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NAA301

Bachelor Thesis Business 15 hp

Tutor: Johan Lindén

Mälardalens Högskola

2011-06-08

Hedge Funds Strategies

Research on market correlation, market

neutrality and portfolio performance

improvements

Oluwayinka Ogunniyi

Ekpenmhene Ojeabulu

Nelson Tabe Arrey

Acknowledgement

Firstly, we would like to give thanks to The Almighty God for a spiritual insight and a sound

mind in the realization of this work. It would have never been a wonderful piece without

inspiration from above. Special gratitude goes to our supervisor, Johan Lindén whose

ingenuity and guidance made this project a dream come true. His endless support both in and

out of class provided us with all the necessary academic ingredients for any present and

future engagements.

______________________________ _____________________________

Oluwayinka Ogunniyi Ekpenmhene Ojeabulu

_________________________________

Nelson Tabe Arrey

Västerås, 2011

Abstract

Date: 2011-06-08

Level: Bachelor Thesis in Economics, 15hp

Authors: Oluwayinka Ogunniyi, Ekpenmhene Ojeabulu and Nelson Tabe Arrey

Tutor: Johan Lindén

Aim: This study analyze the market neutrality of market neutral hedge funds

strategies given that hedge funds seeks to provide positive returns completely

free of market conditions and also consider their performance against the

market. We begin this study with a look into the hedge fund world and a

description of the hedge funds is given so as to provide to the reader a clearer

view of the hedge fund strategies. Then an analysis is made using monthly

data from Barclay hedge fund database, S&P500 and T-bills from the period

of January 2006 to March 2011 to evaluate if market neutral hedge funds can

eliminate market risks using financial theories as a guide.

Conclusion: In conclusion, using a correlation coefficient, the assumption that market

neutral hedge fund strategies can eliminate market risk has to be rejected for

all of the market neutral hedge fund strategies since the result goes against the

relative value strategy theory that hedge fund strategies are expected to be

market neutral.

Table of Contents 1 INTRODUCTION ............................................................................................................................... 5

1.1 BACKGROUND INTRODUCTION .............................................................................................. 1

1.2 PROBLEM DISCUSSION ............................................................................................................ 2

1.3 RESEARCH QUESTION ............................................................................................................. 2

1.4 RESEARCH PURPOSE ............................................................................................................... 2

1.5 DELIMITATION OF THE STUDY ................................................................................................ 2

1.6 METHODOLOGY ...................................................................................................................... 3

1.7 STRUCTURE OF THE THESIS ..................................................................................................... 4

2 FINANCIAL THEORIES ...................................................................................................................... 5

2.1 UNDERLYING MARKET THEORIES ........................................................................................... 5

2.1.1 EFFICIENT MARKET HYPOTHESIS (EMH) ............................................................................. 5

2.1.2 BEHAVIORAL FINANCE ........................................................................................................ 5

2.2 HEDGE FUND THEORY ............................................................................................................. 7

2.2.1 DESCRIPTION OF HEDGE FUND STRATEGIES AND STYLES .................................................. 8

2.2.1.1 MARKET NEUTRAL STRATEGIES (RELATIVE VALUE STRATEGIES) ........................................ 8

2.2.1.2 EVENT DRIVEN HEDGE FUNDS ............................................................................................ 9

2.2.1.3 TACTICAL/DIRECTIONAL HEDGE FUNDS ........................................................................... 11

2.2.1.3 HYBRID HEDGE FUND ........................................................................................................ 13

2.3 CORRELATION AND MARKET NEUTRALITY ........................................................................... 14

2.4 MODERN PORTFOLIO THEORY .............................................................................................. 16

2.4.1 MARKOWITZ PORTFOLIO SELECTION THEORY AND EFFICIENT PORTFOLIO ..................... 16

2.4.2 CAPITAL ASSET PRICING MODEL (CAPM) .......................................................................... 19

2.4.3 RISK -ADJUSTED PERFORMANCE MEASUREMENT OF PORTFOLIO ................................... 21

2.4.3.1 SHARPE RATIO ................................................................................................................... 22

2.4.3.2 TREYNOR RATIO ................................................................................................................ 22

2.4.4 SKEWNESS AND KURTOSIS ................................................................................................ 23

3 ANALYSIS ................................................................................................................................... 25

3.1 CHOICE OF TIME PERIOD ...................................................................................................... 25

3.2 DESCRIPTION OF HEDGE FUND DATA ................................................................................... 26

3.3 RETURN AND STANDARD DEVIATION ................................................................................... 26

3.4 MARKET NEUTRALITY ............................................................................................................ 27

3.4.1 CORRELATION NEUTRALITY HYPOTHESIS ......................................................................... 27

4 RESULTS......................................................................................................................................... 29

4.1 RESULTS REGARDING THE RESEARCH QUESTION ................................................................. 29

4.1.1 CORRELATION COEFFICIENT ANALYSIS OF HF SAMPLES OVER THE WHOLE PERIOD ....... 29

4.1.2 RESULTS OF CORRELATION MARKET NEUTRALITY BASED ON CORRELATION MATRICES

29

4.1.3 CORRELATION MATRIX REGARDING THE WHOLE TIME PERIOD FROM 2006 to MARCH

2011 29

4.1.4 BETA VALUES REGARDING THE WHOLE TIME PERIOD FROM 2006 to MARCH 2011 ....... 30

4.1.5 ASSESSMENT OF CORRELATION/BETA MARKET NEUTRALITY BASED ON A PERFORMANCE

RANKING 31

CONCLUSION ......................................................................................................................................... 34

REFERENCE ............................................................................................................................................ 35

APPENDIX ................................................................................................................................................ 1

List of Figures

Figure 1: Minimum-Variance Frontier and Set of Risky Assets……………………………. 18

Figure2: Expected Return - Beta Relationship (SML)……………………………………… 20

Figure3: Positive and Negative skewness…………………………………………………. . 23

Figure 4: showing the kurtosis……………………………………………………………… 24

List of Tables

Table1: summary of the number of strategies considered under HF……………………….. 26

Table 2: Performance ranking using Treynor Ratio………………………………………… 33

Hedge Funds Strategies - Research on Market correlation, Market

neutrality and portfolio performance improvements

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1 INTRODUCTION

This chapter introduces the topic and provides background information about the history and

development of the hedge fund industry. It also presents the research question, purpose and

an outline of the delimitation.

1.1 BACKGROUND INTRODUCTION

Over the years, there has been a growing awareness of hedge funds amongst both investment

professionals and the general public. They are a prominent feature on the investment

landscape as over two trillion dollars in assets worldwide are managed by hedge funds. The

size of the hedge fund market has grown dramatically in both size and number over the last

few years. Industry experts estimate around 8,000 hedge funds operating globally, mainly in

the USA, with hundreds based in the UK – primarily in the West End. This is without a doubt

due to the stable financial environment witnessed in the 1900s, characterized by the soaring

financial market asset values and increased wealth generated by a rising stock market. There

was an estimated 70 hedge funds in 1990 compared to today with over 50,000 (Lavinio,

Stefano, 1999).

Hedge funds are aggressively managed portfolio of investments that uses advanced

investment strategies such as long-short positions, leveraged and derivative positions in both

domestic and international markets aimed at reducing volatility and risks while trying to

preserve capital and generating high returns under all market conditions. These different

strategies each having its uniqueness yields returns on investments with volatility and risk

that vary largely. Some strategies, not correlated to equity markets can deliver returns with

the possibility of loss extremely low while there are others which maybe more volatile than

mutual funds. A good fund recognizes these differences and combines various strategies and

asset classes together to create long-term investment returns that are stable.

As the market has grown, so has their complexity leading to the development and use of

powerful quantitative methods, sophisticated software and increasingly powerful computers.

Modern hedge funds offer various strategies, many of which do not include traditional

hedging techniques.



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1.2 PROBLEM DISCUSSION

Due to the ability to go short or long and to use derivatives, a view has arisen that hedge

funds can behave market neutral hence offer an investment that has low to zero correlation

with most traditional portfolios. This implies that hedge fund returns can be low or might

even be uncorrelated with market movements. Correlation with market movements depends

amongst others on the investment strategy of the specific fund and the bullishness or

bearishness of the underlying market. It is questionable whether this widespread opinion truly

reflects reality regarding all different kinds of hedge funds or if this is merely a prejudice

fostered by the funds themselves.

1.3 RESEARCH QUESTION

Can a hedge fund that is “market-neutral” eliminate market risk?

1.4 RESEARCH PURPOSE

The aim of this paper is to carry out a research on market correlation, market neutrality and

the improvements of portfolio performance and investigate if a market neutral hedge fund

strategy can eliminate market risk using the correlation coefficient and the beta value.

1.5 DELIMITATION OF THE STUDY

It is important to note that information and performance of the hedge fund industry is guarded

with the utmost secrecy, this implies that the quality value of information used in this thesis

cannot be as complete compared to the information available with regard to other traditional

asset classes such as mutual funds.

Given these hindrances, the authors were able to acquire information about the hedge fund

strategies from the Barclay‟s Database, the risk-free interest rate from the US Federal

Reserve and the S&P500 data from yahoo finance, all in the US. Focus will be placed on the

different hedge fund strategies used in this thesis hence all analysis will be based on the

information acquired from the database stated above.



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1.6 METHODOLOGY

In order to carry out a good investigation to arrive at a reasonable conclusion and fulfill the

academic purpose of the study, the authors chose to apply a formal methodological

framework.

Research Approach

There are two types of research approach, they are qualitative and quantitative. Qualitative

approach is used when the aim of the study is to gain a deeper insight into the setting of the

problem. It involves exploring issues, and understanding phenomenon, more subjective

(describes a problem based on personal experience. This approach applies the use of non-

structured or semi-structured techniques e.g. interviews and open-end questions. Quantitative

approach on the other hand involves numerical information. The aim is to classify features,

count and construct statistical models in order to give a definitive description of what is being

observed. Just as the name goes, information gathered is in the form of numbers and

statistics.

Based on the description given above, this study will make use of the quantitative approach.

The analysis will be based on identifying and selecting various hedge fund strategies and

evaluating the performance of these hedge funds against the market.

Choice of Theories

The authors chose to introduce two mutually exclusive market theories, the Efficient Market

Hypothesis (EMH) and the Behavioral Finance in order to present to the reader an

understanding of how investors act in the market and what the causes of their actions are.

This is important to understand and validate the behavior as well as the performance of the

hedge funds as it builds the basis of the investigation.

Moreover, it is essential to have an understanding of the difference hedge fund classes, hence

the introduction of the classes which will provide a clearer picture of the returns and risk

taking of the different hedge fund strategies. This knowledge is important for the justification

of the results that will be presented later in the study.

The Markowitz Portfolio Theory and Modern Portfolio Theory were chosen for portfolio

considerations because older theories are of no value today. The Treynor Ratio will be used



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as a performance measurement tool to rank the different efficient strategies in order to see if

the strategies that eliminate market risk also outperform the proxy.

Data Collection

There are two primary ways of collecting data, the primary and secondary data collection.

The primary data collection involves interviews, experiments and observations designed

specifically for the study while the secondary data (printed data not designed specifically for

the study) involves literature, sources acquired from websites, and lecture materials etc. the

primary source of data collection is the secondary data.

Data used in this study

Materials used in this thesis are mainly from websites, articles and books all related to the

study area. This information was used to build the theoretical aspect of this thesis. Data from

Barclay Hedge fund, T-bills and S&P indices were used in this study to carry out the analysis

and make conclusions.

1.7 STRUCTURE OF THE THESIS

Chapter 2 – Financial Theories

In this chapter, the authors introduces the theories and also describes in detail the different

hedge fund strategies

Chapter 3 – Analysis

This chapter outlines the analytical procedure in detail. It describes how the results that will

be used in answering the research question are created

Chapter 4 - Results

The results that are used in the research question are presented

Chapter 5 – Conclusions

The research questions are answered and the purpose of the study will be fulfilled.



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2 FINANCIAL THEORIES

This chapter provides the theoretical background information that is needed to create a

better understanding and financial aspects of the research study. It also includes the

description of the different hedge fund strategies used in this study.

2.1 UNDERLYING MARKET THEORIES

To get a better understanding of the strategies and performances of hedge funds, one has to

put into consideration the consistence of their environment, the capital markets. The two

most discussed and used theories, the Efficient Market Hypothesis (EMH) and the Behavioral

Finance theories will be discussed below.

2.1.1 EFFICIENT MARKET HYPOTHESIS (EMH)

EMH suggests that investors cannot expect to outperform the market consistently on a risk-

adjusted basis. The hypothesis does not say that an individual will not outperform the market

since obviously some investors may do exceptionally well for a period of time. The EMH

argues that a security‟s price adjust rapidly to new information and must reflects all known

information concerning the firm. Since price changes rapidly, day-to-day price change will

follow in a random walk over time. Random walk means that price changes are unpredictable

and patterns formed are accidental. The EMH suggests that the financial markets are

efficient. The financial markets are weakly efficient, semi strongly efficient or strongly

efficient. The weak form of EMH claims that all past prices of a stock are reflected in today's

stock price. Therefore, technical analysis cannot be used to predict and beat a market. The

semi strong form of EMH suggests that only information that is not publicly available can

benefit investors seeking to earn abnormal returns on investments. All other information is

accounted for in the stocks price and, regardless of the amount of fundamental and technical

analysis one performs, above normal returns will not be had. Under strong form, all

information in a market, whether public or private, is accounted for in a stock price. Not even

insider information could give an investor the advantage.

2.1.2 BEHAVIORAL FINANCE

Over the past few decades, behavioral finance has become a households name in the finance

industry. Many financial institutions now offer financial services which trades on strategies



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that are partly based on behavioral finance finding. For instance, pension plans in which

people have to decide on how to invest their retirement money use findings from behavioral

finance to help investors improve their investment strategies. And, any hedge funds act based

on strategies originated from behavioral finance.

Behavioral finance aims at improving the understanding of financial markets and its investors

by applying the insights from behavior sciences .i.e. (psychology and sociology). This in

sharp contrast to the traditional finance model, which seeks to understand financial decisions

by assuming that the markets and many of its investors are rational; that is, they acts in an

unbiased fashion and make decisions, by maximizing their self-interest. In essence, the

economic concept of rationality means that economic agent makes the best choices possible

for themselves.

Although appealing, this concept entails strong and unrealistic assumptions about human

behavior and the functioning of financial markets. For example, economic agents are

assumed to process new information correctly and make decisions that are normatively

acceptable (Barberie and Thaler, 2003). Agents must be capable of integrating and

considering many different pieces of information relating to assets and must fully understand

the future consequences of all their actions. Moreover, financial markets must be frictionless,

such that security prices reflect their fundamental values and the influence of irrational

markets participants is corrected by rational traders.

On the contrary, human beings and financial markets do not posses all of these capabilities or

characteristics. For examples, people fail to update beliefs correctly (Tversky and Kahneman,

1974) and have preferences that differ from rational agents (Tversky and Kahneman, 1979).

People have limitations on their capacity process information, and have bounds on

capabilities to solve complex problems (Simon, 1957). Moreover, people have limitations in

their attention capabilities (Kahneman, 1973), and only care about social considerations. In

addition, rational traders are bounded in their possibilities, or may even be absent such that

markets will always correct this „non-rational‟ behavior (Barberie and Thaler, 2003).

For this reason, classic finance theories might give a bad explanation of behavior. In fact,

many studies prove this suggestion in the aggregate behavior of financial markets, the

individual trading behavior of individual investors and the behavior of managers (see



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Campbell 2000, Hirshleifer,2001, Ruback and Wurgler ,2006 and Campbell 2006 for

excellent reviews in the main finance field of asset pricing, Corporate finance and household

finance). For example, numerous evidence shows that the most important tradition asset

pricing theory, the CAPM is inconsistent with much empirical regularity found in cross-

sectional asset pricing data, showing that one group of stocks earn higher (risk-adjusted)

returns than another. Moreover, stock and bonds returns are predictable based on various

macro economic variables, as well as investor‟s sentiment measures (Fama and French, 1988,

1989, Whitelaw, 1994, Cremers, 2002, Avramov, 2004, Bakar and Wurgler, 2007). Hence,

not all information is correctly included in market prices. In addition, another traditional

finance anomaly is the equity premium puzzle which says stock outperforms bonds over long

horizons by a difference that is too large to be explained by any rational asset pricing theory

(Mehra and Prescott, 1985). Furthermore, individual investor generally holds investment

portfolios that are insufficiently diversified or non-preferred (Benartzi, 2001 and Benartzi and

Thaler 2002) and that under-performed benchmarks due to excessive trading (Barber and

Odean, 2000).

On the contrary, the major thought behind behavioral finance is that investment behavior

exists, that differs from what the traditional finance model assumes, and this behavior

influence financial markets. In fact, some recent studies show that the behavioral finance

theories can explain some of the result of traditional finance leave unexplained. Shleifer and

Barberie (2003) explain the high (lows) returns after good (bad) earnings announcements,

high (low) returns for recent winners (losers), and the reversal of these recent winners or

losers returns over long horizon, by modeling various behavioral biases and limitations

investors are subject to.

In fact, findings from behavioral finance have demonstrated to be excellent tools for

improving the decisions of individual investors, especially in investment decisions for

retirement.

2.2 HEDGE FUND THEORY

In the previous section, the theoretical market theories were presented and discussed in detail.

In this section, the different hedge fund strategies and their classes are described in more

details.



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2.2.1 DESCRIPTION OF HEDGE FUND STRATEGIES AND STYLES

This section presents the different hedge fund strategies and styles. This is important because

the analysis of this study will be based on the performance of the hedge funds.

2.2.1.1 MARKET NEUTRAL STRATEGIES (RELATIVE VALUE

STRATEGIES)

This is a strategy undertaken by an investor or investment manager that seek to make profit

from both increasing and decreasing price in a single or many/numerous markets i.e. an

investor goes long in certain instruments while shorting other in such a way that his portfolio

has no net exposure to broad market moves. The goal is to profit from relative mispricing

between related instruments, going long on those that are perceived to be underpriced while

going short on those that are perceived to be overpriced to avoid systematic risk. In Equity

markets, market neutral strategies take several forms and these are explained below.

Equity Market neutral

it is a strategy that emphasizes on fundamental stock picking (tactically, it is a situation in

which investor/analyst uses a systematic form of analysis to conclude that a particular

stock will make a good investment and should be added to his or her portfolio). A portfolio

of long and short positions is maintained to be beta neutral. If the portfolio holds foreign

equities, foreign exchange risk will generally also be hedged away. Long and short positions

are also managed to eliminate net industry, market capitalization, regional or other exposures,

therefore, creating zero net exposure to the market1.

Statistical Arbitrage (stat arb)

It is an equity trading strategy that employs time series methods to identify relative

mispricing between stocks. One technique is pairs trading. Pairs of stocks whose prices tend

to move together. I.e. they are co-integrated and are identified. If the historical price

relationship between them is ever violated, a long-short position is established in the two

stocks in anticipation of the relationship being reestablished. The rationale is that the

abnormal price move was a liquidity effect caused by a large buy or sell order for that stock,

and it will reverse over time. Individual pairs will generally not be market neutral, but the

overall portfolio of pairs can be managed to be market neutral.



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There are various risk neutral strategies and are employed in fixed income market, and are

collectively called Fixed Income Arbitrage.

Fixed income arbitragers try to identify when historical patterns for spreads or term structure

relationships have been violated and put on a long-short position in anticipation of the

historical relationship being reestablished. They also look for situations where credit

risk or liquidity risk is being over compensated and will then put on a long-short position that

earns “positive carry”.

Convertible arbitrage

This strategy exploits mispricing of a firm's convertible bonds. Convertible bonds have

complex exposures to interest rates; the issuer's credit quality, liquidity spreads, the issuer's

stock price, and implied volatility. This makes them extremely difficult to price. Hedge funds

develop sophisticated pricing methodologies and go long or short on convertible bonds they

perceive to be mispriced. To maintain market neutrality, they will generally hedge the

position with positions in the issuer's debt and/or equity.

2.2.1.2 EVENT DRIVEN HEDGE FUNDS

Event driven hedge funds utilize strategies that try to capture profits from specific one time

opportunistic events. This strategy is called event driven due to the fact that it doesn‟t depend

on the overall market performance but is rather driven by special events that may occur

during or after a corporate event such as bankruptcy, merger, acquisition or spinoff. This

implies that managers of hedge fund profit relatively well when employing this strategy given

any market situation (Frush, 2007, p.115). This strategy is usually employed by larger

institutional investors such as hedge funds and private equity firms. This is because tradition

equity investors lack the expertise necessary to analyze such corporate events when they

occur.

There are basically four types of strategies under the event driven strategy. These include

distressed securities, reasonable value, merger arbitrage, and opportunistic events. Although

each strategy is similar, they do have different variations in important ways.



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Merger Arbitrage or Risk Arbitrage

This strategy does not rely on the market, since merger/acquisition transactions takes place

during the periods in the market. It takes advantage of price discrepancies developed by

mergers and acquisitions opportunities. If it‟s perceived that an acquisition has a high

probability of occurring, the fund will usually take a long position in the company being

acquired and a short in the acquirer company. It would be a reversed situation if the

probability of occurrence is low, instead it will take a short position and vice versa.

A disadvantage of this strategy is that they entirely reliant on merger related activity which

implies that the more the merger related activities available, the more the opportunities. The

merger arbitrage strategy generates high profits but compared to the high level of risk

involved, the risk-return relationship is less compelling. The returns of the merger are low but

the standard deviations are limited.

Reasonable Value

This is a strategy employed by hedge fund managers whereby they invest in securities selling

at discounts to their estimated value as a result of being relatively unknown or out of favor in

the investment community. Quite similar to the distress strategy but unlike its counterpart, it

places more emphasis on securities with lower levels of default risk thereby leading to greater

opportunities for hedge fund managers. This strategy is not popular amongst hedge fund

managers.

The strategy is to long a particular security and short sell a comparable security which allows

the hedge fund manager to separate the desired company-specific risk and return potential

and reduce the market risk from both offsetting long and short positions (Frush, 2007, p.117).

This is not a popular strategy amongst hedge fund managers.

Distressed Securities

Distressed companies face financial or other business complications. The announcement of

reorganization to deal with these financial or business issues often cause a price disparity of

the underlying securities, debt or equity with the market. Distressed security managers use

their expertise and understanding to analyze distressed companies to determine whether the

underlying security is undervalued.



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Opportunistic Events/Special Situations

This is a strategy where investments are made on securities experiencing short term event

driven opportunities, are one-timed, but yields returns that are high. It tends to make use of a

combination of strategies depending on the outlook of the hedge fund manager on the

economy. The switching of strategies in the opportunistic event strategy is to utilize the

strategies that will result in the most profitable opportunities. Examples of such event driven

opportunities are initial public offerings (IPOs), seasoned stock offerings and new business

awarded, amongst others. The opportunistic strategy is similar to the merger arbitrage

strategy but there are two differences. First is that the merger arbitrage depends on merger

related activities and not on impromptu opportunities that opportunities call for. Second, the

hedge fund merger arbitrage manager grasps the opportunities as soon as a deal occurs.

Like the other event driven strategies, the opportunistic strategy does not rely on the

performance of the market. Financial instruments used are debt securities, put options

spreads, index out options, common stock, preferred stocks and warrants.

2.2.1.3 TACTICAL/DIRECTIONAL HEDGE FUNDS

The tactical hedge fund strategy most commonly used by the hedge fund managers uses

minimum leverage and instead operates as long-term and low turnover investment. It refers to

strategies that speculate on the direction of market prices of currencies, commodities, equities

and/or bonds.

There are seven types of strategies under the tactical/directional hedge fund strategy. They

include; macro-centric, sector-specific, managed futures, long/short Equity, Emerging

markets, Market timing, and selling short.

Macro-centric

In this strategy, the hedge fund manager invests in securities aimed at making profits from the

changes occurring in the general market (both foreign and domestic) resulting from any

macroeconomic and government influence and intervention. They are usually broad based

investment, playing on foreign exchange movements or investing in market indices. This is

called the high risk „top-down‟ investment approach. Given this strategy, leveraging is

usually used to intensify the investments result. An advantage of this strategy is that it takes



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hold of the inconsistencies between current and forecasted prices. However, the challenge is

in the ability to identify the best time to invest in a certain asset. The source of return lies in

the spread between the intrinsic valuation and the current valuation (Frush, 2007, p. 126)

Sector-Specific

This is a strategy where hedge fund managers having the expertise of a particular sector

invest in securities in that sector owing to the growth prospects, hence the name sector-

specific. These sectors include technology, pharmaceutical, utilities, etc. The strategy is to

invest in long holdings of equities and short sell equities or equity market indices to reduce

the total market risk. However, doing so exposes the hedge fund to sector-specific risk which

in-turn increases the sector-specific return.

Managed Futures

A hedge fund strategy undertaken by hedge fund managers that invests in futures and

currencies on a global basis driven with the focus of making attractive profits. The most

common form of this strategy involves the use of the systematic approach to trade in widely

diversified markets and contracts based on identified trends. Hedge fund managers using this

strategy usually liquidate their positions if trends fails or reverses to fall through as expected.

Equity Long/Short

This strategy, established by Alfred Jones has the tendency of going long or going short on

equity securities. Hedge fund managers go long on securities that they believe will increase in

value and short on securities that will decrease in value. Usually a hedge fund will leverage

its position to maximize any potential profits to be realized. The goal is to reduce the total

risk of the portfolio by minimizing the overall market exposure while at the same time

profiting from the change in the differences or spread between both securities. There are two

types of exposure, they include;

Equity Long/Short Bias

Given the level of exposure, an equity long/short manager can choose to a take a high level of

exposure to the market. Equity long bias and equity short bias strategies lays emphasis on

levels of exposure on either long or short positions respectively. An equity long/short



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manager is said to take a long bias when its level of exposure in the long position is higher

than its level of exposure in its short position. An example is the so-called 130/30 strategies

where hedge funds have a 130% exposure to their long positions and 30% exposure on their

short positions.

The equity short bias is a short term strategy. Not popular amongst hedge funds, since the

equity market tend to move up over time.

Emerging Markets

This hedge fund strategy specializes in investing in the securities of emerging market from

less developed countries with the aim of profiting from market growth or improvement in the

economy which has a positive effect on the emerging market. However, these markets are

considered volatile and subject to fluctuating inflation. The benefit of this strategy is that it

proffers one of the lowest correlations with other hedge fund strategies, thereby reducing

risks and enhancing returns.

Market Timing

Market timing is the strategy of making buy or sell decisions of financial assets (often

stocks), by attempting to predict future market price movements. The market timing strategy

attempts to forecast the future market direction based on the position of market or economic

conditions resulting from technical or fundamental analysis. The strategy aims to profit on the

appropriate timing of securities across markets by moving between different assets classes

depending on the views of the hedge fund manager on the market environment.

2.2.1.3 HYBRID HEDGE FUND

This is a combination of other hedge fund strategies to some degree or another. There are

three types of strategies under this hedge fund style and they include; the multi-strategy

funds, values-based funds and fund of funds. Of the three, fund of fund is the most commonly

used strategy employed by hedge fund managers.

Multi-strategy Funds

This type of strategy involves employing various strategies at the same time to realize both

short and long term profits. Hedge fund managers may choose to employ this strategy in



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order to diversify their portfolios or to avoid limitations on the investment opportunities. This

strategy allows the manager to overweight or underweight the different strategies that provide

the most out of the current investment. This expected volatility with the strategy is variable.

Fund of Funds

It is a diversified portfolio with usually uncorrelated hedge funds. With this fund, hedge fund

managers invest in portfolios of other investment funds rather than investing directly in

securities in an attempt to gain exposure to all of them. The aim of combining different

strategies and asset classes is to provide a more stable long-term investment return while still

delivering lower investment volatility than a single hedge fund. Risks, returns and volatility

can be controlled with the combination of different strategies and funds.

Value-Based Funds

This is a strategy employed by hedge fund managers based on specific religious beliefs and

principles. This hedge fund utilizes directional bets and attempts to take advantage of event

driven opportunities using leveraging and short selling (Frush, 2007, p. 153). The main

distinction between this strategy and other hedge fund strategies is the type of asset that this

strategy cannot invest in, for example, “sin” stock such as stocks in armaments, tobacco,

alcohol and gambling.

2.3 CORRELATION AND MARKET NEUTRALITY

In order to achieve uncorrelated returns in a highly correlated market, a strategy is required

that completely eliminates market risk (beta) so the returns that are generated are derived

exclusively from stocks selection as opposed to market movement. Such strategies are termed

Market- neutral. There are many ways to define market neutrality. All of them are potentially

of interest for risk-averse investors. Some examples of market-neutral are dollar neutrality;

variance and value-at-risk neutrality are some examples. We won‟t discuss these examples on

this paper since it‟s not very important here. Our paper deals with the very basic conception

of correlation neutrality. The relationship between the HF returns and a market index is

expressed by the correlation coefficient.

There are two possible definitions of correlation neutrality. One says that HF returns have to

show a correlation with the underlying market around zero in order to be named market



15

neutral. In the other definition a correlation around zero or below is sufficient. This last

definition evolved because of the favorability of low and negative correlations, since this is

what investors seek for. As we mentioned in the introduction, most assets show highly

positive correlations.

From our point of view, a highly negative correlation does not necessarily signify correlation

market neutrality. Even if it might be desirable, it still implies that market movements

influence the asset. To assume a correlation close to zero as the relevant indicator is therefore

theoretically sounder. For purposes of this study the degree of correlation is more important

than looking at critical values. On the other hand, in order to define a border, this paper

assumes that all correlation coefficients are zero indicating market neutrality. This is a quite

strict assumption but reasonable when emphasizing on diversification potential.

The relevant markets, the HFs have to be compared with the stock or equity markets. Here,

the correlations between assets are higher. So being market neutral in regard to the stock

market is a real challenge. That is why the term “market neutrality” usually refers to the

market neutrality in regard to the stock market. Rising interest rates cause investors to shift

capital from the stock to the bond market. So market neutrality to the stock market is

important since all assets that provide high returns also tend to show high correlations in

regard to this market.

In general, correlation deals with the issue, how two or more variables are related to each

other. This paper will deal with the most common known and easiest case by comparing two

variables, HFs and market benchmarks, regarding their return and standard deviation. The

strength of the linear relationship between those two variables is measured by correlation

coefficients with ranges from –1 ≤ 0 ≤ 1. Correlation is calculated by using the covariance

and the standard deviations of each variable. The formula of the so-called Bravais/Pearson

Correlation Coefficient looks like the following:

(1)



16

2.4 MODERN PORTFOLIO THEORY

Portfolio theory deals with the problem of how to optimally allocates a given some of money

amongst various investments. In our paper we are going to evaluate how HFs can be included

in optimal risky portfolio. We are going to use the basis theories, Markowitz portfolio

selection theory and the CAPM.

2.4.1 MARKOWITZ PORTFOLIO SELECTION THEORY AND EFFICIENT

PORTFOLIO

The basic concept of modern portfolio theory (MPT) was developed by Harry Markowitz in

his 1952 journal of finance article “Portfolio Selection”. He identified the risk-(expected)

return trade-off an individual is facing when making an investment decision. The decision is

not merely about which securities to own but how to divides investor‟s money amongst

different assets in order to create an efficient portfolio which provides the highest expected

returns at a given level of risk. In his theory, he took into account both risk and expected

return. Hence in MPT, the objective of the investors is to maximize utility as utility consider

both returns and risk.

Markowitz developed an analytical tool to spot out efficient portfolios which formed the

efficient frontier. The efficient frontier represents the geometrical of all efficient return-risk

combinations diversification i.e. the investment in different assets which are not correlated

completely positively. To compute the efficient portfolio, it‟s important we calculate the

expected returns, variance/standard deviation and covariance/correlation. The expected return

of the portfolio is defined as the weighted sum of the expected returns of single investments.

E( ) =

=

(2)

where,

E

To compute the variance, it‟s necessary to calculate the interrelation between two securities.

This is because of compensating effects that exists between single components of the



17

portfolio (correlation) which are measured by the covariance. It might leads to a decrease of

the variance of the entire portfolio. Covariance is a statistical measure that expresses the

dependence of the return of developments of two investments. It determines the dimension of

the portfolio risk reduction by a spreading of investment. Variance or standard deviation

usually decline if investment alternatives in a portfolio are not completely positively

correlated. To an optimal portfolio we combine single different investments in such a way

that the resulting combination yields the lowest variance or standard deviation thus reducing

risk.

Covariance of two investments can be shown as follows below:

(3)

where,

The correlation coefficient is a relative measurement for the correlation. It value ranges

between (+1) and (-1). For this reason, the lower the correlation coefficient between return of

the asset in a portfolio the greater are the diversification. The correlation coefficient is the

significant factor in the process of creating portfolio as at a given level of the return the

average risk of investment alternatives in the portfolio can be downscaled by combining

assets with a negative correlation coefficient. It is necessary to mention that diversification

does not eliminate or reduce all risk. Risk that can be diversified is called unique,

nonsystematic or firm-specific risk. Risks that cannot be diversified away are called market

risk, systematic risk or just non-diversifiable risk. If some of the portfolio risk is market risk,

it cannot get eliminated completely through diversification. The motive why diversification

does not affect market risk is that this risk comes from market wide risk sources that affect all

companies in the same way. For instance, changes in technologies, that cause a whole

industry to decline, represent systematic risk.



18

It‟s widely recognized in finance that imposing portfolio weight constraints usually leads to

better out-of-sample performance of mean-variance efficient portfolios. As one can imagine

finding the optimal weights of securities in a portfolio is therefore a difficult process. It can

be done by portfolio optimization software considering the vast number of different securities

available to choose from. Such software uses the mean-variance rule. This means, it first

computes all attainable combinations of given securities, which will result in a kind of bubble

in the standard deviation-expected return graph.

In the next step, the minimum- variance frontier is calculated by looking at the minimum

variance given returns. Investors will never consider holding a portfolio below the minimum

variance point. They will always get higher returns along the positively sloped part of the

minimum-variance frontier. The theory of Efficient Frontier narrows down the different

portfolios from which the investor may choose.

Figure 1: Minimum-Variance Frontier and Set of Risky Assets

The third step is to eliminate all inefficient combinations to get the efficient frontier. All

portfolios that lie on the efficient frontier give the best possible risk-return combinations.

There exist no other portfolios that give a higher return for the same risk. The inefficient

portfolios on the minimum-variance frontier, which have the same risk, but less return than

other possible portfolios, all lie beneath the minimum variance portfolio. For this reason, this

part of the minimum-variance frontier has to be eliminated to keep the efficient part of the

curve only. The minimum-variance portfolio is the portfolio with the lowest variance. After

this estimation the efficient set will be only half of the curve. The efficient frontier can be

expanded through the negation of the restriction that the weight of each asset in the portfolio



19

has to be positive. The negation of this restriction implies the allowance of short sales. This

has to be emphasized since the optimizer that is applied later to investigate the second

research question also lacks this restriction. This makes sense since investors who are

allowed to invest in HFs are often also allowed to perform short sales themselves.

The last step involves finding the optimal risky portfolio. This is done by considering the risk

free asset with standard deviation of zero. This optimal portfolio is the tangency point of the

Capital Allocation Line (CAL) with the steepest possible slope and the efficient frontier. The

CAL is a straight line originating at the risk-free return and the features of the slope. The

investors‟ different degree of risk aversion decides the actual position on the CAL. The slope

is an important performance measure that will be introduced later in this chapter.

The portfolio choice problem is separated into two independent tasks. Firstly, determining the

optimal risky portfolio (the portfolio made up of risky assets) and secondly the allocation

between the risk-free assets (T-bills) versus the risky portfolio depends on the investor‟s

personal preferences for risk-taking (his utility function). This phenomenon is called the

separation property.

2.4.2 CAPITAL ASSET PRICING MODEL (CAPM)

We introduced CAPM here to get a better understanding of the terms “alpha” and “beta”,

which have been mentioned earlier in the text. According to the Portfolio Selection Theory

by Markowitz, all efficient portfolios are position on the efficient frontier. The composition

of these portfolios varies only according to the risk aversion of the investor. The CAPM is

based on the homogeneous expectations assumption. It implies that individual try to optimize

their portfolios. Therefore investors under the CAPM will choose the same portfolio on the

efficient frontier which represents the capital market equilibrium. This is referred to as the

market portfolio. This portfolio is the combination of all types of assets in all markets in

which each asset is weighted by its market capitalization.

The Capital Market Line (CML) is constructed from the market portfolio and the risk free

rate. The CML is the tangency line to the market portfolio on the efficient frontier that passes

through the risk free rate on the expected return axis. The CML is superior to those portfolios

on the efficient frontier except for market portfolio. For instance, since the MVP is positioned



20

on the efficient frontier and has the lowest risk it is considered to be efficient as any other

portfolio on the curve.

The CML is defined as shown below:

(4)

where

The formula shows that the investor receives a risk premium in excess of the

risk free rate as compensation for investing in the risky assets of the market portfolio.

Figure2: Expected Return - Beta Relationship (SML)

The CML graph efficient portfolio is composed of the risk free rate and the market portfolio

according to the risk attitude of an investor. In contrast, the Security Market Line (SML)

graphs the systematic or market risk versus returns of the whole market at a certain time. It

shows all risky marketable securities. The SML can be derived from the CML.

According to CAPM, the beta coefficient is a measure of an asset‟s exposure to systematic

risk. It computes an asset‟s correlated volatility relative to the volatility to the overall market.



21

It shows the relationship between the covariance and the variance of the market portfolio.

The market has a beta of 1(one). If an asset‟s return is higher than market‟s return i.e.

( an asset has a higher normalized systematic risk than market. Hence, it‟s more

volatile.

Beta can be defined as:

(5)

where

market portfolio.

Correlation coefficient between the asset and the market portfolio,m.

We want the reader to understand that beta shows the volatility of a security according to

market movements. Thus beta risk, also known as market risk, is able to explain that part of

the return, which can be generated by investing in assets from a risky market, often the stock

market.

2.4.3 RISK -ADJUSTED PERFORMANCE MEASUREMENT OF PORTFOLIO

We have presented methods for calculating returns and risk. One of the problem that analyst

inevitably encounters is figuring out which investment is more attractive than other. For

analyst to answer this question, they need to be able to compare different investments in

terms of their desirability. The simplest approach to compare portfolios of assets is to create a

comprehensive collection of portfolios. We will focus on statistical measures of asset returns

that can be used to rank portfolios in term of their desirability.

The most commonly used desirability is the amount of return the investor receives relative to

the risk the investor takes to obtain that return. Presuming that more is preferred to less,

different measures has been developed that combines the risk and return into a single statistic

to assess their desirability. As a consequence, each of these measures slightly gives different

information.



22

2.4.3.1 SHARPE RATIO

One of the simplest and most well known measures of risk-adjusted performance is the

Sharpe ratio. This measure is highly used because it is defined simply in terms of mean and

standard deviation of the return. In the framework of CAPM, investors seek to maximize

utility defined as where E is the portfolio expected return,

is the

portfolio variance of return and A is the coefficient of the risk aversion. Maximizing the

utility formulated this way is equivalent to maximizing Sharpe ratio. The Sharpe ratio is

(6)

Expressed in terms of expectation, the equation above is a forward looking or ex-ante

concept. Replacing the expected return and standard deviation with estimated values gives a

historical measure of risk- adjusted returns.

In the hypothetical world of the CAPM, the maximum attainable Sharpe ratio is that of the

Market portfolio. According to CAPM, the Sharpe ratio of the market portfolio is defined as

the reward for a unit of systematic risk whereas in the real world, the ratio of reward to risk

provides a important basis to rank the desirability portfolios.

2.4.3.2 TREYNOR RATIO

In 1965, Treynor developed the Treynor Ratio based on the idea of the CAPM. Treynor

introduces the so called “characteristic line”. It is basically a regression line showing the

relationship between fund‟s returns and benchmark returns. In many cases, absolute-returns

funds will not have a benchmark or the returns to these funds are uncorrelated to the

benchmark. This mean their beta is closed to zero. Beta assumes that systematic risk is the

dominant risk in the portfolio. Beta also explains a large portion of the risk of the fund. If the

fund focuses on stock specific risk, credit risk or futures markets and hedges away the

systematic risk, a measure that computes the risk of the funds using only beta will be

inappropriate. However, Treynor ratio may be a very appropriate statistic for a mutual fund or

long-only hedge fund that‟s benchmarked to an investment index.

Treynor Ratio =

(7)



23

2.4.4 SKEWNESS AND KURTOSIS

Many financial market returns, especially those used in hedge funds trading strategies are not

normally distributed, the analyst should closely measure how the return to their investment

deviates from the assumption of a normal distribution. The Sharpe ratio measures the mean

and variance of the return. These are the first and second moments of the return distribution.

If the returns are not normally distributed, then ranking funds by the Sharpe ratio maybe

misleading. The third moment of the distribution is measured by Skewness while the fourth

moment is called Kurtosis. Normal distribution has a skewness and kurtosis of zero.

Skewness =

(8)

Figure3: Positive and Negative skewness

Investors require positive skewness where the probability of positive returns is higher than if

the distribution were truly normal. Positive skewness often come from the purchase of call or

put options. Investors might wish to avoid funds with negative skewness, where the

probability of negative returns is higher than those implied by normal distribution. Negative

skewness can arise from funds that are sellers of options or those that assume significant risk.

Kurtosis

A distribution with positive kurtosis has a much higher than normal probability of extremely

large or small returns. Financial markets often have leptokurtic distribution characterized by

“fat tail”, where the probability of crashes is much larger than the implied by the normal

distribution. A normal distribution with zero kurtosis assumes that moves of 5 standard

deviations are nearly zero.



24

Figure 4: showing the kurtosis



25

3 ANALYSIS

This chapter presents the analytical data of the research study. The analysis is carried out in

order to put together the results that will be presented and interpreted in the next chapter of

this paper.

The aim of this thesis is to examine if market neutral hedge fund can truly eliminate market

risk. Therefore, this chapter focuses on the concept of market neutrality in order for investor

to make decisive decision on investments.

Many scientific experiments have taken a similar formality of collecting data and then trying

to fit the data into proper mathematical models, therefore finding the relevant scientific rules.

In this project, we adopt the popular Correlation Hypothesis method to find the relationships

between returns of HF and that of the market return S&P500. In order to explore their

performances and ranking among these variables, the risk-free interest rate of monthly T-bills

was used.

Treynor ratio was used to evaluate the performance of each of the market neutral hedge

funds. And Jensen‟s model was used to measure risk-adjusted performance that represents the

average return on individual market neutral HF index over and above that predicted by the

capital asset pricing model (CAPM), given the index beta and the average market return

In order to accomplish this task, we use correlation based on market neutrality which test for

correlation hypothesis and how it is related to beta coefficient.

3.1 CHOICE OF TIME PERIOD

In analyzing the data, one needs to consider an adequate time period. Since development of

the industry in the 1990s, the first years every database has problem to gather information

which led to more researchers to choose time periods beginning from 1994 or later. Low

amount of hedge funds in the time periods between 1990 and 1994 can also be observed in

the Barclays hedge fund database. However, in the year 1994 the overall market performance

changed to a steady upward moving market (bull market), which helps is differentiating

between up and down market movement (bull and bear). Hence, the authors decided to focus

on the time period that clearly shown the features of up and down trend of market, which was

realized in the time period during 2006-2011.



26

3.2 DESCRIPTION OF HEDGE FUND DATA

Our data set consists of selected hedge funds styles/ strategies which is replicable by

investors and which is also representative of the hedge fund industry. We will focus on the

S&P500 as the market index for this paper. This can be seen in the table below.

Hedge Fund

Styles/Strategies

Number of Hedge Fund

Considered

Convertible Index

Distress Security Index

Emerging Market Index

Equity Long Bias Index

Equity Long/Short Index

Equity Market Neutral

Index

Equity Short Bias Index

Event Driven Index

Fixed Income Arbitrage

Index

Fund of Fund Index

Global Macro Index

Merger Arbitrage Index

Multi Strategy Index

Hedge Fund Index

39

60

468

373

622

95

8

112

43

1465

172

34

94

3100

Table1: summary of the number of strategies considered under HF

Since the data were collected from Barclays Hedge fund database, we assumed that all data

are alive and none are dead so we analyze their neutrality against a market index from the

data collected from the database.

3.3 RETURN AND STANDARD DEVIATION

Analyzing correlation coefficients only is not an appropriate way of conducting an academic

analysis. One has to keep in mind that correlation coefficients are based on the covariance,

which is based on the standard deviation. Calculation of the standard deviation is done out of

the monthly or yearly returns. Thus analyzing correlation coefficients starts out with the

analysis of returns. Therefore the authors calculated average annual returns for each HF

strategy and ranked them separately for each year. This procedure was also computed for the

market index which S&P500.



27

This helps the authors to illustrate whether HFs managed to perform as it is stated in theory.

Through comparing the HF performances with the one of the market proxy, the chart also

provides a first sense, how the correlation coefficients might look like. Finally, also the

standard deviation and average return over the whole period for each strategy is provided in

appendix (4) that gives some first information about risk-return relationships.

3.4 MARKET NEUTRALITY

The standard definition uses linear correlation or market betas. This states that a fund is

market neutral if it exhibits zero correlation with the market index or multiple market indices:

, or (9)

(10)

Neutrality of a fund can more generally be thought of as having two dimensions which are

Breadth and Depth.

Breadth: refers to the number of sources of market risk to which fund is neutral. For example

equity market risk. While Depth refers to the completeness of the neutrality of the fund to

these market risks. For example zero correlation.

We focused on neutrality Depth in order to analyze the data we have.

3.4.1 CORRELATION NEUTRALITY HYPOTHESIS

We analyze the relationship between the market neutral funds and the market index using

standard linear correlation hypothesis.

This is equivalent to testing for

Similarly,

and

for all I > 0



28

Therefore, when (Null hypothesis) is true, we accept the hypothesis otherwise we reject

it.

Correlation neutrality is just one of many types of neutrality that may be of interest to a risk-

averse investor. An investor with quadratic utility, or one facing returns that are multivariate

normally distributed, will only require linear correlation as the measure of dependence, and

so this standard concept of market neutrality would suffice. However, neither quadratic utility

nor multivariate normality is an empirically reasonable assumption, particularly for hedge

fund returns (Patton & Andrew J., 2004).



29

4 RESULTS

This chapter presents and interprets the analysis of the data in order to answer the research

question.

4.1 RESULTS REGARDING THE RESEARCH QUESTION

The research question, “can a hedge fund that is market neutral eliminate market risk” will be

answered in this section.

4.1.1 CORRELATION COEFFICIENT ANALYSIS OF HF SAMPLES OVER THE

WHOLE PERIOD

First of all a broader impression of HF correlation to the market proxies is of interest,

therefore the first analysis focuses on the whole time period from January 2006 to March

2011. In order to get an accurate picture, an annual correlation is considered which was

carried out for the period of 6 years. All the correlation coefficient matrices display the

correlation coefficients between the different HF strategies, Hedge fund index and market

index Appendix(1).

4.1.2 RESULTS OF CORRELATION MARKET NEUTRALITY BASED ON

CORRELATION MATRICES

In this section, we take a look at the ability of HFs to perform market neutral in greater detail.

The correlation coefficients regarding HF classes and the market index (S&P500) are

presented and interpreted in order to answer the research question on the basis of hard

numbers. This is done with the study of the correlation coefficients for the whole time period

from 2006 to March 2011. The correlation coefficients matrices can be found in Appendix

(1).

4.1.3 CORRELATION MATRIX REGARDING THE WHOLE TIME PERIOD FROM

2006 to MARCH 2011

In Appendix (1), we present the correlation coefficients based on yearly average returns over

the time period from 2006 to March 2011. As mentioned before, the focus lies primary on the

market index (S&P 500). In justifying the assumption of the correlation coefficients, it was



30

observed that no strategy is able to perform market neutral regarding the postulation that

market neutrality is evident if the coefficient lies in a range from -0.1 to 0.1. On the other

hand, the equity market neutral (0.384) and Fixed income arbitrage (0.30) are the ones that

has lower correlation and therefore have diversification power if they are included in

portfolios. But the overall result seems to reject the common view of HFs being able to

perform market neutral. Another interesting aspect that should be highlighted is the short

strategy, which is the only one that exhibits a significant negative correlation. This is in line

with theory and the investigation of the annual returns in the previous section. This already

indicates that HFs that apply a short strategy have the potential to increase the performance of

optimal portfolios, particularly in downside markets. Finally the correlation coefficients

regarding the risk free index indicate market neutrality for more HF strategies, except equity

market neutral (approx. 0.55), convertibles (approx. -0.31), global macro (0.54), merger

arbitrage (0.45), short biased (0.37) and fixed income arbitrage (0.52).

For further understanding we used the equivalent hypothesis of beta to check for HF

neutrality with the market.

4.1.4 BETA VALUES REGARDING THE WHOLE TIME PERIOD FROM 2006 to

MARCH 2011

Since correlation coefficients do not clearly show that market neutrality can be attain, we

further our research by using betas values to show that HF strategies can attain market

neutrality. This was carried out based on the beta for each strategy from the time period of

2006 to march 2011.

Appendix (2), shows the chart(SML) for HF strategies and that the HF strategies computed

for this period is defensive against the market index S&P500, because their beta values are

not greater than the beta value of the market index S&P500 (approx. 1). From the theories

stated above, relative value strategies explain that market risk can be eliminated. Focusing on

equity market neutrality which is a strategy under relative value strategies, with beta value

(approx. 0.03), Appendix (4) indicates that market risk can be eliminated which implies it

does not have systematic risk. For other types of relative value strategies which include

convertible and fixed income arbitrage indices with beta values (0,19) and (0,18)

respectively, are close to eliminating market risk but does not eliminate it based on the time



31

period considered. A special attention should be given to equity short bias index which has a

negative beta value of (approximately -0.7), this indicates that this strategy focuses on short

selling. The fact that market risks are eliminated does not indicate that the hedge funds will

be free from risk because the sum of a risk comprising of both systematic and unsystematic

risks. Though the systematic risk might be equal to zero, the unsystematic risk still remains

due to under pricing and overpricing of the funds. This can be seen from the appendix (2)

below that the return of the fund does not lie on the line of the security market line (SML).

4.1.5 ASSESSMENT OF CORRELATION/BETA MARKET NEUTRALITY BASED

ON A PERFORMANCE RANKING

Firstly, it is of great importance to investigate the yearly returns of different HF strategies, in

the whole time period of 2006-March2011. This will support the upcoming results of the

correlation analysis through the illustration of return fluctuations of the investigated HF

classes compared to the S&P 500. This reveals the ability to HF managers to create stable

returns over time and their ability to beat the market when looking at the returns.

From our research, relative value strategies should feature lower returns than other strategies.

This is due to the fact that they have lower risk. Amongst the relative value strategies, equity

market neutral performs best (approximately 0.39) compared to convertible arbitrage (0.30)

when comparing in terms of performance using the Treynor ratio. Meanwhile fixed income

arbitrage performs the least (-0.01). Thus, relative value strategies seem to fit to their

theoretical characteristics. They show low returns, low volatility, standard deviation and a

lower correlation with the market than other HFs.

The multi strategy arbitrage most of the time fairly in the market. It can be found amongst

the best fives in the market. Unexpectedly merger arbitrages show a very low risk but also

very high returns in respect to the other strategies.

Considering event driven strategies, we should notice that it has a very high return (7.07%)

but a very low standard deviation (1.43%). The event driven strategy itself performs above

average in most of the whole time periods. It is very defensive since it has a systematic risk

of 0.3. Entirely different, the distressed securities show a very volatile distribution (1.70%)

but it can not achieve satisfying returns.



32

The short selling strategy performs like it is stated in theory. In upward sloping markets,

short positions are not likely to generate high returns, while in downward sloping markets

possibilities arise to gather profits.

Contradictory, equity long clearly shows the performance one expects from a bullish strategy,

which performs very well in bull markets, but cannot achieve this performance in bear

markets.

Equity long/short, a mixture of long and short strategies, performs better than the special

strategy “equity long” in the whole time period. To my surprise it also performed better than

the short strategy. Two reasonable explanations can be given. Firstly, the sample of the short

selling strategies was rather small and in some years heavily biased, due to the fact that only

some HFs were able to perform over a long time period. Hence, there could be monthly or

annually returns, which are only constructed out of 2-3 HFs. This situation supports the

decision to use HF indices in the portfolio analysis. Secondly, it could be the superior

selection skill of equity long/short managers.

The emerging market strategy can be used as a kind of proxy for the whole group. It offers

huge returns. It falls under the three best performing strategies (0.48). Thus it is associated

with a very high volatility.

Now let‟s compare HF returns to a market index in this case, the S&P 500. One has to be

careful when comparing HFs and market index. An index is steadily readjusted in its weights

and securities, which will result in an approximately optimal passive portfolio that reflects the

underlying market. Even passive mutual funds couldn‟t be replicating an index without a

tracking error. The reason being that the transaction costs that are necessary to adjust the

portfolio would hurt the returns considerably. Therefore an index like the S&P 500 is a

challenging benchmark. Nevertheless since HFs want to combine market neutrality with the

idea of absolute return, i.e. a constantly high return despite market movements, some

statements can be made. The S&P 500 S&P500 rank lowest based on our research. This

demonstrates the clear advantage of HFs to minimize losses or even generate profits through

the use of short positions and derivatives.



33

Hedge Fund Styles/Strategies Treynor Ratio (Performance Ranking) Ranking Number

Merger Arbitrage Index 0,481132524 1

Equity Market Neutral Index 0,386423922 2

Multi Strategy Index 0,37134373 3

Convertible Index 0,30919606 4

Event Driven Index 0,208701977 5

Global Macro Index 0,186722986 6

Emerging Markets Index 0,158323325 7

Hedge Fund Index 0,132072421 8

Distressed Securities Index 0,113777676 9

Equity Long/Short Index 0,10871861 10

Equity Long Bias Index 0,07992821 11

Equity Short Bias Index 0,02854622 12

S&P500 0,0214646 13

Fund of Funds Index 0,019316434 14

T-Bills 0

Fixed Income Arbitrage Index -0,013104359 15

Table 2: Performance ranking using Treynor Ratio



34

CONCLUSION

Based on the assumption that market neutrality exists when a correlation coefficient of HF

are low or exhibits zero (0) correlation, No market neutral hedge fund strategy was found to

exhibit this features for the whole period considered in this study. Using the correlation

coefficient, the equity market neutral approximately 0.38 and the fixed income arbitrage,

approximately 0.30 are the only two strategies that were found to be close to being market

neutral. This indicates that they have the tendency to diverse risk if included in the portfolio.

This result goes against the view that hedge fund can perform neutral.

When compared using the beta, it showed that hedge fund strategies can attain market

neutrality given the time period considered. From the results, it showed that hedge fund

strategies computed during the time period were defensive against the market index S&P500

given that their beta values were not greater than the beta value of the market. So therefore,

In line with the theories about relative market neutral funds given above, which state that

market risk can be eliminated if and only if the beta coefficient is zero was satisfied only with

the equity market neutral indices. Although, other relative market neutral such as convertible

and fixed income have a relatively close beta values which are 0.19 and 0.18 respectively.

In conclusion, using a correlation coefficient, the assumption that market neutral hedge fund

strategies can eliminate market risk has to be rejected for all of the market neutral hedge fund

strategies since the result goes against the relative value strategy theory that hedge fund

strategies are expected to be market neutral.



35

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1

APPENDIX

Appendix No 1

Correlation Matrix of Hedge Fund 2006 – March 2011

convertible Distress Emerging ELB ELS Market Neutral SB event fixed in FOF GM MA MS T-Bills HF SP

convertible Index 1,00

Distress Security 0,94 1,00

Emerging Market 0,91 0,97 1,00

Equity Long Bias 0,93 1,00 0,98 1,00

Equity Long Short 0,90 0,98 0,99 0,99 1,00

Equity Market Neutral 0,01 0,32 0,39 0,37 0,43 1,00

Short Bias -0,86 -0,95 -0,85 -0,94 -0,89 -0,31 1,00

Event Driven 0,98 0,98 0,97 0,98 0,97 0,22 -0,89 1,00

Fixed income Arbitrage 0,00 0,23 0,33 0,26 0,30 0,59 -0,24 0,14 1,00

Fund of Funds 0,79 0,94 0,96 0,96 0,97 0,57 -0,88 0,90 0,51 1,00

Global Macro 0,50 0,64 0,77 0,69 0,77 0,72 -0,45 0,66 0,32 0,76 1,00

Merger Arbitrage 0,68 0,78 0,91 0,83 0,89 0,59 -0,60 0,81 0,42 0,88 0,95 1,00

Multi Strategy 0,95 0,99 0,99 0,99 0,99 0,31 -0,90 1,00 0,20 0,93 0,70 0,84 1,00

T-Bills -0,32 -0,19 0,05 -0,11 -0,01 0,55 0,38 -0,15 0,52 0,10 0,55 0,45 -0,11 1,00

Hedge Fund Index 0,92 0,99 0,99 1,00 1,00 0,39 -0,91 0,98 0,27 0,96 0,73 0,86 1,00 -0,06 1,00

S&P500 0,90 0,99 0,94 0,99 0,97 0,38 -0,98 0,95 0,30 0,95 0,61 0,76 0,97 -0,20 0,98 1,00



1

Appendix No 2

Security Market Line (SML)

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

0.08

0.1

-1 -0.5 0 0.5 1

Security Market Line

returns

SML



2

Appendix No 3

Returns and Beta for Hedge Funds

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

Returns

Beta



3

Appendix No 4

Hedge Fund Strategies 2006-March 2011

Geometric Returns, Standard Deviation, Beta values and 3rd

, 4th

Moments

Returns Standard Deviation

Beta Kurtosis Skewness

Convertible Index 6,94% 1,20% 19,90% 2,321014 0,571867

Distressed Securities Index 4,43% 1,70% 32,03% 2,831096 -1,30454

Emerging Markets Index 6,78% 2,96% 37,86% 2,024707 -1,15941

Equity Long Bias Index 5,43% 2,43% 58,19% 2,916896 -1,40205

Equity Long/Short Index 4,22% 1,40% 31,59% 2,490727 -1,38762

Equity Market Neutral Index

2,00% 0,65% 3,15% -2,64584 -0,10818

Equity Short Bias Index -1,16% 2,88% -68,14% 2,838298 1,628193

Event Driven Index 7,07% 1,43% 30,11% 1,533341 -0,5712

Fixed Income Arbitrage Index

0,54% 1,12% 18,15% -1,47759 0,743444

Fund of Funds Index 1,30% 1,22% 26,90% 4,325569 -2,04434

Global Macro Index 5,33% 1,47% 24,34% -1,4541 -0,45358

Merger Arbitrage Index 7,11% 0,63% 13,16% -1,37745 -0,59729

Multi Strategy Index 6,55% 1,15% 15,53% 1,996641 -0,96722

T-Bills 0,78% 0,00% 0,00% -1,97291 0,791038

Hedge Fund Index 5,23% 1,12% 33,66% 2,60452 -1,33235

S&P500 2,73% 3,46% 90,96% 3,776983 -1,78723