hedge fund performance during the internet bubble
TRANSCRIPT
1
Hedge fund performance during the Internet bubble Bachelor thesis finance
Colby Harmon
6325661 /10070168
Thesis supervisor: V. Malinova
2
Table of content 1. Introduction p. 3 2. Literature reviews of studies on mutual funds and hedge funds p. 5 2.1 Evolution of performance measures p. 5 2.2 Studies on performance of mutual and hedge funds p. 6 3. Deficiencies in peer group averages p. 9 3.1 Data bias when measuring the performance of hedge funds p. 9 3.2 Short history of hedge fund data p. 10 3.3 Choice of weight index p. 10 4. Hedge fund strategies p. 12 4.1 Equity hedge strategies p. 12 4.1.1 Market neutral strategy p. 12 4.2 Relative value strategies p. 12 4.2.1 Fixed income arbitrage p. 12 4.2.2 Convertible arbitrage p. 13 4.3 Event driven strategies p. 13 4.3.1 Distressed securities p. 13 4.3.2 Merger arbitrage p. 14 4.4 Opportunistic strategies p. 14 4.4.1 Global macro p. 14 4.5 Managed futures p. 14 4.5.1 Trend followers p. 15 4.6 Recent performance of different strategies p. 15 5. Data description p. 16 6. Methodology p. 18 6.1 Seven factor model description p. 18 6.2 Hypothesis p. 20 7. Results p. 21 7.1 Results period 1997-2000 p. 21 7.2 Results period 2000-2003 p. 22 8. Discussion of results p. 23 9. Conclusion p. 24
3
1. Introduction
A day without Internet today would be cruel and unthinkable. But 20 years ago this
was nothing out of the ordinary. People back then got their news from the newspaper as well
as from news channels. Internet started to have an impact on the world in the mid 90’s, with
the rise of electronic mail, instant messaging and the World Wide Web (Wikipedia, history of
the Internet). This development was also visible in the financial markets. In the late years of
the 1990’s there was a large growth of the commercial internet sector. Due to this growth the
‘dot-com’ industries experienced huge rises in their stock prices. In the period from 1998
through February 2000 the internet industry earned over 1000 percent returns on its public
equity. The internet sector covered roughly 6 percent of the market capitalization of public
companies in the U.S. as well as 20 percent of all publicly traded equity volume (Ofek and
Richardson, 2003).
A bubble in the financial market is characterized by a self perpetuating increase in the
stock prices of a particular industry. This happens when speculators notice the fast increase in
value and decide to buy expecting further rises (Wikipedia). They buy the share because of
the quick rise in price and not because the share is undervalued. This generally causes
companies to be overvalued. When a bubble is overinflated and bursts the prices of the
companies’ shares drop fiercely. This causes many companies to go bankrupt or out of
business.
Finally the internet bubble burst happened on March 10, 2000. On this date the
NASDAQ composite peaked at 5,048.7 (day-end) (Nasdaq). By March 20, 2000, NASDAQ
had lost over 10% of its peak. Nearing 2001 the bubble was collapsing at full speed. Many
internet companies ran out of capital and taken over or liquidated. The stock market crash due
to the internet bubble bursting added up to a loss of 5 trillion dollars in the market value of
companies from March 2000 until November of 2002. The stock market crashed affected all
kinds of investment vehicles, such as mutual funds and hedge funds. For hedge funds this was
the third hit in a time period of three years. First the Asian currency crisis of 1997, then the
Russian debt default of 1998 and now the dotcom crash of 2000.
Hedge funds are characterized as private investment vehicles for wealthy people and
institutional investors. They usually take on the form of limited partnerships, where the
partners and managers invest a large part of their own wealth to align the managers’
incentives with the funds performance (Fung and Hsieh, 1999).
Caldwell (1995) says that the first ever hedge fund was introduced by Albert W. Jones
in 1949 using a strategy based on long and short positions in equity as well as leverage. He
4
used leverage to buy shares and went short on the other side to avoid market risk. Jones thus
referred to his fund being ‘hedged’.
Hedge funds managers enjoy a huge flexibility when it comes to making investment
decisions. This is all thanks to the limited regulatory oversight on hedge funds. Hedge funds,
unlike mutual funds, are not required to register with the SEC and disclose their holdings. The
regulation on limited partnerships makes this possible (Liang, 1999).
The fee structure of hedge funds is specially designed to motivate managers. The fee is
based on factors, such as asset size. There is also a separate incentive fee to align the
managers’ performance with that of the fund. In general, the incentive fees are only paid after
a ‘hurdle rate’ has been met. Most of the hedge funds also make use of a ‘high watermark’
provision. This entails that managers have to make up for previous losses in order to get paid
the incentive fee. All these previously named features make sure that manager’s act in the
invertors’ best interest (Liang, 1999)
Mutual funds generally speaking use relative benchmarks, such as S&P 500 for equity
funds and the Lehman Brothers Aggregate index for bond funds, for their returns. This means
that the funds returns are compared to a benchmark. The relative return is the difference
between the absolute return of an asset and the return of the benchmark. As compared to
hedge funds, which use absolute returns to measure their performance and often take highly
speculative positions (HedgeCo.com)
As a result of benefits hedge funds have gained a lot of popularity. Since the
conception of the first hedge fund in 1949, there has been a huge growth. In the 1980’s there
were around 100 funds. In the early years of 1990 the number of funds grew greatly and now
there are over 10.000 hedge funds worldwide available to investors (Liang, 1999).
This paper expands existing performance of hedge fund literature by taking a closer
look at particular hedge fund strategies in times of financial turmoil. By using asset-based
style factors in a model of hedge fund risk (Fung and Hsieh, 2004), I compare different hedge
fund strategies to the market index in the same period. I find that the hedge fund returns
consistently underperform the market index in the period leading up to the bursting of the
internet bubble. Also, I show that in times of financial turmoil the market return is more
volatile than the returns of the three hedge funds.
In chapter two of this paper there will be a literature review of studies on mutual funds
and hedge funds. In chapter three will be about deficiencies in peer group averages followed
by different types of hedge fund strategies in chapter four. In chapter five I will discuss how I
obtained my data for this research. The methodology section in chapter six will follow this. In
5
chapter seven I will show my results of my tests and this will be followed by a discussion
about the results in chapter eight. Finally in chapter nine will be an overall conclusion.
2. Literature review of studies of mutual funds and hedge funds
In this chapter there will be a discussion on previous literature of performance studies
and performance measures of hedge and mutual funds. First I will give a short historical
insight in the evolution of the performance measures, followed by a discussion on previous
literature in which they use these performance measures.
2.1 Evolution in performance measures
About thirty years ago a commonly used performance measure based on CAPM was
Jensen’s alpha (1968), just as the Sharpe’s (1966) reward-to-variability ratio. These were
often used in the performance evaluation. Due to the more recent literature on cross-sectional
variations in stock return there has been increasingly more interest in multi-factor models.
Studies by Fama and French (1998) and Chan et al. (1996) show that the cross sectional
variations of average returns on U.S. stock show almost no relation to Sharpe’s (1964) beta or
Litners’ capital asset pricing model. Rather they identify other factors that have reliable
powers to explain the cross section of average returns (Capocci and Hübner, 2004). These
factors include the company size, leverage, price/earnings, book-to-market, dividend yield
and the momentum effect (Elton et al., 1996). There are also other multi-factor models
introduced and they include the three-factor model by Fama and French (1993), the Carhart
(1997) four-factor model, and the international Fama and French model (1998).
However, studies in recent years have allowed for some doubt on the usefulness of the
new models. The Fama-French three-factor model gets better results than the classical CAPM
by adding variables as company size and book-to-market equity to the equation. But
according to Kothari and Warner (2001) it also detects significantly abnormal results (like
timing) when none really exist. Additionally, the development of Carharts (1997) four-factor
model proves to be better than the traditional CAPM and Fama-French three-factor model.
Carhart (1997) constructs a 4 factor model based on fama frenches 3 factor model plus
an additional factor that captures Jagadeesh and Titman’s (1993) one year momentum
anomaly. This model is like a model of market equilibrium but then looks at four risk factors.
By adding these variables they have a higher reliable power to explain the cross-section of
average returns (Capocci and Hübner, 2004).
6
Also in literature on hedge funds have there been different models used to evaluate
performance. Fung and Hsieh (1997) extend the asset class factor model by Sharpe (1992)
and also find five dominant investment styles in hedge funds. Schneeweis and Spurgin (1998)
follow in Fung and Hsieh’s footsteps by also using a style-based analysis on a multifactor
approach. On the other side Ackermann et al. (1999) uses a single factor model that focuses
only on risk. Extensions on the Fung and Hsieh (1997) model used by Liang (1999) are
regressions that are based on funds’ characteristics that make use of the Sharpe ratio. A
general asset-class factor consisting of excess returns on passive option-based strategies is
suggested by Agarwal and Naik (2002), as well as on buy-and-hold strategies to benchmark
the performance of hedge funds.
All the above suggests that it is necessary to use multi-factor models, rather than the
CAPM model, to do performance studies. The only problem is that there is no unanimously
accepted model. In this case it is advisable to use a number of models to compare results.
2.2 Studies on performance of mutual and hedge funds
Recently there has been a growing interest in hedge funds. This interest comes from
their increasing prominence in the financial markets (Liang, 1999). But despite this growing
interest there have been only a few performance studies that compare hedge funds to, for
example, the market index or mutual funds. The main reason why this is the case is due to the
private characteristics of hedge funds and there are difficulties to gain access to the funds
data. It is interesting to first look at some studies that compare the two funds in order to find
performance characteristics of hedge funds.
Generally speaking, performance studies comparing mutual funds to hedge funds can
be divided into two categories, either they conclude or deny that hedge funds have
significantly higher realized returns than those that use a passive strategy (Capocci and
Hübner,2004). In these studies they find that with this outperformance there is also an
increase in the volatility for hedge funds compared to mutual funds (Ackermann et al., 1999).
But just as Liang (1999) they also find that although the hedge funds outperform mutual
funds, that they perform worse than the market equity index.
Hedge fund managers often transact in the same markets as traditional fund managers.
However, there is evidence that hedge fund returns have different characteristics than those of
traditional fund managers. Fung and Hsieh (1997) show that hedge fund returns have a much
lower correlation with standard asset returns than mutual funds do. This can be interpreted as
that hedge fund managers are more skilled than traditional fund manager. But this is not
7
always the case. For there is evidence that hedge funds perform poorly when asset markets
perform poorly. A different way to interpret this is that hedge funds are exposed to risk, just
as the mutual funds are, but that the risk is different to the risk of mutual funds. Fung and
Hsieh (2004) use asset based style factors to create hedge fund benchmarks that seize the
common risk factors in hedge funds.
Fung and Hsieh (2001) show that hedge fund strategies often generate option-like
returns and that linear-factor models have difficulty explaining them. Here they model these
hedge fund returns by focusing on the ‘trend-following’ strategy. Trend followers bet on big
moves. Just like option buyers, they profit when the market is volatile. By using look-back1
straddles they can explain trend-following funds’ returns better than standard asset indices.
Furthermore, Agarwal and Naik (2004) find similar results. They look at the
systematic risk exposure of hedge funds using a buy-and-hold strategy and option-based
strategies. Here they also find that the payoffs of the equity oriented hedge funds strategies
exhibit non-linear option like behavior. Besides the non-linear exposure to the equity market
risk, they find that hedge funds exhibit significant risk exposures to the Fama and French
(1993) size and value factors, as well as the momentum factor by Carhart (1997).
Just as Fung and Hsieh (2004), Mitchel and Pulvino tried to identify common return
components by using observable market risk factors. By looking at merger arbitrage funds
Mitchel and Pulvino (2001) created an asset based style factor for these funds. Merger
arbitrage, also known as risk arbitrage, is a strategy that buys the stock of the target firm and
goes short on the stock of the acquirer. This strategy presumes that the announced merger
transaction is completed. By simulating the return of a rule based merger arbitrage strategy,
they show that this asset based style factor for merger arbitrage has the return characteristics
close to those of merger arbitrage funds. Using all the stock and cash merger transactions
from 1968 to 1998 they show that that the returns of merger arbitrage funds had low
correlations with the S&P 500 index returns. Except for when the S&P 500 had a large
decline. This decline went hand in hand with the worst performance of merger arbitrage
funds. They concluded that the systematic risk in merger arbitrage is that many mergers will
be canceled at the same time as the market sharply declines. Thus creating a systematic loss
for merger arbitrage funds.
1 “A look-‐back straddle consists of a pair of look-‐back call and put options. A look-‐back option is a call (put) option giving the holder the retroactive right to buy (sell) the underlying asset at its minimum (maximum) within the look-‐back period.” (Fund and Hsieh, 2004)
8
In an analysis of fixed income funds Fung and Hsieh (2002) look at a different source
of common risk factors and find that these funds are usually exposed to yield spreads. Fixed
income funds usually buy bonds with low credit ratings or less liquidity. After this they hedge
the interest rate risk by going short on U.S. Treasury bonds. Which in comparison have the
highest credit rating and are more liquid. The yield spread is the difference between the yields
of the two bonds.
Often studies apply conventional models for constructing asset class indexes, which
assume that the underlying assets are reasonably homogeneous and the dominant strategy is to
buy the asset and hold it. However, hedge funds diversify the makeup and performance of
their underlying assets. They have dynamic investment styles and may take highly levered
bets. Fung and Hsieh (2004) introduce a different approach to benchmark hedge funds returns
by using asset-based style factors in a model of hedge fund risk. They propose a model that is
similar to models based on arbitrage pricing theory with dynamic risk factor coefficients.
With seven asset-based style factors they explain a large amount of the monthly return
variations.
A different approach in analyzing hedge funds was taken by Capocci and Hübner
(2004). They analyze hedge fund performance using a number of asset pricing models;
including the Carhart (1997) model combined with the Fama and French (1998) and also the
Agarwal and Naik (2002) models. Also Capocci and Hübner add a new factor that takes into
account that some hedge funds invest in emerging bond markets. By doing so they show
evidence of limited persistence for the middle decile funds, but not for the extreme
performers.
Another aspect in the hedge fund world is the lock-up period2 and there have been a
number of studies on this subject. For example, Field and Hanka (2001), Ofek and Richardson
(2000) both report similar price and volume-effects around the end of a lock-up period around
different time periods. Unfortunately, these papers do not give an explanation of these effects.
In order to explain the rise and fall of stock prices in the Internet sector Ofek and Richardson
(2003) use the effects of short sales restrictions and heterogeneity. They estimated that at the
peak, the whole internet sector, hundreds of stocks, was priced as if the average future
earnings growth rate all these firms would exceed the growth rates experienced by a few of
the fastest growing individual firms in the past. Also the required rate of return would be 0%
2 “A lock-‐up period is a window of time in which investors of a hedge fund are not allowed to redeem or sell shares. The lock-‐up period helps portfolio managers avoid liquidity problems while capital is put to work in sometimes illiquid investments.” (investopedia)
9
for the next decade. These extreme levels of valuation would unequivocally lead you to
believe there would be another asset price bubble.
A different view comes from Shiller (2000), he says that the stock price increase was
induced by irrational euphoria amidst individual investors, fed by the media, who maximized
television ratings by supplying pseudo news. There will always be rational and irrational
market participants. However, there are two opposite views on whether the rational traders
correct the price of behavioral traders. The supporters of Friedman (1953) and Fama (1965),
who advocate the efficient market hypothesis, argue that rational speculation would not only
get rid of riskless arbitrage opportunities, but also the exploitation of other forms of
mispricing that need imperfectly hedged and risky trades. This applies to the technology
bubble, because there is not a close substitute that would be able to hedge a short position in
the technology sector.
3. Deficiencies in peer group averages
The models mentioned above all use information obtained from commercial data vendors.
Information on hedge fund returns, risk and fee structure is often not publicly available. This
is because hedge funds are not obliged to disclose this information. There is no centrally
organized group who gathers this information, rather there are only a few commercial data
vendors who posses the information on hedge funds. Due to the fact that the funds are not
obliged to disclose their information, this only happens on a voluntary basis. Thus making the
information prone to biases (Liang, 2000).
A method frequently applied to model hedge fund risk is to use a broad-based index of
hedge funds (Fung and Hsieh, 2004). For example, hedge fund indexes available from Hedge
Fund Research (HFR), Zurich Capital Markets (ZCM) and Credit Suisse. These indexes are
constructed by averaging individual hedge funds. By doing so these indexes are vulnerable to
biases, as shown by Brown et al. (1999). Below we will discuss several biases.
3.1 Data biases when measuring the performance of hedge funds
Indexes created from averaging individual hedge funds can take over errors that were
first in the hedge fund database. Brown et al. (1999) and Fung and Hsieh (2000) both write
about the existence of these data biases. I will now introduce several biases.
10
- Self-selection bias
Hedge funds are not obligated, unlike mutual funds, to publicly disclose their
activities. According to Fung and Hsieh (2004), the self-selection bias occurs when the
sample of funds in the data is not a representative sample of the universe of hedge funds.
Because the hedge funds decision is voluntary only the well performing funds will report and
the poorly performing funds won’t.
- Survivorship bias
Due to the short lifetime of many hedge funds there are many new entrants and many
departures each year. In the case that we would only examine funds that have survived until
now, we would overestimate past returns. This is because many of the worst performing
hedge funds would have not survived and would have disappeared. This type of bias is well
known in mutual funds (Brown et al., 1992)
- Instant-history bias/ Backfill bias
When a fund enters a database, all or part of the its historical data is recorded ex-post
in the database. It is likely that funds only publish their results when their performance is
‘good’. If the performance is ‘bad’, they don’t publish any results. Thus, when the
performance is backfilled, the average return in the database is upwardly biased (Fung and
Hsieh, 2004).
3.2 Short history of hedge fund data
There is another drawback to using hedge funds indexes. It doesn’t matter if they are
based on individual funds or on funds of hedge funds. The drawback is that reliable data on
hedge funds only begins in the 1990’s (Fung and Hsieh, 2004). This period corresponds with
one of the greatest bull markets in the U.S. This bullish period is only shows a few periods of
market declines. Furthermore, it does not give a long enough history to show how hedge
funds perform in different market environments (Fung and Hsieh, 2004)
4.3 Choice of index weights
There are different methods for weighting the shares of each security in an index.
Each method has its advantages and its disadvantages. The three basic methods are price
weighted, value weighted and equal weighted (Financial Education).
11
Price weighted indices
Price weighted indices are calculated by adding up the share price of each security,
followed by dividing the sum of prices by the total number of securities in the index. One of
the main advantages of using this method is that it is easy to calculate. And because of the
ease of obtaining historical pricing information, it allows for back testing of the index. Also
this method uses a constant number of shares, so it is not necessary to re-weight the index
because of daily price fluctuations. One of the disadvantages here is that companies with
higher share prices receive higher weights without any real reason. Thus, the performance of
such a stock is not representative of the overall group. Also actions such as stock splits
require re-weighting of the index, even though they do not alter the value of the securities.
(Financial Education)
Value weighted indices
Value weighted indices are based on the market capitalization of each company in the
index. This kind of indices tracks the returns of all publically traded shares in the index. When
shares are not regularly traded (family ownership), weighting can be based on the float, or
traded shares. By using this method there is an automatic adjustment for corporate actions and
share price changes. Also, the economic changes in the stock market are reflected more
accurately. A disadvantage here is when there are inefficiencies in pricing; this method gives
a higher weight to overvalued stocks and a lower weight to undervalued ones (Financial
Education).
Equal weighted indices
Equal weighted indices invest the same amount in each stock. This method is quite
easy and probably more like the reality of how investors actually weight their holdings. Due
to the fact that price fluctuations can change the weight of each security, it is necessary to
rebalance more often. This method gives a higher relative weight to small companies than to
larger ones (Financial Education)
In my thesis I will use the equally weighted returns of the S&P500. This way of weighting
stocks is also used in similar studies when looking at the performance of hedge funds, Ofek
and Richardson (2003) and Liang (1999).
12
4. Hedge fund strategies
The huge number of hedge fund strategies and the wide array of instruments used
within hedge funds make it hard to get an idea of the hedge fund industry. The different
investments used and the strategies employed lead to a number of different representative
strategies. All strategies can be accredited to six classes: Equity hedge strategy, Relative value
strategy, Event driven strategy, opportunistic strategy, managed futures strategy and multi-
strategies strategy (Hauser, 2005). Due to the fact that these strategies invest in other parts of
the market, their performance is not homogeneous. In order to give you an idea of their recent
performances, see table 1 at the end of this chapter.
4.1 Equity Hedge Strategies
These strategies’ source of return is close to that of traditional investments. The goal is
to profit from stock price increases by means of long positions within the portfolio.
Simultaneously, managers take short positions in an attempt to limit the risk exposure of the
long positions. A frequently seen combination is that of long holdings of equity or derivatives
with short sales of stocks (Hauser, 2005).
4.1.1 Market neutral strategy
Market neutral funds are characterized as funds that constantly look to avoid major
risk factors. The main way to do this is by selling expected overvalued stocks and buying
undervalued stocks. When this strategy is used in a certain sector to remove a particular
industry risk from holdings, managers employ a ‘pairs trading’ strategy. This entails that the
long and short positions originate from the same industry. The key to this strategy lies in the
correct valuation of stocks (Hauser, 2005).
4.2 Relative value strategies
This type of hedge funds looks for gains by capitalizing on irregularities in the pricing
of stocks, bonds or derivatives. They do this by taking positions on forward interest rates, the
spread between different yields and also on price differences between related securities. These
funds are commonly known as arbitrage funds (Financial policy forum).
4.2.1 Fixed income arbitrage
The bond arbitrage has similar characteristics to that of share arbitrage. A manager
wishes to take advantage of price differences at different places. In fixed arbitrage they only
13
look at bonds. Compared to shares bonds have two major differences. The first one being that
bonds have a predetermined maturity. The other difference is that bonds provide fixed
payments of interest until maturity (Hauser, 2005).
When applied to fixed-income arbitrage strategies the goal is to generate a profit due
to the change of arbitrage spreads. The difference between the interest rates is called the
spread. Managers generally make use of corporate and government bonds with the same
characteristics. Usually, the instruments available are government bonds, corporate bonds,
municipal bonds, bond options, bonds futures and other bond derivatives. By using a
combination of these instruments managers try to remove the market risk of the interest rates.
A risk factor for fixed income arbitrage funds is that the spread moves in the other
direction that anticipated. A different risk factor that is applicable is that the underlying
company or country defaults (Hauser, 2005).
4.2.2 Convertible Arbitrage
This is the most complex strategy of all relative value strategies. A convertible bond is
a security made up of partly a traditional bond and the other part is a stock. As a result the
value of this convertible bond is made up of two parts as well. The first part is the fixed
interest rate and the final value of the bond. This is the largest part of the convertible bonds
price. The second part is the right of the owner to trade in the bond for a share at the end of
the maturity. For this reason, the value of the stock impacts the price of the convertible bond,
just as the default risk and the interest level (Hauser, 2005).
4.3 Event Driven Strategies
This type of strategy looks at special events and tries to identify the factors that might
affect corporate valuations. They then try to trade in such a way that they can derive value
when these events take place. The two main strategies are ‘distressed securities’ and ‘merger
arbitrage’ (Hauser, 2005).
4.3.1 Distressed Securities
These are funds that take a position in a companies equity whose securities price is
likely to be affected by situations such as: corporate bankruptcies, reorganizations, distressed
sales and corporate restructurings. The usual way of acquiring these positions is through bank
debt or high yield corporate bonds (Hauser, 2005).
14
4.3.2 Merger Arbitrage
Merger arbitrage, also known as risk arbitrage, makes use of corporate events that are
publicly reported a long time prior to the actual event. They gain from situations like mergers
and acquisitions, spin offs, recapitalizations, share buy backs, exchange offers and leveraged
buyouts. The most frequent strategy is that of the merger and acquisitions. Funds invest in
companies who are a part of the announced mergers and acquisition. The funds then assume
that the deal will be completed. They generally profit by taking a long position in the equities
of the target company and going short on the equities of the acquiring company. The profit
here to be made is when the deal follows through and the acquiring company has to make an
offer that is above the targets actual stock price. The offer has to be higher because otherwise
the shareholders will not sell their shares. This effect of this is that the price of the targets
stock will increase towards the price the acquirer is going to pay. The manager will be able to
make arbitrage profits by financing long positions in the stocks of the target by means of
selling short the acquirers’ stock (Hauser, 2005).
4.4 Opportunistic Strategies
Some managers have decided to change their approach so they can adapt to dynamic
market conditions. By this way they want to take advantage of possible investment
opportunities (Hauser, 2005)
4.4.1 Global Macro
This type of fund relies on macro economic analysis so they can make bets on
expected market movements. The profit to be earned here is from changes in the value of an
entire asset class. Movements can be a consequence of shifts in the world economy.
The risk associated with this strategy is that the anticipated situation will not happen or that
the situation will not have the effect on the position. To take advantage of the anticipated
situation, managers take positions in stocks, bonds, currencies and commodities (Hauser,
2005).
4.5 Managed Futures
Professional money managers, known as commodity trading advisors, usually invest
on the basis of mathematical models. One reason why this type of fund is interesting for
investors is that it provides a diversified exposure to a large variety of markets. Managed
15
futures investment vehicles consist of: future contracts, forward contracts and options. All
these investment vehicles are financial contracts for buying and selling of an index, stock,
bond or commodity at some future date. These contracts are often used in the following way.
Contracts for purchasing a particular asset pose as a long position. Managers may hedge a
portfolio or take advantage of negative price movements by selling contracts for an asset in
the future. By working in this fashion an advantage can be taken by trading advisors of any
price trend (Hauser, 2005).
4.5.1 Trend Follower
The most used strategy used by commodity trading advisors is the systematic trend
following strategy. The goal is to profit from the perpetuation of positions of long term trends
in the market place. It doesn’t matter in what direction the price moves, managed future
trading advisors are able to gain from price trends. If they expect a rising market they can buy
futures positions and if they expect the market to decline they can sell futures positions Fung
and Hsieh, 1997).
4.6 Recent performance of different strategies
As mentioned above there are many different kinds of hedge fund strategies and each
fund performs different than others. In the table 1, you will see the recent performance of
eight different hedge fund styles measured in their year to date returns over the past four
years. Noticeable is that the year to date returns are all positive, except for the year 2011.
Managers of hedge funds attribute the industry wide loss to things like Europe’s debt crisis, a
slower than expected recovery of the U.S. economy and unforeseen events like Japan’s
nuclear disaster. This all came together to create a tricky trading environment in which there
were large and unpredictable stock price swing (Reuters).
16
Differences in performance for hedge fund strategies
Table 1 Year to Date return
Year 2010 2011 2012 2013 Equity Long Short 7.27% -4.58% 6.35% 13.85%
Market Neutral 3.64% 0.16% 1.88% 8.61%
Fixed income arbitrage
11.65% 4.48% 9.32% 8.69%
Convertible arbitrage
12.15% 0.11% 8.74% 8.07%
Event driven 10.01% -3.74% 8.46% 10.81%
Distressed securities 14.01% -5.41% 12.22% 16.85%
Merger arbitrage 6.05% 3.80% 3.82% 3.91%
Global macro 6.74% -3.65% 2.59% 4.81%
Average
9.94%
-1.104%
6.67%
9.45%
Notes: Data obtained from www.barclayhedge.com. YTD describes the performance from the beginning of the year, 1 January, until the year-end.
In this paper the focus is on three hedge fund strategies, namely equity long short,
market neutral and global macro. Table 1 shows that in recent years the equity long short
strategy has earned the highest returns compared to the other two strategies. But on the other
side this fund also has the largest volatility of the three strategies in the past four years.
5. Data description
The data on the market equity returns returns came from CRSP. They cover the
S&P500 equally weighted total returns index on a monthly basis from January 1997 until
January 2004. Thus covering the time span of three years prior to the internet bubble and
three years after the bubble.
Taking the Russel 2000 index total monthy return and subtracting the S&P500 total
monthly return I calculated the size spread factor. This is the spread between the returns of
large capitalization stocks and the returns on small capitalization stocks (Fung and Hsieh,
2004).
For the bond oriented risk factors the data was available at the Board of Governors of
the Federal Reserve System. Here I obtained the monthly change in the 10-year treasury
constant maturity yield. This is applicable to the bond market factor.
17
The credit risk factor was also available at the Board of Governors of the Federal
Reserve System. To calculate the credit spread I first found the monthly change in Moody’s
Baa yield and subtracted the 10-year constant maturity yield.
Fung and Hsieh provided the trend following data themselves. They used this data in
their own research in 2001. This is data on the look-back straddles they used to identify
evidence of trend following.
Data on the particular hedge fund strategies was obtained from
www.barclayshedge.com. This is data on their performance based on 464 funds for the long-
short equity index, 86 funds for the market neutral index and 138 funds for the global macro
index. Because disclosing data of hedge funds is on a voluntary basis, the sample is prone to
biases. In this thesis my sample is prone to the self-selection bias. Barclays hedge database
notes above the table of their returns: “only funds that provide us with net returns are
included in the index calculation”.
Table 2 and 3 show the summary statistics of individual hedge fund returns of the three
strategies prior to the crash and after.
Table 2 Summary statistics 1997-2000 Strategy Number of
funds Obs Mean St. Dev. Min Max
Equity Long-Short
464 39 .0237359 .033276 -.0692 .1099
Global Macro 138 39 .0128923 .0240562 -.0281 .0753 Market Neutral
86 39 .0101308 .0114394 -.01 .0307
Notes: In this table you see the average monthly returns of three hedge fund styles from January 1997 to February 2000. Data obtained from www.barclayshedge.com.
Table 3 Summary statistics 2000-2003 Strategy Number of
funds Obs Mean Std. Dev. Min Max
Equity Long-Short
464 44 .005287 .0162096 -.0287 .0473
Global Macro 138 44 .0087455 .0143275 -.0154 .0556 Market Neutral
86 44 .0052978 .0086224 -.0171 .0297
Notes: In this table you see the average monthly returns of three hedge fund styles from March 2000 to December 2003. Data obtained from www.barclayshedge.com.
18
6. Methodology
In this section will describe what model I will use to test my hypotheses, and will
conclude with a description of my hypotheses.
6.1 Seven-factor model description
The seven-factor asset based style (ABS) model contains three main categories of risk
factors, namely the equity oriented risk factors, bond oriented risk factors and trend following
risk factors. Each category of the risk factor contains its own sub-factors.
The process of creating the ABS factors happens by first extracting the common
sources of risk in hedge fund returns and then linking these common sources of risk to
observable market prices (Fung and Hsieh, 1997).
Equity oriented risk factors
The sub risk factors here are the equity market factor and the size spread factor. The
equity market factor shows the market risk and is measured by looking at the Standard &
Poor’s 500 index for monthly total stock return less the risk free rate of the 10 year treasury
bills.
Fung and Hsieh (2004) conclude that long short equity hedge funds have exposure to
the stock market as well as to the spread between returns on large capitalization stocks and
returns on small capitalization stocks. This is known as the size spread factor. It makes use of
the Wilshire Small Cap 1750 less the Wilshire Large Cap 750 monthly returns.
Bond oriented risk factors
Fung and Hsieh (2002) analyzed fixed income hedge funds and found that these funds
are usually exposed to yield spreads. This is because fixed income funds generally buy bonds
with lower credit ratings and then hedge the interest rate risk by taking a short position in U.S.
T bonds, which have the highest credit ratings. And the difference between the two types of
bonds is called the yield spread.
They also state that yield spreads have the tendency to move together, even more so in
times of market distress. For this reason fixed income funds can be modeled as being exposed
to credit risk.
The credit risk is measured by subtracting the 10 year constant maturity T bond off the
yield on Moody’s Baa bonds.
19
Trend following risk factors
In 2001, Fung and Hsieh modeled a common return component from trend following
funds by using a portfolio of look back straddles. They argued that trend followers bet on big
moves. This is similar to option buyers, they make money when the market is volatile. They
showed that these option portfolios have a high correlation with trend following funds as well
as having the same kind of return characteristics.
In this model I will take into consideration three factors of trend followers, namely
bond, currency and commodity factors. The data on this matter has been provided by Fung
and Hsieh (2001).
The regression is as follows:
Rι-Rf= α + β1(Equity M) +β2(Size Spread)+β3(BondM)+β4(Credit spread)
+β5(BondTF)+β6(CurrencyTF)+β7(CommodityTF)+ε
Ri-Rf stands for the excess returns of the individual hedge funds I will be looking at. I
will regress on the monthly returns of the global-marco strategy, equity long-short strategy
and the market neutral strategy. I subtract the risk-free rate from the hedge fund returns, and I
do the same for the S&P500 returns. Because I do this, the returns that are left are the returns
in excess of the market index. Therefor, the alpha will show the outperformance of the hedge
funds relative to the market equity index when positive and the underperformance when
negative.
Variables Proxy Measure Equity Market Factor SPequalwreturn Standard & Poor's 500 index monthly total
return Size Spread Factor Size Spread Russell 2000 index monthly total return
-/- Standard & Poors 500 monthly total return
Bond Market Factor Treasury10year Change in the 10-year treasury constant maturity yield
Credit Spread Factor Credit Spread Monthly change in Moody's Baa yield -/- 10-year treasury constant maturity yield
Bond Trend Following Factor
Bondlookbackstraddle Return of a portfolio of lookback straddles on bond futures
Currency Trend Following Factor
Currencylookbackstraddle Return of a portfolio of lookback straddles on currency futures
Commodity Trend Following Factor
Commoditylookbackstraddle Return of a portfolio of lookback straddles on commodity futures
20
This model should have a higher explanatory power over the Carhart (1997) four
factor model due to the additional variables. For this reason I will be using this model to
empirically test if these three hedge fund strategies outperformed the market equity index
before and after the bursting of the Internet bubble.
6.2 Hypothesis
In studies done by Ackermann et al. (1999) and Liang (1999) on the performance of
hedge funds, the performance is compared to the performance of mutual funds, or the effect
of incentive fees on hedge fund return is looked at. These studies have found that hedge funds
consistently outperform mutual funds, but many times underperform compared to the market
index. It is interesting how hedge funds perform in times of financial turmoil. For this reason I
will be focusing my research on hedge fund performance in times of the Internet bubble
compared to the market index. My first hypothesis is:
H1: The market outperforms the three hedge fund strategies in the period 1997-2000.
I will be looking at three hedge fund strategies, namely two hedging strategies and one
opportunistic strategy. The first two strategies, equity long-short and market neutral, are
strategies that look to consistently minimize or remove market risk factors. The opportunistic
strategy, global macro, seeks to capitalize by anticipating on expected market movements.
These movements can be a consequence of the world economy (Hauser, 2005).
The time period of January 1997 – February 2000 is the period leading up to the
bursting of the Internet bubble on 10 March 2000. The years 1997 and 1998 have been
turbulent for the financial market due to the Asian currency crisis (1997) and the Russian debt
default (1998) (Liang, 1999). Because I think this was a tricky trading environment, I expect
that the market will outperform the hedge fund strategies.
Other studies, Mitchell and Pulvino (2001) and Fung and Hsieh (1997), have found
that hedge fund strategy returns have a low correlation with the market returns. Mitchell and
Pulvino (2001) primarily looked at merger arbitrage funds and found that in times of poor
performance of the market that the merger arbitrage funds did poorly as well. This was
attributable to the fact that when the market doesn’t perform well, less mergers and
acquisitions, spin offs and recapitalizations occur, which could explain the poor performance.
Brunnermeier and Nagel (2004) show that hedge funds were aware of the internet bubble and
21
argue that the hedge funds only captured the upside and avoided much of the downturn.
Therefor, my second hypothesis is:
H2: In the period of 2000-2003 the three hedge fund strategies outperform the market.
In contrast to Mitchell and Pulvino (2001), I will not test an event-driven strategy, but will
look at how two hedging strategies and one opportunistic strategy performed in the period
after the crash. I expect that the market neutral and equity long short hedge fund strategies
will outperform the market, due to their dynamic hedging qualities. And also expect the
global macro strategy to perform better, because its ability to bet on expected market
movements. As previously mentioned, hedge funds have low correlations with the market
index, so in contrast to Mitchel and Pulvino (2001) I don’t think the strategies will be
influenced as much by the market decline.
7. Results
In this section I will discuss the results I obtained using the previously explained
model. First the results of the period 1997-2000 will be discussed, followed by the results of
the period 2000-1997. In the appendix you can find the results of the regressions.
7.1 Results period 1997-2000
In all three regressions the alphas are negative. This means that the three strategies
performed worse on the return basis than the market equity index. The alphas of the global
macro and market neutral were significant at the 5% level, while the alpha of equity long
short was significant at the 1% level3. The explanation can partially be found in that the
period from 1997-2000 was quite a volatile period for hedge funds. For example, in 1998 a lot
of hedge funds ran into trouble because of the Russian debt crisis, which rapidly followed the
Asian financial crisis of 1997(Liang, 2000). Also this period is the represents the end of one
of the longest bull markets in U.S. stock history (Liang, 2000).
Although the S&P performed better when we look at the returns during the period of
1997-2000, its returns were also more volatile than any of the three hedge fund strategies. The
market equity index shows a volatility of 5.004%, while the highest volatility measured is that
of the equity long short strategy with 3.320%. The strategy with the lowest volatility is the
3 See table 5 in appendix
22
market neutral strategy with 1.154%. Effectively this means it is riskier to invest in the market
than in one of the hedge fund strategies.
It is less risky to invest in hedge funds because they can effectively reduce their risk by doing
dynamic hedging and diversifying their portfolios over different financial instruments.
Table 4 Volatility 1997 -
2000 2000 - 2003
Variable St. Dev. St. Dev. S&P 500 excess
.0500441 .0592835
Long Short excess
.0332079 .0188826
MarketNeutral excess
.0115466 .0099462
GlobalMacro excess
.0244937 .0165327
Notes: Volatility of three hedge fund strategies and S&P 500 index excess returns of the period 1997-
2000 and 2000-2003. Factor definitions can be found in table 5.
7.2 Results period 2000-2003
Observable is that in the period after the bursting of the bubble the alphas of global
macro and market neutral strategy are negative and the alpha of the equity long short strategy
is positive4. This would insinuate that the equity long short strategy outperformed the market
equity index and the other two underperformed. But when we look at the significance of the
results we find that all three results are not significant. Because they are not significant we
unfortunately cannot conclude that these strategies out- or underperformed the market equity
index.
Again it is interesting to look at the standard deviation of the strategies. Here we find,
just as the period before the bursting of the bubble, that the market index returns are more
volatile than the returns of the hedge fund strategies. The equity market index has a volatility
of 5.928% while the highest volatility of the hedge fund strategy is that of the long-short
strategy and is 1.888%. The market neutral strategy has the lowest volatility with 0.994%. 4 See table 6 in appendix
23
After the bursting of the Internet bubble it would be a lot less risky to invest in these hedge
fund strategies.
8. Discussion of results
The factor model that I used is designed to estimate the exposure of a diversified
portfolio of hedge funds. But according to Fung and Hsieh (2004) there is a limitation for
when you try to explain the performance of niche hedge fund styles. As a result you could run
into some insignificant results. They say that if you want to look at more specific hedge fund
styles it better to find risk factors that fit that purpose. Mitchell and Pulvino (2001) showed
this for merger arbitrage funds as well as Fung and Hsieh (2001) showed this for trend
following hedge funds. So in order to find more significant results for the hedge fund styles I
used, the results would be better described by identifying more narrow benchmarks. For
future research additional and more fitting risk factors should be identified that are more
specific to these hedge fund styles.
A different aspect that might have affected my results is the bias that is present in the
hedge fund database of Barclayhedge.com. Due to the lack of regulation for hedge funds to
disclose their results, there will be biases when testing. Fung and Hsieh (2004) and Brown et
al.(1999) have proven this in their research. Unfortunately I was not able to access the larger
hedge fund databases like TASS, HFR and MAR, which are used in other researches and are
more aware of the biases. This might partially explain why my results for the period 2000-
2003 were insignificant.
My results show that the market index constantly outperforms the three hedge fund
strategies. This is in line with the findings of Ackermann et al. (1999) and Liang (1999). As
their results show that the hedge funds perform better than mutual funds, but the results are
lower than the market index. But in contrast to their research, I find, just as Liang (2001), that
the market index returns are more volatile than those of the hedge fund strategies. This may
be attributable to the different dynamic hedging strategies employed by hedge funds to reduce
risk.
24
9. Conclusion
Using a sample of publicly accessible hedge fund data I investigated three hedge fund
strategies’ (global macro, market neutral, equity long-short) performance compared to the
market index in the period 1997-2000 and 2000-2003 using a seven factor model (Fung and
Hsieh, 1997) that takes into account several common risk factors. This period 1997-2000 was
the period just after the Asian currency crisis (1997) and during the time of the Russian debt
default (1998) but before the dotcom crash, also known as the Internet bubble. The period
2000-2003 is the period just after the dotcom crash.
In the period 1997-2000 leading up to the Internet bubble I found that the market
index outperformed all three hedge fund strategies. This for me was not very surprising due to
the prior crises that had just taken place. Although the market index returns were higher than
those of the hedge fund strategies, the hedge funds were a lot less volatile. This I believe is
mainly due to the hedge funds ability to dynamically hedging strategies and their ability to
use different investment instruments to reduce their risk.
The period after the bursting of the bubble of March 10 2000 I unfortunately cannot
say much about because the results of my empirical tests have insignificant values. But just as
the period before, I noticed that the market index was more volatile than the returns of the
hedge fund strategies.
25
Appendix
Regression of hedge fund strategies for period 1997-2003
(standard errors in parentheses)
Table 5 Period 1997-2000 Factor Long-
Short Global Macro Market
Neutral Intercept -.1582046 -.0429741 -.0429741 (.0475069)** (.0190454)* (.0190454)* SPexcess .5097095 .1467164 .1467164 (.0921375)** (.0369376)** (.0369376)** Size Spread -.0000553 -8.32e-06 -8.32e-06 (.0000135)** (5.41e-06) (5.41e-06) Treasury 10y 1.144628 -.1766651 -.1766651 (.6656438) (.2668546) (.2668546) CreditSpread .0816781 -.7974734 .2025266 (.1540973) (.0617771)** (.0617771)** BondLBS -.0240229 .0047225 .0047225 (.0265511) (.0106442) (.0106442) CurrencyLBS .0328006 .0130065 .0130065 (.0269525) (.0108052) (.0108052) CommodityLBS -.0109388 .0100658 .0100658 (.0281665) (.0112918) (.0112918) R-squared 0.6189 0.8874 0.4933 Notes: The dependent variable is the average monthly return of the hedge fund strategy minus the risk free rate over a period of 39 months. Intercept is the measure of outperformance when positive and underperformance when negative. SPexcess = S&P500 returns minus risk free rate; SizeSpread = Russel 2000 index – S&P500 monthly total return; Treasury 10y= month-end to month-end change in U.S. Federal Reserve 10-year constant maturity yield; CreditSpread = month-end to month-end change in the difference between Moody’s Baa yield and the Federal Reserve’s 10-year constant maturity yield; BondLBS = return of a portfolio of lookback straddles on bond futures; CurrencyLBS = return of a portfolio of lookback straddles on currency; CommodityLBS = return of a portfolio of lookback straddles on commodity futures. * Significant at the 5% level
** Significant at the 1% level
26
Regression of hedge fund strategies for period 2000-2003
(standard errors in parentheses)
Table 6 Period 2000-2003 Factor Long-Short Global Macro Market Neutral Intercept .0268054 -.0046194 -.0046194 (.0165253) (.0066437) (.0066437) SPexcess -.0586551 -.0176937 -.0176937 (.0466531) (.0187561) (.0187561) Size Spread .0000227 -6.93e-06 -6.93e-06 (.0000245) (9.86e-06) (9.86e-06) Treasury 10y -.7427976 -1.020.305 -1.020.305 (.8826535) (.3548553)** (.3548553)** CreditSpread .2875544 -.7427893 .2572107 (.1344045)* (.0540349)** (.0540349)** BondLBS -.0193534 -.0104895 -.0104895 (.0151857) (.0061051) (.0061051) CurrencyLBS .0242141 .0123776 .0123776 (.015225) (.0061209) (.0061209) CommodityLBS -.0220933 .0070244 .0070244 (.0211888) (.0085186) (.0085186) R-squared 0.4478 0.8879 0.6605 Notes: The dependent variable is the average monthly return of the hedge fund strategy minus the risk free rate over a period of 44 months. Intercept is the measure of outperformance when positive and underperformance when negative. SPexcess = S&P500 returns minus risk free rate; SizeSpread = Russel 2000 index – S&P500 monthly total return; Treasury 10y= month-end to month-end change in U.S. Federal Reserve 10-year constant maturity yield; CreditSpread = month-end to month-end change in the difference between Moody’s Baa yield and the Federal Reserve’s 10-year constant maturity yield; BondLBS = return of a portfolio of lookback straddles on bond futures; CurrencyLBS = return of a portfolio of lookback straddles on currency; CommodityLBS = return of a portfolio of lookback straddles on commodity futures. * Significant at the 5% level ** Significant at the 1% level
27
References
Ackermann, C., Ravenscraft, D., 1998. The impact of regulatory restrictions on fund performance:
a comparative study of hedge funds and mutual funds. Working paper, University of Notre-Dame
Ackermann, C., McEnally, R., Ravenscraft, D., 1999. The performance of hedge funds: risk, return,
and incentives. Journal of Finance 54, 833-874
Agarwal, V., Naik, N.Y., 2002. Characterizing systematic risk of hedge funds with buy-and-hold and
option based strategies. Working paper, London Business School
Agarwal, V., Naik, N.Y. 2004. Risk and portfolio decisions involving hedge funds. Review of
Financial Studies 17 (1), 63-98
Amin, G.S., Kat, H.M., 2003. Hedge fund performance 1990-2000: Do the “money machines” really
add value? Journal of Financial and Quantitative Analysis 38, 251-263
Barclays Hedge Alternative Investment Database. http://www.barclayhedge.com/
Breeden, D.T., 1979. An intertemporal asset-pricing model with stochastic consumption and
investment opportunities. Journal of Financial Economics 7, 265-276
Brown, S.J., Goetzman, W., Ibbotson, R.G., 1992. Survivorship bias in performance studies. Review
of Financial Studies 5 (4), 553-580
Brown, S.J., Goetzman, W., Ibbotson, R.G., 1999. Offshore hedge funds: survival & performance
1989-1999. Journal of Business 72 (1), 91-117
Carhart, M.M., 1997. On persistence in mutual fund performance. Journal of Finance 52, 57-82
Chan, L.K., Jegadeedh, N., Lakonishok, J., 1996. Momentum strategies. Journal of Finance 51, 1681-
1714
Elton, E.J., Gruber, M.J., Blake, C.M., 1996. Survivor bias and mutual funds performance, Review of
financial studies 9 (4), 1097-1120
Eureka hedge: http://www.eurekahedge.com/
28
Fama, E.F., 1965. The behavior of stock market prices, Journal of Business 38, 34-105
Fama, E.F., French, K.R., 1993. Common risk factors in the returns on stocks and bonds. Journal of
Financial Economics 33, 3-56
Fama, E.F., French, K.R., 1996. Multifactor explanations of asset pricing anomalies. Journal of
Finance 51, 55-84
Fama, E.F., French, K.R., 1998. Value versus growth: the international evidence. Journal of Finance,
1975-1999
Field, L.C., Hanka, G., 2001. The expiration of IPO share lockups. Journal of Finance 56 (2), 471-500
Financial education: http://financial-education.com/2007/06/23/index-weighting-methods/
Financial policy forum: http://www.financialpolicy.org ‘Hedge Funds and Funds of Funds’
Friedman, M., 1953. The case for flexible exchange rates, Essays in Positive Economics.
Fung, W., Hsieh, D.A., 1997. Empirical characteristics of dynamic trading strategies: the case of
hedge funds. Review of Financial Studies 10, 275-302
Fung, W., Hsieh, D.A., 2002. The risk in fixed-income hedge fund styles. Financial Analyst Journal
12 (2), 16-27
Fung, W., Hsieh, D.A., 2004. Hedge fund benchmarks: a risk-based approach. Financial Analyst
Journal 60 (5), 65-80
Hauser, F., 2005, Multi factor models in hedge fund and fund of fund management. University of
Karlsruhe.
HedgeCo : www.hedgeco.net
Hendrikcks, D., Patel, J., Zeckerhauser, R., 1993. Hot hands in mutual funds: short-run persistence of
performance. Journal of Finance 48, 93-130
Investopedia: http://www.investopedia.com/terms
29
Jensen, M.C., 1968. The performance of mutual funds in the period 1945-1964. Journal of Finance 23
(2)
Liang, B., 1999. On the performance of hedge funds. Financial Analyst Journal 55 (4), 72-85
Liang, B., 2001. Hedge funds performance: 1990-1999. Financial Analysts Journal, January/February,
11-18
Managed Account Reports (MAR). http://www.marhedge.com/
Mitchell, M., Pulvino, T., 2001. Characteristics of risk and return in risk arbitrage. Journal of Finance
56, 2135-2175
Nasdaq: http://futures.tradingcharts.com/historical/ND/1999/3/linewchart.html
Ofek, E., Richardson, M., 2000. The IPO lockup period: Implications for market efficiency and
downward sloping demand curves. Working paper, New York University
Ofek, E., Richardson, M., 2003. Dotcom mania: The rise and fall of internet stock prices. Journal of
Finance 58, 1113-1137
Reuters: http://www.reuters.com/article/2012/01/11/us-hedgefunds-idUSTRE80A1CV20120111
Schneeweis, T., Spurgin, R., 1998. Multifactor analysis of hedge funds, managed futures and mutual
fund return and risk characteristics. Journal of Alternative Investments 1, 1-24
Sharpe, W.F., 1964. Capital asset prices: a theory of market equilibrium under conditions of risk.
Journal of Finance 19, 425-442
Sharpe, W.F., 1966. Mutual fund performance. Journal of Business 39 (1), 119-138
Sharpe, W.F., 1992. Asset allocation: management style and performance measurement. Journal of
Portfolio Management 18, 7-19
Shiller, R., 2000, Irrational exuberance. Princeton University Press
30
Wikipedia : http://en.wikipedia.org/wiki/History_of_the_Internet
http://en.wikipedia.org/wiki/Dot-com_bubble