Are Hedge Funds Domiciled in Delaware Different?

Are Hedge Funds Domiciled in Delaware Different? Douglas Cumming Professor and Ontario Research Chair York University - Schulich School of Business 4700 Keele Street, Toronto Ontario M3J 1P3, Canada Na Dai 1 Associate Professor of Finance School of Business SUNY at Albany 1400 Washington Ave Albany, NY 12222, USA Sofia Johan Adjunct Professor in Entrepreneurship and Finance York University - Schulich School of Business 4700 Keele Street, Toronto 4700 Ontario M3J 1P3, Canada 1 Contact information of corresponding author: E-mail: ndai@albany.edu; Tel: 1-518-956-8358. Na Dai owes thanks the Center for Institutional Investment Management at SUNY Albany for data support. Douglas Cumming and Sofia Johan owe thanks to the Social Sciences and Humanities Research Council of Canada for financial support.

2 Are Hedge Funds Domiciled in Delaware Different? Abstract Hedge funds may be established anywhere in the world, either onshore or offshore. Over 60% of U.S. hedge funds however choose to domicile in Delaware, although 95% of those are physically located and managed elsewhere. We explore whether the choice of Delaware as domicile location has implications for the contractual structure, fund performance, and fund survival. We find that Delaware hedge funds exhibit significant differences in contractual structure. They earn higher management and incentive fees, and are more likely to use high watermark provisions. They are less likely to be requested to invest their personal capital. Furthermore, both the redemption notice period and lock up period are significantly longer for Delaware hedge funds. We do not find Delaware hedge funds outperform or underperform funds domiciled elsewhere, nevertheless, we show investors of Delaware hedge funds (fund flows) are more sensitive to funds prior performance. Delaware hedge funds are more likely to be liquidated due to poor performance. These differences between Delaware hedge funds and non-delaware ones have important implications for hedge funds agency conflicts. We show strong evidence that Delaware hedge funds are more likely to increase risk given poor absolute performance. We find some evidence that they are less engaged in short-term return management. All these results hold even after we control for the endogenous nature of funds choosing Delaware as the domicile location. Keywords: Hedge Funds; Delaware; Law and Finance JEL Classification: G23, G24, G28, K22

3 Are Hedge Funds Domiciled in Delaware Different? 1. Introduction Hedge funds are essentially pools of capital that are typically sourced from institutional investors and high net worth individuals (Cumming, Dai and Johan, 2013). Hedge funds may select any jurisdiction to be domiciled, although inevitable the jurisdiction chosen will be determined by more practical, operational factors. For example, the scope of investors that may invest in a hedge fund depends on the regulatory landscape in which the fund is domiciled, as explained in Cumming, Dai and Johan (2013), therefore fund raising will be a factor in domicile choice. In this paper, we document for the first time that over 60% of United States (U.S.) hedge funds choose to domicile in Delaware, which is especially interesting as among those domiciled in Delaware, 95% are physically located, and managed, elsewhere. In the U.S., states compete for corporate charters to increase their revenue from incorporation fees and franchise taxes when companies choose to incorporate locally (Romano, 1985). In the early part of the 20 th century, New Jersey attracted the most out-of-state incorporations (Cary, 1974). Since then, Delaware has emerged as the leader, chartering approximately 43% of the New York Stock Exchange firms and 50% of the Fortune 500 firms (Macey & Miller, 1986; Romano, 1985). Extant research has established that Delaware is a particularly attractive state for incorporation and the winner of the incorporation business due to a few specific reasons. First, it is a small state which earns over 15% of its revenues from incorporations (Romano, 1985, 1993), thereby signaling to the marketplace that it is committed to ensuring responsiveness to corporate interests. Second, Delaware s judges are highly

4 specialized experts in corporate law, and have had years of building up precedents in legal cases that are widely accepted and used throughout the U.S. and in other common law countries around the world (Romano, 1985; Roe, 2003). Delaware is thus the most well recognized state among investors into U.S. companies. U.S. lawyers typically understand the legal structure in their home state and that of Delaware, but not necessarily that of other states (Romano, 1985; Roe, 2003; Bebchuk and Cohen, 2003). Third, Delaware supports takeover bids in a number of ways: raising fewer obstacles to takeovers than other U.S. states; erecting only minor barriers to hostile acquisitions (Delaware has the shortest delay on hostile bids of any U.S. state) in a way that has not reduced shareholder wealth; and lowering acquisition costs by establishing clear precedents and occasionally prohibiting extreme defensive tactics by targets that would allow management to entrench themselves. There is no local constituency of firms physically located in Delaware to push back against bidder-friendly trends in a way that would reduce bidder profits. Thus, in addition to housing a majority of firms, Delaware increases firm value by facilitating the sale of public firms (Daines, 2002). Such incentives have raised the possibility that the competition for corporate charters might on one hand give rise to a race to the bottom if states change their corporate law in ways that favor managerial interests over shareholder interests in order to attract new incorporations (Winter, 1977; Bebchuk, 1992; Bebchuk and Cohen, 2003). On the other hand, incorporation competition may give rise to a race to the top if states compete in a way that maximizes shareholder value. Empirical evidence (Dodd & Leftwich, 1980, Baysinger & Butler, 1985, Romano, 1985; Daines, 2002; Ferris et al., 2006) is consistent with the view that when firms reincorporate from another state to Delaware, share prices seem in the main to go up and not down. This evidence is consistent with the view that the state competition is generally successful

5 in aligning managerial and shareholder interests. It is also consistent with the view that enhanced managerial freedom is a good thing, not a bad thing, for shareholders. While the focus of most empirical studies using U.S. data has centered on the question of whether state competition has been good or bad for shareholders, in this paper we seek to ask whether state competition has been good or bad for fund investors, more specifically hedge fund investors. We address the question of whether or not there are differences in the structure and performance of hedge funds that incorporate in Delaware in view of its glaring popularity as a domicile. To this end, our analyses build on a large and growing literature on hedge fund structure and performance (Ackermann, McEnally, and Ravenscraft, 1999; Agarwal and Naik, 2000a, b, 2004; Agarwal, Daniel, and Naik, 2006; Amin and Kat, 2003; Baquero, Horst, and Verbeek, 2005; Brown, Goetzmann, and Ibbotson, 1999, 2001; Brown and Goetzmann, 2003; Brunnermeier and Nagel, 2004; Cremers, Martijn, and Nair, 2005; Edwards and Caglayan, 2001; Getmansky, 2005; Getmansky, Lo, and Makarov, 2004; Liang, 1999, 2000, 2003; Gupta and Liang, 2005, Teo, 2007), as well as hedge fund activism (Brav et al., 2008a,b; Klein and Zur, 2009) and the structure of hedge funds and strategies (Ding, Getmansky, Liang, and Wermers, 2006; Fung and Hsieh, 1997, 2000, 2001; Goetzmann, Ingersoll, and Ross, 2003; Jorion, 2000). Our analyses are also related to analyses of hedge fund share restrictions (Aragon, 2007) and hedge fund registration (Brown, Goetzmann, Liang, and Schwartz, 2008). Our analyses are likewise related to recent work on international cross-country law and finance analyses of hedge fund regulation in relation to fund structure and performance (Cumming and Dai, 2010; Cumming et al., 2013) in the spirit of La Porta, Lopez-de-Silanes, Shleifer, and Vishny, (1998, 2002, and 2006).

6 What makes an analysis of hedge fund incorporation choices unique and important? First, investors into hedge funds are among the most sophisticated in the financial world. To this end, we would not expect hedge funds in Delaware to earn superior returns, because if there is a premium associated with Delaware law, it will be well-known to hedge fund investors, thereby increasing their price and lowering their return. By examining hedge fund performance among Delaware funds versus other funds, we provide a measure of the degree to which sophisticated investors into hedge funds are familiar with Delaware law. Second, and more importantly, the familiarity with Delaware Law, or lingua franca, means that investors from a diverse set of states and even countries will be on more equal footing and have a more common understanding about the structure and governance of Delaware hedge funds relative to other hedge funds. As a result, we expect that Delaware hedge funds are more likely to have a more pronounced flow-performance relationship. Investors will be more inclined to invest in the future when a Delaware fund does well, and conversely more inclined to withdraw funds in the event of poor performance, because there are more investors that are better aware and more fully informed of the causes and consequences of investing in a Delaware fund relative to non-delaware funds. At the extreme end of the spectrum, we expect Delaware hedge funds are more likely to be liquidated or liquidated more quickly in the event of very poor performance. Third, because investors face less legal uncertainty with the legal and governance structure of Delaware funds, we conjecture that such investors will be more willing to invest in funds that provide Delaware managers with higher fixed and performance fees. Relatedly, investors will be less inclined to require Delaware managers to invest their own capital into the fund that they are managing. Also, investors into Delaware hedge funds will be more likely to

7 accept longer redemption notice periods and lock-in periods relative to non-delaware funds given the greater and more transparent legal environment among Delaware funds. We test these three propositions with the Lipper/TASS database over the period 1994-2010. We provide evidence that is highly consistent with these three propositions. Our evidence is robust to a number of controls, including but not limited to the endogenous selection of Delaware as the choice of jurisdiction. In modeling the choice of jurisdiction we control for a number of things such as the actual physical location of the fund and market conditions, as discussed herein. The stronger flow-performance relationship and the more manager-friendly fund structures (high incentives, high water mark, less personal capital, longer lockup periods and redemption periods, etc.) for the Delaware hedge funds as documented in this paper have implications for the potential agency conflicts of hedge funds. In the second part of the paper, we examine the implications of Delaware domicile for two specific agency conflicts that are well documented in the hedge fund literature, risk shifting (e.g., Brown, Goetzmann, and Park, 2001; and Agarwal, Daniel, and Naik, 2002; Basak, Pavlova, and Shapiro, 2007; Hodder and Jackwerth, 2007; Panageas and Westerfiled, 2009; Aragon and Nanda, 2012) and return misreporting (e.g., Bollen and Pool, 2008, 2009 and Agarwal, Daniel, and Naik, 2011). Incentives to take risk and manage returns can arise from the strong flow-performance sensitivity as investors direct more money into hedge funds that outperform. In contrast, the higher incentive fee and the more frequent usage of the high watermark provision among Delaware funds indicate less risk-shifting behavior than other funds. Longer lockup periods and redemption periods indicate that investors cannot withdraw their capital immediately in response to funds poor performance. Therefore, Delaware hedge fund managers face smaller pressure in

8 terms of manipulating short-term performance. Our empirical evidences show that hedge funds domiciled in Delaware are more likely to increase risk following poor absolute performance. The evidence regarding misreporting, nevertheless, is mixed. This paper is organized as follows. A discussion of why Delaware law matters for hedge funds is provided in section 2. Section 3 introduces the Lipper/TASS dataset and provides summary statistics. Multivariate tests and various robustness checks are presented in section 4. Section 5 provides a summary, concluding remarks and suggestions for future research. 2. Why Delaware Law Matters for Hedge Funds Hedge funds are financial intermediaries that seek to attract capital from an investor base and invest the pool of funds as profitably as possible. It is therefore crucial that the domicile chosen to establish the fund be one that is attractive to investors and also one that facilitates investments from a regulatory, legal, tax and operational perspective. Investments are made in a variety of investments including but not limited to equity and debt securities, derivatives, currency and commodities. From a regulatory perspective and operational perspective, investors seek to invest in hedge funds that are able to execute their mandates within a domicile s regulatory regime and hedge funds require the services of prime brokers and custodians that are able to operate within a domicile. From a tax perspective, both investors and hedge funds seek clear and neutral tax domiciles to maximize investment profits. In this section we explain the two primary advantages of Delaware law for hedge funds: tax transparency, and a stable and clear legal regime that offers contractual flexibility that

9 enables both limited liability and active management by fund managers, investors and service providers. 2.1. Tax transparency Delaware provides an efficient platform for hedge funds to establish their pool of funds under the Delaware Limited Liability Company Acts (DLLC) and the Delaware Revised Model Uniform Limited Partnership Act (DRULPA) 1983. The hedge funds in Delaware are often structured as DRULPAs. As with any limited partnership, it enabled the investors to obtain efficient tax flow through, tax transparency and limited liability, which is crucial in view of the limited oversight investors have over the hedge fund managers investment powers. However, while the funds themselves are structured as DRULPAs, the hedge fund managers typically structure themselves as DLLCs. This is mainly due to U.S. Internal Revenue Service allowing DLLCs to simply elect to opt-into the benefits of a flow-through tax treatment, formerly a treatment only availed to partnerships, not corporations. The ability for hedge fund managers to structure themselves as DLLCs instead of DRULPAs enabled unprecedented contractual flexibility. 2.2. Contractual Flexibility Under Delaware law, a hedge fund structured as a DRULPA required a general partner to assume full liability of the fund as all other partners had limited liability. With the general partner or the hedge fund manager structured as a DLLC instead of another DRULPA the hedge fund manager is able to manage this assumption of full liability as the corporate structure, albeit enjoying the same tax flow-through privileges as a DRULPA, facilitated more protection through corporate law, instead of partnership law. The use of DLLC structure also allowed hedge fund managers to determine management and performance fee flow to management staff more

10 efficiently, especially in view of staff turnover. Fund management duties could also be delegated without the ensuing problems related to partnership. However, there is, as they say, no free lunch. Albeit the full liability effect of general partners is tempered with the use of DLLCs, the lawmakers in Delaware have provided investors with limited liability certain measures of protection not normally availed to limited partners of other limited partnerships, allowing them certain actions which do not inadvertently turn them into general partners, which include acting as a contractor on behalf of the limited partnership; acting as a guarantor of the limited partnership; consulting with or advising a general partner; selling assets of the limited partnership; and making determinations in respect of investments to be made by the limited partnership. 2 Essentially, U.S hedge funds find Delaware an efficient, transparent, low-cost operational environment as there are inexpensive and simple formation processes. There are relatively low disclosure requirements as funds and their general managers are not required to file the DLLC or DRULPA Agreements, and they are not required to file annual reports. Most significantly there isn t a need for managers to maintain a presence or an office or personnel in the domicile. 3. Data and Summary Statistics We describe the data used in our analysis in this section, followed by a discussion of our sample summary statistics. The main database used in our empirical analysis is supplied by Lipper/TASS, a major hedge funds data vendor. Although many funds report a performance history prior to 1994, TASS started collecting hedge fund data only in 1994. To avoid survivorship bias, our sample 2 See Deleware Revised Uniform Partnership Act vised Unif

11 period covers January 1994 through December 2010. We restrict our sample to hedge funds domiciled in U.S. This leaves us a total of 1,714 hedge funds. Majority of the hedge funds are organized either as Limited Partnership (LP) or Limited Liability Companies (LLC). Among the 1,714 hedge funds, 1,078 funds (about 63%) choose to domicile in Delaware. Among the 1,078 funds domiciled in Delaware, only 75 funds also have their business locations in Delaware. In other words, 95% of funds domiciled in Delaware are physically located elsewhere. [Insert Table 1 about here.] As we discussed earlier, Delaware is attractive as a domicile location to hedge funds for many reasons, for instance, lower tax rate, higher quality court, greater flexibility when contracting with investors, among others. The key interest of this paper is to examine whether this choice has any implication for funds structure, performance, risk-taking behavior, and survival. We start with comparing the various characteristics of hedge funds conditional on their domicile locations. As shown in Table 2, hedge funds domiciled in Delaware present significant differences from other funds in several aspects. [Insert Table 2 about here.] First of all, the incentive structure of funds domiciled in Delaware is quite different from others. Funds domiciled in Delaware on average earn a management fee of 1.39% and an incentive fee of 19.20%, which are 0.12% and 1.45% higher than other funds, respectively.

12 Furthermore, about 82% of Delaware hedge funds use high watermark provision, which is almost twice the percentage among funds domiciled elsewhere. 3 Secondly, it seems that managers of hedge funds domiciled in Delaware are able to negotiate for themselves more manager-friendly terms. For instance, Delaware hedge funds apply significantly higher leverage, but are less likely to request fund managers personal capital. They also request longer redemption notice period, and have longer lockup period than other funds. We also compare the performance of Delaware hedge funds with others, measured by average monthly returns, average excess returns, and annual alpha. We do not find significant difference in performance between Delaware hedge funds and others. 4. Empirical Analysis 4.1.The endogeneity of choosing Delaware as domicile location While it is not our major interest to explore why Delaware is so popular as U.S. domestic domicile location for hedge funds in this paper, the endogenous nature of this choice presents a challenge for our empirical analysis that follows. We adopt the Heckman (1976, 1979) two-stage treatment regression framework to examine the robustness of our major empirical findings throughout the paper. In this section, we discuss this approach in details. Our setup of the first stage regression is as follows: 3 Delaware hedge funds and funds domiciled in Delaware used interchangeably in this paper.

13 Delaware Dummy = α + β 1 Business Location + β 2 Inception Year Dummies + β 3 Control Variables Where Delaware Dummy is equal to one if a hedge fund is domiciled in Delaware; Business Location is a dummy variable which is equal to one if the hedge fund is physically located in Delaware; Inception Year Dummies are a set of dummy variables which are equal to one if a hedge fund was founded in a specific year; additional control variables include a Master-Feeder Fund dummy, a LP dummy, a LLC dummy, and dummy variables indicating the investment strategy of the fund. We believe the business location and the inception year of the hedge fund may be correlated with funds choice of the domicile location, but do not obviously affect hedge funds flow-performance relation, their risk choices, and their survival, which are the dependent variables of the second stage regressions that we explore in the following sections. [Insert Table 3 about here.] As shown in Table 3, funds organized as LLC or LP and master-feeder funds are more likely to domicile in Delaware. Furthermore, we show that Delaware become the most popular domicile location to U.S. hedge funds only after 1998. Lamda is estimated off the above probit regression and then included in in the second stage regressions to adjust for the endogeneity that arises from funds self-selection.

14 4.2. Flow-Performance Relation and Domicile Location The flow-performance relation has been examined in the context of hedge funds in many recent studies. Focusing on flows and their relationship to liquidation of funds, Getmansky (2003), shows that similar to mutual funds, the likelihood of a hedge fund liquidated is decreased since investors are chasing individual fund performance. Agarwal and Naik (2004), Goetzmann et al. (2003) and Getmansky (2003) show there are decreasing returns to scale in the performance of hedge funds, and explain their findings by the limited availability of assets that provide superior hedge fund returns. Agarwal et al. (2004, 2006) control for additional factors such as managerial incentives and fees, size and age of funds, that ranking hedge funds based on non-risk adjusted return performance. They find that flows are positively associated with performance, which is different from the findings in Goetzmann et al. (2003). Similarly, Fung et al. (2007) show that the alphas are associated withgreater and steadier capital inflows of funds. Convexity versus concavity of the flow-performance relationship has been debated in the hedge fund literature. Agarwal et al. (2004) find a convex relationship in hedge fund flowperformance; Getmansky (2005) finds a concave flow-performance relationship; and Baquero and Verbeek (2005) find a linear flow-performance relationship. The results depend on database used, time period analyzed and the frequency of the sample. Ding et al. (2007) reconciles these conflicting findings by showing that hedge funds exhibit a convex flow-performance relation in the absence of share restrictions (similar to mutual funds), but exhibit a concave relation in the presence of restrictions. Further, live funds exhibit a concave flow-performance relation due to diseconomies-of-scale, but defunct funds display a convex relation due to the various reasons that they had become defunct.

15 As shown in our summary statistics (Table 2), hedge funds domiciled in Delaware exhibit many different contractual features, such as incentive structures, redemption notice period, lockup period, etc. The existing literature (as discussed above) shows that these factors may have an effect on the flow-performance relation. Based on this reasoning, in this section, we explore whether the flow-performance relation is conditional on the domicile location of a hedge fund. Our main model is as follows: Flow t = α + β 1 Performance t 1 + β 2 Delaware + β 3 Delaware Performance t 1 + β 4 Control Varialbes We measure flows as a proportion of Assets under Management (AUM) by the month t change in net AUM, adjusted for investment returns (Sirri and Tufano, 1998): Flow t = AUM (1 + return ) AUM AUM t t t 1 t 1 We control for prior fund performance with various alternative measures of performance (such as returns and ranked performance), and use different methods to consider nonlinearities, such as with the use of variables for the square of past performance as well as variables that measure tercile performance ranks (as in Ding et al., 2007). For the latter, according to Sirri and Tufano (1998) and Ding et al. (2007), we first estimate the fractional rank for each fund in each period, from 0 to 1 based on the previous month raw return. Then, we construct the fractional terciles ranks as follows: Bottom Tercile Rank: TRank.1 = min (1/3, FRank) Middle Tercile Rank: TRank.2 = min (1/3, FRank-TRank.1)

16 Top Tercile Rank: TRank.3 = min (1/3, FRank-TRank1-TRank2) where FRank is the fractional ranks in each period. The key variable of our interest is the interaction term between Delaware and measures of the fund past performance. From Delaware*Performance t-1, we can infer the incremental effect that being domiciled in Delaware has on the flow-performance relation. We control for other fund specific factors that are important in hedge fund flows, including management fees, incentive fees, high watermark, fund size, the use of leverage, whether fund managers are requested to invest their personal capital, whether the fund accept managed account, redemption notice period, length of lockup period, whether the fund is registered with the SEC, whether the fund is a master-feeder fund, and the organization form of the fund. Finally, we employ a number of dummy variables to control for the hedge fund investment strategies. [Insert Table 4 about here.] The empirical findings are reported in Table 4. In models (1)-(3) we include all hedge funds, where raw returns are used in models (1) and (2) and the fractional terciles ranks are used in model (3). Consistent with the existing literature (Getmansky, 2005; Cumming and Dai, 2009), we find a positive correlation between fund flow and prior return and a significantly

17 negative coefficient on the square term, which indicates a concave relation. The coefficient on the interaction term Delaware*Performance t-1 is significantly positive, suggesting investors are more sensitive to the past performance of funds domiciled in Delaware. As argued in Ding et al (2007), the flow-performance relation could be different between live funds and defunct funds. Thus, in models (4) and (5), we repeat the regressions for liquidated funds and live funds separately. In both cases, we show a significantly positive coefficient on the interaction term Delaware*Performance t-1. Model (6) reports the results from the Heckman 2-stage treatment regression which adjusts for funds self-selection of Delaware as the domicile location. This result remains robust. This finding supports the hypothesis that investors will be more inclined to invest in the future when a Delaware fund does well, and conversely more inclined to withdraw funds in the event of poor performance, because there are more investors that are better aware and more fully informed of the causes and consequences of investing in a Delaware fund relative to non- Delaware funds. 4.3.Fund Survival and Domicile Location Several authors have empirically examined the survival rates of hedge funds (e.g Brown, Goetzmann, and Ibbotson, 1999; Fung and Hsieh, 2000, 2002; Liang, 2000; Gregoriou, 2002; Getmansky, Lo, and Mei, 2004; Baquero, Horst, and Verbeek, 2005; Grecu, Malkiel, and Saha, 2007; Aragon and Nanda 2012). These work document a significant relation between fund survival and fund characteristics including performance, asset size, and investment styles. In this

18 section, we extend these works by considering the relation between fund survival and its domicile location. Specifically, our model is as follows: Liquidated t = α + β 1 Return t 1 + β 2 Delaware + β 3 Delaware Return t 1 + β 4 Control Variables where Liquidated t is a dummy variable which is equal to 1 if a fund was liquidated in a specific year. In Lipper TASS database, a fund is moved to graveyard fund database when it stops reporting performance. This can be caused by many reasons, for instance, funds voluntarily stop reporting although the fund is still operating; funds were liquidated; funds were merged to another entity; etc. We define a graveyard fund as liquidated only when it is explicitly reported so. Return t-1 is the lagged annual raw return of the fund. The coefficient of the interaction term Delaware*Return t-1 indicates how being domiciled in Delaware changes the relation between fund performance and survival. Finally, we include a battery of control variables that are similar to the ones used in earlier tables. [Insert Table 5 about here.] Table 5 reports the results of the above probit regressions. Model (1) does not include the interaction term Delaware*Return t-1, while model (2) does. Model (3) is the Heckman 2-stage treatment regression. The coefficients of lagged return are significantly negative in all models, suggesting poor performance is positively associated with higher probability of liquidation. We do not find domiciled in Delaware itself has significant implication for the survival of funds, nevertheless, we do find a significantly negative coefficient on the interaction term Delaware*Return t-1, even after we control for the self-selection of domicile location in model

19 (3). The negative coefficient of the interaction term Delaware*Return t-1 suggests that the negative association between performance and liquidation is even stronger for funds domiciled in Delaware. This is consistent with our earlier finding from Table 4 that investors (fund flows) are more sensitive to the performance of the fund domiciled in Delaware. Poor performance is more likely to trigger capital withdraw and therefore liquidation of funds domiciled in Delaware. 4.4. Funds Risk Choice and Domicile Location The more pronounced flow-performance sensitivity for Delaware hedge funds and the threat of liquidation upon poor performance arguably would give Delaware hedge fund managers stronger motivation to increase the risk of their investments or manipulate their returns when the fund performance is poor. In this section and the section follows, we explore whether Delaware hedge funds are more likely to have such agency problems. There is a substantial body of literature that examines the risk choices of fund managers. Central to this literature is the notion that fund managers may have the incentive to choose investment strategies that markedly increase or decrease portfolio risk, so-called risk-shifting. For instance, it is argued that fund managers might be especially concerned about their performance relative to that of other funds (i.e., tournaments), thereby inducing relatively poor performers to increase risk. The risk-shifting behavior may not necessarily be in the interest of fund investors. Most of the existing works focus on risk shifting of mutual funds with a few exceptions. Brown, Goetzmann, and Park (2001) and Agarwal, Daniel, and Naik (2002) report evidence of tournament behavior among hedge funds. Both Hodder and Jackwerth (2007) and Panageas and Westerfiled (2009) argue that hedge fund managers investment horizon affects their incentives

20 for risk-shifting. That is, fund managers are more likely to increase risk when they have short investment horizon (for instance, when the fund is likely to be liquidated), and when the fund is below its high watermark. Basak, Pavlova, and Shapiro (2007) and Hodder and Jackwerth (2007) further model that managers incentives for risk-shifting can be mitigated if he or she is exposed to some downside risk, either through a personal capital stake in the fund or through management fees based on end-of-period assets. Aragon and Nanda (2012) find empirical evidences consistent with these predictions. Specifically, they show that high watermark provision is less effective in moderating risk shifting following poor performance when the fund is likely to be liquidated. And they show that managers personal capital stake does reduce their incentives for risk-shifting. The stronger flow-performance sensitivity and the survival-performance relationship as documented in the previous sections indicate that Delaware hedge fund managers would have greater incentive to increase risk when the fund performance is poor. In addition, the many different contractual features, including the incentive contracts and the request for managers personal capital, between hedge funds domiciled in Delaware and those domiciled elsewhere would also have implications for fund managers choices of risks. For instance, the higher incentive fee and the more frequent usage of the high watermark provision among Delaware funds indicate less risk-shifting behavior than other funds. In contrast, the lower percentage of Delaware funds requesting managers personal capital suggests the opposite. To empirically examine the relation between fund managers choices of risks and the domicile location of hedge fund, we follow the methodology used in Aragon and Nanda (2012). Specifically, we estimate the following pooled cross-sectional regression:

21 Risk = α + β 1 LagPerf + β 2 Delaware + β 3 LagPerf Delaware + β 4 LagRisk + β 5 Control Variables Our focus is how changes in fund risk between the first and second halves of the year are related to mid-year performance and how these patterns interact with the Domicile location of the hedge fund controlling for the compensation contract and the presence of personal capital request, among others. ΔRisk is the difference between the sample standard deviations of monthly returns in the second and first halves of the year. Similar to Aragon and Nanda (2012), we require that a fund have the full six monthly observations to be included in the estimate of semi-annual standard deviation. LagRisk is the standard deviation of monthly return during the first six months of a specific year. This is included to control for the potential mean reversion in risk changes. For LagPerf, we use both absolute and relative measures of performance of the fund during the first six months. As shown in Table 6, in models (1) (4), we use the buy and hold return of the fund during the first six months. 4 In models (5) (8), we use the fractional rank of the fund s raw return over the first six months relative to other funds during the same period. As found in Aragon and Nanda (2012), incentive fee, high watermark, and the request of personal capital affect hedge fund managers incentives for risk-shifting. Thus we control for these effects by including interaction terms between LagPerf and these three measures. Other control variables include management fees, incentive fees, high watermark, the use of leverage, whether fund managers are requested to invest their personal capital, whether the fund accept managed account, redemption notice period, length of lockup period, whether the fund is registered with the SEC, whether the fund is a master-feeder fund, and the organization form of 4 We also use average monthly return as a robustness check and get qualitatively similar results.

22 the fund. Finally, we employ a number of dummy variables to control for the hedge fund investment strategies and years of performance. [Insert Table 6 about here.] We find a significantly negative coefficient on LagPerf (model (1)), suggesting hedge fund managers in general have a propensity to increase risk following poor performance. As shown in model (2), the coefficient on the interaction term between LagPerf and Incentive fee is significantly negative, while that of the interaction term between LagPerf and High watermark is significantly positive. This finding suggests although asymmetric compensation contract (high incentive fee) strengthens fund managers incentive for risk-shifting given poor performance, the usage of high watermark significantly curb this incentive. This is consistent with the finding in Aragon and Nanda (2012). We do not find the request of personal capital to significantly affect fund managers incentive for risk-shifting. 5 In model (3), we add the interaction term between LagPerf and Delaware, which is our key interest. The coefficient of this interaction term is significantly negative and the coefficients of LagPerf*Incentive fee and LagPerf*HWM continue to be significantly negative and positive, respectively. The negative coefficient of LagPerf*Delaware indicates that hedge funds domiciled in Delaware have stronger incentive to increase risk following poor performance. This cannot be fully explained by the differences in the compensation contract as we discussed above. This result is robust after we control for funds self-selection of Delaware as domicile location 5 Aragon and Nanda (2012) use the amount of requested personal capital. We choose to use the request of personal capital (a dummy variable) as we find the amount of personal capital is missing for many hedge funds.

23 (model (4)). This finding supports the notion that the more pronounced flow-performance relationship for Delware hedge funds has a significant effect on fund managers incentive for risk-shifting. To examine whether Delaware hedge funds are more likely to increase risk following poor relative performance, the so-called tournament behavior, in models (5) (8), we measure fund performance using the fractional rank of the fund s raw returns during the first half year relative to other funds during the same time period. We find similar results for LagPerf, LagPerf*Incentive fee, and LagPerf*HWM. Interestingly, the coefficient on the interaction term LagPerf*Delaware becomes positive, although only marginally significant. This finding indicates Delaware hedge funds exhibit weaker incentives for the tournament behavior. Together, we show hedge funds domiciled in Delaware are more likely to increase risk given poor absolute performance during the first half year. This result holds after controlling for the contractual differences between Delaware hedge funds and non-delaware ones. We conjecture the stronger relation between performance and fund flow as well as survival for Delaware hedge funds likely incentivizes managers to increase risk upon poor performance. 4.5. Return Misreporting and Domicile Location In this section, we address whether Delaware hedge funds are more likely to misreport returns in order to attract investors. Bollen and Pool (2008, 2009) and Agarwal, Daniel, and Naik (2011) show that investors direct more capital to funds that feature a higher number of months with positive returns. Since managerial compensation is a direct function of the quantity of assets under management, some managers may misreport returns in order to attract and maintain their investor base. Incentives arise from flow-performance sensitivity, as investors direct more

24 money into hedge funds that outperform (e.g., Agarwal, Daniel, and Naik, 2004). The stronger flow-performance sensitivity for Delaware hedge funds suggests that Delaware hedge fund managers have greater incentive to misreport returns. On the other hand, lockup and restriction periods act as disciplining mechanisms which allow investors to withdraw their capital in response to hedge fund poor performance. The shorter the lockup and restriction periods, the more quickly investors can withdraw their capital. As documented in Section 3, Delaware hedge funds often have longer lockup period and restriction period, which suggests that Delaware hedge fund managers face less short-term pressure. Therefore, they have smaller incentives to manipulate returns. We utilize the flags developed in Bollen and Pool (2009) and Agarwal, Daniel, and Naik (2011) to empirically examine this issue. According to Bollen and Pool (2009), there is a returns discontinuity in that comparatively fewer hedge fund returns are reported as being zero or marginally negative relative to the frequency of returns reported as marginally positive (Bollen and Pool, 2009). Bollen and Pool define marginally positive and marginally negative by minimizing the mean square error (MSE) using Silverman (1986) approach, and conclude the appropriate bin width is -0.0058 to +0.0058. We use this bin width as the starting point and then assess robustness to alternative bin widths, and explicitly show the results for bin widths of -0.0048 to +0.0048 and - 0.0068 to +0.0068. For each of these alternative bin widths, we define dummy variables equal to one for the (marginally) positive returns, and dummy variables equal to zero for returns that are zero or (marginally) negative. Models (1)-(3) in Table 7 report probit estimates of the probability that returns are reported as marginally positive, as opposed to zero or negative. The explanatory variables include the Delaware dummy, fund size, and a battery of variables that describe fund

25 characteristics, similar to those used in Tables 4 & 5. The coefficients of the Delaware dummy in models (1) (3) are all significantly positive. However, the significance disappears when we apply the Heckman 2-stage treatment regression framework, as shown in model (6). Among the control variables, we find that the probability of return misreporting is positively correlated with incentive fee and the request of personal capital, which makes intuitive sense. When fund managers compensation and personal capital are at risk, they have greater incentives to manipulate returns. Agarwal, Daniel, and Naik (2011) argue that hedge fund managers compensation are determined by their annual performance, which provides incentives for managers to improve performance as the year comes to a close. They show that hedge funds with high incentives and more opportunities (e.g., in-the-money, higher delta, top 20% performers, shorter lockup periods, shorter restriction periods, higher management fees, etc.) to inflate returns show significantly greater December spike the return in December less the average return from January to November after controlling for risk in both the time series and in the cross-section than those with low incentives and less opportunities to inflate returns. To examine whether Delaware hedge fund behave differently in terms of managing their performance at the year end, we modify the approach of Agarwal, Daniel, and Naik (2011) as follows. Return i,m or Residual i,m = α + β 1 Dec + β 2 Delaware + β 3 Dec Delaware + β 4 Control Variables where Return i,m is the monthly return; Residual i,m is the residual estimated with the timeseries regressions using Fung and Hsieh (2004) 7-factor model. Dec is a dummy variable which is set to equal to 1 if the month of return is December and 0 otherwise. Delaware is a dummy

26 variable which is equal to 1 if the fund is domiciled in Delaware and 0 otherwise. Dec Delaware is the interaction term between Dec and Delaware. If Delaware hedge funds are more incentivized to manage their returns at the year end, we would observe a significantly positive coefficient of the interaction term. The control variables are similar to those used in model (1)- (3) with additional control of year dummies. [Insert Table 7 about here.] Models (4) and (5) report the results of the above regressions. The dependent variable in model (4) is the monthly return and the dependent variable in model (5) is the monthly discretionary return or the monthly residual. Models (7) and (8) repeat models (4) and (5) in the Heckman 2-stage regression framework. We find a significantly positive coefficient of the Dec dummy, significantly positive coefficients on management fee, high water mark, lockup period, and redemption period in the regression of returns, confirming the findings in Agarwal, Daniel, and Naik (2011). The coefficient of the interaction term Dec Delaware turns out to be significantly negative, indicating that Delaware hedge funds actually exhibit weaker December spike. This finding is robust under the Heckman 2-stage framework. In the regression of residuals, we do not find significant coefficients for either the Dec dummy or the interaction term Dec Delaware.

27 5. Conclusions and Discussions This paper explored whether the choice of Delaware as domicile location has implication for the contractual structure, fund performance, risk-taking behavior, return misreporting, and fund survival. We expected that investors familiarity with Delaware Law, or lingua franca, would give rise to a more pronounced flow-performance relationship, and a stronger relationship between poor performance and liquidation. Further, we conjectured that hedge fund investors would be more willing to provide Delaware managers with higher fixed and performance fees, longer redemption notice and lock-in periods, due to greater legal familiarity and legal certainty. At the same time, however, we did not expect Delaware hedge funds to outperform other funds due to the fact that legal familiarity should lead to risk-adjusted equalized returns in equilibrium. The Lipper TASS dataset over the 1994-2010 period is highly consistent with these predictions. Delaware hedge funds earn higher management and incentive fees, are more likely to use high watermark provisions, and less likely to be requested to invest their personal capital. Both the redemption notice period and lock up period are significantly longer for Delaware hedge funds. While Delaware hedge funds do not exhibit materially different performance relative to funds domiciled elsewhere, Delaware hedge funds have steeper or more pronounced flow-performance sensitivities, and are more likely to be liquidated in the event of poor performance. These findings are robust to controls for the endogenous nature of funds choosing Delaware as the domicile location. We further show that the stronger flow-performance sensitivities and the different contractual structures of Delaware hedge funds have important implications for two agency conflicts, risk-taking and misreporting. Specifically, we show strong evidence that Delaware

28 hedge funds are more likely to increase risk following poor absolute performance. They are less motivated to manage short-term performance. Future research could examine other ways in which law interacts with finance in the hedge fund industry in the U.S. and around the world. For instance, recent regulatory proposals such as Dodd-Frank and other similar changes around the world might have significant impacts on hedge funds, and these impacts might be different for Delaware funds versus non-delaware funds. As well, it would be worthwhile to compare such effects among countries that are and are not undergoing regulatory changes.

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33 Appendix: Definition of Variables Variables Acceptsm Change in risk Dec Delaware Delaware*lagreturn Delaware*Trank1 Delaware*Trank2 Delaware*Trank3 HWM LagPerf LagPerf*Delaware LagPerf*HWM LagPerf*Incentive LagPerf*PC lagreturn lagreturnsquare lagrisk Leverage LLC lnlagaum lnlockup Located in Delaware LP Master-Feeder Fund PC Registered Trank1 Trank2 Trank3 Yearlyredemp Definition Dummy variable which is equal to 1 if the fund accepts managed account The difference in standard deviation of monthly returns during the second half of the year and that during the first half of the year Dummy variable which is equal to one if the month of return is December Dummy variable which is equal to one if a hedge fund is domiciled in Delaware Interaction of Delaware dummy and lagreturn Interaction of Delaware dummy and Trank1 Interaction of Delaware dummy and Trank2 Interaction of Delaware dummy and Trank3 Dummy variable which is equal to 1 if a highwater mark provision is included Performance of the fund during the first half of the year Interaction of LagPerf and Delaware Interaction of LagPerf and HWM Interaction of LagPerf and Incentive fee Interaction of LagPerf and PC Fund return of the previous month Square term of fund return of the previous month Standard deviation of monthly returns during the first half of the year Dummy variable which is equal to 1 if leverage is used Dummy variable which is equal to one if a hedge fund is organized as limited liability company Natural logarithm of assets under management in the previous month Natural logarith of lock up period Dummy variable which is equal to one if a hedge fund is physically located in Delaware Dummy variable which is equal to one if a hedge fund is organized as limited partnership Dummy variable which is equal to one if a hedge fund is a master-feeder fund Dummy variable which is equal to 1 if managers are requested to invest their personal capital Dummy variable which is equal to 1 if the fund is registered with the SEC Minimum of 1/3 and the fractional rank of fund return in the previous month Minimum of 1/3 and the difference between the fractional rank of fund return in the previoius month and Trank1 Minmum of 1/3 and the difference between the fractional rank of fund return in the previous month and Trank1 and Trank2 Dummy variable which is equal to 1 if the redemption period is annual

34 Table 1 The Business and Domicile Location of Hedge Funds This table reports the domicile location and business location of 1714 U.S. hedge funds during the period from 1994 to 2010. Domiciled in State Located in State Located and Domiciled in State State N Percentage N Percentage N Percentage Delaware 1078 62.89% 75 4.38% 49 4.55% California 106 6.18% 245 14.29% 86 81.13% New York 105 6.13% 516 30.11% 71 67.62% Illinois 46 2.68% 68 3.97% 17 36.96% Texas 34 1.98% 47 2.74% 22 64.71% Connecticut 16 0.93% 135 7.88% 0 0.00% Massachusetts 16 0.93% 62 3.62% 7 43.75% Florida 15 0.88% 48 2.80% 8 53.33% New Jersey 12 0.70% 29 1.69% 5 41.67% Colorado 8 0.47% 24 1.40% 2 25.00% Nevada 8 0.47% 13 0.76% 2 25.00% Virginia 8 0.47% 26 1.52% 5 62.50% Georgia 7 0.41% 22 1.28% 4 57.14% Maryland 7 0.41% 15 0.88% 3 42.86% Pennsylvania 6 0.35% 24 1.40% 5 83.33% Wisconsin 6 0.35% 6 0.35% 3 50.00% Minnesota 4 0.23% 15 0.88% 2 50.00% North Carolina 4 0.23% 12 0.70% 4 100.00% Michigan 3 0.18% 2 0.12% 1 33.33% Washington 3 0.18% 11 0.64% 2 66.67% Arizona 2 0.12% 5 0.29% 0 0.00% Iowa 2 0.12% 2 0.12% 1 50.00% Kentucky 2 0.12% 5 0.29% 1 50.00% Missouri 2 0.12% 9 0.53% 2 100.00% Ohio 2 0.12% 6 0.35% 2 100.00% Wyoming 2 0.12% 3 0.18% 1 50.00% Alabama 1 0.06% 2 0.12% 0 0.00% Alaska 1 0.06% 2 0.12% 0 0.00% Hawaii 1 0.06% 4 0.23% 1 100.00% Idaho 1 0.06% 1 0.06% 0 0.00% Indiana 1 0.06% 2 0.12% 0 0.00% Maine 1 0.06% 0 0.00% 0 0.00% Mississippi 1 0.06% 0 0.00% 0 0.00% Nebraska 1 0.06% 0 0.00% 0 0.00% New Hampshire 1 0.06% 0 0.00% 0 0.00% New Mexico 1 0.06% 0 0.00% 0 0.00% Tennessee 1 0.06% 5 0.29% 0 0.00% Utah 1 0.06% 6 0.35% 1 100.00% Washington DC 1 0.06% 3 0.18% 0 0.00% Other 197 11.49% 264 15.40% 0 0.00% Total number of funds 1714 100.00% 1714 100.00% 307 17.91%

35 Table 2 Summary Statistics We compare various fund characteristics and performance between funds domiciled in Delaware and those domiciled elsewhere. Medians are reported in parenthesis. ***, **, and * denote statistical significance at 1%, 5%, and 10% confidence level, respectively. Delaware Non-Delaware Difference Average Monthly Returns 0.64% 0.54% 0.10% (0.56%) (0.62%) (-0.06%) Average Excess Returns 0.40% 0.20% 0.20% (0.33%) (0.30%) (0.03%) Alpha 0.32% 0.43% -0.11% (0.26%) (0.24%) (0.02%) Management Fee 1.39% 1.27% 0.12% *** (1.50%) (1.00%) (0.50%) *** Incentive Fee 19.20% 17.75% 1.45% *** (20.00%) (20.00%) (0.00%) *** High Watermark 82.20% 42.27% 39.93% *** Min. Investment ($M) 1.3 5.7-4.4 ** (1.0) (0.5) (0.5) *** Use Leverage 60.58% 61.95% -1.37% Maximum Leverage 108.92% 72.15% 36.77% *** Average Leverage 59.83% 42% 17.46% *** Require Personal Capital 36.92% 55.82% -18.90% *** Investing Managed Account 0.83% 2.20% -1.37% ** Accept Managed Account 26.53% 44.18% -17.65% *** Redemption Notice (Days) 43.81 27.7 16.11 *** (45.0) (30.0) (15.0) *** Lockup Period (Months) 6.38 3.44 2.94 *** (3.0) (0.0) (3.0) *** Registered with SEC 12.15% 7.39% 4.76% *** Master-Feeder Fund 47.59% 28.62% 18.97% *** N of Observations 1078 636

36 Table 3 Why Hedge Funds Choose to Domicile in Delaware? This table reports the probit regression on why some funds choose Delaware as their domicile location. The dependent variable is equal to one if the fund is domiciled in Delaware and zero if not. The independent variables include a set of dummy variables indicating whether the fund is physically located in Delaware, whether the fund is a master-feeder fund, whether the fund is LP or LLC, the inception year of the fund, and fund investment strategies. For fund inception year dummies, we only report those that are significant. Standard errors are reported in parenthesis. ***, **, and * denote statistical significance at 1%, 5%, and 10% confidence level, respectively. VARIABLES Domiciled in Delaware Located in Delaware 0.2169 (0.1707) Master-Feeder Fund 0.4831*** (0.0721) LP 0.6026*** (0.1603) LLC 0.7335*** (0.1877) Inception Year Dummies Yes 2008 1.1802*** (0.3920) 2007 1.034*** (0.3638) 2006 1.3418*** (0.3458) 2005 1.6144*** (0.3372) 2004 1.7963*** (0.3375) 2003 1.2068*** (0.3223) 2002 1.5702*** (0.3332) 2001 1.4965*** (0.3332) 2000 1.2957*** (0.3321) 1999 0.6968** (0.3260) 1998 0.5993* (0.3291) Year dummies before 1998 Not Significant Investment Strategy Dummies Yes N 1714 Pseudo R-square 20.72%

37 Table 4 Flow-Performance Relation and Domicile Location In this table, we examine whether the flow-performance relation is affected by the fund s domicile location. Our dependent variable is the monthly fund flow. The independent variables include measure of performance, its square term, the interaction term between Delaware and measure of performance. In models (1), (2), (4)-(6) we measure fund performance as its lagged monthly return. In model (3) we use the fractional rank to measure fund performance. Model (6) is the Heckman 2-stage treatment regression whether the first stage regression is reported in Table 3. We control for other fund specific factors that are important in hedge fund flows, including management fees, incentive fees, high watermark, fund size, the use of leverage, whether fund managers are requested to invest their personal capital, whether the fund accept managed account, redemption notice period, length of lockup period, whether the fund is registered with the SEC, whether the fund is a master-feeder fund, and the organization form of the fund. Finally, we employ a number of dummy variables to control for the hedge fund investment strategies. Please refer to Appendix for the detailed definitions of these variables. Standard errors are reported in parenthesis. ***, **, and * denote statistical significance at 1%, 5%, and 10% confidence level, respectively. DV: Monthly Fund Flow VARIABLES (1) (2) (3) (4) (5) (6) All Hedge Funds Liquidated HFs Live HFs 2-stage Treatment Regression LagReturn 0.0489*** 0.0546*** 0.0344*** 0.0874*** 0.0547*** (0.0097) (0.0101) (0.0112) (0.0192) (0.0101) LagReturnSsquare -0.0003** -.0003** (0.0001) (0.0001) Delaware*LagReturn 0.0809*** 0.0766*** 0.0836*** 0.0615** 0.0769*** (0.0146) (0.0147) (0.0173) (0.0274) (0.0147) Trank1 1.2214 (0.9231) Trank2 1.7835** (0.8287) Trank3-2.9352*** (0.9513) Delaware*Trank1-0.1608 (1.1862) Delaware*Trank2-0.0047 (1.0350) Delaware*Trank3 1.2303 (1.2037) Delaware 0.2705*** 0.2679*** 0.2799 0.3335*** 0.1224 1.068*** (0.0983) (0.0983) (0.2638) (0.1189) (0.1815) (0.3023) lnlagaum -0.2680*** -0.2708*** -0.2720*** -0.2482*** -0.3518*** -0.2558*** (0.0257) (0.0258) (0.0259) (0.0304) (0.0502) (0.0263) Management fee -0.0982-0.0933-0.0875-0.0327-0.2839** -0.1083 (0.0703) (0.0704) (0.0705) (0.0866) (0.1254) (0.0705) Incentive fee 0.0280*** 0.0282*** 0.0285*** 0.0418*** -0.0082 0.0266*** (0.0082) (0.0082) (0.0082) (0.0104) (0.0147) (0.0083) HWM 0.3738*** 0.3696*** 0.3748*** 0.1715 0.3529* 0.3183*** (0.1083) (0.1083) (0.1084) (0.1378) (0.1882) (0.1098) Leverage -0.0746-0.0710-0.0382-0.1771 0.3045* -0.0732

38 (0.0936) (0.0936) (0.0938) (0.1117) (0.1803) (0.0936) PC 0.1063 0.1036 0.0990 0.0431 0.1132 0.1475 (0.0976) (0.0977) (0.0978) (0.1168) (0.1875) (0.0989) Acceptsm -0.0054-0.0060-0.0043-0.0818-0.0376-0.0004 (0.1009) (0.1009) (0.1010) (0.1217) (0.1923) (0.1009) lnlockup -0.0042-0.0040 0.0024-0.0068-0.0650-0.0182 (0.0380) (0.0380) (0.0381) (0.0450) (0.0752) (0.0384) Yearlyredemp 0.3215** 0.3246** 0.3451** 0.2016 0.2884 0.3625** (0.1429) (0.1429) (0.1432) (0.1637) (0.3125) (0.1435) Registered 0.7218*** 0.7229*** 0.7231*** 0.6580*** 0.9342*** 0.6629*** (0.1398) (0.1398) (0.1399) (0.1625) (0.2863) (0.1414) Master-Feeder Fund -0.2531*** -0.2552*** -0.2660*** -0.0987-0.3964** -0.3692*** (0.0892) (0.0892) (0.0894) (0.1088) (0.1622) (0.0981) LLC -0.4760** -0.4759** -0.4702** -0.3505-0.8956** -0.7363*** (0.1996) (0.1996) (0.1999) (0.2406) (0.3942) (0.2671) LP -0.4337* -0.4344* -0.4297* -0.4942* -0.4050-0.6232*** (0.2444) (0.2444) (0.2447) (0.2996) (0.4617) (0.2065) Constant 4.8230*** 4.8700*** 4.3823*** 4.3329*** 7.0164*** 4.1983*** (0.6195) (0.6199) (0.6444) (0.7424) (1.1748) (0.6647) Investment Strategy Dummies Yes Yes Yes Yes Yes Yes First Stage (Delaware=1) Yes Lamdda -.5230*** (0.1868) Observations 71,603 71,603 71,603 22,633 48,970 71,603 Adjusted R-squared 0.0053 0.0054 0.0035 0.0045 0.0080 Wald Chi-Square 4128.43 Prob > chi2 0.000

39 Table 5 Fund Survival and Domicile Location In this table, we study the relation between funds domicile location and their survival. Our dependent variable is a dummy that is equal to 1 if the fund is liquidated in a specific year. Our key independent variables are Lag Annual Return, Delaware, and their interaction term Delaware*Lag Annual Return. Please refer to Appendix for the detailed definitions of the variables. Standard errors are reported in parenthesis. ***, **, and * denote statistical significance at 1%, 5%, and 10% confidence level, respectively. VARIABLES DV: Dummy=1 if the fund is liquidated (1) (2) (3) 2-stage treatment regression Lag Annual Return -0.0056*** -0.0033** -0.0003** (0.0011) (0.0015) (0.0002) Delaware*Lag Annual Return -0.0048** -0.0005** (0.0021) (0.0002) Delaware 0.0416 0.0696 0.1153*** (0.0594) (0.0607) (0.0227) Lnlagaum -0.0864*** -0.0858*** -0.0091*** (0.0149) (0.0149) (0.0019) Management fee -0.0205-0.0192-0.0037 (0.0407) (0.0406) (0.0052) Incentive fee 0.0000 0.0001-0.0004 (0.0048) (0.0048) (0.0006) HWM -0.1205* -0.1177* -0.0219*** (0.0637) (0.0638) (0.0080) Leverage 0.0437 0.0459 0.0057 (0.0561) (0.0562) (0.0069) PC -0.0603-0.0588-0.0013 (0.0595) (0.0596) (0.0073) Acceptsm -0.1018* -0.0993-0.0126* (0.0612) (0.0612) (0.0074) Lnlockup -0.0287-0.0273-0.0055** (0.0235) (0.0235) (0.0028) Yearlyredemp -0.2440** -0.2448** -0.0159 (0.1014) (0.1016) (0.0102) Registered -0.0022-0.0047-0.0099 (0.0862) (0.0863) (0.0106) Master-feeder Fund 0.1024* 0.1015* -0.0023 (0.0532) (0.0533) (0.0074) LLC 0.0997 0.0930-0.0259 (0.1364) (0.1365) (0.0203) LP -0.1372-0.1439-0.0395** (0.1147) (0.1147) (0.0156) Constant 0.6208* 0.5944* 0.2642*** (0.3472) (0.3479) (0.0493) Investment Strategy Dummies Yes Yes Yes First Stage (Delaware=1) Yes

Lambda -0.0701*** (0.0140) Observations 6,126 6,126 6,126 Pseudo R-squared 0.0473 0.0490 Ward Chi-square 478.33 Prob > chi2 0.000 40

41 Table 6 Risk Shifting and Domicile Location In this table, we examine whether domicile location has implication for funds risk shifting behavior. Our dependent variable is the change in risk between the second half year and the first half year. Our key independent variables include LagPerf, and the interaction terms between LagPerf and HWM, Incentive Fee, PC, and Delaware. In Models (1)-(4), LagPerf is measured as the raw return of the fund during the first six months of a specific year; in Models (5)-(8), we measure LagPerf based on the fractional rank of the fund s raw returns during the first six months relative to other funds during the same time period. Models (4) and (8) adopt the Heckman 2-stage treatment regression framework whether the first stage regression is as reported in Table 3. Please refer to Appendix for the detailed definitions of the variables. Standard errors are reported in parenthesis. ***, **, and * denote statistical significance at 1%, 5%, and 10% confidence level, respectively. VARIABLES DV: Change in Risk Raw Returns Fractional Rank (1) (2) (3) (4) (5) (6) (7) (8) 2-stage 2-stage treatment treatment regression regression LagPerf -0.0319*** -0.0010 0.0002-0.0003-0.7336*** 0.8067* 0.6387 0.6056 (0.0029) (0.0111) (0.0111) (0.0111) (0.1372) (0.4899) (0.4954) (0.4948) Lagperf*HWM 0.0427*** 0.0441*** 0.0442*** 0.8458*** 0.6803** 0.6889** (0.0059) (0.0059) (0.0059) (0.2903) (0.3007) (0.2997) Lagperf*Incentive Fee -0.0194*** -0.0184*** -0.0183*** -0.7403*** -0.7523*** -0.7429*** (0.0039) (0.0039) (0.0038) (0.1658) (0.1661) (0.1658) Lagperf*PC -0.0004 0.0003 0.0005-0.0098-0.0083-0.0093 (0.0057) (0.0057) (0.0057) (0.0174) (0.0174) (0.0173) Lagperf*Delaware -0.0111** -0.0111** 0.5277* 0.5264* (0.0055) (0.0054) (0.2866) (0.2855) LagRisk -0.4485*** -0.4512*** -0.4520*** -0.4520*** -0.4707*** -0.4780*** -0.4812*** -0.4812*** (0.0103) (0.0103) (0.0104) (0.0103) (0.0101) (0.0102) (0.0102) (0.0101) Management fee 0.2408*** 0.2690*** 0.2665*** 0.2544*** 0.2832*** 0.2810*** (0.0661) (0.0663) (0.0661) (0.0669) (0.0671) (0.0669) Incentive fee 0.0085 0.0090 0.0085 0.0405*** 0.0428*** 0.0418*** (0.0081) (0.0081) (0.0081) (0.0130) (0.0130) (0.0130) HWM -0.3329*** -0.2910*** -0.2994*** -0.4976*** -0.3660** -0.3767** (0.1053) (0.1058) (0.1056) (0.1752) (0.1802) (0.1798) Leverage 0.2898*** 0.3160*** 0.3126*** 0.2918*** 0.3123*** 0.3098***

42 (0.0879) (0.0880) (0.0877) (0.0891) (0.0892) (0.0889) PC -0.0007-0.0456-0.0319 0.0043-0.0382-0.0240 (0.0986) (0.0990) (0.0994) (0.0969) (0.0973) (0.0981) Acceptsm -0.1072-0.1175-0.1173-0.0816-0.0925-0.0925 (0.0945) (0.0944) (0.0941) (0.0957) (0.0956) (0.0953) lnlockup 0.0369 0.0385 0.0357 0.0446 0.0439 0.0416 (0.0357) (0.0356) (0.0356) (0.0362) (0.0362) (0.0361) Yearlyredemp 0.1398 0.1655 0.1797 0.1377 0.1606 0.1733 (0.1239) (0.1239) (0.1240) (0.1254) (0.1254) (0.1256) Registered 0.0502 0.0601 0.0480 0.0650 0.0682 0.0596 (0.1357) (0.1355) (0.1355) (0.1406) (0.1404) (0.1402) Master-Feeder Fund -0.1784** -0.1328-0.1797* -0.1867** -0.1422* -0.1832* (0.0832) (0.0838) (0.0933) (0.0842) (0.0848) (0.0945) LLC -0.1022 0.0135-0.1031-0.0861 0.0444-0.0571 (0.2221) (0.2236) (0.2456) (0.2245) (0.2261) (0.2485) LP -0.0219 0.0540-0.0124 0.0180 0.1017 0.0438 (0.1744) (0.1751) (0.1842) (0.1763) (0.1771) (0.1864) Delaware -0.3213*** 0.0218-0.6541*** -0.3549 (0.0980) (0.3181) (0.1724) (0.3525) Constant 0.8497** 0.6015 0.7512 0.5183 1.2745*** 0.4758 0.7130 0.5224 (0.4112) (0.4704) (0.4718) (0.5133) (0.4207) (0.5172) (0.5192) (0.5534) Investment Strategy Dummies Yes Yes Yes Yes Yes Yes Yes Yes Year Dummies Yes Yes Yes Yes Yes Yes Yes Yes First Stage (Delaware=1) Yes Yes Lambda -0.2178-0.1895 (0.1922) (0.1950) Observations 6,488 6,488 6,488 6488 6,488 6,488 6,488 6,488 Adjusted R-squared 0.3358 0.3454 0.3474 0.3261 0.3306 0.3325 Ward Chi-Square 3843.18 3619.95 Prob > chi2 0.000 0.000

43 Table 7 Return Misreporting and Domicile Location This table examines the relation between the fund s domicile location and its incentive for misreporting returns. In models (1)-(3), the dependent variable is equal to 1 if the return is marginally positive, 0 otherwise. Models (1)-(3) repeat the probit regressions for three bin width [-0.0058, 0.0058], [-0.0048, 0.0048], and [- 0.0068, 0.0068], respectively. The dependent variable in model (4) is the monthly return. The dependent variable in model (5) is the monthly discretionary return, which is the residual estimated from the time-series regressions using Fung and Hsieh (2004) 7-factor model. Models (6)-(7)) report the results from the Heckman 2-stage treatment regressions. Please refer to Appendix for the detailed definitions of the variables. Standard errors are reported in parenthesis. ***, **, and * denote statistical significance at 1%, 5%, and 10% confidence level, respectively. VARIABLES (1) (2) (3) (4) (5) (6) (7) (8) BP Approach AND Approach 2-Stage Treatment Regression Bin Width: Bin Width: Monthly Bin Width: [- [-0.0048, [-0.0068, Monthly Discretionary 0.0058, Monthly 0.0048] 0.0068] Return Return 0.0058] Return Bin Width: [-0.0058, 0.0058] Monthly Discretionary Return Dec 1.1384*** 0.0330 1.1396*** 0.0288 (0.1271) (0.1029) (0.1270) (0.1028) Delaware*Dec -0.3151** 0.1853-0.3151** 0.1854 (0.1605) (0.1284) (0.1604) (0.1283) Delaware 0.0763*** 0.0774*** 0.0601** 0.1172** 0.0735* 0.0210-0.0067 0.4901*** (0.0268) (0.0296) (0.0249) (0.0519) (0.0411) (0.0286) (0.1721) (0.1349) LnAUM 0.0295*** 0.0257*** 0.0280*** -0.0411*** -0.0767*** 0.0106*** -0.0434*** -0.0679*** (0.0066) (0.0073) (0.0062) (0.0129) (0.0105) (0.0025) (0.0132) (0.0108) Management fee -0.0300-0.0261-0.0207 0.0943*** 0.0416-0.0158** 0.0954*** 0.0349 (0.0199) (0.0219) (0.0184) (0.0356) (0.0290) (0.0076) (0.0356) (0.0291) Incentive Fee 0.0058*** 0.0062*** 0.0054*** 0.0069* -0.0002 0.0022*** 0.0071* -0.0009 (0.0021) (0.0023) (0.0019) (0.0042) (0.0034) (0.0008) (0.0042) (0.0033) HWM 0.0028-0.0118 0.0079 0.2718*** 0.1368*** -0.0049 0.2751*** 0.1254*** (0.0286) (0.0315) (0.0266) (0.0559) (0.0447) (0.0107) (0.0560) (0.0447) Leverage -0.0594** -0.0482* -0.0528** 0.0198-0.0365-0.0214** 0.0208-0.0403 (0.0238) (0.0262) (0.0222) (0.0472) (0.0370) (0.0090) (0.0471) (0.0370) PC 0.0554** 0.0690** 0.0623*** -0.0223-0.0446 0.0222** -0.0272-0.0259 (0.0251) (0.0277) (0.0233) (0.0494) (0.0389) (0.0096) (0.0497) (0.0393) Yearlyredemp -0.0668-0.0608-0.1008*** 0.1339* 0.0328-0.0219 0.1297* 0.0460 (0.0409) (0.0451) (0.0377) (0.0720) (0.0556) (0.0154) (0.0721) (0.0556) Lnlockup -0.0002-0.0052 0.0033 0.0577*** 0.0298** -0.0010 0.0590*** 0.0255*

44 (0.0097) (0.0106) (0.0090) (0.0192) (0.0151) (0.0037) (0.0193) (0.0151) Acceptsm -0.0167-0.0199-0.0162-0.0630-0.0213-0.0071-0.0635-0.0185 (0.0262) (0.0287) (0.0244) (0.0508) (0.0401) (0.0099) (0.0508) (0.0401) Registered -0.0304-0.0283-0.0075 0.1588** 0.1182** -0.0171 0.1644** 0.0959* (0.0361) (0.0398) (0.0338) (0.0710) (0.0570) (0.0138) (0.0713) (0.0574) Master--Feeder Fund 0.0337 0.0402 0.0312 0.0102 0.0261 0.0121 0.0271-0.0256 (0.0229) (0.0251) (0.0213) (0.0449) (0.0353) (0.0093) (0.0502) (0.0387) LLC -0.0694-0.0870-0.0375-0.0674 0.0613-0.0279-0.0242-0.0033 (0.0624) (0.0678) (0.0580) (0.1234) (0.0633) (0.0272) (0.1359) (0.0663) LP 0.0199-0.0023 0.0239-0.0914 0.0119-0.0682 (0.0533) (0.0577) (0.0493) (0.1006) (0.0219) (0.1051) Constant -0.0258 0.0936 0.0310 0.6888** 1.2469*** 0.5149 0.7728 0.8775 (0.1747) (0.1920) (0.1629) (0.3412) (0.2560) (0.0608) (0.3587) (0.2801) Investment Strategy Dummies Yes Yes Yes Yes Yes Yes Yes Yes Year Dummies Yes Yes Yes Yes Yes Yes Yes Month Dummies Yes Yes Yes First Stage (Delaware=1) Lambda 0.0024 0.0787-0.2654*** (0.0175) (0.1043) (0.0818) Observations 13,645 11,279 15,848 74,789 70,334 13,645 74,789 70334 Pseudo R-squared 0.0193 0.0164 0.0210 0.0193 0.0048 Wald Chi-square 1279.34 5358.64 2936.50 Prob > chi2 0.000 0.000 0.000