Development of an Analytical Framework for Hedge Fund Investment∗

This research attempts to develop an analytical framework for hedge fund investment. Various issues related to the hedge fund investment have been addressed. This paper focuses on three distinct areas of hedge fund research, namely, bias in hedge fund data, the classification of hedge funds, and performance attribution of hedge funds. All studies, reported in this paper, have been carried out using the ZCM/Hedge (formerly Mar/Hedge) database. The bias result varies from +0.13% to -0.44% for different categories. The bias study is carried out for three different lengths of study periods using two different measures of bias. The results show that the bias becomes more negative with the increase in the study period. The performance of disappeared and new portfolios is studied. The validity of the CAPM model is tested using the ZCM/Hedge database. The data does not support any of the implications of the CAPM. Hedge-fund databases vary with the types of funds included and their classifications. Research results on hedge-fund performance may then differ depending on the database, making them difficult to compare. This research uses a cluster-analysis approach to classify hedge funds. The results of the study are compared with the existing classifications of the ZCM/Hedge database. Analytical models to explain hedge fund return are developed using exact factor-pricing models: the macroeconomic factor model and the fundamental factor model. The results of the macroeconomic model show that only two state variables are statistically significant. This lends support to the similarity hypothesis that the macroeconomic factors that explain equity return also have explanatory power for hedge fund returns. Although not all the R-square values are impressive, it appears that the five-factor model does explain approximately 30% to 40% of the variation in hedge fund return. The fundamental factor model is developed using fund attributes that should have an effect on hedge fund return. The analysis is carried out using the OLS and WLS estimation procedures. The results are different for the different estimation procedures, leading to the conclusion that caution is appropriate when interpreting results from OLS coefficients because of presence of heteroscedasticity in the cross-sectional data. It appears from the analysis that the fundamental variables chosen for the model are successful in explaining hedge fund return for hedge funds domiciled in the US, but not for hedge funds domiciled outside the US. ∗ The author is thankful to the Foundation for Managed Derivatives Research for providing a research grant, to Mr. Richard E. Oberuc of LaPorte Asset Allocation System for providing the ZCM/Hedge database and to Lehigh University for the Warren-York Fellowship for this research project. The author is thankful to Dr. D.L. Muething and Dr. L.W. Taylor for their guidance of this research work. The author is also thankful to Dr. Richard J. Kish for his advice. The author is thankful to the participants at the 2002 FMA Doctoral Student Consortium, the 2003 Eastern Economic Association Annual Meeting, and the 9th International Conference on Computing in Economics and Finance for their helpful suggestions and comments. Das Development of an Analytical Framework for Hedge Fund Investment 1 1.0 Introduction Hedge funds, as an alternative investment vehicle, have enjoyed healthy growth in recent years and continue to increase in popularity. High net worth individuals have dominated the hedge fund industry for a long time. An increasing number of institutions are allocating a small portion of assets to alternative investments owing to the longterm success of some hedge funds. Hedge funds became popular for their philosophy of trying to outperform the overall market through individual stock and security selection and by taking market neutral positions in an effort to protect financial capital in times of market volatility. Today, the term ‘Hedge Fund’ is used to describe a wide range of investment vehicles that can vary substantially in terms of size, strategy, and organizational structures. Work has been done on the benefits of adding hedge funds to the traditional investment portfolio, the performance characteristics of hedge funds, and the market impact of hedge funds. The study of performance persistence in the hedge fund industry is a recent phenomenon. Bias is closely linked to the issue of performance persistence; the direction of the bias is not clear even for traditional investments like mutual funds. For hedge funds, the issue becomes more complicated, because it is possible that hedge funds disappear from the database for various reasons. It is necessary to estimate bias to better measure performance and to get an idea of the relative performance. Hedge funds have different performance characteristics depending upon their investment strategy. It is important that bias be estimated for different categories to help measure the performance accurately. Hedge funds provide very limited information to investors, mainly periodic (monthly, quarterly, or annual) returns. Sources of data for the industry are the hedge fund database providers. Four main hedge fund databases are used in academics and industry. There is neither legal definition for hedge funds, nor any industry standard for their classification. The databases vary as to the type of funds to include and in their classification scheme. There appears to be a myriad of classifications in existence. There is a need for a unified approach to the classification of hedge funds. In this research project, an attempt is made to develop an analytical framework for hedge fund investment. This framework will enable the investor to estimate return of a hedge fund, based on the strategies and characteristics influencing the return. The number and diversity of hedge funds suggest that the decision to invest in hedge funds should be based on an analytical framework. This paper proceeds as follows: Section 2 describes the Hedge Fund industry, Section 3 briefly describes the various classification schemes of the database providers, Section 4 describes the literature on hedge fund research, Section 5 measures bias in hedge fund data, Section 6 checks the validity of the CAPM model to explain hedge fund returns Section 7 develops a new approach to classify hedge funds, Section 8 models hedge fund return, and Section 9 concludes. 2.0 Hedge Fund Industry In finance industry terminology, the meaning of hedge is the process of protecting oneself against unfavourable changes in prices. The term ‘hedge fund’ is not defined or separately addressed in any securities or commodity laws. The term has undergone a considerable amount of mutation to represent what it means today compared to what it meant when it first originated in 1949. In 1949, A.W. Jones introduced the concept of the hedge fund. He combined a leveraged long stock position with a portfolio of short stocks in an investment fund with an incentive fee structure. Hedge fund investment practices and strategies have evolved and expanded since then. Some of today’s hedge funds satisfy all criteria of Jones’ fund; namely long/short positions and incentive-based fees. With no legal definition of a hedge fund, any fund that satisfies two criteria of Jones’ fund is identified as a hedge fund. Some hedge funds do not hedge at all. While many hedge fund characteristics have changed significantly, many fundamental features have remained the same. Moreover, hedge funds are no longer unique to the U.S. markets, but exist in many areas around the world. In the United States, they normally offer their shares in private placements and have less than 100 high net-worth investors in order to make use of exemptions provided under the Securities Act of 1933, the Securities Exchange Act of 1934, and the Investment Company Act of l940. In the short history of fifty years, interest in hedge funds and their performance has waxed and waned. In recent years, however, hedge funds have enjoyed healthy growth and appear to have increased in popularity. In particular, the bull market of the late 1980s created more high-net-worth investors. These investors, looking for enhanced returns, started to invest in hedge funds. The renewed interest in hedge funds that began in the late 1980s has not vanished. In 1990, there were about 600 hedge funds worldwide with assets of approximately $38 billion. According to industry publications, at the end of 1998, despite the publicized collapse of Long Term Capital Management (LTCM), there were some 3,300 hedge funds with assets of approximately US$375 billion. The near failure of LTCM in 1998 does not appear to have slowed down the growth of and interest in hedge funds. The LTCM debacle has rightly led to more caution from regulatory authorities and investor interest groups. Das Development of an Analytical Framework for Hedge Fund Investment 2 All estimates suggest that the hedge fund industry has experienced tremendous growth since mid 1980s, measured either by the number of funds or by assets under management. Additional investments in the hedge fund industry in years 2000 and 2001 were US$40 billion and US$80 billion respectively, and the total industry size in the first quarter of the year 2003 is between US$600 billion and US$700 billion. Hedge funds invest in a variety of liquid assets just like mutual funds, but are quite different from mutual funds. For example, under current federal law, hedge funds do not have any management limitations. There are virtually no limits on the composition of the portfolios and no mandatory disclosure of information about holdings and performance. Das et al. (2002a) provides an overview of the hedge fund industry. 3.0 Hedge Fund Database Providers and Classification Four primary databases are popular among researchers and in the investment industry. Providers of these databases offer different services to the industry. The Zurich Capital Markets (ZCM/Hedge) database (formerly MAR/hedge) provides a comprehensive coverage of global hedge funds. The Hedge Fund Research (HFR) database contains more equity-based hedge funds. TASS is the information and research subsidiary of Credit Suisse First Boston Tremont Advisers. Various database providers classify hedge funds, but in different ways. All the four databases have their own indices based on the categories in the database. The index composition is also different for different databases. Hedge fund categories are based on the self-reported style classifications of hedge fund managers that are listed in a particular database. None of the database provides information on the complete hedge fund universe. The databases differ in the definition of the ‘hedge fund’. For example, TASS is the only database that includes the managed futures funds. Unlike hedge funds, managed futures funds limit their activities to the futures market. Following issues are observed about the performance data for various databases. • A major limitation of most hedge fund databases is that they typically have data only on funds still in existence or that are new and growing. • Most hedge fund indices do not include performance of closed funds. • Only those funds that choose to report are included in the database. Not much can be done with this issue due to the industry structure. ZCM/Hedge and TASS have historical performances of all funds that are included in their database. Historical performances are not included (no backfiling) in index construction, but are available for fund analysis. • HFR, ZCM/Hedge, and VanHedge have all inclusive selection criteria; they include all funds in their database that classify them as hedge funds. TASS has its own selection criteria. • The classification method varies across databases making them difficult to compare. Hedge fund managers employ a diverse array of strategies. The database providers classify hedge funds based on the voluntary information that they collect from the hedge fund managers. Style definitions and the number of categories of hedge funds differ among the database providers. The classification of hedge funds by various database providers is briefly described here. The ZCM/Hedge database classifies hedge funds into four general classes and ten broad categories of investment styles, as reported by the managers of the hedge fund. The classes are ‘onshore’ hedge funds (HF-US), ‘offshore’ hedge funds (HF-NON), ‘onshore’ fund-of-funds (FOF-US), and ‘offshore’ fund-of-funds (FOF-NON). Some of the categories have further sub-classifications. TASS is the information and research subsidiary of Credit Suisse First Boston Tremont Advisers. It has nine categories of hedge funds, classified based on the investment styles of hedge fund managers. Figure 3.1 shows the classification of the ZCM/Hedge and TASS database. The Hedge Fund Research (HFR) has twenty-six categories of hedge funds. Some of these categories are merely a type of financial instrument or a geographic area for investment. This classification can be reorganized into eleven categories. Some of the categories have further sub-classifications. The VanHedge maintains an extensive database of hedge funds. It provides consultancy and detailed generic performance data on hedge fund styles. VanHedge database can be organized into thirteen categories and five subcategories. 3.1 Alternative Classification Requirement There exists a lot of variation in the definitions, calculation methodologies, assumptions, and data employed by the different managers and databases. It is necessary to benchmark hedge fund manager practices relative to their peers as hedge funds follow diverse strategies. Multiple peer groups may be relevant depending on the strategies employed by the manager, and it is important to clearly identify a peer for the various hedge fund strategies. This may not be an easy task since hedge fund managers refrain from disclosure. Das Development of an Analytical Framework for Hedge Fund Investment