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Hedge Fund Performance During the Internet Bubble Bachelor Thesis Finance

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Hedge Fund Performance During the Internet Bubble Bachelor Thesis Finance Hedge fund performance during the Internet bubble Bachelor thesis finance Colby Harmon 6325661 /10070168 Thesis supervisor: V. Malinova 1 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 2 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 3 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 4 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.
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