hedge funds; an alternative to traditional asset classes?

GHENT UNIVERSITY

FACULTY OF ECONOMICS AND BUSINESS ADMINISTRATION

ACADEMIC YEAR 2015 – 2016

Hedge Funds; an Alternative to Traditional Asset Classes?

Master’s Dissertation submitted to obtain the degree of

Master of Science in Business Engineering

Maxime Pirotte

Under the guidance of

Prof. Dr. Koen Inghelbrecht

II

Confidentiality agreement:

PERMISSION

I declare that the content of this Master’s Dissertation can be consulted and/or reproduced if the

sources are mentioned.

Maxime Pirotte

III

Dutch summary

In 2007 deed Warren Buffet een weddenschap van 1 miljoen dollar met Protégé Partners, een

hedgefund, dat hedge funds de S&P 500 zouden onderpresteren, dit na aftrek van kosten, over een

periode van tien jaar.

Met ongeveer 19 maanden te gaan gaat Warren Buffet de weddenschap zeker winnen. Toch zouden

de hedge fund managers beweren dat de S&P 500 niet de beste benchmark is. Zij beweren dat het

doel van de hedge funds is om risico's te beperken en een constant rendement te leveren.

In een wereld van lage rente, lage kapitaalmarktrente en lage dividenden, hebben de vergoedingen

die hedge funds vragen, een drastische invloed op de netto-opbrengsten. In de jaren 1990, het

gouden tijdperk van hedge funds, konden hedge fund managers gemakkelijk dubbele cijfers

rendement per jaar leveren. Dus moeten hedge fund managers hun kosten verlagen in de nabije

toekomst als ze willen hun activiteiten behouden.

Echter, in deze studie gebruikten we eerder rendement voor aftrek van kosten. We kwamen tot de

conclusie dat hedge funds inderdaad wel beter presteren dan de S&P 500 en obligaties in goede

periodes. Maar hedge funds hadden problemen om beter te presteren dan deze benchmarks in tijden

van crisis. Obligaties waren zelfs een beter en veiliger alternatief in deze moeilijke tijden.

We kwamen ook tot de conclusie dat hedge fund managers liever in kleinere aandelen investeren. De

meeste Event-driven managers hadden daarentegen de voorkeur voor aandelen met een hoge book-

to-market ratio's. Bovendien volgen sommige Event-driven managers een momentum-strategie, terwijl

anderen een momentum contrarian volgen.

Hedge funds hebben ook een lagere standarddeviatie dan aandelen en obligaties. Maar omdat hedge

funds een hoge correlatie hebben met aandelen in tijden van crisis, hebben ze weinig alternatieven

voor diversificatie.

De belangrijkste bijdrage van dit proefschrift aan de bestaande literatuur, is dat we geprobeerd

hebben om de aantrekkelijkheid van hedge funds te beschrijven tijdens verschillende periodes.

Performances en correlatie met aandelen en obligaties worden voor de eerste keer in een pre- en

post-crisisperiode gerapporteerd. Er zijn echter een aantal beperkingen aan deze studie. Eerst en

vooral, onze rendementen waren vóór de kosten. Bij het analyseren na de kosten, kon je een heel

ander beeld krijgen over de aantrekkelijkheid van hedge funds. Ten tweede is het bewezen dat de

hedge fund databases biases vertonen, verschillende databases geven immers verschillende

resultaten. Ten derde belichten hedge funds niet vaak hun activiteiten, waardoor het moeilijker wordt

om conclusies over hedge funds te trekken.

Alle statistieken in dit onderzoek hebben betrekking op het gemiddeld rendement. Natuurlijk zullen er

altijd managers zijn die de markt overtreffen. Maar hoe krijg je ze in die positie? Als het zo makkelijk

was, zou er sprake zijn van onder presterende managers? Met een industrie van meer dan 10.000

individuele fondsen, geleid door ervaren managers, zijn er toch voldoende beleggingsmogelijkheden

die niet kunnen worden gezien door anderen? Nee, want er zijn te weinig mogelijkheden voor te veel

geld, dus zal de gemiddelde prestatie verslechteren. Dus verwacht men dat in de toekomst het aantal

hedge funds zal verminderen.

IV

Acknowledgements

I would like to thank everyone that helped me directly or indirectly during my work on this thesis. I will

start by thanking my parents for the constant support.

I would like to thank Sen Gu, student at the Boston University, Joeri Kiekens, student at Tokyo

Institute of Technology, and Eron Durnez, student at the University of Ghent, for the constant

feedback.

Last but certainly not least, I would like to thank Prof. Dr. Koen Inghelbrecht, for making it possible for

me to do a research on the topic of hedge funds. For giving me insightful comments which helped me

to enrich my research, while being there to help me overcome any potential difficulties that would

arise.

V

Table of Contents

Confidentiality agreement: ...................................................................................................................... II

Dutch summary ...................................................................................................................................... III

Acknowledgements ................................................................................................................................ III

0. Introduction ...................................................................................................................................VIII

0.1 Structure of the thesis ............................................................................................................IX

1. Hedge funds explained ................................................................................................................... 1

1.1 What are hedge funds? ........................................................................................................... 1

1.2 Investment strategies .................................................................................................................... 2

1.2.1 Global Macro .......................................................................................................................... 2

1.2.2 Equity long/short approach (Equity-Driven) ........................................................................... 3

1.2.3 Relative value (Arbitrage)....................................................................................................... 3

1.2.4 Event-Driven .......................................................................................................................... 4

1.3 Hedge fund performance .............................................................................................................. 5

1.4 Bonds and Equities ....................................................................................................................... 7

1.5 Measuring Risk ............................................................................................................................. 8

1.5.1 Illiquidity.................................................................................................................................. 8

1.5.2 Lack of regulation, fraud and operational risk ........................................................................ 8

1.5.3 Vulnerability to systematic risk ............................................................................................... 9

1.6 Biases in hedge fund data ............................................................................................................ 9

1.6.1 Survivorship bias .................................................................................................................... 9

1.6.2 Selection bias ....................................................................................................................... 10

1.6.3 Backfill bias .......................................................................................................................... 10

1.7 Statistical characteristic of Hedge funds ..................................................................................... 10

1.7.1 Non-normality ....................................................................................................................... 11

1.7.2 Non-linearity ......................................................................................................................... 12

1.7.3 Autocorrelation ..................................................................................................................... 12

1.8 Hedge funds and the financial crisis ........................................................................................... 12

1.8.1 Did they contribute to the financial crisis? ............................................................................ 12

1.8.2 How did their performance change after the crisis? ............................................................ 13

2. Performance measurements model and Data collection .............................................................. 14

2.1 The capital asset pricing model .................................................................................................. 14

2.2 The three factor model of Fama and French (1993) and its international version (Fama and

French, 1998) .................................................................................................................................... 14

2.3 The four-factor model of Carhart (1997) ..................................................................................... 15

2.4 The seven factor model of Fung and Hsieh (2004) .................................................................... 16

VI

2.5 Data Collection ............................................................................................................................ 16

3. Results .......................................................................................................................................... 18

3.1 Hedge fund performances ..................................................................................................... 18

3.1.1 Sharpe Ratio ........................................................................................................................ 18

3.1.2 The capital asset pricing model (CAPM) .............................................................................. 20

3.1.3 The 3 Factor and the Carhart (4 Factor) Model ................................................................... 25

3.1.4 The 7 Factor model .............................................................................................................. 28

3.2 Correlation with traditional asset classes .............................................................................. 30

4. Conclusion ..................................................................................................................................... 34

Bibliography .......................................................................................................................................... 36

VII

List of Figures

Figure 1: Hedge Fund Strategies ............................................................................................................ 2

Figure 2: Hedge Fund Returns Compared to Indices ............................................................................. 5

Figure 3: Hedge fund returns (%) ......................................................................................................... 18

Figure 4: Credit Suisse HFI; Monthly Alpha .......................................................................................... 22

Figure 5: Credit Suisse HFI; Monthly Alpha; 1994-2001 ...................................................................... 23

Figure 6: Credit Suisse HFI; Monthly Alpha; 2002-2006 ...................................................................... 23

Figure 7: Credit Suisse HFI, Monthly Alpha; 2007-2015 ...................................................................... 24

Figure 8: Credit Suisse HFI; Yearly Alpha ............................................................................................ 24

Figure 9: Moving Correlation; Credit Suisse HFI & S&P 500; 24m....................................................... 33

Figure 10: Moving Correlation; Credit Suisse HFI & Bonds; 24m......................................................... 33

Figure 11: Hedge funds after fees ........................................................................................................ 34

List of Tables

Table 1: Sharpe Ratio ........................................................................................................................... 19

Table 2: Std Dev ................................................................................................................................... 19

Table 3: Kurtosis ................................................................................................................................... 20

Table 4: Skewness ................................................................................................................................ 20

Table 5: CAPM; 1994-2015................................................................................................................... 21

Table 6: CAPM; 1994-2001................................................................................................................... 21

Table 7: CAPM; 2002-2006................................................................................................................... 22

Table 8: CAPM 2007-2015.................................................................................................................... 22

Table 9: 3 Factor Model; 1994-2015 ..................................................................................................... 26

Table 10: Carhart Model; 1994-2015 .................................................................................................... 26

Table 11: 3 Factor Model; 1994-2001 ................................................................................................... 26



Table 14: Carhart Model; 1994 -2001 ................................................................................................... 27



Table 17: 7 Factor Model; 1994 - 2015 ................................................................................................. 29




Table 21: Correlation; 1994-2015 ......................................................................................................... 31




VIII

0. Introduction

Despite increasing interest in hedge funds, few studies have been carried out on hedge funds

performance and correlation with other asset classes. This can partly be explained by their private

characteristics and the difficulties encountered to have access to individual funds data.

The objective of this study is to evaluate the performance of hedge fund investments and determine

whether hedge funds are a more interesting investment opportunity compared to traditional asset

classes.

Nowadays, hedge funds have become more developed and specialised. In the past the term “hedge

fund” referred to investments that were used for hedging or protecting the investor. However, with their

increased complexity, speculative nature and looser financial regulations they embrace an abundance

of investment opportunities. While putting them in high demand, further increasing their growth over the

years, hedge funds have also received a reputation for being manipulative and have received hard

criticism in the media.

So we wonder, are hedge funds able to deliver the promised returns and do they hedge investors

against market risk?

This study will be divided into two parts;

We will examine hedge fund performances, while using different measurements models.

Determining the correlation of performances between, not only, hedge funds and bonds but

also hedge funds and equities.

We will try to ascertain whether or not hedge funds are an attractive investment and if they are less

risky than other products on the market.

The time range to be examined is 1994-2015, additionally it will be further divided into 3 sub-periods.

Period 1, will be from 1994 to 2001, Period 2 will be from 2002-2006, and Period 3 will be from 2007-

2015. The reason for dividing our research in these time ranges is that 2002-2006 is considered a bull

market and 2007-2015 is considered a bear market.

Furthermore, hedge fund returns need first get corrected for biases, fat tails and autocorrelation. Then,

together with the returns of other asset classes they will be compared to each other, during different

time periods.

Hedge funds claim that even during moments of crisis’s they can hedge risk and deliver superior returns

compared to traditional asset classes. Our goal is to find out whether this hypothesis holds and if hedge

fund are indeed an interesting alternative to traditional asset classes.

IX

We will thus try to examine the following questions;

Are returns of hedge funds superior during times of crises?

Does the correlation between hedge funds return and traditional asset classes’ change

during different financial situations?

How does the hedge fund volatility change during the examined periods?

In order to answer these question we will be analysing different indices and strategies used. We will be

using the Credit Suisse Hedge Fund Index (Credit Suisse HFI). Furthermore, the strategies covered will

be the Global Macro, Arbitrage, Event-Driven and Equity-Driven strategies.

0.1 Structure of the thesis

This study will be divided in 4 chapters. In chapter 1, we will give a short introduction to the world of

hedge funds. The main characteristics of the hedge fund industry will be discussed. Also, the 4

strategies that will be used in the research will be presented. Furthermore, we will also talk about hedge

funds during the financial crisis.

In chapter 2 we will explain each model used.

Chapter 3 will be about our analysis and results. We will examine how each strategy fared during

different periods. We will also compare some of our results to other studies. As hedge funds haven’t

been examined sufficiently, it will be quite interesting to find out whether hedge funds indeed deliver the

excess results they promise. We will then compare the hedge funds’ returns to the S&P 500 and the 10

year US Government Bond. We will also look at the correlations of each strategy with the S&P 500 and

bonds.

Finally, in chapter 4 we will give a summary of our findings.

Chapter 5 will consist of the references.

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1. Hedge funds explained

One of the liveliest part of the financial sector over the last decade has been the hedge-fund industry.

With little regulation, motivation of profit-sharing incentive fees, and drawn to the far edge of the

investment universe, hedge funds have been taking enormous risks compared to the less willing market

participants.

As it is quite hard to invest in individual hedge funds directly, most investors invest in so-called funds of

hedge funds (FoHF). In return for a typically not-insignificant fee, FoFH (claim to) take care of the many

unavoidable, time-consuming and complex issues that come with investing in a highly opaque asset

class such as hedge funds.

As the incentive to earn more has increased, hedge funds now provide liquidity in every major market.

However these last few years a fundamental shift in perception towards hedge funds can be seen, from

being the favourites of the investment world to the black sheep of the family.

1.1 What are hedge funds?

"There is no common definition of what constitutes a hedge fund; it can be described as an unregulated

or loosely regulated fund which can freely use various active investment strategies to achieve positive

absolute returns" (Garbaravicius and Dierick, 2005).

"The term 'hedge fund' is used to describe a wide variety of institutional investors employing a diverse

set of investment strategies. Although there is no formal definition of 'hedge fund,' hedge funds are

largely defined by what they are not and by the regulations to which they are not subject. As a general

matter, the term 'hedge fund' refers to unregistered, private investment partnerships for wealthy

sophisticated investors (both natural persons and institutions) that use some form of leverage to carry

out their investment strategies." (Becker B. and Doherty-Minicozzi C., Hedge Funds in Global Financial

Markets 3, 2000)

With many competing (and sometimes contradictory) definitions of hedge funds, the expressions

“hedge fund” has no universally accepted definition. A first definition came in the 1950s to describe any

investment fund that uses short-selling and leverage. Usually hedge fund are private investment

partnerships, which use a wide variety of trading strategies and employ a large mixture of trading

techniques and instruments.

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In short we can define a hedge fund as:

An alternative investment that uses a pool of money from investors with the goal of achieving an active

return, or alpha, with the help of a number of different investment strategies.

Brentani (2004) points out that hedge funds feature two important aspects: first, their main objective is

to generate positive returns while minimizing risk. Secondly, hedge funds try to control losses.

1.2 Investment strategies

Though possessing certain characteristics that distinguish themselves from

traditional investment vehicles, the magnitude of available investments

tools available to hedge funds managers, allow for different strategies to be

employed.

The strategy determines 1) the goals of given fund, 2) the ways to achieve

them, and 3) from the investors’ perspective, the opportunities offered.

These 3 factors determine the returns and risk. Furthermore it is necessary

to segment hedge funds to have a practical framework to refer to. As no

general classification exists, the classification we use will consist of four

main categories.

Edwards and Caglayan (2001) found by using monthly hedge fund returns,

during the periods of January 1990 to August 1998, that 25% of hedge

funds earned positive excess returns and that the magnitude of these

returns differed with different investment strategies.

Bali, Brown et al. (2012) found that hedge funds following directional trading

strategies, such as global macro, had the ability to accurately adjust their

exposure to changes in the market and, thus, a positive and stronger link exists between their

systematic risk and future returns. However, for non-directional strategies, such as convertible

arbitrage, fixed income arbitrage …, the relation between systematic risk and future returns is

insignificant.

1.2.1 Global Macro

The hedge funds that use the global macro investment style are arguably the largest funds. Furthermore

they are the most performance oriented entities, and they attract the most of media attention and

coverage.

Hedge fund strategies

Global Macro

Equity long/short

Relative value (arbitrage)

Event Driven

FIGURE 1: HEDGE FUND

STRATEGIES

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Macro trading seeks to benefit from speculation on the fluctuation of currencies, commodities, equities

and bonds. Macro investing utilizes the macroeconomic principles in a search for arbitrage opportunities

or profit opportunities in the global markets. With a top-down approach, managers seek to profit from

the fundamental economic, political and market factor analysis. Once a promising area is specified,

high leverage is used as a profit maximizing tool. Global macro managers rely often strongly on

derivatives.

Global macro portfolio managers spend a lot of time identifying trends or events which could increase

security, and their aim is to make the investments just before price changes occur (Lhabitant 2004).

Since the financial and economic crisis, liquidity has dried up, causing big names in the industry to close

their funds, due to their inability to match their historic 20%-plus returns. Due to lower interest rates, it

has become harder for many macro fund managers to long on bonds, a strategy that was often used

during market shocks (Hedge Fund Review Oct 2012).

Historically, the biggest dispersion among the hedge funds strategy was with the macro funds. Berman

(2014) found that the average dispersion between the 75th and 25th percentile of macro fund managers

declined from 27% in 2009 to around 12% in 2012.

1.2.2 Equity long/short approach (Equity-Driven)

As the name suggests, investment in equities are made, with a composition of long and short positions,

that have as purpose to decrease market exposure. The equity long/short approach is based on a

traditional, fundamental analysis of the market, where shorting creates an opportunity to generate profits

even in situations of market decline.

Even though this shorting technique might seem to have been advantages, certain drawback have to

be considered. With short selling there are higher trading costs (as a result of leverage), moving prices

require even more monitoring, and have negative results during bull markets.

20% of long/short equity hedge funds delivered persistent, significant and stable positive returns (Fung

and Hsieh, 2011). Furthermore, by using futures and/or options the hedge fund manager can create

flexibility to change the current position (Plesner, 2003).

1.2.3 Relative value (Arbitrage)

In this strategy, a broad range of securities, equity, debt options and futures are pursued. The

underlying principle of this strategy is pricing discrepancies between similar, related instruments in the

market.

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Convertible bonds are a primary subject of interest for arbitrage managers. When arbitrage

opportunities are spotted, then hedge funds come into play. The investment strategy is vulnerable to

different kinds of risks, such as interest, credit and equity risk. It is then the task of a hedge fund

manager to be protected against the mentioned set of risk.

Interest risk is dealt by selling interest rate future contracts or interest rate swaps.

Equity risk is often hedged by shorting the equity. This technique requires more effort than the interest

rate risk, as the sensitivity of bond prices change over time.

Credit risk acts as the biggest challenge. As convertible bonds are often unsecured, subordinated,

issued by companies with high volatility of earnings, high leverage, the credit risk is often strengthened.

A possible solution is the use of an asset swap. The asset swap allows the bond to be divided into two

core components; an equity call option and a fixed income part.

Calamos (2011) found that convertible arbitrage bonds establish a market-neutral profile with low

correlation with equity.

Furthermore, Hutchinson and Gallagher (2004) found that convertible arbitrageurs generate absolute

positive returns of 3% per annum.

1.2.4 Event-Driven

In the event driven style of hedging, hedge fund managers place their interests in debt, equity and trade

claims of companies going through specific phases of their life cycle. The assumption is that, companies

undergoing ownership changes, restructuring or market trouble are expected to have an influence of

share prices. Hoping to benefit from undervalued asset prices, hedge fund managers will then invest in

these assets.

The advantages hedge fund managers have over traditional investors in this sector comes mainly from

the types of analysis performed and expertise involved (but not only these factors). Banks and

individuals often have unreasonable fears, further strengthened by the lack of capable analysts’

coverage on such securities. Low liquidity and long redemption periods are a concern too.

Having a bigger market knowledge and limited interest from other investors, managers invest in

distressed securities hoping for future value appreciation. Though the valuation process does not come

easily. Valuing distressed companies is a complicated process, requiring in-depth knowledge of

valuation, law and company’s business.

Merger arbitrage is a strategy taking advantages of opportunities deriving from companies going

through corporate control changes. Leveraged buyouts (LBO), mergers and hostile takeovers are a

major interest.

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1.3 Hedge fund performance

FIGURE 2: HEDGE FUND RETURNS COMPARED TO INDICES

The foremost reason for investing in hedge funds is the assumed superior performance in comparison

to traditional asset classes, especially during bear markets. Hedge funds tend to provide risk-adjusted

performance that provides investor with diversification benefits even during dire macroeconomic

environments. As hedge funds are subject to almost no regulations and restrictions, they supposedly

deliver better long-term results.

Although there is an increasing number of papers being published on hedge fund performances, there

is no common consensus whether hedge fund performances persists or not.

Capocci and Hübner (2004) show that hedge funds prefer investing in smaller stock and emerging

market bonds. By using these specific strategies, 25% of hedge funds generate excessive positive

returns. They also conclude that it is better to ride with the flow than against the flow, as momentum

strategies outperform contra momentum strategies.

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Smaller funds outperform larger funds (Ding et al., 2009). On a risk-adjusted basis, however, larger

funds outperform the small funds. In other words, this means that smaller funds take on higher risk than

larger hedge funds. Because larger funds hold less liquid assets, the type of strategies they use is also

different than the smaller funds (Ding et al., 2009).

Ackermann (1999) examined data from 1988 to 1995, and found out that hedge funds outperform

mutual funds but not equities and bonds even being much more volatile. Further analysis was done on

performances on hedge funds after they year 2000, and they reach different results that the ones of

Ackermann.

Lhabitant (2004) concluded that hedge funds significantly outperform assets classes, especially equity,

while exhibiting lower volatility.

Research suggests that from 1994-2000 hedge funds outperformed equities (Stulz 2007).

Evidence has been found that hedge fund strategies exhibit low to medium correlation with equities and

bonds (Lhabitant, 2004). Brooks and Kat (2001) documented that hedge funds have a high level of

correlation with equities, however, they also found that hedge funds have low levels of correlation with

bonds.

We want to find out whether fund managers are able to deliver a persistent performance, while

outperforming their benchmark. While people can be lucky or have insider information, the reason

investment managers exist, is because people want to achieve repeated positive returns time and time

again. Not only that, investment managers provide investors with additional advantages, such as

diversification, but the (high) fees involved sometimes deter potential investors. Studies have also

shown that buying past winner and selling past losers, can generate persistence up to a one-year

horizon (Jegadeesh and Titman, 1993). Thus, you might expect that fund managers should be able to

recreate these returns. But on the other hand, the theory of the efficient market hypothesis suggests

that it isn’t possible to persistently outperform the market, especially after fees. In the hedge fund

industry, the question is asked even more. Why should you invest in hedge funds, accounting their

higher fees, and can they deliver that persistent positive absolute returns they promise? The literature

on persistent performance goes back to Sharpe (1966), and his study on mutual funds. Sharpe (1966),

found little evidence of the persistence of outperformance of mutual funds. Other studies find that the

persistence performance of mutual funds is weak. Grinblatt and Titman (1995) conclude that there is

little evidence of persistent abnormal evidence, and the benchmark used greatly affected the

conclusions. Fama and French (2010) also reached to the same conclusion, after costs had been

accounted for.

Ackerman et al. (1999) find that in the 1988-1995 period, hedge funds outperformed mutual funds, but

not market indices, both in terms of Sharpe ratio and absolute returns. However, it is important to note,

that in the period studied, market returns were exceptionally high. During the same period, Brown et al.

(1999) find that hedge funds have positive outperformance in terms of Jensen’s alpha and Sharpe ratio,

but find no evidence that this outperformance is persistent. Furthermore, over a longer studied period,

Page | 7

Brown and Goetzmann (2003), found very little evidence of performance persistence on a yearly level.

They also underline that hedge funds are not a homogenous investment class. Malkiel and Saha (2005)

find that performance appears to be persistent in some years and not in others on the one-year horizon.

Furthermore, they notice that risks are higher and returns lower than thought previously. Zhong (2008),

confirmed this lower performance, and shows that alpha returns have been decreasing over time due

to high performers reversing towards the mean, while poor performers remain constant. Finally, Eling

(2009) underline the variation of performance and suggest that the identification of performance

persistence is very sensitive to the statistical technique used.

On a quarterly horizon, Agarwal and Naik (2000), find strong evidence of persistent performance of

hedge funds, both before and after fees. Different studies contest the length and the effect of

performance persistence. Bares et al. (2003) find that, on the short-term, persistence is the strongest

on a one-month horizon. Koh et al (2003) and Boyson (2008) find similar conclusions. On the other side

of the spectrum, Kosowksi et al. (2007) find that hedge funds consistently outperform on a yearly

horizon. Furthermore, Manser and Schmid (2009) find the same conclusions when performance is

measured on a risk-adjusted basis. Ammann et al. (2010) study hedge funds over a 3 year period. They

find that that funds which are less correlated with their peers persist more.

There have also been multiple studies that debate on the length and extend of performance persistence.

On one side, the long-term side, Kosowski et al. (2007) document that hedge funds persistently

outperform up to a yearly horizon. On the three-year horizon, Ammann et al. (2010) show that

persistence depends on the used strategy of fund managers. On the other side, the short-term side,

Bares et al. (2003) find that persistence is strongest on the one-month horizon. Koh et al. (2003) and

Boyson (2008) also document similar findings.

1.4 Bonds and Equities

Equities and bonds have been studied actively over the years. Depending on the years studied, different

conclusions would be made over their returns and correlation. There is no clear consensus of the

results, especially due to the recent shifts in the financial markets landscape. Fama and Schwert (1977)

find negative correlation between returns. This conclusion is found in multiple studies done in the late

1980’s (Keim and Stambaught (1986), Ferson (1989), …).

In the early 1990’s multiple studies state that the correlation between stocks and bonds is stable and

positive over time, which is supposedly caused by a common discount rate effect (Campbell and

Ammer, 1993; Shiller and Beltratti, 1992). However, more recent studies claim to contradict this relation.

Although the correlation between the stocks and bonds are positive on average, there is considerable

time variation and several periods of extended negative correlation. These changes, however, can

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occur within short periods of time. There are several examples of consecutive months with changing

signs of their stock-bond return correlation (Baker and Wurgler, 2012; Baele et al., 2010).

1.5 Measuring Risk

In the early days of its existence, hedge funds were a highly elitist investment tool. In the late ’80s, only

the right contacts and sufficient amount of money could guarantee access to alternative investment.

The increasing popularity and acceptance of hedge funds increased the need for a tool aimed at a wide

variety of investors. Providing indices helped the industry develop into the stage it is in, but were also

an answer for the growing need for accurate and timely measures of valuation, return and risk.

So what is the catch? After all there is no such thing as a free lunch.

1.5.1 Illiquidity

Generally hedge funds are illiquid. Thus, it wouldn’t be a core investment, except for the most

specialised investors. Investors have to have sufficient expertise to judge whether the liquidity terms

offered by the hedge fund managers are in line with the liquidity of the underlying securities in the

portfolio.

More often than not, managers do not know the risk with borrowing short and lending long, clients do

not really understand the asset class, and managers are willing to put existing clients at a disadvantage

to win new businesses. Which brings us to the next point.

1.5.2 Lack of regulation, fraud and operational risk

As part of the shadow banking system, hedge funds lie outside the light of the Fed, the SEC, the CFTC,

etc. Therefore, it is impossible to determine the definite systematic risk. Chan et al. (2004) presented

indirect evidence that the level of systematic risk in the hedge fund industry has risen.

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1.5.3 Vulnerability to systematic risk

Hedge funds are not guaranteed to make money in all market conditions. In fact they are a risky asset

class and such they have always been vulnerable to systematic shocks. In 2008, the Tremont Index

(“Broad Index”) finished the year down 19%. That being said, there are a number of strategies and

managers that are able to generate outsized return during periods of severe stress. Similarly, once the

worst is over, hedge funds tend to make money, even when the equities continue to lose money.

Liang (1999) found that hedge funds offer lower systematic risks than mutual funds.

1.6 Biases in hedge fund data

Nowadays, investors are able to access historical data more easily of any given security or mutual fund.

However, this is not true regarding hedge funds. Both the scope and the quality of the data vary among

hedge fund database vendors.

There are multiple reasons why hedge fund databases are incomplete. First, hedge fund managers can

participate in databases voluntary, which can lead to sampling biases (Fung W., Hsieh A., 2002).

Voluntary participation means that only a portion of the total population of hedge funds can be observed.

Second, most hedge fund databases were created in the 1990s. Thus information on hedge funds

before the 1990s contain measurement biases.

Third, different databases have different criteria on how to include funds.

Though there are multiple biases that are the consequences of analysing hedge fund data, only three

of them will be mentioned – survivorship bias, selection bias and backfill bias.

1.6.1 Survivorship bias

This is the tendency for funds with poor performances to be dropped, because of poor results. This will

result in an overestimation of the past return of hedge funds.

In other words, a mutual fund company’s selection of funds today include only those funds which have

been successful in the past. Many underperforming funds are closed and merged into other funds to

hide poor performances. This will result in an overestimation of past returns, as the poor returns have

been dropped from the database.

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Usually, funds cease to exist due to poor performance. This makes the returns of databases be biased

upwards and risk skewed downwards (Fung and Hsieh, 2001).

Survivorship bias only has a very small influence on performance (Fung and Hsieh, 2011). This is due

to the fact that the reporting bias, successful fund which are closed to new investors often stop reporting

to databases, partly offset the survivorship bias (Fung 2008). Furthermore, survivorship bias decreases

as the industry matures.

1.6.2 Selection bias

This type of bias occurs when we choose non-random data for statistical analysis. The bias exists due

to the flaw in the selection process. This type of bias can produce distorted results. The effect of self-

selection bias can’t be easily quantified but its level can be considered negligible compared to other

biases (Fung and Hsieh, 2000).

Fung and Hsieh (2001) also argue that self-selection bias occur in 2 different forms. First, many famous

hedge fund believe that they do not need to participate as they are sufficiently well known. As the most

successful funds do not report to the databases, skewing happens in the database, showing weaker

performance. Second, newer, well performing funds are more likely to report to a database, as they are

seeking new investors. This effect will skew the database to show better performance.

1.6.3 Backfill bias

Another important type of important source of bias is the backfill bias. It occurs when funds joining a

given database are allowed to backfill their historical returns even though they were not part of the

database in the previous years (Llabitant, 2004). The backfill bias is estimated to be around 1.4%

annually according to Fung-Hsieh (2000, 2001).

1.7 Statistical characteristic of Hedge funds

Hedge funds generally suffer from the following characteristics; Non-normality, non-linearity between

hedge fund returns and traditional asset classes and autocorrelation.

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1.7.1 Non-normality

Multiple studies have documented the existence of non-normality in hedge funds returns. Brooks and

Kat (2002) tested a sample of 48 hedge funds indices to determine their statistical composition. They

found the Jarque and Bera (1987) normality test to be significant for each hedge fund index they tested.

Given these findings they determined that hedge fund returns are not normally distributed. Two main

reasons of a non-normal distribution is the existence of kurtosis, often called “fat tails” and the existence

of skewness in the hedge fund data.

1.7.1.1 Kurtosis

Kurtosis is a measure of flatness of the distribution. Heavier tailed distribution have larger kurtosis

measures. The normal distribution has a kurtosis of 3.

Investors view investment vehicles with a high positive kurtosis as unfavourable, as they have a higher

possibility of have extreme returns.

Brooks and Kat (2002) found that the hedge fund indices which they examined had a relatively high

level of kurtosis. This implies that hedge fund are subject to extreme returns.

1.7.1.2 Skewness

Skewness measures the degree of symmetry of distribution around the mean. A normal distributed

dataset will have a skewness equal to zero. Distributions with positive skewness are called right-

skewed. While distributions with a negative skewness are called left-skewed.

Skewness has an effect on returns. A right-skew return distribution provides frequent small losses and

few large returns. While a left-skewed return distribution frequent small positive returns and few extreme

losses. Investors will be more attracted to positively skewed returns, as they provide large positive

returns (though they occur rarely). While left-skewed distributions will cause extreme losses which may

jeopardize investor’s wealth.

Brooks and Kat (2002) found that hedge funds indices were on average negatively skewed.

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1.7.2 Non-linearity

Hedge fund returns have been found to be non-linear. This means that the returns of hedge funds

typically exhibit a nonlinear relationship with market returns. This nonlinearity indicated that returns of

hedge funds are generally more strongly correlated with the market when it is depreciating that when it

is appreciating. (Mitchell and Pulvino, 2001).

1.7.3 Autocorrelation

Brook and Kat (2002) found that hedge fund returns show significant correlation. Autocorrelation is a

situation in which a time series data is influence by its own historical values.

1.8 Hedge funds and the financial crisis

1.8.1 Did they contribute to the financial crisis?

The financial crisis in 2007, which was originally triggered by a complex interplay of policies, turned the

focus of regulatory controls towards the hedge fund industry, often blaming them for the start of it all.

However, several academics (e.g. Ben-David, Franzoni, et al. 2012) later argued that it had not been

the hedge fund’ intermediary position that had further extended the proportion on which the whole

financial system had collapsed on.

Another important note is that hedge fund riskiness is its capacity to leverage itself. Hedge funds try to

come over as market neutral, and tell that they have low risks, though in fact the use of high leverage

suggests to make them risky investment opportunities. From 2005 to 2007, the gross hedge fund

leverage stayed between ranges of 1.0 – 1.3. However, not long before the market crash, the cross-

sectional leverage ratio increased to 1.6 (Ang, Gorrvvy et al, 2011). Though using leverage may boost

profits, unexpected losses will be more difficult to manage, and these losses could be transmitted to

financial institutions who were granting these loans in the first place.

Hedge funds use two arguments to deny causing the subprime crisis. First, hedge funds had nothing to

do with the creation of the toxic securities that were at the centre of the crisis. Second, hedge funds

argue that they were not the only financial institutions carrying these toxic securities.

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1.8.2 How did their performance change after the crisis?

As this part is only a literature review, and not a concrete analysis of hedge fund performances on my

part, I will have to refer to a later part of this thesis if you want to find my findings (see: 3. Results).

So what has been the impact of the crisis on hedge fund performances? Fung and Hsieh (2000), Dai

and Shawky (2013) studied their performances after the financial crisis. They found that the

performances of hedge funds had been deeply impacted by the crisis, with the largest funds taking the

biggest hits. Additionally, they found that diversification within the hedge funds did not have much of an

impact on preventing poor performance caused by systematic risk.

Though the financial crisis had a disastrous effects on the entire financial sector, its impacts was not

equally spread throughout the system.

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2. Performance measurements model and Data

collection

Our study will contain different performance measurement models. The first model which will be used

is the CAPM. We will also be using multi-factor models such as the Fama and French (1998) three-

factor model and its international version of 1998 (Fama and French, 1998). We shall also be using the

seven-factor model of Fung and Hsieh (2004). Finally we will compare the CAPM with the multi-factor

models.

2.1 The capital asset pricing model

The first performance model we will be using is a single index model based on the classical CAPM

(Sharpe, 1964). Its equation is as follows:

RPt – RFt = αp + βp (RMt - RFt) + εPt t= 1, 2 … N

where 𝑅Pt = return of fund P at time t; RFt = risk-free return at time t; RMt = return of the market portfolio

at time t; εPt = error term; αP and βP are the intercept and the slope of the regression, respectively.

There are of course limits to the CAPM, it is based on unrealistic assumptions, it is difficult to test the

validity of the CAPM, and betas do not remain stable over time. (Naylor, Tapon, 2009).

2.2 The three factor model of Fama and French (1993) and its

international version (Fama and French, 1998)

The three factor model of Fama and French (1993) is estimated from an expected form of the CAPM

regression. It takes the size and the book-to-market ratio of the firms into account. The equation is a

follows:

Rt – RFt = αp + βp1 (RMt - RFt) + βp2SMBt + βp3HMLt + εPt t= 1, 2 … N

where SMBt = Small minus Big and HMLt = High minus Low. These factors aim at isolating the firm-

specific components of returns.

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The SMB factor measures the excess return of a stock with low market capitalization over stocks with

high market capitalization. The HML factor calculates the return difference between stocks with the

highest and lowest book-to-market value.

The international version of the model (Fama and French, 1998), consider 12 major EAFE (Europe,

Australia, and Far East) countries and several emerging markets. They propose an international factor

mimicking for book-to-market equity (HML). The formula is the following:

RPt – RFt = αp + βp1 (RMt - RFt) + βp2IMHLt + εPt t= 1, 2 … N

where IMHLt = an international version of HMLt.

Even thought the Fama and French 3-Factor Model is highly favoured compared to the CAPM model,

the CAPM model is still considered the default model for estimating cost of equity and returns in spite

of its limitations.

However, even this model is criticised. One of this criticism is that the 3-factor model is accused of data-

mining. (Oxera, 2006).

2.3 The four-factor model of Carhart (1997)

Carhart’s (1997) four-factor model is an extension of the Fama and French (1993) factor model. Carhart

adds an additional momentum effect, already taking account of the size and book-to-market ratio. The

momentum effect is defined as buying stocks that were past winners and selling past losers (Grinblatt

M., Titman S., Wermers R., 1995). The model is found with the following regression

RPt – RFt = αp + βp1 (RMt - RFt) + βp2SMBt + βp3HMLt + βp4PR1YRt + εPt t= 1, 2 … N

where PR1YRt = the factor-mimicking portfolio for the momentum effect (also called the momentum

factor (MOM)).

Daniel et al. (1997) stress that this models assumes that in the absence of stock selection or timing

abilities, the coefficients of four zero-investment factor-mimicking portfolios are appropriate measures

of multidimensional systematic risk. It identifies a matching passive portfolio return for each fund return.

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2.4 The seven factor model of Fung and Hsieh (2004)

The Fund and Hsieh (2004) alpha for each strategy is computed via a time-series regression of the

portfolio’s realized excess returns of the seven factors with constant factor loadings. The formula is as

follows:

RPt – RFt = αp + βp1 (RMt - RFt) + βp2SMBt + βp310YGBondt + βp4Spr10YBaat + βp5PTFSBDt +

βp6PFTSFXt + βp7PTFSCOMt + εPt t=1, 2 … N

where 10YGBond = change in 10 year constant maturity government bond yield, Spr10YBaa = change

in spread between this 10 year bond and Moody’s Baa yield, PTFSBD = return on bond look back

straddle, PFTSFX = Return of PTFS Currency Lookback Straddle PTFSCOM = return on commodity

look back straddle.

2.5 Data Collection

The hedge fund world is shrouded in secrecy, in other word most hedge fund managers refuse to publish

any data related to their performance or the strategy they follow. Data is often only available to certified

investors who are willing to pay large amounts of money in order to have access to the detailed records

of every fund.

However, they are many suppliers of statistics related to the performance of hedge funds. For our study

of hedge funds, we will be using data from the Credit Suisse Hedge Fund Index. For each strategy that

we want to study, we will be using data from Hedge Fund Research, a provider of hedge fund indexes,

with over 150 different indices. We will also be using monthly total returns as they can be considered

more accurate than quarter or annual one and less noisy than daily returns.

Concerning equities, the index chosen is the S&P 500, as it is considered the best representation of the

US stock market.

For bonds, we will be using the US Benchmark 10 year DS Govt. Index.

Furthermore, extra data will be needed for some of the models that we will be using. For the Fama and

French Model (3 Factor Model), we will be using the SMB and HML factor which is given on their site

(http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/index.html). Where SMB = 1/3 (Small Value +

Small Neutral + Small Growth) – 1/3 (Big Value + Big Neutral + Big Growth) and HML = 1/2 (Small

Value + Big Value) – 1/2 (Small Growth + Big Growth). For the momentum factor, we also used data

from the Fama and French website, with Mom = 1/2 (Small High + Big High) – 1/2 (Small Low + Big

Low).

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For the seven factor model of Fung and Hsieh, we will be using the data available on their website

(https://faculty.fuqua.duke.edu/~dah7/HFData.htm).

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3. Results

3.1 Hedge fund performances

FIGURE 3: HEDGE FUND RETURNS (%)

In the graph above we have the monthly returns of the Credit Suisse HFI, we notice that there is a high

volatility of returns over the examined period. Returns are obviously lower in times of crisis. However,

only looking at returns doesn’t give an accurate representation of reality. How did hedge fund returns

compare to the S&P 500 or the bonds? Did they outperform or underperform? That is why we will be

using multiple models to find out how hedge funds performed compared to the benchmark.

3.1.1 Sharpe Ratio

First we are going to work with the Sharpe Ratio, which helps measures risk. It was first introduced by

William F. Sharpe in 1966.

𝑆𝑅 =𝐸(𝑅𝑝) − 𝑟𝑓

σp

With E(Rp), the expected portfolio returns, rf, risk-free rate and σp, the portfolio standard deviation.

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Hedge Fund Returns (%)

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TABLE 1: SHARPE RATIO

Sharpe Ratio 1994-2015 1994-2001 2002-2006 2007-2015

Credit Suisse HFI 0.22 0.21 0.58 0.17

Equity 0.22 0.35 0.30 0.08

Global Macro 0.21 0.19 0.39 0.14

Arbitrage 0.35 0.48 0.82 0.23

Event-Driven 0.29 0.41 0.42 0.12

s&p 500 0.14 0.18 0.10 0.12

Bonds 0.12 0.05 0.10 0.19

Sharpe ratio assumes that returns are normally distributed, so higher moments are not taken into

account, while penalizing volatility. Lo (2002) states if autocorrelation exists, the Sharpe Ratio can be

overstated by 65%.

An overview of the Sharpe Ratios is presented in the table above (Table 1). It is evidential that in the

time of the bull-market that the hedge funds indices provide higher Sharpe Ratios.

Over the studied periods, the Sharpe Ratio decreases drastically during times of crisis, even to the point

where they were outperformed by bonds. However, during blooming periods, hedge funds outperform

their peers. We should notice, however, that the equity-driven strategy was the only strategy to have a

lower Sharpe Ratio in the 2002-2006 period.

We can conclude that hedge funds strategy outperform the S&P 500 for any given risk. However, in the

times of crisis, some strategies have difficulties achieving this result. Furthermore, bonds are the

“safest” alternative during times of crisis.

Furthermore, we will have a quick look at Std. Dev, Kurtosis and Skewness.

TABLE 2: STD DEV

Sharpe Ratio 1994-2015 1994-2001 2002-2006 2007-2015


Equity 2.58 2.87 1.76 2.57

Global Macro 1.82 2.32 1.51 1.39

Arbitrage 1.19 1.02 0.54 1.50

Event-Driven 1.90 1.91 1.71 1.94

s&p 500 4.29 4.40 3.87 4.53

Bonds 2.16 1.95 2.28 2.34

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TABLE 3: KURTOSIS

Kurstosis 1994-2015 1994-2001 2002-2006 2007-2015

Credit Suisse HFI 3.02 0.99 (0.25) 3.44

Equity 2.23 1.47 (0.31) 1.99

Global Macro 1.25 0.57 1.50 (0.11)

Arbitrage 15.96 17.87 (0.24) 10.75

Event-Driven 4.07 7.09 1.43 3.19

s&p 500 1.15 0.64 0.91 1.43

Bonds 2.29 0.68 0.49 3.54

TABLE 4: SKEWNESS

Skewness 1994-2015 1994-2001 2002-2006 2007-2015

Credit Suisse HFI (0.14) 0.03 (0.14) (1.27)

Equity (0.19) 0.22 (0.45) (0.85)

Global Macro 0.28 0.02 0.35 0.37

Arbitrage (2.73) (2.89) (0.51) (2.31)

Event-Driven (1.18) (1.47) (0.81) (1.17)

s&p 500 (0.67) (0.68) (0.46) (0.70)

Bonds 0.39 0.04 (0.59) 0.95

Hedge funds promise to lower risk while delivering higher returns. When look at the standard deviations,

hedge funds usually provide much lower standard deviations that the S&P 500 and bonds. Only the

equity-driven strategies provide higher standard deviations.

For risk-averse investors, excess kurtosis combined with negative skewness is not desirable, as it

indicated a probability of greater losses. Jarque-Bera test defines a null hypothesis stating that data is

normally distributed, however when doing the Jarque-Bera test on the 5% significance level, we come

to the conclusion that most indices are not normally distributed.

3.1.2 The capital asset pricing model (CAPM)

The first performance model used is the CAPM. The intercept, is called Jensen’s alpha, seen as the

measurement of a fund’s ability to out-perform or under-perform the market. The slope, β, is a measure

of risk. It measures how sensitive the index is to the market. The CAPM calculates the returns through

two sources: the return generated from the investment style and the excess return generated from the

performance factor, which is generally recognized as “manager skill”. Manager skill is measured by the

Jensen’s alpha. A positive and significant alpha indicates a positive manager skill.

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The hedge fund industry has been able to escape regulations that aim to protect potential investors.

Due to the lack of transparency in the hedge fund industry, it doesn't surprise us that people associate

this lack of transparency to the positive risk adjusted returns (alpha) that hedge funds create.

The following graphs display the alphas during different periods.

In our CAPM model, for the Credit Suisse HFI, there is a positive and significant alpha. However, when

we divide our data for each sub period, for the years of 2007-2015, we notice that there isn’t a significant

alpha, meaning that in that period, hedge funds weren’t able to achieve a significant excessive return.

When looking at each strategy there is a significant alpha for all strategies except for the Global Macro

strategy in 1994-2001 period and 2007-2015 period, and the Event-Driven and Equity-Driven strategies

in 2007-2015. Only the Arbitrage strategy was able to achieve a significant alpha.

The betas estimated are rather low. Also looking at the p-value, we see that Premium Market Return

does not have a significant effect on hedge fund return. The betas of each strategy also increases

during times of crisis, this could indicate to a higher systematic risk.

Though as the CAPM is a rather simple model, we will further investigate whether this conclusion is

true or not.

TABLE 5: CAPM; 1994-2015

1994-2015 Alpha p-value βMkt-Rf R²


Equity 0.28 0.002221 0.48 0.68

Global Macro 0.29 0.006745 0.15 0.13

Arbitrage 0.32 7.85E-08 0.1 0.37

Event-Driven 0.34 6.09E-06 0.33 0.60

TABLE 6: CAPM; 1994-2001



Equity 0.65 0.000495 0.50 0.64

Global Macro 0.26 0.216042 0.26 0.28

Arbitrage 0.41 2.16E-05 0.12 0.27

Event-Driven 0.57 9.13E-05 0.30 0.51

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TABLE 7: CAPM; 2002-2006

2002-2006 Alpha p-Value βMkt-Rf R²

Credit Suisse HFI 0.52 1.69E-05 0.16 032

Equity 0.35 0.012003 0.39 0.65

Global Macro 0.54 0.006671 0.09 0.04

Arbitrage 0.41 5.97E-08 0.07 0.20

Event-Driven 0.55 8.88E-05 0.36 0.63

TABLE 8: CAPM 2007-2015



Equity (0.10) 0.422405 0.49 0.78

Global Macro 0.16 0.229471 0.06 0.05

Arbitrage 0.22 0.039595 0.23 0.51

Event-Driven 0.03 0.767807 0.35 0.69

FIGURE 4: CREDIT SUISSE HFI; MONTHLY ALPHA

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Credit Suisse Monthly Alpha + ε

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FIGURE 5: CREDIT SUISSE HFI; MONTHLY ALPHA; 1994-2001

FIGURE 6: CREDIT SUISSE HFI; MONTHLY ALPHA; 2002-2006

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Credit Suisse HFI Alpha + ε, 2002-2006

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FIGURE 7: CREDIT SUISSE HFI, MONTHLY ALPHA; 2007-2015

FIGURE 8: CREDIT SUISSE HFI; YEARLY ALPHA

Most hedge funds, particularly those investing in equities, claim that if they were to release information,

even in small quantities, it could hurt their returns, as they would be revealing their strategies to the

public and competitors. However, since the 2008 crisis, there has been greater demand for

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Credit Suisse Alpha + ε, 2007-2015

Page | 25

transparency. However, it should be noted that before increased regulation is implemented, people

need to learn more about hedge funds, as the wrong regulation could go against investors whom the

regulators are trying to protect (Aharzwal et al, 2010).

Furthermore, you'd expect that hedge fund managers to display exceptional investment skills, as they

charge exceptionally high fees to customers. There has also been a link shown between secrecy and

returns. Hedge fund disclosure may contain information which could allow potential followers to replicate

or shirt a funds' investment strategy at the fraction of the cost. Kat and Palaro (2006) show that reporting

hedge funds facilitates hedge fund replication. Hasanhodzic and Lo (2007), show that hedge fund

replication is feasible at a cost that does not exceed performance fees. The fact that there are hedge

funds that require secrecy seems to indicate that some funds are worried about the information content

of their disclosure (Agarwal et al., 2012).

Replication of hedge fund returns is also feasible at a cost that does not exceed performance fees

(Hasanhodzic and Lo, 2007). Kat and Palaro (2006) further confirm that it is easy to replicate hedge

funds, even with voluntary reporting. Furthermore, as some hedge funds want their strategies to require

secrecy, it seems to indicate that at least some funds are worried about the information content of their

disclosures. However, there are some hurdles that prevent exact copies of strategies (Bacmann et al,

2008).

3.1.3 The 3 Factor and the Carhart (4 Factor) Model

As mentioned earlier, we will be investigating market returns through multiple models, the 3 Factor

model of Fama French and the Carhart model are two of these models, and in the next section we will

discuss the 7 factor model.

Over the examined period, when using the 3 Factor Model and the Carhart model on the Credit Suisse

HFI, all factors are significantly different than zero, except for the HML-factor. This result also occurs

for the Macro and Equity-Driven strategies. However, for the Event-driven and the Arbitrage strategies,

it is the momentum factor which is not significantly different than zero, while the HML-factor is

significantly different than zero. Furthermore, when looking at each different strategy, in the 3-Factor

model, the HML-factor is not significantly different than zero for the Global Macro and Equity-Driven

strategies. The same holds true for in the Carhart Model. However, for the Event-Driven and Arbitrage

strategies, it is the momentum factor, which is not significantly different than zero.

When divided in each sub period, we notice that the momentum factor does not prove to be a strong

indicator of hedge fund behaviour. In nearly all cases, for the SMB-factor, there was a significant effect,

however this wasn’t the case for the HML-factor. There were also some cases where the alpha was not

significantly different than zero (for example for the Macro-strategy).

Page | 26

As for the alphas, in the period 2007-2015, no strategy was able to generate a significant alpha. Also

the betas of the Premium Market Return increases during times of crisis.

TABLE 9: 3 FACTOR MODEL; 1994-2015

Alpha P-value βMkt-Rf βSMB βHML R²

Credit Suisse

HFI

0.28 0.003668 0.26 0.10 (0.02) 0.42

Equity 0.28 0.000363 0.43 0.21 (0.04) 0.77

Global Macro 0.30 0.004574 0.12 0.07 (0.05) 0.16

Arbitrage 0.31 2.24E-07 0.16 0.05 0.06 0.40

Event-Driven 0.32 4.69E-06 0.31 0.16 0.08 0.67

TABLE 10: CARHART MODEL; 1994-2015

Alpha P-value βMkt-Rf βSMB βHMl βMOM R²

Credit Suisse

HFI

0.20 0.031108 0.30 0.09 0.02 0.11 0.48

Equity 0.24 0.002247 0.45 0.21 (0.02) 0.05 0.78

Global Macro 0.24 0.022144 0.16 0.06 (0.02) 0.08 0.20

Arbitrage 0.31 1.82E-07 0.16 0.05 0.06 (0.01) 0.40

Event-Driven 0.32 4.73E-06 0.31 0.16 0.08 (0.01) 0.67


1994-2001 Alpha P-value βMkt-Rf βSMB βHML R²

Credit Suisse

HFI

0.41 0.067144 0.37 0.20 0.12 0.41

Equity 0.68 8.67E-07 0.45 0.28 0.01 0.83

Global Macro 0.21 0.304063 0.29 0.17 0.11 0.35

Arbitrage 0.35 9.6E-05 0.16 0.10 0.11 0.40

Event-Driven 0.48 2.6E-05 0.36 0.25 0.19 0.72

Page | 27



Credit Suisse

HFI

0.35 0.001884 0.16 0.12 0.15 0.51

Equity 0.09 0.353582 0.36 0.26 0.18 0.85

Global Macro 0.27 0.156892 0.08 0.19 0.23 0.25

Arbitrage 0.31 1.32E-05 0.07 0.05 0.09 0.36

Event-Driven 0.31 0.006822 0.35 0.20 0.19 0.79



Credit Suisse

HFI

0.08 0.488023 0.31 (0.05) (0.14) 0.60

Equity (0.15) 0.198072 0.51 0.03 (0.16) 0.80

Global Macro 0.12 0.346468 0.10 (0.11) (0.10) 0.11

Arbitrage 0.19 0.073529 0.25 (0.02) (0.08) 0.53

Event-Driven 0.02 0.885416 0.35 0.01 (0.05) 0.69

TABLE 14: CARHART MODEL; 1994 -2001

1994-2001 Alpha P-value βMkt-Rf βSMB βHMl βMOM R²

Credit Suisse

HFI

0.20 0.34128 0.41 0.18 0.17 0.17 0.52

Equity 0.58 1.09E-05 0.47 0.27 0.03 0.08 0.85

Global Macro 0.10 0.625727 0.32 0.15 0.13 0.09 0.38

Arbitrage 0.35 0.000123 0.16 0.10 0.11 0.00 0.40

Event-Driven 0.51 1.64E-05 0.35 0.25 0.18 (0.02) 0.72



Credit Suisse

HFI

0.35 0.001968 0.18 0.11 0.14 0.03 0.51

Equity 0.09 0.354457 0.40 0.24 0.16 0.05 0.85

Global Macro 0.27 0.161132 0.09 0.19 0.23 0.01 0.25

Arbitrage 0.31 1.58E-05 0.08 0.05 0.09 0.00 0.36

Event-Driven 0.31 0.006922 0.33 0.21 0.20 (0.02) 0.79

Page | 28



Credit Suisse

HFI

0.07 0.49863 0.31 (0.05) (0.13) 0.01 0.61

Equity (0.14) 0.21048 0.50 0.03 (0.18) (0.04) 0.80

Global Macro 0.11 0.363914 0.11 (0.11) (0.07) 0.04 0.13

Arbitrage 0.19 0.057244 0.24 (0.02) (0.12) (0.06) 0.56

Event-Driven 0.02 0.861753 0.35 0.01 (0.07) (0.02) 0.70

Mithcell and Pulvino (2001) found that for Event-Driven strategies, hedge funds exhibit a significant

excess return of 27%.

All strategies considered, we find that hedge funds deliver excessive returns, prefer smaller stocks.

Most Event-driven managers prefer stocks with high book-to-market ratios. Furthermore, some Event-

driven managers follow a momentum strategy, while others follow a momentum contrarian.

Overall it seems that both models do a good job in describing hedge fund behaviour when looking R².

Only for the Global Macro strategies is still less the case.

3.1.4 The 7 Factor model

The last model discussed is the seven factor model of Fung and Hsieh.

When looking at the whole studied period, 1994-2015, we come to the conclusion that hedge funds

outperform the market. However, when we decide to divide the studied period over sub-periods, we

come to the conclusion that hedge funds weren’t able to outperform the market in the crisis period.

Furthermore, the Equity-Driven and Global Macro strategy weren’t able to outperform the market in the

2002-2006 period.

We also find that for most strategies that the SMB-factor is significantly different than zero.

All strategies considered, we find that hedge funds deliver excessive returns and prefer smaller stocks.

Furthermore, the betas of the market premium return increased during the times of crisis.

Overall it seems that both models do a good job in describing hedge fund behaviour when looking R².

Once again, only for the Global Macro strategies is still less the case.

Page | 29

TABLE 17: 7 FACTOR MODEL; 1994 - 2015

Alpha p-value βMkt-Rf βSMB β10YGBOND βSpr10year βptfsbd βptfsfx βptfscom R²

Credit Suisse

HFI

0.40 0.002538 0.27 0.12 (0.02) (0.025) 0.00 0.00 (0.01) 0.44

Equity 0.40 0.002065 0.44 0.23 0.11 (2.65) 0.16 (0.20) (1.69) 0.78

Global Macro 0.42 0.002883 0.15 0.10 (3.44) (2.63) 0.55 0.60 (1.24) 0.22

Arbitrage 0.28 0.004202 0.15 0.03 0.21 0.54 (0.78) (0.49) (0.27) 0.40

Event-Driven 0.40 0.000668 0.30 0.14 0.33 (1.30) (0.43) (0.58) (0.37) 0.67


1994-2001 Alpha p-value βMkt-Rf βSMB β10YGBOND βSpr10year βptfsbd βptfsfx βptfscom R²

Credit

Suisse HFI

0.86 0.005832 0.31 0.16 (0.09) (0.20) 0.01 0.00 0.00 0.50

Equity 0.64 0.000981 0.45 0.27 (0.01) 0.03 (0.01) 0.01 0.01 0.83

Global Macro 0.41 0.159107 0.25 0.12 0.03 (0.03) (0.02) 0.01 0.03 0.39

Arbitrage 0.36 0.005043 0.10 0.04 (0.01) 0.03 (0.02) 0.00 0.00 0.42

Event-Driven 0.49 0.004648 0.26 0.15 (0.02) 0.06 (0.02) 0.00 0.00 0.70



Credit

Suisse HFI

0.48 0.021702 0.14 0.10 (0.01) 0.00 0.00 0.00 (0.01) 0.44

Equity 0.25 0.109715 0.34 0.27 (0.02) 0.00 (0.01) 0.00 0.02 0.84

Global Macro 0.49 0.044306 0.10 0.13 (0.03) 0.02 0.02 0.03 0.03 0.47

Arbitrage 0.36 0.000448 0.05 0.07 (0.01) (0.01) (0.01) 0.00 0.00 0.33

Event-Driven 0.37 0.039147 0.31 0.22 (0.02) 0.01 (0.01) 0.00 0.01 0.76

Page | 30



Credit Suisse

HFI

0.11 0.462844 0.29 (0.04) (0.01) 0.00 (0.01) 0.00 (0.01) 0.59

Equity (0.33) 0.070577 0.47 0.00 0.01 0.03 (0.01) 0.00 (0.01) 0.80

Global Macro 0.05 0.791539 0.14 (0.12) 0.01 0.01 0.01 0.02 0.01 0.25

Arbitrage (0.08) 0.562127 0.21 (0.04) 0.01 0.04 (0.01) (0.01) (0.01) 0.61

Event-Driven (0.20) 0.204922 0.32 0.00 0.01 0.03 (0.01) (0.01) (0.01) 0.74

3.2 Correlation with traditional asset classes

Literature says, that thanks to the weak correlation between hedge funds and other securities, when a

hedge fund is added to a traditional portfolio, it will most likely improve the risk-return of this said portfolio

(Fung and Hsieh 1997). Furthermore, Fung and Hsieh (1997) found that hedge funds have much lower

correlations to standard asset than mutual funds. However, this doesn’t mean that hedge funds perform

poorly when asset markets perform poorly.

Studies show that hedge fund return are much less correlated to standard asset return than mutual fund

returns. (Fung and Hsieh, 1997). A possible reason for this conclusion is that hedge fund managers are

more skill-full than traditional fund managers. However, this view does not hold true as hedge funds

also perform poorly when asset markets perform very poorly. Another feasible argument is that risks

that hedge funds take are different than risks taken by mutual fund managers.

Hedge fund strategies are generally highly correlated with each other. First indicators suggest that

hedge fund strategies tend to follow the market in bearish periods, this tendency might be due to

investors following protection strategies.

Over the whole examined period, most hedge fund strategies exhibit a low to negative returns with

bonds. However, they exhibit a much higher level of correlation with equities, especially during times of

crisis. The hedge funds strategies also exhibit high levels of correlation with each other.

When looking at figure 8 and 9, we come to the same conclusions, hedge funds exhibit higher levels of

correlation with stocks during periods where there is a crisis, while their correlation with bonds is

negative.

Page | 31

This means that in times of crisis, hedge funds are not much safer than equities, while bonds are the “safer” alternative during those difficult times.

TABLE 21: CORRELATION; 1994-2015

1994-2015 Credit

Suisse HFI

Equity -

Driven

Global

Macro

Arbitrage Event-

driven

s&p Bonds

Credit

Suisse HFI

1

Equity 0.77 1

Global

Macro

0.77 0.55 1

Arbitrage 0.65 0.73 0.33 1

Event-

Driven

0.76 0.88 0.50 0.82 1

S&p 0.57 0.76 0.32 0.58 0.72 1

Bonds 0.07 (0.25) 0.16 (0.21) (0.27) 0.20 1


1994-2001 Credit

Suisse

HFI

Equity -

Driven

Global

Macro

Arbitrage Event-

driven

s&p Bonds

Credit

Suisse HFI

1

Equity 0.69 1

Global

Macro

0.88 0.63 1

Arbitrage 0.48 0.58 0.39 1

Event-

Driven

0.69 0.80 0.64 0.76 1

S&p 0.50 0.67 0.46 0.48 0.62 1

Bonds 0.15 (0.02) 0.28 (0.03) (0.05) 0.12 1

Page | 32


2002-2006 Credit

Suisse HFI

Equity -

Driven

Global

Macro

Arbitrage Event-

driven

s&p Bonds

Credit

Suisse HFI

1

Equity 0.83 1

Global

Macro

0.70 0.50 1

Arbitrage 0.84 0.67 0.53 1

Event-

Driven

0.86 0.92 0.44 0.68 1

S&p 0.55 0.78 0.14 0.42 0.77 1

Bonds 0.00 (0.31) 0.25 0.01 (0.24) (0.42) 1


2007-2015 Credit

Suisse HFI

Equity -

Driven

Global

Macro

Arbitrage Event-

driven

s&p Bonds

Credit

Suisse HFI

1

Equity 0.91 1

Global

Macro

0.59 0.41 1

Arbitrage 0.89 0.87 0.26 1

Event-

Driven

0.92 0.94 0.34 0.93 1

S&p 0.73 0.86 0.22 0.70 0.81 1

Bonds (0.34) (0.45) 0.03 (0.35) (0.45) (0.35) 1

Page | 33

FIGURE 9: MOVING CORRELATION; CREDIT SUISSE HFI & S&P 500; 24M

FIGURE 10: MOVING CORRELATION; CREDIT SUISSE HFI & BONDS; 24M

0

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Moving Correlation - S&P 500

-0.8

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Moving Correlation - Bonds

Page | 34

4. Conclusion

In 2007, Warren Buffet struck a $1m bet with Protégé Partners, a fund of hedge funds, that the hedge-

fund portfolio would underperform the S&P 500, after fees, over a period of ten years.

FIGURE 11: HEDGE FUNDS AFTER FEES

With around 19 months to go, Mr Buffet is almost certain to collect. However, hedge fund managers

would claim that the S&P 500 is not the best benchmark. They would claim that the goal of hedge funds

is to limit risk and deliver consistent returns.

In a world of low interest rates, low bond yields and low dividends, fees charged by hedge funds affect

net returns drastically. In the 1990s, the golden age of hedge funds, hedge fund managers and

managers of the likes of George Soros could easily deliver double-digit returns every year. All things

considered hedge fund managers should expect to lower their fees in the near future if they want to

maintain their operations.

However, in this study we used before-fees returns. We came to the conclusion that hedge funds indeed

did outperform the S&P 500 and bonds in blooming periods. However, hedge funds had difficulties

outperforming these benchmarks in time of crisis. Bonds were even the better and safer alternative

during these difficult times. Furthermore, hedge funds had a rather high correlation with equities in times

of crisis, and a negative correlation with bonds during the same period.

We also came to the conclusion that hedge fund managers prefer smaller stocks. Most Event-driven

managers prefer stocks with high book-to-market ratios. Furthermore, some Event-driven managers

follow a momentum strategy, while others follow a momentum contrarian.

Page | 35

Hedge funds also seem to perform better than equities and bonds while exhibiting lower standard

deviations. However, even if hedge fund managers can outperform equities, due to their high correlation

during times of crisis, it takes away any diversification power they have.

The major contribution of this thesis to the existing literature, is that we tried to describe the

attractiveness of hedge funds during multiple periods. Performances and correlations with equities and

bonds is reported for the first time in a pre- and post-crisis period. However, there are a few limitations

to this study. First and foremost, our returns were before fees. When analysing returns after fees, you

could get a very different image on the attractiveness of hedge funds. Second, it has been proven that

hedge fund databases suffer from biases, with different databases giving different results. Third, hedge

funds don’t often disclose their activities, making it more difficult to draw conclusions over all hedge

funds.

All statistics in this research refer to average returns. Of course there will always be managers who

outperform the market. But how do you spot them in advance? If it were that easy, would there be any

under-performing managers? With an industry of over 10,000 individual funds, run by managers who

are extremely clever and work diligently, are there enough investment options which cannot be seen by

others? No, as there are too few opportunities for too many funds, average performances will worsen.

So expect in the future the number of hedge funds to decrease.

Page | 36

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