Performance Persistence and Flow Restrictions in Hedge Funds · prediction of performance persistence in hedge funds by Glode and Green (2011). To our knowledge, this is the first

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Performance Persistence and Flow Restrictions in Hedge Funds

Kee-Hong Bae*

York University

and

Junesuh Yi**

Dongguk University

This draft: January 2012

* Department of Finance, Schulich School of Business, York University, Toronto, ON, Canada, e-mail: kbae@schulich.

yorku.ca ** Department of Finance, Dongguk Business School, Dongguk University, Seoul, Korea, e-mail: [email protected]

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Performance Persistence and Flow Restrictions in Hedge Funds

Abstract

This paper examines the impact of flow restrictions on superior persistence of performance in hedge funds.

We find that not only money outflow restrictions such as redemption notice period, lock period, and payout

period, but also money inflow restrictions such as minimum investment amount, closed-end, and closing to

individual investors are positively associated with winners‟ persistence. Out of two resources with respect to

persistence, outflow restrictions are observed to be a more important factor for persistence than inflow

restrictions.

We also document that managerial incentives have influence on winners‟ persistence. Thus, we explore which

feature is a more salient determinant for continuous excellent performance persistence. Interestingly, while

each of funds with higher restrictions and funds with higher incentives display higher ratio of persistence,

funds with lower incentives given higher restrictions exhibit higher ratio of persistence than funds with higher

incentives. This suggests that persistence is subject to flow restrictions rather than to incentives. The empirical

results in this paper are resonant with the theoretical prediction of performance persistence by Glode and

Green (2011) that reveal a determinant to affect persistence not from managerial skills but from unique

structure of hedge funds composed of limited and general partners.

Key words: Hedge fund, performance persistence, flow restriction, managerial incentive

JEL classification: G11, G23

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I. Introduction

What is an essential source to hold superior performance in hedge fund? While the hedge fund

has been considered to be an alternative investment vehicle compared with traditional investment

instruments and many academic papers have examined the related issues, there is little known about

the concrete features of hedge funds generating continuously excellent performance. Under the

circumstances with little information or severe information asymmetry due to the unregulated

natures of hedge funds, the investors would of course wish to invest in trustworthy funds

consistently performing well.

In fact, several previous researches have documented the performance in hedge funds. On the

contrary, only a few studies have attempted to examine the persistence of performance. Even most

of them investigate whether performance persistence exists in hedge fund industry, but hardly pay

attention to the features of hedge funds showing persistence by individual hedge fund. Performance

is not necessarily identical to performance persistence. It is possible that the performance of a

particular fund is inferior in a subsequent period even if the performance of a fund delivers a

superior performance in a previous period. Hence, funds may present differential extent of

persistence at different estimation periods of returns. Furthermore, most of literature investigates

persistence mainly focused on investment managers as mutual fund studies do. It is well known that

performance of mutual funds depends on stock picking skill and timing ability of fund managers.

However, hedge funds have natures distinctive from mutual funds in many aspects. Aside from

basic differences such as unregulated investment companies, usage of various investment

techniques including short selling, derivatives, or leverage, and pursuing absolute returns, hedge

funds have a unique, organized structure as a limited partnership. Investors generally contribute

capital as limited partners and investment managers operate funds serving as general partners.

Accordingly, funds are mostly raised from large and accredited institutional investors and fund

managers receive compensation based on their performance like in a limited partnership corporation.

In this way, money flows and compensation of hedge funds particularly present pretty differentiated

structures compared with other investment vehicles.

Money flows are typically restricted not only at set up but at withdrawal stage as funds are

solicited from limited investors. Regarding money inflows to hedge funds, most funds require the

minimum investment amount of $1 million beyond the qualification on net income for individual

investors and on net worth for institutions. Hedge funds even may not be open to individual

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investors. The funds also operate in a closed-end form that does not accept further investment after

inception, maintaining the limited number of shares. Besides, there are more restrictive terms

associated with money outflows from funds. Investors cannot withdraw their money during lockup

period when investors are initially required to hold their money in the fund. Although the lockup

period ends, investors are not free to exit their money due to redemption period and notice period.

Hedge funds do not redeem investors‟ shares on a daily basis like mutual funds but on a quarterly or

yearly base. Therefore, investors who desire to take the money out should notify hedge fund in

advance of redemption period. If investors want to receive any return from their investment in the

hedge funds‟ capital, they are also required to wait during payout period. Such the various

restrictive provisions related to money inflows and outflows of hedge funds lead to share illiquidity

for investors, compared to mutual funds where investors can buy and sell their shares whenever

they want.

From the viewpoint of general partners in limited partnership, hedge fund managers are

rewarded by performance-based fee contracts. The standard fee arrangement of hedge fund industry

is “2 and 20”. Namely, hedge fund managers receive a fixed management fee corresponding to 2

percent of assets under management and an incentive fee of 20 percent of the profits above a

predetermined rate. This rate is named as hurdle rate generally using Treasury bill rate plus a spread.

Because fund managers can have incentive fee only if they perform higher than the hurdle rate, this

asymmetric compensation contract is called as option-like structure. Another important provision of

compensation for general partners is high water mark. If fund managers make a loss in some period,

they can receive the incentive fee only after they have recovered the highest net asset value

previously seen at the end of the fiscal year.

Under the circumstances of restrictive money flows and incentive-centered fee structures on

distinguished organizational form, the performance persistence of hedge funds can be justified

through other resources rather than the ability of fund managers. In particular, share illiquidity

caused by money flow restrictions may be more likely to affect performance persistence. Investors

can neither easily withdraw their money after exhibition of worse performance nor make new

investment in the fund after confirmation of better performance. These rigid fund flows allow fund

managers to establish a more creative and innovative trading strategy and more flexible portfolio

construction, possibly resulting in superior performance persistence.

Hence, our paper conjectures that the magnitude of flow restrictions may be one of the

determinants for fund presenting good performance persistence and investigates the relationship

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between flow restrictions and performance persistence. We separate flow restriction provisions into

inflow and outflow restrictions, and explore the impact of each of the flow restrictions on

performance persistence. We also understand the likelihood that the incentive structure among

several characteristics of funds can influence on performance persistence or that the incentive

structure and flow restriction can simultaneously affect persistence. Thus, we analyze the

relationships among these three variables.

As the results of empirical tests, flow restrictions are positively associated with performance

persistence. Outflow restrictions in particular are observed to have more impact on performance

persistence. As performance measures for performance estimation, we use parametric method by

regression of excess returns on risk factors including liquidity premium and non-parametric method

by contingency table. We also find that funds with aggressive incentive structure exhibit superior

performance persistence and that managerial incentives are highly correlated with flow restrictions.

Consequently, we discover that flow restrictions are a more important determinant than managerial

incentives to retain better performance.

This study is in line with Glode and Green (2011) in that the source of performance persistence

in hedge fund is not drawn from manager‟s ability but from innovative trading strategies or

techniques. They provide a theoretical model that rationalizes the performance persistence of hedge

fund based on hedge funds‟ unique organizational form differentiated from other investment

vehicles. Performance persistence of hedge funds can be explained through information secrecy

between limited partners and general partners. According to them, fund managers need to share

informational rents with the incumbent investors to prevent them from revealing information about

trading strategies to other managers. Consequently, general partners and limited partners need to

restrict fund flows even when expected profitability is high to maintain performance persistence.

We consider the information secrecy between limited partner and general partner in their paper as

flow restrictions of fund managers to investors. This is because the effect to performance

persistence with minimization of money movement by keeping up confidentiality about valuable

information is almost identical the flow restrictions on agreement between funds and investors‟

contract. Therefore, the empirical test results of this paper are resonant with the theoretical

prediction of performance persistence in hedge funds by Glode and Green (2011).

To our knowledge, this is the first research about the relationship between performance

persistence and flow restrictions in hedge funds. Previous literatures have covered in a fragmentary

manner compared to this topic such as share restriction and performance, restrictions and incentives,

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and persistence and estimation intervals. The second contribution of this paper is to figure out the

source of performance persistence reflecting specific organizational form composed of limited and

general partners on hedge funds. Thirdly, this paper takes into account restrictions and incentives

simultaneously to estimate their impact on performance persistence and demonstrates which factor

is salient. Finally, we scrutinize money inflow restrictions such as close to public or closed-end

provisions as well as money outflow restrictions.

The paper proceeds as follows. Section 2 discusses the background of objectives and the

purpose of the paper as well as existing related literature. Section 3 describes the data and

methodologies including parametric and non-parametric method. Section 4 presents the empirical

results and Section 5 concludes the paper.

II. Background

Although a number of academic papers on hedge funds have been recently published, most

studies are incorporated to find out risk factors to correctly estimate performance. Considering the

interest of investors, practitioners, and academicians, and the ripple effect to hedge fund industry, it

is a wonder that there is no comprehensive study that investigates determinants for performance

persistence. Of course, there are several studies related to persistence, share restrictions and

performance, and managerial incentives and performance respectively.

In terms of performance persistence of hedge funds, most papers have examined it focusing on

fund managers‟ ability. These studies provide the mixed results depending on types of estimated

returns, evaluation period, and superior or inferior funds. Especially, it is suggested that there are

several layers with respect to the existence periods of persistence. While the early researches insist

that performance persistence happens at quarterly level. (Brown, Goetzmann, and Ibbotson (1999),

Agarwal and Naik (2000), Bares, Gibson, and Gyger (2003), and Baquero, Horst, and Verbeek

(2005)), the recent studies assert that the period being performance persistence is longer than

quarterly horizon. Kosowski, Naik, and Teo (2007) provide evidence it over a 1 year horizon and

Jaganathan, Malakhov, and Novikov (2010) report it on even 3 years. Kosowski et al. (2007)

explain the reason that prior researches do not find long-term persistence is because they

imprecisely measure performance too much relying on frequency probability of short-term period.

Taking a step forward, Jaganathan et al. (2010) contend that estimation period of persistence should

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be as at least 3 years due to the specific circumstances related to share illiquidity such as lockup,

redemption, and notice period.

Regarding flow restrictions, the literature mainly concentrates on share restrictions related to

money outflow. Ding et al. (2009) and Baquero and Verbeek (2009) examine the impact of share

restrictions on the flow-performance relationship and find conflicting results. Ding et al. (2009) find

that flow-performance relation exhibit concave or convex form depending on share restrictions. In

contrast, Baquero and Verbeek (2009) demonstrate a linear relationship between flow and

performance irrelevant to share restrictions. Aragon (2007) examines the relation between share

restriction and fund return. He presents that funds with restrictions deliver higher excess return than

those without restrictions and these abnormal returns are disappeared after controlling the

restrictions. He interpret that the differences of excess return between two fund groups are because

the liquidity reservoir caused by share restrictions allow fund managers to operate their assets more

freely. On the contrary, Sadka (2010) demonstrate that liquidity risk is an important determinant in

hedge fund returns but the impact of liquidity risk on returns is independent of the share restrictions,

inconsistent with Aragon (2007)

The empirical analyses of relationship between managerial incentives and performance present

conflict results. While Ackermann, McEnally, and Ravenscraft (1999) and Edwards and Caglayan

(2001) provide evidence that funds with higher incentive fees structure exhibit better performance,

Brown et al. (1999) demonstrate that performance is not affected by incentive fees. Also Panageas

and Westerfield (2009) discover that fund managers would not place large weight on risky assets, in

spite of option-like compensation arrangement by high water mark contract.

The influence of both managerial incentives and restrictions on performance is documented by

Agarwal, Daniel, and Naik (2009) that find that a higher degree of managerial discretion as well as

greater managerial incentives deliver superior performance. The level of incentives are calculated

based on manager‟s option delta, managerial ownership proxied by cumulative value of the

incentive fee reinvested on fund, and high water mark provision, and the degree of managerial

discretions are estimated by lockup, notice, and redemption period. It is more likely that they

conduct comprehensive analyses about managerial discretion and incentives connected with

performance. This paper provides additional contribution to their study in that we estimate

persistence not performance, consider inflow restrictions as well as outflow restrictions, and reveal

more salient one factor of the two, restrictions and incentive, to keep performance persistence.

Meanwhile, Brown et al. (2008) analyze other characteristics of hedge funds such as capital

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structure (leverage, margin) and governance structure (75% ownership, domestic direct corporation,

indirect owner) for examination of the value of disclosure by SEC requirements. They find that

operational risk factors are correlated with lower leverage and higher ownership. In other words,

funds with regulatory problem have less leverage and more concentrated management structure.

Aragon, Liang, and Park (2011) exploit the differences of capital flow and performance between on-

and off-shore funds and find that risk adjusted abnormal returns are observed in stand-alone onshore

funds more imposed share restrictions than offshore funds.

III. Data and Methodology

3-1. Data

We use the TASS hedge fund data that is known as the most comprehensive database comparing

to other hedge fund data venders1. The TASS database provides monthly returns, net asset value

(NAV), asset under management (AUM), fund strategies, fund characteristics related restrictions

(open or close to public, open-end or closed-end, minimum investment amount, redemption notice

period, lockup period, payout period), and other fund characteristics (management fee, incentive fee,

high water mark, leverage, margin, domicile country, management company location, and so on).

Our Sample period extends from January 1994 to March 2008 including dead funds as well as

lives. To alleviate survivorship bias, the period begins with 1994 because the TASS database

supplies the information of dead funds after January 1994 named as defunct fund. As of March

2008, the number of sample funds is 10,838 consisting of 6,327 live and 4,511 dead funds.

Out of total sample funds, we basically exclude funds that report returns as foreign currency and

gross of all fees, and whose primary category is „fund of hedge funds‟ and „undefined‟. Composed

of portfolios with other hedge funds, „fund of hedge funds‟ exhibit different features from individual

hedge funds in fee structure, trading strategy, risk management, and due diligence cost. Afterwards,

we employ two ways, non-parametric and parametric method as performance measure for

persistence. In non-parametric method, performance persistence of funds is determined by

comparing style-adjusted return of each year for two years. On the other hand, in parametric method

„winners‟ or „losers‟ are resolved using abnormal returns computed by regression analysis with 30

monthly returns.

1 Liang (2000)

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Therefore, for the non-parametric method based on yearly interval, we eliminate funds with less

than 12 months of return history and with inception date after 2007. For parametric test based on 3

years interval, we discard funds with inception date after 2003 and with less than 60 months of

return data. Moreover, we delete the observations of funds with less than 6 monthly returns a year

and with missing value or zeros of asset under managements at the end of year in both non-

parametric and parametric tests.

In addition, we remove the observations of inception and termination year of funds in order to

mitigate backfill bias that funds report returns prior to their added date to database and liquidation

bias that returns are not reported prior actually closing the funds. Consequently, through the

combination of selection procedure for sample and observation controlling several biases frequently

happened in hedge funds, we finally extract 3,756 sample (2,276 live and 1,486 dead funds) and

17,365 yearly observations for non-parametric test, and 2,001 (1,035 live and 966 dead funds)

sample and 211,258 monthly observations for parametric test.

Table 1 reports descriptive statistics2. The average (median) yearly return of entire sample is

12.23% (9.92%) and flows increase 45.74% a year. They show quite different percent by strategy.

While emerging market achieves 17.49% yearly returns on average as the highest among strategies,

dedicated short bias delivers only 1.85% of returns as the lowest. On the other hand, dedicated short

sale records the smallest fund size but largest fund flows. Long/short equity hedge accounts for the

most number of samples.

Insert Table 1 about here

3.2 Methodology

To investigate the impact of flow restrictions on persistence, once we identify funds with

superior persistence using non-parametric and parametric methods. Next, we examine the

relationship between restrictions and persistence using restriction difference analyses among funds

classified by the extent of persistence drawn by non-parametric method, and using regression

analyses of persistence drawn by parametric method on restrictions. After exploring other

characteristics beyond restrictions and extracting uncovered character to affect persistence, we

examine the relationship between restrictions and uncovered character. Finally, we demonstrate

2 This is about samples drawn from non-parametric method. The patterns of descriptive statistics for samples drawn from

parametric method are very close to those for non-parametric method.

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which feature, restrictions or uncovered character, is salient factor to influence on persistence in

hedge funds through comparing winners‟ ratio among the groups classified by restrictions and

incentives.

3.2.1 Performance persistence

Performance persistence is basically estimated by comparing the performance measures in

previous period with the performance measures in subsequent period. We use two different

performance measures, non-parametric method based on contingency table and parametric on

regression.

For non-parametric test, we calculate style-adjusted returns of funds. Unlike other investment

vehicles, hedge funds employ the various trading strategies, showing that return distributions of

funds within a particular fund strategy are remarkably different from those within other fund

strategy. Hence, we need to adjust returns of individual fund with returns of funds within same

strategy. Style-adjusted return of a fund is calculated by deduction of average return of all funds

employing identical strategy with a fund from a fund‟s raw return. Based on style-adjusted returns,

we construct contingent table of winners and losers. If a style-adjusted return on yearly base of a

fund is higher than median return of all funds employing same strategy with a fund, it becomes a

winner (W), otherwise, it is a loser (L). Therefore, funds denoted as winners for two consecutive

periods (WW) are classified by funds with superior performance persistence3. We also extend

evaluation period to multi-period, three consecutive years. In multi-period analysis, funds being

winners for three periods (WWW) are identified as funds existing performance persistence.

Panel A of Table 2 describes summary statistics of yearly returns on both previous and

subsequent periods by three classes (WW, WL or LW, and LL) in two periods persistence. The ratio

of funds displaying superior performance persistence is 28.59% out of total observed funds. Funds

displaying superior persistence achieve yearly returns of 14.06% and 12.94% on each period.

Overall returns of the second period are lower than returns of the first period in all classes, implying

that returns in hedge fund industry have been decreasing in recent years. On the other hand,

skewness and kurtosis of funds with superior persistence are higher in the second period, suggesting

that funds with extreme positive returns have increased.

3 If a fund is losers in two consecutive periods, it is denoted as LL. WL denotes winners in previous period but losers in

subsequent period, and LW denotes the reverse.

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The relative performance persistence by non-parametric test cannot necessarily provide genuine

superior persistence of funds because the entire peer group may have inferior performance.

Therefore, we estimate absolute performance persistence using performance measure by the

multifactor model. For this parametric test, we employ modified Fung and Hsieh model (hereafter

MFH model) that liquidity factor is added to Fung and Hsieh (2004) seven factor model as in

equation (1).

(1)

where is the monthly return of a fund i in excess of one month T-bill rate. EMKT (equity

market) is S&P 500 minus risk free rate. SIZE (size spread) is Wilshire small cap 1750 minus

Wilshire large cap 750 till the end of 2006 and Russell 2000 minus S&P 500 thereafter. BMKT

(bond market) is the monthly change in the 10 year treasury constant maturity yield. CRED (credit

spread) is the monthly change in the Moody's Baa yield less 10-year treasury constant maturity

yield. TFB (bond trend-following factor), TFCR (currency trend-following factor), and TFCM

(commodity trend-following factor) are the return of PTFS (primitive trend-following strategy) of a

bond lookback straddle, of a currency lookback straddle, and of a commodity lookback straddle,

respectively4.

Meanwhile, is Sadka (2006) liquidity factor on permanent-variable price impact

component extracted from the Trades and Quotes (TAQ) tick-by-tick data. Liquidity is important

factor to estimate performance of hedge funds especially associated with share restrictions. Aragon

(2007) indicates the reason that fund returns with share restrictions are higher than those without

restrictions is due to liquidity premium by share illiquidity. With respect to his finding, however,

Sadka (2010) argues that the liquidity premium could be controlled by using existing liquidity risk

factor.

4 The three trend-following factors of Fung and Hsieh (2001) are provided by David Hsieh web site at

http://faculty.fuqua.duke.edu/~dah7/DataLibrary/TF-FAC.xls

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We calculate alphas over consecutive non-overlapping 3 year intervals. That is, to be estimated

performance persistence, funds have at least 60 monthly returns5. This is a quite long time and may

lead to likelihood of survivorship bias. However, hedge funds commonly restrict money outflows

by provisions such as lockup and redemption notice period for 2 or 3 years. Moreover, it is observed

that the duration of persistence has been lengthened by developing new estimation methods with

respect to short-term period returns in recent studies. In addition, because parametric test evaluates

not relative persistence but absolute persistence, it is hard to say that survivorship bias is

problematic. In other words, it does not matter whether funds survive in the long time on the

parametric test since this paper examines the existence of persistence on individual funds not on

entire hedge fund industry.

As the result of regression analysis in equation (1), if an alpha of a fund is positive and

statistically significant in one period, it becomes a winner (WW)6. To be classified as the funds

existing performance persistence, funds should display positive and statistically significant alphas in

two consecutive periods (WWWW)7.

Panel B of Table 2 displays summary statistics of monthly returns on both previous and

subsequent periods by five classes in two periods persistence8. The number of funds displaying

superior performance persistence is 1,313 out of 8,005 fund observations, which is only 16.4% of

the total observed funds. Regardless of existence of persistence, average returns of funds are

commonly lower in second period as in panel A. The percentage of funds with persistence is lower

than 22% for alpha persistence by April 2005 of Jananathan (2010). This is more likely to be caused

by recently decreasing rate of returns over the entire hedge fund industry as shown in panel A and B.

Moreover, liquidity risk factor used in this study is attributable to depreciate the abnormal return as

in Sadka (2010). Average yearly return of funds with superior performance is 12.48% in the first

and 11.04% in the second period, which is a bit lower than those in panel A.

5 Funds have minimum 30 monthly observations in each period to calculate alpha. 6 If an alpha of a fund is positive but insignificant, the fund is denoted as W, negative but insignificant, it is L. and

negative and significant, then it is LL. 7 As the results of regression analyses for two estimation periods, we have nine different outcomes depending on signal

and significance of alphas, WWWW, WWW, WW, WWL(=LWW), WWLL(=WLLW= LLWW), WLL(=LWW), LL, LLL,

and LLLL. 8 We classify nine cases in drawn from note 7 by five depending on the extent of performance persistence. WWWW:

alphas are positive and statistically significant in two consecutive periods, WWW and WW: alphas are positive and at

most one alpha is significant, LLL and LL: alphas are negative and at most one alpha is significant, LLLL: alphas are

negative and statistically significant in two consecutive periods, and the others (WWL, LWW, WWLL, WLLW, LLWW,

WLL, LWW): one of alphas is positive and the other of alpha is negative.

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3.2.2. Persistence and restrictions

We estimate flow restrictions through six related features of funds. Open- or closed-end,

opening or closing to public, and minimum investment amount are used as proxies for inflow

restrictions and redemption notice period, lockup period, and payout period are used as proxies for

outflow restrictions. Summary statistics related to flow restrictions exhibit in Table 3.


Median of redemption notice period and payout period are 30 days and 10 days, respectively.

On the contrary, the median lockup period is 0 month and 68.8% of funds do not impose lockup

period. Average (median) minimum investment amount is $8.64 ($0.5) millions. 58% of funds are

open-end and only 17% of funds allow individual investors to invest in the funds.

The relationship between performance persistence and flow restrictions is examined using

probit and ordered-probit regressions as in equation (2). Dependent variable is persistence and

explanatory variables are proxies representing flow restrictions. Size, flow, and volatility of funds

are included in explanatory variables for control purposes. Strategy dummies are also added to

control the variability of performance relying on fund category.

(2)

where is 1 if a fund i is a winner in previous and subsequent periods and is 0

otherwise in probit model. In ordered probit model, it ranges from 1 to 5 depending on the extent of

performance persistence9. equals 1 if a fund i is open-end and 0 if a fund i is closed-end.

equals 1 if a fund i is open to public and 0 otherwise. is the log of the

minimum investment amount of fund i. and are redemption

notice period (days), lockup period (months), and payout period (days) of fund i, respectively.

9 It equals 5 if a fund i is denoted as WWWW in note 8, 4 denoted as WWW and WW, 3 denoted as WWL, LWW,

WWLL, WLLW, LLWW, WLL, and LWW, 2 denoted as LL and LLL, and 1 denoted as LLLL.

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is the log of estimated assets of fund i , is the money flows of fund i10

, and

is the standard deviation of monthly returns of fund i at the end of year t-211

. Meanwhile,

we witness that there are considerable missing value or zeros for estimated assets, implying that size

and flow variables might not be very accurate. Therefore, funds with the top and bottom 1% of them

are winsorized to control for outliers. is a strategy dummy that equals 1 if fund i

belongs to strategy s and equals 0 otherwise12

.

One of drawbacks in analyses by equation (2) is likely that the parameters of restriction

variables are extracted from the last available date of fund data, even if these variables may change

over time. However, hedge funds are observed to rarely alter their contract features throughout the

life. Ding et al. (2009) compare these features using two different version of the TASS database,

January 2001 and September 2005 and find that only 10% (9%) of individual hedge funds change

their subscription (redemption) period. Liang and Schwatz (2008) also find no more than 2% of

funds reform the incentive and management fee structure from 1998 to 2006.

Another possible shortcoming in equation (2) is that a particular fund may be able to have

multiple observations in dependent variable despite having an identical explanatory variable.

Therefore, we run probit and ordered probit regressions by clustering the observations by funds,

because clustered standard errors are able to correct for within fund correlation13

.

3.2.3 Persistence and other characteristics

The impact of other characteristics beyond flow restrictions on persistence is analyzed using

equation (3). Explanatory variables are classified by four categories, incentives, ownership, capital

structures, and locations. Incentives include incentive fee, management fee, and high water mark

provision. Even if management fee is devised to cover the operating cost of the managers, it can be

considered as one of the incentive features because management fee of larger funds can deliver a

substantial part of a manager‟s profit. Ownership category includes a variable whether principals

have money invested in funds. Using leverage and margin in leverage are included in capital

structure category. Location is composed of variables whether funds are on- or off-shore and

10 Flows are calculated by the following equation.

where is the assets under

management of fund i at the end of year t-1 and is the return for fund i during the year t. 11 t-2 is not a calendar year but a year prior to beginning of the persistence estimation because estimation period is three

years interval. For instance, if we estimate persistence of fund i from 1996 to 2001, t-2 becomes 1995. 12 To be full rank for the equation, the number of dummy variables for ten strategies is nine. 13 We also make clustering by two dimensions, fund and year at the same time using Cameron, Gelbach, and Miller

(2006).

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whether management companies are located in big cities. Control variables used in equation (2) are

also employed in equation (3).

where is as defined in equation (2). and are

management fee and incentive fee charged by fund i , respectively. equals 1 if

fund i has a high water mark provision, and equals 0 otherwise. equals 1 if

principals of management company of fund i have invested in a fund i and equals 0 otherwise.

is 1 if a fund i use leverage, and is 0 if a fund i does not use leverage.

equals 1 if a fund i leverages using margin borrowing and equals 0 other wise. equals 1 if

a fund i is onshore and equals 0 if a fund i is offshore. equals 1 if headquarter of

management company of fund i is located in big city, and equals 0 otherwise14

. ,

, , and are as defined as in equation (2).

3.2.4 Persistence, restrictions, and incentives

To analyze the relationship between persistence and both of restrictions and incentives at the

same time, we use two kinds of ordered probit regression models whose dependent variable is the

extent of persistence. As explanatory variables, one includes scaled inflow restrictions, outflow

restrictions, and incentives, and the other contains a classification variable representing the extent of

restrictions and incentives.

We scale the parameters of restriction and incentive in a fund from 0 to 1 by percentile of

probability distribution on each variable. Because inflow restrictions, outflow restrictions, and

incentives include three variables, scaled them provide the range from 0 to 3, respectively, implying

that the higher scales score is the stronger restrictions or the more incentives. For binary distribution

of open end and open to public variables, scale score is endowed with 1 if a fund is closed-end or

does not raise money from individual investors.

14 Big cities are defined as cities where more than 30 funds‟ management companies are located and are following 8 cities;

New York, London (UK), San Francisco, Hamilton (Canada), Greenwich, Boston, Chicago, and Stamford.

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To investigate which factor between restrictions or incentives is more important to persistence,

we also conduct probit regression of persistence on the magnitude of restrictions and incentives. For

this, we construct portfolios based on the degree of restrictions and incentives. Funds are

categorized to be high (low) if scaled incentives or restrictions of funds are above (below) median

of total funds, resulting in four kinds of value in classification variable (HH, HL, LH, and LL). For

instance, if a fund whose incentives and restrictions are above median, then the fund is classified

HH. We create matrix columns using parameterization method for the classification variable and

specify less than full rank. Therefore, parameter estimates of each class by GLM (generalized linear

method) present the difference in the effect of each class compared to the last class.

IV. Empirical results

4-1. Performance persistence and flow restrictions

4.1.1. Non-parametric method

The results of difference analyses on flow restrictions among funds classified based on the

extent of persistence drawn by non-parametric method are shown in Table 4. We find obviously

clear results that the funds with superior performance persistence exhibit higher degree of flow

restrictions. In panel A analyzing two period persistence, the funds belong to WW display higher

minimum investment amount, more closed-end, less opening to public, and longer redemption,

payout, and lockup periods. Differences among classes of persistence are statistically significant in

all restriction variables15

. Restrictions of WW are substantially more rigid than those of LL. Funds

with persistence show 5.6 days longer redemption period, 4.3 days longer payout period and 1.4

months longer lockup periods than funds belongs to LL. The fund ratios of open-ended and opening

to public for WW are less than those for LL. In panel B that we extend the persistence estimation

period to three consecutive years, all restriction variables except „open to public‟ also present

differences with statistically significant among classes of persistence16

.

15 Basically we conduct significant test using GLM (general linear model) for difference analyses among funds classified

by persistence. To control heteroscedasticity that presents difference variances of restriction variables among classes for

persistence, we also use Welch‟s and Kruskal-Walli test with respect to variables showing heteroscedasticitic variance as a

result of Levin‟s test. The results using these tests are very close to those reported in the paper. We can provide results on

request. 16 In three periods persistence, we have four classes depending on the number of winners, WWW: winners in three

periods, WW (=WWL, WLW, LWW): winners in two periods, LL (=WLL, LLW, LWL): winners in one period, and LLL:

losers in three periods.

17


Consistent with the above results, funds with persistence have more rigid flow restrictions

provisions when we use the scaled restrictions in lieu of individual restriction proxies in panel C

and D of Table 4. In two and three periods analyses, scaled inflow and outflow restrictions as well

as scaled total restrictions exhibit statistically significant differences between WW and LL or

WWW and LLL. Especially, the difference of outflow restrictions between two classes is bigger

than that of inflow restrictions. While the average scaled inflow restrictions of WWW and LLL in

three estimation periods are 1.79 and 1.66, the average scaled outflow restrictions of WWW and

LLL are 1.60 and 1.36, respectively, meaning that the difference of outflow restrictions is as almost

twice as that of inflow restrictions.

Table 5 shows flow restrictions and performance persistence of funds by trading strategies. As

we expect, in most strategies, the funds with persistence present the higher total flow restrictions as

well as inflow and outflow restrictions17

. In equity market neutral, event driven, fixed income,

global, and long/short equity strategies, funds of winners for two consecutive periods show

statistically significant differences from funds of losers in all three scaled restrictions. Especially,

the average of scaled total restrictions of WW is as much as 0.5 higher than that of LL in fixed

income and global strategies. The scaled outflow restrictions difference between WW and LL is

maximized in fixed income strategy as 0.35. Meanwhile, funds belong to emerging markets,

managed futures, and multi-strategy do not display the difference of scaled inflow restrictions.


4.1.2. Parametric method

Subsequent to restrictions difference analyses among funds classified by relative performance

persistence drawn by non-parametric method, we investigate the relation impact of restrictions on

persistence estimated by absolute performance computed through parametric method. Table 6

shows the results of relation between flow restrictions and persistence drawn by MFH model in

equation (1).

17 We remove „dedicated short bias‟ among ten trading strategies due to the insufficient number of samples.

18


In both simple/multiple probit and ordered probit regression analyses using equation (2)18

, we

find that restriction provisions have influence on performance persistence. In detail, minimum

investment amount, redemption notice period, and lockup period are positively associated with

superior performance persistence in all models. Payout period is also positively related to

persistence and open to public is negatively related to persistence as expectation in simple model.

These relationships exhibit clearer in ordered probit model. Except open-ended in simple probit,

and open-ended and open to public in multiple probit model, all restriction variables are observed to

affect performance persistence. Open ended and open to public variables provide negative sign as

expectation but insignificant parameter estimates in some models. It implies that outflow restriction

provisions are more influence on persistence rather than inflow restriction provisions.

By strategy, the impact of scaled inflow or outflow restrictions on persistence is estimated using

probit and ordered probit model that dependent variable is classes sorted on persistence as in Table

6 and explanatory variables are scaled inflow and outflow restrictions including control variables.

Inconsistent with results of non-parametric test, the scaled restrictions are positively associated with

persistence in not as many as strategies in Table 7. Only funds belong to event strategy present that

both inflow and outflow restrictions have significantly positive relations with persistence in probit

model. In ordered probit model, while fixed income, global, long/short equity hedge, managed-

futures, and multi-strategy exhibit significant estimates for scaled outflow restrictions, emerging

market and event driven show significant estimates for scaled inflow restrictions. In analyses with

respect to entire samples, we find that not only scaled outflow restriction but inflow restrictions

have significant influence on persistence.

As the results of test through both non-parametric and parametric tests, we reveal that

restrictions affect performance persistence. Moreover, outflow restrictions are more positively

associated with persistence of hedge fund, which is line with Aragon (2007) and Agarwal, Daniel,

and Naik (2009) that provide the evidence the impact of individual flow outflow restrictions such as

lockup period and redemption period on performance.


18 Dependent variables are 0 or 1 for probit, and 1 to 5 as defined in note 9 for ordered probit model.

19

4.2 Performance persistence and fund characteristics

In this section, we explore the relationship between other characteristics beyond flow

restrictions and persistence. For this, we segregate other characteristics into four categories,

incentives, ownership, capital structure, and location. Table 8 displays the results of these analyses.


We conduct probit /ordered probit regression of persistence on each category in model 1 to 4

and on all variables including four categories in model 5. In panel A for probit model of Table 8,

only high water mark among variables within incentive category in model 1 and 5 display positively

significant estimate, consistent with Agarwal et al. (2009). In addition, onoff variable belongs to

location category in model 4 and 5 exhibits significant estimate, consistent with Aragon et al. (2011)

that onshore funds deliver higher performance than offshore funds. No variables connected with

ownership and capital structure display significant estimate.

On the other hand, in ordered probit model of panel B, we find that all variables included in

incentive provisions in model 1 and 5 show positively significant estimates. Namely, funds with

persistence have more likely to impose high water mark, higher incentive fee, and higher

management fee. In model 5 including variables in all category, we cannot find any other variables

with statistically significant estimates except variables included in incentives. In turn, we suggest

that incentives including incentive fee, high water mark, and management fee may be determinants

to affect persistence of performance in hedge funds.

4.3. Persistence and Incentives

In the above sections, we observe that flow restrictions and incentives among characteristics

affect performance persistence of hedge funds. We examine the relationship between restrictions

and incentives in this section.

Panel A of Table 9 display the results of regression of restrictions on individual proxies

representing incentives. Dependent variables are scaled total, inflow, and outflow restrictions, and

explanatory variables are management fee, incentive fee, high water mark, and control variables.

While incentive fee and high water mark are observed to have positive relation with flow

restrictions, management fee have negative connection with them. It implies that funds with strict

20

flow restrictions are likely to enjoy more their incentives after satisfying with investors‟ expected

rate of returns. It also indicates that funds provide the opportunity for investors to choose

appropriate funds on their investment purpose, either higher returns attributable to an illiquidity of

their money by strict flow restrictions or lower returns with low management fee. In the analyses

that explanatory variables are scaled from 0 to 1 by percentile of probability distribution on each

incentive variables, the results are exactly same as those drawn from analyses using original figures.


Panel B of Table 9 display correlation coefficient of scaled restrictions and scaled incentives.

Consistent with the above outcomes, scaled total, inflow, and outflow restrictions are positively

correlated with scaled incentive fee and scaled high water mark, and are negatively correlated with

scaled management fee.

4.4 Performance persistence, restrictions, and incentives

We recall that flow restrictions and managerial incentives are determinants for superior

performance persistence and restrictions are positively correlated with incentives. In this section, we

investigate which feature is more salient factor to affect persistence by comparing winner ratios on

groups constructed by the extent of restrictions and incentives, and by probit regression of

persistence on class variable whose value is determined by the degree of restriction and incentives.

Panel A of Table 10 displays the results of regression of persistence on scaled inflow restrictions,

outflow restrictions, and incentives. While only scaled outflow has significantly positive estimate in

probit model, the above three variables are positively associated with superior persistence in

ordered probit model. Namely, outflow restriction is discovered as more important determinant than

any other factors.


For the search for more salient factor between restrictions and incentive with respect to superior

persistence, we construct four groups classified by the extent of incentives and restriction. That is, if

a fund has scaled score of restriction above the median of entire sample, a fund is denoted as high

(H) and is denoted as low (L) otherwise. If a fund‟s scaled incentive score is higher than median

21

incentive score of all samples, a fund belongs to high (H) and belongs to low (L) otherwise. Thereby,

all funds are classified as four groups (HH, HL, LH, and LL)19

. Median of scaled total, inflow,

outflow restrictions, and incentives are 3.17, 1.75, 1.43, and 1.83 respectively.

Panel B of Table 10 displays frequencies and proportion of winners on each group. As we

expect, funds classified by high restriction or high incentive show more frequency and higher

proportion of winners. For example, while 22.6% (18.1%) of funds with high total restrictions

(incentives) exhibit superior performance persistence, 10.5% (14.7%) of funds with low total

restrictions present it. However, if we analyze frequency and proportion of winners on each group,

the results are changed. Interestingly, funds with high restrictions and high incentives (HH) have

lower winner ratio than funds with high restrictions and low incentives (LH). While winner ratio for

HH is 21.2%, winner ratio of LH is 25.4% in groups classified by scaled total restrictions and

incentives. The differences between two groups are widened in groups classified by scaled outflow

restrictions and incentives. On the other hand, funds with high restrictions and high incentives (HH)

have always higher winner ratio than funds with low restrictions and high incentives (HL)

regardless of total, inflow, and outflow restrictions. Namely, although funds with higher restrictions

and higher incentives respectively display higher ratio of persistence, funds with lower incentives

exhibit higher ratio of persistence than funds with higher incentives if we consider interact effect

between restrictions and incentives and fix condition as high restrictions. These results suggest that

total restrictions and outflow restrictions are more important factor than incentives to affect

persistence of performance in hedge funds.

Finally, we conduct probit regression of persistence on class variables categorized four groups

including control variables. For dependent variable of class, we create matrix columns using

parameterization method. The results are shown in Panel C of Table 10. The parameter estimates of

each group present the difference in the effect of each class compared to funds with high incentives

and restrictions (HH). Funds included in LL and HL classified by total restrictions or outflow

restrictions obviously display lower superior persistent ratio than HH. However funds belong to LH

show higher winner ratios comparing to ratios of HH in spite of insignificant estimates20

. Therefore,

we can conclude that restrictions matter more to superior persistence of performance in hedge funds

than incentives. In the mean time, in groups classified by inflow restrictions and incentives, funds

19 The first letter represents incentives and the second represents total, inflow, and outflow restrictions. 20 In analyses with explanatory variables excluding control variables, funds belong to LH exhibit statistically significant

higher winner ratio than funds in HH.

22

with high inflow restrictions and high incentives do not display higher winner ratio than those of

funds with low inflow restrictions and high incentives.

4.5 Robustness

This section addresses the robustness of the main results. We conduct regression analyses using

interaction variables with proxies for restrictions and performance in previous period. That is, we

can investigate whether performance persistence is attributable to restrictions by analyzing the

relationship between performance of subsequent period and combining variables with proxies for

restrictions and performance of previous period as following equation (4).

where and are abnormal returns of subsequent and previous period for three year

intervals of fund i, drawn from MFH model. , , ,

and of fund i are defined as in equation (2).

and of fund i are same as defined in equation (2) at time t21

.

Panel A of Table 11 displays the relation between performance at subsequent period and

combined effect of restrictions and performance at previous period for funds with superior

persistence. We find that combined variables of minimum investment amount, redemption period,

payout period, and lockup period with performance at previous period exhibit significantly positive

estimates. It is interpreted that performance at the first period interacted with restrictions are

associated with performance at the second period. The combined variables with open-ended and

open to public report negative sign but insignificant.


On the other hand, we do not find any positive relations in Panel B of Table 11 analyzing the

relations for funds without superior persistence. No combined variables with individual proxy for

restrictions exhibit positive relation with performance at the second period. Rather, the combined

21 t is last year of subsequent period for three year intervals.

23

variables of previous performance with minimum investment amount and open to public show

significant reversed sign to Panel A. Hence, we suggest that the results provide the clear evidence

that funds with superior persistence restrict fund flows.

V. Conclusions

This paper examines the impact of flow restrictions on superior persistence of performance in

hedge funds. We find that money outflow restrictions such as redemption notice period, lock period,

and payout period are positively associated with winners‟ persistence. In addition, we provide the

evidence that funds with barrier to entry for investment by high minimum investment amount,

closed-end, and opening to only institutions are more likely to deliver the consistent superior

performance. It implies that fund managers who are confident in achievement of good performance

seem to impose rigid contractual provisions associated with flow restrictions. Out of two resources

with respect to persistence, outflow restrictions are observed to more important factors for

persistence than inflow restrictions.

We also document that managerial incentives such as incentive fees, high water mark, and

management fees have influence on winners‟ persistence. Thereby, we explore which feature

between flow restrictions or managerial incentives is more salient determinant for continuous

excellent performance persistence. Interestingly, while funds with higher restrictions and higher

incentives respectively display higher ratio of persistence, funds with lower incentives exhibit

higher ratio of persistence under higher restrictions condition. This result suggests that persistence

is subject to flow restrictions rather than incentives.

This paper makes several contributions in the following ways. First, our study is the first one

about the relation between performance persistence and flow restrictions or persistence and both

restrictions and incentives simultaneously. Second, we provide the empirical evidence with the

theoretical prediction by Glode and Green (2011) that reveal determinant to affect persistence not

from managerial skills but from unique structure form of hedge funds composing of limited and

general partners. Third, we consider entry barrier to invest in hedge funds such as closed-end form

and closing to individual investors beyond restrictions related to withdrawn.

We believe that these results have important implications for investors to choose an appropriate

fund and for practitioners to make contractual features in hedge fund industry. We expect that future

research issues associated with this paper seem to be characters of funds with rigid flow restrictions.

24

In other words, if we understand that funds whose restrictions are strict deliver superior

performance persistence, then which funds are willing to impose these restrictions?

25

<References>

Ackermann, G., R. McEnally, and D. Ravenscraft, 1999, The performance of hedge funds: Risk,

return, and inventives, , Journal of Finance 54, 833-874.

Agarwal, V., N. D. Daniel, and N. Y. Naik, 2009, Role of managerial incentives and discretion in

hedge fund performance, Journal of Finance 64, 2221-2256.

Agarwal, V. and N. Y. Naik, 2000, Multi-period performance persistence analysis of hedge funds,

Journal of Financial Quantitative Analysis 35, 327-342.

Aragon, G. O., 2007, Share restrictions and asset pricing: Evidence from the hedge fund industry,

Journal of Financial Economics 83, 33-58.

Aragon, G. O., B. Liang, and H. Park, 2011, Onshore and offshore hedge funds: Are they twins?,

working paper.

Avramov, D., R. Kosowski, N. Y. Naik, and M. Teo, 2011, Hedge funds, managerial skill, and

macroeconomic variables, Journal of Financial Economics 99, 672-692.

Bali, T. G., S, J. Brown, and M. O. Caglayan, 2011, Do hedge funds‟ exposures to risk factors

predict their future returns?, Journal of Financial Economics 101, 36-58.

Baquero, G., J. R. Horst, and M. Verbeek, 2005, Survival, look-ahead bias and the persistence in

hedge fund performance, Journal of Financial Quantitative Analysis 40, 493-517.

Baquero, G. and M. Verbeek, 2009, A portrait of hedge fund investors: Flows, performance, and

smart money, working paper.

Bares, P. A., R. Gibson, and S. Gyger, 2003, Performance in the hedge fund industry: An analysis of

short and long-term persistence, Journal of Alternative Investment 6, 25-41.

Brown, S. J., W. N. Goetzmann, and R. G. Ibbotson, 1999, Offshore hedge funds: Survival and

performance 1898-1995, Journal of Business 72, 91-118.

Brown. S., W. Goetzmann, B. Liang, and C. Schwarz, 2008, Mandatory disclosure and operational

risk: Evidence from hedge fund registration, Journal of Finance 63, 2785-2815.

Ding, B., M. Getmansky, B. Liang, and R. Wermers, 2009, Share restrictions and investor flows in

the hedge fund industry, working paper.

Edwards, F. R. and M. O. Caglayan, 2001, Hedge fund performance and manager skill, Journal of

Futures Markets 21, 1003-1028.

Fung, W. and D. A. Hsieh, 2004, Hedge fund benchmarks: a Risk-based approach, Financial

Analysts Journal 60, 65-80.

Glode, V. and R. C. Green, 2011, Information spillovers and performance persistence for hedge

funds, Journal of Financial Economics 101, 1-17.

26

Hong, H., J. D. Jubic, and J. C. Stein, 2005, Thy neighbor‟s portfolio: Word-of-mouth effects in the

holdings and trades of money managers, Journal of Finance 60, 2801-2824.

Jaganathan, R., A. Malakhov, and D. Novikov, 2010, Do hot hands exist among hedge fund

managers? An empirical evaluation, Journal of Finance 65, 217-255.

Kosowski, R., N. Y. Naik, and M. Teo, 2007, Do hedge funds deliver alpha? a Bayesian and

bootstrap analysis, Journal of Financial Economics 84, 229-264.

Liang, B., 2000, Hedge funds: The living and the dead, Journal of Financial Quantitative Analysis

35, 309-326.

Panageas, S. and M. Westerfield, 2009, High water marks: high risk appetites? Convex

compensation, long horizons, and portfolio choice, Journal of Finance 64, 1-36.

Sadka., R., 2006, Momentum and post-earnings-announcement drift anomalies: The role of liquidity

risk, Journal of Financial Economics 80, 309-349.

Sadka, R., 2010, Liquidity risk and the cross-section of hedge fund returns, Journal of Financial

Economics 98, 54-71.

27

Table 1. Descriptive statistics

This table reports descriptive statistics of 3,756 (2,276 live and 1,486 dead funds) sample funds and 17,365

yearly observations by trading strategy. The TASS database classifies hedge funds into ten primary categories.

Sample period is from January 1994 to March 2008. Mean and median returns are the average and mean of

yearly returns for funds belong to identical strategy. Size is average assets under management (AUM) at the

end of year of funds belong to identical strategy. Flow is calculated by the following equation.

where is the assets under management of fund i at the end of year t-1 and

is the return for fund i during the year t. Volatility is annualized monthly standard deviation of all funds within

identical strategy.

Trading Strategy N Observations Return Return

Size($M) Flow

(%)

Volatility

(%) Mean(%) Median(%)

Convertible Arbitrage 168 804 9.01 8.39 168.05 47.94 20.84

Dedicated Short Bias 36 177 1.85 2.85 35.07 67.36 78.45

Emerging Markets 294 1,523 17.49 13.82 157.99 36.21 67.82

Equity Market Neutral 273 1,157 8.55 7.12 109.87 54.11 24.07

Event Driven 433 2,139 12.15 10.86 231.33 52.41 22.52

Fixed Income Arbitrage 218 955 9.41 8.53 220.94 58.10 17.56

Global Macro 232 899 10.06 7.29 235.78 49.00 46.11

Long/short Euiqty Hedge 1,452 6,711 13.68 11.03 119.53 45.09 51.99

Managed Futures 439 2,058 10.57 8.70 70.01 30.83 72.89

Muti-Strategy 211 942 12.14 10.23 294.43 51.25 27.62

Total 3,756 17,365 12.23 9.92 152.84 45.74 45.96

28

Table 2. Summary statistics for performance persistence

This table reports summary statistics of performance persistence for two estimation periods in which

performance is measured in non-parametric and parametric methods. Panel A shows yearly returns statistics of

three groups (WW, LL, WL or LW) classified by the extent of persistence drawn by non-parametric method.

If a style-adjusted return (raw return-average return of all funds within same strategy) on yearly base of a fund

is higher than median return of all funds employing same strategy with a fund, it becomes a winner (W),

otherwise, it is a loser (L). Therefore, if funds are winners (losers) in two consecutive periods, funds are

denoted as WW (LL). WL denotes winners in previous period but losers in subsequent period, and LW

denotes the reverse. Panel B shows yearly returns statistics of five groups (WWWW, WWW or WW, LL or

LLL, LLLL, the others) classified by the extent of persistence drawn by parametric method. Performance of

funds are estimated by modified Fung and Hsieh model (MFH model) that liquidity factor is added to Fung

and Hsieh (2004) seven factor model as following equation.

where is the monthly return of a fund i in excess of one month T-bill rate. EMKT (equity market) is

S&P 500 minus risk free rate. SIZE (size spread) is Wilshire small cap 1750 minus Wilshire large cap 750 till

the end of 2006 and Russell 2000 minus S&P 500 thereafter. BMKT (bond market) is the monthly change in

the 10 year treasury constant maturity yield. CRED (credit spread) is the monthly change in the Moody's Baa

yield less 10-year treasury constant maturity yield. TFB (bond trend-following factor), TFCR (currency trend-

following factor), and TFCM (commodity trend-following factor) are the return of PTFS (primitive trend-

following strategy) of a bond lookback straddle, of a currency lookback straddle, and of a commodity

lookback straddle, respectively. is Sadka (2006) liquidity factor on permanent-variable price impact

component extracted from the Trades and Quotes (TAQ) tick-by-tick data. Therefore, the definition of each

group is as follows. WWWW: alphas are positive and statistically significant in two consecutive periods,

WWW and WW: alphas are positive and at most one alpha is significant, LLL and LL: alphas are negative

and at most one alpha is significant, LLLL: alphas are negative and statistically significant in two consecutive

periods, and the others (WWL, LWW, WWLL, WLLW, LLWW, WLL, LWW): one of alphas is positive and

the other of alpha is negative.

Panel A. Group classified using Non-parametric performance measure

Previous period

Subsequent period

n mean median std skew kurtosis

mean median std skew kurtosis

WW 3,836 14.06 8.90 15.33 2.00 4.91

12.94 7.72 14.70 2.29 6.38

WL+LW 5,891 2.32 0.69 16.91 1.17 4.65

0.69 -0.62 16.63 1.20 4.57

LL 3,692 -8.75 -5.89 8.97 -1.47 4.93

-9.51 -6.60 9.37 -1.70 2.91

Panel B. Group classified using parameter performance measure

Previous period

Subsequent period

n mean median std skew kurtosis

mean median std skew kurtosis

WWWW 1,313 12.45 10.03 8.02 1.49 2.68

11.04 8.60 8.18 2.35 7.45

WW+WWW 3,514 10.58 7.78 9.55 1.86 4.26

8.78 6.15 8.71 2.08 5.57

The others 2,660 5.03 3.91 13.32 0.31 2.40

0.13 -1.34 11.55 0.68 2.88

LL+LLL 505 -7.94 -5.43 8.09 -2.28 7.77

-8.18 -5.86 8.68 -2.27 6.45

LLLL 13 -18.40 -14.83 15.43 -0.81 -0.54

-24.38 -28.47 19.53 -0.24 -1.30

29

Table 3. Summary statistics for flow restrictions provisions

This table reports summary statistics of flow restrictions provisions. Redemption notice period is time period

that has to be given to the fund before shares can be redeemed. Lockup period is minimum period an investor

is initially required to hold its money in the fund. Payout period is time period before an investor will receive

money as returns back. Open ended equals 1 if a fund is open-end and equals 0 if a fund is closed-end. Open

to public equals 1 if a fund opens to public and equals 0 if a fund closes to public. The parameters of

restrictions are scaled from 0 to 1 by percentile of probability distribution on each variable. For binary

distribution of open-end and open to public variables, scale score is endowed with 1 if a fund is closed-end or

closing to public and is endowed with 0 otherwise. Inflow restrictions and outflow restrictions include three

variables, so that scaled them provide the range from 0 to 3, respectively.

Mean SD Min Q1 Median Q3 Max Mode

Minimum Investment

Amount($M) 8.64 2.30 0 0.25 0.50 1 100 1

Redemption Notice

Period(days) 33.75 26.93 0 14 30 45 180 30

Payout Period(days) 14.83 21.53 0 0 10 30 640 0

Lockup Period(Months) 3.82 6.50 0 0 0 12 90 0

Open Ended 0.58 0.49 0 0 1 1 1 1

Open To Public 0.17 0.37 0 0 0 0 1 0

Scaled Total Restrictions 3.24 1.11 0.61 2.36 3.19 4.12 5.9 2.79

Scaled Inflow Restrictions 1.74 0.74 0 1.16 1.75 2.49 2.99 1.75

Scaled Outflow Restrictions 1.49 0.61 0.61 1.04 1.44 1.98 2.97 0.61

30

Table 4. Flow restrictions and performance persistence for non-parametric method

This table reports flow restrictions among funds classified by the extent of persistence drawn by non-

parametric method. Panel A shows the magnitude of each restriction variable on three groups (WW, LL, WL

or LW) classified by two periods persistence drawn by non-parametric method. If funds are winners (losers)

in two consecutive periods, funds are denoted as WW (LL). WL denotes winners in previous period but losers

in subsequent period, and LW denotes the reverse. Panel B shows the magnitude of each restriction variable

on four groups (WWW, WW. LL, LLL) classified by three periods persistence drawn by non-parametric

method. If funds are winners (losers) in three consecutive periods, funds are denoted as WWW (LLL). If

funds are winners (losers) in two periods, funds are denoted as WW (LL). Panel C displays the scaled inflow

and outflow restriction score on three groups classified by two periods persistence drawn by non-parametric

method. Panel D displays the scaled inflow and outflow restriction score on four groups classified by three

periods persistence drawn by non-parametric method. Inflow restrictions and outflow restrictions include

three variables, so that scaled them provide the range from 0 to 3, respectively. Significant test use GLM

(general linear model) for difference analyses among funds classified by persistence. To control

heteroscedasticity that presents difference variances of restriction variables among classes for persistence, we

also use Welch‟s and Kruskal-Walli test with respect to variables showing heteroscedasticitic variance as a

result of Levin‟s test. ***

,**

, and * indicate significance at the 1%, 5%, 10% level.

Panel A. Two period (raw)

WW LW+WL LL Fvalue WW-LL t

Open Ended 0.5938 0.6089 0.6498 13.54*** -0.0559 -5.01***

Open To Public 0.1491 0.1536 0.1666 2.39*** -0.0175 -2.08**

Minimum Investment($M) 1.0330 0.8439 0.8768 14.59*** 0.1562 3.61***

Redemption Notice Period 38.22 34.81 32.57 42.55*** 5.64 8.93***

PayOut Period 15.55 13.81 11.23 40.87*** 4.32 9.56***

LockUp Period 4.67 3.79 3.28 42.38*** 1.39 8.93***

Panel B. Three period (raw)

WWW WW LL LLL Fvalue WWW-LLL t

Open Ended 0.6028 0.6069 0.6306 0.6695 7.64*** -0.0667 -4.05***

Open To Public 0.1494 0.1456 0.1517 0.1649 1.11 -0.0155 -1.24

Minimum Investment($M) 1.1054 0.8999 0.8225 0.9528 10.61*** 0.1526 2.28***

Redemption Notice Period 39.58 36.16 33.62 32.70 25.65*** 6.88 7.12***

PayOut Period 15.84 14.17 12.36 9.97 27.33*** 5.87 9.3***

LockUp Period 5.13 4.00 3.52 3.07 31.8*** 2.06 8.7***

Panel C. Two period (scaled)

WW WL+LW LL Fvalue WWW-LLL t

Total Restriction 3.36 3.22 3.08 62.5*** 0.28 11.16***

Inflow Restriction 1.79 1.74 1.68 21.35*** 0.11 6.47***

Outflow Restriction 1.56 1.47 1.39 81.69*** 0.17 12.77***

Panel D. Three period (scaled)

WWW WW LL LLL Fvalue WWW-LLL t

31

Total Restriction 3.39 3.26 3.16 3.02 37.94*** 0.37 9.98***

Inflow Restriction 1.79 1.76 1.72 1.66 10.96*** 0.13 5.2***

Outflow Restriction 1.60 1.49 1.43 1.36 56.33*** 0.24 12.21***

32

Table 5. Flow restrictions and performance persistence for non-parametric method by trading strategy

This table reports scaled total restriction, inflow restriction, and outflow restriction among three groups (WW,

LL. WL or LW) classified by the extent of two periods persistence drawn by non-parametric method. If funds

are winners (losers) in two consecutive periods, funds are denoted as WW (LL). WL denotes winners in

previous period but losers in subsequent period, and LW denotes the reverse. The parameters of restrictions

are scaled from 0 to 1 by percentile of probability distribution on each variable. For binary distribution of

open-end and open to public variables, scale score is endowed with 1 if a fund is closed-end or closing to

public and is endowed with 0 otherwise. Inflow restrictions and outflow restrictions include three variables, so

that scaled them provide the range from 0 to 3, respectively. Panel A shows the magnitude of scaled total

restriction composed of six variables, Panel B is associated with scaled inflow restriction, and Panel C is

related to scaled outflow restrictions. Significant test use GLM (general linear model) for difference analyses

among funds classified by persistence. ***

,**

, and * indicate significance at the 1%, 5%, 10% level.

Panel A. Scaled total restrictions

WW WL+LW LL Fvalue WW-LL t

Convertible Arbitrage 3.54 3.50 3.28 4.04**

0.26 2.62***

Emerging Markets 2.64 2.52 2.59 1.45 0.06 0.75

Equity Market Neutral 3.49 3.58 3.23 11.92***

0.27 3.37***

Event Driven 3.94 3.69 3.48 25.34***

0.46 6.94***

Fixed Income Arbitrage 3.72 3.59 3.17 16.69***

0.55 5.36***

Global Macro 3.04 2.94 2.51 14.24***

0.53 4.98***

Long/short Euiqty Hedge 3.56 3.42 3.31 21.58***

0.25 6.53***

Managed Futures 2.36 2.29 2.19 4.18**

0.17 2.85***

Muti-Strategy 3.40 3.35 3.16 3.28**

0.24 2.5**

Panel B. Scaled inflow restrictions

Convertible Arbitrage 1.99 1.92 1.68 10.65***

0.31 4.32***

Emerging Markets 1.25 1.24 1.29 0.5 -0.04 -0.81


0.13 2.21**

Event Driven 2.04 1.94 1.90 5.53***

0.14 3.18***

Fixed Income Arbitrage 1.97 1.99 1.78 6***

0.19 2.75***

Global Macro 1.69 1.67 1.41 8.2***

0.28 3.56***


0.10 3.57***

Managed Futures 1.38 1.36 1.32 0.94 0.06 1.36

Muti-Strategy 1.75 1.76 1.65 1.76 0.10 1.49

Panel C. Scaled outflow restrictions

Convertible Arbitrage 1.55 1.57 1.58 0.19 -0.03 -0.59

Emerging Markets 1.38 1.26 1.27 5.69***

0.11 2.67***


0.14 2.93***

Event Driven 1.89 1.74 1.57 40.98***

0.32 8.98***

Fixed Income Arbitrage 1.74 1.61 1.39 22.81***

0.35 6.64***

Global Macro 1.34 1.26 1.08 14.11***

0.26 5.2***


0.15 7.46***

Managed Futures 0.97 0.93 0.87 8.02***

0.10 3.95***

Muti-Strategy 1.65 1.59 1.51 3.33**

0.14 2.57**

33

Table 6. Flow restrictions and performance persistence for parametric method

This table reports the results of probit or ordered probit regression of persistence on flow restriction variables

as following equation.

where is 1 if a fund i is a winner in previous and subsequent periods and is 0 otherwise

in probit model. In ordered probit model, it ranges from 1 to 5 depending on the extent of performance

persistence. It equals 5 if a fund i is denoted as WWWW in note 8, 4 denoted as WWW and WW, 3 denoted as

WWL, LWW, WWLL, WLLW, LLWW, WLL, and LWW, 2 denoted as LL and LLL, and 1 denoted as LLLL.

equals 1 if a fund i is open-end and 0 if a fund i is closed-end. equals 1 if a fund i

is open to public and 0 otherwise. is the log of the minimum investment amount of fund i.

and are redemption notice period (days), lockup period (months), and

payout period (days) of fund i, respectively. is the log of estimated assets of fund i , is the

money flows of fund i , and is the standard deviation of monthly returns of fund i at the end of year

t-2. is a strategy dummy that equals 1 if fund i belongs to strategy s and equals 0 otherwise.

Panel A show the results of probit regression, and Panel B exhibit the results of ordered probit regression. ***

,**

, and * indicate significance at the 1%, 5%, 10%

Panel A. Probit regression

simple multiple

coefficient Chi-sq coefficient Chi-sq

OpenEnded -0.0521 0.81 -0.1177 1.63

OpenToPublic -0.1591 3.35* -0.0086 0.00

Minimum investment 0.1926 80.16***

0.1029 6.78***

RemptionNoticePeriod 0.0118 118.09***

0.0068 17.33***

PayOutPeriod 0.0047 12.87***

0.0005 0.07

LockUpPeriod 0.0189 18.24***

0.0153 5.48**

size

-0.0291 1.07

flow

-0.0049 0.05

std

-10.3386 41.61***

Panel B. Ordered Probit regression

simple Multiple

coefficient Chi-sq coefficient Chi-sq

OpenEnded -0.0597 1.78 -0.0413 0.44

OpenToPublic -0.1735 6.87***

-0.0564 0.39

Minimum investment 0.1705 143.51***

0.1202 24.49***

RemptionNoticePeriod 0.0104 104.23***

0.0050 9.95***

PayOutPeriod 0.0078 40.98***

0.0039 7.83***

LockUpPeriod 0.0199 33.17***

0.0107 4.72**

size

-0.0597 9.76***

flow

-0.0009 0.00

std

-4.5425 39.11***

34

Table 7. Flow restrictions and performance persistence for parametric method by trading strategy

This table reports the results of probit or ordered probit regression of persistence on scaled inflow and outflow

restrictions by trading strategy as following equation.

where is 1 if a fund i is a winner in previous and subsequent periods and is 0 otherwise in

probit model. In ordered probit model, it ranges from 1 to 5 depending on the extent of performance



and are scaled inflow and outflow restriction of fund i.The parameters of

restrictions are scaled from 0 to 1 by percentile of probability distribution on each variable. For binary

distribution of open-end and open to public variables, scale score is endowed with 1 if a fund is closed-end or

closing to public and is endowed with 0 otherwise. Inflow restrictions and outflow restrictions include three

variables, so that scaled them provide the range from 0 to 3, respectively. Panel A show the results of probit

regression, and Panel B exhibit the results of ordered probit regression. For total fund analyses, strategy

dummy variable is added in the equation. ***

,**

, and * indicate significance at the 1%, 5%, 10%.

Panel A. Probit regression

Restriction Inflow Restriction outflow

coefficient chi-sq coefficient chi-sq

Convertible Arbitrage 0.1886 0.78 -0.1076 0.11

Emerging Markets 0.1569 1.19 -0.0163 0.01

Equity Market Neutral -0.0405 0.02 0.4466 0.85

Event Driven 0.2726 3.47* 0.4058 4.66**

Fixed Income Arbitrage -0.2031 0.88 0.9158 10.26***

Global Macro -0.2810 0.66 1.2087 6.09**

Long/short Euiqty Hedge 0.1526 1.65 0.1663 0.93

Managed Futures 0.2307 1.64 1.1795 9.95***

Muti-Strategy 0.0490 0.07 0.6546 8.93***

Total 0.1096 2.79* 0.3848 16.55***

Panel B. Ordered Probit regression

Restriction Inflow Restriction outflow


Convertible Arbitrage 0.2415 1.73 -0.0820 0.08

Emerging Markets 0.3052 4.89** 0.1716 0.87

Equity Market Neutral -0.0880 0.15 0.4167 1.70

Event Driven 0.2644 4.20** 0.2503 2.20

Fixed Income Arbitrage -0.2208 1.32 0.7188 7.21***

Global Macro 0.1814 0.70 0.8350 7.72***

Long/short Euiqty Hedge 0.0050 0.00 0.2586 7.06******

Managed Futures 0.1447 2.14 0.8224 12.23***

Muti-Strategy 0.2001 0.96 0.6909 6.59**

Total 0.0988 4.88** 0.3803 35.18***

35

Table 8. Performance persistence and fund characteristics

This table reports the results of probit or ordered probit regression of persistence on fund characteristics such

as incentives, ownership, capital structures, and location as following equation.

where is 1 if a fund i is a winner in previous and subsequent periods and is 0 otherwise in

probit model. In ordered probit model, it ranges from 1 to 5 depending on the extent of performance



and are management fee and incentive fee charged by fund i .

equals 1 if fund i has a high water mark provision, and equals 0 otherwise. equals 1 if

principals of management company of fund i have invested in a fund i and equals 0 otherwise.

is 1 if a fund i use leverage, and is 0 if a fund i does not use leverage. equals 1 if a fund i

leverages using margin borrowing and equals 0 other wise. equals 1 if a fund i is onshore and

equals 0 if a fund i is offshore. equals 1 if headquarter of management company of fund i is located in

big city, and equals 0 otherwise. Explanatory variables are fund‟s contractual provisions associated with

incentive in model 1, ownership in model 2, capital structure in model 3, and location in model 4. All

characteristic variables are included in model 5. Panel A show the results of probit regression, and Panel B

exhibit the results of ordered probit regression. ***

,**


Panel A. Probit

model 1 model 2 model 3 model 4 model 5

coeff chi-sq coeff chi-sq coeff chi-sq coeff chi-sq coefft chi-sq

ManagementFee 0.0594 0.60

0.1011 1.59

IncentiveFee 0.0005 0.00

0.0027 0.09

HighWaterMark 0.1748 4.17**

0.1600 2.79*

PersonalCapital

-0.0974 1.44

-0.0703 0.67

Leveraged

0.0561 0.16

0.0214 0.02

Margin

-0.0491 0.13

0.0070 0.00

onoff

0.2057 6.39** 0.1814 4.17**

city

0.1133 2.10 0.1421 3.00

Panel B. Ordered probit

ManagementFee 0.0764 2.56

0.0938 3.43*

IncentiveFee 0.0118 3.31*

0.013 3.66*

HighWaterMark 0.2092 11.36***

0.2133 10.26**

PersonalCapital

-0.0436 0.55

-0.0159 0.07

Leveraged

0.105 1.41

0.0243 0.07

Margin

-0.0401 0.23

0.0156 0.04

onoff

0.1097 3.16* 0.0901 1.97

city

0.051 0.77 0.0825 1.84

36

Table 9. Restrictions and Incentives

This table reports the relationship between restrictions and incentives. Panel A display the results of

regression of restrictions on individual proxies representing incentives. Dependent variables are scaled total,

inflow, and outflow restrictions, and explanatory variables are management fee, incentive fee, high water

mark, and control variables. mfee and ifee are scaled management fee and incentive fee. Panel B exhibits

correlation coefficient of scaled restrictions and scaled incentives. ***

,**

, and * indicate significance at the 1%,

5%, 10%.

Panel A. Regression Analyses

(1) raw explanatory variables

Total Restriction Inflow Restriction Outflow Restriction

coefficient t coefficient t coefficient t

ManagementFee -0.2450 -8.02*** -0.1297 -5.46*** -0.1100 -8.25***

IncentiveFee 0.0149 3.5*** 0.0089 2.87*** 0.0076 3.37***

HighWaterMark 0.8482 18.1*** 0.3450 10.09*** 0.5069 20.57***

(2) scaled explanatory variables

mfee -0.4914 -6.33*** -0.3078 -5.36*** -0.1688 -4.15***

ifee 0.4751 4.61*** 0.3299 4.33*** 0.1783 3.32***

HighWaterMark 0.8927 20.19*** 0.3530 10.79*** 0.5468 23.48***

Panel B. Correlation coefficients between scaled restrictions and scaled incentives

Total Restriction

Inflow

Restriction

Outflow

Restriction mfee ifee

Inflow Restriction 0.8607***

Outflow Restriction 0.7603*** 0.3237***

mfee -0.1351*** -0.1169*** -0.0892***

ifee 0.1773*** 0.1397*** 0.1702*** 0.1004***

HighWaterMark 0.4404*** 0.2647*** 0.4888*** -0.0316 0.2374***

37

Table 10. Performance persistence, Restrictions, and Incentives

This table reports the relationship between performance persistence and both of restrictions and incentives. Panel A displays the results of probit or ordered probit regression of persistence on scaled inflow restrictions,

outflow restrictions, and incentives. Panel B shows frequencies and proportion of winners on groups

classified by the extent of restrictions and incentives. If a fund has scaled score of restriction above the

median of entire sample, a fund is denoted as high (H) and is denoted as low (L) otherwise. If a fund‟s scaled

incentive score is higher than median incentive score of all samples, a fund belongs to high (H) and belongs to

low (L) otherwise. Thereby, all funds are classified as four groups (HH, HL, LH, and LL: fist letter is

incentives). We conduct probit regression of persistence on class variables categorized four groups. (if control

variables are included (excluded) in model, it is denoted as full (multiple)). For dependent variable of class,

we create matrix columns using parameterization method. The results are shown in Panel C. The parameter

estimates of each group present the difference in the effect of each class compared to funds with high

incentives and restrictions (HH). ***

,**


Panel A. Regression of persistence on scaled inflow, outflow, and incentives

probit

ordered probit


Inflow Restriction 0.1075 2.70 0.0801 3.37*

Outflow Restriction 0.3782 14.20*** 0.295 18.14***

Incentives 0.0186 0.06 0.1323 6.12**

Panel B. Frequencies and Proportion of Winners

Total Restrictions

Low High Total

Incentives Low 9.57%(256/2675) 25.39%(322/1268) 14.66%(578/3943)

High 12.22%(172/1407) 21.21%(563/2655) 18.09%(735/4062)

Total 10.48%(428/4082) 22.56%(885/3923)

Inflow Restrictions

Low High Total

Incentives Low 10.69%(245/2291) 20.16%(333/1652) 14.66%(578/3943)

High 13.85%(201/1451) 20.45%(534/2611) 18.09%(735/4062)

Total 11.92%(446/3742) 20.34%(867/4263)

Outflow Restrictions

Low High Total

Incentives Low 10.32%(290/2809) 25.40%(288/1134) 14.66%(578/3943)

High 12.44%(157/1262) 20.64%(578/2800) 18.09%(735/4062)

Total 10.98%(447/4071) 22.01%(866/3934)

Panel C. Regression

Multiple

Full

coefficient chi-sq

coefficient chi-sq

(1) Total Restrictions

LL -0.9333 46.33***

-0.6229 15.70***

HL -0.6587 18.46***

-0.384 5.84**

LH 0.2349 2.88*

0.1775 1.57

(2) Inflow Restrictions

LL -0.7641 26.68***

-0.367 5.03**

HL -0.4693 10.15***

-0.0142 0.01

LH -0.0182 0.02

0.1002 0.50

(3) Outflow Restrictions

LL -0.8151 38.80***

-0.489 11.15***

HL -0.6048 13.48***

-0.4222 5.55**

LH 0.269 3.52*

0.1483 0.99

38

Table 11. Robustness test

This table reports the results of regression of the abnormal return at subsequent period on restriction variables

interacted with the abnormal returns at the previous period as following equation.

where and are abnormal returns of subsequent and previous period for three year intervals of

fund i, drawn from MFH model. , , , and of fund i are defined as in equation (2). and of fund i are same as

defined in equation (2) at time t. Panel A displays the relation between performance at subsequent period and

combined effect of restrictions and performance at previous period for funds with superior persistence. Panel

B shows this relationship for funds without persistence. ***

,**


Panel A. Funds with performance persistence

coefficient t

lag alpha*Open Ended -0.0002 -0.01

lag alpha*Open To Public -0.0376 -1.43

lag alpha*Minimum Investment 0.0627 8.12***

lag alpha*Redemption Notice Period 0.0018 5.99***

lag alpha*PayOut Period 0.0005 2.58***

lag alpha*LockUp Period 0.0052 4.37***

Panel B. Funds without performance persistence

lag alpha*Open Ended -0.0034 -0.14

lag alpha*Open To Public 0.0606 2.01**

lag alpha*Minimum Investment -0.0461 -5.31***

lag alpha*Redemption Notice Period -0.0007 -1.35

lag alpha*PayOut Period 0.0005 0.94

lag alpha*LockUp Period 0.0016 0.93

Documents

Performance Persistence and Flow Restrictions in Hedge Funds · prediction of performance persistence in hedge funds by Glode and Green (2011). To our knowledge, this is the first