance associated with investing in hedge fund.

Although hedge funds are popular in terms of an investment vehicle, there are various issues. The issues related are its cost/ management fee structures, collection of data, survivorship bias and selection bias. Various performance measure techniques are available for hedge funds too. I will be looking at some of the performance measurement approaches.

1.4 Purpose

There are several purposes for this paper. First is to explore why hedge fund are considered as an investment vehicle with a short description of different characteristics and styles of hedge funds. Second is to examine why hedge funds are attractive for investors and fund managers by presenting different theories where risk and returns of hedge funds are investigated in order to evaluate the performance measures. Third purpose is to investigate the issues related to the investment in hedge funds where several sets of issues are evaluated and various performance measures are identified. Also this paper ends with a brief overview of Asian hedge funds, their recent development into the hedge funds worlds and its characteristics.

1.5 Methodology and Limitation

This dissertation is based on desk research. Most of the data and information used was obtained from published documents, journal, articles, newspapers and other electronic sources. When conducting a research two methods can be used. They are quantitative and qualitative methods. Qualitative research involves collection of variety of empirical materials, case study, interviews, surveys and observation while quantitative research uses statistical measures and numbers. They “seek explanations and predictions that will generalise to other persons and places” (Thomas, 2003: pp 2). Due to the absence of interviews and surveys and because it uses statistical and mathematical measures, this study can be categorised as quantitative rather than qualitative.

One of the main aims of this paper is to investigate hedge funds as an investment tool and using statistical and mathematical measures for performance measures, risk and returns, which is why it can be classified as positivistic. Since the majority use of mathematical and hard data, it can easily be described as quantitative and during the collection of data it has been made sure that it is accurate, which proves the objective nature if the research.

“The positivistic approach is mainly based in quantitative data and observation with the purpose of testing theories and it does not leave much room for the author's own interpretation and opinions” (Sundqvist 2009:pp 14).

This paper has also the characteristics of deductive research approach since the data analysis is mainly based on theories which already exist and it merely adds knowledge to theories about using hedge funds as alternative investment.

One of the most important aspects while conducting a study is to see that it does not span over large research fields (Sundqvist 2009: pp5). This research paper is also limited in many ways and the biggest limitation is the unavailability and accuracy of data. Detailed information is available but however the data can be very expensive and may not be entirely relevant to my area of interest. They might be useful to give a detailed insight into the topic but the practicality of the cost will not allow for this. Detailed research is also very time consuming as the area of hedge fund is quite vast and there is a possibility that such findings will not cover every aspect of my intended topic. The issues on the accuracy of data has been discussed in Chapter 2 i.e. data biases. Empirical studies have also been conducted regarding the quality of data from different database vendors. However, this issue does not invalidate the findings of this paper.

1.6 Structure


2.1 Introduction

There is no one particular definition of hedge fund as mentioned earlier. According to the Investment Company Act 1940 of the US, hedge funds were defined by their low degree of regulatory controls. In comparison to mutual funds, hedge funds were seen to have higher level of risk. This led to a 100-investor limit as well as wealth requirement of the investors. Fung and Hsieh (1999) claim that another reason for 100-investor limit is the use of leverage and short selling in hedge funds. The limit restrictions were later abandoned and wealth requirement lowered.

Many definitions of hedge funds have been cited-most of them mainly based on its characteristics. Some of them are:

“Investment companies that by their charter can buy on margin, sell short, hold warrants, convertible securities and commodities and otherwise engage in aggressive trading tactics in order to profit from forcasting market swings.”- Polhman, Ang and Hollinger (1978)

“A mutual fund that employs leverage and uses various techniques of hedging”- Soros (1987)

“hedge funds are vehicles that allow private investors to pool assets to be invested by a fund manager. Unlike mutual funds, hedge funds are commonly structured as private partnerships and thus subject to only minimal SEC regulation. Moreover, because hedge funds are only lightly regulated their managers can pursue investment strategies involving, for example, heavy use if derivatives, short sales and leverage.”- Bodie, Kane and Marcus (2008).

Murguia and Umemoto (2004) claims that the reason why there is no proper definition of hegdge funds is because they are not classified by the different asset classes but by the type of strategies employed by the fund mangers is what classifies them. Such strategies range from very aggressive to conservative, which is the reason why there is no clear definition.

2.2 Risk, Return and Performance Measurement

Several studies have been carried out about hedge funds performance and risk issues. Fung and Hsieh (1997a) extend Sharpe (1992) style analysis and conclude that there are more diversified hedge fund strategies and suggested that hedge fund strategies are more dynamic. The literatures also conclude that option-based factors can enhance the power of explaining hedge fund returns. Brown, Goetzmann and Ibbotson (1999) examine the performance of offshore hedge funds and attribute fund performance to style effects rather than managerial skills.

Brown, Goetzmann and Liang (2003) found, in a study using the TASS database, that fund of hedge funds reduce by a third the standard deviation of monthly hedge fund returns, as well as significantly reduce the value at risk of hedge fund investment. Hence, fund of hedge funds can also provide significant diversification potential. A well-diversified fund of hedge fund manager can therefore take advantage of market-specific risks while maintaining low correlations to stock, bond, and currency markets. As a result of which the fund of hedge fund manager can provide superior returns and generate alpha which reflects managerial skills. More generally, since fund of hedge funds deliver more consistent returns with lower volatility than individual hedge funds, they are considered to be ideal for diversifying traditional portfolios.

Koh, Koh, Lee and Phoon(2004) state that traditional asset allocation optimizes the use of equities, bonds, real estate and private equity to invest in a portfolio that maximizes returns and minimizes the portfolio risk. Thus, hedge funds become vital in enhancing returns in an investment portfolio.

Following the growth in hedge fund industry, fund-of-hedge funds (FOF) have become more and more popular. Liang (2003) states that FOF mixes various strategies and asset classes together and creates more stable long-term investment returns than any of the individual funds. It invests in underlying hedge funds and diversifies the fund specific risks and relieves burdens on investor to select and monitor managers, and providing asset allocation in dynamic market environments. Fund-of-funds require less initial investment as compare to hedge funds and therefore are more affordable for small investors. To participate in the investment, small investors may be willing to pay extra fees as it might be the only way for them.

Previous studies in this area by Brown, Goetzmann and Liang (2002) conclude that combining hedge funds with fund-of-funds not only causes the double counting but also hides the difference in fee structures between hedge funds and fund-of-funds. Liang (2003) state that a hedge funds charges a management fee and incentive fee while a fund-of-funds not only charges these fees at a fund-of-fund level but also passes hedge fund level fees in the form of after fee returns to the fund-of-fund investors whether or not the fund-of-funds make a profit.

Brown, Goetzmann and Liang (2002) examine this issue and propose an alternative fee which provide a better incentive for fund-of-fund managers and reduce the cost for investors under the current fee structure, which is that the fund-of-fund managers absorb the underlying hedge fund fees and establish their own incentive fees at the fund-of-fund level. Liang (2003) conclude that because of the above issues fund-of-funds need to be separated from hedge funds in academic studies and address the difference in performance, risk and fee structures.

However, the FOF mangers can add value to the portfolio through selection, construction and continuous monitoring of the portfolio. They provide professional services and have access to the information that are expensive and difficult to obtain otherwise. The FOF mangers quite often use different investment strategies and styles through a diversified portfolio of individual fund managers. Considering these advantages for an investor, investing in fund of hedge funds is not cheap. The cost can be as high as the cost of buying a building, according to Koh, Koh, Lee and Phoon (2004). This structure allows for more diversified portfolio and much reduced risk at the fund level which comes at a price. More diversified the portfolio is it is more likely that it will incur more incentive fees.

Ackermann et al. (1999) and Liang (1999) who compare the performance of hedge funds to mutual funds and several indices find that hedge funds constantly obtain better performance than mutual funds, although lower than the market indices considered. They also indicate that the returns in hedge funds are more volatile than both the returns of mutual funds and those of market indices. Ackermann and Ravenscraft (1998) emphasize that the stronger legal limitations for mutual funds than for hedge funds hinder their performance. According to Brown, Goetzmann, Hiraki, Otsuki and Shiraishi (2001) and Brown, Goetzmann and Park (2001), hedge funds showing good performance in the first part of the year reduce the volatility of their portfolio in the second half of the year.

Fung and Hsieh (1997) and Schneeweis and Spurgin (1997) prove that the insertion of hedge funds in a portfolio can significantly improve its risk-return profile, thanks to their weak correlation with other financial securities. This low correlation is also emphasized by Liang (1999) and Agarwal and Naik (2000). Amin and Kat (2001) find that stand-alone investment hedge funds do not offer a superior risk-return profile, but that a great majority of funds classified as inefficient on a stand-alone basis are able to produce an efficient payoff profile when mixed with the S&P500. They obtain the best results when 10–20% of the portfolio value is invested in hedge funds. Taking all these results into account, hedge funds seem a good investment tool.

There are many persuasive reasons why investing in hedge funds are considered as “alternative investments”. There is much probability that the incentive fee for fund mangers can be so large that it absorbs all the fund return. Also some uninformed investors may be misled about the risks and returns on hedge funds as it relies heavily on statistical compilation from the database vendors which is filled with data biases such as survivorship bias and selection bias. These biases arises a concern whether the hedge funds indices are a good measure of performance in the hedge funds industry.

2.2.1 Survivorship Bias

Fung and Hsieh (2001a) found that estimates of survivorship biases differed across two commonly used databases, HFR and TASS. The survivorship bias was much higher in TASS than that in HFR. They estimated that survivorship bias would over-report hedge fund mean returns by about 1.5% to 3% per annum.

Brooks and Kat (2001) stated that around 30% of newly established funds do not survive the first three years, primarily due to poor performance. Thus, not including defunct funds (no longer report its returns) is likely to lead to over-estimation of the returns and profile of hedge fund industry. It is an important bias for the hedge fund because according to Malkiel and Saha (2005) the returns earned by currently existing hedge funds are reflected by the databases available during this time. The returns from the hedge funds that existed at some point in the past but nevertheless are non existence at present (i.e. dead funds) or the defunct funds are not included. Malkie and Saha (2005) also conclude that while estimating the survivorship bias by fund categories, a substantial difference between live and defunct funds in all categories was found including the substantial survivorship in fund of fund category, which contradicts the claim of Laam (2003) that survivorship bias in fund of fund category is relatively small.

Because of the short average life of hedge funds survivor bias is a substantial topic of discussion in hedge fund literature. Malkiel and Saha (2005) report that less than 25 percent of funds operating in 1996 were still reporting to databases in 2004. Gregoriou, Hübner, Papageorgiou, and Rouah (2005) discuss the mortality of commodity trading advisers and report that the median fund survived only 4.4 years. A survivorship bias refers to the fact that the returns on non surviving funds are not reported. Generally, these non-surviving fund's returns are poor and when their returns and the returns earned by their investors are unrecorded, this can exaggerate the returns earned by an average investor. In the literature, authors report a wide range of estimates of survivor bias; the returns to live funds exceed the returns to the combination of live and defunct funds by between 0.6 percent and 3.6 percent per year. By not including failed funds in return calculations, hedge fund returns will be overstated by this amount.

2.2.2 Selection Bias

By selection bias, it means that the fund mangers have the power to select and report only those funds which have good performance. Before the information on the funds can be released to third party, the hedge fund consultant needs the consent of the fund manager, which creates the possibility of selection bias. It is a belief that the hedge fund managers only want to include the funds with good performance, which means that the returns on the database are higher than the returns on all existing funds. Therefore, the database vendors may not have the true picture on the performance of the all the available hedge funds.

Selection bias in hedge funds are said to have no estimates of its size, however Fung and Hsieh (1997a) found evidence of it being limited. Fung and Hsieh (2000) also state that managers with superior performance and who are not interested in attracting more capital opt out to participate in databases. They also gave example of George Soros's Quantum Fund which has been closed since 1992 despite of its legendary performance. They also later conclude that to study the selection bias in hedge funds accurately, the input from the investors that do not disclose the performance to vendors are needed.

Park (1995) studies a subset of selection bias called “instant history bias” which arises when a new fund is added into a database and its historical performance are “backfilled” by the database vendors. Capocci and Hubner (2004) state this corresponds to the demand of fund managers who market themselves if they have good track record. Fung and Hsieh (2000) found 1.4% difference per year in returns from 1994-1998 using a 12 months incubation period (number of days from inception to entry into database).

Following Park (1995), Brown et al. (1999), and Fung and Hsieh (2000), Capocci and Hubner (2004) estimate the bias in database in two steps. One was called the observable one and the other adjusted observable one (returns after deleting first 12, 24, 36 and 60 months). For the period 1984 -2000, they found observable monthly return averaged 1.49% and the adjustable observable one was approximately 0.9% per year which was much lower that Fung and Hseih (2000) of 1.4%. While estimating for the period of 1994-2000, the found the bias was 1.2% much closer to Fung and Hseih (2000). Capocci and Hubner (2004) conclude the difference can be explained by the difference in time period covered and the database used and that longer the estimation period bigger is the bias.

Amnec and Goltz (2008) claim although there are several problems with hedge fund indices they also find several solutions to construct an investable hedge funds index. The solution includes:

* Transparency of the method

* A methodology that guarantees a high degree of representativeness as well as precise classification of components

* Minimum liquidity of the indices

* Prohibition of practices such as backfilling

* Information on risk factor exposure

2.3 Performance Measurements

One of the main aims of this paper is to investigate the performance measures of hedge funds. One of the most well known performance measures which I have chosen is the Sharpe Ratio in order to make comparisons easy to interpret. Other alternative performance measures are described in section…..

2.3.1 Sharpe Ratio

Sharpe ratio is a measure of risk-adjusted performance. It is also referred to as “risk-to-volatility” ratio. The measure was first presented by Sharpe (1966) in his article “Mutual Fund Performance” where he evaluated the performance of 34 mutual funds. He defines it as “….simple yet theoretically meaningful measures that considers both average return and risk”.

In analysis of hedge funds, Sharpe ratio is often used as performance measure and comparisons are made between the Sharpe ratio of other funds or indices.

The Sharpe ratio of a portfolio can be calculated as: (Bodie, Kane and Marcus,2008)

Sp = E (r p) – r f_

δ p


E (r p) = return on the portfolio

r f = the risk-free rate

δ p = risk of the portfolio measure as standard deviation

According to the above formula, the portfolio that has the highest Sharpe ratio has the most favourable relationship between risk and return, therefore, is mostly preferred by the investors. The Sharpe ratio is easily computed and interpreted and is frequently used in practice and theoretical research (Modigliani and Modigliani, 1997). However, Amin and Kat (2003) state in the performance measurement of hedge funds, Sharpe ratio is not free of heavy criticisms because of the non normal distribution of hedge fund returns.

The Sharpe ratio is appropriate when the portfolio under consideration represents the entire risky investment. Hence, if the Sharpe ratio is calculated for individual hedge fund returns, it is implicitly assumed that all investor's wealth is allocated to the hedge fund. To measure the hedge fund performance in a portfolio context, that is, only a small portion of investor's wealth is allocated to the hedge fund, the Sharpe ratio must be calculated on the basis of the portfolio returns including the hedge fund.(Eling and Schuhmacher(2007),p6)

2.3.2 Skewness and Kurtosis

“Skewness” refers to asymmetry of the distribution. A distribution with an asymmetric tail extending out to the right is referred to as “positively skewed” while a distribution with an asymmetric tail extending out to the left is referred to as “negatively skewed” (Wuensch, 2007).

Positive skewness represent the standard deviation will overestimate the risk because positive deviation from expected return also increases the estimation of volatility. Similarly, negative skewness represents the standard deviation underestimating the risk.

“Kurtosis” measures the size of the tails of the return distribution. High kurtosis means the distribution has fat tail. The normal distribution will have skewness of 0 and kurtosis of 3 (Malkiel and Saha, 2005).

Positive kurtosis also known as leptokurtic which has an effect that causes underestimation of extreme events. Mesokurtic is a normal kurtosis while platykurtic is a negative kurtosis.

Skewness and Kurtosis are crucial in the understanding of hedge funds performance measures and how it is applicable to hedge funds. Previous studies show that the traditional asset returns can be characterised by their mean and standard deviation as they are close to normal.

But in contrast to normal asset classes, according to Amin and Kat (2003), hedge funds have abnormal distribution i.e. negative skewness and high kurtosis. Because of which, the risk-averse investors might be worried about their investment as mean-variance fails to consider these high movements in return distribution. Mean- variance analysis is only appropriate when the distribution of return is normal; therefore, the reliability of the analysis depends on the degree of non-normality. Despite the fact that hedge fund returns are not generally normally distributed, many investors still use mean-variance analysis.

According to Fung and Hsieh (1999a), “... when returns are not normally distributed (as it is the case for hedge funds), the first two moments (i.e. mean and standard deviation) are not sufficient to give an accurate probability.” They found that hedge fund returns are fat-tailed.

Brooks and Kat (2001) found that “….while hedge funds may offer relatively high means and low variances, such funds give investors third and fourth moment attributes that are exactly the opposite to those that are desirable. Investors obtained a better mean and a lower variance in return for more negative skewness and higher kurtosis.”

Koh et al. (2004) conclude the dynamic trading strategies of hedge funds render traditional mean-variance measures meaningless. While some hedge funds may have low standard deviations, this does not mean they are relatively “riskless”. In fact, they harbour skewness and kurtosis, which makes them “risky”.

2.3.3 Correlations of Returns

Fung and Hsieh (1997), Liang (1999) and Amin and Kat (2001) state there is a weak or low correlation between hedge funds and other securities. Therefore, by adding hedge funds to a portfolio, the investor can significantly improve risk-return trade-off. Having an asset with a low correlation allows the investor to diversify the risks. However, Koh, et al. (2004) argues correlation-based diversification may not be valid in the case of hedge funds.

Fung and Hsieh (2001) stated that “… Risk management in the presence of dynamic trading strategies is also more complex.” There is a lot of freedom given to hedge fund managers to generate returns that are not correlated with those of other asset classes. But, this freedom comes at a price. Dynamic trading strategies influence hedge funds to extreme events. As a result, correlations may come at a cost. They cautioned that “periodically the portfolio can become overly concentrated in a small number of markets” and market exposures converge. They later conclude this would lead to an “implosion” due to diversification.

Lavino (2000, p177) argued that many hedge funds are not consistently and continuously negatively or poorly correlated with other asset classes over time and that it also may not have meaningful standard deviations. In fact, many hedge funds have distributions with fat-tails, and so normality assumptions on the distribution of hedge fund returns are generally not correct. This means it is not appropriate that the use of correlation as a measure to execute portfolio diversification.

Lo (2001) armoured this view. He discussed one of the main aims of the investors is to diversify their returns, as hedge fund returns seem uncorrelated with market indexes such as the S&P 500. However, uncorrelated events can become synchronized in a crisis, with correlation changing from 0 to 1 overnight. These situations are examples of “phaselocking” behaviour encountered in physical and natural science.

2.4 Other Performance Measures

The mere use of means and standard deviations for the measurement of risks and returns for hedge funds are found to be quite inadequate by the above discussions. Skewness and kurtosis statistics would help but when hedge funds are added to a portfolio of other assets simple correlations measures are not sufficient to diversity portfolio risks as it has been mentioned in above discussions.

For above mentioned reasons, Sortino ratio is preferred, which was first presented by Sortino and Price (1994), and has much in common with previously discussed Sharpe ratio. This ratio differentiates between deviations on the upside and on the downside and they are more consistent with an investor's concern over their risk on losses in their investment. However, they have one major difference- it uses “downside deviation” rather than using standard deviation as the denominator. Downside deviation (DD) is value that represents potential loss that may arise from the risk as measured against Minimum Acceptable Return (MAR). The MAR is usually risk free rate, zero or user defined. The numerator of Sortino ratio is the difference between the return on the portfolio(R) and the MAR (Koh et al. (2004),p 15). The ratio can be calculated as:

Sortino ratio = (R – MAR)/ DD

Sortino and Price (1994) state:

“Performance is not just a matter of who got the highest return, or who took the least risk, but a question of who provides the best risk-adjusted return.”

Lavino (1999) defined another measure to explain how high skewness of a hedge fund returns may be connected to hedge fund manager's selection of high reward and low variance opportunities. He captured this as follows:

d- Ratio = Abs (d/U)


d = number of returns less than zero times their value

U = number of returns greater than zero times their value

Abs = absolute value

The d-Ratio compares the value and frequency of a manager's winners to losers to capture the skewness in returns. This may be used as a proxy for fund's risk with d = 0 representing distribution with no downside and d = infinity representing one where the manager does not make any positive return (Koh et al. (2004)).

In order to analyse performance of hedge fund managers, we also need to analyse the manager's skill. Good performance in hedge funds is viewed as transitory. One of the ways to examine this is to see if it is mean- reverting i.e. whether or not the performance will reverse and converge into some kind of long term value. This can be done with Hurst Ratio which measures the persistence of individual returns directly without a comparison to a median. An advantage of Hurst ratio is that its efficiency is not based on the assumption on the return distribution.


Hurst Ratio is defined as follows: (Koh et al. (2004))

Hurst Ratio = log M / (log N - log a)


M(t) = Max(t) - Min(t))/S(t)

N = length of shorter sub-periods into which a manager's return record

has been subdivided

t = number of sub-periods into which a manager's return record has

been sub divided

S(t) = standard deviation of data over sub-period t

a = constant term that is negligible if track record is five years or less

A Hurst Ratio comprised between 0 and 0.5 indicates reverse persistence. It means that a manager's return tend to fluctuate randomly but converge to a stable value in long term. A ratio of 0.5 indicates random performance i.e. returns in one period are not affected by the returns in another period. These hedge funds are more riskier as short term gains in one period may be followed by losses in another period. A ratio comprised between 0.5 and 1 indicates positive persistence i.e returns are persistent.

The Sortino, d and Hurst Ratio gives additional insight to the performance and risks in investing in hedge funds, empirical research shows that further work is needed before these methods can be used. The next section examines some practical issues relating performance measurement.

2.5 Practical Issues

As mentioned earlier in this paper that hedge funds has various data issues as many information are not easily available. Even if one possesses a reliable data it is very difficult to statistically compute measure of risk adjusted return. These issues make it tricky to have a simple evaluation to fully measure risk and return.

Koh, Lee and Phoon (2002) have identified 6 types of practical issues that increase the “riskiness” of hedge funds: style purity, consistency, fund size, use of leverage, liquidity and asset concentration. They also note “some of these problems are closely linked to one another and create extraneous risks, which may not be correctly priced by the usual risk adjusted return measures.”

Hedge funds are thought to have pure and consistent style, which according to Koh et al. (2002) is not true. They do not always function exactly as their classifications specify and therefore it is not viable to classify hedge funds precisely.

Style purity of hedge fund is less consistent compared to mutual funds. Fund and Hsieh (2001b) suggest using factor analysis to differentiate the core “factors” that drive the return for funds which may help distinguish one fund from another. This in turn may enable an investor to detect style purity and consistency.

Till (2001) suggest with a number of alternative investment strategies “investors earn their returns due to assuming risk positions in a risk-averse financial world, rather than from inefficiencies in the market place”. This implies that returns are made from “risk transfer” rather than managerial abilities. And if this is the case then in order to achieve superior returns, the selection skill to choose appropriate hedge fund style and the manager who can implement style consistency becomes important. This leads to a notion that instead of using measures like variance and skewness, style purity and consistency are important to measure exposure to hedge funds risk.

A hedge fund's size affects its risk and return at a very significant level, and its risk increases proportionately with its asset under management (AUM). The reason behind this is after a hedge fund hits an “optimal size” it becomes very difficult to keep the same strategy or have the “opportunities for execution” i.e. use of leverage (Koh, Lee and Phoon (2002)). They also observe as soon as the target of the fund is reached, the fund managers tend to close the funds for further investments. This behaviour is a clear indication that fund managers understand the trade-offs between size and performance.

One of the main reasons for hedge fund managers to use leverage is to magnify potential returns. According to Weisman and Abernathy (2000) it is important to guard against excessive use of leverage and lack of liquidity. They point out that “if one were to construct a non-diversified, illiquid and/or leveraged portfolio and let it grow over time, it would eventually lead to bankruptcy of the fund, if a misfortune strikes.”

Koh et al. (2002) also identified following to account for various practical risks that was discussed above.

Table 5: Discount to Risk-Adjusted Returns to Account for Various Practical Risk

Source: Koh et al. (2002)

They argue that risk- adjusted return with penalty (as seen in Table 5) is more meaningful to an investor as standard deviations merely fail to alert investors about risks such as liquidity and leverage. According to them, if we were to compare two hedge funds with similar attributes, and want to know which has better risk-adjusted return, the hedge fund with less leverage, is less concentrated, invests in more liquid asset and is more diversified will be preferred than the one which has similar risk adjusted return but has taken more risks to achieve the same result and also because it has more chances of surviving in long term.

The risk- adjusted return has been defined as:

“This measure assumes that all the named variables are observable, measurable and reliable. The benchmark return may be a stock index, a peer measure or the interest rate of the 90-day Treasury bill. The risk measure may be the “tracking error”, “standard deviation”, or some other measure”. (Koh et al.(2002))

2.6 Cost and Fees

Following the growth in hedge fund industry, fund-of-hedge funds (FOF) have become more and more popular. Liang (2003) states that FOF mixes various strategies and asset classes together and creates more stable long-term investment returns than any of the individual funds. It invests in underlying hedge funds and diversifies the fund specific risks and relieves burdens on investor to select and monitor managers, and providing asset allocation in dynamic market environments. Fund-of-funds require less initial investment as compare to hedge funds and therefore are more affordable for small investors. To participate in the investment, small investors may be willing to pay extra fees as it might be the only way for them.

Previous studies in this area by Brown, Goetzmann and Liang (2002) conclude that combining hedge funds with fund-of-funds not only causes the double counting but also hides the difference in fee structures between hedge funds and fund-of-funds. Liang (2003) state that a hedge funds charges a management fee and incentive fee while a fund-of-funds not only charges these fees at a fund-of-fund level but also passes hedge fund level fees in the form of after fee returns to the fund-of-fund investors whether or not the fund-of-funds make a profit.

Brown, Goetzmann and Liang (2002) examine this issue and propose an alternative fee which provide a better incentive for fund-of-fund managers and reduce the cost for investors under the current fee structure, which is that the fund-of-fund managers absorb the underlying hedge fund fees and establish their own incentive fees at the fund-of-fund level. Liang (2003) conclude that because of the above issues fund-of-funds need to be separated from hedge funds in academic studies and address the difference in performance, risk and fee structures.

However, the FOF mangers can add value to the portfolio through selection, construction and continuous monitoring of the portfolio. They provide professional services and have access to the information that are expensive and difficult to obtain otherwise. The FOF mangers quite often use different investment strategies and styles through a diversified portfolio of individual fund managers. Considering these advantages for an investor, investing in fund of hedge funds is not cheap. The cost can be as high as the cost of buying a building, according to Koh, Koh, Lee and Phoon (2004). This structure allows for more diversified portfolio and much reduced risk at the fund level which comes at a price. More diversified the portfolio is it is more likely that it will incur more incentive fees.

2.7 Conclusion

In this chapter, a brief examination of hedge funds and its characteristics has been given. It also investigates downside faced by investors to make decisions based on available data from database vendors. It has also been established that commonly used statistics such as mean, standard deviation and correlation is not meaningful because of non-normality of returns of hedge fund distribution. Alternatively the use of other performance measures such as Sortino, d and Hurst ratio has been suggested. Issues with data collection and data biases such as selection bias and survivorship bias has been examined and has been established that it affects the returns and performance of hedge funds as a whole.

Several studies have been carried out about hedge funds and its performance measures. Modigliani and Modigliani (1997) has concluded that Sharpe ratio is practical for performance measures. Eling and Schuhmacher (2007) mentions it should be measured in a portfolio context rather than the entire investment. Many academic studies have also been based on how hedge funds outperform mutual funds and comparison between these two in terms of risk, return and performance. Fung and Hsieh conclude that hedge fund strategies are more dynamic than that of mutual funds. However, there are not many studies where hedge fund performance has been compared to popular market indices like S&P 500. In Chapter 4, I have further analysed and compared performance statistics from Credit Suisse/Tremont Hedge funds and S&P500, its annualised return and standard deviation.

Although hedge funds are fast becoming popular investment strategies, it has many issues related to its performance measures, risk and return. There are various ways in which its performance can be measures but the data available are full of biases which make it difficult to give a clear and precise overview. Also collection of such data is not an easy task for new investors as it is not easily available to general public. The management fees charged by hedge fund managers are arguable as it charges 10-20% of investment.


3.1 Research Methods

3.1.1 Practical Method (Secondary Data Collection)

In this paper, he main source for collecting and describing several theories and subject are information from books and articles. This type of sourcing data and information is known as secondary data collection. The secondary sources used in this thesis have formed its theoretical foundation. The literature found has been taken from books and articles used from online library at University of Wales. The main search results for articles were found in the databases such as Business Premier (EBSCO), JSTOR an Social Science Research Network (SSRN). By sorting the articles by relevance to the subject matter the next step was to read the abstract and then finalise the articles which were used in this thesis. Besides using database mentioned above, I also used internet search engine such as Google.com in order to understand the scope of the subject.

3.1.2 Quantitative Research Method

In order to increase the reader's understanding, in this section explanation of some research methods used has been described. Two methods can be used while conducting studies in social science: quantitative and qualitative. A scientific approach of quantitative research method has been employed throughout the paper. As explained earlier in chapter 1, quantitative methods are when mathematical and statistical measures are used when conducting the research. It is a synonym for data analysis procedures such as graphs or statistics that generates or uses numerical data (Saunders et al. 2009:pp 151).

In this thesis, most of the analysis is carried out through the use of statistical and numerical data. In order to answer the main research questions, data has been gathered mainly using the report from data based vendors such as Credit Suisse/ Tremont hedge fund. Other vendors such as Hedge Fund Research (HFR),Managed Account Reports (MAR) and Standard and Poors (S&P) has also been mentioned in order to further understand the literature and statistics of hedge funds.

3.2 Resesarch Philosophy

This section examines the research philosophy which is related to the development of the knowledge and the nature of the knowledge (Saunders et al. 2009: pp 107). A scientific method employed throughout the paper is epistemology approach which means the way reality should be studied and can be classified into two forms: positivistic and interpretivist (Saunders 2009: pp 113). Positivistic approach is when a researcher uses resources for the analysis of facts whereas interpretivist philosophy embraces feelings and attitude. As mentioned earlier in chapter 1, as positivistic approach is based on the quantitative data and observation, this paper can therefore be classified as positivistic epistemology rather than interpretivist. In positivistic approach, the observation leads to the production of credible data. For example, by observing the returns and performance of different hedge funds over time, annualised return, standard deviation and risk-adjusted return has been produced.

The paper can also be explained as a deductive approach as existing theories on hedge funds are used to test the topic. Deduction approach emphasised on the scientific principles, moving theory to data and collection of quantitative data (Saunders 2009: pp 127); which is all present in this thesis. The deductive approach is associated with quantitative research approach (Bryman and Bell 2007: pp 14)

Also mentioned in Chapter 1 is the objective nature of the research. Objectivity refers to the fact “…. that there is transparency in the procedures for assigning raw materials to categories so that the analyst's personal biases intrude as little as possible in the process.”(Bryman and Bell 2007:pp 303). As most of the data in the thesis is collected from another source such as previous academic studies and database vendors; it is highly unlikely that there is a presence of personal biases and fabrication of data. The reliability and validity of the research will be discussed further in this section.

Hence, the research method consists of deductive approach combined with positivistic epistemology which proves the statement that this paper uses quantitative method of research and is objective.

3.3 Research Design

As mentioned earlier, the deductive and positivistic nature of the paper, it aims to fulfil few objectives. Firstly the paper has a descriptive nature as it gives an overview of understanding of hedge funds and its different categories. Secondly, it takes a form where the analysis of reasons as to why investors should invest in hedge funds using existing theories. Thirdly, the risk, returns and performance measurement analysis is carried out. This paper can be said to have a normative objective because the analysis is based on the descriptive part and also on the analysis whether or not hedge funds are valuable investment tool for the investors.

To help understand the research strategy used in the thesis Saunder et al (2007) have considered various strategies that can be used in a research. The one that is used in this thesis is archival research which means the use of administrative records and documents as the principal source of data. This strategy can be linked to secondary data analysis because these data were originally collected for different purpose. For example, the purpose of data available from data vendors is to attract new investors but these data have extensively been used for academic studies and researches. One of the biggest constraints of archival research is that they may not contain precise information need for the research, data may be missing or access may be refused for confidentiality reasons (Saunders et al. 2007:pp 150). This is precisely the case in this research because the most of the data available from data vendors are to attract new investors and important information is only available to fund managers. Also a fee needs to be paid in order to access a lot of these data. For this reason, this research has been designed in a way so as to make the most of the data that is available to general public.

3.4 Data and Data Providers

The data has been collected using data from database vendors such as Credit Suisse/ Tremont Hedge fund, which is the index, constructed using the TASS database of more than 2600 hedge funds. The CSFB/Tremont Hedge Fund Index is the only asset-weighted hedge fund benchmark which was designed to establish a standard for tracking and comparing hedge fund performance against other major asset classes, like the S&P 500, on a global basis. Its web site provides interactive tools that allow users to customize their research.(www.hedgeindex.com, 2002) http://www.hedgeindex.com/hedgeindex/en/PressRelease.aspx?DocID=214&cy=USD

S&P 500 ….

Information on hedge funds is available on a voluntary basis. This information is released on a monthly basis to inform existing investors or to attract new ones. Data collectors then in turn make them available to qualifying public. The term qualifying has been used because, in order to get this information, I had to provide them with information so that they decide whether or not I am qualified to receive such information. Databases such as MAR, HFR and TASS is mostly used in academic and commercial studies of hedge funds. As mentioned earlier, in this thesis I have used data collected exclusively from Credit Suisse/Tremont Hedge funds which defines 10 strategies. It provides 2600 individual hedge funds and the indices available are from January 1994 till date. Some of the database and their description is provided in Appendix.

3.5 Limitation

This thesis is limited in number of ways. First of these limitations is that the data availability of hedge funds. The detailed data on hedge funds are only provided to hedge funds managers and investors. In order to get all the data is very costly and the data obtained may not be entirely relevant to the chosen topic.

Secondly, the data available are not free of any biases. As discussed in Chapter 2 (Section 2.2), available data from the databases are full of data biases such as survivorship and selection biases which question the accuracy of the data. This is a result of voluntary nature of participation while performance reporting which then leads to incompleteness of information.

Thirdly, the practical issues also mentioned in Chapter 2 (Section 2.5) make it difficult to have simple evaluation to measure risk and return.


4.1 Investing in Hedge Funds

In 1990 the entire hedge fund industry was estimated at $20 billion. At the end of 2008, global hedge fund industry was estimated to be worth $1 trillion with 8350 active funds. It has gained a lot of popularity in the last decade and is one of the fastest growing industries. While hedge funds are well established in US and Europe, they have also been growing rapidly in Asia.

Mean and Standard Deviation of Returns

Table 1

Jan 2000 – Nov 2009


Mean Return


Standard Deviation (%)

Risk-Adjusted Return

Event driven








Global / Macro




Market neutral








Emerging Market




Convertible Arbitrage




Dedicated Short Bias




Fixed Income Arbitrage




Source: Credit Suisse/ Tremont hedge index


* The mean returns are annually compounded returns over the period 2000 to November 2009,

* The annualized standard deviations were computed from of the standard deviation of monthly returns for each investment style.

* Risk-adjusted returns are obtained by dividing the mean return by the standard deviation.

Table 1 gives statistics about the various categories of hedge funds and past performance. The global/macro hedge funds provided the best mean return over the period studied, while the event-driven funds had the lowest standard deviation of returns. On a risk adjusted basis which is obtained by dividing the mean return by the standard deviation, the category of fund that ranks highest is the global/macro funds followed closely by event-driven funds. Hedge funds are not required to publicly disclose performance and holdings information unlike the registered insurance companies, which might be construed as solicitation materials. This is the reason why which makes it more difficult for investors to evaluate hedge fund managers

Table 2

Credit Suisse/Tremont Hedge Fund Index Performance Statistics (as of 2009)

3 Months 1.44%

6 Months -1.73%

1 Year -15.79%

3 Year Annualized Return -0.44%

5 Year Annualized Return 4.07%

Source: Credit/Suisse/Tremont Hedge fund

Table 3

Standard and Poor's Global 500 Index Performance Statistics (as of 2009)

1 Year 31.69%

3 Year Annualized Return -4.56%

5 Year Annualized Return 3.07%

Source: S&P Indices (www.standardandpoors.com)

Hedge funds have posted attractive returns. A five year annualised return of 4.07% posted by Credit Suisse, higher than the S&P 500 of 3.07% (see table 2 & 3). Hedge funds are seen as natural hedge to control downside risk because they employ investment strategies believed to generate returns that are uncorrelated to traditional asset classes. Hedge funds differ in strategies- a macro fund such as quantum fund generally take a directional view by betting in particular bond market or a currency movement. Other funds specialise in corporate events such as mergers or bankruptcies. They also vary widely in investment strategies and the amount of financial leverage.

In the recent financial crisis, hedge funds have been heavily criticised in terms of their strategies and also for the fact that in 2008, they have had hard time fulfilling their absolute return targets. There have been other criticisms towards hedge fund regarding this particular crisis. Stromqvist (2009) writes that ever since the growth of hedge fund industry there has always been discussions regarding the role of hedge funds in a financial crisis. The main focus of the criticism was on highly leveraged hedge funds and that they may have a large impact on price stability on both currencies and equities.

In an article written in The Times, Dillow (2008) observes that even though average return of hedge funds in 2008 has been poor, “they have not been a serious source of instability in the wider financial system”.

Regardless of the recent financial crisis, hedge funds still generate a growing number of interests all around the world. The information about the hedge funds are of private nature, and therefore it is difficult to obtain information about the operations of individual hedge funds and reliable summary statistics about the industry as a whole.

In 2008, while overall performance in hedge fund industry was negative, not all asset classes performed badly all together at the end of the year. Managed Futures and Dedicated Short mangers posted double digit returns of 18.3% and 14.9% respectively (see table 4).

Table 4

Cumulative Return

Broad Index (19.1%)

Convertible Arbitrage (31.6%)

Dedicated Short 14.9%

Emerging Markets (30.4%)

Equity Market Neutral (40.3%)

Event Driven (17.7%)

Fixed Income Arbitrage (28.8%)

Global Macro (4.6%)

Equity Long Short (19.8%)

Managed Futures 18.3%

Multi-Strategy (23.6%)

*YTD Dec 2008

Source: Credit Suisse/Tremont Hedge Fund Index (January 2009)


It is a common belief that investing in hedge funds can have superior returns. Many success stories have emerged in the past and the most popular of which is the George Soros story. In September of 1992, he risked $10 billion on a singlecurrencyspeculation when he shorted the British pound, which gave him an international fame. He was right, and in a single day he successfully generated a profit of $1 billion – ultimately, it was reported that his profit on the transaction almost reached $2 billion. Therefore, he is famously known as the "the man who broke theBank of England."

The greates investor: George Soros,

http://www.investopedia.com/university/greatest/georgesoros.asp 16-12-09

Traditional asset allocation makes the most of the use of equities, bonds, real estate and private equity to invest in a portfolio that maximizes returns and minimizes the portfolio risk. Therefore, in an investment portfolio hedge funds can play a vital role in maximising returns. Moreover, in a bear market, many investment and fund mangers find it dull to just beat the market index, which may have negative returns. They generally prefer to go short or avoid long positions to have positive returns. Choosing an appropriate hedge fund to invest increases the possibility of obtaining positive “absolute returns”.

It is also generally believed that hedge funds have returns that are generally uncorrelated with the traditional asset classes. In fact, hedge funds may even have a lower risk profile. For example, Morgan Stanley Dean Witter (2000) reported that hedge funds “exhibit a low correlation with traditional asset classes, suggesting that hedge funds should play an important role in strategic asset allocation”.

The answer to the question “Why invest in Hedge funds?” simply is “to make money.” The common analogy in all hedge funds strategies and the underlying rationale for investing in hedge funds is the search for absolute returns. This is sometimes called "alpha". "Alpha" is the extra return a skilled manager can produce over and above the market return (or "beta"). Whereas many conventional fund managers aim simply to outperform their chosen benchmark index, hedge fund managers seek to produce positive gains in all market conditions.


4.2 Why invest in hedge funds?

In an article by David Harper, he mentioned here are two basic reasons forinvestingin a hedge fund:

* to seek higher net returns (net of management and performance fees)

* to seekdiversification.

4.2.1 Potential higher net return

Higher returns in hedge funds are not always guaranteed; therefore the investors tend to look for good hedge fund managers to invest in good strategy and it is reasonable for the investors to expect higher return for the level of risk involved. Although it is arguable that selecting a good manager is the only way to earn attractive returns. Many experts believe that bigger is not always better. In hedge funds, the investment technique and process can not be taught or replicated. It is built around individual manager's skill, knowledge and experience. This is one of the reasons why small funds are sometimes the better funds.

Time is a critical factor while investing in hedge funds. The article by David Harper mentions “…often-cited statistics fromCredit Suisse/TremontIndex in regard to hedge fund performance during the 1990s are revealing”. While looking at a bullish market from January 1994 to September 2000- S&P 500 Index outperformed every major hedge fund by 6% in annualised return. However, some of the strategies performed very differently. For example, dedicated short suffered the most with -0.83% (negative) annualised return. However, in the bearish market, hedge funds can be an attractive asset class compared to mutual funds. Looking at the period since 1994 to 2009, theCredit Suisse/Tremont Hedge Fund Index caught up and beat S&P 500 with net average annual return of 9.32% (see table 7) versus 6.47% for the S&P 500. The variation in the performances between these two periods signifies that the time is a critical factor while considering investments and that hedge funds have higher potential return specially in the bear market.

Table 5: S&P 500 Annualise Return (YTD 2009)

Source: www.standardandpoors.com

4.2.2 Seeking Diversification

One of the main benefits of investing in hedge funds is its diversification benefits. If an uncorrelated asset is added to the portfolio it can reduce the total portfolio risk. Hedge funds are said to be uncorrelated with most market indices as they use derivatives, short sale and non equity investments. Harper also mentions that correlation varies by strategy and somehow remains consistent. He also presents a reasonable hierarchy in his article to prove his statement. However, as mentioned in the literature, using correlation alone as a measure to execute portfolio diversification is not appropriate.

Like any other investments, hedge funds are not free of multiple risks that is involved. Each strategy has its own risk. For example, reduction in stock prices in event-driven and long/short is open to the elements of short-squeeze. Traditionally risk is measure in terms of volatility or annualised standard deviation of returns. Empirical and academic studies show looking at the average; hedge funds are less volatile than the market. Table 6 shows that the volatility of S&P 500 from 1994 to 2009 is 15% where as the hedge fund only show about 8% (Credit Suisse)

Table 7: Credit Suisse/ Tremont Hedge Funds (1994- 2009)

Total Return


Annualised Total Return


Annualised Standard Deviation


Sharp Ratio


Source: www.hedgeindex.com

As mentioned in Chapter 2, several studies prove that hedge fund returns are not normally distributed. Amin and Kat (2003), Fung and Hseih (1999) and Brook and Kat (2001), all mention that hedge fund returns tend to be skewed. To be more specific, they are said to be negatively skewed which means they bear “fat tails”. 2 demonstrates that the hedge fund return have fat tail as the displays the tendency of negative skewness and high kurtosis. The curves have longer left tail indicating negative tail. This can further be examined using data provided by database vendors.

The normal distribution of kurtosis is 0, but table 8 shows the kurtosis of 18 which means it is at its peak. This suggests that it is more variable and wider is shape i.e. the distribution has fat tail. “The skewness is a measure of the asymmetry in the distribution curve with negative skewness indicating that the curve has a longer left (or negative) tail. That is, there are a greater number of extreme negative daily returns than extreme positive daily returns.” (Cook Pine Capital (2008):pp 3)

Fat tails are a problem, especially while measuring risk. They are said to be characterised by positive returns but very few extreme losses. Empirical studies show that for number of years, hedge fund managers have neglected the effects of fat tail in hedge fund strategies. However, in recent years such strategies finally revealed the consequences and failed to deliver. For this reason, instead of using Sharpe ratio and volatility to measure risk, downside risk measures such as Sortino ratio will be more rational. It has been mentioned in chapter 2, that Sortino ratio is very similar to Sharpe ratio but it takes downside deviation as a denominator instead of standard deviation.

4.3 Issues with hedge fund

Hedge funds are considered as very attractive investment opportunities; however, it is not free of speculations and issues. Some of the issues that have been discussed in the literature are:

* cost and management fee structures

* collection of data

* data biases such as survivorship and selection biases

* performance measurement

4.3.1 Cost and Management fee structures

Investing in just one hedge fund can be time consuming and immense due diligence and increases the risk. Due to this reason, fund of hedge funds (FOF) have become popular. These are funds that are pooled together that assign their capital amongst different hedge funds (see 3). FOF needs to be registered with SEC (Security and Exchange Commission) which is not the case with hedge funds.

Although there are several advantages of FOF, which include diversification and expertise of fund managers, the biggest disadvantage is the cost and its double fee structure. The investor need to pay the fund manager a management fee which also may include performance fee in addition to fees that are normally paid to the underlying hedge fund (David Harper).

Hedge funds typically charge a management fee of 1-2% of the assets plus a management fee of 20% of the hedge funds profit (www.sec.gov). Harper also continues to mention that the fund managers might also charge additional 10% depending on the fund strategy which means under this arrangement, the investor would pay 2% annually and 30% of the gains, which make it a serious cost issue.

4.3.2 Collection of data

Hedge funds generally do not disclose their activities to the public, as they are organized as private limited partnerships, and frequently as offshore investment vehicles. This has resulted in frequent complaints about the lack of transparency. Fortunately, many funds do release selective information to publicize themselves and their performance to attract new investors. These data are collected by a small number of data vendors and fund advisors. A few large advisors and vendors are currently publishing performance data and indices/sub-indices periodically corresponding to the various investment strategies. A listing of Hedge Funds Databases and some descriptive details is provided in the Appendix.

However, voluntary participation in performance reporting leads to incompleteness of information regarding the hedge fund population as a whole. Thus, sampling biases are present whenever an investor analyses a hedge fund database on a stand-alone basis.

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