The Strategic Listing Decisions of Hedge Funds

Transcription

1 The Strategic Listing Decisions of Hedge Funds Philippe Jorion and Christopher Schwarz* 2013 Forthcoming, Journal of Financial and Quantitative Analysis ABSTRACT The voluntary nature of hedge fund database reporting creates strategic listing opportunities for hedge funds. However, little is known about how managers list funds across multiple databases or whether investors are fooled by funds listing decisions. In this paper, we find that hedge funds strategically list their small, best performing funds in multiple outlets immediately while preserving the option to list their other funds in additional databases later. We generally find that investors react rationally to these fund listings based on the predictability of performance. Finally, our results lead to specific guidelines on handling backfilled returns to minimize biases. JEL Classifications: G11 (portfolio choice), G23 (private financial institutions), G32 (financial risk management) Keywords: hedge funds, advertising, public database, performance evaluation * Philippe Jorion (phone: , [email protected]) is at The Paul Merage School of Business at the University of California at Irvine and Pacific Alternative Asset Management Company (PAAMCO). Christopher Schwarz (phone: , [email protected]) is at The Paul Merage School of Business at the University of California at Irvine. We thank the editor, an anonymous referee, and participants at the Oxford-Man Institute Hedge Fund conference for helpful comments. Steve Jupp and Scott Whitton provided useful discussions of the TASS and HFR databases, respectively. 1

2 I. Introduction The hedge fund industry has undergone tremendous growth over the last two decades. Investors have been attracted to hedge funds for several reasons, including the industry s diversification benefits. Unlike mutual funds that can attract investors through public advertising, hedge funds achieved their growth even though they are prohibited by law from general solicitation. 1 While there are alternative ways to draw investors attention, one manner in which hedge fund companies can attract investors is by listing their funds in various hedge fund databases. 2 These listings help potential accredited investors learn of a fund s existence, performance, and contact information. Given that tens of thousands of funds have listed in these resources, it is clear that the industry views hedge fund databases as effective solicitation channels. The decision to list a hedge fund in a database is voluntary because hedge funds are under no legal obligation to do so. 3 The voluntary nature of this reporting allows for fascinating insights into the industry because it gives management companies the opportunity to game the system in their attempt to gain the attention of investors. As is well known, funds that list in databases are widely 1 In the United States, Rule 502(c) of the 1933 Securities Act prohibits general solicitation or general advertising in connection with private placement offerings. Thus, hedge funds may not be offered or sold by print, radio, and television advertisements, nor by mass mailings or most other means of public communication. 2 Listing hedge fund information in a database is not interpreted as falling under Rule 502(c). The SEC has issued letters that indicate it will take no action in specific situations, generally when communications, though public, are not deemed to have been made by or on behalf of an issuer. For instance, web database sites can provide access to hedge fund information provided they comply with guidelines set forth by the SEC (1998) in a so-called LAMP no-action letter. 3 For example, Fung and Hsieh (2009) note that only 45 of the 100 single-manager hedge fund firms in the 2008 Institutional Investor (II Top 100) report to one or more of four major hedge fund databases. 2

3 observed to backfill high returns. Since backfilling is so prevalent, hedge fund managers must assume that some investors use this abnormally high performance to make investment decisions, although no examination of this question has been performed to date. Strategic listings can take many other forms, however. Management companies have the option to list one or several of their funds in one or several databases, simultaneously or sequentially. Since listing is essentially costless, once a fund chooses to list in a hedge fund database, it would seem logical to list simultaneously in all databases. 4 Yet Fung and Hsieh (2009) report that only 7% of hedge funds are listed in all four large hedge fund databases they study. This would be expected if all investors subscribe to all databases. However, even though these databases are relatively cheap to acquire, it seems likely that many investors do not subscribe to all databases. 5 The goal of this paper is to examine whether hedge fund companies strategically utilize hedge fund listings to attract the attention of investors and, if so, whether investors are indeed fooled by these strategic actions. Rather than focus on which funds choose to list to any database, we focus our analyses on the heterogeneity of listing decisions conditional on listing. We report a number of 4 Listing in a hedge fund database is labor intensive but not very expensive relative to other costs. Also, intermediaries allow prepackaged listing services into several databases. Software providers also create automatic access to multiple databases. In this instance, we are only considering funds that have already decided to list themselves in any hedge fund databases and have therefore given up a large portion of their secrecy. Other researchers, such as Agarwal et al. (2012) and Aiken et al. (2011) examine the circumstances under which hedge funds decide to initially list to any hedge fund database. They do not examine heterogeneity in those listing decisions conditional on listing. 5 For example, the cost of the live HFR database is around $4,000. Other databases have similar costs. 3

4 new findings using the listing decisions of hedge funds in two large databases, Lipper TASS and Hedge Fund Research (HFR). 6 First, we document that hedge fund management companies indeed take strategic advantage of the voluntary nature of hedge fund reporting. We observe that, both across and within management companies, small, better performing hedge funds are more likely to report to multiple databases immediately. Small funds have greater incentives to list immediately in multiple databases since they need sufficient assets to be economically viable. At the same time, these small funds also need high performance to give them the ability to attract investment. 7 We then observe that, after initially listing to only one database, many funds list in a second database after a significant delay. For example, in our sample, one-third of funds that list in a second database do so one year or more after their initial listing. Thus, some hedge fund companies must believe that there is a tradeoff between immediately listing in a second database versus keeping the option to list again. As with the initial listings, these delayed second listings take place when fund assets are low and after another period of good performance. Second, we generally find evidence that hedge fund investors are not fooled by these strategic decisions. We document that investor flows to newly listed funds are significantly higher than for other funds, particularly when their backfilled performance is high. While chasing 6 TASS is the acronym for Trading Advisor Selection System. Although there are several other hedge fund databases, examining listing decisions requires precise information about the listing date. Only TASS and HFR provide this information, which is why our main tests rely on these two databases. We do, however, perform tests using a third database to confirm our findings. TASS and HFR also represent a significant percentage (63%) of funds that are available in the major hedge fund databases (Agarwal et al. (2009)). 7 These observations coincide with the statements by the data vendors that asset size seems to play a large role in funds listing decisions. 4

5 artificially high backfilled performance may seem questionable, the rationality of such behavior depends on whether chasing past performance is smart in the sense that previous performance has predictive content. In this case, investor behavior seems rational because performance persistence is higher between the pre- and post-listing periods than for the general hedge fund population. 8 We also find that a new listing s excess flows do not spillover to the company s other mature funds. In addition, we also observe that hedge funds receive no excess flows from a delayed second listing. This behavior again seems rational because persistence after a delayed second listing is not different from the overall hedge fund population. On the other hand, we find that flows are greater to funds that immediately list in multiple databases rather than a single one, even though performance persistence is not significantly different between these two groups of funds. While this investor behavior may not seem rational, multiple simultaneous listings do lower search costs for investors. Third, our findings about investor flows also provide insights into the economic efficiency of hedge funds strategic listing decisions. Small funds are acting efficiently by listing in both databases immediately because these second listings do indeed result in needed additional flows. It is unclear, however, whether large funds, especially those with good performance, are efficient by retaining options to list in a second database at a later date since delayed second listings do not result in greater flows. While the benefits from delayed second listings are small, the costs of delaying second listings for these funds may also be small. Large funds are in less in need of capital. More importantly, while flow-related diseconomies of scale from immediate second listings are not likely 8 These results differ from Aggarwal and Jorion (2010a), who also find more persistence early in the life of hedge funds. They specifically exclude backfilled data, however. 5

6 an issue for the small funds, large funds could be concerned that they cannot absorb this additional capital without substantial negative effects on their performance. 9 Thus, the lack of an immediate second listing by a large fund may reflect an equilibrium between the costs and benefits of a delayed second listing rather than an inefficiency. This equilibrium hypothesis is supported by the fact that delayed second listings occur after declines in fund assets. Finally, our results also have implications for researchers with respect to the handling of backfilled data. A researcher with access to only one database should always use the listing dates when provided for several reasons. First, based on our TASS-HFR data, there is a large amount of variability in backfill period length. Second, while approximately half of an individual database s funds will be listed in a second database, the average listing date differs by only three months. More importantly, only 15% of funds in an individual database will have listing dates in a second database that differ by more than 12 months. Hence, there is not much loss of informational content by using the listing date. In addition, even if a fund has listed in another database earlier, we document that the delayed listing to the researcher s database is likely to arise after a period of good performance. While this performance is real in the sense that it was available for outside investment, the researcher s data would ignore another major segment of the population, which consists of funds that previously listed in another database but choose not to report to the researcher s database. 9 Presumably, one reason why many funds do not list to any database is because they are closed to investment due to concerns about diseconomies of scale. Berk and Green (2004) hypothesize that past good performance leads to inflows that result in the deterioration of fund performance over time. Because hedge funds managers rely heavily on performance based fees (e.g. Goetzmann et al. (2003)), they may decide to limit investment when their funds are near their optimal sizes. Indeed, Getmansky et al. (2009) and Liang and Schwarz (2011) both find that hedge funds limit investor flows when size starts to affect performance. 6

7 Since such funds are likely to have poor relative performance, using returns prior to the add dates would create a positive backfill bias. Following the same line of reasoning, when a researcher is using multiple databases, the earliest listing date should be used. Using the latest date would remove the performance of funds that performed well and listed in a second database while retaining the relatively poor performance of those funds that never listed in a second database. Thus, not using the earliest date would result in a negative bias. This paper is structured as follows. Section II reviews the literature while Section III describes the data and provides an overview of listing decisions. Section IV examines the strategic nature of listing decisions. Section V examines investor reactions to strategic listing decisions. Concluding comments are contained in Section VI. II. Literature Review This paper complements previous research in several areas. Hedge fund databases have long been used for academic study (e.g., starting with Fung and Hsieh (1997), Ackermann et al. (1999), and Brown et al. (1999)). More recently, some studies have merged several hedge fund databases rather than rely on a single database. Since hedge funds only voluntarily report to these sources, this creates an expanded sample (e.g., Agarwal, Daniel and Naik (2009), Fung and Hsieh (2009), Agarwal et al. (2012)). By now, it is well known that hedge fund databases suffer from numerous biases. Ackermann et al. (1999), for example, discuss survivorship bias and backfill bias. These studies, however, have not examined the information that can be inferred from discrepancies 7

8 across databases. 10 In particular, the strategic listing of some funds across multiple databases both in their timing and distribution has not been studied yet. Given that the decision to list in one of these databases is voluntary, some papers examine the decision to list in any database. For example, Agarwal et al. (2012) use 13(f) filings to compare returns of hedge funds advisors that list and do not list to a database. They find higher advisor level performance before listing, which is consistent with a backfill bias. Their primary focus is on differences between reporting and non-reporting funds, which is distinct from examining strategic listing decisions across databases. Aiken et al. (2011) also study this issue by comparing listed funds to non-listed funds based on a narrow sample of registered funds of funds. Similarly, their focus is on the decision to list to any database rather than strategic choices across sources. The primary purpose of listing in a database is for marketing the fund. 11 By law, hedge funds are not allowed to solicit the general public. Thus, listing in a hedge fund database represents an opportunity to draw the attention of many accredited investors. In some sense, this is similar to the ability of mutual funds to advertise, even though advertising is costly for mutual funds. In that industry, Jain and Wu (2000) find that fund performance is significantly higher pre-advertising, implying that fund operators choose to advertise funds with superior performance. This behavior is similar to the practice of hedge funds to list after good performance. Mutual funds performance deteriorates after advertising, indicating that their performance prior to advertising does not represent skill. This is confirmed by Carhart (1997) and Carhart et al. (2002), who find no evidence of performance persistence in mutual fund returns. 10 To date, only two papers have compared information across hedge fund databases. Liang (2000, 2003) finds some discrepancies in return data. However, he does not explore differences in listing decisions. 11 In TASS, for example, less than 1% of all funds enter the database closed to investment. 8

9 In the hedge fund industry, however, performance seems persistent. Baquero et al. (2005), for example, report evidence of persistence at a 12-month horizon, even after correcting for lookahead bias. 12 In another study, Aggarwal and Jorion (2010a) find that new funds that do not backfill generally have higher performance persistence than other funds, especially in earlier years. However, no research has compared the persistence of backfilled performance to that immediately after listing. Of course, the primary purpose of advertising is to attract investor flows. Sirri and Tufano (1998) argue that consumers face a bewildering array of investment choices and that search costs can be decreased by advertising. They report that funds that spend more on marketing are rewarded with a stronger performance-flow relationship. Jain and Wu (2000), Barber, Odean and Zheng (2005), and Gallaher, Kaniel and Starks (2009) also find that mutual fund advertising successfully attracts retail investor flows. Some work, such as Baquero and Verbeek (2009) and Getmansky et al. (2009), find that hedge fund flows are generally affected by the same factors as mutual funds, although the flow-performance relationship is more linear as compared to the convex shape observed in the mutual fund space. However, to date, no study has examined the impact of strategic listing decisions on investor flows. An important question concerning these investor flows is their rationality. The key question when assessing rationality is whether investor flows are able to predict returns, in other words whether this is smart money. Evidence of the smartness of mutual fund investors is mixed. Early work by Zheng (1999) and Wermers (2003) suggests that mutual fund flows are able to predict future returns. However, more recent work (i.e., Frazzini and Lamont (2008), Ben-Rephael et al. 12 Jagannathan et al. (2010) find evidence of persistence over three-year horizons. Kosowski, Naik, and Teo (2007) report mild evidence of persistence using Ordinary Least Squares alphas but much stronger evidence in a Bayesian framework. 9

10 (2012)) finds significant evidence that mutual fund investors, except at very short horizons, are dumb money. While the majority of mutual fund investors are retail, hedge fund investors should be more sophisticated because they are restricted to accredited investors such as high net worth individuals and institutional investors. However, the evidence of smart money in the hedge fund industry is mixed. While Getmansky et al. (2009) finds evidence of smart money as flows predict future hedge fund returns, Ozik and Sadka (2010) as well as Baquero and Verbeek (2009) cannot confirm this hypothesis. A. Databases III. Data This study combines two widely-employed hedge fund databases, TASS and HFR. 13 These are the only major databases that contain the date added to the database field necessary for our analysis. 14 We use the March 2010 version of the TASS database, which has 13,897 funds, and the December 2009 version of the HFR database, which has 14,460 funds. Both databases contain live and defunct funds since 1994, which eliminates survivorship bias after that date. Database providers sell their product in competition with each other. Because subscribers want a large sample of funds in the database they purchase, breadth of coverage is a crucial feature. Funds are not charged for listing in the databases. New funds are added in one of two ways. Funds seeking greater exposure to potential investors can initiate contact with TASS or HFR. 15 Alternatively, the database providers actively search for funds to add. They encourage the fund 13 We also make limited use of the CISDM database. However, most of our analyses relies on TASS and HFR only. 14 Our analyses depend on the accuracy of the date added to the database field. We confirm that these dates seem accurate by observing that all of the backfill bias is removed just one month after the month added to the database. 15 This includes the HFR fund-of-funds business, which has approximately $3 billion under management. 10

11 management company to report many different types of funds (both the onshore and offshore equivalents as well as different share classes in various currencies) in order to give more choices to potential global investors. Importantly, database vendors do not screen on any fund characteristics when contacting funds. The vendors are mostly concerned about whether the fund has an actual return record, as both TASS and HFR do not allow pro forma, or simulated, returns. Regardless of whether the fund manager or the database provider initiates contact, the fund manager must agree to provide the information. Thus, all listing decisions we evaluate represent voluntary listings. The client bases of both databases are a broad mix of institutional investors (pension funds, endowments, funds of funds), consultants, high-net-worth family offices, hedge funds, and academics. Thus, most users use the database to help select hedge funds. Neither database vendor specified catering toward a specific clientele, so there is no reason to believe structural differences between the databases exist that would result in market segmentation or a clientele effect. 16 B. Fund Selection We first impose the usual filters on our sample. First, to focus on hedge funds only, we eliminate any funds of funds. Second, we require fund values to be denominated in U.S. dollars. Generally, assets under management for each share class (currency) reflect the master fund s assets 16 Although the databases do not provide precise breakdowns of their client bases, we look at two measures indicative of clientele effects. First, we examine the fractions of funds that are onshore versus offshore. Individual high net worth individuals will tend to invest in onshore funds; tax-exempt institutional funds invest in offshore funds. For TASS, 36% of the funds are onshore; this ratio is not materially different, at 49% for HFR. The other variable examined is the size of minimum investment. Both databases have a median requirement of $500,000. Therefore, the two databases seem to cater to a similar clientele. Note that these values differ from values in Table 1 as those data are subsets of each database. 11

12 under management. By keeping only USD funds, we eliminate duplicate entries for the same entity. Third, we eliminate funds that do not report returns on a monthly basis, or net of fees. Finally, we remove funds that do not have the date added to database field available because this information is essential for the analysis. In addition to the above filters, we place constraints on the added to database dates to deal with other data issues. 17 We then identify funds that are listed in both databases. Unlike mutual funds, hedge funds do not have unique identifies such as ticker symbols to match funds quickly across multiple databases. Vendors also use different abbreviations and lengths for fund names, which makes automatic name matching difficult. In addition, unlike mutual funds that should be included in every database due to their public disclosure requirements, hedge funds may or may not be included in both databases. We therefore match funds at the individual entry level between TASS and HFR using a manual procedure, relying mostly on fund names. When returns are overlapping, we default to TASS data. Otherwise, we combine the two databases to build the longest time series possible Because our database download dates are not the same month, we only keep funds with an add date of June 30, 2009 or before. Next, we noticed that the earliest add date in HFR is May 1996, which has 1,242 funds. This probably represents the date HFR started to track this field. As a result, we remove funds with add dates on and prior to May 1996 in both databases. A similar issue affects the TASS database, as reported in Aggarwal and Jorion (2010b). TASS merged with Tremont in the late 1990s. Tremont funds were given the opportunity to add their fund to TASS, which introduced a hidden survivorship bias. The add dates for these funds are not the date the funds were added to Tremont, but rather the date they were added to TASS, in groups. Therefore, we eliminate TASS funds with add dates between April 1999 and November 2001 when the TASS add date is later than the HFR add date. 18 Because return information may not be the same in both databases, we also rerun our results using HFR returns as the default. We also reran the results averaging any overlapping returns. In both cases, the results are similar to those reported here. 12

13 We also default to TASS descriptive information for overlapping funds. Finally, we map TASS style classifications to HFR s categories, as explained in Appendix A. 19 After we complete this match, we then perform another procedure to remove any remaining duplicate share classes. Because different share classes may have unique names, we use a return based filter to remove duplicates. We first sort funds by the management company. Within the same management company, we then compute the correlations between all funds with the same strategy. If the correlation between two funds is 0.99 or higher, we consider these two entries the same fund and therefore only retain the share class with the earlier date added to the database. Because this procedure can also link funds that list one share class in TASS while listing a different share class in HFR, this procedure also links approximately 250 additional TASS and HFR funds. 20 Our final sample consists of 8,310 funds. Of these, 2,663 are in both databases; 1,847 funds are in TASS alone; 3,800 are in HFR alone. 21 Thus, 32% of the funds are listed in both databases. Summary information is presented in Table 1. This includes the incentive and management fee, the 19 Agarwal et al. (2009) also map strategies from different databases to common styles when combining databases. 20 Most share classes have the same database listing pattern (i.e., all are in TASS only, all are in HFR only, or all are in both databases). In untabulated results, we compare the listing properties of those funds we matched by hand using fund names and those funds matched through the return matching procedure that combines different share classes. We find similar listing properties across both groups. Thus, it appears listing different share classes and listing the same share class across databases are homogenous listing strategies. Performing our analyses without combining share classes leads to similar conclusions. 21 We are happy to offer assistance to other researchers with implementing our matching procedure when combining databases. While the number of TASS only funds seems low given that the starting number of funds in both HFR and TASS are similar, the filters eliminate a much higher number of TASS funds. For example, only 65% of TASS funds have returns in U.S. Dollars, against 80% for HFR funds. Also, 27% of HFR funds are fund of funds versus 37% of TASS funds. 13

14 lockup period, and the minimum investment. Return and Assets show the distribution of monthly fund returns and assets, in millions of dollars. Excess Return and Excess Assets show return and asset levels above the sector, or style, averages for that month. Because we are interested in what conditions lead to different fund listing decisions and how investors react to those decisions, we do not remove any backfilled returns when compute these averages. We do, however, winsorize the top and bottom 1% of assets and returns of our sample to minimize the impact of outliers. Panel A, B, and C presents results for funds in TASS only, in HFR only, and in both databases respectively. Overall, the three groups are fairly homogeneous. Funds that exist in both databases tend to have slightly more assets. <Insert Table 1 about here> C. Comparison of Database Add Dates By design, the two databases contain an add date field, which allows for a comparison of listing dates. Since the reason for listing in any hedge fund database is to market a fund to potential investors, one would presume that a fund would list in all of the databases at nearly the same time. Interestingly, this is not the case. We compute the difference between TASS and HFR add dates for funds listed in both databases, where positive values represent later listings in TASS. We also compute the absolute values of the differences, which are reported in Table 2. <Insert Table 2 about here> On average funds list in TASS approximately three months later than in HFR. This slight delay could be due to administrative differences between the two database providers rather than a 14

15 preference for HFR over TASS. The average absolute difference between the two databases, however, is over one year. This cannot be explained by administrative issues. Out of our 2,663 matched funds, 894 funds (i.e., 33%) have a difference of twelve months or more. Moreover, 10% of our matched sample has date differences of three years or more. Figure 1 describes the histogram of date differences. Clearly, management companies do not choose to add their funds to these databases at the same time. <Insert Figure 1 about here> Many prior studies assume a homogenous backfill period for all hedge funds, such as 12 or 24 months. However, Aggarwal and Jorion (2010a) note that more than half of funds have backfill periods longer than 12 months. Fung and Hsieh (2009) note that this can extend to ten years. Figure 2 plots the distribution of backfilled times for our sample. <Insert Figure 2 about here> While we find an average backfill period of 27 months, which is close to two years, we also find that backfill periods can vary greatly. Out of our 8,310 funds, only 649 funds (8%) have a backfill period of approximately two years, taken as the 21 to 27 months interval. This demonstrates that precise information about the add date field is critical and, thus, explains why we do not use databases that do not contain the date added to the database field. This evidence should reinforce the use of the date added to the database field to measure performance. It is sometimes argued that this field should not be used because the fund could have a prior listing in another database. In fact, for the funds that are common, Table 2 shows that the mean difference in dates is less than three months. More importantly, only a small fraction (15%) 15

16 of the funds in an individual database would have listing dates in a second database that differ by more than one year. Thus, in the absence of more information, there is no reason to discard this field. IV. Determinants of Hedge Fund Listing Decisions A. Initial Listing Decisions The primary reason for hedge funds to list in a database is to market themselves to potential investors. Prior literature notes that hedge funds often backfill returns and that not all funds list to all databases. However, no other study has explored the listing decision of a specific fund across databases. The previous section indicates that this is a complex process because there is a large amount of variability in listing decisions. Some funds list to one database only rather than two. Even for funds that appear in both databases, a large dispersion can exist between the first and second listings dates. The question is whether these decisions reflect strategic actions by hedge fund managers and whether this affects investors. For the analysis of listing decisions, we define three groups of funds. The first set consists of those listed in one database only (1 database, 1DB ) during their entire lives. The second consists of funds listing in two databases simultaneously. Funds that list in both TASS and HFR within six months of each other are defined as funds listing simultaneously in both databases with no delay (2 databases, no delay, 2ND ). The third set consists of funds listed in both databases, but with an add date difference of one year or more (2 databases, with delay 2WD ). For all three groups, we use the earliest date added to database date to examine fund characteristics around the initial listing date. We then compute the average return in excess of the style return each month for 16

17 the 24 months prior to and after the initial listing decision. 22 Finally, we compute average excess monthly returns for various intervals around the initial listing decision. Because identifying 2WD funds conditions on future information at the time of the initial listing decision, we also split our sample into two groups only. The first consists of 2ND funds defined as above. The second is the complement, i.e., all funds that do not list to two databases within six months. We label these Initial 1DB funds. We report results using this split in Panel A of Table 3. Results for the three group splits based on future conditioning are reported in Panel B. <Insert Table 3 about here> Consistent with the backfill bias reported in the literature, we note that all groups exhibit significantly higher performance in the period prior to the first listing. Even so, there are large differences between groups. In Panel A, 2ND funds outperform other funds prior to the initial listing date by a significant margin. Panel B, where we condition on future information about 2WD funds, shows that 1DB funds have significantly lower performance. In contrast, 2WD funds have higher performance, close to that of 2ND funds. Since these two groups represent different listing decisions, performance is not the only factor driving listing decisions. To relate listing to asset gathering, Figure 3 plots average assets, in excess of the style average, for our three groups of funds prior to and after the initial add date Excess returns do not require the estimation of any parameters, which would introduce noise and minimum period requirements. On the other hand, they do not control directly for fund-specific risk, other than the sector. This is only an issue, however, if fund-specific risk were correlated with the selected explanatory variables. 23 A graph of Initial 1DB and 2ND funds shows a similar relationship between size and the decision to immediately list in two databases. 17

18 <Insert Figure 3 about here> Clearly asset size is also an important factor when listing funds. Both 1DB and 2ND funds have steadily falling assets prior to their initial listing. Because the hedge fund industry was experiencing massive growth over this period, non-advertised, incubating funds, which would have no inflows, would have declining excess assets. When funds finally decide to list, 2WD funds are approximately $25 million larger than 1DB funds, which are in turn approximately $20 million larger than 2ND funds. Given that the median fund size is close to $25 million, the economic difference between these groups is large. Finally, the graph offers striking evidence of a sharp increase in asset growth after the first listing date for all three groups. 24 Next, we turn to a multivariate analysis of fund characteristics that lead to listing decisions, using a logistic regression with 1DB funds as the base case. We use as independent variables the total excess return over a 12-month window prior to the listing month, the log of the excess assets in millions of dollars just prior to initial listing, 25 and style dummies as well as year dummies for the initial listing. Finally, we include the fund s incentive fee, management fee, logged minimum investment, and lockup period. Results are reported in Table To ensure that the excess asset results are not due to large outliers we reran the results using median excess asset levels. The relationship between the three groups is unchanged. As expected, since a small group of funds commands a large portion of flows, the slope of asset growth is more muted after the add date. 25 Since excess assets can be negative, we add a constant (500) to all excess asset values to ensure positive values. We have added other constants and find similar results. 26 In untabulated results, we investigate the impact of operational risk on fund listing decisions. We compute the omega score variable developed in Brown et al. (2008) and include that variable in the logistic model. That paper used TASS 18

19 <Insert Table 4 about here> The multivariate analysis confirms the previous results. In Panel A, the coefficient on returns and assets are positive and negative, respectively. 27 This means that better performing, smaller funds are more likely to be reported to both databases immediately (case 2ND). Better performing, larger funds are reported to a second database with some delay (case 2WD). 28 One drawback of this analysis is that it relies solely on TASS and HFR data. As a robustness check, we also considered CISDM funds. CISDM does not have an added to the database field but we can still identify funds that choose not to list in either of the other two databases. 29 We then run a similar logistic regression using only excess assets and excess returns because CISDM lacks characteristic information for a significant portion of its funds. The results data only. Thus, some variables are not available in HFR, such as AcceptsManagedAccounts and PersonalCapital. For these variables, we set all HFR funds to zero. Overall, we find that funds with higher operational risk are less likely to list in more than one database. It could be that funds with higher operational risk, while wanting to attract capital, do not want to draw too much attention to their operations. For example, as found in Brown et al. (2012), larger funds are more likely to be selected for a due diligence report. Other results are unaffected by the inclusion of the omega score variable. 27 We verified these results are not impacted by the fact that excess assets are measured as of the end of the excess return period. Excluding either variable from the logistic model does not impact the results of the other variable. Additionally, the univariate results support the conclusions of the model. 28 Since 2ND funds are advertising their performance to more potential investors immediately after listing, they have more incentive to smooth their returns. In unreported results, we examine the autocorrelation of returns over the first two years after listing. We find that the autocorrelation of returns is higher for the 2ND funds, at 0.10, versus 0.08 for Initial 1DB funds. 29 The matching procedure used to identify CISDM funds not listing in HFR or TASS is the same procedure outlined to match HFR and TASS. 19

20 are as expected. CISDM funds that never choose to list in TASS or HFR are larger and have worse performance. This explains why their managers never pursued listing their fund in HFR or TASS. Overall, our results suggest that these initial listing decisions are not random. Hedge fund management companies indeed seem to take strategic advantage of the voluntary nature of hedge fund reporting. B. Length of Backfill Period While other papers (e.g. Ackermann et al. (2009), Agarwal et al. (2012), Aiken et al. (2012)) examine the potential bias caused by hedge fund listing decisions, one unexplored issue is what determines the length of the backfill period. In untabulated results, we examine the cross-sectional distribution of backfill periods. As in the prior section, we find that the need for more assets and good performance play the most important role in the timing of fund listings. Funds that delay listing for longer periods tend to have larger assets and poorer performance compared to other funds at the same point in their life cycles. In addition to these two variables, we also find that funds listing sooner have lower minimum investment requirements and lower incentive fees, which we would expect for funds more anxious to attract investors. We also note that the drivers of the backfill period are consistent across the TASS, HFR, and combined databases. C. Listing to a Second Database after a Delay Many funds initially decide to list in one hedge fund database only. We also observe that some funds decide to list themselves in a second database after a considerable delay. In this section, we discuss the reasons for these delayed second listings. In untabulated results, we repeat the analysis in Table 3 but align on the second listing date. Compared to funds that never subsequently list in a second database, 2WD funds experience significantly greater returns before the second 20

21 listing. Like mutual fund advertising and the initial hedge fund database listing, the decision of a second listing is a strategic choice that comes after good performance. The asset sizes of these funds tend to decline before the second listing date but increases thereafter as after the first listing. 30 These results are confirmed by a logistic analysis similar to Table 4. Finally, after the second add date, the performance of 2WD funds drops significantly, indicating that this second period of outperformance is not indicative of higher levels of skill compared to 2ND and 1DB funds. Overall, this behavior reflects the tradeoff between an immediate second listing and holding the option to list later after a period of good performance and when additional funds are needed. Section V examines the value of this option. D. Intra-Management Company Evidence of Strategic Listings The previous evidence demonstrates that the decision of whether and when to list a hedge fund in a database is strategic. Likewise, management companies must decide which funds to list among the many funds they offer. The decision to list a fund is essentially costless. In addition, management companies that already have one fund listed in a database must be aware of its existence and of the listing procedures. Thus, a choice not to list must be an active decision. Our prior results would lead us to expect that management companies choose to list their best performing funds or those funds with the greatest need for assets in both databases. This section investigates the extent to which management companies list their funds across our databases. We identify funds that appear in only one database but whose management company has funds in both databases, which we call 1DB2CO. This is a subset of our 1DB group. We 30 We find this is true even of funds that wait three years or more between listings. There are no characteristic differences between those funds that wait three or more years as compared to one or two years to list. The only variation between the two groups is the timing of the asset decline and high performance. 21

22 label the rest of the original 1DB funds, whose management companies only list funds in one of the two databases, as 1DB1CO. We also have the usual 2DB funds. On average, management companies with at least one fund listed in both databases list only 60% of their funds in both HFR and TASS; 17% of funds are listed in TASS only and 24% of funds are listed in HFR only. 31 Thus, many management companies selectively choose to list funds in one database only. Table 5 examines the reasons for this behavior, comparing excess returns across the 1DB2CO and 2ND groups in Panel A. Panel B includes a multivariate analysis, as in Table <Insert Table 5 about here> The fact that 2ND funds strongly outperform 1DB2CO funds demonstrates that management companies choose to list their best performing funds in multiple databases. In untabulated results, 1DB2CO funds also significantly underperform 1DB1CO funds. Similar to previous results, companies tend to list their smaller funds in more databases. Thus, funds that are only listed in one database tend to be larger and have lower performance. These results demonstrate that strategic listing decisions occur both across and within management companies. 31 The lower percentage of TASS only funds relative to HFR only funds matches the ratio of HFR and TASS funds in our 8,310 fund sample. 32 For the sake of brevity we do not report the results for the 2WD funds in Table 5. The conclusions of that group are unchanged from those previously reported in Tables 3 and 4. The slight difference between the Table 3 and Table 5 results for 2ND funds are due to the company information requirement for this section. 22

23 V. Listing Decisions and Investor Flows Hedge funds list in public databases to increase their exposure and ultimately to attract inflows. In the prior section, we found significant evidence that hedge fund management companies make strategic use of the voluntary nature of hedge fund database reporting. Funds are more likely to advertise themselves immediately in multiple outlets if their performance is higher and assets are low. It is also clear that fund management companies exercise their option to list sequentially in several databases. The next important issue is how this behavior affects investors. In this section, we examine how these supposedly sophisticated investors respond to listing decisions and whether these responses are rational. A. Investor Response to Initial Backfilled performance As indicated, hedge fund management companies tend to list funds with higher returns and with a greater need of assets in several databases. Although the relationships discovered in the prior section were previously unknown, academic researchers have long been aware that the potential for backfill bias is one of the drawbacks of voluntary reporting (e.g. Park (1995) and Ackerman et al. (1999)). Our results also find that backfilled performance is approximately 5% higher than nonbackfilled performance. This leads to the question of how investors react to new listings and if so, whether they discount backfilled returns. In this section, we examine quarterly investor flows, based on the Sirri and Tufano (1998) model which defines net inflows as Flow = t Assets - Assets t t-1 t Assets t Return (1) Here, inflows are measured from the relative asset growth after accounting for the natural growth due to returns. 23

24 We then regress this flow variable on prior year performance ranked by style, along with the prior quarter flows, the log of assets at the beginning of the quarter, the average style flows during the same quarter, the prior year standard deviation of returns, fund fees, the log of minimum investment, and the lockup period. To avoid distortions due to the effect of small funds, we remove any fund whose assets are less than $10 million. Because the relationship between investor flows and prior performance may be asymmetric, we use a piecewise-linear approach with the performance rank split at the median fund performance. 33 Finally, to investigate whether investors discount backfilled returns, we create a dummy variable that is one if any of the fund s performance over the prior year is backfilled. This can also be viewed as a flag for a new fund. Since backfilled performance is known to be artificially high, it seems rational that investors would discount that performance through a weaker relationship between prior performance and flows, all else equal. The model is estimated every quarter from 1996 to June 2009, using the Fama-Macbeth (1973) approach; standard errors are computed using the Newey-West (1987) technique with eight lags. Results are reported in Table 6. In the first model, the backfill dummy variable captures a level effect. In the second model, the dummy variable is also interacted with performance to capture both the level effect and the effect of backfilled returns on the flow-performance relationship. <Insert Table 6 about here> 33 Specifically, define the rank variable as x. For x below the median, the Low variable is x and the High variable is 0. For x above the median, Low is 50% and High is (x-50%). 24

25 Surprisingly, funds with backfilled returns have significantly higher flows than funds without backfill returns. Model 1 shows that funds with backfilled returns have on average 2.4% per quarter higher flows than others. This flow increase is related to the performance of the fund, as shown in Model 2. A new fund with poor backfilled performance receives the same flows as an older fund. For example, a backfilled fund with a performance rank of zero would receive roughly the same flows as a non-backfilled fund ( % %= -1.5%). In contrast, a top performing new fund, with percentile ranking of 100%, receives 7.8% more flows than an older fund (7.8% = % %). 34 Other findings in the table, such as the sensitivity of flows to performance, are consistent with the prior literature. 35 One confounding factor with this flow analysis is non-database marketing. For example, the listing date could also signal the start of a concerted marketing campaign to raise assets that includes road shows, capital introductions, and so on. It is therefore impossible to disentangle the listing effect itself as compared to other factors. 36 Even so, regardless of how investors find out about newly listed hedge funds, their decision to invest is based on backfilled performance. These results, at first glance, are not consistent with the notion that hedge fund investors are sophisticated. As is well known, backfilled returns are artificially high. Making investment decisions based on backfilled returns, however, may be perfectly rational if backfilled performance is especially informative about future performance. To examine this possibility, we examine the performance persistence of both backfilled and non-backfilled returns. We first sort funds in 34 Because hedge fund returns are known to be autocorrelated (Getmansky et al. (2004)), we rerun our flow analysis with the first and second quarter lagged performance omitted. We find similar results. 35 See for example Chevalier and Ellison (1997) and Sirri and Tufano (1998) for mutual funds and Baquero and Verbeek (2009) and Getmansky et al. (2009) for hedge funds. 36 The same issue affects studies of magazine advertising with mutual funds. 25

26 quartiles based on their excess returns ranked by style during the 12 months prior to the listing date. This is then compared to the performance during the subsequent periods of 0 to 12 months and 0 to 24 months after listing. We also compute the probability of survival over those periods. As a baseline comparison, we evaluate non-backfilled persistence for our entire sample of funds at all times after the listing date, that is, using only returns after the date added to database. Results are reported in Table 7. Panel A describes the transition from the backfill period to the non-backfill period and Panel B covers the non-backfill period. <Insert Table 7 about here> The results suggest that the additional attention paid by investors to backfilled performance is indeed warranted and rational. We find significant persistence of backfilled returns at the oneyear and two-year intervals. Funds in the top quartile of backfilled performance outperform funds in the lowest quartile by almost 6% in one year and 11.7% over two years (non-annualized). By comparison, results for our non-backfilled sample are noticeably weaker. Persistence at a one-year horizon is slightly significant but the magnitude of the effect is about 2% lower. In addition, twoyear persistence is positive, but insignificant and much lower in magnitude. 37 The differences across the two panels are significant at the 1% confidence level. 38,39 37 Note that the differences between the two groups are not caused by a greater look-ahead bias for the backfilled sample. Indeed the table shows that survival rates are similar for both groups. 38 To compute the statistical significance of the differences, we first compute the persistence related excess returns of the highest quartile each period by subtracting the lower quartile excess return average from the individual entries in the high quartile panel. We then compute the t-statistic for the difference between these returns in post backfill period and the overall database with standard errors clustered by fund since the same fund will likely appear in multiple times. 26

27 B. Spillover Effect from New Listings and Multiple Database Listing Decisions The previous section shows that new funds listings draw significant attention from investors but that this is rational due to the persistence of performance. A related question is whether new listings could have spillover effects on existing funds belonging to the same management company. Previous literature in the mutual fund industry reports that fund companies are able to use the star performance of some of their funds to attract inflows into other funds (e.g., Nanda, Wang, and Zheng, 2004). However, we find that the introduction of a new fund has no impact on other funds that belong to the same management company. 40 We also investigate whether strategic listing decisions to multiple databases affect investor flows by examining whether listing to two databases immediately or a delayed second listing attracts additional excess flows. Our results suggest that, indeed, listing immediately in two databases does tend to draw greater inflows. We estimate that listing immediately in two databases generates an additional flow effect of 3.5%, which is economically important. As before, the interaction model indicates that these additional flows are conditional on past performance. At the same time, we also find that listing in a second database after a year or more is not associated with an immediate 39 Kosowski et al. (2006), when examining mutual fund performance and performance persistence, note that distributional properties may be violated when examining the tails of a cross-sectional distribution. Specifically, higher performance by prior outperforming funds may be due to higher levels of skewness and kurtosis. While in our case we are examining much larger sections (quartiles) of the distribution, we examine the skewness and kurtosis of our quartile portfolios and overall return distributions to ensure these characteristics are not driving our results. We find that our high quartile funds (Q4) tend to have equal or even marginally lower skewness and kurtosis. Thus, our findings are not driven by these distributional characteristics. 40 The results from this section are omitted to preserve space. They are available upon request. 27

28 increase in investor flows. 41 Perhaps investors have learned about the existence of these funds through other channels by the time the funds decide to list a second time. Another explanation is that a delayed second listing may be accompanied by less marketing effort. 42 Based on these findings, we can also evaluate investor rationality. To determine whether these investor reactions are rational, we compute the performance persistence of funds that list themselves to multiple databases. We find that persistence of 2ND funds is not significantly different from other listing funds. Therefore there seems to be no justification for the higher flows to 2ND funds based on performance. However, one possible rational explanation is that an immediate second listing reduces search costs for investors. We also find that 2WD funds persistence around their second listings is not greater than the overall hedge fund population. Since 2WD funds do not receive any excess flows from their delayed second listings, this persistence result suggests hedge fund investors are acting rationally in this case. VI. Conclusions Hedge funds have limited opportunities to market themselves to investors. One of the few methods to promote a fund is by listing it in a public hedge fund database. Given that the cost of 41 We also examined whether having a fund listed in two databases is generally associated with greater inflows in general, not just at the second listing date. We find effects that are similar to the spillover results. A fund with a performance rank of zero in both databases receives 1.6% less flows than a performance rank zero fund in only one database. This flow loss occurs for essentially all funds with below median performance whereas funds with above median performance receive no flow impact. Listing across multiple databases makes it less difficult for investors to compare returns, which will hurt poorly performing funds. 42 Any non-database marketing would bias results toward finding a delayed listing effect. However, even with this potential positive bias, we do not find any such effect. 28

29 listing is minimal, one would therefore expect that, once a hedge fund has decided to list in a database, it would choose to list to all databases to maximize exposure to potential accredited investors. However, this is not what we observe. Thus, the purpose of this paper is to determine whether simultaneous and sequential listings to multiple databases are strategic and how these listing decisions impact investor flows. We find that hedge fund management companies make strategic use of the voluntarily nature of database listing, indicating that they believe they can fool investors. Small funds with the highest backfilled returns are immediately listed in multiple databases. Funds that initially list in one database list in a second database after another period of good performance and a decrease in assets. While these strategic listings have the potential to fool investors, we find that investors largely react rationally to database listings. Newly listed funds receive significantly more flows from investors, even though their returns are backfilled. However, this behavior is rational because we find that the performance from the backfilled to the post-listing period is much more persistent than for the general hedge fund population. Additionally, we find that investor reactions to concurrent and delayed second listings are also largely rational. Finally, we use the investor flow results to examine the efficiency of hedge fund listing decisions. Small funds with good performance that are both in need of assets and able to attract investors are acting efficiently. By listing themselves to multiple databases immediately, they are indeed obtaining additional flows. However, it is somewhat unclear if large funds are acting efficiently. While the benefits of delayed second listings are small since they are associated with no increase in flows, the cost of delaying a second listing could also be small since large hedge funds may be concerned about the effect of large inflows on their performance. Overall, our results lend support to the sophisticated nature of both hedge fund investors and management companies. 29

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33 Table 1: Summary Fund Statistics This table presents summary statistics on our final sample. Incentive Fee and Management Fee are the fund s incentive and management fee in percent. Lockup Period is the fund s lockup period in months. Minimum Investment is the fund s minimum investment in thousands of dollars. Return and Assets are the fund s monthly returns and assets in millions of dollars. Excess Return and Excess Assets are the fund s monthly return and asset levels above the style averages for that month. N is the number of funds or fund-months. Panel A presents results for TASS only funds, Panel B shows HFR funds only, and Panel C shows funds in both. Sample period is January 1994 to June Panel A: TASS Only Funds N Mean Median Std. Dev. Incentive Fee Management Fee Lockup Period Minimum Investment Return 111, Assets 74, Excess Return 111, Excess Assets 74, Panel B: HFR Only Funds N Mean Median Std. Dev. Incentive Fee Management Fee Lockup Period Minimum Investment Return 223, Assets 175, Excess Return 223, Excess Assets 175, Panel C: Both Database Funds N Mean Median Std. Dev. Incentive Fee Management Fee Lockup Period Minimum Investment Return 199, Assets 171, Excess Return 199, Excess Assets 171,

34 Table 2: Add Date Differences across Databases This table describes the distribution of the difference in add dates for funds that list in both TASS and HFR. The raw difference is computed as the TASS add date minus the HFR add date, leading to positive values for funds listed later in TASS. Raw Difference Absolute Difference Mean Std. Dev Percentile Percentile Percentile Percentile Median Percentile Percentile Percentile Percentile

35 Table 3: Comparison of Performance around the Initial Listing Date This table compares excess monthly returns for three groups of funds around the initial listing date. In Panel A, a fund is labeled 2ND if it is listed in both HFR and TASS within six months of each other and Initial 1DB otherwise. Thus, there no conditioning on forward information. In Panel B, a fund is labeled 1DB if it is only listed in one database over the entire period, 2ND if it is listed in both HFR and TASS within six months of each other, and 2WD if it is listed in both databases but is listed in the second database after one year or more from its initial listing date. This involves forward conditioning. Avg. is the average monthly fund return above the style average for that particular calendar month. Period is defined relative to the first month added to any of these databases. t-values are computed using Newey-West (1987) with two lags. Significance at the 5% and 1% confidence levels denoted by * and **. Panel A: No Forward Conditioning Initial 1DB 2ND 2ND Initial 1DB Period Avg. t-value Avg. t-value Avg. t-value -24 to % 8.94** 0.51% 12.41** 0.26% 7.02** -24 to % 9.45** 0.46% 7.87** 0.27% 4.43** -12 to % 13.62** 0.57% 11.54** 0.25% 5.91** 0 to % -6.11** -0.04% % 2.08* 13 to % ** -0.20% -5.92** 0.03% to % ** -0.12% % 2.08* Panel B: Forward Conditioning 1DB 2ND 2WD Period Avg. t-value Avg. t-value Avg. t-value -24 to % 7.06** 0.51% 12.41** 0.46% 10.19** -24 to % 5.21** 0.46% 7.87** 0.40% 6.28** -12 to % 11.29** 0.57% 11.54** 0.51% 10.66** 0 to % -7.67** -0.04% % 5.42** 13 to % ** -0.20% -5.92** 0.06% to % ** -0.12% % 3.53** 2ND 1DB 2ND 2WD 2WD 1DB Avg. t-value Avg. t-value Avg. t-value -24 to % 8.72** 0.05% % 5.46** -24 to % 5.91** 0.06% % 3.24** -12 to % 6.33** 0.05% % 4.83** 0 to % 4.15** -0.23% -2.81* 0.45% 9.47** 13 to % 2.99* -0.26% -4.94** 0.40% 6.37** 0 to % 4.89** -0.25% -5.06** 0.43% 10.80** 35

36 Table 4: Multivariate Analysis of Initial Listing Decisions This table presents results for logistic models for the hedge fund listing decisions. In Panel A, the dependent variable is one if the fund is a 2ND fund and zero otherwise. In Panel B, a multivariate logistic model is run. The base case for this model is funds that list in one database only. A fund is labeled 2ND if it is listed in both HFR and TASS within six months of each other and 2WD if it is listed in both databases with a delay of one year or more. Excess Return is the fund s total excess return over twelve months prior to its initial listing. Excess Assets is the natural log of the fund s excess assets the month prior to listing. Incentive Fee and Management Fee are in percent. Lockup Period is in months. Minimum Investment is the log of the fund s minimum investment. Significance at the 5% and 1% confidence levels denoted by * and **. Panel A: No Forward Conditioning 2ND Value Chi-sq Excess return ** Log of Excess assets * Management fee Incentive fee Lockup period Minimum Investment ** Style Dummies Y Year Dummies Y Number of Observations 3432 Pseudo R-squared 7.89% Panel B: Forward Conditioning 2ND 2WD Value Chi-sq Value Chi-sq Excess return ** ** Log of Excess assets ** Management fee * Incentive fee Lockup period * Minimum Investment ** Style Dummies Y Y Year Dummies Y Y Number of Observations 3432 Pseudo R-squared 14.46% 36

37 Table 5: Analyses of Intra-Management Company Listing Decisions This table examines how fund characteristics impact intra-management company listing decisions. In Panel A, excess style returns for three groups of funds around the initial listing date are reported. A fund is labeled 1DB2CO if it is listed in one database but its management company has at least one other fund listed in both databases and 2ND if it is listed in both HFR and TASS within six months of each other. Avg. is the average monthly fund return above the style averages for that particular calendar month. Period is defined relative to the first date added to any of these databases. Panel B reports results from a multivariate logistic model. The base case is funds that list in only one database and whose management company does not cross list. Excess Return is the fund s total excess return over twelve months prior to its initial listing. Excess Assets are the fund s excess assets the month prior to listing. Incentive Fee and Management Fee are in percent. Lockup Period is in months. Minimum Investment is the log of the fund s minimum investment. Significance at the 5% and 1% confidence levels denoted by * and **. Panel A: Excess Returns 1DB2CO 2ND 2ND - 1DB2CO Period Avg. t-value Avg. t-value Avg. t-value -24 to % % 12.53** 0.47% 11.34** -24 to % % 7.84** 0.49% 6.65** -12 to % 3.14** 0.57% 11.65** 0.47% 9.98** 0 to % -8.79** -0.05% % 2.37* 13 to % -8.09** -0.20% -6.33** 0.20% 2.91* 0 to % -7.38** -0.12% -2.38* 0.18% 3.79** Panel B: Multivariate Analysis 1DB2CO 2ND Value Chi-sq Value Chi-sq Excess return ** ** Excess assets ** Management fee Incentive fee ** Lockup period ** Minimum Investment ** Style Dummies Y Y Year Dummies Y Y Number of Observations 3423 Pseudo R-squared 15.55% 37

38 Table 6: Effect of Backfilled Returns on Investor Flows This table estimates the effect of backfilled returns on the performance-flow relationship. Fund flows are estimated each quarter as the percentage increase in assets after controlling for organic growth. These quarterly flows are then regressed against ranked performance during the previous year, the prior quarter s flows, the standard deviation of returns over the prior year, the log of assets at the beginning of the quarter, mean style flows during the same quarter, fees, the log of minimum investment, and the lockup period. Prior performance is measured by returns ranks by style from 0% to 100%. Prior Return Rank-Low and Prior Return Rank-High are piecewise-linear variables set at Min(x,50%) and Max(x-50%,0), respectively. The Backfilled dummy variable is set at one if any backfilled returns are included in the prior year s return calculation and zero otherwise. Coefficients are computed using the Fama-Macbeth (1973) approach; standard errors and p-values are computed using the Newey-West (1987) technique with eight lags. Significance at the 5% and 1% confidence levels denoted by * and **. Model 1 Model 2 Value t-value Value t-value Prior Return Rank-Low ** ** Prior Return Rank-High ** ** Prior Qtr Flow ** ** Prior Std. Dev ** ** Log(Assets t-1 ) ** ** Mean Style Flow t ** ** Incentive Fee Management Fee Log(MinInvestment) * * Lockup Period Backfilled (BF) ** Prior Rank-Low BF * Prior Rank-High BF ** Average Nb. Observations Average Adjusted R-squared 8.72% 8.71% 38

39 Table 7: Persistence of Backfilled Performance This table examines the persistence of hedge fund returns. Panel A examines persistence around the listing date. For each fund, we compute the total 12 month excess return prior to the first listing date. Prior performance is then fractionally ranked by style and split into quartiles, where Q4 represents the best performing funds. We then report the average excess return for each quartile over the 0-12 and 0-24 month subsequent period (non-annualized). The table also shows the difference between the top and bottom quartiles as well as its p-value. This is computed from a panel regression on dummy variables for each quartiles, with standard errors clustered by year of listing. Panel B performs the same analysis sorting returns after the backfill period only. This procedure is run cross-sectionally for each calendar quarter and average values are computed using Fama-Macbeth (1973). Standard errors and p-values are then computed from the time series of coefficients using a Newey-West (1987) correction with four and eight lags for the one- and twoyear forecast horizons. We also compute the survival probability for both groups. Finally, we also compute p-values for difference tests comparing performance persistence of the two panels. Panel A: Persistence of Backfill Performance BF Quartile Months 0-12 P(Survival) Months 0-24 P(Survival) Q1-3.00% 74.51% -6.36% 60.09% Q2-1.23% 81.01% -2.04% 68.02% Q3-1.80% 86.70% -0.91% 73.34% Q4 2.87% 84.95% 5.36% 73.48% Q4 Q1 5.87% 11.72% p-value Panel B: Non-Backfill Persistence Prior Quartile Months 0-12 P(Survival) Months 0-24 P(Survival) Q1-0.17% 70.76% 2.70% 54.71% Q2-0.15% 81.15% 0.32% 66.57% Q3 0.34% 86.30% 1.38% 73.95% Q4 3.45% 89.03% 6.52% 78.99% Q4 Q1 3.62% 3.82% p-value p-value of difference test

40 Figure 1: Histogram of Differences between Dates Added to Databases This figure shows the histogram of the difference between the add dates in HFR and TASS. The vertical axis is the number of funds in that bin while the horizontal axis is the difference measured in months. 40

41 Figure 2: Cumulative Frequency of Backfill Period Length This figure shows the cumulative frequency of the backfill period for funds in our sample, measured as the difference between the fund s first return date and the date added to the first database. The vertical axis is the cumulative proportion of funds in that bin; the horizontal axis is the backfill period measured in months. Cumulative Frequency 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Number of Months of Backfilled Returns 41

42 Figure 3: Excess Assets around Funds Initial Listings This figure displays the average excess assets of funds that list only in one database ( 1DB ), funds that list in two databases with no delay ( 2ND ) and funds that listed in two databases with delay ( 2WD ). The vertical axis shows the average excess assets in millions of dollars. The horizontal axis shows the number of months prior to the fund being listed in a database 42

43 Appendix A: Style Classifications The analysis requires the same set of style classification across the TASS and HFR databases. We used the overlapping sample of funds to determine the most common mapping of a TASS sector into HFR. The table displays the mappings. Original Style Name Database Merged Style Dedicated Short Bias TASS Equity Hedge Emerging Markets TASS Equity Hedge Equity Hedge HFR Equity Hedge Equity Market Neutral TASS Equity Hedge Long/Short Equity Hedge TASS Equity Hedge Event-Driven HFR Event-Driven Event Driven TASS Event-Driven Global Macro TASS Macro Macro HFR Macro Managed Futures TASS Macro Convertible Arbitrage TASS Relative Value Multi-Strategy TASS Relative Value Options Strategy TASS Relative Value Other TASS Relative Value Fixed Income Arbitrage TASS Relative Value Relative Value HFR Relative Value Undefined TASS Relative Value 43