Electronic copy available at: http://ssrn.com/abstract=967788

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Onshore and Offshore Hedge Funds: Are They Twins?

George Aragon

Arizona State University

Bing Liang University of Massachusetts Amherst

Hyuna Park

Minnesota State University*

First Draft: February 26, 2006 This Version: January 27, 2011

Abstract

Contrary to offshore hedge funds, US-registered (“onshore”) funds are subject to strict marketing prohibitions, accredited investor requirements, limited number of investors, and tax disadvantage. We exploit this difference to test predictions about organizational design, capital flow, and fund performance. We find that onshore funds impose stronger share restrictions such as a lockup provision than offshore funds, but hold more liquid assets. Our results show that capital flows are less sensitive to past performance in onshore funds than in offshore funds due to regulation on advertising, and the flow sensitivity difference affects performance. Liquidity-adjusted alpha is positive and significant (0.94% per month) only for stand-alone onshore funds that have not been affected by strong capital flows from offshore investors through a master-feeder structure.

Key words: offshore hedge funds, lock-up provision, liquidity risk, master-feeder structure                                                        * George Aragon is at W.P. Carey School of Business, Arizona State University, Tempe, AZ 85287-3406, phone: (480) 965-5810, e-mail: George.Aragon@asu.edu. Bing Liang is at Isenberg School of Management, University of Massachusetts, 121 Presidents Drive, Amherst, MA 01003-9310, phone: (413) 545-3180, e-mail: bliang@som.umass.edu. Hyuna Park is at the College of Business, Minnesota State University, Mankato, 150 Morris Hall, Mankato, MN 56001, phone: (507) 389-5406, e-mail: hyuna.park@mnsu.edu. Previous versions of this paper were circulated under the title of “Share Restrictions, Liquidity Premium, and Offshore Hedge Funds”. We are grateful for comments from Turan Bali, Arnoud Boot, Stephen Brown, Tom Fraser, Mila Getmansky, Hossein Kazemi, Bernard Morzuch, Joseph Reising, Tom Schneeweis, Paula Tkac, Mingming Zhou, and participants at the 2007 FMA annual meeting, the 2007 China International Conference in Finance (CICF), the Center for International Securities and Derivatives Markets (CISDM) 2007 annual research conference, the 2008 Financial Intermediation Research Society (FIRS) Conference, and the seminar participants at the University of Amsterdam, Binghamton University, Koc University, University of Massachusetts Amherst, and Minnesota State University Mankato. We are responsible for any error.

Electronic copy available at: http://ssrn.com/abstract=967788

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JEL classification: G11, G12, G23, G32

Electronic copy available at: http://ssrn.com/abstract=967788

3

Onshore and Offshore Hedge Funds: Are They Twins?

Abstract

Contrary to offshore hedge funds, US-registered (“onshore”) funds are subject to strict marketing prohibitions, accredited investor requirements, limited number of investors, and tax disadvantage. We exploit this difference to test predictions about organizational design, capital flow, and fund performance. We find that onshore funds impose stronger share restrictions such as a lockup provision than offshore funds, but hold more liquid assets. Our results show that capital flows are less sensitive to past performance in onshore funds than in offshore funds due to regulation on advertising, and the flow sensitivity difference affects performance. Liquidity-adjusted alpha is positive and significant (0.94% per month) only for stand-alone onshore funds that have not been affected by strong capital flows from offshore investors through a master-feeder structure.

Key words: offshore hedge funds, lock-up provision, liquidity risk, master-feeder structure

JEL classification: G11, G12, G23, G32

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1. Introduction

The hedge fund industry has grown rapidly in the last decade. In particular, offshore

funds registered in low-tax jurisdictions such as the Cayman Islands and British Virgin Islands

have grown much faster than US-registered (“onshore”) funds (27.2% vs. 15.2% per annum

during 1994-2008 in terms of total assets under management (AUM) according to the Lipper

TASS hedge fund data). As of December 2008, 64.3% of the total assets are managed by

offshore funds, while onshore funds manage only 22.5% of the total assets. In contrast, the

proportion was 20.3% offshore vs. 31.6% onshore in December 1993.

The fast growth of offshore funds can be explained by capital flows from institutional

investors who increased allocation to alternative investments after the U.S. equity market

downturn in the early 2000s. Agarwal and Naik (2005) report that there has been a shift in the

type of hedge fund investor: in the early 1990s, the typical hedge fund investor was a high net-

worth U.S. individual investor (who needs onshore funds), but today the typical investor is an

institutional investor (who prefers offshore funds due to tax reasons).1

With the rapid growth of the industry, hedge fund research has also proliferated.

Researchers have examined whether this growth is accompanied by positive risk-adjusted

performance. Some studies find that top-performing hedge funds can consistently deliver alpha,

and their performance cannot be explained by luck (Fung and Hsieh (1997, 2004), Ackermann et

                                                       1 LePree (2008) shows that U.S. tax-exempt institutional investors such as endowments and pension funds prefer offshore funds to onshore funds in order to avoid unrelated business income tax (UBIT). As offshore hedge funds are corporations, income from leveraged strategies can be converted into dividends and avoid UBIT. However, onshore hedge funds cannot provide such benefits to institutional investors because they are pass-through entities such as a limited partnership in order to avoid double taxation in the United States (McCrary (2002)). See Section 3.4 and Table 1 for details on the legal structural difference between onshore and offshore hedge funds.

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al. (1999), Brown et al. (1999), Agarwal and Naik (2004), Kosowski et al. (2007), Fung et al.

(2008), and Jagannathan et al. (2010)).2

Despite the growing importance of offshore funds, most studies treat onshore and

offshore hedge funds as a monolithic group.3 However, onshore hedge funds face regulatory

constraints on capital formation that do not apply to offshore funds. Specifically, most onshore

hedge funds rely upon a regulatory exemption pursuant to either Section 3(c)(1) or Section

3(c)(7) of the Investment Company Act of 1940.4 The exemptions impose restrictions on the

number and type of investors, and also the manager’s ability to advertise the sale of securities to

potential investors. In contrast, offshore funds are not constrained with respect to both the level

of investor capital in the fund and the flow of new capital into the fund.

Existing research suggests that constraints on raising capital could significantly affect

fund performance, investor flows, and organizational design. In this paper, we examine these

differences using a large sample of onshore and offshore hedge funds over the time period of

1994-2005. We report several new empirical findings. First, we find that onshore funds impose

tighter restrictions on investor redemptions, including longer lockup periods, higher minimum

investment, less frequent funding cycles, and longer redemption notice periods than offshore

funds. All the differences are statistically significant at the 1 percent level.

Second, we find that onshore funds manage assets with higher liquidity and lower

liquidity risk than offshore funds on average. The evidence suggests that share restrictions are

more likely when equity funding is restricted, because onshore funds cannot advertise their

                                                       2 Recent research also finds positive alphas of hedge funds can be interpreted as a compensation for holding illiquid fund shares. See, e.g, Liang (1999), Aragon (2007), Bali, Gokcan and Liang (2007), Ding et al. (2009). 3 As commercial hedge fund databases such as TASS provide a combined data set of onshore and offshore hedge funds, most previous studies do not differentiate offshore funds from onshore funds in their research. One exception is Brown, Goetzmann, and Ibbotson (1999) who study offshore fund performance and attrition specifically by using data from the U.S. Offshore Funds Directory. 4 Among the 1,524 onshore hedge funds in our data, 1,400 funds (91.86%) are exempt from registration and only 124 funds are registered investment advisers.

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performance and must limit the number of accounts. Our findings also suggest that offshore

funds can more efficiently manage illiquid assets, because they face lower equity funding risk

than their onshore counterparts.

Third, we find that onshore funds are associated with lower assets under management and

a significantly lower sensitivity of investor flows to past performance. In addition, the reduced

flow/performance sensitivity is more pronounced following lagged positive performance, where

advertising constraints are more likely to bind. Overall the evidence supports our key identifying

assumption that the exemption requirements faced by onshore funds constrain the amount and

flow of investor capital under management.

Finally, we find that alpha is positive and significant only for stand-alone onshore funds

(0.94% per month). In contrast, the average alpha of either offshore funds or onshore funds that

are part of a master-feeder (MF) structure is insignificant. The offshore MF vehicle is a means to

raise additional capital from non-U.S. investors and U.S. institutional investors.5 We interpret

this finding as consistent with the predictions of Berk and Green (2004, hereafter BG), Pastor

and Stambaugh (2010, hereafter PS), and Goetzmann, Ingersoll, and Ross (2003). Specifically,

hedge fund managers who set up the MF structure to accept capital flows from both onshore and

offshore investors lose their ability to deliver alpha due to decreasing returns to scale, while

those who operate within the capacity limit of their strategy as a stand-alone (SA) onshore fund

could have delivered high risk-adjusted performance.

Previous research finds that mutual fund investors chase performance but performance is

not persistent. To explain this seemingly puzzling flow-performance relationship, BG (2004)

builds a theoretical model in which investor capital is supplied competitively as investors update

                                                       5 See Section 3.1 for the details on how a hedge fund manager can use the MF structure to attract capital from both onshore and offshore investors.

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their beliefs about manager ability. A central prediction of their model is that investors are

sensitive to past returns and supply capital such that excess returns are zero on average.

Consistent with BG (2004), Fung et al. (2008) find that the inflow of new capital to better

performing funds-of-funds leads to the erosion of superior performance over time.

Recently, PS (2010) extends the BG model by assuming that the decreasing returns to

scale happens at the entire industry level instead of the individual fund level while investors face

endogeneity and disinvest less in the poor performing industry. An important difference between

these two models is, alpha can be positive in the PS model, but zero alpha is a necessary

condition for equilibrium in the BG model. One caveat is that these models assume no incentive

fee because they are developed to explain the flow-performance relation in mutual funds.

However, incentive fee is an important part of the compensation package for hedge fund

managers.6

Our results are supportive of the competitive markets view for our sample of hedge

funds. In particular, we find no evidence of positive alpha on average among offshore hedge

funds, where the premise of competitive supply of capital is more likely to be satisfied. In

addition, we reach a similar finding for US-registered managers that also manage offshore

accounts as part of a MF structure. Apparently, capital flows from offshore investors reduce a

manager’s ability to deliver alpha due to decreasing returns to scale. In fact, we find positive

alpha only among stand-alone onshore funds, precisely where capital levels and flows are most

constrained.

We interpret the positive alpha observed only in stand-alone onshore funds as evidence of

successful funds’ unwillingness to accept new money due to a fixed amount of arbitrage profits

in capital markets and the fee structure of hedge funds. Goetzmann, Ingersoll, and Ross (2003)                                                        6 See Table 1 Panel C for the summary statistics of hedge fund incentive fee. 

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find that hedge funds with superior performance do not tend to sell new shares and experience

net share repurchases in contrast to similar studies in the mutual fund industry. They conjecture

that the option-like incentive fee structure of hedge funds exist because managers cannot trade on

superior performance to increase compensation through growth.7 Consistent with this

interpretation, we find that stand-alone hedge funds are closed to new investments more often

than MF hedge funds (13.04% vs. 9.28%).

To our knowledge, this is the first paper that analyzes the impact of regulatory

environment on share restrictions, capital flow, and performance of onshore and offshore hedge

funds by separating stand-alone funds from master-feeder funds. We are also the first to analyze

the risk-adjusted performance of hedge funds after controlling all three types of liquidity risk:

market liquidity (a systematic risk factor as in Sadka (2010)), asset liquidity (idiosyncratic

illiquidity that causes serial correlation as in Getmansky, Lo, and Makarov (2004)), and share

liquidity (share restrictions as in Aragon (2007)).8

The rest of this paper is organized as follows: Section 2 develops testable hypotheses, and

Section 3 describes data and summary statistics. Section 4 presents empirical results and Section

5 provides robustness check. Finally, Section 6 concludes.

                                                       7 Strasburg and Gongloff (2010) report an example of a successful hedge fund manager who has been closed to new investments for more than a decade, and returns capital to investors because he believes that an “enormous amount of capital” has a negative impact on his long-term track record. 8 We distinguish asset liquidity from share liquidity, while previous research assumes a positive relation between share restrictions and asset illiquidity and uses share restrictions such as a lockup provision as a proxy for asset illiquidity. See Section 3.2, 3.3, and 4.4 for details on how we control market liquidity, asset liquidity, and share liquidity when measuring risk-adjusted performance of hedge funds.

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2. Background and Testable Hypotheses

In this section we discuss the regulatory framework motivating our empirical work. We

also discuss related literature to develop the specific testable hypotheses that are tested later in

the paper.

2.1. Regulatory Framework

Although a typical public investment company in the United States is required to be

registered with the US Securities and Exchange Commission (SEC), hedge funds are largely

exempted from the Investment Company Act of 1940 mainly due to the legal structure and the

special types of investors. Normally, US-registered hedge funds are organized as partnerships

with limited and qualified investors from wealthy individuals or institutions, who are deemed to

be financially sophisticated and have little need for protection by government regulations.

In particular, under Sections 3(c)1 and 3(c)7 of the Investment Company Act of 1940,

3(c)1 hedge funds with no more than 100 accredited investors and 3(c)7 hedge funds with

unlimited number of “qualified purchasers” are exempted, respectively9. Hedge funds are also

exempted from registering their securities by the Securities Act of 1933 if they do not seek

funding from the general public. Finally, the Securities Exchange Act of 1934 requires hedge

funds with more than 499 investors to report on a quarterly basis so a 3(c)7 fund can effectively

avoid quarterly reporting by having 499 investors or less.

                                                       9 An accredited investor must meet at least one of the following requirements: 1) earn an individual income of more than $200,000 per year, or a joint income with spouse of $300,000, in each of the last two years and expect to reasonably maintain the same level of income, 2) have a net worth exceeding $1 million, either individually or jointly with spouse, and 3) be a general partner, executive officer, director or a related combination thereof for the issuer of a security being offered. A qualified investor is an individual or institution with at least $5 million in assets to invest with.

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Therefore, the US-registered onshore funds in general do not seek funds from the general

public and have less than 100 or 499 investors depending on whether they are a 3(c)1 or 3(c)7

fund. Under these restrictions, managers will impose stricter share restrictions than their offshore

counterparts in order to keep investor capital and deter redemptions. Based on the above

observations, we develop the following testable hypotheses to capture the fundamental difference

between onshore funds and offshore funds.

2.2. Testable Hypotheses

The first hypothesis relies on the idea that equity capital represents an important source of

funding for hedge fund managers, and their capital withdrawals can reduce fund performance

when investment opportunities are favorable. For example, Shleifer and Vishny (1997) argue that

when arbitrage requires capital, arbitrageurs can become most constrained when they have the

best opportunities, because fund investors are informationally disadvantaged about the fund’s

strategy. Hedge funds can manage their risk by limiting the size of redemptions and/or replacing

exiting capital. However, since onshore funds face regulatory constraints on the number of

accounts and the ability to issue new shares, we expect these funds to more often impose

redemption restrictions, like lockups and notice periods, to reduce the size of redemptions.10

H1 [Share Restriction Hypothesis]: Due to regulatory requirements on the number of investors

and the way to offer fund shares, onshore funds impose greater share restrictions than their

offshore counterparts: they impose longer lockup periods, less frequent redemption and

                                                       10 The role of lockups in managing redemptions in open-ended funds has been examined by Chordia (1996), Nanda, Narayanan, and Warther (2000), Lerner and Schoar (2004), and Aragon (2007). The prevailing mechanism is that lockups allow funds to attract investors that are less likely to experience a liquidity shock, and therefore have a longer investment horizon. A similar mechanism drives the equilibrium described by Amihud and Mendelson (1986).

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subscription periods, larger initial investment requirements, in order to attract long-term

investors, keep investor capital and deter redemptions.

The second hypothesis relies on the idea that a manager’s portfolio choice depends on the

fund’s exposure to investor liquidity shock. Edelen (1999) shows that investor liquidity shocks

can increase non-discretionary trading costs and reduce fund profitability. These non-

discretionary trading costs are likely to be larger for funds holding less liquid assets. Hedge

funds can manage this risk by holding more liquid assets and/or reversing these shocks by raising

new capital. However, since offshore funds face regulatory constraints on the number of

accounts and the ability to issue new shares, we expect these funds to hold more liquid assets, so

that redemptions are met at lower cost. Hence, we have the following testable hypothesis:

H2 [Asset Illiquidity Hypothesis]: Due to regulatory requirements on the number of investors and

the way to offer fund shares, onshore funds manage assets with greater liquidity and lower

liquidity risk than offshore funds.

The third hypothesis is based on the idea that investor search costs are an important

determinant of fund flow. In particular, Sirri and Tufano (1998) and Huang, Wei, and Yan

(2007) report empirical evidence suggesting that greater marketing efforts by mutual funds

deliver strong performance-flow sensitivity, and especially for high performing funds. Likewise,

we expect that the regulatory constraints on onshore funds’ marketing efforts will lead to lower

performance sensitivity as compared to offshore funds. This leads to our third hypothesis:

H3 [Fund Flow Hypothesis]: Due to restrictions on both the number of investors and the public

issuance of fund shares, investors face greater search costs when investing with onshore funds.

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As a result, the flow-performance relation is less sensitive for onshore fund than offshore funds,

especially for high-performing funds.

Our final hypothesis relies on the idea that fund performance is unlikely to persist when

investor capital is sensitive to past performance. For example, BG (2004) and PS (2010) assume

that managers face decreasing returns to scale from asset management, and that investors will

supply capital competitively as they learn from past performance. In the Berk and Green

equilibrium, therefore, abnormal fund returns are eliminated by investors as they direct more

capital to superior managers. A key assumption in their model is that there is perfect competition

among investors for manager skill. However, as we argue above, the regulatory constraints faced

by onshore funds make it difficult to advertise performance to investors, leading to a reduced

flow/performance relation. Therefore, we expect greater performance among onshore funds,

where investor capital does not chase away performance. Meanwhile, offshore funds (who

presumably are not subject to the same regulatory requirements) will show lower risk-adjusted

performance. Therefore, we have the following testable hypothesis for fund performance:

H4 [Fund Performance Hypothesis]: Offshore funds conform more closely to the BG (2004)

assumption that investor capital is supplied competitively and without search costs, due to the

absence of regulation on the number of investors, accredited investor requirement, and strict

marketing prohibitions. As a result, we expect lower risk-adjusted performance among offshore

funds because fund profits are chased away by unrestricted capital flows.

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3. Data and Summary Statistics

In this section we discuss the hedge fund data used in the empirical analysis. We also

discuss the variables used to benchmark fund returns and how to measure market liquidity risk

and asset illiquidity. Finally, we present summary statistics for the key variables of the sample.

3.1. TASS Database

We obtain individual hedge fund data from Lipper TASS, which is one of the largest used

in academic research. The database provides monthly net-of-fee returns, assets under

management (AUM), and other fund characteristics such as investment style, legal structure,

domicile country where a fund is registered, management company, fee structure, and share

restriction provisions. The investment styles of hedge funds we analyze are convertible arbitrage,

dedicated short bias, equity market neutral, emerging markets, event driven, fixed income

arbitrage, global macro, long-short equity hedge, and multi-strategy funds. We exclude managed

futures as this style focuses specifically on futures which are different from hedge funds or funds

of hedge funds.11

We do not include funds that report i) returns in a foreign currency, instead of US dollars,

ii) quarterly (instead of monthly) returns, or iii) gross return (instead of net-of-fee returns). We

include both live funds and defunct funds to avoid survivorship bias. As TASS does not retain

data on defunct funds before 1994, our sample period starts in January 1994 and ends in

November 2005. To mitigate backfill bias, we delete the first two years of return data.12 Based

on the above criteria, we have 3,573 funds left in our sample for our portfolio level analysis,

                                                       11 Liang (2004) indicates that there are differences between managed futures and hedge funds in terms of performance, risk, attrition, and correlation structures with major market indices. 12 Another method to reduce backfill bias is to use the date on which each fund was added to the database and to delete the returns before the date if the date is available. As a robustness check, we repeat our tests using both methods of adjusting backfill bias and find that our main results do not change.

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among which 2,111 are live funds and 1,462 are defunct funds. There are 2,049 (1,200 live and

849 defunct) offshore funds and 1,524 (911 live and 613 defunct) onshore funds.13 Later, when

we estimate alpha to evaluate the risk-adjusted performance at the individual fund level, we

further require a minimum return history of twenty-four months. There are 2,233 funds (1,230

offshore and 1,003 onshore) that meet these criteria.

We further partition the sample of offshore and onshore funds depending on whether they

are part of a master-feeder (MF) structure. A MF structure is devised for hedge fund managers

who wish to market a fund to both onshore and offshore investors. Instead of managing two

different portfolios side-by-side, a MF manager usually sets up one “master” company and two

“feeders”: one feeder is a limited partnership for onshore investors and the other feeder is an

offshore corporation for offshore investors. The sole investment of these two feeders is an

ownership interest in the master, which is typically an offshore limited liability company. The

actual portfolio investment is made at the master company level.14

We use the management company information provided by TASS to define master-feeder

(MF) funds and stand-alone (SA) funds. If there are both onshore and offshore funds with the

same investment style managed by the same company, we classify them as MF funds and the

other funds are SA funds. Among the 1,230 offshore funds with at least twenty four monthly

returns, 341 are MF funds and 889 are SA funds. Among the 1,003 onshore funds, 389 are MF

funds and 614 are SA funds. We recognize that there can be cases where a MF onshore

(offshore) fund is misclassified as a SA fund if the counterpart offshore (onshore) fund in the MF

                                                       13 We classify a hedge fund as an onshore fund if it is registered in the United States. Offshore funds are registered in the Cayman Islands, British Virgin Islands, Bermuda, Bahamas, Guernsey, Netherlands Antilles, Mauritius, Liechtenstein, or Saint Kitts and Nevis. 14 See Buscema (1996) and McCrary (2002) for detailed description on the master-feeder structure of hedge funds.

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structure does not report to the database. However, the bias is against finding any difference

between MF funds and SA funds.

Finally, when analyzing the relation between share illiquidity and asset illiquidity, we use

five share restriction variables available in TASS: lockup period, redemption frequency (RF),

redemption notice period (RNP), subscription frequency (SF), and the minimum investment

amount (MinInv). The lockup period specifies a time interval during which a new investor is not

allowed to redeem the shares of a fund without a penalty. As in previous research, we use a

lockup dummy variable instead of the lockup period because the lockup period is clustered

around zero (for 68% of the funds) and twelve month (for 26% of the funds), and have little

variability. RF and SF show how often the fund processes the redemption and subscription

requests from investors. RNP is the amount of advance notice that investors should give a fund

manager before cashing in the fund shares. Note that lockup is a one-time restriction applied only

to new investors while other variables are rolling restrictions applied to all investors.

3.2. Benchmarking Hedge Fund Returns

To estimate alpha, we use the seven risk factors of Fung and Hsieh (2004). That is, i) the

excess return on the S&P 500 index, ii) the size factor as in Fama and French (1993), iii) the

monthly change in the 10-year treasury constant maturity yield, iv) the change in the credit

spread of the Moody’s Baa bond over the 10-year treasury bond, v), vi), and vii) the excess

returns on portfolios of look back straddle options on currencies, commodities, and bonds as in

Fung and Hsieh (2001).15

                                                       15 We thank Kenneth French and David Hsieh for providing downloadable data on their websites. The size factor was obtained from http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html, The trend-following factors were downloaded from http://faculty.fuqua.duke.edu/~dah7/HFRFData.htm.

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In addition to the seven risk factors, we include another systematic risk factor in time

series regressions to obtain market-liquidity adjusted alpha. The added factor is the liquidity risk

factor as in Pastor and Stambaugh (2003, hereafter PS) and Sadka (2006). Sadka (2010)

examines the cross-section of hedge fund returns and finds that funds that significantly load on

market liquidity risk outperform low-loading funds and this outperformance is independent of

share restrictions.16

3.3. Measuring Asset Illiquidity and Market Liquidity Risk

Lo (2001) and Getmansky, Lo, and Makarov (2004, hereafter GLM) show that hedge

fund returns are often serially correlated, and the most likely explanation is illiquid exposure

although return smoothing is also a possibility. Therefore, Lo (2001), GLM (2004), and

Khandani and Lo (2007) suggest using the first-order serial correlation coefficient (ρ) of a fund’s

returns as a measure of asset illiquidity.

GLM (2004) also suggests another measure of asset illiquidity by distinguishing between

a fund’s reported returns and economic returns. The idea is that the reported returns of illiquid

portfolios only partially reflect the true economic returns contemporaneously but the economic

returns are incorporated to reported returns eventually. That is, the reported return in period t

( 0tR ) satisfies the following equations:

00 1 1 2 2t t t t k t kR R R R R (1)

0 1 0,1,2, ,i for all i k and (2)

0 1 2 1k (3)

                                                       16 The results reported in this paper are based on Sadka’s (2006) liquidity factor, but we also tested the PS (2003) liquidity factor as a robustness check and found similar results.

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where tR is the fund’s true economic return in period t.

As in GLM (2004), we assume that the demeaned economic returns are mean-zero, normal

random variables, and use the previous sixty-month return history of a fund to estimate the

parameters in Equation (1) by maximum likelihood estimation. 0 represents the fraction of a

fund’s economic return that is simultaneously incorporated in its reported return. Hence low 0

means a more illiquid portfolio. Therefore, we call = 1- 0 the GLM (2004) measure of asset

illiquidity.

We test both the GLM (2004) illiquidity measure () and the first-order serial correlation

coefficient () as a measure of asset illiquidity, and find similar results.17 Note that both higher

and higher indicate higher asset illiquidity.

In addition to the idiosyncratic GLM illiquidity measure, we estimate the systematic

market-liquidity-risk beta for each fund where market liquidity is defined as in PS (2003) and

Sadka (2006). They show that market liquidity is a priced risk factor in the cross-section of stock

returns. The PS (2003) liquidity factor is based on the principle that order flows induce greater

return reversal when liquidity is low, while Sadka’s (2006) liquidity factor is based on the

permanent variable component of the intraday price impact of stock trades. The impact of market

liquidity on hedge funds has also been confirmed by the Long-term Capital Management debacle

in 1998 and the recent liquidity crisis happened from 2007 to 2009.

                                                       17 The only difference is that the requirement of sixty-month return history to estimate parameters in Equation (1) reduces our sample size from 2,233 (1,230 offshore and 1,003 onshore) to 1,400 (740 offshore and 660 onshore). Therefore, the results reported in this paper are using the first-order serial correlation coefficient () as a measure of asset illiquidity to maintain a large sample size.

 

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Recently, Sadka (2010) applies the market liquidity factor to the cross-section of hedge

fund returns, and reports that funds with significant loading on market liquidity risk outperform

the low-loading funds by 8 percent on an annual basis over the period 1994-2007. Using a two-

way sorting approach based on market liquidity loading and share liquidity, he also shows that

this outperformance is independent of share restrictions. To build on these findings, we include a

market liquidity factor in the time-series regression to estimate alpha in addition to the seven risk

factors of Fung and Hsieh (2004).

3.4 Summary Statistics

Table 1 presents summary statistics for our main sample of hedge funds. The majority

(2,049) of the 3,573 hedge funds in our sample are offshore funds. We also tabulate the number

of offshore funds by domicile country. The vast majority of offshore funds are domiciled in the

Cayman Islands (1,193), followed by the British Virgin Islands (351) and Bermuda (103). The

table also shows that offshore funds have more assets under management on average compared

to onshore funds. This is consistent with our hypothesis that the registration exemption

requirements faced by onshore funds, like the limit on the number of investor accounts and

restrictions on public advertising, restrict capital flows to onshore funds.

Finally, Table 1 shows that 87 percent of onshore funds are limited partnerships (LPs)

while only 4.3 percent of offshore funds endorse such a structure. In the case of offshore funds,

the most frequently observed legal structure is open ended investment company (47.5 percent),

but the proportion varies across locations (42.1 percent for Cayman Island and 70.9 percent for

Bahamas). Corporate structures are less frequent among onshore funds because corporations are

subject to double taxation in the United States.

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4. Analysis and Results

In this section we report results from testing the four hypotheses developed in Section 2.

4.1. Share Restriction Hypothesis

In Table 2 we report results from comparing the usage of share restrictions between

onshore and offshore funds. Our findings indicate that onshore funds impose tighter share

restrictions than offshore funds. For example, the average lockup period of onshore funds (5.7

months) is more than twice the lockup period of offshore funds (2.4 months). On average,

onshore funds require higher minimum investment amount, and have longer redemption,

redemption notice, and subscription periods than those of offshore funds. All the differences are

statistically significant at the 1 percent level with the t-statistics ranging from 3.64 to 19.61.

Overall, our results support the share restriction hypothesis according to which, due to regulatory

requirements on the number of investors and the way to offer fund shares, onshore funds are

more likely to use lockups to attract long-term investors, retain investor capital and deter

redemptions.

4.2. Asset Illiquidity Hypothesis

In Table 3 we report the results from comparing market liquidity risk and asset illiquidity

between onshore and offshore funds. We find that offshore funds hold assets with both greater

illiquidity and higher market liquidity risk. For example, the Sadka (2006) liquidity beta is 28.47

for offshore funds on average, as compared to 9.32 for onshore funds. A similar, though

statistically insignificant, pattern is also found when measuring liquidity risk using the PS (2003)

factor. In addition, we find that offshore funds hold assets with greater illiquidity, measured

20

either using the first-order return autocorrelation or the GLM measure. Taken together, we

interpret these results as support for the asset illiquidity hypothesis-offshore funds can manage

asset illiquidity better than their onshore counterparts through easy equity issuance. In contrast,

onshore funds cannot as easily reverse investor outflows by raising new equity, thereby making

it comparatively more costly for these funds to hold illiquid assets. This finding is consistent

with Brunnermeier and Pedersen (2009) who argue that a shock to a hedge fund’s capital can

lower the liquidity of assets that it trades.

The above results show that the organizational form and asset illiquidity of offshore

funds differs significantly from that of onshore funds. Specifically, offshore funds have lower

share restrictions and hold asset with greater illiquidity and market liquidity risk. This is

somewhat surprising in light of prior research showing a positive relation between share

restrictions and asset illiquidity. For example, Aragon (2007) reports a positive relation and

argues that share restrictions allow funds to efficiently manage illiquid assets. By separating

offshore funds from onshore funds, we extend the literature and suggest that a fund’s domicile

(i.e., onshore or offshore) is an important control variable in testing the relation between share

restrictions and asset illiquidity.

In Table 4 we present results from a multivariate logit analysis of the fund’s decision to

use a lockup provision. We estimate the model separately for the onshore and offshore fund

subsamples. As explanatory variables we include asset illiquidity (ρ), illiquidity factor loading

(βSadka), fund age, and a limited partnership (LP) dummy. We also include the investment style

dummies to adjust for the style effect. To make parameter estimates comparable, we normalize

continuous variables to have a mean of zero and a standard deviation of one across all funds.

21

Consistent with the prior literature (see Aragon (2007)), we find that both asset illiquidity

and the age of a fund are related to lockups. In particular, lockups are less common among funds

with greater asset liquidity as the need has been reduced. Younger funds are more likely to

impose lockup as managers are eager to cumulate assets. This result holds for both onshore and

offshore funds, although the magnitude of the coefficient is stronger for offshore funds. The

partnership structure restricts managers from getting more investors so they need a lockup

provision to prevent money withdraw; this is true especially for onshore funds.

4.3 Fund Flow Hypothesis

To examine the flow-performance relationship, we use a methodology similar to Sirri and

Tufano (1998) and Fung et al. (2008). Specifically, we measure capital flows into a fund during

a year by using the growth rate of net new money, which is defined as Flowi,t= (TNAi,t-TNAi,t-1

(1+ Ri,t))/ TNAi,t-1. TNAi,t is fund i’s total net assets at the end of year t, and Ri,t is the fund’s

return during the year. That is, Flowi,t represents the percentage growth of a fund during the year

in excess of the growth that would have occurred if no new money had flowed in. As in previous

research, the top and bottom 1 percent of the flows are winsorized to mitigate the effect of

outliers.

We run a piecewise linear regression of investor flows on relative performance variables

Lowt, Midt, and Hight. These variables are defined using a fractional rank (FRANK) that

represents a fund’s percentile performance relative to other funds in the same investment style

during the same period. FRANK ranges from 0 to 1. The bottom performance quintile (Lowt) is

defined as Min (0.2, FRANKt-1), the middle three performance quintiles are combined into one

22

group labeled as Midt, defined as Min (0.6, FRANKt-1 – Lowt), and the highest performance

quintile (Hight) is defined as Min (0.2, FRANKt-1 – Lowt – Midt).

For example, if a fund’s FRANK was 0.82 last year, its Lowt is 0.2, Midt is 0.6, and Hight

is 0.02. We include the logarithm of the size in the previous period (Log (TNAt-1)) as a control

variable because an equal dollar flow has a larger percentage impact on smaller funds. We also

include the standard deviation of fund returns, flows to the investment style, share restrictions,

fees, high-water mark (HWM) dummy, leverage dummy, and open-to-public dummy variables

as control variables. We conduct the regressions annually from 1994-2005, then calculate the

Fama-MacBeth (1973) coefficients as well as the t-statistics.

In Table 5 we report the results from estimating the piecewise linear regression of

investor flows on relative performance. We find that the sensitivity of net fund flows to past

performance, especially strong performance, is greater for offshore funds. For example, a change

in rank from 80th percentile to 90th percentile increases inflows by 11.5% for offshore funds, and

the increase is significant at the 5 percent level. However, the same jump in rank in onshore

funds does not lead to a statistically significant increase in inflows. This result supports our key

identifying assumption that offshore funds find it easier to raise capital from investors who chase

past performance. For example, offshore funds face fewer restrictions on advertising than

onshore funds, thereby attract more capital after generating superior performance, not

mentioning onshore funds can close for new money when reaching their design capacity. In

addition, it is easier for offshore funds to attract new investors because these funds do not face a

limit on the number of accounts. This result helps motivate our prediction that, due to decreasing

returns to scale, offshore funds have lower performance than onshore funds.

23

4.4. Fund Performance Hypothesis

In this subsection we compare the performance of onshore funds with offshore funds. In

Table 6 we compare the monthly returns, Sharpe Ratios, and Fung and Hsieh (2004) seven-factor

alphas for the two fund subgroups. In Panel A we find that offshore funds generally have worse

performance than onshore funds regardless of the performance measure. For example, the seven-

factor alpha of onshore funds is 0.77%, higher than 0.55% for offshore funds. The 0.22% spread

between the two fund groups is statistically significant at the 1% level and transfers to an annual

performance difference of 2.64%.

A similar pattern holds when we measure performance using raw returns or the Sharpe

Ratio. In addition, the higher average performance among onshore funds is very stable across

different style categories. For example, the onshore/offshore alpha spread is positive for all

styles except for convertible arbitrage funds, where the difference is insignificant from zero. This

suggests that the performance difference is not driven by omitted factors from our benchmark

model.

In Panel B of Table 6 we compare performance after further subdividing the sample

funds depending on whether a fund has a lockup provision. The results again show a positive

differential between onshore and offshore funds, although this difference is larger for funds

without a lockup (0.18%) than those with a lockup (0.07%).18 Note that in Panel B of Table 6,

the difference between the lockup funds and non-lockup funds is 0.26% for onshore funds and

0.37% for offshore funds. Therefore, offshore funds earn a larger lockup premium than onshore

                                                       18 The t-statistic for the performance difference between onshore lockup funds and offshore lockup funds is 1.19 and it is 4.38 for the performance difference between onshore non-lockup funds and offshore non-lockup funds.

24

funds, consistent with the results in Table 3 that offshore funds have greater asset illiquidity and

liquidity risk.

Our earlier results highlight significant differences between onshore and offshore funds

in the degree of share restrictions, asset liquidity, and market liquidity risk. Prior research shows

that these variables have significant explanatory power on hedge fund returns.19 Therefore, we

also compare performance of onshore and offshore funds after further controlling for differences

in market liquidity, asset liquidity, and share liquidity.

We estimate this “liquidity-adjusted alpha” using a two-step procedure. First, we use a

time-series regression with the excess return of a hedge fund as the dependent variable, and the

seven factors of Fung and Hsieh (2004), plus the market liquidity factor of Sadka (2006) as

explanatory variables to obtain alphas. Then, we use a cross-sectional regression of the estimated

eight-factor model alphas ( i ) on asset illiquidity () and share restriction variables as in

Equation (4).

where RNP2 and MinInv2 are included to test whether the relation between alpha and share

restriction is linear, and LockupRNP is used to test whether an extra period of redemption notice

matters for investors who have agreed for a lockup period.

The results are reported in Table 7. Overall our main finding here is consistent with Table

6-onshore funds perform better than offshore funds. For example, we estimate a liquidity-

adjusted alpha of 0.67% for onshore funds with a significance level at 1%, but for offshore funds

                                                       19 A positive relation between fund returns and share restrictions, like lockups and notice periods, is reported by Liang (1999), Bali, Gokcan and Liang (2007), Liang and Park (2007), and Aragon (2007). More recently, Sadka (2010) finds that funds that significantly load on liquidity risk subsequently outperform low-loading funds by about 8% annually over the period 1994-2007.

)4(

ˆ

62

52

4

32110

dummiesstyleRNPLockupMinInvRNP

MinInvRNPLockup

iii

iiii

25

this number is only 0.08% and statistically insignificant. In addition, the positive coefficients on

the share restrictions variables are consistent with prior findings on share illiquidity premium in

hedge fund returns. However, the higher returns attributable to fund lockups, or, “lockup

premium”, has greater economic and statistical significance for offshore funds (0.37%) than

onshore funds (0.15%).

We further subdivide our sample funds depending on whether they are part of a master-

feeder (MF) or stand-alone (SA) structure. Strikingly, Table 7 shows that the greater

performance of onshore funds is driven entirely by the positive liquidity-adjusted alphas of the

SA onshore funds. In contrast, onshore funds that are part of a MF structure have similar

performance to their offshore counterparts. We interpret this finding as the decision of hedge

fund managers who observe a limited amount of arbitrage profits in capital markets not to raise

additional capital because they want to avoid the diluting effect on the outstanding claims. For

example, when hedge fund managers compete to attract capital, “hot hands” can raise enough

capital without going offshore and choose to remain as SA onshore funds because size matters

less under the incentive fee structure of hedge funds.

In contrast, managers with lower performance are more likely to use MF structure to

attract more capital seeking for a size-related compensation.20 As advertising constraint makes

flow to onshore funds become less sensitive to superior performance, investors cannot chase

away performance through the channel of decreasing returns to scale in the SA onshore funds.

Incentive fees in hedge funds may have motivated SA onshore fund managers (who started their

career with wealthy US individual investors and built a superior track record) not to accept more

                                                       20 This finding is consistent with Nohel, Wang, and Zheng (2010), who examine side-by-side management of hedge funds and mutual funds. They find that hedge fund managers who accept capital from both hedge fund investors and mutual fund investors underperform their peers. 

26

capital from offshore investors in order to prevent the decreasing returns to scale problem of

their arbitrage strategies. Meanwhile, the performance of MF onshore funds is lower because the

fund manager (who is seeking higher compensation through growth) circumvents the regulation

on the number of investors by accepting additional capital from offshore investors through a MF

structure.

5. Robustness

We find that the liquidity-adjusted alpha of SA onshore funds is positive and significantly

different from zero. To show that this alpha is not generated by pure luck, we apply the robust

bootstrap methodology developed by Kosowski et al. (2006) to our analysis as follows.21

First, we conduct the time-series regression of the excess return on the eight risk factors,

and save the parameter estimates and the time-series of residuals for each fund. Then, we do the

cross-sectional regression of the estimated eight-factor model intercept on asset illiquidity and

share illiquidity as in Equation (4), and save the t-statistic of the intercept ( t ).

Second, we draw 143 months with replacement from January 1994 to November 2005.

Then, for each fund, we create the bootstrapped excess return (BER) observations using the

parameter estimates and residuals of the time-series regression without including the estimated

intercept. That is, the bootstrapped returns have a true alpha of zero. Call the new excess return

data generated by this procedure as bootstrap sample BER 1. Note that this procedure is designed

to keep the higher order correlation between the regressors and residuals as well as the cross-

sectional correlation of the residuals across funds in the original return data.

                                                       21 See Kosowski et al. (2007) and Fung et al. (2008) for details on bootstrap analysis of hedge fund performance.

27

Third, we delete funds that have less than twenty-four return observations from BER 1.

Then, using BER1, we repeat the time-series and the cross-sectional regressions in the first step

and find the t-statistic of the intercept from the cross-sectional regression ( 1t ).

Finally, we repeat the second and third steps 1,000 times to generate BER 1,…, BER

1,000, and { 1t ,…, 000,1

t }. We compare t from the original return data with the 99th percentile of

the bootstrapped empirical distribution of the t-statistics of alpha to examine whether the

probability of obtaining a t-statistic higher than or equal to t is lower than 1 percent.

We find that the t-statistics of alphas ( t ) of all funds, onshore funds, and the SA onshore

funds are all higher than the 99th percentiles of the bootstrapped empirical distributions. This

confirms that the liquidity-adjusted alpha of hedge funds reported in this paper cannot be

attributed to pure luck.

6. Conclusion

This paper analyzes the impact of regulation on share restrictions, capital flow, and

performance of hedge funds. By separating i) offshore funds from onshore funds, ii) stand-alone

funds from master-feeder funds, and iii) illiquidity of hedge fund assets from illiquidity of hedge

fund shares, we make three contributions to the hedge fund literature.

First, we find that the positive relation between share restrictions and asset illiquidity

reported in previous research should be reexamined. By distinguishing onshore funds from

offshore funds, we show that onshore funds on average impose stronger share restrictions than

offshore funds but invest in more liquid assets in order to better manage equity funding risk

caused by restrictions on advertising and the number of investors.

28

Second, we find that onshore and offshore hedge funds have a different flow-

performance relationship and this difference affects risk-adjusted performance. We show that

capital flow to onshore funds is less sensitive to past performance due to prohibition on

advertising, and high capital flow to offshore funds chase away alpha due to decreasing returns

to scale.

Finally, we analyze the risk-adjusted performance of hedge funds after controlling all

three types of liquidity risk (market liquidity, asset liquidity, and share liquidity), and find that

liquidity-adjusted alpha is positive and significant only in stand-alone onshore funds. Master-

feeder funds that accept capital from both onshore and offshore investors underperform stand-

alone funds. This finding is consistent with previous research that shows increased capital flows

to actively managed funds lead to decreasing returns to scale as documented by Berk and Green

(2004) as well as Pastor and Stambaugh (2010).

29

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34

Table 1 Legal Structure of Hedge Funds by Domicile Country

This table compares offshore hedge funds with onshore funds in terms of fund size and legal structure. The data is from the TASS database, and the sample period is from January 1994 to November 2005. AUM_Total is the total assets under management (AUM) as of November 2005.

Domicile Country No. of Funds

AUM _Average ($mm)

AUM _Total ($ billion)

Legal Structure (%)

Limited Partnership

Limited Liability

Company

Open Ended Investment Company

Exempted Company

Open Ended Mutual Fund

Others

Onshore Funds 1,524 83.6 71.1 87.0 9.8 1.4 0.0 0.0 1.8

Offshore Funds 2,049 170.7 193.7 4.3 6.6 47.5 12.7 5.2 23.6

Cayman Islands 1,193 157.5 101.7 4.8 7.9 42.1 21.4 2.9 20.9

British Virgin Islands 351 201.5 40.9 3.4 4.3 67.6 0.0 2.8 21.9

Bermuda 216 169.8 22.7 3.7 9.7 46.8 1.9 27.2 10.7

Bahamas 103 137.4 3.8 3.9 1.0 70.9 0.0 1.0 23.2

Others 186 216.3 24.5 3.8 2.7 31.7 1.1 1.1 59.6

All Funds 3,573 132.5 264.8 39.6 8.0 27.8 7.3 3.1 14.2

   

35

Table 2 Share Restrictions and Other Characteristics of Hedge Funds by Investment Style and by Domicile Country

This table compares offshore funds and onshore funds in terms of share restrictions, size, age, and fees. Reported numbers are sample averages across all funds within the same investment style. Panel A shows share restriction variables; Panel B displays fund size and age; Panel C presents fees and other fund characteristics. ***, **, and * denote that the difference in the characteristics of offshore funds and onshore funds is significantly different from zero at the 1%, 5% and 10% level, respectively.

Panel A: Share Restrictions

Investment Style

Lock-Up Period (months)

Minimum Investment ($mm)

Redemption Notice Period (Days)

Redemption Frequency (Days)

Subscription Frequency (Days)

On-shore

Off-shore

t-stat On-

shore Off-

shore t-stat

On-shore

Off-shore

t-stat On-

shore Off-

shore t-stat

On-shore

Off-shore

t-stat

Convertible Arbitrage

3.43 3.38 0.06 1.16 0.97 0.63 40.9 42.2 -0.30 82.2 66.5 1.49 37.8 31.3 2.35**

Dedicated Short Seller

4.58 4.59 0.00 0.57 0.63 -0.58 27.2 21.8 0.84 132.7 44.1 3.03*** 60.1 32.1 3.21***

Emerging Markets

4.00 1.68 2.21** 0.56 0.38 2.43** 33.9 27.0 1.61 92.8 50.9 3.83*** 40.5 31.0 2.27**

Equity Market Neutral

4.51 1.15 5.35*** 0.71 0.68 0.30 32.7 25.2 2.88*** 78.5 38.8 6.95*** 47.5 30.7 5.25***

Event Driven 6.21 4.54 2.21** 1.31 1.11 1.27 51.8 48.9 0.97 160.2 90.1 6.55*** 51.8 35.5 4.66*** Fixed Income

Arbitrage 4.58 1.98 3.56*** 1.18 1.07 0.53 44.2 31.4 3.31*** 92.3 65.3 2.41** 40.7 32.4 2.69**

Global Macro 3.91 1.19 3.16*** 0.85 0.76 0.47 27.5 18.1 3.25*** 63.1 38.0 4.52*** 37.9 30.8 2.24** Long/Short

Equity Hedge 6.40 2.36 13.55*** 0.78 0.59 4.08*** 36.8 29.4 6.36*** 117.0 51.6 17.24*** 50.3 34.0 10.47***

Multi-Strategy 4.59 2.43 2.44** 1.16 1.08 0.20 39.6 40.4 -0.16 80.6 61.9 1.90 39.4 31.4 2.74***

All Funds 5.69 2.39 15.60*** 0.90 0.73 3.64*** 38.7 31.4 8.12*** 112.4 56.4 19.61*** 47.9 32.9 13.95***

 

36

Panel B: Size and Age

Investment Style

Number of Funds AUM _Total ($ billion) AUM _Average ($mm) Age (months)

Onshore Offshore Onshore Offshore Onshore Offshore t-statistic Onshore Offshore t-statistic

Convertible Arbitrage 70 112 1.4 6.1 52.4 216.7 -2.80*** 74.3 62.8 1.69*

Dedicated Short Seller 19 17 0.8 0.6 49.6 56.4 -0.16 103.2 75.0 1.40 Emerging Markets 45 255 1.5 32.2 47.7 160.2 -4.45*** 61.8 65.6 -0.50

Equity Market Neutral 124 164 5.5 8.7 56.0 124.5 -1.71* 52.0 46.0 1.37

Event Driven 219 256 16.0 34.0 191.6 257.1 -1.43 72.6 56.8 3.18***

Fixed Income Arbitrage 78 149 4.8 18.4 100.0 200.3 -3.26*** 52.8 50.2 0.46

Global Macro 69 198 1.9 15.2 57.1 148.7 -2.37** 61.4 52.6 1.32

Long/Short Equity Hedge 819 793 30.9 62.1 61.2 132.2 -4.97*** 64.3 52.9 5.06***

Multi-Strategy 81 105 8.4 16.3 123.3 315.7 -2.50** 55.3 52.5 0.44

All Funds 1,524 2,049 71.1 193.7 83.6 170.7 -7.51*** 64.2 54.9 5.93***

Panel C: Fees and Other Characteristics

Investment Style Management Fee (%) Incentive Fee (%) High-Water Mark (%) Leveraged (%)

Onshore Offshore t-stat Onshore Offshore t-stat Onshore Offshore Onshore Offshore

Convertible Arbitrage 1.22 1.34 -1.64* 17.9 19.0 -1.38 65.7 65.2 70.0 81.2

Dedicated Short Seller 1.17 1.35 -1.11 19.4 18.8 0.34 52.6 52.9 36.8 35.3

Emerging Markets 1.45 1.53 -1.04 17.9 17.5 0.42 44.4 43.9 57.8 57.6

Equity Market Neutral 1.21 1.43 -4.28*** 18.9 19.9 -1.57 66.1 72.0 52.4 57.3

Event Driven 1.26 1.42 -3.81*** 18.7 19.1 -0.82 67.6 68.4 51.6 59.0

Fixed Income Arbitrage 1.24 1.36 -1.57 19.1 19.8 -0.92 79.5 61.1 78.2 77.9

Global Macro 1.47 1.57 -0.83 18.5 17.8 0.76 68.1 48.0 72.5 80.3

Long/Short Equity 1.15 1.32 -8.16*** 19.2 19.1 0.49 71.7 62.9 61.9 64.1

Multi-Strategy 1.44 1.47 -0.35 19.8 18.1 2.27** 76.5 67.6 61.7 59.0

All Funds 1.22 1.40 -10.70*** 19.0 18.8 1.01 69.8 60.7 60.9 65.1

 

37

Table 3 Illiquidity of Assets in Hedge Funds by Investment Style and by Domicile Country

This table compares offshore funds and onshore funds in terms of market illiquidity beta and asset illiquidity measures. βPS represents for the Pastor-Stambaugh (2003) liquidity beta, βSadka is the Sadka (2006) liquidity beta,

Autocorrelation () is the first-order autocorrelation in fund returns, and GLM Measure () is the liquidity measure similar to Getmansky, Lo, and Makarov (2004). ***, **, and * denote that the difference in the characteristics of offshore funds and onshore funds is significantly different from zero at the 1%, 5% and 10% level, respectively.

Investment Style

Market Illiquidity Asset Illiquidity

PS (2003)

Factor Loading (βPS) SADKA (2006)

Factor Loading (βSadka) Autocorrelation () GLM Measure ()

On-shore

Off-shore

t-stat On-

shore Off-

shore t-stat

On-shore

Off-shore

t-stat On-

shore Off-

shore t-stat

Convertible Arbitrage

-2.47 -3.30 0.75 -25.19 -31.74 0.38 0.39 0.39 0.16 0.31 0.33 -0.69

Dedicated Short Seller

-2.40 -5.60 0.75 -60.84 -4.46 -0.70 0.05 0.06 -0.16 -0.04 0.11 -2.49**

Emerging Markets

8.03 8.50 -0.14 21.43 110.63 -1.20 0.19 0.17 0.62 0.12 0.15 -0.75

Equity Market Neutral

0.58 0.70 -0.12 36.86 25.12 0.55 0.09 0.06 0.75 0.13 0.08 0.81

Event Driven 1.11 0.51 0.56 53.96 41.62 0.75 0.22 0.22 0.01 0.21 0.22 -0.20

Fixed Income Arbitrage

0.06 0.45 -0.26 -25.48 17.50 -1.91* 0.28 0.25 0.76 0.27 0.22 1.11

Global Macro 1.84 2.08 -0.11 18.79 46.63 -0.93 0.01 0.08 -2.58*** -0.06 -0.01 -0.85

Long/Short Equity Hedge

2.62 2.79 -0.21 3.31 9.32 -0.43 0.10 0.12 -1.42 0.08 0.13 -3.02***

Multi-Strategy 3.04 -0.97 1.45 -22.82 -24.25 0.03 0.16 0.18 -0.57 0.12 0.19 -1.47

All Funds 1.92 2.30 -0.71 9.32 28.47 -2.17** 0.14 0.16 -2.07** 0.11 0.15 -2.88***

38

Table 4 Logit Analysis of the Lockup Provision

This table reports the parameter estimates and Pseudo-R2 from the logistic regression of lockup provision. Independent variables are asset illiquidity (ρ), market illiquidity (βSadka), fund age, and a limited partnership dummy variable (LP). Investment style dummy variables are also included as control variables. To make estimates comparable, variables are normalized to have a mean of zero and a standard deviation of one across all funds. ***, **, and * denote statistical significance at the 1%, 5% and 10% level, respectively.

All Funds Onshore Offshore

Panel A. Univariate Analysis with Asset Illiquidity

Asset Illiquidity (ρ) 0.21*** 0.13* 0.42*** Pseudo-R2 (%) 3.18 2.68 5.54

Panel B. Univariate Analysis with Market Illiquidity

Market Illiquidity (βSadka) 0.00 0.10 -0.08 Pseudo-R2 (%) 2.46 2.59 3.55

Panel C. Multivariate Analysis

Asset Illiquidity (ρ) 0.30*** 0.18* 0.46*** Market Illiquidity (βSadka) 0.01 0.10 -0.04 Age -0.53*** -0.63*** -0.40** LP 1.10*** 0.45* 0.17 Pseudo-R2 (%) 10.13 6.18 5.84

 

 

39

Table 5 The Effect of Performance on Capital Flows: Onshore vs. Offshore Hedge Funds

This table presents the effect of relative performance on capital flows to onshore and offshore funds. Similar to Sirri and Tufano (1998) and Fung et al. (2008), capital flows are measured by using the growth rate of net new money, which is defined as Flowi,t = (TNAi,t - TNAi,t-1*(1+ Ri,t))/ TNAi,t-1. TNAi,t is fund i’s total net assets at time t, and Ri,t is the fund’s return over the prior period. The coefficient estimates are presented from the piece-wise linear regression of investor flows on relative performance variables, Low, Mid, and High, which are defined using a fractional rank (FRANK) that represents a fund’s percentile performance relative to other funds in the same investment style in the same period. FRANK ranges from 0 to 1. The bottom performance quintile (Lowt) is defined as Min (0.2, FRANKt-1), the middle three performance quintiles are combined into one grouping labeled as Midt, defined as Min (0.6, FRANKt-1 – Lowt), and the highest performance quintile (Hight) is defined as Min (0.2, FRANKt-1 – Lowt – Midt). For example, if a fund’s FRANK was 0.98 last year, its Lowt is 0.2, its Midt is 0.6, and its Hight is 0.18. As control variables, risk, size, flows to the investment style, share restrictions, fees, high-water mark (HWM) dummy, leverage dummy, and open-to-public dummy variables are included. The regressions are run annually, and standard errors and t-statistics are calculated from the annual results as in Fama and MacBeth (1973). t-statistics are given in parenthesis below the coefficient estimates. ***, **, and * denote statistical significance at the 1%, 5% and 10% level, respectively.

  ALL Onshore Offshore

Intercept 2.36

(6.50) *** 2.81

(5.26) *** 2.66

(8.58) ***

Relative Performance Bottom Performance Quintile (Low)

0.82 (2.93)

** 0.29 (0.42)

0.88 (1.32)

2nd-4th Performance Quintiles (Mid)

0.67 (5.80)

*** 0.72 (7.64)

*** 0.73 (3.79)

***

Top Performance Quintile (High)

0.86 (1.91)

*

0.74 (1.14)

1.15

(2.74)

**

Std. dev. Monthly Returns -0.04

(-4.66) *** -0.03

(-2.39) ** -0.05

(-7.37) ***

Log (TNAt-1) -0.14

(-8.56) *** -0.17

(-7.52) *** -0.16

(-9.07) ***

Flow to the Investment Style 0.18

(1.44) -0.01

(-0.96) 0.01

(2.54) **

High Water Mark (HWM) 0.20

(6.34) *** 0.19

(6.49) *** 0.21

(5.25) ***

Lockup Period 0.00

(0.72) 0.01

(1.36) 0.00

(0.87)

Redemption Frequency 0.00

(0.59) 0.01

(2.74) ** 0.06

(2.09) *

Subscription Frequency -0.06

(-3.65) *** -0.03

(-1.95) ** -0.08

(-1.87) *

Management Fee 0.04

(1.60) 0.02

(0.58) -0.03

(-0.89)

Incentive Fee 0.01

(1.83) * 0.01

(1.60) 0.00

(0.39)

Open to Public 0.12

(1.67) 0.01

(0.20) 0.09

(1.18)

Leveraged 0.05

(1.03) -0.01

(-0.15) 0.05

(1.11)

Adj-R2 (%) 10.89 14.51 11.17

40

 

Table 6 Performance and Risk for Onshore and Offshore Hedge Funds

Panel A compares offshore hedge funds with onshore hedge funds in terms of performance and risk. Risk-adjusted performance is measured by the Sharpe ratio and the seven-factor model alpha as in Fung and Hsieh (2004). Reported numbers are sample averages. Panel B lists a two-way sorting result: i) onshore vs. offshore, ii) lock-up vs. non-lockup, and compares the risk-adjusted performance. ***, **, and * denote that the difference in the characteristics of offshore funds and onshore funds is significantly different from zero at the 1%, 5% and 10% level, respectively.

Panel A: Performance of Onshore vs. Offshore Hedge Funds by Investment Styles

Investment Style

Average Return (%) Standard Deviation (%) Sharpe Ratio Seven-Factor Model Alpha

Onshore Offshore t-statistic Onshore Offshore t-statistic Onshore Offshore t-statistic Onshore Offshore t-statistic

Convertible Arbitrage 0.50 0.26 1.92* 1.96 1.86 0.40 0.21 0.04 0.95 0.52 0.54 -0.19

Dedicated Short Seller 0.48 -0.42 2.29** 6.51 6.26 0.20 0.07 -0.14 2.30** 0.52 0.44 0.33 Emerging Markets 0.84 0.57 0.61 6.26 6.26 -0.01 0.07 0.18 -1.22 0.62 0.60 0.07

Equity Market Neutral 0.51 0.50 0.05 2.12 1.83 1.53 0.21 0.12 0.88 0.48 0.33 1.74*

Event Driven 0.80 0.57 2.71*** 2.76 1.91 3.95*** 0.27 0.27 -0.01 0.83 0.57 3.09***

Fixed Income Arbitrage 0.56 0.36 2.38** 1.97 2.00 -0.11 0.55 0.34 1.32 0.69 0.40 2.99***

Global Macro 0.08 0.26 -0.59 4.73 3.68 1.98** -0.01 -0.02 0.10 0.61 0.31 2.06**

Long/Short Equity 0.69 0.54 1.67* 4.80 4.35 2.51** 0.16 0.10 1.97** 0.84 0.65 3.48***

Multi-Strategy 1.02 0.68 1.74* 2.91 2.78 0.28 0.34 0.28 0.68 0.99 0.47 2.80***

All Funds 0.67 0.49 2.73*** 3.97 3.69 2.42** 0.20 0.14 2.41** 0.77 0.55 6.16***

 

41

Panel B: Performance of Onshore vs. Offshore Hedge Funds by Lockup Periods

Lockup Non-lockup Difference

(αlockup- αnon-lockup) Number (percent) Seven-Factor Model Alpha Number (percent) Seven-Factor Model Alpha

All Funds 687 (30.8%) 0.90 1,546 (69.2%) 0.54 0.36***

Onshore Funds 443 (44.2%) 0.92 560 (55.8%) 0.66 0.26***

Offshore Funds 244 (19.8%) 0.85 986 (80.2%) 0.48 0.37***

Difference (αonshore - αoffshore)

0.07 0.18***

 

42

Table 7 Liquidity-adjusted Alpha

This table presents the parameter estimates and adjusted-R2s from cross-sectional regressions of The independent variable is a fund’s alpha from the time-series regression of the fund’s excess return on Fung and

Hsieh (2004) factors and Sadka (2006) liquidity factor ( i

^

), and explanatory variables are asset illiquidity (), share restrictions like a lockup dummy variable (Lockup), redemption notice period (RNP) in months, and minimum investment amount (MinInv) in millions of dollars., and style dummy variables. t-statistics are reported in parentheses. ***, **, and * represent statistical significance at the 1%, 5%, and 10% level, respectively.

Intercept Asset Illiquidity Lockup RNP MinInv RNP2 MinInv2 LockupRNP Adj-R2

All Funds 0.35 (4.09)***

0.26 (2.58)***

0.28 (3.44)***

0.26 (5.10)***

0.00 (0.00)

-0.03 (-1.86)*

0.00 (0.11)

-0.07 (-1.34)

4.83

Onshore 0.67 (5.48)***

0.20 (1.33)

0.15 (1.49)

0.21 (2.95)***

0.01 (0.10)

-0.04 (-2.13)**

-0.01 (-0.07)

-0.04 (-0.58)

3.31

SA 0.94 (5.65)***

0.08 (0.35)

0.17 (1.16)

0.18 (1.55)

0.04 (0.73)

-0.01 (-0.25)

-0.01 (-0.89)

0.01 (0.10)

4.59

MF 0.11 (0.63)

0.62 (3.42)***

0.05 (0.34)

0.25 (3.14)***

-0.03 (-1.54)

-0.04 (-2.35)***

0.01 (1.17)

-0.01 (-0.19)

6.85

Offshore 0.08 (0.66)

0.36 (2.58)***

0.37 (2.80)***

0.21 (2.88)***

-0.02 (-0.45)

-0.01 (-0.32)

0.01 (0.52)

-0.11 (-1.31)

4.13

SA 0.10 (0.63)

0.28 (1.59)

0.51 (2.77)***

0.06 (0.72)

0.06 (0.96)

0.03 (0.86)

-0.01 (-1.02)

-0.16 (-1.43)

3.87

MF 0.07 (0.40)

0.61 (2.94)***

0.13 (0.82)

0.13 (1.29)

-0.02 (-0.43)

-0.04 (-1.27)

0.01 (1.01)

-0.01 (-0.09)

6.05

 

iiiiiiii dummiesstyleRNPLockupMinInvRNPMinInvRNPLockup 62

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