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An Analysis Of Hedge Fund Performances


Hedge funds are actively managed portfolios that hold positions in publicly traded securities. Gaurav S. Amin and Harry M. Kat (2000) stated on their report that “A hedge fund is typically defined as a pooled investment vehicle that is privately organized, administrated by professional investment managers, and not widely available to the public�. It charges both a performance fee and a management fee. It allows a flexible investment for a small number of large investors (usually the minimum investment is $1 million) can use high risk techniques. 1Now days it is very clear that in the matter of alternative investment mutual fund is not performing well. As a high absolute returns and typically have features such as hurdle rates and incentive fees with high watermark provision hedge fund gives a better align to the interests of managers and investors. 2Moreover mutual funds typically use a long-only buy-and-hold type strategy on standard asset classes, which help to capture risk premia associate with equity risk, interest rate risk, default risk etc. However, they are not very helpful in capturing risk premia associate with dynamic trading strategies. That is why hedge fund comes into the picture.

This is the year of 2009, which takes the greatest history of the world in the following century. In the year of 2008 the world saw the greatest fall down of the world economy. Lots of people missing their jobs, lots of company were stopped. The world economy faced the highest losses in the history. These all factors are showing only one way to makeover from that greatest downfall that is hedging. 3The last couple of decades have witnessed a rapidly growing in the hedge funds. Relative to traditional investment portfolios hedge funds exhibit some unique characteristics; they are flexible with respect to the types of securities they hold and the type of the position they take.

1 Agarwal, V. and Naik, N. (2000). “Multi-period performance persistence analysis of hedge fund s�. The journal of financial and quantitative analysis. Vol. 35, No,3. PP-327.

2 Agarwal, V. and Naik, N. (2004). “Risks and portfolio decisions involving hedge funds�. The review of financial studies, Vol. 17, No.1. PP-64.

3 Journal of banking and finance 32(2008) 741-753- “Hedge Fund Pricing and Model Uncertainty� by Spyridan D. Vrontos, Ioannis D. Vrontos, Daniel Giomouridies.

4The number of FOHFs increase by 40% between 2001 and 2003, and now comprised almost two third of the $650 billion invested in the USA’s hedge fund market. Due to its nature it is difficult to estimate the current size of hedge fund industry. 5Van Hedge Fund Advisors estimates that by the end of 1998 there were 5380 hedge fund managing $311 in capital, with between $800 billion and $1 trillion in total assets, which indicates the higher number of recent new entries. So far, hedge fund is based on American phenomena. About 90% hedge fund managers are based in the US, 9% in Europe and 1% in Asia and elsewhere. Now a day’s around 5883 hedge funds are trading around the world. (*Barclay Hedge database)

4 Financial times, 29th October, 2003.

1.1 Categories of Hedge fund investment objectives:

Event Driven:

Distressed securities- manager focuses on securities of companies in reorganization and bankruptcy, ranging from senior secured debt to the common stock of the company.

Risk arbitrage- manager simultaneously buys stock in a company being acquired and sells stock in its acquirers.


International- manager pays attention to economic change around the world (except the United States) but more bottom-up oriented in that managers tend to be stock-pickers in markets they like. Uses index derivatives to a much lesser extent than macro managers.

Emerging- Manager invests in less mature financial markets of the world, e.g. Hong Kong, Singapore, Pakistan, India. Because shorting is not permitted in many emerging markets, managers must go to cash or other markets when valuations make being long unattractive.

Regional- Manager focuses on specific regions of the world, example- Latin America, Asia, and Europe.

Global macro: Opportunistic trading manager that profits from changes in global economies typically based in major interest rate shifts. Uses leverage and derivatives.

Market neutral:

Long/short stocks- half long/half short. Manager attempts to lock-out or neutralize market risk.

Convertible arbitrage- Manager goes long convertible securities and shorts the underlying equities.

Stock index arbitrage- Manager buys a basket of stocks and sells short stock index futures, or the reverse.

Fixed income arbitrage- Manager buys T-bonds and sells short index futures or the reverse.

Short sales:

Manager takes a position that stock prices will go down. Used as a hedge for long only portfolios and by those who feel market is approaching a bearish trend.

U.S Opportunistic:

Value – Manager focuses on assets, cash flow, book value, out-of-favor stocks.

Growth – Manager invests in growth stocks, revenues, earnings, and growth potential are keys.

Short term – Manager holds positions for a short time frame.

Fund of fund:

Capital is allocated among a number of hedge funds, providing investors with access to managers they might not be able to discover or evaluate in their own. Usually has a lower minimum than a hedge fund.

Source: Carl Ackermann, Richard McEnally, and David Ravenscraft, “The performance of hedge funds: Risk, Return and Incentives,� Journal of finance 54, no.3 (June 1999) figure 1, page-843. Reproduced from a hedge fund database firm named Managed Account Report (MAR) Inc, and distributed through LaPorte Asset Allocation System.

2. Literature review:

Despite the increasing interest and recent development, few studies have been carried out on hedge funds comparing to other investment tools like mutual funds. “An analysis of Hedge Fund performance 1984-2000� by Capocci Daniel using one of the greatest hedge fund database ever used on his working paper (2796 individual funds including 801 dissolved), to investigate hedge funds performance using various asset-pricing models, including an extension from of Carhart’s (1997) model combined with Fama and French (1998), Agarwal and Naik (2000) models that take into account the fact that some hedge funds invest in emerging market bond. At the end they found that their model does a better job describing hedge funds behaviour. That appears particularly good for the Event Driven, Global Macro, US Opportunistic, Equity non-Hedge and Sector funds.

Since the early 1990s, when around 2000 hedge funds were managing assets totalling capital of $60 billion, the subsequent growth in the number and asset base of hedge funds has never really been refuted. The industry only suffered from a relative slowdown in 1998, but since then has enjoyed a renewed vitality with an estimated total of 10,000funds managing more than a trillion US dollars by the end of 2006. The growing trend of the sector remained remarkably sustained during the stock market collapse that started in March 2000, when the NASDAQ composite Index reached an all-time high of 5,132 and finished three years later with a floor level of 1,253. In the meantime, the global met asset value (NAV) of hedge funds continued to grow at a steady rate of 10.6% (Van Hedge Funds Advisors International, 2002), contrasting with a decrease of 2.7% in the worldwide mutual fund industry ( Investment Company Institute, 2003). In 2001, Capocci and Hubner(2004) estimated that there were 6,000 hedge fund managing around $400 billion. In 2007, Capocci, Duquenne and Hubner (2007) estimated that there were 10,000 hedge funds managing around $1 trillion. This is a growth of 11% in the number of funds and 26% in assets over six years (6PhD thesis paper by Daniel P.J. Capocci).

Other studies from practitioners Hennessee (1994), and Oberuc (1994) also showed an evidence of superior performance in the case of hedge funds. Ackernann and Al. (1999) and Liang (1999) who compared the performance of hedge funds to mutual funds and several indices, found that hedge funds constantly obtained better performance than mutual funds. Their performance was not better than the performance of the market indices considered. They also indicated that the returns in hedge funds were more unstable than both the returns of mutual funds and those of market indices. According to Brown and Al. (1997) hedge funds showing good performance in the first part of the year reduce the volatility of their portfolio in the second half of the year (Capocci Daniel- An analysis of hedge fund performance 1984-2000). Taking all these results into account hedge funds seems a good investment tool.

6 PhD thesis paper by Daniel P.J. Capocci. Electronic copy available at: http//

3. Research design and Methodology:

In this section I would like to describe the empirical methodology to be used to measure the performance of hedge fund as well as the performance of FTSE 100 and S&P 500. My aim is to identify which will give the better return for an investor. To investigate hedge funds performance and performance of FTSE 100 and S&P 500 my study will follow some models like 4-factor model from of Carhart’s (1997) model, the 3-factor model from Fama and French (1993) models, the Sharpe ratio (1966) and Jensen’s alpha (1968) and CAPM.

I divide my research into three sections. First section will analyse the performance of hedge funds, FTSE 100 and S&P 500. This section sets out the models of performance measurement I will use. Second section will made correlation between Hedge fund vs. FTSE 100 and Hedge fund vs. S&P 500 to find out the better portfolio. Third section will exposes a discussion as well as a description of my database and finally concludes the paper.

3.1. Performance measure models:

The 4-factor model from Carhart (1997)

Carhart’s (1997) 4-factor model is an extension of the Fama and French (1993) factor model. It not only takes into account the size of the firms, the book to market ratio, but there is an additional factor for the momentum effect. Grinblatt, Titman and Wermers (1995) define this effect as buying stocks that were past winners and selling past losers. This model is estimated with the following regressions:

Rpt-Rft=αp+βpi (Rmt –Rft) + βp2 SMBt +βp3 HMLt + βp4 PR1YRt + ept t= 1,2,.........,T


SMBt= the factor mimicking portfolios for size;

HMLt= the factor mimicking portfolio for book to market equity;

PR1YRt= the factor mimicking portfolio for the momentum effect7

7 for a description of the construction of PR1YR see Carhart (1997).

As stressed by Daniel et al. (1997), this model, which is effectively a four factor Jensen measure, assumes that betas with respect to the returns of four zero investment factor mimicking portfolios, are appropriate measures of multidimensional systematic risk. According to this model, in the absence of stock selection or timing abilities, the expected return for a fund is the sum of the risk free return and the products of the betas with the factor risk premium, which are simply the expected returns of each of these zero investment portfolios. The Carhart (1997) approach identifies a matching passive portfolio return for each fund return. This passive return, which is subtracted from the fund return to generate αp, is a weighted average of the returns of the Carhart factor portfolios and the return of a one month T-bill (Capocci Daniel 2001, Journal- European Private Bankers, Nov, 2001).

The 3-factor model from Fama and French (1993):

Fama and French (1993) 3 factor model is estimated from an expected form of the CAPM regression. It takes the size and the book to market ratio of the firm into account. It uses the time series approach from Black, Jensen, and Scholles (1972) in the sense that the monthly returns on stocks are regressed on the returns to a market portfolio of stocks and mimicking portfolios for size and book to market. It is estimated from the following extension of the CAPM regression:

Rpt-Rft=αp+βpi (Rmt –Rft) + βp2 SMBt +βp3 HMLt + ept t= 1,2,.........,T


SMBt= the factor mimicking portfolios for size, and

HMLt= the factor mimicking portfolio for book to market equity.

SMLt which comes from small minus big meant to mimic the risk factor in returns related to size, and HMLt which comes from high minus low meant to mimic the risk factor in returns related to book to market equity8. HML (respectively SMB) is neutral relative to the size effect (respectively to the book to market). This means that these factors do a good job isolating the firm-specific components of returns (Fama and French 1993, 1995, 1996 and 2000).

8 See Fama and French (1993) for a precise description of the construction of SMBt and HMLt.

The Sharp Ratio (1966):

The Sharp ratios (1966) calculate the ratio of the average excess return and the return standard deviation of the fund that is being evaluated. As such it measures the excess return per unit of risk. Assuming all asset returns to be normally distributed, the CAPM tells us that in equilibrium the highest attainable Sharpe ratio is that of the market index. In more general terms, the market index’s sharp ratio represents the set of return distributions that is obtained when statically combining the market index with cash. With the market index being highly diversified, these distributions offer the highest achievable expected return for every possible standard deviation (Gaurav S. Amin and Harry M.Kat (2002), Hedge fund performance 1990-2000).

Jensen’s Alpha (1968):

Jensen’s alpha was introduced in Jensen (1968) and equals the intercept of the regression:

(Rh-Rf)= α + β (Ri- Rf) + eh,

Where Rh is the fund return, Rf is the risk free rate and Ri is the total return on the market index. Like the Sharpe ratio, Jensen’s alpha is rooted in the CAPM. According to the CAPM, in equilibrium all (portfolios of) assets with the same beta will offer the same expected return, any positive deviation therefore indicates superior performance (Gaurav S. Amin and Harry M.Kat (2002), Hedge fund performance 1990-2000).

Capital Asset Pricing Model:

The first performance model that will be used is a capital asset pricing based single index model (CAPM). This model developed by Sharpe (1964) and Linter (1965) is the oldest performance evaluation model. Its formula is the following:

Rpt – Rft = αp + βp (Rmt-Rft) + ept t= 1,2,........., T


Rpt= return of fund p in month t, Rft= risk free return on month t, Rmt= return of the market portfolio on month t, ept= the error term, αp and βp= the intercept and the slope of the regression estimated.

The intercept of this equation, αp commonly called Jensen’s alpha (1968) is usually interpreted as a measure of out or under performance relative to the market proxy used. There are several extension of this model have been developed like- the Breeden (1979) intertemporal CAPM or the Ferson and Schadt (1996) CAPM that allows time variation in the expected returns and the risk (Capocci Daniel 2001, An analysis of hedge fund performance 1984- 2000).

4. Data Preparation:

For data preparation my first step will be to collect the monthly data of the hedge fund index, FTSE 100 and S&P 500. For my data collection I will use some sources like- Credit Suisse/ Tremont Hedge Fund Index (CSTHFI hereafter) which is an appropriate representative of the entire hedge fund industry, there are three biggest database of hedge fund in the world these are Managed Account Reports (MAR), Hedge Fund Research, Inc (HFR) and TASS Management (TASS). These databases were the most used in academic and commercial hedge fund studies. For the FTSE 100 and S&P 500 I will use yahoo finance.

4.1. Bias in Hedge fund data:

According to Ackermann et al. (1999) and to Fung and Hsieh (2000), two upward biases exist in the case of hedge funds. They do not exist in the case of mutual funds, and they both have an opposite impact to the survivorship bias. Survivorship bias is an important issue in mutual funds performance studies (see Carhart and al. 2000). This bias is present when a database contains only funds that have data for the whole period studies. In this case, there is a risk of overestimating the mean performance because the funds that would have ceased to exist because of their bad performance would not be taken into account. The two upward biases exist because, since hedge funds are not allowed to advertise, they consider inclusion in a database primarily as a marketing tool. The first phenomenon stressed by Ackermann and al. (1999) and called the self-selection bias is present because funds that realize good performance have less incentive to report their performance to data providers in order to attract new investors. The second point called instant history bias or backfilled bias (Fung and Hsieh 2000) occurs because after inclusion a fund’s performance history is backfilled. This may cause an upward bias because funds with less satisfactory performance history are less likely to apply for inclusion than funds with good performance history (Capocci Daniel 2001, An analysis of hedge fund performance 1984- 2000). To avoid these biases I will try to take all funds both living and dissolved into account.

Once I have collected all the data that I need I will use SPSS to test the correlation between my two benchmarks FTSE 100 and S&P 500.

5. Contingency Plan:

To make my research effective I made a well constructed plan. I have drafted a project plan (Appendix A) with scheduled dates for when I intend to complete sections for submission. After completing my final exam I will jump in to this field. Advises from previous students who completed their dissertation, I made my project plan flexible to keep some things in mind like supervisor’s holiday and any unforeseen events such as my illness. I will try to keep a good communication with my supervisor for checking that I am in right track. I plan to make some formal meetings with my supervisor to discuss my progress and I will try to inform him about the state of my work. It is hard to spending too much time over one task and going off track, I hope I will manage this if there is no rush at the very last minute. Another worry is the collecting and analysing the data, that is why I plan to collect the data early June once I have finished my research design. If I face any kind of difficulties I will inform him and make a cut-off point where I should stop searching the board data and start my own primary data. As I do all SPSS classes and briefly touched about this, I think it will be easy to analyze the data but I need to increase a bit of use of control on it by practicing more. So I will set aside time for collecting data and practice more SPSS for regression analysis. I hope if all these go well, I will make my dissertation very effectively.

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