Does investor sentiment play a role in hedge fund return?AbstractThe current estimate of the hedge fund industry is over one trillion dollar and growth seems to continue with more than 8,000 funds. We study the existence of investor sentiment in the hedge fund industry using MorningStar database. We calculate fund flows as a measure of investor sentiment and analyze the fundamental characteristics of hedge funds that are favored by investor sentiment. We find an overwhelming evidence of investor sentiment in the hedge fund industry. We also find that investor sentiment is not governed by the fundamental characteristics of the hedge funds. I. INTRODUCTION Hedge funds have enjoyed healthy growth through the years and continue to increase in popularity, especially among high net-worth individuals. Recently, an increasing number of institutions have allocated a small portion of their assets to these alternative investments owing to their long-term success. 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. The strategies include selling stocks short on a bet they will fall and using borrowed money. Many of them hedge against declines in the market, but the techniques vary greatly. One commonality surrounding hedge funds is the limited amount of information provided to potential investors. Typically information is limited to periodic (monthly, quarterly, or annual) returns. Even the leading hedge-fund databases provide incomplete information drawn from the fund-offering documents such as contractual provisions (fee structure, minimum investment size, and withdrawal provisions), descriptions of investments, styles of investment, and the periodic return. The current estimate of the industry size is over one trillion dollar. The average hedge fund remains small, with less than $200 million in assets, compared with one billion dollar for the average mutual fund. In 2004, the average fund lagged behind the S&P index. Hedge fund performance was uneven in 2005. On an average hedge funds actually lost money in 4 out of 11 months. In this paper we document the existence of investor sentiment in the hedge fund industry. We calculate fund flows as a measure of investor sentiment and analyze the fundamental characteristics of hedge funds that are favored by investor sentiment. The paper proceeds as follows: Section I gives a brief history of the hedge fund industry. Section II provides a review of the literature. Data, modeling, and results are outlined in Section III. Section IV summarizes our findings and contributions. II. A BRIEF HISTORY OF THE HEDGE FUND INDUSTRY In 1949, A.W. Jones introduced the concept of a hedge fund by combining a leveraged long stock position with a portfolio of short stocks in an investment fund with an incentive fee structure. From this simple concept, hedge fund investment practices and strategies continue to evolve. Consequentially, many hedge fund characteristics have changed significantly, but many of the fundamental features have remained the same. Moreover, hedge funds are no longer unique to the U.S. markets, but have become a fixture in the global marketplace. In the United States, the funds normally offer their shares in private placements and are limited to 100 or fewer high net-worth investors in order to make use of regulatory exemptions provided under the Securities Act of 1933, the Securities Exchange Act of 1934, and the Investment Company Act of 1940. Interest in hedge funds and their performance has waxed and waned over time, but recent publicity has lead to hedge funds enjoying healthy growth. For instance, the high net-worth investors created through the bull market of the late 1980s started to invest in hedge funds as a means of enhancing their returns. 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 $375 billion. Additional investments at the turn of the century have pushed the hedge fund industry over the $1 trillion mark. Although hedge funds invest in a variety of liquid assets similar to mutual funds, they are quite different. Under current federal law, hedge funds have no limitations on management, virtually no limits on the composition of the portfolios, and no mandatory disclosure of information about holdings or performance. III. LITERATURE REVIEW The systematic study of hedge funds is a recent phenomenon, encouraged primarily by the availability of data. Most of the literature is less than a decade old, and focuses on performance attribution, performance evaluation, characteristics, and the impact on the financial markets. When modeling hedge fund performance as a group, researchers typically model hedge fund performance by treating all the hedge funds in a database as a single group. Examples include Schneeweis and Spurgin (1998), Ackermann et al. (1999). Researchers have also attempted to extract strategies from observed returns to reclassify hedge funds based on observed return characteristics. Examples include Fung and Hsieh (1997), Brown and Goetzmann (2001). The second research focus, performance evaluation, is essentially concerned with comparing the return earned on a hedge fund with the return earned on some other standard investment asset. Research in this area can be divided into three groups: benchmarking, performance persistence, and performance in a portfolio context. Key benchmarking research supports the fact that hedge funds outperform mutual funds, even on a risk adjusted basis. See, for instance, Ackermann et al. (1999), Brown et al. (1999), Edwards and Liew (1999), Agarwal and Naik (2000), Edwards and Caglayan (2001), Kao (2002), Amin and Kat (2003) and Malkiel and Saha (2005). The third research area focuses on hedge fund characteristics. This area is the broadest focus group, starting with general characteristics and progressing to performance attributes, as in Brown et al. (2001). Characteristics of the hedge fund industry, including the fee structure, data conditioning biases, and the risk/return characteristic of various hedge fund strategies have been studied. For instance, see Park and Staum (1998), Schneeweis and Spurgin (1998), and Ackermann et al. (1999) for a thorough discussion of hedge fund characteristics. Returns are summarized in Edwards (1999), Fung and Hsieh (1999), and Lamm et al. (1999). Goetzmann et al. (1998) evaluate compensation issues. In the last area, researchers study the role of hedge funds in the financial market crisis and the implications for policy. For instance, the role of hedge funds in the Asian crisis is documented in Yago et al. (1998, 1999), Eichengreen and Mathieson (1998), and Brown et al. (2000, 2001). The collapse of LTCM is referenced in Edwards (1999). A summary of the empirical work on hedge funds leads to the following conclusions: * hedge fund returns are volatile * the inclusion of hedge funds in diversified portfolios raises efficiency of portfolios * hedge funds have a low correlation with traditional asset classes * fund-of-hedge funds offer diversification benefits to some extent * hedge funds may have risk-adjusted performance persistence * diminishing-return-to-scale may exist in the hedge fund industry * hedge funds did not have any direct role in precipitating risk in the financial market * incentive fee structures do not lead hedge fund managers to take more risk because of the possibility of non-survival * hedge funds follow very dynamic strategies Many papers have documented the relationship between investor sentiment and performance in mutual fund industry as well as the stock markets in general. For example, Lamont and Frazzini (2005), Ippolito (1992), Chevalier and Ellison (1997), Sirri and Tufano (1998) study this relationship in the mutual fund industry. IV. DATA AND METHODOLOGY The databases popular among researchers and the investment community include Center for International Securities and Derivatives Markets (CISDM/Hedge) database (formerly, MAR/hedge), which provides a comprehensive coverage of global hedge funds; Hedge Fund Research (HFR) database, which contains more equity-based hedge funds; and TASS, the information and research subsidiary of Credit Suisse First Boston Tremont Advisers. The database providers all offer hedge fund classifications and indices, unfortunately without much in common. Hedge fund categories listed in a particular database are based on the self-reported style classifications of the hedge fund managers. In addition, none of the databases provide information on the complete hedge fund universe. The databases also differ on their definition of a 'hedge fund'. For example, TASS is the only database that includes managed futures funds, which limit their activities to futures market. Since hedge fund managers employ a diverse array of investment strategies, the database providers must provide some sort of classification scheme. Although all the major databases rely on the voluntary information provided by the hedge fund managers, style definitions and the number of hedge fund categories differ among the database providers. The data used for this study is the monthly hedge fund return of the Center for International Securities and Derivatives Markets/Hedge (CISDM/Hedge) database and MorningStar. CISDM/Hedge database was made available by University of Massachusetts for this research. The CISDM/Hedge database provides monthly returns for all the funds. The study period for the present research has been selected to be between January 1994 and December 2004. CISDM/Hedge data has 184,095 observations of monthly returns for 2,930 funds. Some more funds had to be dropped from the study due to the unavailability of some key data that could not be derived from the available information. A study period dataset from January 1994 to December 2004 is constructed from the available dataset. The available monthly return observations that are used for the study are 167,009 for 2,930 funds and their distribution is shown in Table 1. The MorningStar has individual hedge fund data for approximately 1500 hedge funds domiciled in US. Of course we will have to consider the availability of data for individual hedge funds and it is estimated that the data will have an attrition rate of 50%. The researchers are analyzing the data available from 1995 to 2009. We will also look at the 23 categories of HF data in MorningStar. This will also give an idea of the impact that financial crisis has had on hedge fund asset flow. This study considers after-fee returns and before-fee returns. In general, hedge funds charge two types of fees: an asset management fee and an incentive fee. The asset management fee is based on amount of the assets in the fund, usually 1%, or 2% per year. The incentive fee or the "carried interest" is the hedge fund manager's share in a fund's profit. Usually this is 20 percent and is paid annually in the United States. For offshore hedge funds, the incentive fee is calculated monthly or quarterly. Two other important features of a hedge fund fee structure are the hurdle rate and the high water mark. The CISDM/Hedge database provides information on annual fee structure for each of the hedge funds. Subtracting 1/12th of the stated percent fee from the monthly return approximates the administrative fee. Both the hurdle rate and the high water mark feature are considered for computing the incentive fee. For example, the incentive fee was subtracted only if the fund in question had a positive cumulative return since it last charged an incentive fee and had crossed the hurdle rate. This takes care of the loss recovery requirement, the minimum return requirement and assures that there is no double counting of fees. Our study uses an approach similar to that of Lamont and Frazzini (2005). We study the incremental flow of funds to different categories and individual hedge funds. We do not answer the question as to why funds flow into the hedge fund industry, which is observed from the stellar increase in the size of the industry from $38 billion in 1990 to over $1 trillion in 2005. Rather, we try to analyze that, given the fact that industry size keeps growing what are the factors responsible for the disproportionate growth of some hedge funds when other funds are experiencing net outflow. We carry out our analysis at fund level and also at the category level. We use asset under management (AUM) to calculate the flow. The first question that we answer is about investor sentiment in general. Our hypothesis is stated below. [H.sub.0] : There is an evidence of investor sentiment as measured by net disproportionate flow of funds in some hedge funds. [H.sub.a]: There is no evidence of investor sentiment in the hedge fund industry. We measure investor sentiment as the level of differential flow. Under normal circumstances, every hedge fund should experience an inflow (outflow) of funds that is proportional to the percentage of the hedge fund industry assets that the fund owns. This, we call the theoretical flow for the hedge fund. In short, with nothing changing, we expect the hedge fund's representation in the industry, in terms of its share to remain fixed. Differential flow is the extra flow of funds that a hedge fund receives over and above its theoretical flow (proportional flow). The differential flow is calculated as a difference of real flow and theoretical flow. The model is expressed using the following equations: [RflM.sup.i.sub.m] = [AUM.sup.i.sub.m] - [AUM.sup.i.sub.m-1] (1 + [r.sup.i.sub.m] (1) [RflY.sup.i.sub.t] = [12.summation over (i=1)] [RflM.sup.i.sub.m] (2) [TflY.sup.i.sub.t] = [AUM.sup.i.sub.t-1] / [AUM.sup.Agg.sub.t-1] x [FL.sup.Agg.sub.t] (3) [AUM.sup.Agg.sub.t-1] = [N.summation over (i=1)] [([AUM.sub.t-1]).sub.i] (4) [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (5) Diff ([Fl.sup.i.sub.t]) = [RflY.sup.i.sub.t] - [TflY.sup.i.sub.t] (6) where [RflM.sub.i.sup.m] is the monthly real flow for hedge fund i in month m, [AUM.sup.i.sub.m] is the assets under management for hedge fund i in month m, [AUM.sup.i.sub.m-1] is the assets under management for hedge fund i in month m-1, [r.sup.i.sub.m] is the monthly return for hedge fund i in month m, [RflY.sup.i.sub.t] is the annual real flow for hedge fund i in year t, [TflY.sup.i.sub.j] is the theoretical annual flow for hedge fund i in year t, [AUM.sup.i.sub.t-1] is the year-end assets under management for hedge fund i in year t-1, [AUM.sup.Agg.sub.t-i] is the year-end assets under management for all the funds in the database or all funds in the category for year t-1. [FL.sup.Agg.sub.t] is the flow of funds (new money) to the hedge fund industry (with N funds) or to the category (with N funds) for year t, and Diff([FL.sup.i.sub.t]) is the differential flow of funds to hedge fund i in year t, Equations 1 through 6 describe the model that we use to calculate investor sentiment. When Diff([FL.sup.i.sub.t]) is positive there is a net inflow of differential funds and when it is negative the hedge fund experienced an outflow of funds. It is important to realize that we are not interested in measuring inflow or outflow. We measure differential inflow or outflow experienced by that fund. With a growing industry, it is expected that there will be net inflow of funds. Our variable of interest is the disproportionate flow of funds, represented by Diff([FL.sup.i.sub.t]). The asset under management figure that is reported in the database consists of two parts. There is some increase or decrease in assets under management that is solely due to the return of hedge fund. Then there is also the increase or decrease in assets under management that is due to inflow or outflow of funds. We calculate monthly real flow as return is updated on a monthly basis. This takes into consideration the impact of return on real flow. This is more accurate than calculating yearly flow using average annual return as the annual return may not be a good representation of the ups and downs in monthly return that hedge funds generally experience. The data on assets under management does not appear to have been updated on a monthly basis, though most of the funds in the database do have assets under management reported on a monthly basis. Careful observation and analysis revealed that asset under management is updated only in the last month of the year, when the hedge fund managers have to calculate fees that they can charge the investors. Equation 2 gives the annual real flow, obtained by summing up all the monthly real flow for a particular hedge fund. We calculate differential flow for three different scenarios. In scenario 1, we calculate differential flow with hedge being part of the universe of hedge funds, which in our case is the complete database. In scenario 2, we calculate differential flow with hedge fund being a subset of the category it belongs to. In scenario 3, we calculate category differential flow with category being a subset of the hedge fund database. Hypothetically assume that the industry consists of 10 funds with assets under management of 10 million each. The industry size is 100 million. Following year, the industry experiences an inflow of $10 million and a capital gain of 2 million. The industry size is now 112 million. Under theoretical flow measure, we would expect each hedge fund to receive $1 million new money (10 percent of 10 million). Instead hedge fund 1 receives 1.5 million of the new money, whereas hedge fund 2 receives only 0.5 million. In this case hedge fund 1 has experienced a differential flow of +0.5 million whereas hedge fund 2 has a differential flow of -0.5 million. So investor sentiment favored hedge fund 1. To test this hypothesis of investor sentiment, we sort all the hedge funds on the basis of differential flow, the variable of interest. We report results of three years from our study period of 11 years. Table 2 shows 10 BIG OUTFLOW hedge funds for each of the three years (1994, 1999, and 2004) out of the study period (1994 to 2004). The differential flow of hedge funds is calculated with respect to total hedge funds, which in our case is the total database. These are the hedge funds that experienced a net differential outflow for the representative year. Hedge fund 4607 has a negative real flow (outflow) of $150 million. If hedge fund 4607 had received flows (new money) in proportion to its previous (1993) year-end size, it would have had an inflow of $2755 million. Having found an evidence of investor sentiment in the hedge fund industry, the next step in our analysis is to see what causes this investor sentiment. Is there a particular 'type' of hedge fund that investors prefer? To answer this question, we come up with a set of hypothesis described below. The hypotheses related to the investor sentiment hypothesis stated above are: [H.sub.0]: Category membership has an effect on net differential flow of funds experienced by the hedge fund. [H.sub.a]: Category membership has no effect on net differential flow of funds experienced by the hedge fund. [H.sub.0]: Amount of leverage has an effect on net differential flow of funds experienced by the hedge fund. [H.sub.a]: Amount of leverage has no effect on net differential flow of funds experienced by the hedge fund. [H.sub.0]: Size has an effect on net differential flow of funds experienced by the hedge fund. [H.sub.a]: Size has no effect on net differential flow of funds experienced by the hedge fund. [H.sub.0]: Portfolio allocation has an effect on net differential flow of funds experienced by the hedge fund. [H.sub.a]: Portfolio allocation has no effect on net differential flow of funds experienced by the hedge fund. The last column in Table 2 provides the category membership of the hedge fund in question. For year 1994 (panel A) it appears that the funds that experienced differential outflow in general belonged to the category 'Global Macro'. Similar analysis for year 1999 and 2004 reveals that hedge funds that have been experiencing a net differential outflow in general belong to the category 'Market Neutral'. In general, we find that the funds that have experienced differential net outflow belong to the category 'Global Macro' for 1994 to 1998, and 'Market Neutral' for 1999 to 2004 with the exception of the year 2001. Table 3 shows top 10 BIG INFLOW (with respect to total funds) hedge funds. For year 1994 (panel A) it appears that the funds that experienced differential inflow belong to the category 'Global Macro' (5 out of 10 hedge funds). Similar analysis for year 1999 and 2004 reveals that hedge funds that have been experiencing a net differential inflow belong to the category 'Regional Established' and 'Event Driven' respectively. In general, we find that the funds that have experienced differential net inflow belong to the category 'Market Neutral' for 1998 to 2003 except year 1999 (4 out of 11 years of study) and various categories for the other years of study. Comparing the results of Table 2 and Table 3, it is not clear if category membership has had anything to do with the differential outflow or differential inflow of funds. For the year 1994, the hedge funds that experienced differential outflow belonged to the category 'Global Macro' but so did the funds that experienced differential inflow. Even for the results across the years, category membership does not seem to be a dominant feature that would explain the differential flow of these hedge funds. Differential inflow and outflow results for hedge funds with respect to category are shown in Table 5 and Table 6 respectively. Note that the category 'US Opportunistic" disappeared from the database in 1996. For year 1994 (panel A) it appears that the funds that experienced differential outflow belonged to different categories. Similar analysis for year 1999 and 2004 reveals that hedge funds that have been experiencing a net differential outflow belong to the category 'Global Regional Established" and 'Market Neutral' respectively. For differential inflow with respect to category, it appears that the funds in general belong to the category 'Global Macro' for 1994, and "Market Neutral' for 1999 and 2004. Analysis of differential flow (inflow and outflow) with respect to category funds for the complete study period reveals that most of these funds belong to category "Market Neutral' (8 out of 11 years of study for outflow, and 7 out of 11 years of study for inflow). We can conclude that category membership does not seem to have an impact on the differential flow (inflow and outflow) of funds experienced by some hedge funds. Specifically, when we look at the differential inflow and outflow of funds it appears that there has been some redistribution of funds within the category 'Market Neutral' resulting in both maximum differential inflow and maximum differential outflow occurring in funds that belong to 'Market Neutral'. Since category membership does not give much detail, we further analyzed these BIG INFLOW HFs (10 hedge funds with maximum differential inflow) and BIG OUTFLOW HFs (10 hedge funds with maximum differential outflow) to help us determine what are the characteristics of the 'IN FASHION HFs', if any. But before we proceed with this analysis, it is important to probe into the differential flow results (Table 2 to Table 5) a little more. When we compare the results of differential inflow of funds with respect to total hedge funds (Table 3 and Table 4) with differential flow of funds with respect to category funds (Table 5 and Table 6), we observe that the Fund ID does not appear to change much. In general, the hedge funds that experienced a net differential inflow (outflow) with respect to total funds also had differential inflow (outflow) with respect to category funds. For example, it appears that for the year 1994 the hedge funds that experienced outflow of funds with respect to total funds and also with respect to category funds are 4607, 4608, 3385, 4548, 3341, 3622, and 1239 (7 out of 10 funds). These funds had a differential outflow of funds when in general the total hedge fund and the respective categories were experiencing an inflow (or less outflow) of funds. This reinforces our analysis that category membership does not appear to have an impact on the differential flow experienced by hedge funds. Also, it is important to realize that hedge fund classification varies among different databases. The classification schemes provided by different database providers are overlapping and many-a-times confusing from the perspective of the investor. Table 6 and Table 7 provide the general characteristics of these BIG OUTFLOW HFs and BIG INFLOW HFs with respect to total funds respectively for the three representative years (1994, 1999, and 2004). The characteristics that we looked into are leverage, size, and portfolio allocation. Leverage does not seem to have any impact on the net differential flow (inflow and outflow) of funds experienced by hedge funds. Both the BIG OUTFLOW HFs and BIG INFLOW HFs have in general a low leverage except for a few funds. As far as the size of hedge fund is concerned we were interested in seeing if there is any diseconomies-of-scale for investor sentiment that can be inferred from the differential inflow or outflow of funds. It appears that investors do not prefer any specific size. For example, hedge fund 93 (Table 7, panel C) has a size of $15.7 billion and is one of the BIG INFLOW HFs for the year 2004. This hedge fund entered the database in 1998 and is five years old. There does not appear to be any threshold size for hedge funds to be favored by investors. The last hypothesis that we test is to see if portfolio allocation has any effect on net differential flow of funds experienced by the hedge fund. It is observed from Table 6 that the BIG OUTFLOW HFs invest in stocks and options for all the three years. For the year 1994, the BIG OUTFLOW HFs invested in futures as well, whereas for the year 2004, they invested in warrants and futures as well. Analyzing the portfolio allocation of BIG OUTFLOWHFs for all the eleven years of study we find that these funds in general do not invest in currency, warrants and futures. Analyzing the BIG INFLOW HFs gives us some interesting results. For the years 1994 to 1998, the BIG INFLOW HFs invested in stocks, bonds and options. Then for the years 1999 and 2000, these funds as a group stopped investing in options. For year 2001 and 2002 there was a trend to invest in all the instruments (stocks, bonds, currency, warrants, options and futures). A conservative trend is observed for the year 2003 and 2004 where these funds are investing only in stocks and bonds. Similar results are found for MorningStar database but when the study period is broken into pre and post crisis, the results are different. Table 8 and Table 9 provide the general characteristics of these BIG OUTFLOW HFs and BIG INFLOW HFs with respect to category funds respectively for the three representative years (1994, 1999, and 2004). The results are similar to the BIG OUTFLOW HFs and BIG INFLOW HFs with respect to total funds. We can thus conclude that portfolio allocation has no effect on net differential flow of funds experienced by the hedge fund. Table 10 provides the results of differential flow of category for the three representative years of study. For the year 1994, the category 'Global Macro' had the maximum differential outflow whereas category 'Global Regional Emerging' has maximum differential inflow of funds. Panel B and panel C report the results for the year 1999 and 2004 respectively. Table 11 provides the organization of categories sorted on the basis of the results of differential flow for each year of study. For the years 1994 to 1998 the category 'Global Macro' had maximum outflow, whereas category "Market Neutral" had maximum outflow for the years 1999 and 2004. Further analysis reveals that investors in aggregate were pulling out of the category 'Global Macro' and redirecting their sentiment to the category "Market Neutral" 'Market Neutral' was the in-fashion category from 1995 to 2001 except for the year 1999. The year 1999 appears to have experienced a fad in investor sentiment, with investors favoring the category 'Global Regional Established'. This category has emerged again as in-fashion category in the year 2004. In fact for each of the years 1999, 2002 and 2003 a different category was in-fashion. The category 'Event Driven' made its entry as an in-fashion category in the year 2002. This category continued to attract investors as can be seen from Table 11. [FIGURE 1 OMITTED] V. CONCLUSION In this paper we document the existence of investor sentiment in the hedge fund industry. To test the hypothesis of investor sentiment, we sort all the hedge funds on the basis of differential flow, the differential flow (inflow and outflow) of funds experienced by individual hedge funds and different categories. We find an overwhelming evidence of investor sentiment in the hedge fund industry for both individual hedge funds and categories. For individual hedge funds we analyze to see if category membership has any impact on the differential flow of funds. We find that the funds that have experienced differential net outflow belong to the category 'Global Macro' for 1994 to 1998, and 'Market Neutral' for 1999 to 2004 with the exception of the year 2001. In aggregate it appears that category membership does not impact the flow of funds. Even for the results across the years, category membership does not seem to be a dominant feature that would explain the differential flow of funds. There appears to be some redistribution of funds within the same category resulting in both maximum differential inflow and maximum differential outflow occurring in hedge funds that belong to the same category. We analyze further to see if the investor sentiment is driven by the fundamental characteristics of hedge funds, namely size, leverage, and portfolio allocation. Leverage and size do not appear to be the discerning factor for investor sentiment. There does not appear to be any threshold size for hedge funds that is favored by investors. We also find that portfolio allocation has no effect on net differential flow of funds. Investor sentiment is not governed by the fundamental characteristics of the hedge funds. We calculate the differential flow of funds for all the categories for each year of study. It appears that investors in aggregate were pulling out of the category 'Global Macro" and redirecting their sentiment to the category 'Market Neutral" "Market Neutral" was the in-fashion category from 1995 to 2001. The category 'Event Driven' made its entry as an in-fashion category in the year 2002. This category continued to attract investors till the end of our study period. The results are quite interesting for pre and post financial crisis for hedge fundsusing Morningstar database. It appears that hedge funds have lost their charm in attracting new money. It could be because of regulations or because of lack of confidence in the global economy. References Ackerman, Carl, Richard McEnally and David Ravenscraft (1999), "The Performance of Hedge Funds: Risk, Return, and Incentives," The Journal of Finance, 833-874. Agarwal, Vikas, Naveen D. Daniel and Narayan Y. Naik (2004), "Flows, Performance, and Managerial Incentives in Hedge Funds", EFA 2003 Annual Conference Paper No. 501. Brown, Stephen J., William N. Goetzmann and James Park (2001), "Careers and Survival: Competition and Risk in the Hedge Fund and CTA Industry," The Journal of Finance, (56:5), 1869-1886. Brown, Stephen J. and William N. Goetzmann (1995), "Performance Persistence," The Journal of Finance 50: 2, 679-698. Brown, Stephen J., William N. Goetzmann and James Park (2000), "Hedge funds and the Asian Currency Crisis of 1997," The Journal of Portfolio Management, 95-101. Brown, Stephen J., William N. Goetzmann and Roger G. Ibbotson (1999), "Offshore Hedge Funds: Survival and Performance 1989-1995," Journal of Business 72: 1, 91-117. Chen, Nai-Fu, Richard Roll and Stephen A. Ross (1986), "Economic Forces and the Stock Market," The Journal of Business 59: 3, 383-403. Connor, Gregory (1995), "Phe Three Types of Factor Models: A Comparison of Their Explanatory Power," Financial Analysts Journal 51: 3, 42-46. Fama, Eugene F. and James D. MacBeth (1973), "Risk, Return, and Equilibrium: Empirical Tests," The Journal of Political Economy 81: 3, 607-636. Franklin, R. Edwards and Jimmy Liew (1999), "Hedge Funds Versus Managed Futures as Asset Classes," Journal of Derivatives, 45-64. Franklin, R. Edwards (1999), "Hedge Funds and the Collapse of Long Term Capital Management," Journal of Economic Perspectives 13: 2, 189-210. Fung, William and David A. Hsieh (2002), "Benchmarks of hedge-fund performance: Information content and measurement bias," Financial Analysts Journal, 23-34. --(2001a), "The Risk in Hedge Fund Strategies: Theory and Evidence from Trend Followers," The Review of Financial Studies 14:2, 313-341. --(2001b), "Performance Characteristics of Hedge Funds and Commodity Funds: Natural versus Spurious Biases," Journal of Financial and Quantitative Analysis 35: 3, 291-307. --(1997), "Survivorship Bias and Investment Style in the Returns of CTAs: The Information Content of Performance Track-Records," Journal of Portfolio Management 24: 1, 30-41. Goetzmann, William N., Jonathan Ingersoll Jr. and Stephen A. Ross (1998), "High Water Marks," NBER Working Paper, February. Lamont, Owen and Andrea Frazzini (2005), "Mutual Fund Flows and the Cross-Section of Stock Returns," Yale International Center for Finance Working Paper No. 05-09, May. McFall, Lamm Jr. (1999), "Portfolio of Alternative Assets: Why not 100% Hedge Funds," The Journal of Investing, 87-97. Park, James M. and Jeremy C. Staum (1998), "Performance Persistence in Alternative Investment Industry," Working Paper Columbia University. Schneeweis, Thomas (1998), "Managed Futures, Hedge Fund and Mutual Fund Performance: An Equity Class Analysis," The Journal of Alternative Investments, 11-14. Schneeweis, Thomas and Richard Spurgin (1998), "Estimation: A Multi-factor Analysis of Hedge Fund, Managed Futures, and Mutual Fund Return and Risk Characteristics," Journal of Alternative Investments, 1-24. Schneeweis, Thomas (1998), "Dealing with the myths of Hedge Fund Investment," The Journal of Alternative Investments, 11-14. NANDITA DAS, Associate Professor of Finance, Delaware State University, Delaware, DE
Table 1
Total Available Data and Study Period Dataset Composition
Total Database
Category Funds Observations
Number % Number %
Event Driven 321 11.0 22,548 12.2
Global International 90 3.1 6,732 3.7
Global Regional Established 721 24.6 49,579 26.9
Global Regional Emerging 215 7.3 13,875 7.5
Global US 182 6.2 9,101 4.9
Global Macro 198 6.8 11,762 6.4
US Opportunistic 38 1.3 1,760 1.0
Long Only/Leveraged 42 1.4 2,475 1.3
Market Neutral 816 27.8 48,349 26.3
Sector 266 9.1 15,010 8.2
Short Sellers 41 1.4 2,904 1.6
Total 2,930 100 184,095 100
Study period (1994-2004)
Category Funds Observations
Number % Number %
Event Driven 321 11.0 20,360 12.2
Global International 90 3.1 6,186 3.7
Global Regional Established 721 24.6 45,368 27.2
Global Regional Emerging 215 7.3 13,534 8.1
Global US 182 6.2 6,053 3.6
Global Macro 198 6.8 9,807 5.9
US Opportunistic 38 1.3 707 0.4
Long Only/Leveraged 42 1.4 2,404 1.4
Market Neutral 816 27.8 45,570 27.3
Sector 266 9.1 14,596 8.7
Short Sellers 41 1.4 2,424 1.5
Total 2,930 100 167,009 100
Table 2
Differential Outflow of Funds with respect to Total Funds
Fund ID Real Flow Theoretical Differential
(US$M) Flow (US$M) Flow (US$M)
Panel A. 1994
4607 -150.01 2755.02 -2905.03
4608 -196.44 1641.61 -1838.05
3385 -272.22 245.26 -517.49
4548 -143.65 326.71 -470.36
3341 -182.37 270.40 -452.77
3622 -179.98 110.70 -290.68
3172 -69.84 217.47 -287.31
1239 0.15 261.98 -261.83
1603 24.91 252.89 -227.98
317 83.84 303.24 -219.40
Panel B. 1999
1167 -2627.59 1255.10 -3882.69
1601 -342.58 1572.70 -1915.29
3786 -482.95 465.62 -948.57
1776 -436.61 266.78 -703.39
4340 -246.61 441.72 -688.33
3690 -126.88 539.60 -666.48
4482 -349.28 293.58 -642.86
6506 40.22 624.27 -584.05
4178 -120.13 372.79 -492.92
863 -319.42 164.38 -483.80
Panel C. 2004
1167 -4381.47 2303.59 -6685.07
1181 -874.55 1778.45 -2652.99
3930 -1091.97 683.40 -1775.37
1795 -457.26 638.34 -1095.59
2931 -735.78 147.95 -883.73
526 -432.30 443.37 -875.67
1584 203.43 964.85 -761.42
831 75.84 755.62 -679.77
317 -162.90 397.46 -560.36
932 328.28 840.00 -511.72
Fund ID Category
Panel A. 1994
4607 Global Macro
4608 Global Macro
3385 Global Regional Established
4548 Global Macro
3341 Global US
3622 Market Neutral
3172 Global US
1239 Global International
1603 Event Driven
317 Global Macro
Panel B. 1999
1167 Market Neutral
1601 Global Macro
3786 Market Neutral
1776 Global Macro
4340 Market Neutral
3690 Global Regional Established
4482 Global Regional Emerging
6506 Global Regional Established
4178 Event Driven
863 Market Neutral
Panel C. 2004
1167 Market Neutral
1181 Market Neutral
3930 Market Neutral
1795 Global Regional Established
2931 Market Neutral
526 Market Neutral
1584 Global Regional Emerging
831 Market Neutral
317 Global Macro
932 Event Driven
Table 3
Differential Inflow of Funds with respect to Total Funds
Fund ID Real Flow Theoretical Differential
(US$111) Flow (US$M) Flow (US$M)
Panel A. 1994
4285 179.24 26.91 152.33
1872 187.35 12.38 174.97
6.506 335.34 122.02 213.32
3690 333.02 112.47 220.55
3977 1011.10 730.29 280.81
1601 1516.16 1229.96 286.20
4252 738.06 296.34 441.72
4483 872.18 96.09 776.09
4482 1706.49 167.97 1538.53
2998 2313.57 17.12 2296.45
Panel B. 1999
1795 436.82 195.52 241.30
304 277.98 30.00 247.97
3531 263.39 9.53 253.86
3073 405.38 83.18 322.21
245 330.69 2.06 328.64
1798 460.41 69.61 390.80
4496 845.46 328.09 517.37
6574 649.74 34.06 615.68
1602 1151.83 464.32 687.52
3977 3446.87 1210.66 2236.21
Panel C. 2004
503 1582.89 548.96 1033.93
1887 1281.58 209.60 1071.98
1233 1847.09 758.89 1088.20
892 1438.57 152.42 1286.16
1014 3286.29 1943.59 1342.71
1633 1869.80 494.54 1375.26
6468 1748.68 253.11 1495.57
289 3522.05 203.52 3318.53
1194 5922.02 109.09 5812.92
93 10444.81 1365.59 9079.21
Fund ID Category
Panel A. 1994
4285 Global Macro
1872 Market Neutral
6.506 Global Regional Established
3690 Global Regional Established
3977 Global Macro
1601 Global Macro
4252 Global Macro
4483 Global Macro
4482 Global Regional Emerging
2998 Global Regional Established
Panel B. 1999
1795 Global Regional Established
304 Market Neutral
3531 Global Regional Established
3073 Market Neutral
245 Global International
1798 Global Regional Established
4496 Market Neutral
6574 Global Regional Established
1602 Global International
3977 Global Macro
Panel C. 2004
503 Market Neutral
1887 Event Driven
1233 Global International
892 Event Driven
1014 Event Driven
1633 Market Neutral
6468 Global Macro
289 Event Driven
1194 Global Regional Established
93 Global Regional Established
Table 4
Differential Outflow of Funds with respect to Category Funds
Fund ID Real Flow Theoretical Differential
(US$M) Flow (US$M) Flow (US$M)
Panel A. 1994
4607 -150.01 1451.34 -1601.35
4608 -196.44 864.80 -1061.24
1239 0.15 959.46 -959.31
1233 27.51 867.40 -839.89
3385 -272.22 353.40 -625.62
710 88.06 541.74 -453.68
3341 -182.37 243.85 -426.22
4548 -143.65 172.11 -315.76
1236 57.09 356.70 -299.61
3622 -179.98 105.31 -285.29
Panel B. 1999
1167 -2627.59 218.51 -2846.10
6506 40.22 1733.74 -1693.51
3690 -126.88 1498.59 -1625.47
1601 -342.58 759.23 -1101.81
2998 -148.26 924.62 -1072.88
4847 407.68 1384.41 -976.73
3867 472.45 1428.96 -956.51
3148 -32.94 890.01 -922.95
672 -85.71 689.88 -775.60
1776 -436.61 128.79 -565.40
Panel C. 2004
1167 -4381.47 914.91 -5296.38
1795 -457.26 1178.73 -1635.99
1181 -874.55 706.34 -1580.89
3930 -1091.97 372.73 -1464.69
932 328.28 1366.49 -1038.21
509 236.81 1118.50 -881.69
2931 -735.78 35.25 -771.03
1584 203.43 936.07 -732.64
931 -83.45 646.82 -730.27
508 149.51 816.28 -666.77
Fund ID Category
Panel A. 1994
4607 Global Macro
4608 Global Macro
1239 Global International
1233 Global International
3385 Global Regional Established
710 Global Regional Emerging
3341 Global US
4548 Global Macro
1236 Global International
3622 Market Neutral
Panel B. 1999
1167 Market Neutral
6506 Global Regional Established
3690 Global Regional Established
1601 Global Macro
2998 Global Regional Established
4847 Global Regional Established
3867 Global Regional Established
3148 Global Regional Established
672 Global Regional Established
1776 Global Macro
Panel C. 2004
1167 Market Neutral
1795 Global Regional Established
1181 Market Neutral
3930 Market Neutral
932 Event Driven
509 Event Driven
2931 Market Neutral
1584 Global Regional Emerging
931 Event Driven
508 Event Driven
Table 5
Differential Inflow of Funds with Respect to Category Funds
Fund ID Real Flow Theoretical Differential
(US$M) Flow (US$M) Flow (US$M)
Panel A. 1994
6506 335.34 175.82 159.52
4285 179.24 14.17 165.06
3690 333.02 162.06 170.96
1872 187.35 11.77 175.58
4252 738.06 156.11 581.95
3977 1011.10 384.71 626.39
4482 1706.49 925.13 781.37
4483 872.18 50.62 821.56
1601 1516.16 647.94 868.22
2998 2313.57 24.67 2288.90
Panel B. 1999
601 252.78 2.58 250.20
1798 460.41 193.33 267.08
2934 283.09 15.60 267.49
304 277.98 5.22 272.75
245 330.69 2.48 328.21
3073 405.38 14.48 390.90
6574 649.74 94.60 555.14
1602 1151.83 559.36 592.47
4496 845.46 57.12 788.34
3977 3446.87 584.45 2862.42
Panel C. 2004
2544 941.93 0.88 941.05
1233 1847.09 848.62 998.47
425 1159.31 103.89 1055.41
892 1438.57 247.95 1190.63
503 1582.89 218.03 1364.86
6468 1748.68 271.31 1477.38
1633 1869.80 196.41 1673.38
289 3522.05 331.08 3190.97
1194 5922.02 201.45 5720.57
93 10444.81 2521.67 7923.14
Fund ID Category
Panel A. 1994
6506 Global Regional Established
4285 Global Macro
3690 Global Regional Established
1872 Market Neutral
4252 Global Macro
3977 Global Macro
4482 Global Regional Emerging
4483 Global Macro
1601 Global Macro
2998 Global Regional Established
Panel B. 1999
601 Market Neutral
1798 Global Regional Established
2934 Market Neutral
304 Market Neutral
245 Global International
3073 Market Neutral
6574 Global Regional Established
1602 Global International
4496 Market Neutral
3977 Global Macro
Panel C. 2004
2544 Market Neutral
1233 Global International
425 Market Neutral
892 Event Driven
503 Market Neutral
6468 Global Macro
1633 Market Neutral
289 Event Driven
1194 Global Regional Established
93 Global Regional Established
Table 6
Characteristics of BIG OUTFLOW HFs with Respect to Total Funds
Fund ID Leverage AUM Portfolio
(US$M) S B C W O F
Panel A. 1994
4607 1.1 6500.00 Y N N N Y N
4608 1 3745.00 Y Y Y Y Y Y
3385 X 386.75 N N N N Y Y
4548 X 460.00 Y N N N Y N
3341 1.2 408.40 Y N Y N N Y
3622 X 84.10 Y N N N N N
3172 X 567.80 N N Y N N Y
1239 1 680.40 Y N N N N N
1603 X 655.00 X X X X X X
317 1.1 816.93 Y Y N Y Y Y
Panel B. 1999
1167 1.5 1719.21 Y N N Y Y N
1601 X 5826.60 X X X X X X
3786 X 1067.70 X X X X X X
1776 1 408.18 Y N N N Y N
4340 2 1039.42 Y N N Y Y N
3690 30 2146.16 Y N N N N N
4482 1 935.60 Y N N N Y N
6506 1.5 2679.43 Y Y Y Y Y N
4178 1 1197.47 Y N N N Y N
863 1 257.68 N Y N N Y Y
Panel C. 2004
1167 1.5 3191.42 Y N N Y Y N
1181 1.5 4459.18 Y N N N N N
3930 2 2414.36 Y Y N Y Y Y
1795 X 1683.92 X X X X X X
2931 6 805.00 Y Y Y N Y Y
526 X 924.30 X X X X X X
1584 1 3469.27 Y N N N N N
831 2 3331.97 Y N N N Y Y
317 1.1 1100.76 Y Y N Y Y Y
932 2 3100.00 Y N N N Y Y
Fund ID Category
Panel A. 1994
4607 Global Macro
4608 Global Macro
3385 Global Regional Established
4548 Global Macro
3341 Global US
3622 Market Neutral
3172 Global US
1239 Global International
1603 Event Driven
317 Global Macro
Panel B. 1999
1167 Market Neutral
1601 Global Macro
3786 Market Neutral
1776 Global Macro
4340 Market Neutral
3690 Global Regional Established
4482 Global Regional Emerging
6506 Global Regional Established
4178 Event Driven
863 Market Neutral
Panel C. 2004
1167 Market Neutral
1181 Market Neutral
3930 Market Neutral
1795 Global Regional Established
2931 Market Neutral
526 Market Neutral
1584 Global Regional Emerging
831 Market Neutral
317 Global Macro
932 Event Driven
Note: S=Stocks, B=Bonds, C=Currency, W=Warrants, O=Options and
F=Futures
Table 7
Characteristics of BIG INFLOW HFs with Respect to Total Funds
Fund ID Leverage AUM Portfolio
(US$M) S B C W O F
Panel A. 1994
4285 1 260.00 Y N N N N N
1872 1 220.71 N Y N N N N
6506 1.5 628.72 Y Y Y Y Y N
3690 30 605.33 Y N N N N N
3977 2 2500.00 N Y N N N N
1601 X 4900.00 X X X X X X
4252 3 1500.00 Y Y N Y Y Y
4483 1 1050.00 Y Y N N N N
4482 1 1900.00 Y N N N Y N
2998 1 1800.00 Y Y N N Y Y
Panel B. 1999
1795 X 1400.00 X X X X X X
304 X 390.00 X X X X X X
3531 30 273.60 N Y N N Y Y
3073 X 351.48 X X X X X X
245 1.2 519.20 Y N N N N Y
1798 X 890.00 X X X X X X
4496 1 1947.60 Y Y N N N N
6574 1.2 801.16 Y Y N N N N
1602 X 2976.80 Y Y Y Y Y Y
3977 2 9000.00 N Y N N N N
Panel C. 2004
503 1.3 3472.26 N Y N N N N
1887 2 2070.00 Y N N N N N
1233 1.1 4624.50 Y N N N Y Y
892 1 2100.00 N Y N N N N
1014 1 9578.00 N Y N N N N
1633 X 3585.30 N X X X X X
6468 1.25 2702.00 Y N Y N N N
289 1 4306.00 Y N N Y Y N
1194 1.3 6303.65 Y N Y N N Y
93 1 15700.00 Y Y Y N N N
Fund ID Category
Panel A. 1994
4285 Global Macro
1872 Market Neutral
6506 Global Regional Established
3690 Global Regional Established
3977 Global Macro
1601 Global Macro
4252 Global Macro
4483 Global Macro
4482 Global Regional Emerging
2998 Global Regional Established
Panel B. 1999
1795 Global Regional Established
304 Market Neutral
3531 Global Regional Established
3073 Market Neutral
245 Global International
1798 Global Regional Established
4496 Market Neutral
6574 Global Regional Established
1602 Global International
3977 Global Macro
Panel C. 2004
503 Market Neutral
1887 Event Driven
1233 Global International
892 Event Driven
1014 Event Driven
1633 Market Neutral
6468 Global Macro
289 Event Driven
1194 Global Regional Established
93 Global Regional Established
Table 8
Characteristics of BIG OUTFLOW HFs with respect to Category Funds
Fund ID Leverage AUM Portfolio
(US$M) S B C W O F
Panel A. 1994
4607 1.1 6500.00 Y N N N Y N
4608 1 3745.00 Y Y Y Y Y Y
1239 1 680.40 Y N N N N N
1233 1.1 617.20 Y N N N Y Y
3385 X 386.75 N N N N Y Y
710 1.3 424.28 Y Y Y Y Y N
3341 1.2 408.40 Y N Y N N Y
4548 X 460.00 Y N N N Y N
1236 0.71 275.10 Y N N N N N
3622 X 84.10 Y N N N N N
Panel B. 1999
1167 1.5 1719.21 Y N N Y Y N
6506 1.5 2679.43 Y Y Y Y Y N
3690 30 2146.16 Y N N N N N
1601 X 5826.60 X X X X X X
2998 1 1100.00 Y Y N N Y Y
4847 1 2937.80 Y Y Y N Y Y
3867 0 2519.00 N Y N Y N N
3148 X 794.40 Y N N N Y N
672 1 1268.28 N Y N Y N N
1776 1 408.18 Y N N N Y N
Panel C. 2004
1167 1.5 3191.42 Y N N Y Y N
1795 X 1683.92 X X X X X X
1181 1.5 4459.18 Y N N N N N
3930 2 2414.36 Y Y N Y Y Y
932 2 3100.00 Y N N N Y Y
509 0.5 2515.00 Y N N N N N
2931 6 805.00 Y Y Y N Y Y
1584 1 3469.27 Y N N N N N
931 10 1234.81 N Y Y Y Y Y
508 1.5 1810.00 Y N N N N N
Fund ID Category
Panel A. 1994
4607 Global Macro
4608 Global Macro
1239 Global International
1233 Global International
3385 Global Regional Established
710 Global Regional Emerging
3341 Global US
4548 Global Macro
1236 Global International
3622 Market Neutral
Panel B. 1999
1167 Market Neutral
6506 Global Regional Established
3690 Global Regional Established
1601 Global Macro
2998 Global Regional Established
4847 Global Regional Established
3867 Global Regional Established
3148 Global Regional Established
672 Global Regional Established
1776 Global Macro
Panel C. 2004
1167 Market Neutral
1795 Global Regional Established
1181 Market Neutral
3930 Market Neutral
932 Event Driven
509 Event Driven
2931 Market Neutral
1584 Global Regional Emerging
931 Event Driven
508 Event Driven
Table 9
Characteristics of BIG INFLOW HFs with Respect to Category Funds
Fund ID Leverage AUM Portfolio
(US$M) S B C W O F
Panel A. 1994
6506 1.5 628.72 Y Y Y Y Y N
4285 1 260.00 Y N N N N N
3690 30 605.33 Y N N N N N
1872 1 220.71 N Y N N N N
4252 3 1500.00 Y Y N Y Y Y
3977 2 2500.00 N Y N N N N
4482 1 1900.00 Y N N N Y N
4483 1 1050.00 Y Y N N N N
1601 X 4900.00 X X X X X X
2998 1 1800.00 Y Y N N Y Y
Panel B. 1999
601 1.5 310.00 Y N N N N N
1798 X 890.00 X X X X X X
2934 1 607.00 Y Y N N Y N
304 X 390.00 X X X X X X
245 1.2 519.20 Y N N N N Y
3073 X 351.48 X X X X X X
6574 1.2 801.16 Y Y N N N N
1602 X 2976.80 Y Y Y Y Y Y
4496 1 1947.60 Y Y N N N N
3977 2 9000.00 N Y N N N N
Panel C. 2004
2544 1.25 986.00 Y N N N Y N
1233 1.1 4624.50 Y N N N Y Y
425 1.1 2017.81 N Y N N N N
892 1 2100.00 N Y N N N N
503 1.3 3472.26 N Y N N N N
6468 1.25 2702.00 Y N Y N N N
1633 X 3585.30 N X X X X X
289 1 4306.00 Y N N Y Y N
1194 1.3 6303.65 Y N Y N N Y
93 1 15700.00 Y Y Y N N N
Fund ID Category
Panel A. 1994
6506 Global Regional Established
4285 Global Macro
3690 Global Regional Established
1872 Market Neutral
4252 Global Macro
3977 Global Macro
4482 Global Regional Emerging
4483 Global Macro
1601 Global Macro
2998 Global Regional Established
Panel B. 1999
601 Market Neutral
1798 Global Regional Established
2934 Market Neutral
304 Market Neutral
245 Global International
3073 Market Neutral
6574 Global Regional Established
1602 Global International
4496 Market Neutral
3977 Global Macro
Panel C. 2004
2544 Market Neutral
1233 Global International
425 Market Neutral
892 Event Driven
503 Market Neutral
6468 Global Macro
1633 Market Neutral
289 Event Driven
1194 Global Regional Established
93 Global Regional Established
Table 10
Differential Flow of Category with Respect to Total Funds
Category Real Flow Theoretical Differential
(US$B) Flow (US$B) Flow (US$B)
Panel A. 1994
Global Macro 4.776 9.066 -4.290
Event Driven 0.679 1.385 -0.706
Global US 1.416 1.571 -0.154
Market Neutral 1.645 1.729 -0.084
US Opportunistic 0.120 0.145 -0.025
Long Only/Leveraged 0.015 0.017 -0.003
Sector 0.298 0.215 0.083
Short Sellers 0.241 0.092 0.149
Global Regional Established 3.731 2.589 1.142
Global International 2.631 0.718 1.912
Global Regional Emerging 2.415 0.438 1.976
Panel B. 1999
Market Neutral 1.819 10.448 -8.629
Global Macro 4.407 9.128 -4.722
Event Driven 0.789 3.807 -3.017
Global Regional Emerging 0.171 1.520 -1.349
Long Only/Leveraged 0.157 0.118 0.039
Short Sellers 0.427 0.230 0.196
Global International 1.667 1.384 0.283
Sector 2.010 1.665 0.344
Global Regional Established 26.338 9.484 16.854
Panel C. 2004
Market Neutral 12.354 31.105 -18.751
Sector 1.164 2.529 -1.365
Global Regional Emerging 5.569 5.741 -0.171
Long Only/Leveraged 0.120 0.136 -0.016
Short Sellers 0.089 0.102 -0.013
Global US 0.070 0.003 0.067
Global Macro 4.422 4.125 0.297
Global International 4.498 4.022 0.476
Event Driven 19.185 11.793 7.392
Global Regional Established 26.362 14.276 12.086
Category Flow (%)
Panel A. 1994
Global Macro -47.32
Event Driven -50.97
Global US -9.82
Market Neutral -4.87
US Opportunistic -17.31
Long Only/Leveraged -14.61
Sector 38.35
Short Sellers 162.57
Global Regional Established 44.09
Global International 266.23
Global Regional Emerging 450.78
Panel B. 1999
Market Neutral -82.59
Global Macro -51.72
Event Driven -79.27
Global Regional Emerging -88.73
Long Only/Leveraged 32.69
Short Sellers 85.39
Global International 20.47
Sector 20.66
Global Regional Established 177.72
Panel C. 2004
Market Neutral -60.28
Sector -53.98
Global Regional Emerging -2.98
Long Only/Leveraged -11.51
Short Sellers -12.38
Global US 2095.22
Global Macro 7.19
Global International 11.82
Event Driven 62.68
Global Regional Established 84.66
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