Duration of Poor Performance, Fund Flows, and

Risk-Shifting by Hedge Fund Managers1

Ying Li

2

A. Steven Holland3

Hossein B. Kazemi4

Abstract

A typical hedge fund manager receives greater compensation when the fund has a strong

absolute or relative performance. Asymmetric performance fees and fund flow-performance

relationship may create incentives for risk-shifting, estimated in our study by the change in fund

return volatility in the middle of the year. However, hedge funds that cannot attract new funds or

have had poor performance for a long period may face different incentives. The combination of

these two observations confronts hedge fund managers with a complex strategic decision

regarding the optimal level of their funds’ return volatility. While an increase in return volatility

generally increases the expected payoff of the compensation contract, excessive volatility is not

sustainable. This paper empirically examines the factors that affect hedge fund managers’

decisions to risk-shifting. We show that (1) if the fund has had prior poor performance, the

magnitude of risk-shifting is larger; (2) as the duration of poor performance increases, risk-

shifting is reduced; (3) if the fund is experiencing capital outflows, the magnitude of risk-shifting

is smaller and (4) funds that have outflows and also use leverage or have short redemption notice

periods display a smaller degree of risk-shifting. (JEL G11, G23, G32)

Keywords: hedge fund, fund flow, performance, incentive, risk

1 We wish to thank George Aragon, Stephen Brown, Peter DeMarzo, Rajib Doogar, Mila Getmansky, Nikunj

Kapadia, Bing Liang, PK Sen, Mark Westerfield, and conference participants at the EFM 2011 Alternative

Investments Symposium at York University for their many helpful comments. We are responsible for any errors. 2 Corresponding author, Assistant Professor of Business, University of Washington Bothell. Email: YLi@uwb.edu,

Tel: 425-352-3413

3 Professor of Business, University of Washington Bothell. Email: sholland@uwb.edu.

4 Professor of Finance, University of Massachusetts, Amherst. Email: kazemi@isenberg.umass.edu.

1. Introduction

Hedge fund managers receive payoffs that are convex functions of the fund’s

performance, measured in both absolute and relative terms. On one hand, a typical manager

earns 20% performance fee (Liang, 2000), which is based on the fund’s absolute performance,

and is usually not subject to a claw-back policy. On the other hand, a convex fund flow-

performance relation has been observed in the hedge fund industry (Agarwal et al., 2002), which

means fund flows respond more to a high ranking of relative performance than they do to a low

ranking of relative performance. Since both performance fee and relative performance ranking

are usually assessed annually at the end of a calendar year, the payoff structure, which is similar

to that of a call option, may lead the managers to engage in mid-year risk-shifting, i.e., a mid-

year change of return volatility (Carpenter, 2000; Aragon and Nanda, 2012).

While it is generally believed that convex incentive structures lead to risk-shifting, the

questions of why and under what conditions risk-shifting occurs remain unanswered (Ross,

2004). Previous studies provide evidence supporting the presence of risk-shifting due to the

implicit incentive from the convex flow-performance relation, also known as the “tournament

incentive” (Brown et al., 1996). However, there is mixed evidence on how the explicit incentive,

which is based on the convex compensation contract, influences risk-shifting (Brown et al.,

2001; Aragon and Nanda, 2012). Clearly, the impact of the explicit incentive on the risk-shifting

behavior of managers is of general interest because stock options are used extensively in

executive compensation contracts. While executives have the power to influence their

compensation contracts, hedge funds usually set up their compensation structure at inception

with little alteration going forward (Agarwal et al., 2009). Hedge funds, therefore, offer a

laboratory to test the relation between convex payoff and risk-taking behavior with less concern

about endogeneity.

This paper attempts to address the gap in the literature by focusing on the joint impact of

hedge fund managers’ expected payoffs from their compensation contracts, direction of fund

flows into or from their funds, and past performance on their risk-shifting decisions. Since

managers hold a portfolio of investor-specific1 performance fee options, we show that not only

1 Though theoretical models of optimal risk-taking by fund managers refer to a unique fund-level HWM (Carpenter,

2000; Panageas and Westfield, 2009), in practice, in order to avoid the free rider problem, hedge funds issue new

share classes as new investors contribute capital to the fund. This means, the HWM is defined at the investor level

the most recent performance, but cumulative past performance matters for their risk-shifting

decisions. Risk-shifting is not a strategy that can repeatedly be used, as a fund’s return volatility

will become exceedingly unattractive. We show that the duration of a fund’s poor performance, a

factor that, to the best of our knowledge, has not been explored in prior research, influences the

risk-shifting decision of managers. Furthermore, we show that two important fund

characteristics, the length of redemption notice period required by a fund and, especially, the use

of leverage by the fund manager, interact with fund flows to impact a manager’s risk-shifting

policy. Taking the history of a fund’s performance into account also helps reconcile the

previously reported mixed evidence regarding the relation between the moneyness of the

performance fee option and risk-shifting policies of fund managers.

Our main source of data is the Morningstar/CISDM database over the sample period of

January 1994 through December 2012. Similar to Aragon and Nanda (2012), we construct a

proxy for the high-water mark (HWM) of a hedge fund by tracking its net asset value (NAV)

over a three-year window using reported net-of-fees returns. Our empirical framework to identify

the conditions that may influence a hedge fund manager’s risk-shifting decision includes two

variables that are measured after the fund achieves its HWM: performance and fund flow. For

performance, we also consider the duration of underperformance, to assess the long-term

feasibility of risk-shifting.

We use the performance and fund flow estimates along with the HWM proxy to identify

the “state” that describes (1) the moneyness of a hedge fund manager’s compensation option

portfolio and (2) the fund flow condition. Then, we focus on the interactions between (1) the

state variables and the first half-year performance and (2) the state variables and fund

characteristics in a regression setting to investigate hedge funds’ state-dependent risk-shifting

behavior.

We document several new findings. First, a hedge fund manager’s risk-shifting is more

pronounced when the fund is in a state that its performance fee option is partly out-of-the-money.

The magnitude of risk-shifting for a fund in such a state is more than double that of a fund in the

state where the performance fee option is at-the-money. Most hedge funds have performed well

during our sample period with about 75% of managers having at-the-money performance fee

and not at the fund level. The incentive to change the fund’s volatility, therefore, is based on the sensitivity of the

value of a portfolio of call options with different strike prices (i.e., HWMs) to the changes in the volatility of the

fund’s portfolio.

option. We find less risk-shifting among these managers, which may explain the mixed evidence

reported by Brown et al. (2001) and Aragon and Nanda (2012). Second, risk-shifting ceases if a

fund’s NAV is below its HWM proxy for an extended period. We find that a fund reacts to poor

performance by increasing its return volatility in the short-run, but does not engage in risk-

shifting after being underwater for more than one year. Third, when facing outflows, a fund that

employs leverage usage or imposes short redemption notice on its investors engages in a smaller

magnitude of risk-shifting.

Besides performance, fund flows, leverage, and redemption periods, a number of other

manager characteristics such as the manager’s physical location, education, and risk-aversion

could influence a fund’s risk-shifting incentives. A manager is typically part of the ownership

structure of a hedge fund, and, therefore, remains with the fund throughout its life (Liang, 2000).

Consequently, regressions with fund fixed effects greatly alleviate the concern of biased

estimates due to the presence of omitted time-invariant variables.2 Our empirical findings are

robust to using fund fixed effects regressions, and are robust to variations in our methodology,

including varying the definitions of poor performance and fund flows. The findings also appear

to be robust to the impact of potential biases, as our results are similar when we use a sample that

is less subject to backfill bias (the “no backfill bias” sample) and the full sample.

Our study is related to a large literature on hedge funds’ managerial risk-taking incentives

and behavior conditional on fund characteristics. A number of empirical studies have examined

hedge fund managers’ risk-taking by investigating the impacts of survival and career concerns

(Fung and Hsieh, 1997; Brown et al., 2001; Aragon and Nanda, 2012), presence of high-water

mark provisions and managerial stakes (Aragon and Nanda, 2012), usage of derivatives (Chen,

2013), and past performance history (Aragon and Nanda, 2012; Ray, 2012). Past studies also

document a convex relationship between fund flows and performance for mutual funds

(Chevalier and Ellison, 1997; Sirri and Tufano, 1998), and for hedge funds (Agarwal et al.,

2002), how fund flows are related to risk taking decisions in the mutual fund industry (Basak et

al., 2007; Hu et al., 2011) and how a less convex flow-performance relation leads to less risk-

shifting in the pension fund industry (Del Guercio and Tkac, 2002). Additional studies examine

the interaction of leverage policy and risk taking strategies (Brown et al., 2001; Ang et al., 2011),

2 Firm fixed effects regression models are widely used in the corporate finance literature to reduce endogeneity

concerns due to time-invariant omitted variables. For example, Adams and Ferreira (2009), Oikonomou, Brooks,

and Pavelin (2012).

and explore the interaction between fund flows, performance, and managerial incentive in the

hedge fund industry (Agarwal et al., 2002 & 2009). The importance of liquidity provisions in

affecting hedge fund risk-return profiles is examined in Getmansky et al. (2004) and Aragon

(2007). Li and Kazemi (2007) and Cumming and Dai (2010) investigate the conditional

properties of hedge funds’ return distributions.

We add to this vast literature by showing that hedge fund managers use risk-shifting as a

transient strategy to maximize payoffs. We also show that fund outflows play an important role

in hedge fund managers’ risk-shifting decisions. Because we control for fund-level fixed effects,

our results are less prone to endogeneity concerns due to time-invariant non-observable missing

variables that may influence a fund’s risk-shifting decision.

The paper’s structure is as follows. Section 2 presents the methodology, testable

hypotheses, and the data, Section 3 discusses the research design and results, Section 4 presents

tests for robustness, and Section 5 concludes.

2. Methodology, hypotheses and data

2.1 Methodology

Following Aragon and Nanda (2012), our research variable is the change in the return

volatility of each hedge fund in our sample during the second half of the year compared to the

volatility in the first-half year. From the limited data disclosed by hedge funds, we use a rolling

three-year window to estimate the maximum HWM of each fund’s current investments and to

obtain a history of its fund flows. With the help of the HWM proxy, we estimate two states for

each hedge fund: (1) Past performance-related state, which we refer to as the Moneyness of its

performance fee option and (2) fund Flow. We use these estimates to examine the responses of

hedge fund managers to poor performance in the first half of the fourth year. The maximum

value of each fund’s NAV during the three-year window is compared to the fund’s NAV at the

beginning of the fourth year (evaluation year) as the measure of moneyness of the performance

fee option:

,

,

,

i t

i t

i t

NAVMoneyness

HWM (1)

In the above definition of Moneyness, 𝑁𝐴𝑉𝑖,𝑡 is the net asset value of a share of hedge

fund 𝑖 as of the end of the three-year (36-month) window (the beginning of the evaluation year),

and 𝐻𝑊𝑀𝑖,𝑡 = max(𝑁𝐴𝑉𝑖,𝑡−𝜏; 0 ≤ 𝜏 < 36) is the maximum level of NAV over the three-year

window.

If Moneyness is equal to one, the manager’s performance fee option for all assets under

management is at the money. If Moneyness is less than one, the manager’s performance fee

option is out-of-the-money for at least some of the assets under management. Clearly, our

estimate of the moneyness of the performance fee option is subject to errors. We check for the

robustness of our results by changing the cutoff threshold of Moneyness in Section 4.

It is important to note that Moneyness is a conditioning variable that refers to the status of

the performance fee option at the beginning of the evaluation year.3 Furthermore, depending on

the timing of the investment, each share class will have its own specific HWM, and, therefore, its

specific degree of moneyness. This means that the variable defined as Moneyness in Equation

(1) should be interpreted as a proxy for the aggregated moneyness of all share classes of a given

fund at the beginning of the year. In addition, unless there are inflows, a fund that has negative

performance during the first half of the year will not collect a performance fee unless it displays

positive performance during the second half of the year.

We estimate the fund-level aggregate flow by comparing the actual assets under

management as of mid-evaluation year to the expected assets under management calculated

based on the fund’s cumulative return since the time the HWM was achieved. The aggregated

fund-level flow over [𝑡𝐻𝑊𝑀, 𝑡 + 6]is calculated following Sirri and Tufano (1998):

, 6 , , 6 ,

, , 6

,

1HWM HWM

HWM

HWM

i t i t t i t

i t t

i t

AUM AR AUMFlow

AUM

(2)

Where HWMt is the time when the HWM was reached, t is the beginning of the evaluation year,

𝐴𝑈𝑀𝑖,𝑡+6is the amount of assets under management as of June 30 of the evaluation year,

𝐴𝑈𝑀𝑖,𝑡𝐻𝑊𝑀is the amount of assets under management at the time when the HWM was reached,

and 𝐴𝑅𝑖,𝑡𝐻𝑊𝑀,𝑡+6 is the accumulative return over the window [𝑡𝐻𝑊𝑀, 𝑡 + 6].

We define two dummy variables, M and F, to describe the state a fund is in depending on

its moneyness and flow conditions. We set M equal to 1 if Moneyness < 0.9 as of the beginning

of the evaluation year, indicating that the fund has had meaningful negative returns after

3 The results do not change if Moneyness is measured mid-year. The results are available from the authors upon

request.

reaching its HWM. We set F equal to 1 if , indicating that the fund has experienced

outflows after reaching its HWM.4

2.2 Hypotheses

As hedge fund managers react to convex payoffs, whether they are from the embedded

option in their compensation contracts or the asymmetric flow-performance relation, our first

hypothesis is straightforward:

Hypothesis 1: Fund volatility increases following poor performance in the first half of the

year.

Hypothesis 2 relates a hedge fund manager’s risk-shifting to the state the fund may find

itself in depending on Moneyness and Flow. If a large proportion of the manager’s performance

fee option portfolio is out-of-the-money at the beginning of the year, the manager will need to

increase the volatility by a larger amount to obtain the same amount of benefits obtained by the

fund if only a small portion of the performance fee option is out-of-the-money. Thus, the

moneyness determines the magnitude of change in volatility needed to bring about a given

change in the value of the performance fee option.

We hypothesize that fund outflows affect how a fund weighs the cost of risk-shifting.5

That is, if a fund has negative performance but no outflows, the manager may conclude that

investors still have a positive view of the fund, and, therefore, the costs associated with risk-

shifting are not significant. However, if a fund experiences outflows following poor

performance, costs associated with increased risk-shifting will be deemed significant, and the

magnitude of risk-shifting by such a fund is likely to be small. This argument leads to our

second hypothesis:

Hypothesis 2: The increase in return volatility following poor performance in the first

half of the year is greater for a fund that has an out-of-the-money performance fee option and

has experienced no fund outflows.

4 We use dummy variables (M and F) instead of continuous variables for Moneyness and Flow to mitigate some

multicollinearity issue and to allow errors of margin since Moneyness and Flow are based on the estimated HWM

proxy and therefore are not exact measures. We also conduct robustness checks for various M and F definitions in

Section 4. 5 Fund flow has been used as a measure for investors’ sentiment. (For example, Jain and Wu, 2000; Barber, Odean,

and Zheng, 2004; Frazzini and Lamont, 2008).

0Flow

In addition, we explore the roles fund characteristics play in the dynamics of fund flows,

the moneyness of the performance fee option, and the manager’s risk-shifting policy. Past studies

have examined how certain fund characteristics influence hedge fund managers’ risk-shifting

incentive and behavior. For example, using a theoretical model, Hodder and Jackwerth (2007)

argue that managerial stake reduces the incentive for risk-shifting; Aragon and Nanda (2012)

provide empirical evidence supporting this argument. We focus on two other characteristics that

are likely to stand out due to their close relationship with fund flows and risk: the redemption

notice period and the use of leverage. Getmansky et al. (2011) and Aragon et al. (2014) show

that the flow-performance relationship turns concave from convex when the fund imposes share

restrictions. Redemption notice period is a type of share restriction, and short redemption notice

periods allow investors to react quickly to changes in the fund’s investment style, and, therefore,

risk-shifting could lead to increased outflows.

Hypothesis 3a: Conditional on fund outflows, the increase in return volatility is smaller

for hedge funds that have short redemption notice periods.

Just as funds with short redemption notice periods could face increased cost associated

with shifting risk, funds that use leverage may also find such a policy costly. In the absence of

any changes in a fund’s investment and leverage policy, return volatility of the fund will

endogenously increase following a period of poor performance. On the other hand, when the

fund experiences outflows, managers may be more wary of the cost of increasing volatility and

refrain from doing so. Furthermore, even if the manager wanted to increase volatility, lenders

may impose active constraints on risk-shifting policies of a fund (Brown et al., 2001). Both

scenarios would result in a smaller increase in volatility.

Brown et al. (2001) find empirical evidence for non-passive leverage policy, Ang et al.

(2011) show hedge funds are experts at managing their leverages compared to listed financial

intermediaries, and Lan et al. (2013) suggest active leverage policy in a theoretical model; based

on these studies, we argue that levered funds will find it too costly to increase their return

volatility if they have experienced fund outflows.

Hypothesis 3b: Conditional on fund outflows, the increase in return volatility is smaller

for hedge funds that use leverage.

Our last hypothesis is about the impact of the duration of time that a fund’s portfolio of

performance fee options has been partly out-of-the-money on its risk-shifting decision. On one

hand, if a fund has not collected performance fees for a long time, the fund may have attempted

risk-shifting before or the fund’s performance fee option is deeply out-of-the-money so that risk-

shifting may no longer help. Thus, it may not react to poor first half-year performance. On the

other hand, the manager of such a fund may consider closing the fund and walking away from it.

Such a manager may decide to increase the volatility as a last attempt to collect performance fees

and extend the life of the fund (see Hodder and Jackwerth, 2007; Drechsler, 2014). Thus, the

fund may further increase its return volatility after poor first-half year performance.

Hypothesis 4: Duration of poor performance affects the risk-shifting behavior of hedge

fund managers.

2.3 Hedge fund sample

We use the Morningstar-CISDM Hedge Fund Database for this study.6 This database

provides information on performance and various characteristics of thousands of hedge funds,

funds of hedge funds, and CTAs.7 For each fund, we observe monthly net-of-fees returns, NAV,

major trading strategy claimed by the fund, whether the fund is listed on an exchange, and the

regulatory agency for the fund.8 For most funds, we are also able to obtain information on their

initiation months; assets under management (AUM); currency of AUM; performance fee rate;

management fee rate; whether they use leverage; and liquidity constraints they impose including

lockup period, redemption notice period, and redemption frequency. Following Sirri and Tufano

(1998), we use monthly AUM and returns to calculate aggregated fund flows occurring after a

fund has achieved its HWM. We do not have data on manager’s ownership share of the fund, or

the reasons for a fund to appear in the defunct database.

We focus on US-dollar-denominated individual hedge funds and exclude funds of hedge

funds and CTAs.9 Each hedge fund is required to have 48 months of return history

10 and

6 The Morningstar Hedge Fund Database is built on the CISDM database, which used to be Zurich Hedge Fund

Universe, formerly known as the MAR database. 7 Funds of hedge funds are portfolios of individual hedge funds. CTAs are funds that specialize in futures trading.

8 We use net-of-fees returns for this study. We recognize that the HWM and fees are based on returns computed

before all management fees and performance fees are subtracted, hence gross returns will be most appropriate to use.

However, past research demonstrates little difference from using net-of-fee returns versus using gross returns. 9 The incentive to shift risk is less obvious for funds of hedge funds. For example, Agarwal et al.(2002) do not find

funds of hedge funds to exhibit strategic risk-taking behavior and attribute it to the inflexibility of the business.

information on characteristics, including fund size (AUM), and the strategy followed by the

manager (style). We are aware that these requirements may introduce certain biases into our

sample selection process. We perform robustness checks to understand the potential impact of

these biases. After applying the above filters, we are left with 11934 individual observations for

the full sample and 8199 observations for the no backfill bias sample.11

12

3. Empirical results

3.1 Summary statistics

We examine monthly return data from January 1994 to December 2012 to evaluate hedge

funds’ risk-shifting behavior from 1997 to 2012. We include both live and defunct funds to

minimize survivorship bias. To mitigate unwarranted noise from outliers, we also winsorize our

sample at 1% and 99% for the following measures: fund’s return volatility in the first and second

half years, the moneyness of a fund’s performance fee option and fund flow.

The summary statistics of our sample are reported in Table 1. There are 11934 individual

observations appearing in the database between January 1994 and December 2012 with the

number increasing every year through 2007 steadily and declining thereafter. Panel A shows that

the mean level of AUM at the beginning of each evaluation year increased from $131.83 million

in 1997 to $234.92 million in 2008 and then fell to $155.81 million in 2012. Panel B reports

the number of fund-year observations for the eight styles (namely, Convertible Arbitrage, Debt-

related, Equity-related, Event Driven, Global Macro, Merger Arbitrage, Multi-strategy and

Volatility) in our study. It shows that Equity-related is the most prominent style of funds in our

sample, representing about 62% of both the full and no backfill samples.

Panels C and D report the summary statistics of our dependent variable, independent

variables and control variables used in base case regressions for both no backfill bias and full

CTAs are a group of managers that trade on highly liquid futures and there is less variation in the liquidity provision

at CTAs. However, robustness checks show that our results are robust to including all funds of hedge funds. These

additional results are available upon request. 10

We relax this requirement in robustness tests and our results are qualitatively the same. 11 The Morningstar-CISDM Hedge Fund Database uses a dummy variable for high-water mark provision, where a

“1” means the fund has a high-water-mark. About 88% of the hedge funds in our sample report that they have a

high-water mark (10002 out of the full sample and 6851 out of the no backfill sample, some funds have missing

information). We report results below by assuming all hedge funds with missing information have high-water mark

provisions. The results from a sample that excludes funds with those funds with missing information on their high-

water mark provisions are largely the same. The results are available upon request. 12

No backfill bias sample excludes returns that are “backfilled”, which occur prior to when the fund was added to

the database, to minimize the backfill bias as discussed in Fung and Hsieh (2000).

samples. Out of 8199 fund-year observations in the no backfill bias sample, 2020 have M = 1,

while 4502 have F = 1, and 1520 funds have both M and F equal to 1, indicating that these funds

have had negative flows and performance decline of more than 10% since reaching HWM during

the previous 36 months. Out of the 11934 fund-year observations in the full sample, 2809 have

had such negative return history (M = 1), 6180 have had negative aggregate fund flows (F = 1),

2037 have had both M and F equal to 1. The summary statistics show that even though poor

performance is likely to result in fund outflows, the distinction between poor performance and

outflows is meaningful and necessary. While M and F are correlated, they capture separate fund

characteristics.

Finally, Panel E reports the distribution of funds with and without leverage usage and

with and without short redemption notice periods under different moneyness and flow conditions

for both no backfill bias and full samples. As we can observe from Panel E, more fund-year

observations are associated with leverage usage, and more fund-year observations are associated

with short redemption notice periods. For example, for the no-backfill-bias sample, there are

4403 and 5163 fund-year observations that report leverage usage and impose short redemption

periods, respectively. There are 1338 and 3036 fund-year observations that do not use leverage

and impose long short redemption periods, respectively.

[Table 1 about here]

We also report the correlation coefficients for our variables in Table 2. The Pearson

correlation coefficient for M and F is 0.23, suggesting that multicollinearity should not be a

major concern.

[Table 2 about here]

3.2 Regression analysis

To investigate the risk-shifting of hedge fund managers when they are in states defined

by M and F, we use multivariate regression to estimate the following equation over the period of

1997-2012, with a focus on the interaction terms 𝑀 × 𝑃𝑒𝑟𝑓, 𝐹 × 𝑃𝑒𝑟𝑓, 𝑇𝑖𝑚𝑒 × 𝑃𝑒𝑟𝑓, 𝐹 ×

𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒 and 𝐹 × 𝑆ℎ𝑜𝑟𝑡𝑁𝑜𝑡𝑖𝑐𝑒:

1 2 3 4 5 6 7

8 9 10 11 12

j j j j j

j j

DifStd M Perf M Perf F F Perf TimeUnder TimeUnder Perf

F Leverage F ShortNotice LagStd AuCorr SecondFlow

FundCharacteristic M FundCharacteristic F FundCharacte

j

j

k k

k

ristic

Dummy

(3)

where M = 1 if Moneyness < 0.9 and 0 otherwise,13

and F = 1 if Flow < 0 and 0 otherwise.

The performance of a fund during the first half of the year, which is denoted by Perf, is a

critical variable. We use three different measures of the variable Perf: (1) NegRet, a dummy

variable that equals one if the cumulative return over the first half of an evaluation year is

negative and zero otherwise, (2) LowAbsRetRank, a dummy that equals one if a fund’s

cumulative return over the first half-year is below the median of all funds and zero otherwise,

and (3) LowRelRetRank, a dummy that equals one if the cumulative return over the first-half of

the year is below the median of funds within a given style and zero otherwise. If funds engage in

mid-year risk shifting, the coefficient on Perf will be positive as funds with poor first half-year

performance increase volatility in the second half-year. We initially focus on NegRet to explore

how the explicit incentives arising from out-of-the-money compensation contracts affect hedge

fund risk shifting. We consider the other two variables (LowAbsRetRank and LowRelRetRank)

in additional tests in Section 4.

The interaction terms M Perf and F Perf indicate how the magnitude of volatility

change following poor first half-year performance reacts to changes in M and F, i.e., when the

fund is in a certain M- and F- dependent state.

TimeUnder, an abbreviation for time-underwater, is a continuous integer that ranges

between 0 and 35 and is defined as

, ,t ,

,

,

36 0.9

0 0.9

HWM i i t

i t

i t

t if Moneyness

TimeUnder

if Moneyness

(4)

for fund i as of time t, the beginning of each evaluation year. TimeUnder is an estimate of the

number of months that a fund’s NAV has been less than 0.9 of its HWM, suggesting that the

fund has not collected performance fees for at least some of its assets under management during

13 We conduct robustness checks to evaluate the sensitivity of our results in the next section.

those months. The interaction term 𝑇𝑖𝑚𝑒 × 𝑃𝑒𝑟𝑓 indicates the magnitude of risk-shifting in

response to the duration of past poor performance.

We control for lagged fund volatility (PStd), the percentage of net flow in the second half

year (SecondFlow), and return autocorrelation (AuCorr) because they have previously been

documented to have an impact on fund volatility. PStd controls for mean reversion in risk

changes that may be induced by mis-measurement (Koski and Pontiff, 1999; Aragon and Nanda,

2012). We expect 𝛽10to be negative and greater than -1. As pointed out in Getmansky, et al.

(2004), autocorrelation in fund returns may be a symptom of return smoothing by fund

managers, and may lead to a downward bias in estimating the true return volatility. Since less

liquid strategies usually have higher autocorrelation (Getmansky et al., 2004), higher AuCorr

reflects less liquid asset holdings, which may exhibit lower volatility. Both the downward-

estimation bias and the less-liquid-asset-holding predict a negative coefficient estimate for

AuCorr. We therefore expect 𝛽11 to be negative. SecondFlow controls for a spurious relation

between mid-year performance and changes in fund risk (Ferson and Warther, 1996; Koski and

Pontiff, 1999).

We also control for a number of fund characteristics that are likely to influence a fund’s

risk-shifting decisions. These include fund size, leverage usage, and the length of the redemption

notice period:

Small = 1 if a fund has below-median AUM and 0 otherwise. On one hand, smaller funds

collect less management fees, so the performance fee is more important, suggesting a positive

effect on volatility (more aggressive risk taking). On the other hand, smaller funds cannot afford

to lose assets, and higher volatility could lead to greater losses and loss of funds. This

observation suggests a negative effect on volatility (less aggressive risk taking).

Leverage = 1 if a fund reports to the database that it uses leverage and 0 otherwise. As

suggested before, funds that use leverage should be less aggressive in increasing their return

volatility if they have experienced outflows.

ShortNotice = 1 if a fund has a redemption notice shorter than 30 days and 0 otherwise.

All else constant, funds that offer relatively short redemption notices are expected to be less

aggressive in increasing their return volatility following outflows. We expect the signs of 𝛽8 and

𝛽9,the coefficients for the interaction terms F Leverage and FShortNotice to both be

negative.

In addition to the variables discussed above, we estimate Equation (3) with style fixed

effects and year fixed effects as control variables and separately with fund fixed effects and year

fixed effects. Style fixed effects are dummy variables that represent the different styles of hedge

funds.14

If a fund belongs to a certain style, then the dummy variable corresponding to that style

is set equal to one. The base case used in our regression is equity-related style, which is the

largest category. Year fixed effects are dummy variables that represent each year the fund

manager makes decisions on volatility. There are 16 such evaluation years (1997-2012) for our

sample and the base year is 1997.15

The reported t-statistics are based on robust standard errors

that cluster at fund levels.

Table 3 reports the results from estimating Equation (3) for the no-backfill-bias sample,

with Perf represented by NegRet.16

Columns (1) – (2) report results from a pooled OLS

regression with controls of style and year fixed effects and Columns (3) – (4) report results from

a fixed fund effects regression with year fixed effects.

In Column (1), M, F, and their interactions with NegRet, Small are included. We do not

include the interaction terms between M, F and Leverage and ShortNotice to conserve the

number of observations used for the regression since there are missing values for Leverage and

ShortNotice. The estimated coefficient for NegRet is positive and significant, consistent with

Hypothesis 1: poor performance in the first half of the year leads to increased return volatility.

The estimated coefficients for M and F are both insignificant but the estimate for interaction term

𝑀 ×𝑁𝑒𝑔𝑅𝑒𝑡is positive and significant, with a large coefficient more than double the magnitude

of that on NegRet. This indicates the magnitude of risk-shifting in response to poor performance

is larger for funds that have a larger proportion of their compensation option portfolio out-of-the-

money, consistent with Hypothesis 2. The estimated coefficient for Small is positive and

significant, suggesting that funds with smaller sizes and no outflows have higher risk-shifting.

14

Hedge funds are categorized into various styles to reflect the fact that they may carry very different risk profiles as

they employ different strategies. For our sample, there are 24 categories: Europe HF global emerging markets

equity, HF Asia/pacific long/short equity, HF bear market equity, HF China long/short equity, HF convertible

arbitrage, HF debt arbitrage, HF distressed securities, HF diversified arbitrage, HF emerging markets long-only

equity, HF emerging markets long/short equity, HF equity market neutral, HF Europe long/short equity, HF event

driven, HF global long/short equity, HF global macro, long-only debt, long-only equity, long-only other, long/short

debt, merger arbitrage, multi-strategy, U.S. long/short equity, U.S. small cap long/short equity, volatility. We

summarize these styles into eight based on their assets classes: equity-related, debt-related, convertible arbitrage,

emerging markets, event driven, global macro, multi-strategy and volatility-related. 15

We use 1997 as the base year. Our results do not change with the selection of base year. 16

Results from the full sample are qualitatively the same as those from the no backfill bias sample and are not

reported in this and the following tables to save space. These results are available upon request.

The estimated coefficient for 𝐹 × 𝑆𝑚𝑎𝑙𝑙 is negative and significant and of nearly equal absolute

value to the coefficient for Small, indicating that small funds with outflows have little or no risk-

shifting. The estimated coefficient for TimeUnder is positive and significant, indicating that

higher risk-taking follows longer duration of underperformance. The estimated coefficient for

𝑇𝑖𝑚𝑒𝑈𝑛𝑑𝑒𝑟 × 𝑁𝑒𝑔𝑅𝑒𝑡, however, is negative and significant, indicating less risk-shifting for

hedge funds as the duration of underperformance increases. These findings are consistent with

Hypothesis 4 that the duration of underperformance influences hedge fund managers’ risk-

shifting behavior. The estimated coefficients for AuCorr and PStd are both negative and

significant (and greater than -1 for PStd), consistent with our expectation.

We report results in Column (2) for the regression that includes the additional interaction

terms 𝐹 × 𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒 and 𝐹 × 𝑆ℎ𝑜𝑟𝑡𝑁𝑜𝑡𝑖𝑐𝑒. The number of observations goes down from 7765

to 5497 and the estimated coefficients for NegRet, Small, 𝑀 ×𝑁𝑒𝑔𝑅𝑒𝑡, 𝐹 × 𝑆𝑚𝑎𝑙𝑙, TimeUnder,

𝑇𝑖𝑚𝑒𝑈𝑛𝑑𝑒𝑟 × 𝑁𝑒𝑔𝑅𝑒𝑡 and AuCorr have the same sign and statistical significance as those in

Column (1). The coefficient estimate for F becomes highly significant with a magnitude of

0.621. The coefficient estimates for Leverage and ShortNotice are both positive and significant

while those for F Leverage and F ShortNotice , are negative and significant (-0.504 and -

0.289, with t-values of -3.243 and -2.280, respectively). The results suggest that (a) the presence

of leverage and short redemption notice period is associated with larger mid-year volatility

change when funds have not experienced outflows and (b) the presence of leverage and short

notice periods does not increase volatility for hedge funds that experience outflows and may

even decrease it.

The findings point to fund flows as a critical variable in the decision making of fund

managers: a fund that experiences outflow but does not use leverage or have short redemption

notice period has higher fund volatility while a fund that experiences outflow and uses leverage

and has short redemption notice period has lower fund volatility. These results are consistent

with Hypotheses 2 and 3. Furthermore, none of the coefficients on M, 𝑀 × 𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒 and

𝑀 × 𝑆ℎ𝑜𝑟𝑡𝑁𝑜𝑡𝑖𝑐𝑒 is significant, suggesting no difference in risk-shifting for hedge funds with

either leverage usage or short redemption notice period when part of the performance fee option

portfolio is out-of-the-money. The estimated coefficient for 𝐹 × 𝑁𝑒𝑔𝑅𝑒𝑡is negative but

insignificant, suggesting that risk-shifting is mitigated for hedge fund managers who experience

outflows. The estimated coefficient for SecondFlow is positive and significant, suggesting

higher fund flow accompanies higher volatility.

Results in Column (3) and (4) are based on estimating Equation (3) using fund-level fixed

effects rather than style-level fixed effects which alleviates the endogeneity issues that could

arise from unobservable time-invariant variables. The estimated coefficients for NegRet,

𝑀 ×𝑁𝑒𝑔𝑅𝑒𝑡, TimeUnder and 𝑇𝑖𝑚𝑒𝑈𝑛𝑑𝑒𝑟 × 𝑁𝑒𝑔𝑅𝑒𝑡 have the same sign and statistical

significance as those using pooled OLS regressions in Columns (1) and (2). The coefficient

estimate for F Leverage remains negative and significant and the estimate for F ShortNotice

is negative but no longer significant. A number of control variables, including Small, Leverage,

and ShortNotice, AuCorr also lose their significance. Since fixed effects regression removes

cross-sectional variation and relies on within-fund variation only, it does not have much power

for variables that have little variation at fund level (Zhou, 2001), which may explain the loss of

significance of F ShortNotice and the control variables.

In summary, results from the regression of Equation (3) using the no backfill bias sample

are consistent and robust for our Hypothesis 1, 2, 3b, and Hypothesis 4. We also find supporting

evidence for Hypothesis 3a, even though it is not as robust as the other findings.

[Table 3 about here]

3.3 Time spent underwater

Next, we explore in more detail how the duration of underperformance is associated with

changes in a hedge fund’s return volatility during the second half of the year. With a mean of

7.02 months, and a range between 0 and 35 months, TimeUnder is highly skewed with a median

of 2 months, and a 90th

percentile of 24 months. It is therefore possible that the relationship

between risk-shifting and TimeUnder is non-linear. To explore that possibility, we construct

three dummies for funds with partly out-of-the-money performance fee option portfolio and

various length of time being under-water: M_ST is set to one if a fund has M = 1 for less than 12

months and zero otherwise, M_MT is set to one if a fund has M = 1 for between 12 and 24

months and zero otherwise, M_LT is set to one if a fund has M = 1 for over 24 months and zero

otherwise. M_ST, M_MT, and M_LT are more evenly distributed with a mean of 9.98%, 7.90%,

and 6.76%, respectively. We include both M_ST and M_LT, re-estimate Equation (3), and report

estimation results from both fund fixed effects and pooled OLS regressions in Columns (1) and

(2) (M_MT serves as the base case) of Table 4, respectively. The coefficient for M_ST is negative

(and statistically significant for the regression estimates using fund fixed effects) and the

coefficient for M_LT is positive and significant generally. The difference between the

coefficient for M_ST and M_LT suggests that for funds experiencing positive returns, a longer

duration of poor performance leads to greater volatility of returns. The estimate for 𝑀_𝑆𝑇 ×

𝑁𝑒𝑔𝑅𝑒𝑡 is positive and significant, with a large magnitude, more than two times that for NegRet

(1.435 vs 0.447 in Column (1)). Therefore, a fund manager tends to be more aggressive in risk-

shifting after poor first half-year performance if the fund’s NAV has been less than the HWM for

a year or less. When the duration of underperformance extends over to more than one year,

hedge fund managers have less risk-shifting, supporting Hypothesis 4.17

[Table 4 about here]

4. Additional tests

4.1 Alternative performance measures

In this section we consider measures of the performance of the hedge fund relative to

other funds. Specifically, we investigate whether and how M and F influence hedge fund risk-

shifting decisions when poor first half-year performance (𝑃𝑒𝑟𝑓) is measured either by the rank

of absolute performance among all hedge funds (LowAbsRetRank) or by the rank of relative

within-style performance (LowRelRetRank) and focus on the coefficient estimates on the

interaction terms 𝑀 × 𝑃𝑒𝑟𝑓, 𝐹 × 𝑃𝑒𝑟𝑓, 𝑇𝑖𝑚𝑒 × 𝑃𝑒𝑟𝑓.

Panel A of Table 5 reports the results from the estimation of Equation (3) with the two

alternative performance measures. The findings are qualitatively similar to those in Table 3,

suggesting larger magnitudes of tournament-like risk-shifting due to already out-of-the-money

performance fee option. These findings, again, show that Moneyness and Flow are important

factors for risk-shifting related to tournament incentives as well.

17

An alternative explanation is related to different behavior for defunct funds. We explore whether the higher

volatility change at funds with long-time being under-water is related to the chaos before a fund ceases operation by

investigating the risk-shifting behavior in the final year of funds that are in the defunct funds database. We do not

find differences from the funds that are in the live funds database. Since defunct fund database includes both funds

that liquidate and cease operation and those stop reporting to the database, it is highly likely that we do not have

return data for the actual final year of a fund that ceases operation.

Panel B of Table 5 reports the impact of duration of underperformance on risk-shifting

using the two alternative performance measures, LowAbsRetRank, and LowRelRetRank and the

results are similar: the estimates for 𝑀_𝑆𝑇 × 𝐿𝑜𝑤𝐴𝑏𝑠𝑅𝑒𝑡𝑅𝑎𝑛𝑘 and 𝑀_𝑆𝑇 × 𝐿𝑜𝑤𝑅𝑒𝑙𝑅𝑒𝑡𝑅𝑎𝑛𝑘

are positive and significant yet insignificant for the interaction terms between 𝑀_𝐿𝑇 and the two

alternative performance measures. Our findings indicate as before that a fund shifts risk by more

in response to poor first half-year performance if the duration of poor performance is less than

one year.

[Table 5 about here]

4.2 Subsample analysis and alternative cutoffs for M and F

To examine further details for the impact of outflows on the propensity to risk-shifting

for a fund with out-of-the-money performance fee option, we analyze a subsample of hedge

funds with 1M and free of backfill bias, and report the results in Table 6. We conduct

robustness checks using different thresholds for setting the values of M and F equal to one.18

There are three thresholds (Moneyness <1, or 0.9, or 0.8) that are used to set M = 1 and another

three thresholds (Flow<0, or <‒0.1%, or <‒0.2%) that are used to define F = 1.19

For example,

the results reported in Column 2 are obtained by examining only those funds where moneyness is

less than or equal to 0.9 and when Flow is less than or equal to 0.

The results in Table 6 show that, first, the coefficients for NegRet are significant and

positive for all cases. Therefore, regardless of the definition for M = 1 and F = 1, a fund tends to

increase its return volatility following poor first half-year performance. Second, the coefficients

for the interaction term 𝐹 × 𝑁𝑒𝑔𝑅𝑒𝑡 are negative and significant except for the cases in which

Moneyness is defined to be a ratio of NAV to HWM less than 1. Therefore, for the subsample

funds with M=1, fund outflows mitigate risk-shifting for hedge funds unless the performance fee

option is relatively close to be at-the-money. Table 6 provides further support for our Hypotheses

2.

[Table 6 about here]

18

In robustness checks with various definitions of M using changing thresholds for Moneyness, we set TimeUnder to

0 whenever M=0. 19

Note that if Moneyness is equal to 1, it means that HWM is achieved at the end of the 36-month window and the

performance fee option is at-the-money.

We also use the entire no-backfill bias sample to conduct additional tests for robustness

of our findings to the thresholds assumed for Moneyness and Flow, which define M and F. We

vary these thresholds for M and F and report results based on the same nine combinations as

those in Table 6. The results of the interactions of these nine cutoffs with poor performance in

the first half-year are qualitatively the same.20

4.3 Discussion

Brown et al. (1996) and Brown et al. (2001) both use contingency table tests to

investigate how the number of mutual/hedge fund managers with above- and below-median

style-adjusted variance ratios in the second half-year vary with their performance in the first half-

year. They both find supporting evidence for risk-shifting due to tournament-like behavior.

Brown et al. (2001) do not find evidence that hedge fund managers increase volatility in the

second half-year after negative return in the first half-year for their sample over the period of

1989-1995. We reproduce the contingency table test using our sample of data over the period of

1997-2012 and present results for all funds, funds with M = 1 (funds with more than 10%

performance decline since achieving HWM), and funds with M = 0 (funds with less than 10%

performance decline since achieving HWM), respectively, in Table 7. The Chi-square statistic

is statistically significant for the group of all funds across the three performance measures

(NegRet, LowAbsRetRank, and LowRelRetRank), providing support for risk-shifting in hedge

funds with respect to convex payoff due to both asymmetric performance fee option and

asymmetric flow-performance relation. Despite the smaller number of funds in the group of all

funds with M = 1, this group has the strongest tendency to engage in risk-shifting after poor first

half-year performance. However, when the performance measure is NegRet, which suggests the

fund has a negative return in the first half-year, there is no evidence of risk-shifting for the group

of funds with M = 0. This is consistent with the findings in Brown et al. (2001) which show that,

despite of their asymmetric compensation contracts, hedge funds do not react by increasing

volatility after negative first half-year returns. Our results show that this is indeed the case for

hedge funds with close to at-the-money compensation option at the beginning of the year.

However, when hedge fund managers have a large amount of out-of-the-money compensation

options at the beginning of the year, they are more aggressive in risk-shifting. Hedge funds

20

These results are available upon request from the authors.

generally performed well during the 1990s (Ackermann et al., 1999) and, thus, we expect that

most of the funds in the Brown et al. (2001) sample to have M = 0.

[Table 7 about here]

Chen (2013) shows that hedge funds that use derivatives have smaller risk-shifting

compared to those that do not. Since when hedge funds report to the database that they use

leverage, they could mean the usage of futures, swaps and other derivatives besides bank or

primary brokerage credit, it is plausible that our results on the lower volatility change for funds

that use leverage conditional on outflow may be due to their usage of derivatives instead. To

examine that possibility, we exclude hedge funds that use derivatives and re-estimate Equation

(3). Our results remain the same, and the coefficient for 𝐹 × 𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒is negative and

significant, with a similar magnitude (t-stat= -2.69).21

4.4 More robustness tests

The results reported so far are based on the HWM observed over a rolling three-year

window starting from Jan 1, 1994, where we treat the maximum NAV over such a window as a

proxy for HWM of a typical investor. We relax the requirement of a three-year window and use

the maximum NAV as a proxy for HWM for a fund with at least four years of return history over

a period which starts from Jan 1, 1994 and ends right before the beginning of the evaluation year.

This maximum NAV can be viewed as the maximum HWM of a hedge fund’s investors starting

after Jan 1, 1994 at the fund. Using this alternative definition of HWM, we re-calculate summary

statistics and re-estimate Equation (3) with the three performance measures (NegRet,

LowAbsRetRank, and LowRelRetRank); we find our main results remain unchanged. Our

findings are also robust to using the full sample of hedge funds. 22

We explore whether the fund’s moneyness and flow conditions are associated with the

flow-performance relationship examined in Chevalier and Ellison (1997) and Sirri and Tufano

(1998). We find that the flow-performance relation documented by previous papers is weaker for

hedge funds with partly out-of-the-money performance fee option portfolio.23

We may

hypothesize that managers are aware of the weaker relation when they decide whether to

21

These results are available from the authors upon request. 22

These results are available upon request. 23

These results are available upon request.

implement a risk-shifting policy. If the performance-flow relationship is indeed weaker, the cost

associated with potential outflows will be lower, and, therefore, the manager may choose a more

aggressive risk-shifting policy.24

5. Conclusion

In response to Ross (2004)’s call, we show that time-varying performance- and flow-

related conditions influence hedge funds’ risk-shifting behavior. We also show that hedge fund

managers adjust their risk-taking in a way that is consistent with their fund characteristics. We

find that the most recent half-year performance and cumulative past performance influences risk-

shifting as does the direction of fund flows. We also show that risk-shifting behavior is driven

to a great extent by funds that whose performance fee option portfolio has been partly out-of-the-

money for a short period. The use of leverage or short redemption notice periods reduces risk

shifting for funds that are experiencing outflows.

Our results help depict how hedge funds use volatility strategies that are based on

performance, flow history, and fund characteristics over time. Our findings highlight the

importance of the explicit incentive for risk-shifting due to the convex payoff structure of

compensation contracts for hedge fund managers, an issue of interest to the investment

community and to scholars of corporate finance. More research is needed, however, especially to

fully understand the relations between risk-taking and various combinations of flow and

performance, as well as how leverage usage is related to risk-taking.

24

In a related study but a different context, Del Guercio and Tkac (2002) show that pension fund managers have

much less tournament-like risk-shifting incentives due to much less convex flow-performance relation.

References

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

risk, return, and incentives. Journal of Finance 54, 833-874.

Adams, R.B. and D. Ferreira, 2009. Women in the boardroom and their impact on corporate

governance and performance. Journal of Financial Economics 94, 291-309.

Agarwal, V., N. Daniel, and N. Y. Naik, 2002. Determinants of money-flow and risk-taking

in the hedge fund industry. Unpublished working paper, Georgia State University.

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

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

Ang, A., S. Gorovyy, G.B. van Inwegen, 2011, Hedge fund leverage. Journal of Financial

Economics 102, 102-126.

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, H. Park, 2014, Onshore and offshore hedge funds: are they twins?

Management Science 60, 74-91

Aragon, G. O. and V. Nanda, 2012, Tournament behavior in hedge funds: High water marks,

fund liquidation, and managerial stake. Review of Financial Studies 25, 937-974

Basak, S., A. Pavlova, and A. Shapiro, 2007, Optimal Asset Allocation and Risk Shifting in

Money Management. Review of Financial Studies 20, 1583-1621

Barber, B., T. Odean, and L. Zheng, 2004, Out of sight, out of mind: the effects of expenses

on mutual fund flows. Journal of Business 78, 2095-2120.

Brown, S., W. Goetzmann, and J. Park, 2001, Careers and survival: Competition and risk in

the hedge fund and CTA industry. Journal of Finance 56, 1869-1886

Brown, K. C., W. V. Harlow, and L. T. Starks, 1996, Of tournaments and temptations: an

analysis of managerial incentives in the mutual fund industry. Journal of Finance 51, 85-110

Carpenter, J., Does option compensation increase managerial risk appetite. 2000, Journal of

Finance 55, 2311-2331

Chevalier, J., and G. Ellison, 1997, Risk taking by mutual funds as a response to incentives.

Journal of Political Economy 105, 1167-2000

Cumming, D. and N. Dai, 2010, Hedge fund regulation and misreported returns. European

Financial Management 16, 829-857

Del Guercio, D. and P. Tkac, 2002, The determinants of the flow of funds of managed

portfolios: mutual funds versus pension funds, Journal of Financial and Quantitative Analysis 37,

523-557.

Drechsler, I. 2014, Risk choice under high-water marks. forthcoming, Review of Financial

Studies, doi: 10.1093/rfs/hht081.

Ferson, W. and V. Warther, 1996, Evaluating Fund Performance in a Dynamic Market.

Financial Analyst Journal 52, 20-28.

Frazzini, A. and O. A. Lamont, 2008, Dumb money: mutual fund flows and the cross-section

of stock returns, Journal of Financial Economics 88, 299-322.

Fung, W. and D. A. Hsieh, 1997, Empirical characteristics of dynamic trading strategies: The

case of hedge funds, Review of Financial Studies 10, 275-302

Fung, W. and D. A. Hsieh, 2000, Performance Characteristics of Hedge Funds and CTA

Funds: Natural versus Spurious Biases, Journal of Financial and Quantitative Analysis 35, 291-

307

Getmansky, M., A. Lo, and I. Makarov, 2004, An econometric model of serial correlation and

illiquidity in hedge fund returns, Journal of Financial Economics 74, 29-610

Getmansky, M., B. Liang, C. Schwarz, and R. Wermers, 2011, Share restrictions and investor

flows in the hedge fund industry. Working paper, University of Massachusetts, Amherst

Goetzmann, W. N., J. E. Jr. Ingersoll, and S. A. Ross, 2003, High-water marks and hedge

fund management contracts, The Journal of Finance 58, 1685-1717

Hodder, J.E. and J. C. Jackwerth, 2007, Incentive contracts and hedge fund management,

Journal of Financial and Quantitative Analysis 42, 811-26

Hu, P., J. R. Kale, M. Pagani, and A. Subramanian, 2011, Fund flows, performance,

managerial career concerns, and risk taking, Management Science 57, 628-646

Jain, P. and J. Wu, 2000, Truth in mutual fund advertising: evidence on future performance

and fund flows. Journal of Finance 55, 937-958.

Koski, J. L. and J. Pontiff, 1999, How are derivatives used? Evidence from the mutual fund

industry. Journal of Finance 54, 791-816.

Lan, Y., N. Wang, and J. Yang, 2013, The economics of hedge funds, Journal of Financial

Economics 110, 300-323

Li, Y. and H. B. Kazemi, 2007, Conditional properties of hedge funds: Evidence from daily

return, European Financial Management 13(2), pp.211-238

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

Analysis 35, pp.309-326

Oikonomou, I., C. Brooks, and S. Pavelin, 2012.The Impact of Corporate Social Performance on

Financial Risk and Utility: A Longitudinal Analysis. Financial Management 41, 483-515.

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

Ray, S., 2012, The downside risk of high water marks: an empirical study, Journal of

Investment Management, Second Quarter

Ross, S. A., 2004, Compensation, incentives, and the duality of risk aversion and riskiness,

Journal of Finance 59, 207-225.

Sirri, E. and P. Tufano, 1998, Costly search and mutual fund flows, Journal of Finance 53,

1589-1622.

Zhou, X., 2001, Understanding the determinants of managerial ownership and the link

between ownership and performance: comment, Journal of Financial Economics 62, 559-571.

Appendix. Definition of Variables

PStd Fund volatility calculated using monthly returns in the first half of an evaluation year

𝑃𝑆𝑡𝑑𝑖 = 𝜎𝑡,𝑡+6 ∗ 100

NStd Fund volatility calculated using monthly returns in the second half of an evaluation

year. 𝑁𝑆𝑡𝑑𝑖 = 𝜎𝑡+7,𝑡+12 ∗ 100

DifStd (NStd-PStd) *100, dependent variable

HWM

𝑡𝑖,𝐻𝑊𝑀

High-water mark proxy as of the beginning of the evaluation year based on the

previous 36-month window. 𝐻𝑊𝑀𝑖,𝑡 = max(𝑁𝐴𝑉𝑖,𝑡−𝜏; 0 ≤ 𝜏 < 36) The month when hedge fund 𝑖 reaches its HWM.

Moneyness

, , ,/

i t i t i tMoneyness NAV HWM , where 𝑁𝐴𝑉𝑖,𝑡 is net asset value per hedge fund share

at the beginning of the evaluation year.

Flow 𝐹𝑙𝑜𝑤𝑖,𝑡+6 =𝐴𝑈𝑀𝑖,𝑡+6−(1+𝐴𝑅𝑖,𝑡𝐻𝑊𝑀,𝑡+6)×𝐴𝑈𝑀𝑖,𝑡𝐻𝑊𝑀

𝐴𝑈𝑀𝑖,𝑡𝐻𝑊𝑀

where 𝐴𝑈𝑀 is the asset under

management and 𝐴𝑅𝑖,𝑡𝐻𝑊𝑀,𝑡+6 is the accumulative return between 𝑡𝐻𝑊𝑀 and 𝑡 + 6,

(June 30th of the evaluation year). Flow is a proxy for the net flow from the month a

fund achieves its HWM to Jun 30 of the evaluation year. M A dummy variable that equals 1 if Moneyness < 0.9 and 0 otherwise.

M_ST A dummy variable that equals 1 if M=1 and TimeUnder is less than 1 year and 0

otherwise.

M_MT A dummy variable that equals 1 if M=1 and TimeUnder is more than 1 year and less

than 2 years. It is 0 otherwise.

M_LT A dummy variable that equals 1 if M=1 and TimeUnder is longer than 2 years and 0

otherwise.

F A dummy variable that equals 1 if Flow < 0 and 0 otherwise.

NegRet A dummy variable that equals 1 if cumulative return over the first half of the

evaluation year is negative and 0 otherwise

LowAbsRetRank

LowRelRetRank

Leverage

Small

A dummy variable that equals 1 if accumulative return over the first half of the

evaluation year is ranked below median among all funds and 0 otherwise

A dummy variable that equals 1 if accumulative return over the first half of the

evaluation year is ranked below median among funds within a given style and 0

otherwise

A dummy variable that equals 1 if the fund reports that it uses leverage and 0

otherwise

A dummy variable that equals 1 if the fund’s asset under management is below-

median and 0 otherwise.

ShortNotice A dummy variable that equals 1 if a fund has a short redemption notice that is less

than or equal to 30 days and 0 otherwise.

TimeUnder

SecondFlow

The number of months a fund has been under its maximum NAV in a three-year

window

Net flow to the fund from Jul 1 to December 31 of the evaluation year.

AuCorr First-order autocorrelation coefficient on a fund's return in a 3-year historical return

window.

Table 1. Summary Statistics of Hedge Funds Sample, 1994 - 2012

Summary statistics are reported for the hedge funds sample from the Morningstar-CISDM

database. We include only funds that have returns in Morningstar-CISDM after January 1994 to

minimize survivorship bias. To be included in the sample, each fund must have at least four

years of return history, information on assets under management, management fee and

performance fee rates, strategy, as well as returns for the entire four-year window. The sample is

winsorized at 1% and 99% values of DifStd, Moneyness and Flow to minimize the impact from

outliers. The Appendix provides detailed definition for each variable.

Panel A. Hedge fund counts and fund size by evaluation year.

Window Evaluation

Year

Frequency Percent Min 25th

Pctl

50th

Pctl

75th

Pctl

Max Mean

94-96 1997 74 0.58 0.20 18.00 43.94 90.40 2818.00 131.83

95-97 1998 122 0.99 0.20 20.05 58.60 120.37 3054.00 153.59

96-98 1999 185 1.68 0.20 15.00 45.93 111.40 3054.00 131.07

97-99 2000 289 2.43 0.20 16.00 48.00 132.10 3054.00 146.74

98-00 2001 406 3.37 0.20 17.36 45.57 139.00 3054.00 153.74

99-01 2002 537 4.64 0.20 17.30 49.20 140.03 2580.27 150.35

00-02 2003 730 5.96 0.20 14.80 47.10 120.00 2829.00 131.05

01-03 2004 924 7.48 0.20 19.08 60.42 166.70 2877.00 167.64

02-04 2005 1132 9.01 0.20 19.42 65.00 198.05 3054.00 193.01

03-05 2006 1267 10.3 0.20 18.77 66.67 199.80 3054.00 196.45

04-06 2007 1294 10.62 0.20 18.80 65.00 210.39 3054.00 209.95

05-07 2008 1155 10.19 0.20 21.00 72.49 237.50 3054.00 234.92

06-08 2009 1071 9.25 0.20 13.00 41.33 131.54 3054.00 166.30

07-09 2010 1004 8.55 0.20 15.60 46.00 141.00 3054.00 162.05

08-10 2011 941 8.02 0.28 17.81 50.76 140.56 3054.00 163.93

09-11 2012 805 6.92 0.28 15.25 45.39 136.79 3054.00 155.81

94-12 11934 100% 0.20 17.40 54.19 164.80 3054.00 177.39

Panel B. Style Distribution

No Backfill-Bias sample Full sample

Style Counts

Convertible Arbitrage 275 398

Debt-related 989 1524

Equity-related 5072 7387

Event Driven 558 765

Global Macro 544 795

Merger Arbitrage 126 154

Multi-strategy 604 861

Volatility 31 50

Panel C. Summary Statistics for No backfill Bias Sample.

No back Fill Sample M = 1 F = 1 1M F

Variable N Mean Std N Mean Std N Mean Std N Mean Std

PStd 8193 3.34 3.03 2014 5.40 3.94 4496 3.51 3.15 1514 5.29 3.91

NStd 8194 3.42 3.60 2015 4.75 4.62 4497 3.47 3.51 1515 4.60 4.27

𝐷𝑖𝑓𝑆𝑡𝑑 8193 0.08 3.12 2014 -0.64 4.42 4496 -0.05 3.08 1514 -0.69 4.14

𝑀 8199 0.25 0.43 2020 1.00 0.00 4502 0.34 0.47 1520 1.00 0.00

𝐹 8199 0.55 0.50 2020 0.75 0.43 4502 1.00 0.00 1520 1.00 0.00

M F � 8199 0.19 0.39 2020 0.75 0.43 4502 0.34 0.47 1520 1.00 0.00

𝑇𝑖𝑚𝑒𝑈𝑛𝑑𝑒𝑟 8199 7.02 9.80 2020 17.26 9.81 4502 9.54 10.75 1520 17.84 9.79

𝑆𝑚𝑎𝑙𝑙 8199 0.50 0.50 2020 0.62 0.48 4502 0.55 0.50 1520 0.61 0.49

𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒 5741 0.77 0.42 1341 0.75 0.43 3175 0.78 0.42 1019 0.77 0.42

𝑆ℎ𝑜𝑟𝑡𝑁𝑜𝑡𝑖𝑐𝑒 8199 0.63 0.48 2020 0.67 0.47 4502 0.64 0.48 1520 0.66 0.47

𝐴𝑢𝐶𝑜𝑟𝑟 8193 0.14 0.22 2014 0.19 0.22 4496 0.16 0.22 1514 0.20 0.22

Panel D. Summary Statistics for Full Sample. Full Sample M = 1 F = 1 1M F

Variable N Mean Std N Mean Std N Mean Std N Mean Std

PStd 11925 3.34 3.13 2800 5.45 4.18 6171 3.49 3.20 2028 5.25 3.91

NStd 11926 3.41 3.62 2801 4.84 4.66 6172 3.47 3.56 2029 4.63 4.28

𝐷𝑖𝑓𝑆𝑡𝑑 11925 0.08 3.16 2800 -0.61 4.45 6171 -0.03 3.12 2028 -0.62 4.10

𝑀 11934 0.24 0.42 2809 1.00 0.00 6180 0.33 0.47 2037 1.00 0.00

𝐹 11934 0.52 0.50 2809 0.73 0.45 6180 1.00 0.00 2037 1.00 0.00

M F � 11934 0.17 0.38 2809 0.73 0.45 6180 0.33 0.47 2037 1.00 0.00

𝑇𝑖𝑚𝑒𝑈𝑛𝑑𝑒𝑟 11934 6.61 9.51 2809 17.03 9.83 6180 9.00 10.42 2037 17.39 9.75

𝑆𝑚𝑎𝑙𝑙 11934 0.50 0.50 2809 0.62 0.49 6180 0.55 0.50 2037 0.61 0.49

𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒 8012 0.78 0.41 1761 0.76 0.43 4191 0.79 0.41 1294 0.78 0.42

𝑆ℎ𝑜𝑟𝑡𝑁𝑜𝑡𝑖𝑐𝑒 11934 0.63 0.48 2809 0.67 0.47 6180 0.63 0.48 2037 0.66 0.48

𝐴𝑢𝐶𝑜𝑟𝑟 11925 0.14 0.22 2800 0.18 0.22 6171 0.15 0.22 2028 0.20 0.22

Panel E. Distribution of Leverage Usage and Short Redemption Period.

Full Sample No Backfill Sample

Leverage ShortNotice Leverage ShortNotice

0 1 0 1 0 1 0 1

All 1747 6265 4470 7464 1338 4403 3036 5163

1M 428 1333 925 1884 330 1011 658 1362

0M 1319 4932 3545 5580 1008 3392 2378 3801

1F 898 3293 2266 3914 714 2461 1625 2877

0F 849 2972 2204 3550 624 1942 1411 2286

M F =1 289 1005 700 1337 235 784 519 1001

0M F 1458 5260 3770 6127 1103 3619 2517 4162

Table 2. Correlation Matrix with P-values in Parentheses.

Pairwise correlation coefficients are reported for our main variables in regression analyses for the no backfill bias sample. There are 8199 individual hedge fund-

year observations over the period of 1997–2012 in the no backfill bias sample. The Appendix provides detailed definition for each variable. MF, M×NegRet,

and F×NegRet represent interaction between Moneyness and Flow, Moneyness and NegRet, as well as Flow and NegRet, respectively.

1 2 3 4 5 6 7 8 9 10 11 12 13

1 DifStd 1.00

2 M -0.13 1.00

[<.00]

3 F -0.04 0.23 1.00

[<.00] [<.00]

4 MF -0.12 0.83 0.43 1.00

[<.00] [<.00] [<.00]

5 NegRet 0.10 0.09 0.08 0.07 1.00

[<.00] [<.00] [<.00] [<.00]

6 M×NegRet 0.06 0.54 0.12 0.44 0.49 1.00

[<.00] [<.00] [<.00] [<.00] [<.00]

7 F×NegRet 0.07 0.13 0.41 0.22 0.73 0.46 1.00

[<.00] [<.00] [<.00] [<.00] [<.00] [<.00]

8 TimeUnder -0.03 0.60 0.28 0.53 0.13 0.35 0.18 1.00

[0.00] [<.00] [<.00] [<.00] [<.00] [<.00] [<.00]

9 Small 0.01 0.14 0.10 0.11 0.07 0.12 0.08 0.16 1.00

[0.19] [<.00] [<.00] [<.00] [<.00] [<.00] [<.00] [<.00]

10 Leverage 0.01 -0.02 0.02 0.00 0.02 -0.02 0.02 -0.02 -0.07 1.00

[0.67] [0.20] [0.10] [0.84] [0.21] [0.13] [0.07] [0.12] [<.00]

11 ShortNotice 0.00 0.05 0.02 0.03 0.07 0.05 0.06 0.06 0.13 -0.07 1.00

[0.65] [<.00] [0.05] [0.01] [<.00] [<.00] [<.00] [<.00] [<.00] [<.00]

12 PStd -0.32 0.39 0.06 0.31 0.15 0.22 0.11 0.21 0.15 -0.04 0.10 1.00

[<.00] [<.00] [<.00] [<.00] [<.00] [<.00] [<.00] [<.00] [<.00] [0.01] [<.00]

13 AuCorr -0.04 0.11 0.07 0.13 -0.05 0.02 -0.01 0.06 -0.12 0.01 -0.15 -0.04 1.00

[0.00] [<.00] [<.00] [<.00] [<.00] [0.03] [0.27] [<.00] [<.00] [0.28] [<.00] [<.00]

Table 3. Regressions with Moneyness and Fund Flows: No Backfill Bias Sample

This table reports pooled OLS and fund fixed effects (FE) estimates from Equation (3), the regression of mid-year

change in fund volatility on the fund’s negative return in the first half-year conditional on the past performance and

fund flow and fund characteristics using the no backfill bias sample. The no backfill bias sample includes a total of

8199 individual hedge fund-year observations (7765 observations when we include SecondFlow as a control,

representing 1625 individual funds) over the period of 1997–2012 and 5741 of the observations (5497 observations

when we include SecondFlow as a control, representing 1058 individual funds) have information on leverage usage.

The Appendix provides detailed definition for each variable. Numbers in parentheses are t-statistics based on robust

standard errors clustered at fund levels. Columns (1) – (2) report results from pooled OLS regressions and Columns

(3) – (4) report results from a fund fixed effects regression. The symbols ***, **, and * denote significance levels of

1%, 5% and 10%, respectively.

(1) (2) (3) (4)

VARIABLES OLS OLS Fund FE Fund FE

M -0.198 -0.279 -0.484*** -0.398

(-1.289) (-0.901) (-3.239) (-1.318)

F 0.036 0.621*** 0.078 0.335*

(0.480) (3.355) (1.044) (1.908)

NegRet 0.483*** 0.664*** 0.503*** 0.535***

(3.682) (4.182) (3.699) (3.148)

𝑀 × 𝑁𝑒𝑔𝑅𝑒𝑡 1.287*** 1.340*** 1.360*** 1.447***

(4.703) (3.872) (5.041) (4.314)

𝐹 × 𝑁𝑒𝑔𝑅𝑒𝑡 -0.123 -0.287 -0.161 -0.205

(-0.657) (-1.211) (-0.864) (-0.881)

𝑀 × 𝑆𝑚𝑎𝑙𝑙 0.357** 0.316 -0.009 -0.098

(2.044) (1.598) (-0.050) (-0.454)

𝑀 × 𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒 0.069 -0.041

(0.265) (-0.155)

𝑀 × 𝑆ℎ𝑜𝑟𝑡𝑁𝑜𝑡𝑖𝑐𝑒 0.014 -0.162

(0.070) (-0.766)

𝐹 × 𝑆𝑚𝑎𝑙𝑙 -0.330*** -0.241* -0.133 -0.103

(-2.639) (-1.692) (-1.047) (-0.718)

𝐹 × 𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒 -0.504*** -0.343**

(-3.243) (-2.052)

𝐹 × 𝑆ℎ𝑜𝑟𝑡𝑁𝑜𝑡𝑖𝑐𝑒 -0.289** -0.036

(-2.280) (-0.281)

TimeUnder 0.020*** 0.023*** 0.021*** 0.026***

(4.118) (4.514) (3.949) (4.223)

𝑇𝑖𝑚𝑒𝑈𝑛𝑑𝑒𝑟 × 𝑁𝑒𝑔𝑅𝑒𝑡 -0.023** -0.025* -0.023** -0.021

(-2.146) (-1.852) (-1.990) (-1.473)

Small 0.354*** 0.296*** 0.145 0.039

(3.598) (2.611) (1.066) (0.239)

Leverage 0.229* 0.311

(1.922) (0.328)

ShortNotice 0.232** -1.825

(2.186) (-1.507)

AuCorr -0.250* -0.386** -0.199 -0.343

(-1.687) (-2.345) (-0.919) (-1.487)

SecondFlow 0.208 0.443*** 0.237* 0.306*

(1.442) (2.721) (1.670) (1.854)

PStd -0.367*** -0.351*** -0.725*** -0.731***

(-18.984) (-15.592) (-24.296) (-20.129)

Constant 1.325*** 0.942*** 2.963*** 3.670***

(4.809) (2.669) (9.050) (3.108)

Observations 7,765 5,497 7,765 5,497

R-squared 0.299 0.281 0.439 0.423

Style FE Yes Yes

Year FE Yes Yes Yes Yes

Fund FE Yes Yes

Number of fundID 1,625 1,058

Table 4. The Impact of Duration of Underperformance: No Backfill Bias Sample

This table focuses on the impact of duration of underperformance and reports pooled OLS and fund fixed effects

(FE) estimates from Equation (3), the regression of mid-year change in fund volatility on the fund’s poor

performance in the first half-year for the no backfill sample. The performance measure is NegRet. The sample

includes a total of 8199 individual hedge fund-year observations over the period of 1997–2012 and 7765 of the

observations have information on SecondFlow, representing 1625 individual funds. Instead of M and TimeUnder,

which is the number of months a fund has been below its HWM, three dummy variables, M_ST, M_MT and M_LT

are used to capture the possible nonlinear relationship between DifStd and NegRet conditional on M and different

amount of TimeUnder. M_ST is set to 1 if M=1 and TimeUnder is less than 12 months and 0 otherwise; M_MT

equals 1 if M=1 and TimeUnder is between12 months and 24 months and 0 otherwise; M_LT equals 1 if M=1 and

TimeUnder is over 24 months and 0 otherwise. The Appendix provides definitions for each variable. Numbers in

parentheses are t-statistics based on robust standard errors clustered at fund levels. The symbols ***, **, and *

denote significance levels of 1%, 5% and 10%, respectively.

(1) (2)

VARIABLES OLS Fund FE

M_ST -0.024 -0.417**

(-0.160) (-2.532)

M_MT

M_LT 0.958*** 0.534**

(4.534) (2.189)

F -0.101 0.026

(-1.614) (0.426)

NegRet 0.470*** 0.477***

(3.340) (3.398)

𝑀_𝑆𝑇 × 𝑁𝑒𝑔𝑅𝑒𝑡 1.175*** 1.435***

(3.491) (3.802)

𝑀_𝑀𝑇 × 𝑁𝑒𝑔𝑅𝑒𝑡

𝑀_𝐿𝑇 × 𝑁𝑒𝑔𝑅𝑒𝑡 0.663 0.544

(1.534) (1.241)

𝐹 × 𝑁𝑒𝑔𝑅𝑒𝑡 -0.143 -0.144

(-0.810) (-0.819)

Small 0.267*** 0.057

(4.351) (0.474)

AuCorr -0.220 -0.175

(-1.475) (-0.812)

SecondFlow 0.163 0.222

(1.133) (1.550)

PStd -0.363*** -0.730***

(-19.121) (-24.323)

Constant 1.350*** 3.032***

(4.886) (9.215)

Observations 7,765 7,765

R-squared 0.299 0.440

Number of fundID 1,625

Fund FE Yes

Year FE Yes Yes

Style FE Yes

Table 5. Regression Results with Alternative Performance Measures: No Backfill Bias Sample

This table reports results using alternative performance measures in estimation of Equation (3), the regression of

mid-year change in fund volatility on the fund’s alternative poor performance measures in the first half-year

conditional on the performance and fund flow and fund characteristics for the no backfill sample. The sample

includes a total of 8199 individual hedge fund-year observations over the period of 1997–2012 yet only 5497 of the

observations have information on both Leverage and SecondFlow. The two alternative poor performance measures

are LowAbsRetRank and LowRelRetRank. Panel A focuses on the impact of M and F and Panel B focuses on the

impact of duration of underperformance. The Appendix provides detailed definition for each variable. Numbers in

parentheses are t-statistics based on robust standard errors clustered at fund levels. The symbols ***, **, and *

denote significance levels of 1%, 5% and 10%, respectively.

Panel A The Impact of Moneyness and Flow with Alternative Performance Measures

(1) (2) (3) (4)

Alt-Perf= Alt-Perf= Alt-Perf= Alt-Perf=

LowAbsRetRank LowAbsRetRank LowRelRetRank LowRelRetRank

VARIABLES Fund FE OLS Fund FE OLS

M -0.447 -0.476 -0.437 -0.489

(-1.465) (-1.477) (-1.346) (-1.451)

F 0.325* 0.551*** 0.320 0.621***

(1.712) (2.831) (1.623) (3.025)

Perf 0.394*** 0.300*** 0.315*** 0.275**

(3.444) (2.850) (2.706) (2.579)

𝑀 × 𝑃𝑒𝑟𝑓 1.136*** 1.328*** 1.059*** 1.274***

(4.265) (5.045) (3.577) (4.729)

𝐹 × 𝑃𝑒𝑟𝑓 -0.078 0.003 -0.061 -0.125

(-0.480) (0.020) (-0.379) (-0.798)

TimeUnder 0.031*** 0.034*** 0.024*** 0.027***

(3.954) (4.971) (3.180) (4.126)

𝑇𝑖𝑚𝑒𝑈𝑛𝑑𝑒𝑟 × 𝑃𝑒𝑟𝑓 -0.025** -0.036*** -0.009 -0.021**

(-2.064) (-3.341) (-0.760) (-1.994)

𝑀 × 𝑆𝑚𝑎𝑙𝑙 -0.108 0.315 -0.111 0.324

(-0.503) (1.583) (-0.511) (1.629)

𝑀 × 𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒 -0.037 0.072 0.017 0.117

(-0.136) (0.274) (0.062) (0.442)

𝑀 × 𝑆ℎ𝑜𝑟𝑡𝑁𝑜𝑡𝑖𝑐𝑒 -0.170 0.008 -0.171 0.029

(-0.798) (0.041) (-0.796) (0.142)

𝐹 × 𝑆𝑚𝑎𝑙𝑙 -0.110 -0.275* -0.104 -0.268*

(-0.749) (-1.886) (-0.712) (-1.839)

𝐹 × 𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒 -0.322* -0.482*** -0.329* -0.484***

(-1.899) (-3.077) (-1.928) (-3.062)

𝐹 × 𝑆ℎ𝑜𝑟𝑡𝑁𝑜𝑡𝑖𝑐𝑒 -0.053 -0.292** -0.042 -0.296**

(-0.406) (-2.255) (-0.319) (-2.284)

Small 0.063 0.318*** 0.047 0.311***

(0.386) (2.769) (0.289) (2.732)

Leverage 0.391 0.221* 0.393 0.231*

(0.405) (1.864) (0.408) (1.932)

ShortNotice -1.752 0.229** -1.805 0.229**

(-1.435) (2.131) (-1.453) (2.149)

AuCorr -0.377 -0.418** -0.405* -0.434***

(-1.632) (-2.534) (-1.733) (-2.611)

SecondFlow 0.315* 0.403** 0.325* 0.402**

(1.882) (2.444) (1.895) (2.393)

PStd -0.718*** -0.335*** -0.722*** -0.338***

(-19.902) (-14.836) (-20.145) (-15.150)

Constant 3.360*** 0.829** 3.474*** 0.873**

(2.824) (2.260) (2.868) (2.389)

Observations 5,497 5,497 5,497 5,497

R-squared 0.419 0.277 0.420 0.277

Number of fundID 1,058 1,058

Fund FE Yes Yes

Year FE Yes Yes Yes Yes

Style FE Yes Yes

Panel B The Impact of Duration of Underperformance with Alternative Performance Measures

(1) (2) (3) (4)

Alt-Perf= Alt-Perf= Alt-Perf= Alt-Perf=

LowAbsRetRank LowAbsRetRank LowRelRetRank LowRelRetRank

VARIABLES Fund FE OLS Fund FE OLS

M_ST -0.506*** -0.236 -0.319 -0.056

(-2.661) (-1.282) (-1.384) (-0.269)

M_LT 0.633** 1.166*** 0.449 0.958***

(2.082) (4.294) (1.605) (3.926)

F -0.016 -0.148* 0.005 -0.091

(-0.205) (-1.879) (0.056) (-1.174)

Perf 0.331*** 0.199** 0.349*** 0.265***

(3.498) (2.137) (3.686) (2.858)

𝑀_𝑆𝑇 × 𝑃𝑒𝑟𝑓 1.207*** 1.255*** 0.803** 0.896***

(4.153) (4.838) (2.566) (3.284)

𝑀_𝐿𝑇 × 𝑃𝑒𝑟𝑓 0.169 0.139 0.600 0.586

(0.406) (0.362) (1.478) (1.527)

𝐹 × 𝑃𝑒𝑟𝑓 0.010 0.020 -0.016 -0.086

(0.081) (0.157) (-0.119) (-0.675)

Small 0.071 0.268*** 0.060 0.268***

(0.592) (4.367) (0.498) (4.373)

AuCorr -0.198 -0.229 -0.224 -0.251*

(-0.922) (-1.545) (-1.038) (-1.694)

SecondFlow 0.229 0.132 0.223 0.132

(1.585) (0.927) (1.526) (0.922)

PStd -0.719*** -0.349*** -0.722*** -0.352***

(-24.060) (-18.280) (-24.179) (-18.594)

Constant 2.885*** 1.265*** 2.927*** 1.273***

(8.442) (4.403) (8.538) (4.428)

Observations 7,765 7,765 7,765 7,765

R-squared 0.438 0.297 0.437 0.296

Number of fundID 1,625 1,625

Fund FE Yes Yes

Year FE Yes Yes Yes Yes

Style FE Yes Yes

Table 6. The Effects of Varying M and F Cutoffs: No Backfill Bias Subsample with M=1

This table reports estimates from a subsample analysis using Equation (3) to examine when using different cutoffs, how flow condition, F , influences mid-year change in

fund volatility in response to the fund’s negative return in the first half-year for the no backfill sample. The subsample includes funds that have M=1 only, based on

different cutoffs for Moneyness and Flow. There are three cutoffs for Moneyness and Flow, respectively, resulting in nine combinations of Moneyness and Flow. The

three cutoffs for Moneyness: equal to 1, 0.9, and 0.8; the three cutoffs for Flow: equal to 1, -0.1, -0.2. The Appendix provides detailed definition for each variable

Numbers in parentheses are t-statistics based on robust standard errors clustered at fund levels. The symbols ***, **, and * denote significance levels of 1%, 5% and

10%, respectively.

Variable

1 2 3 4 5 6 7 8 9

Moneyness<1 Moneyness<0.9 Moneyness<0.8 Moneyness<1 Moneyness<0.9 Moneyness<0.8 Moneyness<1 Moneyness<0.9 Moneyness<0.8

Flow<0 Flow<0 Flow<0 Flow<-0.1 Flow<-0.1 Flow<-0.1 Flow<-0.2 Flow<-0.2 Flow<-0.2

Intercept 1.322*** 1.031 -1.299 1.53*** 1.224 -0.165 1.594*** 1.448* 0.173

[3.488] [0.934] [-0.509] [4.641] [1.267] [-0.077] [5.442] [1.724] [0.093]

F 0.699** 1.127 2.03 0.49* 1.068* 0.95 0.578* 1.133** 0.807

[2.213] [1.628] [1.61] [1.663] [1.755] [1.039] [1.797] [1.969] [1.062]

NegRet 0.695** 2.03*** 3.17*** 0.769*** 1.809*** 2.658*** 0.671*** 1.532*** 2.359***

[2.573] [3.287] [3.003] [3.848] [3.983] [3.401] [3.907] [4.166] [3.631]

F NegRet -0.17 -1.434** -2.150* -0.37 -1.399*** -1.743** -0.257 -1.278** -1.62**

[-0.564] [-2.193] [-1.961] [-1.457] [-2.644] [-2.035] [-0.996] [-2.555] [-2.083]

F Small -0.257 -0.292 -0.837 0.02 0.17 0.063 0.187 0.494 0.49

[-1.116] [-0.714] [-1.262] [0.096] [0.455] [0.112] [0.901] [1.417] [1.031]

F Leverage -0.608** -0.804 -1.423 -0.531** -0.892** -0.805 -0.653** -1.046** -0.739

[-2.379] [-1.497] [-1.636] [-2.227] [-1.998] [-1.229] [-2.539] [-2.372] [-1.245]

F ShortNotice -0.394* -0.275 -0.06 -0.376* -0.619 -0.028 -0.384* -0.596* -0.037

[-1.792] [-0.609] [-0.088] [-1.911] [-1.602] [-0.054] [-1.877] [-1.666] [-0.077]

All controls: Yes Yes Yes Yes Yes Yes Yes Yes Yes

Fixed Style

Effects Yes Yes Yes Yes Yes Yes Yes Yes Yes

Fixed Year

Effects Yes Yes Yes Yes Yes Yes Yes Yes Yes

Nobs 3186 1341 801 3186 1341 801 3186 1341 801

Adj. R2 0.277 0.324 0.358 0.278 0.327 0.353 0.277 0.326 352

Table 7. Contingency Table Test with Variance Ratios

This table reports the proportion of hedge funds falling in each classification for three groups of the no backfill sample: all funds,

funds with M = 1, and funds with M = 0. There are 8199 individual hedge fund-year observations over the period of 1997-2012 in the

no backfill sample. The Appendix provides detailed definition for the performance variables. Each classification is a combination of a

particular performance measure (Perf) and the value of variance ratio. Following Brown et al. (2001), the variance ratio is defined as

the ratio of variance of return in excess of style benchmark for the second six-month period to the variance of the first six-month

excess return. Variance ratio low is defined as a variance ratio less than the median for all funds in the calendar year, and variance

ratio high is defined as a variance ratio greater than or equal to the median for all funds. The Chi-square numbers represent the χ2(1)

statistics from the 2 x 2 contingency tables. The symbols ***, and ** denote significance levels of 1% and 5%, respectively.

Perf All Funds

Variance Ratio

Funds with M=1

Variance Ratio

Funds with M=0

Variance Ratio

Low High Chi-

square

Low High Chi-square Low High Chi-square

NegRet 0 50.36 49.64 51.36 48.64 49.99 50.01

1 47.44 52.56 4.79** 44.27 55.73 7.61*** 48.69 51.31 0.64

LowAbsRetRank 0 52.88 47.12 55.97 44.03 51.73 48.27

1 45.84 54.16 34.28*** 43.53 56.47 29.50*** 46.74 53.26 12.51***

LowRelRetRank 0 52.52 47.48 55.73 44.27 51.32 48.68

1 46.38 53.62 26.14*** 44.03 55.97 26.09*** 47.28 52.72 8.21***