Indirect Incentives of Hedge Fund Managers - … JOURNAL OF FINANCE •VOL. LXXI, NO. 2 APRIL 2016 Indirect Incentives of Hedge Fund Managers JONGHA LIM, BERK A. SENSOY, and MICHAEL

THE JOURNAL OF FINANCE bull VOL LXXI NO 2 bull APRIL 2016

Indirect Incentives of Hedge Fund Managers

JONGHA LIM BERK A SENSOY and MICHAEL S WEISBACHlowast

ABSTRACT

Indirect incentives exist in the money management industry when good current per-formance increases future inflows of capital leading to higher future fees For theaverage hedge fund indirect incentives are at least 14 times as large as direct in-centives from incentive fees and managersrsquo personal stakes in the fund Combiningdirect and indirect incentives manager wealth increases by at least $039 for a $1increase in investor wealth Younger and more scalable hedge funds have strongerflow-performance relations leading to stronger indirect incentives These results havea number of implications for our understanding of incentives in the asset managementindustry

HEDGE FUND MANAGERS ARE AMONG the most highly paid individuals todayKaplan and Rauh (2010) estimate that in 2007 the top five hedge fund man-agers earned more than all SampP 500 firmsrsquo CEOs combined The payoff tobecoming a top hedge fund manager is therefore enormous The logic of Holm-strom (1982) Berk and Green (2004) and Chung et al (2012) provides a frame-work for understanding hedge fund managersrsquo careers Investors allocate capi-tal to funds based on their perception of managersrsquo abilities which is a functionof fund performance Good performance increases a managerrsquos lifetime incomedirectly through contractual incentive fees earned at the time of performanceIt also increases a managerrsquos lifetime income indirectly through higher futurefees both from increased flows of new investment to the fund and from themechanical increase in the fundrsquos asset base The extremely high level of payfor top hedge fund managers thus suggests that indirect incentives are likelyto be a significant component of managersrsquo total incentives particularly earlyin a managerrsquos career

lowastJongha Lim is with California State University at Fullerton Berk A Sensoy is with Ohio StateUniversity Michael S Weisbach is with Ohio State University NBER and SIFR We thank NengWang for graciously providing code for evaluating the Lan Wang and Yang (2013) model AndreaRossi provided unusually diligent research assistance For helpful comments and discussions wethank Jack Bao Jonathan Berk Niki Boyson Yawen Jiao Michael OrsquoDoherty Tarun RamadoraiJosh Rauh Ken Singleton Luke Taylor Sterling Yan two anonymous referees an anonymousAssociate Editor and seminar and conference participants at the 2014 AFA meetings the 2014Spring NBER Corporate Finance Meeting American University Arizona State University Cali-fornia State University at Fullerton Fordham University Harvard Business School NortheasternUniversity Ohio State University the University of Florida the University of Missouri and theUniversity of Oklahoma We have read The Journal of Financersquos disclosure policy and have noconflicts of interest to disclose

DOI 101111jofi12384

871

872 The Journal of Finance Rcopy

In this paper we estimate the magnitude of these indirect incentives of hedgefund managers In particular we address the following questions For an incre-mental percentage point of returns to investors how much additional capitaldoes the market allocate to that hedge fund How much of this additional cap-ital do hedge fund managers end up receiving as compensation in expectationHow does this ldquoexpected future pay for todayrsquos performancerdquo compare in mag-nitude with the direct incentive fees that hedge fund managers earn from theincremental returns How do these effects differ across types of funds and overtime for a particular fund And what are the implications of the existence ofsuch indirect incentives for hedge fund investors and for our understanding ofcontracting more broadly

We first estimate the relation between hedge fund performance and inflowsto the fund using a sample of 2998 hedge funds from 1995 to 2010 As predictedby learning models of fund allocation and consistent with prior work on mutualfunds and private equity funds this relation is substantially stronger for newerfunds whose managersrsquo abilities the market knows with less certainty For theaverage fund the estimates imply that a one-percentage-point incrementalreturn in a given quarter leads to a 15 increase in the fundrsquos assets undermanagement (AUM) from inflows of new investment over the next three yearsFor a new fund the same incremental return results in a 21 increase inAUM from inflows In addition performance has a greater impact on flows forfunds engaged in more scalable strategies These results are consistent withthe view that investors continually update their assessment of managers andadjust their portfolios accordingly

The way in which the inflow-performance relation affects managersrsquo com-pensation depends on the fee structure in hedge funds Typically hedge fundmanagers receive a management fee equal to 15 of AUM together with in-centive fees equal to 20 of profits above a high-water mark (HWM) Goodperformance increases managersrsquo future income because fees will be earnedon inflows of new investment and also because the asset value of existinginvestors becomes larger and closer to the HWM Valuing a managerrsquos com-pensation requires a contingent claims modeling framework to account for thefact that incentive fees are effectively a portfolio of call options on the fundrsquosassets We use four such models which allows us to evaluate the sensitivity ofthe estimates to different modeling frameworks and parameter choices

The first model that we use is the model of Goetzmann Ingersoll and Ross(2003 hereafter GIR) GIR provide an analytical formula for calculating thefraction of a dollar invested in the fund that in expectation will be receivedby the fundrsquos managers over the life of the fund The other three models in-corporate two real-world features that are missing from the GIR model andcould have a material impact on a managerrsquos future compensation futureperformance-based flows and the managerrsquos endogenous use of leverage inthe fundrsquos portfolio Each of these features leads to greater compensation andhence greater indirect incentives than would otherwise be the case The GIRestimates therefore provide a lower bound on the magnitude of the indirectincentives faced by hedge fund managers

Indirect Incentives of Hedge Fund Managers 873

The second model that we use augments the GIR model to accommodatefuture performance-based flows The third model is that of Lan Wang andYang (2013 hereafter LWY) in which the manager can endogenously choose theamount of leverage to use at each point in time Finally we present estimatesusing an extension of the LWY model that allows for performance-based flowsas well as endogenous leverage LWY nests GIR which assumes no leverageat any time as a special case so all of our estimates can be thought of asimplications of different versions of the LWY model

Each of these models provides an estimate of the present value of man-agersrsquo compensation per dollar invested in the fund Together with the flow-performance relations these estimates allow us to calculate the magnitude ofindirect incentives facing hedge fund managers For an incremental percent-age point or dollar of current returns to the fundrsquos investors we calculate thepresent value of the additional lifetime income the fundrsquos managers receive inexpectation due to inflows of new investment and the increase in the value ofexisting investorsrsquo assets

As a benchmark for assessing the importance of this indirect pay for perfor-mance we compare its magnitude to the direct performance pay that managersreceive from incentive fees and changes in the value of their own investment inthe fund We use the Agarwal Daniel and Naik (2009) framework to estimatethe change in the value of managersrsquo current compensation (coming from bothincentive fees and the managerrsquos own stake in the fund) for an incrementalreturn

Our estimates indicate that a one-percentage-point increase in returns gen-erates on average $331000 in expected direct incentive pay consisting of$142000 in incremental incentive fees and $190000 in incremental profits onmanagersrsquo personal stakes Using the GIR model with parameter choices thatyield lower-bound estimates we calculate that managers also receive $531000in expected future fee income consisting of $248000 in future fees earned onthe new investment that occurs in response to the incremental performanceand $282000 in extra future fees earned on the increase in the value of exist-ing investorsrsquo assets in the fund Because a one-percentage-point increase inreturn is equal to $211 million for an average-sized fund in our data thesecalculations imply that on average managers receive 16 cents in direct pay andat least 23 cents in indirect pay for each incremental dollar earned for fundinvestors The indirect career-based incentive effect is thus at least 14 timeslarger than the direct income that managers receive from incentive fees andreturns on their personal investments Again this indirect-to-direct incentiveratio of 14 corresponds to model and parameter choices that lead to a lowerbound of estimates of indirect incentives Under other plausible choices theratio would be substantially higher The average indirect-to-direct incentiveratio is 35 across all the models and parameter values we consider

We next find that indirect incentives are even larger for young funds Fornew funds we estimate indirect incentives to be six to 12 times as large asdirect incentives given the parameters used in LWY The importance of indirectincentives declines monotonically as a fund ages largely as a consequence of


weakening flow-performance relations but continues to be larger than directincentives until the fund is at least 15 years old The importance of indirectincentives also depends on the style of the fund For an average fund followinga style unlikely to be capacity-constrained indirect incentives are 32 to 73times as large as direct incentives while they are 25 to 60 times as large fora fund that is likely to be constrained and hence unable to grow as much inresponse to good performance

Overall our estimates suggest that regardless of the choice of model orreasonable model parameters the total incentives facing hedge fund managersare substantial and much larger than direct incentives alone would implyAlthough direct incentives are themselves substantial indirect incentives inthe hedge fund industry comprise the majority of managersrsquo total incentives

These estimates of substantial indirect incentives in the hedge fund indus-try have a number of implications First they are potentially important forunderstanding hedge fund contracting Hedge fund management contracts arestructured in a sophisticated manner yet perhaps surprisingly we find no ev-idence that direct compensation schemes adjust with the indirect incentivesthat their managers face The lack of such adjustment reflects a larger puzzlein our understanding of markets for alternative assets in that important con-tractual parameters most notably the 20 incentive fee vary little both acrossasset classes and across funds within asset classes

Second institutional investors often state that the financial incentives ofa potential asset manager are an important consideration when deciding be-tween alternative managers Presumably all of a managerrsquos incentives in-cluding both direct and indirect incentives matter in making this choice Theresults discussed above provide estimates of how indirect and total incentivesvary across types of funds and across similar funds of different ages whichshould be relevant for potential investors In addition indirect incentives varysystematically across types of potential investments The results here and inChung et al (2012) provide estimates of indirect and total incentives for hedgefunds and private equity funds Below we also provide estimates of indirectincentives for a sample of 11911 actively managed equity mutual funds overthe period 1995 to 2010 These estimates suggest that indirect incentives inmutual funds are substantial but smaller than those for hedge funds rangingbetween 28 and 66 of the hedge fund estimates

Finally since Fama (1980) and Holmstrom (1982) incentives generated frommanagerial career concerns have been an important part of the theory of thefirm However there are virtually no estimates of their magnitude The esti-mates provided here for hedge fund managers are among the first attempts tomeasure the importance of indirect incentives The fact that career-generatedincentives are so powerful in this industry suggests that they could be equallyimportant in other industries in which they are likely to be harder to estimate

This paper proceeds as follows Section I discusses how we quantify thedirect and indirect components of hedge fund pay for performance SectionII describes the data Section III presents estimates of the flow-performance


relation Section IV estimates managersrsquo direct and indirect incentives SectionV discusses implications of our estimates Section VI concludes

I Quantifying the Magnitude of Pay for Performance of HedgeFund Managers

A Direct Pay for Performance

Hedge fund managersrsquo compensation generally consists of management feeswhich are a percentage of AUM (often around 15) plus incentive fees whichare a percentage (usually 20) of profits or of profits earned above the HWMIn addition hedge fund managers usually make a personal investment in thefund The direct pay for performance that a manager receives comes from theincentive fees as well as his personal investment in the fund both of whichincrease in value with the fundrsquos performance Quantifying these direct perfor-mance incentives is complicated because of the option-like features containedin the hedge fund managerrsquos incentive fee contract In particular the incen-tive fee contract resembles a portfolio of call options one per investor in thefund The exercise price of each option is determined by the investorrsquos time ofentrance into the fund the fundrsquos hurdle rate and the historical HWM levelpertaining to the investorrsquos assets Thus even if different managers have thesame 20 incentive fee rate their actual direct pay-performance sensitivitywill vary depending on the distance between the current asset value and theexercise prices of these options

To estimate the direct pay-performance sensitivity we use Agarwal Danieland Noikrsquos (2009) total delta approach which measures the impact of an in-cremental one-percentage-point return to fund investors on the value of themanagerrsquos incentive fees plus the increase in the value of the managerrsquos ownownership stake The total delta of the managerrsquos claim to the fund is equalto the sum of these individual optionsrsquo deltas plus the delta of the managerrsquospersonal stake in the fund We follow Agarwal Daniel and Naikrsquos (2009) andassume that the managerrsquos initial stake is zero but the stake grows over timeas managers reinvest all of their incentive fees back into the fund1 Details ofthis calculation are described in Appendix A

B Indirect Pay for Performance

In addition to the pay for performance from incentive fees and their owninvestment in the fund hedge fund managersrsquo lifetime income changes withperformance through a reputational effect good performance increases themarketrsquos perception of a managerrsquos ability leading to higher inflows of newinvestment to the fund Ultimately a fundrsquos managers will receive part of

1 Assuming instead that the managerrsquos initial stake is 1 or 2 of AUM increases the estimatesof indirect incentives by 5 and 10 respectively and the estimates of direct incentives by 2and 5 respectively These changes do not affect our conclusion that indirect incentives are largerelative to direct incentives


these inflows as future management and incentive fees Furthermore goodperformance mechanically increases the value of existing investorsrsquo stakes inthe fund A portion of this increase will likewise be paid over time in future feesto the fund manager The expectation of this future income depends on todayrsquosperformance leading to what we refer to as indirect incentives

To evaluate the magnitude of these indirect incentives we first need esti-mates of the way in which performance affects expected inflows to the fundThese estimates are discussed in Section III below We also need a model ofthe present value of a managerrsquos expected lifetime compensation as a fractionof fund assets This model should predict for each incremental dollar undermanagement the increase in the managerrsquos expected compensation over thefuture lifetime of the fund We use four such models

The first model is that of GIR The GIR model leads to the lowest estimatesof the sensitivity of future income to todayrsquos performance so it can be thoughtof as providing a lower bound on our estimates of indirect incentives The GIRmodel is a contingent-claims model of the fraction of fund assets that accrue tothe fund manager in expected future compensation Key features of this modelare that compensation is a fixed management fee plus a percentage of profitsabove a HWM the fundrsquos asset value follows a martingale with drift generatedby the managerrsquos alpha investors continuously withdraw assets (eg an en-dowment investor might withdraw 5 per year) and an investor liquidates hisor her position following a sufficiently negative return shock (so that expectedfund life is finite)

The GIR model does not account for all factors that could affect managersrsquofuture compensation and in turn indirect incentives In particular the GIRmodel does not allow the fundrsquos asset value to grow through future performance-based fund flows Under appropriate assumptions the effect of such flows inthe context of the GIR model is to increase the variance of the fundrsquos AUM2

For this reason our second model reports estimates from a variant of the GIRmodel in which fund variance is augmented

Another factor not accounted for by the GIR model is the fact that a fundrsquosportfolio is endogenously chosen with managers adjusting their portfolios tomaximize their incomes given a particular incentive scheme Following LWYour third model allows managers to perform such adjustments by levering theirfunds strategically Given that hedge fund managers typically do use leverageestimates of indirect incentives that incorporate this feature are likely to bemore accurate

Finally we present estimates from a fourth model also introduced by LWYthat augments the basic LWY model to include future performance-based fundflows Note also that the LWY model nests the GIR model as a special case in

2 Suppose as an approximation that the flow-performance relationship is linear in logs andflows are contemporaneous with returns Suppose that without flows the log AUM of the fundevolves as a martingale as in the GIR model st+1 = st + et+1 With performance-based flows st+1 =st + get+1 where g gt 1 captures the flow effect Therefore even with performance-based flows thelog AUM would still follow a martingale so the GIR model still applies but with a higher variancethan in the case of no flows We thank the Associate Editor for suggesting this argument to us


which leverage is zero so all four sets of estimates can be viewed as comingfrom variations of the LWY model

Using different models to estimate indirect incentives allows us to gaugethe importance of different factors especially the importance of futureperformance-based fund flows and endogenous portfolio choice in determin-ing the fraction of a fundrsquos value that will ultimately accrue to managers infuture fees3 Details on all four models our choices of model parameters andthe associated calculations are described in Appendix B

For each of the models above we estimate the present value of the total(management plus incentive) future fees that the manager earns on an extradollar of AUM To calculate indirect pay for performance we multiply thispresent value by an estimate of the number of incremental dollars of AUMthat result from a one-percentage-point increase in returns to investors Thelatter increase consists of two parts namely the mechanical increase in thevalue of existing investorsrsquo stakes plus incremental inflows of new investmentIn this way the models for a managerrsquos fee value together with our estimatesof the flow-performance relation facing hedge fund managers provide an esti-mate of the present value of the incremental future revenue that the hedgefund manager expects to earn as a result of a one-percentage-point improve-ment in current net returns4 This calculation results in an incentive measuredenominated in dollars per percentage point change in returns We also con-duct the same calculation for a one-dollar improvement in investor return toobtain a measure in terms of dollars in managerial wealth per dollar returnedto shareholders Both of these measures are commonly used in the literatureon manager incentives

Because the GIR and LWY models estimate present values no further ad-justment for the riskiness of future income is required Also the estimates donot require that the manager continue to manage the fund in the future underthe assumption that the present value of the managerrsquos claims to future feeincome can be monetized when the manager departs

II Hedge Fund Data

Our data come from the TASS database which covers about 40 of thehedge fund universe (Agarwal Daniel and Naik (2009)) Summary statisticsfor key sample fund characteristics reported in Table I are very close to thosefor the sample considered by Agarwal Daniel and Naik (2009) who mergeand consolidate four major databases (CISDM HFR MSCI and TASS) Wetherefore conclude that our sample of hedge funds is representative of thehedge fund universe

3 In addition to GIR and LWY there have been a number of other attempts to value managersrsquoclaims to hedge funds Important contributions include Panageas and Westerfield (2009) Drechsler(2014) and Guasoni and Oblόj (2013)

4 Net returns can be improved either through increased gross returns or decreased costs borneby the fund such as financing costs security lending fees and settlement charges The incentiveswe measure are therefore incentives to achieve both


Table ISummary Statistics

This table presents descriptive statistics for the sample of 2998 funds during the 1995 to 2010sample period Time-varying variables are reported at the fund-quarter level and other time-invariant contractual characteristics are reported at the fund level Quarterly flow is the differencebetween the reported quarter-end AUM and the quarter-beginning AUM times (1 + Quarterlyreturns) scaled by quarter-beginning AUM Quarterly returns are the reported quarterly net-of-fee returns AUM is assets under management in millions measured at the end of each quarterAge is the number of quarters from the fundrsquos inception date measured at the beginning of eachquarter Volatility is the standard deviation of monthly (net-of-fee) returns over the 12 monthsprior to each quarter multiplied by the square root of three to arrive at a quarterly figure Alltime-varying variables except Age are winsorized at the 1st and 99th percentiles Management feesare the percentage of the assets charged by the fund as regular fees in annual terms Incentive feesare the percentage of positive profits received by the manager as performance incentives HWMis an indicator variable that equals one if the fund has a HWM provision and zero otherwiseLeverage is an indicator variable that equals one if the fund uses leverage and zero otherwiseOpen-to-public is an indicator variable that equals one if the fund allows public investment andzero otherwise On-shore is an indicator variable that equals one if the fund is domiciled in theUnited States and zero otherwise Total redemption period is the sum of the notice period and theredemption period measured in quarters where the notice period is the time the investor has togive notice to the fund about an intention to withdraw money from the fund and the redemptionperiod is the time that the fund takes to return the money after the notice period is over Lockupperiod is the minimum time in quarters that an investor has to wait before withdrawing investedmoney Lockup period is considered to be zero for a fund that has no lock-up provision Subscriptionperiod is the time delay measured in quarters between investing in a fund and actually purchasingfund shares Constrained is an indicator variable that equals one if the fundrsquos strategy belongs toConvertible Arbitrage Fixed Income Arbitrage Emerging Markets and Event Driven strategiesand zero otherwise (Ding et al (2009))

Variable N Mean 25th Pctl Median 75th Pctl SD

Quarterly flow () 50333 798 minus413 034 1014 3701Quarterly returns () 50333 256 minus091 211 566 905AUM ($ million) 50333 2108 110 393 1300 6370Age (quarters) 50333 1756 609 1319 2434 1532Volatility () 50333 533 300 441 804 243Management fees ( annualized) 2998 15 10 15 20 05Incentive fees () 2998 193 200 200 200 34HWM 2998 0694 0000 1000 1000 0461Leverage 2998 0685 0000 1000 1000 0464Open-to-public 2998 0191 0000 0000 0000 0394On-shore 2998 0230 0000 0000 0000 0421Total redemption period (quarters) 2998 1085 0556 0667 1333 1015Lockup period (quarters) 2998 0974 0000 0000 0000 2077Subscription period (quarters) 2998 0356 0333 0333 0333 0190Style

Constrained 2998 0303 0000 0000 1000 0460Constrained = 1

Convertible Arbitrage 2998 0034Emerging Markets 2998 0120Event Driven 2998 0096Fixed Income Arbitrage 2998 0052

Constrained = 0Equity Market Neutral 2998 0073Global Macro 2998 0078LongShort Equity Hedge 2998 0420Multi-Strategy 2998 0077Other 2998 0049


Our sample period is from January 1995 to December 2010 We focus onthe post-1994 period because TASS started reporting information on ldquodefunctrdquofunds only after 19945 We exclude managed futuresCTAs and funds-of-fundswhich have different incentive fees and are likely to have different inflow-performance relations from typical individual hedge funds We also excludeclosed-end hedge funds as subscriptions in these funds are only possible duringthe initial issuing period so future flows are not possible This initial filterleaves us with 4939 open-end hedge funds6

We drop funds for which TASS does not contain information on organizationalcharacteristics such as management fees incentive fees and HWM provisionsIn addition we consider only funds with an uninterrupted series of net assetvalues and returns so that we can calculate inflows Further we restrict oursample to funds with at least 12 consecutive monthly returns available duringthe sample period If a fund stops reporting returns and then resumes at afuture date we use only the first sequence of uninterrupted data Finally weexclude funds with an incentive fee of zero because there can be no direct payfor performance for these funds

We conduct the analysis using quarterly data because we wish to include onlylagged (not contemporaneous) returns in the flow-performance specificationsand to have a relatively short gap between returns and flows7 To construct aquarterly sample we drop all fund-calendar quarters that have return infor-mation only for a fraction of the period We also require that a sample fundhave a subscription period less than or equal to three months so that quarterlyinflows are not restricted This sample construction process leaves us with asample of 2998 funds (50333 fund-quarter observations)

Table I presents descriptive statistics Time-varying variables such as Quar-terly flow and Quarterly returns are measured at the fund-quarter level andother contractual characteristics such as Management fees and Incentive feesare measured at the fund level8 All time-varying variables except fund age(Age) are winsorized at the 1st and 99th percentiles to minimize the effect ofoutliers

5 Defunct funds include funds that are liquidated merged or restructured as well as those thatstopped reporting returns to TASS (Fung et al (2008))

6 Some funds are closed to new investors but unfortunately we do not know whether a particularfund is taking new money at any point in time so we cannot exclude funds on the basis of thispolicy Including closed funds causes us to understate the flow-performance relation for funds thatare not closed

7 Prior versions of this paper presented estimates using annual data with contemporaneousannual returns included in the flow-performance specifications (Lim Sensoy and Weisbach (2013))The estimates of indirect incentives using annual data are very close to those reported here

8 TASS provides information on fundsrsquo organizational characteristics as of the last available dateof fund data Like most previous studies we also assume that these organizational characteristicsdo not change throughout the life of the fund Agarwal Daniel and Naik (2009) argue that fundsrsquoorganizational characteristics are unlikely to change much over time based on their discussionswith practitioners which suggests that if a manager wants to impose new contractual terms it iseasier for him to start a new fund with different terms than to go through the legal complicationsof changing an existing contract


Mean Quarterly flow is 80 with a median of 03 so the distributionis highly skewed Mean Quarterly returns is 26 the median is 21 Aver-age fund size (AUM) is $2108 million The remaining variables reflect time-invariant contractual features Summary statistics on these characteristics arevery close to those reported in prior studies (eg Agarwal Daniel and Naik(2009) Baquero and Verbeek (2009) Aragon and Nanda (2012)) The variableManagement fees is the annual percentage of fund assets (AUM) received bythe manager as compensation and has a sample mean (median) of 15 (15)The variable Incentive fees is the percentage of profits above the HWM receivedby the manager as compensation and has a sample mean (median) of 193(20) Over two-thirds of our sample funds 694 have a HWM provision685 report that they use leverage 191 are open to public investors andabout a quarter are on-shore funds

The fee data consist of the fees that are currently publicly quoted by thefunds These data could potentially misrepresent the true fees relevant forour calculations for three reasons First funds sometimes provide fee reduc-tions to particular strategic investors that are not reflected in the database(Ramadorai and Streatfield (2011)) Although we cannot investigate this pos-sibility directly it is unlikely to have a major impact on our conclusions Forexample if the true incentive fee (management fee) averaged over all investorswere 10 lower than what is reported in the database our estimates of indirectincentives using the base GIR model would be overstated by about 1 (5) Asecond issue is that fees can change over time Agarwal and Ray (2012) andDueskar et al (2012) find that fee changes are infrequent and tend to reflectpast performance when they do occur so that fee increases follow good perfor-mance and fee decreases follow poor performance This effect is magnified inindirect incentives because good performance today leads not only to inflowsbut also to higher proportional fees on those inflows Third it is possible thatthere are fee changes unobservable to researchers To the extent that observ-able and unobservable fee changes are positively correlated this possibilityleads us to understate indirect incentives

We consider three variables that reflect potential restrictions on the be-havior of flows First we use Total redemption period defined as the sum ofthe notice period and the redemption period where the notice period is thetime the investor has to give notice to the fund about an intention to with-draw money from the fund and the redemption period is the time that thefund takes to return the money after the notice period is over The Lockupperiod is the minimum time that an investor has to wait before withdrawinginvested money The Subscription period is the time delay between investingin a fund and actually purchasing fund shares The mean total redemption pe-riod lock-up period and subscription period are 109 097 and 036 quartersrespectively


III Estimating the Sensitivity of Fund Inflows to Performance

To understand the impact of performance on fund flows we employ aBayesian learning framework that presumes investors are continually eval-uating managers trying to assess their abilities (see Berk and Green (2004)Chung et al (2012)) A fundrsquos performance provides information about the man-agerrsquos ability so an observation of performance will lead investors to updatetheir assessment of his ability and allocate more capital to a fund if their es-timate of the managerrsquos ability increases The magnitude of the updates andhence the sensitivity of inflows to performance should depend on the infor-mativeness of the signal relative to the precision of the prior estimate of thefund managerrsquos ability The sensitivity of inflows to performance should alsodepend on the extent to which ability can be scaled to replicate a fundrsquos returndistribution on new capital

Measuring the indirect incentives of hedge fund managers requires an es-timate of the relation between fund performance and future inflows A longliterature beginning with Ippolito (1992) estimates this relation to be rela-tively strong in the mutual fund industry9 It is also positive in the privateequity industry (see eg Kaplan and Schoar (2005)) However the results forhedge funds are less clear Goetzmann Ingersoll and Ross (2003) report a neg-ative and concave relation whereas other studies including Agarwal Danieland Naik (2003) Fung et al (2008) Baquero and Verbeek (2009) and Dinget al (2009) find a positive one

A Empirical Specification

We estimate the following specification

Flowit = β0 +11sumj=1

β0+ j Returnitminus j + γ Xtminus1 + λY + Fixed effects + εit (1)

where Flowit represents flows for fund i in quarter t10

Following the literature on flows to mutual funds (eg Sirri and Tufano(1998) Chevalier and Ellison (1999)) or hedge funds (eg Fung et al (2008)Agarwal Daniel and Naik (2009)) we compute quarterly flows of capital intoa fund as follows

Flowit = AUMit minus AUMitminus1(1 + Returnit

)AUMitminus1

(2)

where AUMit and AUMitminus1 are the AUM of fund i at the end of quarters t andtminus1 respectively and Returnit is the net-of-fee return for fund i in quarter t

9 See Brown Halow and Starks (1996) Sirri and Tufano (1998) Chevalier and Ellison (1999)Barclay Pearson and Weisbach (1998) Del Guercio and Tkac (2002) Bollen (2007) Huang Weiand Yan (2007) and Sensoy (2009)

10 We restrict our estimates to quarterly specifications but prior versions of this paper alsoinclude annual and monthly specifications with similar results to those reported below


This definition expresses flows as a fraction of beginning-of-period (end ofprior period) AUM which is a natural benchmark from the perspective of afund manager assessing his or her incentives going forward For instance theoption deltas that comprise direct incentives are defined in terms of beginning-of-period AUM An alternative approach to computing fund flows is to scalethe denominator of equation (2) by (1 + Returnit) which expresses flows as afraction of what end-of-period AUM would have been in the absence of flowsAlthough this alternative definition is less intuitive for our purpose it leads tosimilar estimates of indirect incentives

The vector X in equation (1) consists of time-varying fund characteristicsthat include lagged flows the natural logarithm of AUM for fund i at tminus1 thenatural logarithm of one plus fund irsquos age in quarters at tminus1 and fund volatil-ity over the previous 12 months11 The vector Y includes time-invariant fundcharacteristics that include Management fees Incentive fees Total redemptionperiod Lockup period Subscription period and a set of indicator variables thatequal one if the fund has an HWM provision uses leverage is open to publicinvestors and is an on-shore fund All specifications include fixed effects forthe nine styles listed in Table I interacted with calendar quarter fixed effectsThese time-by-style effects capture all shocks observed or unobserved that arecommon to funds of a given style in a given quarter including the returns topeer funds and inflows to other funds of the same style12 Reported standarderrors are robust to heteroskedasticity and account for double clustering byfund and time period13

B Estimates of the Flow-Performance Relation

We present estimates of the flow-performance relation in Panel A ofTable II In Column (1) we include returns in the 11 quarters prior to thecurrent quarter In Column (2) we add a number of fund-level controls In eachspecification the coefficients on returns are positive and statistically signif-icant in most cases and decline sharply over time so the coefficient on themost recent quarterrsquos return is the largest If we sum the coefficients on the 11prior quarters the sum in Column (1) is 144 and in Column (2) is 150 Thesecoefficients imply that a one-standard-deviation increase in returns (9) willlead to about a 13 increase in fund size

11 For young funds that have for example only one yearrsquos worth of return history we cannotcompute lagged returns and flows In such cases we ldquodummy outrdquo missing lagged variables toretain observations To do so we set missing values of lagged flows and returns to zero and includean indicator for missing values

12 Time-by-style fixed effects perform the same adjustment as a factor model regression underthe assumption that the factor loadings are the same for all funds of a given style within a giventime period

13 We focus on linear specifications Although there is some evidence of nonlinear effects in thedata (using splines quadratics etc) they are small in magnitude and have little impact on theestimates obtained with linear specifications


Table IIEstimates of Flow-Performance Sensitivity

This table presents the OLS coefficient estimates of equation (1) and corresponding standard errors(in parentheses) using quarterly data The dependent variable is Quarterly flow as defined byequation (2) See Table I for definitions of all independent variables The sample period is from 1995to 2010 We only employ the funds with a subscription period less than or equal to three months toavoid quarterly inflows being restricted by subscription periods Equation (1) is augmented in PanelB to include interaction terms of the log of the fundrsquos age plus one with prior performance and inPanel C to include interaction terms of Constrained with performance variables All specificationsin Panel A and B include style fixed effects quarter fixed effects and quarter-by-style fixed effectsOnly quarter fixed effects are included in Panel C due to multicollinearity Standard errors aredouble clustered by fund and year and indicate statistical significance at the 1 5and 10 level respectively

(1) (2)

Dependent var = Quarterly flow(t) Coef (SE) Coef (SE)

Panel A Base-case

Quarterly returns(tminus1) 0501 (0045) 0496 (0043)Quarterly returns(tminus2) 0314 (0039) 0263 (0036)Quarterly returns(tminus3) 0209 (0034) 0190 (0028)Quarterly returns(tminus4) 0117 (0035) 0111 (0030)Quarterly returns(tminus5) 0089 (0030) 0098 (0027)Quarterly returns(tminus6) 0054 (0033) 0066 (0030)Quarterly returns(tminus7) 0083 (0027) 0103 (0025)Quarterly returns(tminus8) 0044 (0022) 0060 (0022)Quarterly returns(tminus9) 0012 (0029) 0034 (0029)Quarterly returns(tminus10) minus0010 (0030) 0019 (0029)Quarterly returns(tminus11) 0025 (0027) 0056 (0026)Quarterly flow(tminus1) 0158 (0008)Log(Age + 1) minus0004 (0005)Log(AUM(tminus1)+1) minus0024 (0001)Volatility minus0141 (0037)Management fees 0211 (0386)Incentive fees 0005 (0053)HWM indicator 0020 (0004)Leveraged indicator 0000 (0004)Open-to-public indicator minus0002 (0005)On-shore indicator minus0032 (0005)Log(Total redemption period +1) 0034 (0007)Log(Lock-up period +1) minus0005 (0003)Log(Subscription period +1) minus0023 (0015)Indicators for missing lagged returns Yes YesFixed effects

Style Yes YesQuarter Yes YesStyle times Quarter Yes Yes

Number of observations 50333 50333Adjusted R2 0162 0194F-test for joint significance of F-stat (p-value) F-stat (p-value)

All lagged returns 1940 (0000) 2077 (0000)

(Continued)


Table IImdashContinued

(1) (2)


Panel B Age interactions

Quarterly returns(tminus1)timesLog(Age in quarters+1) minus0319 (0035) minus0285 (0034)Quarterly returns(tminus2)timesLog(Age in quarters+1) minus0171 (0027) minus0125 (0025)Quarterly returns(tminus3)timesLog(Age in quarters+1) minus0051 (0036) minus0033 (0036)Quarterly returns(tminus4)timesLog(Age in quarters+1) minus0003 (0032) 0001 (0032)Quarterly returns(tminus5)timesLog(Age in quarters+1) minus0041 (0030) minus0042 (0029)Quarterly returns(tminus6)timesLog(Age in quarters+1) 0038 (0033) 0040 (0031)Quarterly returns(tminus7)timesLog(Age in quarters+1) 0013 (0033) 0007 (0032)Quarterly returns(tminus8)timesLog(Age in quarters+1) 0033 (0033) 0034 (0033)Quarterly returns(tminus9)timesLog(Age in quarters+1) 0003 (0040) minus0004 (0040)Quarterly returns(tminus10)timesLog(Age in quarters+1) 0132 (0038) 0133 (0035)Quarterly returns(tminus11)timesLog(Age in quarters+1) 0018 (0040) 0007 (0041)Quarterly returns(tminus1) 1325 (0117) 1233 (0113)Quarterly returns(tminus2) 0773 (0095) 0602 (0087)Quarterly returns(tminus3) 0354 (0114) 0287 (0110)Quarterly returns(tminus4) 0124 (0112) 0110 (0106)Quarterly returns(tminus5) 0200 (0100) 0215 (0092)Quarterly returns(tminus6) minus0067 (0112) minus0056 (0104)Quarterly returns(tminus7) 0033 (0106) 0079 (0099)Quarterly returns(tminus8) minus0066 (0107) minus0046 (0107)Quarterly returns(tminus9) 0004 (0128) 0053 (0127)Quarterly returns(tminus10) minus0416 (0122) minus0383 (0114)Quarterly returns(tminus11) minus0038 (0126) 0037 (0130)Log(Age + 1) minus0026 (0007) minus0000 (0006)Indicators for missing lagged returns Yes YesOther controls No YesFixed effects



All lagged returns 1788 (0000) 1570 (0000)All age interaction terms 1214 (0000) 1084 (0000)

Panel C Scalability interactions

Quarterly returns(tminus1)timesConstrained minus0215 (0048) minus0215 (0047)Quarterly returns(tminus2)timesConstrained minus0096 (0043) minus0078 (0039)Quarterly returns(tminus3)timesConstrained minus0102 (0038) minus0107 (0037)Quarterly returns(tminus4)timesConstrained 0013 (0034) 0013 (0036)Quarterly returns(tminus5)timesConstrained minus0009 (0032) minus0022 (0032)Quarterly returns(tminus6)timesConstrained 0026 (0034) 0013 (0034)Quarterly returns(tminus7)timesConstrained minus0044 (0036) minus0057 (0036)Quarterly returns(tminus8)timesConstrained 0032 (0037) 0033 (0036)Quarterly returns(tminus9)timesConstrained minus0085 (0030) minus0090 (0031)Quarterly returns(tminus10)timesConstrained minus0023 (0036) minus0007 (0036)Quarterly returns(tminus11)timesConstrained minus0007 (0037) minus0004 (0038)

(Continued)




(1) (2)


Quarterly returns(tminus1) 0559 (0049) 0554 (0046)Quarterly returns(tminus2) 0346 (0045) 0291 (0040)Quarterly returns(tminus3) 0241 (0039) 0224 (0032)Quarterly returns(tminus4) 0099 (0037) 0095 (0034)Quarterly returns(tminus5) 0077 (0034) 0094 (0030)Quarterly returns(tminus6) 0042 (0040) 0062 (0036)Quarterly returns(tminus7) 0101 (0032) 0126 (0030)Quarterly returns(tminus8) 0034 (0026) 0049 (0026)Quarterly returns(tminus9) 0034 (0033) 0060 (0033)Quarterly returns(tminus10) minus0002 (0035) 0028 (0033)Quarterly returns(tminus11) 0015 (0032) 0050 (0031)Constrained indicator 0012 (0007) 0010 (0006)Indicators for missing lagged returns Yes YesOther controls No YesFixed effects

Quarter Yes YesNumber of observations 50333 50333Adjusted R2 0156 0189F-test for joint significance of F-stat (p-value) F-stat (p-value)

All lagged returns 1980 (0000) 2065 (0000)All scalability interaction terms 476 (0000) 424 (0000)

Theoretically a learning framework such as Berk and Green (2004) suggeststhat the sensitivity of fund flows to performance should depend on the precisionof the prior distribution of ability The precision of the prior distribution islikely to be related to the experience of the fund managers Intuitively a moreexperienced manager is more of a ldquoknown quantityrdquo so given an observationof performance an observer will update her assessment of his ability less thanif the same performance were observed from a new manager In addition thesensitivity of inflows to performance should depend on the extent to which it ispossible to replicate the current distribution of returns if the fund increases insize in other words the fund strategyrsquos ldquoscalabilityrdquo

To evaluate these implications we estimate the extent to which the sensitiv-ity of inflows to performance depends on the fundrsquos age To do so we estimateequation (1) including interaction terms of prior performance plus the log ofone plus the fundrsquos age and present these estimates in Panel B of Table IIIn each estimated equation the sum of the interaction coefficients is negativeand the coefficients are jointly statistically significant with a majority of theeffect arising from the two quarters immediately preceding the focal quarterThe negative coefficients on the interaction terms mean that as hedge fundsget older the effect of performance on inflows declines


A fundrsquos strategy likely affects the sensitivity of inflows to performance be-cause some strategies can be replicated with more capital while others willface diminishing returns For example arbitrage strategies (eg Convert-ible Arbitrage) in which opportunities disappear as they are exploited areunlikely to be infinitely scalable by nature Strategies that invest in illiq-uid assets and have high market impact costs (eg Event Driven) are alsomore likely to face capacity constraints (Aragon (2007) Teo (2009) Getmansky(2012)) However strategies that involve liquid instruments (eg LongShortEquity Equity Market Neutral) are less prone to capacity constraints Ra-madorai (2013) finds a negative effect of capacity constraints on hedge fundreturns

To evaluate whether scalability affects the flow-performance relation werely on the classification of Ding et al (2009) who consider Convertible Arbi-trage Emerging Market Event Driven and Fixed Income Arbitrage strategiesto be ldquocapacity constrainedrdquo The other strategies (Equity Market NeutralGlobal Macro LongShort Equity Multi-Strategy and Others) are classified asldquounconstrainedrdquo We create an indicator variable equal to one if the fund iscapacity constrained and zero if it is unconstrained We interact this variablewith the past performance variables and present the results in Panel C ofTable II

As with the previous estimates the coefficients on lagged performance arepositive and statistically significantly different from zero However the coeffi-cients on these variables interacted with the ldquoConstrainedrdquo indicator variableare negative and statistically significant for the three most recent quartersimplying that the strategies we consider to be constrained are less respon-sive in size to a performance shock Even though a shock to performance forldquoconstrainedrdquo funds would cause the market to update its assessment of fundmanagersrsquo abilities the fact that they are less scalable limits the extent towhich investors are willing to change their investments in these funds as aresult

A caveat to these results is that if hedge funds misreport their returnsestimates of the flow-performance relation may be biased In particular Bollenand Pool (2009) show that the potential for misreporting is strongest whenreturns are slightly negative In this case the relation between flows and trueperformance would be weaker than the relation between flows and performancereported to the database leading our estimates of indirect incentives (withrespect to true performance) to be overstated To gauge the magnitude of thiseffect we reestimate the flow-performance equations under the assumptionthat all reported positive returns were accurate but all negative returns werein fact upward-biased by 10 The resulting coefficients are about 26 lowerthan those reported above which suggests that misreporting may lead to abouta 26 overstatement of the part of indirect incentives (reported below) thatcomes from new inflows


IV Calculating Indirect and Direct Pay for Performance

In this section we use the models discussed in Section I and the Appendicestogether with the estimates presented in Section III to quantify the magni-tude of direct and indirect pay for performance sensitivities facing hedge fundmanagers

To calculate direct pay for performance we follow Agarwal Daniel andNaikrsquos (2009) total delta approach using the parameters discussed in AppendixA This approach takes the perspective of a manager at the beginning of the pe-riod calculating the sensitivity of his claim to the fundrsquos assets to performancerealized over the upcoming period Based on a set of assumptions discussedin Appendix A we calculate direct incentives arising from each individual in-vestorrsquos assets as well as the managerrsquos personal stake and then sum themup to obtain the total direct pay for performance for each fund-quarter in oursample We take the average of these fund-quarter estimates to be our estimateof typical direct pay for performance sensitivities

For indirect pay for performance the coefficients on returns in Table II carrythe interpretation of incremental inflows as a percentage of beginning-of-periodAUM for an incremental percentage point of returns As previously discussedwe use four models to estimate the fraction of an incremental dollar investedin the fund that accrues to the manager in expected future management andincentive fees the base GIR model the GIR model augmented to account for fu-ture performance-based flows the LWY model (which incorporates endogenousleverage) and the LWY model extended to account for future performance-based flows For every fund-quarter we use each of the four models to calculatethe fraction of each incremental dollar that in expectation will accrue to man-agers Then as with direct pay for performance we take the average of thefund-quarter estimates to be our estimate of typical indirect pay for perfor-mance sensitivities We repeat this calculation for a number of alternativeparameter choices Details of the models parameter choices and calculationsare provided in Appendix B

We use common parameters for each model to ensure an apples-to-applescomparison across models For each of the four models we calculate indirectincentives using eight sets of parameters obtained from two choices of each ofthree parameters The three parameters we vary are those that because of theireconomic interpretation are likely to have a quantitatively important impacton our estimates of indirect incentives A particularly important parameter isb which represents the minimum asset value relative to the HWM that theinvestor will tolerate before withdrawing all of his or her money from the fundIf b = 0 the fund is never liquidated for poor performance Positive values of bimply a positive probability of performance-related liquidation each period andtherefore a finite expected fund life We consider b = 08 as recommended byGIR which means that a 20 loss results in liquidation of an investorrsquos stakeWe also present estimates using b = 0685 which LWY use in their analysis

Another important parameter is δ + λ which is the fraction of an investorrsquoscapital that he or she withdraws each period for exogenous reasons GIR set


this parameter equal to 5 per year which corresponds to the typical spendingrules of institutional investors such as endowments or pension plans LWYuse 10 which although too high for such institutions may be appropriatefor other investor types The higher this parameter the lower the indirectincentives because higher withdrawal rates mean that new money will stayin the fund for a shorter period of time We present estimates using quarterlyequivalents of 5 and 10

The final parameter that we vary is the managerrsquos future expected gross-of-fee risk-adjusted performance α Following GIR we present estimates forquarterly equivalents of α = 0 and 3 which correspond to levered valuesin the LWY framework LWY calibrate their model to an unlevered α = 122which is close to a levered value of 3 given a typical hedge fund leverage

In both GIR and LWY α is exogenous and time-invariant In particular itis not related to current or future inflows of new investment If inflows lead tolower future performance our estimates of indirect pay for performance wouldbe overstated Although Naik Ramadorai and Stromqvist (2007) AgrawalDaniel and Naik (2009) and Fung et al (2008) do find that inflows result inlower future performance this relation is not apparent in our more recent dataMoreover given the magnitude of such relations identified by prior work anyoverstatement of indirect pay for performance is likely to be small For instanceour estimates of the flow-past performance sensitivity presented in Panel Aof Table II imply that a one-percentage-point incremental return in a givenquarter leads to a 15 increase in AUM over the next 11 quarters Accountingfor the magnitude of the deleterious effects of flows on future performanceestimated by Agrawal Daniel and Naik (2009) would reduce this estimate byonly 001 of AUM

A Estimates of Direct and Indirect Incentives

The estimates of direct incentives are summarized in Panel A of Table IIIThese calculations indicate that the expected dollar increase in incentive feesfor an incremental one-percentage-point increase in quarterly net return equals$142000 (Row (1)) or roughly 68 cents for every dollar returned to investors(Row (4)) This figure is lower than the typical 20 incentive fee rate becausethe option is always out of the money (when the option is in the money theincentive fee is immediately paid and the strike is reset) The change in man-agersrsquo personal stakes averages $190000 for an incremental 1 return (Row(2)) and 95 cents for every dollar returned to investors (Row (5)) Total directincentives average $331000 for a one-percentage-point increase in fund value(Row (3)) or 163 cents for each additional dollar created (Row (6))

Panels B through E of Table III perform comparable calculations for indirectincentives using each of the four models to value the fundrsquos future fee incomeFor example the estimates in Panel B are from the base-case GIR model thatdoes not allow for endogenous leverage or future performance-based fund flowsConsider first the estimates reported in Column (1) (with b = 0685 α = 0 andδ + λ = 5) These estimates indicate that for a one-percentage-point increase


Tab

leII

ID

irec

tan

dIn

dir

ect

Pay

for

Per

form

ance

Th

ista

ble

pres

ents

esti

mat

esof

dire

ctan

din

dire

ctpa

yfo

rpe

rfor

man

ce(i

nce

nti

ves)

ata

quar

terl

yfr

equ

ency

A

llre

port

edst

atis

tics

are

aver

ages

take

nov

eral

lfu

nd-

quar

ters

inth

eda

taP

anel

Apr

esen

tses

tim

ates

ofdi

rect

ince

nti

ves

calc

ula

ted

asde

scri

bed

inA

ppen

dix

AD

irec

tin

cen

tive

sar

ede

fin

edas

the

expe

cted

pres

ent

valu

edo

llar

chan

gein

man

ager

wea

lth

from

dire

ctin

cen

tive

fees

plu

sth

em

anag

errsquos

own

ersh

ipst

ake

resu

ltin

gfr

oman

incr

emen

tal

one-

perc

enta

ge-p

oin

tor

one-

doll

arin

crea

sein

fun

dre

turn

sP

anel

sB

toE

pres

ent

esti

mat

esof

indi

rect

ince

nti

ves

calc

ula

ted

usi

ng

fou

rdi

ffer

ent

mod

els

asde

scri

bed

inA

ppen

dix

BI

ndi

rect

ince

nti

ves

are

defi

ned

asth

eex

pect

edpr

esen

tva

lue

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ange

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anag

erw

ealt

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omfu

ture

fees

earn

edfr

omin

flow

sof

new

inve

stm

ent

plu

sth

ein

crea

sein

the

valu

eof

exis

tin

gas

sets

resu

ltin

gfr

oman

incr

emen

talo

ne-

perc

enta

ge-p

oin

tor

one-

doll

arin

crea

sein

retu

rns

toin

vest

ors

Est

imat

esof

indi

rect

ince

nti

ves

are

pres

ente

dfo

rei

ght

com

bin

atio

ns

ofth

epa

ram

eter

sbα

an

dδ+λ

A

llpa

ram

eter

choi

ces

are

prov

ided

inTa

ble

CI

inA

ppen

dix

C

Th

en

um

ber

offu

nd-

quar

ters

use

din

all

colu

mn

sof

Pan

els

Ato

Cis

497

76(5

57fu

nd-

quar

ter

obse

rvat

ion

sar

edr

oppe

dfr

omth

efu

llre

gres

sion

sam

ple

beca

use

the

sum

ofth

eva

lues

ofal

lin

vest

orsrsquo

stak

eis

zero

for

thes

eca

ses

acco

rdin

gto

the

com

puta

tion

sde

scri

bed

inA

ppen

dix

A)

Th

en

um

ber

ofob

serv

atio

ns

use

dto

esti

mat

eth

eLW

Ym

odel

(Pan

elD

)an

dth

eex

ten

ded

LWY

mod

el(P

anel

E)

are

som

ewh

atsm

alle

rbe

cau

seth

eO

DE

ineq

uat

ion

(B3)

fail

sto

hav

ea

nu

mer

ical

solu

tion

for

cert

ain

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bin

atio

ns

ofpa

ram

eter

sT

he

nu

mbe

rof

fun

d-qu

arte

rsu

sed

ines

tim

atio

nav

erag

es49

731

inP

anel

Dan

d48

033

inP

anel

E

b=

068

5b

=0

8

α=

0α

=3

α=

0α

=3

δ+λ

=5

δ+λ

=10

δ+λ

=5

δ+λ

=10

δ+λ

=5

δ+λ

=10

δ+λ

=5

δ+λ

=10

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Pan

elA

Dir

ect

ince

nti

ves

Per

1ch

ange

infu

nd

valu

e($

M)

(1)

Dir

ect

effe

ctfr

omin

cen

tive

fees

014

20

142

014

20

142

014

20

142

014

20

142

(2)

Dir

ect

effe

ctfr

omm

anag

ersrsquo

pers

onal

stak

e0

190

019

00

190

019

00

190

019

00

190

019

0(3

)To

tald

irec

tef

fect

033

10

331

033

10

331

033

10

331

033

10

331

Per

1$ch

ange

infu

nd

valu

e($

)(4

)D

irec

tef

fect

from

ince

nti

vefe

es0

068

006

80

068

006

80

068

006

80

068

006

8(5

)D

irec

tef

fect

from

man

ager

srsquope

rson

alst

ake

009

50

095

009

50

095

009

50

095

009

50

095

(6)

Tota

ldir

ect

effe

ct0

163

016

30

163

016

30

163

016

30

163

016

3

(Con

tin

ued

)


Tab

leII

ImdashC

onti

nu

ed

b=

068

5b

=0

8

α=

0α

=3

α=

0α

=3

δ+λ

=5

δ+λ

=10

δ+λ

=5

δ+λ

=10

δ+λ

=5

δ+λ

=10

δ+λ

=5

δ+λ

=10

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Pan

elB

In

dire

ctin

cen

tive

ses

tim

ated

from

the

Goe

tzm

ann

In

gers

oll

and

Ros

s(G

IR2

003)

mod

el

Per

1ch

ange

infu

nd

valu

e($

M)

(1)

Indi

rect

effe

ctfr

omn

ewm

oney

048

20

355

109

40

589

030

80

248

068

90

436

(2)

Indi

rect

effe

ctfr

omch

ange

inva

lue

ofex

isti

ng

asse

ts0

449

033

90

903

051

70

340

028

20

671

044

9(3

)To

tali

ndi

rect

effe

ct0

932

069

51

998

110

60

648

053

11

360

088

4(4

)In

dire

ctd

irec

t2

812

106

033

341

961

604

112

67P

er1$

chan

gein

fun

dva

lue

($)

(5)

Indi

rect

effe

ctfr

omn

ewm

oney

022

80

169

048

80

272

014

50

116

030

50

195

(6)

Indi

rect

effe

ctfr

omch

ange

inva

lue

ofex

isti

ng

asse

ts0

193

014

60

383

022

20

138

011

40

269

018

0(7

)To

tali

ndi

rect

effe

ct0

421

031

50

871

049

40

283

023

00

574

037

5(8

)In

dire

ctd

irec

t2

581

935

353

031

741

413

522

30

Pan

elC

In

dire

ctin

cen

tive

ses

tim

ated

from

the

GIR

mod

elau

gmen

ted

for

perf

orm

ance

-bas

edfl

ows

Per

1ch

ange

infu

nd

valu

e($

M)

(1)

Indi

rect

effe

ctfr

omn

ewm

oney

054

50

396

113

50

622

034

60

279

070

10

455

(2)

Indi

rect

effe

ctfr

omch

ange

inva

lue

ofex

isti

ng

asse

ts0

474

035

50

892

051

80

354

029

40

642

044

0(3

)To

tali

ndi

rect

effe

ct1

018

075

12

028

114

00

700

057

31

343

089

5(4

)In

dire

ctd

irec

t3

072

276

123

442

111

734

052

70P

er1$

chan

gein

fun

dva

lue

($)

(5)

Indi

rect

effe

ctfr

omn

ewm

oney

025

70

189

050

90

289

016

20

130

031

20

205

(6)

Indi

rect

effe

ctfr

omch

ange

inva

lue

ofex

isti

ng

asse

ts0

208

015

60

386

022

80

147

012

20

263

018

0(7

)To

tali

ndi

rect

effe

ct0

465

034

50

895

051

70

309

025

20

575

038

6(8

)In

dire

ctd

irec

t2

852

125

503

171

901

553

532

37

(Con

tin

ued

)


Tab

leII

ImdashC

onti

nu

ed

b=

068

5b

=0

8

α=

0α

=3

α=

0α

=3

δ+λ

=5

δ+λ

=10

δ+λ

=5

δ+λ

=10

δ+λ

=5

δ+λ

=10

δ+λ

=5

δ+λ

=10

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Pan

elD

In

dire

ctin

cen

tive

ses

tim

ated

from

the

Lan

Wan

gan

dYa

ng

(LW

Y20

13)

mod

el

Per

1ch

ange

infu

nd

valu

e($

M)

(1)

Indi

rect

effe

ctfr

omn

ewm

oney

055

40

366

133

70

691

042

50

315

072

50

454

(2)

Indi

rect

effe

ctfr

omch

ange

inva

lue

ofex

isti

ng

asse

ts0

583

036

11

245

067

30

551

038

70

821

052

2(3

)To

tali

ndi

rect

effe

ct1

137

072

72

582

136

40

976

070

21

546

097

6(4

)In

dire

ctd

irec

t3

432

197

804

122

952

124

672

95P

er1$

chan

gein

fun

dva

lue

($)

(5)

Indi

rect

effe

ctfr

omn

ewm

oney

025

70

169

057

50

305

019

80

146

032

10

204

(6)

Indi

rect

effe

ctfr

omch

ange

inva

lue

ofex

isti

ng

asse

ts0

241

015

10

498

027

20

217

015

30

321

020

5(7

)To

tali

ndi

rect

effe

ct0

498

032

01

073

057

70

415

030

00

642

040

8(8

)In

dire

ctd

irec

t3

051

966

583

542

551

843

942

51

Pan

elE

In

dire

ctin

cen

tive

ses

tim

ated

from

the

LWY

mod

elau

gmen

ted

for

perf

orm

ance

-bas

edfu

nd

flow

s

Per

1ch

ange

infu

nd

valu

e($

M)

(1)

Indi

rect

effe

ctfr

omn

ewm

oney

055

40

366

214

41

148

042

50

315

092

50

545

(2)

Indi

rect

effe

ctfr

omch

ange

inva

lue

ofex

isti

ng

asse

ts0

583

036

12

126

115

20

551

038

71

053

063

7(3

)To

tali

ndi

rect

effe

ct1

137

072

74

270

230

00

976

070

21

978

118

2(4

)In

dire

ctd

irec

t3

432

1912

89

694

295

212

597

357

Per

1$ch

ange

infu

nd

valu

e($

)(5

)In

dire

ctef

fect

from

new

mon

ey0

257

016

90

938

049

50

198

014

60

402

024

0(6

)In

dire

ctef

fect

from

chan

gein

valu

eof

exis

tin

gas

sets

024

10

151

085

30

453

021

70

153

040

40

246

(7)

Tota

lin

dire

ctef

fect

049

80

320

179

10

948

041

50

300

080

50

487

(8)

Indi

rect

dir

ect

305

196

110

05

822

551

844

942

99


in returns the incremental future fees from inflows of new investment average$482000 and the incremental future fees from the increase in value of existingassets average $449000 The total is $932000 which is 281 times the directchange in compensation through incentive fees and the managersrsquo personalstakes Expressed as a fraction of an incremental dollar in the fund managersreceive 228 cents from new money and 193 cents from the change in the valueof existing assets Together these effects are 258 times the direct incentivesthat managers receive for the same change14

The remaining columns of Panel B present analogous calculations for al-ternative parameter choices In general higher choices of b and δ + λ reducethe size of indirect incentives while a higher α raises them Intuitively ahigher b implies a lower tolerance for negative return shocks by investors soin expectation future fees and hence indirect incentives will be lower whenb is higher Similarly a higher value of δ + λ means that assets exit the fundmore quickly for nonperformance-related reasons which effectively shortensa fundrsquos expected life so future fees will be lower In contrast a higher αmeans that future returns are expected to be higher so the likelihood of hittingthe liquidation boundary defined by b declines and fees will be a percentageof higher asset values so their expectation increases with α Nonetheless re-gardless of the choice of parameters indirect incentives are substantially largerthan direct incentives with indirect-to-direct incentive ratios varying from 14to 60

Considering the other models presented in Panels C D and E it is evi-dent that for any set of parameters the indirect incentives coming from thealternative models are higher than those from the base-case GIR model Theaugmented GIR model and the two LWY models differ from the base-case GIRmodel by accounting for future performance-related fund flows andor portfolioallocations that are chosen endogenously to incorporate managersrsquo incentives

In the case of the augmented GIR model future performance-based flows areequivalent to an increase in fund volatility Volatility increases the value of themanagerrsquos claim to each incremental dollar in the fund because the managerrsquosincentive fees are options on the fund15 Indirect incentives calculated usingthe augmented GIR model and presented in Panel C are about 9 larger thanfor the base GIR model when α = 0 and are about 2 larger when α = 3

The LWY model allows the manager to choose the leverage of the fund inresponse to incentives When managers can endogenously adjust their futureportfolios they will do so only when it benefits them This effect increases thevalue to the manager of an incremental dollar in the fund compared to when

14 The indirect-to-direct incentives ratios are calculated by dividing average indirect incentivesby average direct incentives for both ldquoper incremental percentage point of returnsrdquo and ldquoper incre-mental dollarrdquo calculations The two ratios are not the same because for each fund-quarter directand indirect incentives per 1 increase in returns are scaled by (1 of) AUM which varies overtime to reach direct and indirect incentives per $1 change to fund value

15 In the GIR framework the standard intuition that volatility increases option value is partiallybut not entirely offset by the fact that greater volatility increases the probability of liquidationgiven a fixed b


they do not have this option Indirect incentives calculated using the LWYmodel and presented in Panel D range from 2 to 51 larger than in the baseGIR model depending on the model parameters

The LWY model augmented for performance-based fund flows accounts notonly for endogenous allocation but also inflows of new investment in the futureif future performance is good Both channels increase the value to the managerof an incremental dollar in the fund They also interact in that with the possi-bility of future inflows the manager endogenously takes more risk This effectis especially strong when large α is combined with low b as the downside fromrisk-taking (hitting the liquidation boundary represented by b) is less likely tooccur in this case Thus indirect incentives from the augmented LWY modelpresented in Panel E are about 39 higher than in the base-case GIR modelwhen the liquidation threshold is tight (b = 08) When the liquidation thresh-old is looser (b = 0685) indirect incentives are about 12 higher comparedto the base GIR model for unskilled managers (α = 0) and 105 higher forskilled ones (α = 3)

Overall the ratio of indirect to direct incentives varies from 14 to 129 acrossthe 32 model and parameter combinations examined here with an average of35 The three alternative models typically deliver estimates of indirect in-centives that are roughly 25 higher than the base-case GIR model becauseperformance-based inflows and endogenous portfolios both increase future pay-ments to fund managers Regardless of the specific calculations used it is ev-ident that indirect incentives for most hedge fund managers are considerablylarger than their direct incentives16

B Direct and Indirect Incentives by Fund Age

Panel B of Table II documents that the magnitude of the flow-performancerelation declines as a fund ages consistent with the logic of Berk and Green(2004) Because the indirect incentives that managers face are due in largepart to the influence of returns on flows it is likely that managersrsquo indirectincentives also decline with a fundrsquos age

Table IV examines this hypothesis by calculating indirect incentives for fundsof different ages with parameter choices given in Column (4) of Table III (ieb = 0685 α = 3 and δ + λ = 10 these are the parameter choices used byLWY) Here we focus our discussion on changes in manager wealth per in-cremental dollar returned to investors because older funds are systematicallylarger complicating comparisons across age groups of changes in wealth perincremental percentage point of returns However for completeness we reportthe latter incentive measure as well in Table IV

Panel A presents the average direct pay for performance for funds of differentages Direct pay for performance appears to increase with fund age For a new

16 Indirect incentives are also large in the case in which the manager has the option to resetthe HWM by strategically abandoning the fund and starting a new one This case can also beaccommodated in the LWY framework The estimates of indirect incentives would fall in betweenthose for the base LWY model and the LWY model augmented for performance-based flows


Tab

leIV

Dir

ect

and

Ind

irec

tP

ayfo

rP

erfo

rman

ceb

yA

geG

rou

pT

his

tabl

epr

esen

tses

tim

ates

ofdi

rect

and

indi

rect

pay

for

perf

orm

ance

(in

cen

tive

s)at

aqu

arte

rly

freq

uen

cys

imil

arto

Tabl

eII

Ibu

tbr

oken

dow

nby

fun

dag

eR

epor

ted

stat

isti

csar

eav

erag

esta

ken

over

all

fun

dsof

agi

ven

age

All

esti

mat

esin

this

tabl

eu

seth

equ

arte

rly

equ

ival

ent

ofth

epa

ram

eter

sb

=0

685α

=3

an

dδ+λ

=10

t

hes

ear

eth

epa

ram

eter

sch

osen

byLW

Y

Fu

nd

age

(yea

rs)

01

23

45

67

89

1011

1213

14

15

Pan

elA

Dir

ect

ince

nti

ves

Per

1ch

ange

infu

nd

valu

e($

M)

(1)

Tota

ldir

ect

effe

ct0

100

150

180

230

290

380

460

510

580

720

851

031

321

281

221

04P

er1$

chan

gein

fun

dva

lue

($)

(2)

Tota

ldir

ect

effe

ct0

100

110

120

140

160

180

210

230

240

260

280

290

310

320

360

35

Pan

elB

In

dire

ctin

cen

tive

ses

tim

ated

from

the

GIR

mod

el

Per

1C

han

gein

fun

dva

lue

($M

)(1

)To

tali

ndi

rect

effe

ct0

590

820

940

981

081

121

221

311

401

631

812

042

272

261

961

50(2

)In

dire

ctd

irec

t6

055

435

134

273

752

982

632

572

402

272

131

981

721

771

611

44P

er1$

chan

gein

fun

dva

lue

($)

(1)

Tota

lin

dire

ctef

fect

057

053

050

048

047

045

044

043

042

042

042

042

042

042

042

039

(2)

Indi

rect

dir

ect

551

478

407

333

290

242

209

187

172

159

150

146

134

130

117

110

Pan

elC

In

dire

ctin

cen

tive

ses

tim

ated

from

the

GIR

mod

elau

gmen

ted

for

perf

orm

ance

-bas

edfu

nd

flow

s

Per

1ch

ange

infu

nd

valu

e($

M)

(1)

Tota

lin

dire

ctef

fect

062

084

096

100

110

117

127

134

143

171

186

207

233

234

207

153

(2)

Indi

rect

dir

ect

631

560

524

435

385

311

273

261

245

239

219

201

177

183

170

147

Per

1$ch

ange

infu

nd

valu

e($

)(1

)To

tali

ndi

rect

effe

ct0

590

550

530

500

490

470

460

450

440

440

440

440

430

440

440

40(2

)In

dire

ctd

irec

t5

765

004

253

483

032

542

191

961

801

681

561

511

391

361

231

13

(Con

tin

ued

)


Tab

leIV

mdashC

onti

nu

ed

Fu

nd

age

(yea

rs)

01

23

45

67

89

1011

1213

14

15

Pan

elD

In

dire

ctin

cen

tive

ses

tim

ated

from

the

LWY

mod

el

Per

1ch

ange

infu

nd

valu

e($

M)

(1)

Tota

lin

dire

ctef

fect

071

100

116

119

133

140

154

172

186

203

225

253

288

262

228

172

(2)

Indi

rect

dir

ect

728

666

633

515

466

371

332

336

319

283

264

246

218

204

187

166

Per

1$ch

ange

infu

nd

valu

e($

)(1

)To

tali

ndi

rect

effe

ct0

650

610

590

560

550

530

520

520

510

500

500

510

500

500

500

45(2

)In

dire

ctd

irec

t6

305

504

743

883

432

882

472

262

071

891

771

741

601

541

411

27

Pan

elE

In

dire

ctin

cen

tive

ses

tim

ated

from

the

LWY

mod

elau

gmen

ted

for

perf

orm

ance

-bas

edfu

nd

flow

s

Per

1ch

ange

infu

nd

valu

e($

M)

(1)

Tota

lin

dire

ctef

fect

118

167

191

194

224

235

263

302

327

347

386

434

500

442

382

285

(2)

Indi

rect

dir

ect

120

811

16

104

98

447

816

265

685

915

614

854

544

213

793

453

132

74P

er1$

chan

gein

fun

dva

lue

($)

(1)

Tota

lin

dire

ctef

fect

106

098

095

091

091

088

086

087

086

084

084

087

085

085

087

075

(2)

Indi

rect

dir

ect

102

68

907

706

305

674

794

113

813

503

182

982

992

742

642

452

14


fund an incremental dollar returned to fund investors results in 10 cents inincremental incentive fees and returns on the managerrsquos personal stake Fora 10-year-old fund this quantity increases to 28 cents Much of this increasehowever occurs because of our assumption that managers reinvest their feesin which case their ownership increases over time To the extent that managersstart their funds with a positive stake and do not reinvest all of their fees directpay for performance will not increase as fast as suggested by this table

Panels B to E present estimates of indirect incentives using each of the fourmodels to evaluate the fraction of fund value going to managers in the form offuture fees The estimates for each model imply that indirect incentives declinesubstantially as a fund ages For new funds an incremental dollar returned toinvestors today results in at least $057 in expected future fees This indirecteffect is about six times as important as the direct effect of performance oncurrent income from incentive fees and gains on the managerrsquos own stake Theindirect effect declines sharply with the fundrsquos age but remains larger thanthe direct effect even for 15-year-old funds

C Direct and Indirect Incentives and Fund Scalability

Indirect incentives result from managers being able to increase their fundsrsquosize when performance has been good Their ability to do so depends on theability of managers to scale their investments Some funds are relatively un-constrained in that they adopt strategies that are scalable implying that thefund can likely invest new capital with the same ex ante return distributionas existing capital In contrast other more constrained funds typically cannotaccept more capital without significantly reducing expected returns

Table V provides estimates of indirect and direct incentives for funds clas-sified as ldquoNot Capacity Constrainedrdquo and ldquoCapacity Constrainedrdquo accordingto the classifications discussed in Section IIIB The direct incentives of eachtype are very similar 16 cents for each incremental dollar returned to in-vestors for unconstrained funds compared to 17 cents for constrained fundsThe differences in indirect incentives however are substantial 52 cents foreach dollar returned to investors for unconstrained funds compared to 43 centsfor constrained funds using the GIR model This difference implies an aver-age indirect-to-direct incentives ratio of about 32 for the unconstrained fundscompared to 25 for the constrained funds Using other models the indirect-to-direct incentives ratio ranges between 34 and 73 for the unconstrainedfunds and between 26 and 60 for the constrained funds Although our proxyfor fund scalability is imperfect these differences suggest that indirect incen-tives are relatively more important in funds adopting more scalable investmentstrategies

The cross-sectional differences in indirect incentives by fund scalability pointto broader issues that are likely to affect the scalability of the hedge fundindustry and hence indirect incentives as a whole In particular current trendsin industry growth and regulation suggest that it is possible that managers willbe less able to effectively deploy additional capital in the future than has been


Table VDirect and Indirect Pay for Performance by Scalability

This table presents estimates of direct and indirect pay for performance (incentives) at a quarterlyfrequency similar to Table III but broken down by the scalability of the fundrsquos strategy ldquoCapacityconstrainedrdquo strategies are Emerging Market Fixed Income Arbitrage Event Driven and Con-vertible Arbitrage All other strategies LongShort Equity Equity Market Neutral Global MacroMulti-Strategy and Others are not considered capacity constrained Reported statistics are aver-ages taken over all fund-quarters within a grouping All estimates in this table use the quarterlyequivalent of the parameters b = 0685 α = 3 and δ+λ = 10 these are the parameters chosenby LWY

Not Capacity Constrained Capacity Constrained

Panel A Direct incentives

Per 1 change in fund value ($M)(1) Total direct effect 0340 0314Per 1$ change in fund value ($)(2) Total direct effect 0160 0169

Panel B Indirect incentives estimated from the GIR model

Per 1 change in fund value ($M)(1) Total indirect effect 1213 0862(2) Indirectdirect 357 275Per 1$ change in fund value ($)(1) Total indirect effect 0516 0427(2) Indirectdirect 323 253

Panel C Indirect incentives estimated from the GIR model augmented forperformance-based fund flows


Panel D Indirect incentives estimated from the LWY model


Panel E Indirect incentives estimated from the LWY model augmented forperformance-based fund flows



Table VIDirect and Indirect Pay for Performance by Presence

of a High-Water MarkThis table presents estimates of direct and indirect pay for performance broken down by thepresence of an HWM provision Reported statistics are averages taken over all funds in a givencategory All estimates of indirect incentives are obtained from the base LWY model with parametervalues b = 0685 α = 3 and δ+λ = 10

(1) (2) (3)All Funds Funds with

funds with HWM no HWM

(1) Number of observations used in estimation 49776 35549 14227(2) AUM ($ million) 213 239 148Pay for performance per 1 change in fund value ($M)(3) Direct effect from incentive fees 0142 0132 0165(4) Direct effect from managersrsquo personal stake 0190 0188 0193(5) Total direct effect 0331 0320 0358(6) Indirect effect from new money 0691 0775 0483(7) Indirect effect from change in value of

existing assets0673 0813 0323

(8) Total indirect effect 1364 1587 0805(9) Indirectdirect 412 496 225Pay for performance per 1$ change in fund value ($)(10) Direct effect from incentive fees 0068 0054 0105(11) Direct effect from managersrsquo personal stake 0095 0074 0146(12) Total direct effect 0163 0128 0251(13) Indirect effect from new money 0305 0310 0291(14) Indirect effect from change in value of


(15) Total indirect effect 0577 0613 0486(16) Indirectdirect 354 480 194

true in the past (ie in our sample period) If so the flow-performance relationwill decline and so will the magnitude of indirect incentives All of our estimatesshould be interpreted with this caveat in mind

D Direct and Indirect Incentives and High-Water Marks

The importance of indirect incentives could vary across funds depending onthe presence of an HWM provision because of both potential differences inflow-performance sensitivities and the impact of the HWM on fees

Table VI presents estimates of direct and indirect incentives for funds withand without HWM provisions We find that funds without HWM provisionshave higher direct incentives while funds with HWM provisions have higherindirect incentives Consequently funds with HWM provisions have substan-tially higher indirect-to-direct incentives ratios For an incremental dollarreturn to the fundrsquos investors the indirect-to-direct ratio is 194 for funds withno HWM provision and 480 for those with HWM and the difference betweenthe two is statistically significant


The fact that indirect incentives are substantially higher for funds withHWM complements the finding of Patton Ramadorai and Streatfield (2015)that the direction and magnitude of return misreporting is larger for funds withan HWM provision One reason for this finding could be the higher indirectincentives we document which suggest greater gains from misreporting

V Implications of These Estimates of Indirect Incentives

The existence of large indirect incentives for hedge fund managers has im-plications for our understanding of a number of issues including the structureof contracts in hedge funds the portfolio choices of investors and contractingbeyond the asset management context In this section we highlight these im-plications their empirical predictions as well as a number of questions thatemerge from our analysis that are potential topics for future research

A Implications for Hedge Fund Contracting and Managersrsquo Total Incentives

Hedge fund contracts are set up in a sophisticated manner with a fee struc-ture containing a combination of management fees and incentives that useHWMs that adjust their effective strike price Presumably these contracts aredesigned to solve a principal-agent problem of some sort Yet as emphasizedby Gibbons and Murphy (1992) what matters in a principal-agent problem isthe total pay for performance that agents receive not the distribution betweenits direct and indirect components

A1 Total Incentives over Time and the Relation between Directand Indirect Incentives

In the hedge fund industry our finding that indirect incentives decrease withfund age together with established evidence that explicit contracts tend to besticky implies that managersrsquo total incentives decline as a fund ages17 Whetherit is optimal for total incentives to decline and for contracts to be sticky givendeclining indirect incentives is an interesting question for future researchIf as seems likely attrition in the industry results in more experienced man-agers being higher quality on average than novice managers a theory ratio-nalizing these results would need to explain why the process of generatinghedge fund returns is such that better managers should receive weaker totalincentives

Although direct incentives in the hedge fund industry do not (given the stick-iness of the contracts) appear to increase to compensate for declining indirect

17 Funds do at times change their fee structure but such changes are infrequent Deuskar et al(2012) report a number of findings about the factors that lead hedge funds to change their feesNone of the findings in Deuskar et al (2012) suggest that changes in the hedge fund fee structureare driven by a desire to offset changes in indirect incentives faced by fund managers


Table VIIThe Relation between Direct and Indirect Incentives

This table presents the relations between various contractual terms and Constrained OLS coeffi-cient estimates and corresponding standard errors (in parentheses) are reported The dependentvariable is the incentive fee rate and HWM indicator in Columns (1) and (2) respectively Allanalyses are conducted at the fund level Standard errors are clustered by fund inception year and indicate statistical significance at the 1 5 and 10 level respectively

(1) (2)Incentive fee rate HWM indicator

Dependent var= Coef (SE) Coef (SE)

Constrained minus00020 (0002) minus00394 (0025)Log(Inception AUM+1) 00008 (0000) 00121 (0009)Leveraged indicator 00067 (0001) minus00136 (0027)Open-to-public indicator 00024 (0001) 01287 (0031)On-shore indicator 00045 (0002) minus00571 (0019)Log(Total redemption +1) minus00019 (0003) 00163 (0038)Log(Lock-up period+1) 00003 (0001) 01491 (0028)Log(Subscription period+1) 00194 (0006) minus04330 (0099)Number of observations 2998 2998Adjusted R2 0016 0072

incentives as a fund ages it is possible that contracts signed at fund inceptionare related to the strength of the indirect incentives that managers face In par-ticular because funds with less scalable investment strategies have a weakerflow-performance relation holding contractual terms fixed they have weakerindirect and total incentives than funds with more scalable strategies So wemight expect direct contractual incentives to be stronger in less scalable fundsto offset the weaker flow-performance relation Alternatively it is possible thatthe type of manager who manages a less scalable fund is more likely to be onewhose total incentives should be weaker In that case constrained funds couldhave weaker direct incentives than unconstrained funds

To evaluate the extent to which contracting adjusts in the presence of in-direct incentives we measure the relation between direct incentives and thescalability of an fundrsquos strategy Direct incentives are a function of the incentivefee rate which usually remains constant over the life of the fund In additionthe presence of an HWM provision mitigates a fundrsquos direct incentives to someextent Therefore to assess whether being constrained in its ability to acceptcapital because of a less scalable strategy affects the direct incentives that afund provides its managers we estimate equations predicting both the fundrsquosincentive fee rate and whether a fund employs an HWM In these equationswe include the indicator variable Constrained that equals one if a firm followsa less scalable strategy using the Ding et al (2009) classification of strategiesas well as other variables that are likely to be associated with a firmrsquos directincentives

We present estimates of these equations in Table VII In neither equa-tion is the estimated coefficient on Constrained statistically significant or of


meaningful magnitude Consequently these estimates suggest that funds donot adjust their direct compensation plans based on the extent to which theirmanagers receive indirect incentives

The lack of a relation between a hedge fundrsquos contractual fee structure andindirect market-based incentives reflects a larger puzzle about the structureof compensation in alternative asset classes Both hedge funds and privateequity funds generally use a combination of management fees that are pro-portional to capital under management and performance fees which are usu-ally 20 of profits regardless of other factors that ostensibly should affectthe magnitude of the profit-sharing percentage Although the performancefee differs more in the hedge fund industry than in private equity18 the factthat hedge funds do not appear to adjust their incentive fees based on indi-rect incentives that are large in magnitude and have wide variation acrossfunds and over time is difficult to reconcile with extant optimal contractingtheories

A2 Hedge Fund Managersrsquo Careers and Adverse Consequences of Incentives

The estimates provided above suggest that hedge fund managersrsquo careerpatterns should be heavily influenced by the returns earned by the fund whilethey are young and are establishing a reputation Since young fund managershave limited liability and the option of leaving the industry the estimatesdiscussed above imply that young hedge fund managers receive extremely largepayoffs for good performance but do not fully bear the downside risk from anyactions they take Therefore we expect the pay of managers who earn highreturns to increase substantially while there should be a high exit rate frommanagers who do not perform well

These incentives potentially lead to some adverse outcomes for investors Theexit option will lead managers to become prone to increase the risk of the fundeven if doing so comes at a cost to investors In addition if young fund managershave the option of taking investments that increase short-term returns at theexpense of the long term as suggested by Stein (1989) their incentives to doso are particularly large Thus we expect young managers to be more likely totake investments that have payoffs that are observable over shorter horizonsthan older managers In addition to the extent that accounting manipulationor fraud can increase short-term payoffs with any potential cost occurring inthe future we expect to observe such manipulation or fraud more frequentlyfrom younger managers The extent to which indirect incentives lead to theseactivities that adversely affect investors in practice is unknown

18 Gompers and Lerner (1999) Metrick and Yasuda (2010) and Robinson and Sensoy (2013)provide evidence on private equity fund compensation


B Implications for Investors

B1 Differences in Hedge Fund Managersrsquo Incentives

Institutional investors often highlight the importance of the incentives oftheir fund managers in making their portfolio decisions (see Swenson (2000))Since indirect incentives are such an important part of hedge fund managersrsquototal incentives they should be taken into account by investors investing inhedge funds and other kinds of assets Higher indirect incentives increase thepayoff to managers from increasing returns but also potentially lead them toincrease risk and manipulate returns The estimates presented above suggestthat younger funds and funds in more scalable sectors have higher indirectand total incentives Portfolio theory is not clear on the optimal composition ofa portfolio of funds whose incentives are different from one another Howeverit does seem clear that the differences in indirect incentives we document arefactors that a portfolio manager would like to be aware of when making portfolioallocations to hedge funds

B2 Differences in Incentives across Asset Classes

Indirect incentives are present not just for managers of hedge funds but alsofor managers of all asset classes Consequently total incentivesmdashincludingboth direct and indirect componentsmdashare relevant not only for portfolio de-cisions within a particular asset class but also for portfolio decisions acrossdifferent asset classes Estimates of total incentives facing asset managers areavailable for some asset classes in addition to hedge funds

Probably the most important ldquoalternative assetrdquo other than hedge funds isprivate equity funds This investment class has very similar direct incentiveschemes as hedge funds In addition they have substantial indirect incentivesChung et al (2012) estimate that private equity fund managersrsquo indirect in-centives are approximately the same size as their direct incentives

Mutual funds are another asset class for which one can measure indirectincentives An important difference from hedge funds is that mutual fundsdo not charge incentive fees or carried interest so direct contractual incen-tives do not exist However the flow-performance relation facing mutual fundmanagers potentially leads to substantial indirect incentives To understandhow mutual funds and hedge funds compare in terms of the magnitude andimportance of indirect incentives we conduct similar calculations for mutualfunds as we do for hedge funds These calculations also require estimates of theflow-performance sensitivity as well as a valuation model of the mutual fundmanagerrsquos expected lifetime compensation

To estimate the flow-performance sensitivity in as comparable a manneras possible to our hedge fund estimates we construct a sample of mutualfunds from the CRSP Survivorship Bias Free Mutual Fund Database over thesame 1995 to 2010 period used in the hedge fund sample Because we focuson flows into actively managed funds we exclude index funds and exchange-traded funds (ETF) We also exclude fixed income funds international funds


and sector funds to facilitate comparison with prior literature We then applythe same filtering rules as we used with hedge funds that is we require theavailability of contractual information and at least 12 consecutive monthlyobservations within the sample period We also drop all fractional quarters

Our sample consists of 307079 fund-quarters associated with 11911 fundsThe average mutual fund in our sample contains $322 million in assets and iseight years old The management fee defined as the total expense ratio minusthe 12B-1 fee averages 11 of total net assets Further descriptive statisticsare provided in Table CII in Appendix C

We estimate the mutual fund analog of equation (1) using quarterly obser-vations from this mutual fund sample and report the results in Table CIIIin Appendix C19 In doing so we omit hedge fund-specific variables such asthe incentive fee that are not relevant for mutual funds and include style byquarter fixed effects for mutual fund styles listed in Table CII in Appendix CWe find that the same patterns hold for mutual funds as for hedge funds thecoefficients on past returns are all positive and statistically significant in mostcases and decline almost monotonically over time The sum of coefficients onpast returns is about 178 (based on Column (2) of Table CIII) implying thatflows are somewhat more responsive to performance in the mutual fund indus-try than in the hedge fund industry This pattern may be attributed to the factthat search costs and transaction costs are much lower for mutual funds thanfor hedge funds which leads to higher flow-performance sensitivity (HuangWei and Yan (2007))

We obtain the present value of mutual fund fees per dollar of AUM using thesame LWY framework that we use for hedge funds Within this framework amutual fund can be modeled as a fund in which leverage is not allowed and theincentive fee is zero Therefore the LWY model with no leverage (equivalentto the GIR model) with the incentive fee rate (k) set to zero characterizes thepresent value of fees for a mutual fund In addition since mutual funds do nothave an HWM provision the contractual growth rate of HWM (g) is set to zeroand the ratio between the investorrsquos wealth and HWM (w) is set to one Forcomparison with the hedge fund estimates we present estimates for the sameeight combinations of the parameters b α and δ + λ For all other parametersfund-specific values are used

Table VIII provides estimates of indirect incentives in mutual funds In con-trast to the hedge fund analyses we do not compare indirect incentives to direct

19 We estimate the same linear specification as we used with hedge funds to obtain compa-rable flow-performance sensitivity estimates Alternatively to incorporate the well-documentednonlinearity (convexity) in the flow-performance relation for mutual funds we estimate a sim-ple piecewise specification that partitions negative returns and positive returns The resultingflow-performance sensitivity estimate is about 26 larger when returns are positive than whatis implied from the linear specification which leads to 17 larger indirect incentives than thosereported in Table VIII When we take into account convexity effects by including quadratic laggedreturn terms in the regression the resulting estimate suggests that indirect incentives are about13 larger than those reported in Table VIII when evaluated at the 75th percentile of quarterlyreturns


Tab

leV

III

Ind

irec

tP

ayfo

rP

erfo

rman

cefo

rM

utu

alF

un

ds

Th

ista

ble

pres

ents

esti

mat

esof

indi

rect

pay

for

perf

orm

ance

(in

cen

tive

s)fo

rm

utu

alfu

nds

ata

quar

terl

yfr

equ

ency

usi

ng

the

LWY

mod

el(e

quiv

alen

tto

the

GIR

mod

elu

nde

rth

ese

tup

for

mu

tual

fun

ds)

Wit

hin

this

fram

ewor

ka

mu

tual

fun

dis

asp

ecia

lca

sein

wh

ich

leve

rage

isn

otal

low

edan

dth

ein

cen

tive

fee

(k)

isze

roM

utu

alfu

nds

hav

en

oH

WM

prov

isio

na

nd

ther

efor

eth

eco

ntr

actu

alH

WM

grow

thra

te(g

)is

set

toze

roan

dth

era

tio

betw

een

anin

vest

orrsquos

wea

lth

and

HW

Mle

vel

(w)

ison

eat

all

tim

es

Est

imat

esof

indi

rect

ince

nti

ves

are

pres

ente

dfo

rei

ght

com

bin

atio

ns

ofth

epa

ram

eter

sbα

an

dδ+λ

All

oth

erpa

ram

eter

valu

esar

efu

nd-

spec

ific

Rep

orte

dst

atis

tics

are

aver

ages

take

nov

eral

lfu

nd-

quar

ters

(307

079

)in

the

data

Flo

w-p

erfo

rman

cese

nsi

tivi

tyto

com

pute

the

indi

rect

effe

ctfr

omn

ewm

oney

isob

tain

edfr

omth

eco

effi

cien

tes

tim

ates

inC

olu

mn

(2)

ofTa

ble

CII

Iin

App

endi

xC

Row

s(4

)an

d(9

)(R

ows

(5)

and

(10)

)re

port

the

mag

nit

ude

ofin

dire

ctin

cen

tive

sfo

rm

utu

alfu

nds

inco

mpa

riso

nto

the

esti

mat

esfo

rh

edge

fun

dsfr

omth

eG

IRm

odel

(LW

Ym

odel

)

b=

068

5b

=0

8

α=

0α

=3

α=

0α

=3

δ+λ

=5

δ+λ

=10

δ+λ

=5

δ+λ

=10

δ+λ

=5

δ+λ

=10

δ+λ

=5

δ+λ

=10

(1

)(2

)(3

)(4

)(5

)(6

)(7

)(8

)

Per

1ch

ange

infu

nd

valu

e($

mil

lion

s)(1

)In

dire

ctef

fect

from

new

mon

ey0

392

025

90

769

036

70

214

015

60

414

022

4(2

)In

dire

ctef

fect

from

chan

gein

valu

eof

exis

tin

gas

sets

022

10

146

043

30

206

012

00

088

023

30

126

(3)

Tota

lin

dire

ctef

fect

061

30

405

120

20

573

033

40

243

064

70

351

(4)

of

the

hed

gefu

nd

esti

mat

esfr

omth

eG

IRm

odel

658

58

3

602

51

8

516

45

9

476

39

6

(5)

of

the

hed

gefu

nd

esti

mat

esfr

omth

eLW

Ym

odel

539

55

7

465

42

0

343

34

7

419

35

9

Per

$1ch

ange

infu

nd

valu

e($

)(6

)In

dire

ctef

fect

from

new

mon

ey0

139

009

50

260

013

30

074

005

50

136

007

9(7

)In

dire

ctef

fect

from

chan

gein

valu

eof

exis

tin

gas

sets

007

80

053

014

60

075

004

10

031

007

60

044

(8)

Tota

lin

dire

ctef

fect

021

60

148

040

60

208

011

50

086

021

20

123

(9)

of

the

hed

gefu

nd

esti

mat

esfr

omth

eG

IRm

odel

514

47

1

466

42

1

406

37

5

369

32

7

(10)

of

the

hed

gefu

nd

esti

mat

esfr

omth

eLW

Ym

odel

435

46

4

378

36

0

277

28

8

330

30

0


incentives Direct incentives are small in the mutual fund industry because mu-tual funds do not charge incentive fees and mutual fund manager ownership istypically very small (eg Ma and Tang (2014)) In terms of indirect incentivesTable VIII shows that for an incremental one-percentage-point return to thefundrsquos investors the manager of an average-sized mutual fund ($322 million)receives as high as $1202000 in expected future compensation which corre-sponds to 47 of what the LWY model suggests an average hedge fund managerreceives under the same parameter assumptions and 60 of what the GIRmodel implies for hedge funds Depending on the parameter choices the rela-tive magnitude of the indirect effect in mutual funds ranges between 40 and66 of the hedge fund counterpart within the GIR framework (Row (4)) and be-tween 34 and 56 within the LWY framework (Row (5)) Expressed as a frac-tion of an incremental dollar return to the fund mutual fund managers receive86 to 406 cents which corresponds to 33 to 51 of the size of indirect incen-tives facing hedge fund managers within the GIR framework (Row (9)) and 28to 46 within the LWY framework (Row (10)) Indirect incentives are substan-tially smaller in mutual funds than in hedge funds but nonetheless constitutean important component of the incentives motivating mutual fund managers

C Implications for Understanding Contracting More Broadly

Ever since Fama (1980) and Holmstrom (1982) it has been recognized thatmarket-based incentives are an important element of corporate governanceand also motivate many workers who are not top managers Yet while therehas been some work estimating their importance for CEOs of large industrialcorporations it is difficult to evaluate their magnitude quantitatively20 Theestimates provided here for hedge fund managers are some of the first esti-mates of the importance of indirect incentives for managers other than CEOsof large corporations The cross-sectional pattern of the estimates is consis-tent with a Bayesian learning process by which the market provides theseincentives

An open question is the extent to which our estimates compare to market-based incentives of more typical managers While most managers do not havethe upside potential of hedge fund managers they also do not have directincentive schemes that are nearly as powerful Consequently relative to directincentive pay indirect market-based incentives could potentially be equally oreven more important to most workers as they are for hedge fund managers

VI Conclusion

Managersrsquo incentives to perform well come not only through direct pay-forperformance plans but also through the managerial labor market The market

20 See Boschen and Smith (1995) and Taylor (2013) for evidence of the importance of indirectincentives for CEOs and Hermalin and Weisbach (2014) for a general discussion of indirect incen-tives and their measurement


draws inferences about managersrsquo abilities from their observed performanceand rewards or penalizes them accordingly providing an additional channelthrough which managersrsquo performance can affect their welfare The moneymanagement industry is one place where the managerial labor market providessubstantial incentives since investors can observe managersrsquo performance andreallocate capital easily between alternative investments This paper estimatesthe magnitude of this effect for hedge fund managers

Our estimates indicate that indirect incentives are particularly large forhedge fund managers On average for an incremental dollar returned to in-vestors managersrsquo expected lifetime income increases by at least 39 cents 23cents of which comes from indirect incentives arising from managersrsquo abilityto earn fees on the increased AUM that follows incremental performance Thelarge indirect incentives for hedge fund managers come from the fact that in-flows to hedge funds are highly sensitive to performance and the fee structurein hedge funds is such that managers expect to receive a large fraction of eachdollar invested in the fund as compensation over time

The existence of substantial indirect incentives facing hedge fund managershas a number of implications Hedge funds appear to be organized to provideoptimal incentives to their managers Yet direct incentives do not appear toadjust for differences in indirect incentives across funds or over a fundrsquos lifecycle Investors who care about fund managersrsquo incentives should care abouttheir total incentives including both direct and indirect components Bothprivate equity funds and mutual funds also appear to have substantial indirectincentives although not as large as those facing hedge fund managers Finallyasset management is but one industry in which it is possible to measure indirectincentives facing managers Undoubtedly such incentives are also importantfor many other types of jobs Whether indirect incentives are of comparableimportance outside of the money management industry would be an excellenttopic for future research

Initial submission July 30 2013 Final version received May 23 2015Editor Kenneth Singleton

Appendix A Calculating Direct Pay for Performance

Our proxy for direct pay for performance is given by total delta as definedin Agarwal Daniel and Naik (2009) Total delta at a point in time is definedas the expected dollar change in the managerrsquos wealth for a one-percentage-point increase in the fundrsquos return over the following year The total delta canbe decomposed into two parts the portion coming from investorsrsquo assetsmdashthemanagerrsquos option deltamdashand the portion coming from the managerrsquos owner-ship stake in the fund The managerrsquos option delta is in turn the sum of thedeltas associated with the different investors in the fund because of the factthat different investors in the fund face different spot prices (S) and exerciseprices (X) depending on the timing of their entrance into the fund Following


Agarwal Daniel and Naik (2009) for each fund-investor-quarter we computethe individual optionrsquos delta using the Black-Sholes model for a one-quartermaturity European call option as follows

Individual Option Delta = N(Z) times S times 001 times k (A1)

where Z = ln(SX) + T(r+σ 22)σT05 S is the investor-specific spot price(ie the market value of the investorrsquos assets) k is the contractual incentive feerate and X is the investor-specific exercise price Given the structure of hedgefund contracts X is the HWM level for the investorrsquos assets (ie the historichigh that the investorrsquos asset has reached since her investment in the fund) ifthe fund has an HWM provision and simply the market value of the investorrsquosassets if the fund has no such provision In either case X can be increased by ahurdle rate if applicable Because incentive fees are paid out at the end of eachquarter SX can never be greater than one Therefore SX measures whetherthe option is at the money and if not the degree to which the option is out ofthe money The variable T is the time to maturity of the option (one quarterin this case) r is the natural logarithm of one plus the risk-free rate which ismeasured as the three-month LIBOR rate that is in effect at the beginning ofeach quarter σ is the standard deviation of monthly (net-of-fee) returns overthe prior 12-month period multiplied by the square root of three to arrive ata quarterly figure and N() is the standard normal cumulative distributionfunction

A complication in computing individual option deltas is that various in-vestorsrsquo assets are pooled together for management so they earn the samerate of gross return but different investors will in general have different spotprices (S) and exercise prices (X) depending on when they entered the fundUnfortunately the exact times and prices at which each investor entered eachfund are not publicly available To compute the investor-specific spot price (S)and exercise price (X) for each investor we make the following assumptions

1 The first investor enters the fund at the end of quarter 0 (beginning ofquarter 1) There is no capital investment by the manager at inceptionTherefore the entire assets at inception come from a single investor

2 All cash flows including fee payments investorsrsquo capital allocation andthe managerrsquos reinvestment take place once a quarter at the end of eachcalendar quarter

3 The exercise price (X) for each option is reset at the end of each quarterand applies to the following quarter

4 All new capital inflows come from a single new investor5 When capital outflows occur we adopt the FIFO rule to decide which

investorrsquos money leaves the fund In particular the asset value of thefirst investor is reduced by the magnitude of outflow If the absolutemagnitude of outflow exceeds the first investorrsquos net asset value thenthe first investor is considered as liquidating her stake in the fund andthe balance of outflow is deducted from the second investorrsquos assets andso on


6 The hurdle rate is LIBOR if the fund has an HWM provision and 0otherwise

7 Managers reinvest all of their incentive fees into the fund

An algorithm for estimation is as follows

1 First we solve the following recursive problem iteratively to back outgross returns (gross) using observable information on net-of-fee returns(net) assets under management (AUM) and the parameters of the com-pensation contract (kc)

nett =sumi

Sitminus1 times (1+grosst) minus ifeeit minus mfeeit+MStminus1 times (1+grosst)

AUMtminus1minus 1

(A2)

where ifeeit = Max[(Sitminus1times(1+grosst) minus Xitminus1) 0]timesk mfeeit = Sitminus1timescand MSt denotes the market value of the managerrsquos stake in the fundWe start the algorithm with the following initial values

S10 = X10 = AUM0

MS0 = 0

(A3)

2 We update the market value of the managerrsquos stake as follows

MSt = MStminus1 times (1 + grosst) +sum

i

i f eeit (A4)

3 The new spot price and exercise price of investor i are updated recursivelyas follows

Sit = Sitminus1 times (1 + grosst) minus i f eeit minus mf eeit (A5a)

Xit =

Max[Sit Xitminus1] times (1 + LIBOR) if with HWM provisionSit if without HWM provision (A5b)

4 The net flow into the fund is defined as the difference between the re-ported value of quarter-end AUM and the current market value of allexisting investorsrsquo assets and the managerrsquos assets

Flowt = AUMt minus(sum

i

Sit + MSt

) (A6)

If (A6) gt 0 then we assume that a new investor enters the fund withthe beginning spot price and exercise price equal to Flowt If (A6) lt 0then we apply the FIFO rule as described above


5 Using S and X for each investor and equation (A1) we compute thedelta from each investorrsquos assets and then sum them up to computethe managerrsquos option delta The total delta of the fund is the sum ofthe managerrsquos option delta and the delta from the managerrsquos stake whichis equal to 001MSt because the manager retains all returns from in-vesting his own assets

Appendix B Calculating Indirect Pay for Performance

We employ four models to compute the present value of the total futurefees (both management and incentive fees) per dollar in the fund Each of thesemodels provides this value as a function of the ratio of the asset value to itsHWM that is the stake-to-strike (SX) ratio of the assets which we denote byw This subsection describes each of these models in turn discusses our choicesof model parameters (a summary of parameter choices is provided in Table CI)and describes the calculations of indirect incentives

B1 Baseline GIR Model

Goetzmann Ingersoll and Ross (2003) provide a closed-form solution for thevalue of total fees as follows

f (w) = 1c + δ + λminus α

[c + (δ + λminus α)k + [η(1 + k) minus 1]cb1minusη

γ (1 + k) minus 1 minus bγminusη[η(1 + k) minus 1]wγminus1

minusbγminusη(δ + λminus α)k + [γ (1 + k) minus 1]cb1minusη

γ (1 + k) minus 1 minus bγminusη[η(1 + k) minus 1]wηminus1

]

(B1)

where γ and η are the larger and smaller roots of the following characteristicquadratic equation

(γ

η

)equiv

12σ

2 + c minus r minus α minus cprime + g plusmnradic( 1

2σ2 + c minus r minus α minus cprime + g

)2 + 2σ 2 (r + cprime minus g + δ + λ)

σ 2 (B2)

There are 10 parameters in the valuation equation (B1) c and k are thecontractual management fee and incentive fee rate respectively cprime is the ac-counting choice of costs and fees allocated to reducing HWM σ is the volatilityof fund returns g is the contractual growth rate in the HWM level (ie hurdlerate) which is usually zero or the risk-free rate α is the risk-adjusted expectedgross-of-fee return on the fundrsquos assets reflecting manager skill δ + λ is thetotal withdrawal rate which is the sum of the regular payout rate to investors(δ) and the exogenous liquidation probability of the fund (λ) b is the lowestacceptable fraction of the HWM below which the investor loses confidence inthe fund and liquidates all of his position and r is the risk-free interest rateThe parameter b captures a performance-triggered liquidation boundary Forexample with b = 08 an investor liquidates his entire stake if it falls in valueby 20 from its HWM


We estimate the parameters of equation (B1) from observable fund-leveldata whenever possible In particular for c and k we use an individual fundrsquoscontractual information available from TASS We compute σ as the squareroot of three times the standard deviation of monthly returns over the priorcalendar year The value of w for each existing investor is computed based onS and X obtained from the estimation procedure described in Appendix A Forr we use the three-month LIBOR at the beginning of each quarter

For the rest of the parameters that are not observable or reasonably es-timable we adopt the quarterly equivalent of the baseline parameter valuesused in GIR or LWY α = 0 and 3 δ + λ = 5 and 10 b = 0685 and 08g = r if a fund has a HWM provision and zero otherwise and cprime = 021 Theseparameter choices are summarized in Table CI in Appendix C

B2 GIR Model Augmented for Performance-Based Fund Flows

One of the limitations of the base-case GIR model is that it does not allow forperformance-based fund flows One way to address this issue is to assume asan approximation that the flow-performance relationship is linear in logs andthat flows are contemporaneous with returns Without performance-inducedflows the log AUM of the fund would evolve as a martingale so st+1 = st +et+1 With performance-based flows st+1 = st + get+1 where g gt 1 captures theflow effect The log AUM still follows a martingale Given these assumptionsthe GIR model still applies but with a higher variance than in the case of noflows

We implement this approach to incorporating fund flows in the follow-ing manner First we estimate the relation between log(AUMtAUMtminus1) andlog(1 + Returnt) controlling for all the other variables used in Table II exceptthe lagged return terms When we estimate this equation the coefficient onthe logged contemporaneous return equals 1247 Then at the fund-quarterlevel we multiply the GIR volatility σ by this coefficient to provide a volatilityparameter for the extended GIR model that is likely to reflect performance-based flows Empirically the mean quarterly volatility is boosted from 533to 665 One complication in using the new volatility parameter is the way itinteracts with the liquidation boundary parameter b Because the increase involatility is associated with flows and not returns per se the greater volatilityshould not result in a greater likelihood of performance-based liquidation Toensure this does not happen we decrease b such that under the new meanvolatility performance-based liquidation is as likely as before For examplewith b = 08 (0685) an investor withdraws all assets following a minus 200(315) return which is 375 (591) standard deviations away from one under

21 In the GIR model cprime is determined by an unobservable accounting choice Its value is notexplicitly discussed but r + cprime minus g is assumed to be 5 which implies a choice of cprime = 5 becauser always equals g in GIR In contrast the LWY model does not have this parameter and thereforeeffectively assumes it is equal to zero Since cprime cannot be reasonably estimated from the data and apositive value for it serves to increase indirect incentives substantially we follow LWY and assumethat it equals zero throughout the paper


the volatility parameter without flows Now under the new volatility parame-ter a 375 (591) standard deviation drop translates into a minus 249 (393)return or equivalently b = 0751 (0607) In summary relative to the base GIRmodel the augmented model multiplies all fund-quarter-level volatility esti-mates by 1247 and adjusts b as described above

B3 Baseline LWY Model

A drawback of the GIR model is that it does not allow the manager to alterhis or her portfolio endogenously However in practice hedge fund managershave incentives to change their investment strategies and portfolio allocationdynamically to maximize their value function One way they potentially adjusttheir portfolios is through the use of leverage dynamically trading off thebenefit and the downside risk of leverage by allocating the fundsrsquo AUM betweenthe alpha-generating strategy and the risk-free asset To address this issue weemploy the model provided by Lan Wang and Yang (2013) which is a dynamicframework to value a hedge fund managerrsquos compensation contract under theendogenous leverage choice of the manager22

In the baseline LWY setting the managerrsquos value function f(w) solves thefollowing ODE

(β minus g + δ + λ) f (w) = cw + [π (w)αprime + r minus g minus c]w f prime (w)

+ 12π (w)2σ prime2w2 f primeprime (w)

(B3)

subject to the boundary conditions

f (b) = 0 (B4)

f (1) = (k + 1) f prime (1) minus k (B5)

where αprime and σ prime represent unlevered alpha and volatility respectivelyThe optimal leverage policy π (w) is dynamically determined as follows

π (w) =

min

αprime

σ prime2ψ(w) π ψ (w) gt 0

π ψ (w) le 0 (B6)

22 There are other hedge fund valuation models that incorporate endogenous portfolio alloca-tion by hedge fund managers For example Panageas and Westerfield (2009) provide closed-formsolutions for endogenously determined hedge fund leverage and valuation in a setting with noperformance-induced liquidation boundary (ie b = 0) and no management fees (ie c = 0) Drech-sler (2014) also solves for the managerrsquos optimal leverage choice and derives closed-form solutionsalbeit by counterfactually assuming that management fees are a constant fraction of the HWMnot the AUM Overall Drechsler (2014) reaches similar results to LWY when incorporating con-siderations such as performance-triggered liquidation risk management fees and the managerrsquosrestart option We use the LWY model because of its generality as it embeds as special cases bothPanageas and Westerfield (2009) and GIR (with leverage exogenously fixed at zero)


where ψ(w) is a risk-neutral managerrsquos endogenous risk attitude Risk attitudeψ(w) is in turn defined by

ψ (w) equiv minus f primeprime (w)f prime (w)

(B7)

Relative to the GIR model the baseline LWY model requires calibrations ofthree additional parameters the unlevered alpha (αprime) the unlevered volatility(σ prime) and the managerrsquos discount rate (β) We equate β to r by assuming thatthe manager and the investor use the same discount rate Following LWY weadopt the average leverage of 213 reported by Ang Gorovyy and van Inwegen(2011) The unlevered alphas (αprime) implied by levered alphas of 0 and 3 are0 and 14 respectively Likewise the unlevered volatility (σ prime) is obtainedfor each fund-quarter by dividing the estimated quarterly volatility σ in thedata by 21323 Parameter choices are summarized in Table CI In generalthe ODE in equation (B3) does not have an analytical solution and must beapproximated numerically

B4 LWY Model Augmented for Performance-Based Inflows

LWY provide an extension of their baseline model that includes futureperformance-based fund flows LWY assume that new money arrives over in-cremental time interval (t t + t) and show that dIt evolves as follows

dIt = i[dHt minus (g minus δ) Htdt

] (B8)

where I gt 0 denotes the sensitivity of dIt with respect to the fundrsquos instanta-neous (gross) profits

Now the value of total fees f(w) satisfies ODE (B3) subject to the followingboundary conditions

f (b) = 0 (B9)

f (1) = (k + 1) f prime (1) minus k1 + i

(B10)

We choose i = 08 following LWY In general a larger value for i leads toa larger π (w) and therefore a larger f(w) because the manager becomes lessrisk-averse when there are more rewards at the upside Our implementation ofthe augmented LWY model mirrors that of the baseline LWY model describedabove

23 The unlevered volatility (σ prime) is floored and capped at 14 and 42 respectively because theODE (B3) cannot be solved when σ prime takes an excessively small or large value


B5 Calculating Indirect Incentives

Indirect incentives have two components future fees earned from the in-cremental inflows of new investment that follow incremental performance andfuture fees earned on the increase in the value of existing investorsrsquo stakes thatfollows incremental performance The latter component occurs both because thevalue of existing investorsrsquo stakes is higher and also because it moves closer tothe applicable HWM

For the first component inflows of new investment the present value of totalfees for each dollar of new money coming into the fund f(w)new can be computedusing one of the four valuation models discussed above with w determined asfollows

wnew =

1 if the fund has no hurdle rate1

1+r if the fund has a hurdle rate(B11)

Indirect pay for performance coming from new money flows per 1 incremen-tal return is calculated as f(w)new times the sum of the regression coefficients onlagged returns (with appropriate age or strategy interactions) given in Column(2) of the appropriate Panel of Table II multiplied by 1 of beginning-of-yearAUM

To compute indirect pay for performance from changes in the value of existinginvestorsprime stakes we proceed as follows First we compute the f(w) fraction foreach fund-quarter for each existing investor f (wi)base

old assuming that the fundearns an baseline return equal to the mean return earned by all funds of thesame age and investment strategy To obtain investor-specific wi under thebaseline return we take each investorrsquos wi at the beginning of the quarter(calculated as described in Appendix A) and multiply by (1 + baseline return)Then if the result is less than one and the fund has an HWM provision wi isset to the result divided by (1 + r) If the result is greater than one and the fundhas a HWM set wi to 1(1 + r)24 Finally if the fund does not have an HWMset wi to one We adjust wi in this way because if the result is greater than onethen the option becomes in the money incentive fees are paid and the strikeresets We sum these individual investorsrsquo f(w) fractions over all investors inthe fund as follows

f (w)baseold =

sumi

xi f (wi)baseold where xi = Sisum

iSi (B12)

We next rerun these calculations for existing investors assuming that thefund earns an additional one-percentage-point return in addition to the base-line return ( f (w)base+1

old ) Then we estimate the impact of an incremental 1return on the managerrsquos future pay due to the increase in the asset values of

24 As in Appendix A we assume that an fund has a hurdle rate whenever it has an HWMprovision


existing investors as the difference between f (w)base+1old times AUM times (1+baseline

return + 1) and f (w)baseold times AUM times (1 + baseline return)

Finally indirect incentives per incremental one-dollar increase in fund re-turns are calculated for each fund-quarter by dividing indirect incentives perone-percentage-point increase in returns by 1 of beginning-of-quarter AUM

Appendix C Tables

Table CISummary of Parameter Choices

This table summarizes the parameters for the four models used to calculate indirect incentives

Parameter Baseline GIR Augmented GIR Baseline LWY Augmented LWY

c Fund specificannual rate4

Fund specific annualrate4



k Fund specific Fund specific Fund specific Fund specificσ (σ prime) Fund-quarter

specificquarterlyvolatility =standarddeviation ofmonthlyreturns over theprior 12 monthperiod times radic

3

Fund-quarter specificquarterly volatilitytimes 1247

Fund-quarter specificcorrespondingunlevered volatility(σ prime) = quarterlyvolatility213


α (αprime) Quarterlyequivalent of0 3

Quarterly equivalentof 0 3

Quarterly equivalentof 0 3correspondingunlevered alpha (αprime)= 0 14

Quarterly equivalent of0 3 correspondingunlevered alpha (αprime) =0 14

δ+λ Quarterlyequivalent of5 10



Quarterly equivalent of5 10

b 0685 08 0607 0751 0685 08 0685 08r 3-Month LIBOR 3-Month LIBOR 3-Month LIBOR 3-Month LIBORg = r if HWM = r if HWM Equated to r Equated to r

= 0 if no HWM = 0 if no HWMβ na na Equated to r Equated to rcprime 0 0 na naw(=SX) Fund-quarter-

investorspecific

Fund-quarter-investorspecific



i na na na 08


Table CIISummary Statistics for the Mutual Fund Sample

This table presents descriptive statistics for the sample of 307079 fund-quarter observationsassociated with 11911 mutual funds during the 1995 to 2010 sample period Data are obtainedfrom the Center for Research in Security Prices (CRSP) Survivorship Bias Free Mutual FundDatabase Only actively managed domestic equity funds are considered excluding index fundsexchange-traded funds exchange-traded notes international funds fixed income funds and sectorfunds Quarterly flow Quarterly returns Age and Volatility are constructed in the same way asthe corresponding variables for hedge funds Management fees are the expense ratio minus 12b1fees as the percentage of total assets measured at the end of each quarter All variables exceptthe style indicator are reported at the fund-quarter level Quarterly flow Quarterly returns andVolatility are winsorized at the 1st and 99th percentiles


Quarterly flow () 307079 505 minus495 minus055 666 2533Quarterly returns () 307079 159 minus406 252 800 1034AUM ($ million) 307079 3222 66 384 1847 9362Age (quarters) 307079 3128 1144 2138 3735 3613Volatility () 307079 834 522 760 1053 406Management fees ( annualized) 307079 108 086 105 125 082Style

Large-cap (EDCL) 11911 0002Mid-cap (EDCM) 11911 0108Small-cap (EDCS) 11911 0171Micro-cap (EDCI) 11911 0007Growth amp Income (EDYB) 11911 0230Growth (EDYG) 11911 0438Income (EDYI) 11911 0030Hedged (EDYH) 11911 0014Dedicated short bias funds (EDYS) 11911 0001


Table CIIIEstimates of Flow-Performance Sensitivity for Mutual Funds

This table presents the OLS coefficient estimates of equation (1) and corresponding standard errors(in parentheses) for the sample of mutual funds over the period 1995 to 2010 The dependentvariable is Quarterly flow as defined by equation (2) Style fixed effects quarter fixed effectsand quarter-by-style fixed effects are included in all models Standard errors are double clusteredby fund and year and indicate statistical significance at the 1 5 and 10 levelrespectively

(1) (2)


Quarterly returns(tminus1) 0647 (0060) 0539 (0057)Quarterly returns(tminus2) 0405 (0056) 0284 (0051)Quarterly returns(tminus3) 0252 (0046) 0176 (0042)Quarterly returns(tminus4) 0216 (0045) 0171 (0038)Quarterly returns(tminus5) 0170 (0040) 0125 (0034)Quarterly returns(tminus6) 0135 (0053) 0106 (0044)Quarterly returns(tminus7) 0100 (0044) 0083 (0036)Quarterly returns(tminus8) 0060 (0044) 0049 (0037)Quarterly returns(tminus9) 0086 (0041) 0078 (0034)Quarterly returns(tminus10) 0114 (0040) 0104 (0031)Quarterly returns(tminus11) 0075 (0042) 0063 (0035)Quarterly flow(tminus1) 0253 (0009)Log(Age + 1) minus0023 (0002)Log(AUM(tminus1)+1) minus0010 (0001)Volatility minus0024 (0059)Management fees minus0143 (0329)Indicators for missing lagged returns Yes YesFixed effects


Number of observations 307079 307079Adjusted R2 0123 0192F-test for joint significance of F-stat (p-value) F-stat (p-value)All lagged returns 2029 (0000) 1853 (0000)

REFERENCES

Agarwal Vikas Naveen Daniel and Narayan Naik 2003 Flows performance and managerialincentives in the hedge fund industry Working paper Georgia State University

Agarwal Vikas Naveen Daniel and Narayan Naik 2009 The role of managerial incentives anddiscretion in hedge fund performance Journal of Finance 44 2221ndash2256

Agarwal Vikas and Sugata Ray 2012 Determinants and implications of fee changes in the hedgefund industry Working paper Georgia State University

Ang Andrew Sergiy Gorovyy and Greg van Inwegen 2011 Hedge fund leverage Journal ofFinancial Economics 102 102ndash126

Aragon George 2007 Share restrictions and asset pricing Evidence from the hedge fund industryJournal of Financial Economics 83 33ndash58

Aragon George and Vikram Nanda 2012 Tournament behavior in hedge funds High-watermarks fund liquidation and managerial stake Review of Financial Studies 25 937ndash974


Baquero Guillermo and Marno Verbeek 2009 A portrait of hedge fund investors Flows perfor-mance and smart money Working paper Erasmus University

Barclay Michael J Neil D Pearson and Michael S Weisbach 1998 Open-end mutual funds andcapital gains taxes Journal of Financial Economics 49 3ndash43

Berk Jonathan and Richard Green 2004 Mutual fund flows and performance in rational marketsJournal of Political Economy 112 1269ndash1295

Bollen Nicolas PB 2007 Mutual fund attributes and investor behavior Journal of Financial andQuantitative Analysis 42 683ndash708

Bollen Nicolas PB and Veronika K Pool 2009 Do hedge fund managers misreport returnsEvidence from the pooled distribution Journal of Finance 64 2257ndash2288

Boschen John F and Kimberly J Smith 1995 You can pay me later The dynamic response ofexecutive compensation to firm performance Journal of Business 68 577ndash608

Brown Keith C W Van Harlow and Laura T Starks 1996 Of tournaments and temptations Ananalysis of managerial incentives in the mutual fund industry Journal of Finance 51 85ndash10

Chevalier Judith and Glenn Ellison 1999 Career concerns of mutual fund managers QuarterlyJournal of Economics 114 389ndash432

Chung Ji-Woong Berk A Sensoy Lea H Stern and Michael S Weisbach 2012 Pay for perfor-mance from future fund flows The case of private equity Review of Financial Studies 253259ndash3304

Del Guercio Diane and Paula A Tkac 2002 The determinants of the flow of funds of managedportfolios Mutual funds vs pension funds Journal of Financial and Quantitative Analysis37 523ndash557

Deuskar Prachi Zhi Jay Wang Youchang Wu and Quoc H Nguyen 2012 The dynamics of hedgefund fees Working paper University of Oregon

Ding Bill Mila Getmansky Bing Liang and Russ Wermers 2009 Share restrictions and investorflows in the hedge fund industry Working paper University of Massachusetts

Drechsler Itamar 2014 Risk choice under high-water marks Review of Financial Studies 272052ndash2096

Fama Eugene F 1980 Agency problems and the theory of the firm Journal of Political Economy88 288ndash307

Fung William David A Hsieh Narayan Y Naik and Tarun Ramadorai 2008 Hedge fundsPerformance risk and capital formation Journal of Finance 63 1777ndash1803

Getmansky Mila 2012 The life cycle of hedge funds Fund flows size competition and perfor-mance Quarterly Journal of Finance 2 57ndash109

Gibbons Robert and Kevin J Murphy 1992 Optimal incentive contracts in the presence of careerconcerns Theory and evidence Journal of Political Economy 100 468ndash505

Goetzmann William N Jonathan E Ingersoll Jr and Stephen A Ross 2003 High water marksand hedge fund management contracts Journal of Finance 43 1685ndash1717

Gompers Paul and Josh Lerner 1999 An analysis of compensation in the US venture capitalpartnership Journal of Financial Economics 51 3ndash44

Guasoni Paolo and Jan Oblόj 2013 The incentives of hedge fund fees and high-water marksMathematical Finance doi 101111mafi12057

Hermalin Benjamin E and Michael S Weisbach 2014 Understanding corporate governancethrough learning models of managerial competence NBER Working paper 20028

Holmstrom Bengt 1982 Managerial incentive problems A dynamic perspective in Essays inHonor of Lars Wahlbeck (Swedish School of Economics Helsinki)

Huang Jennifer Kelsey D Wei and Hong Yan 2007 Participation costs and the sensitivity offund flows to past performance Journal of Finance 62 1273ndash1311

Ippolito Richard A 1992 Consumer reaction to measures of poor quality Evidence from themutual fund industry Journal of Law and Economics 35 45ndash70

Kaplan Steve N and Antoinette Schoar 2005 Private equity performance Returns persistenceand capital flows Journal of Finance 60 1791ndash1823

Kaplan Steven N and Joshua Rauh 2010 Wall Street and Main Street What contributes to thehighest incomes Review of Financial Studies 23 1004ndash1050


Lan Yingcong Neng Wang and Jinqiang Yang 2013 The economics of hedge funds Journal ofFinancial Economics 110 300ndash323

Liang Bing 2001 Hedge fund performance 1990ndash1999 Financial Analysts Journal 57 11ndash18Lim Jongha Berk A Sensoy and Michael S Weisbach 2013 Indirect incentives of hedge fund

managers NBER Working paper 18903Ma Linlin and Yuehua Tang 2014 Portfolio manager ownership and mutual fund performance

Working paper Northeastern UniversityMetric Andrew and Ayako Yasuda 2010 The economics of private equity funds Review of Finan-

cial Studies 23 2303ndash2341Naik Narayan Y Tarun Ramadorai and Maria Stromqvist 2007 Capacity constraints and hedge

fund strategy returns European Financial Management 13 239ndash256Panageas Stavros and Mark M Westerfield 2009 High-water marks High risk appetites Convex

compensation long horizons and portfolio choice Journal of Finance 64 1ndash36Patton Andrew J Tarun Ramadorai and Michael Streatfield 2015 Change you can believe in

Hedge fund data revisions Journal of Finance 70 963ndash999Ramadorai Tarun 2013 Capacity constraints investor information and hedge fund returns

Journal of Financial Economics 107 401ndash416Ramadorai Tarun and Michael Streatfield 2011 Money for nothing Understanding variation in

reported hedge fund fees Working paper University of OxfordRobinson David T and Berk A Sensoy 2013 Do private equity fund managers earn their fees

Compensation ownership and cash flow performance Review of Financial Studies 26 2760ndash2797

Sensoy Berk A 2009 Performance evaluation and self-designated benchmark indexes in themutual fund industry Journal of Financial Economics 92 25ndash39

Sirri Eric and Peter Tufano 1998 Costly search and mutual fund flows Journal of Finance 531589ndash1622

Stein Jeremy C 1989 Efficient capital markets inefficient firms A model of myopic corporatebehavior Quarterly Journal of Economics 104 655ndash669

Swenson David F 2000 Pioneering portfolio management An unconventional approach to insti-tutional investment (Simon amp Schuster New York NY)

Taylor Lucian A 2013 CEO wage dynamics Estimates from a learning model Journal of Finan-cial Economics 108 79ndash98

Teo Melvyn 2009 Does size matter in the hedge fund industry Working paper Singapore Man-agement University


In this paper we estimate the magnitude of these indirect incentives of hedgefund managers In particular we address the following questions For an incre-mental percentage point of returns to investors how much additional capitaldoes the market allocate to that hedge fund How much of this additional cap-ital do hedge fund managers end up receiving as compensation in expectationHow does this ldquoexpected future pay for todayrsquos performancerdquo compare in mag-nitude with the direct incentive fees that hedge fund managers earn from theincremental returns How do these effects differ across types of funds and overtime for a particular fund And what are the implications of the existence ofsuch indirect incentives for hedge fund investors and for our understanding ofcontracting more broadly

We first estimate the relation between hedge fund performance and inflowsto the fund using a sample of 2998 hedge funds from 1995 to 2010 As predictedby learning models of fund allocation and consistent with prior work on mutualfunds and private equity funds this relation is substantially stronger for newerfunds whose managersrsquo abilities the market knows with less certainty For theaverage fund the estimates imply that a one-percentage-point incrementalreturn in a given quarter leads to a 15 increase in the fundrsquos assets undermanagement (AUM) from inflows of new investment over the next three yearsFor a new fund the same incremental return results in a 21 increase inAUM from inflows In addition performance has a greater impact on flows forfunds engaged in more scalable strategies These results are consistent withthe view that investors continually update their assessment of managers andadjust their portfolios accordingly

The way in which the inflow-performance relation affects managersrsquo com-pensation depends on the fee structure in hedge funds Typically hedge fundmanagers receive a management fee equal to 15 of AUM together with in-centive fees equal to 20 of profits above a high-water mark (HWM) Goodperformance increases managersrsquo future income because fees will be earnedon inflows of new investment and also because the asset value of existinginvestors becomes larger and closer to the HWM Valuing a managerrsquos com-pensation requires a contingent claims modeling framework to account for thefact that incentive fees are effectively a portfolio of call options on the fundrsquosassets We use four such models which allows us to evaluate the sensitivity ofthe estimates to different modeling frameworks and parameter choices

The first model that we use is the model of Goetzmann Ingersoll and Ross(2003 hereafter GIR) GIR provide an analytical formula for calculating thefraction of a dollar invested in the fund that in expectation will be receivedby the fundrsquos managers over the life of the fund The other three models in-corporate two real-world features that are missing from the GIR model andcould have a material impact on a managerrsquos future compensation futureperformance-based flows and the managerrsquos endogenous use of leverage inthe fundrsquos portfolio Each of these features leads to greater compensation andhence greater indirect incentives than would otherwise be the case The GIRestimates therefore provide a lower bound on the magnitude of the indirectincentives faced by hedge fund managers





































II Hedge Fund Data




































)AUMitminus1

(2)















(1) (2)


Panel A Base-case





(Continued)



(1) (2)









(Continued)




(1) (2)



























Tab

leII

ID

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Pay

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Th

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Est

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ided

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Pan

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sum

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eth

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Ym

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(Pan

elD

)an

dth

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mod

el(P

anel

E)

are

som

ewh

atsm

alle

rbe

cau

seth

eO

DE

ineq

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(B3)

fail

sto

hav

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nav

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731

inP

anel

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d48

033

inP

anel

E

b=

068

5b

=0

8

α=

0α

=3

α=

0α

=3

δ+λ

=5

δ+λ

=10

δ+λ

=5

δ+λ

=10

δ+λ

=5

δ+λ

=10

δ+λ

=5

δ+λ

=10

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Pan

elA

Dir

ect

ince

nti

ves

Per

1ch

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infu

nd

valu

e($

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(1)

Dir

ect

effe

ctfr

omin

cen

tive

fees

014

20

142

014

20

142

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20

142

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20

142

(2)

Dir

ect

effe

ctfr

omm

anag

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pers

onal

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190

019

00

190

019

00

190

019

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190

019

0(3

)To

tald

irec

tef

fect

033

10

331

033

10

331

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033

10

331

Per

1$ch

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infu

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e($

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)D

irec

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irec

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fect

from

man

ager

srsquope

rson

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009

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095

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50

095

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50

095

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095

(6)

Tota

ldir

ect

effe

ct0

163

016

30

163

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30

163

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163

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3

(Con

tin

ued

)


Tab

leII

ImdashC

onti

nu

ed

b=

068

5b

=0

8

α=

0α

=3

α=

0α

=3

δ+λ

=5

δ+λ

=10

δ+λ

=5

δ+λ

=10

δ+λ

=5

δ+λ

=10

δ+λ

=5

δ+λ

=10

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Pan

elB

In

dire

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cen

tive

ses

tim

ated

from

the

Goe

tzm

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In

gers

oll

and

Ros

s(G

IR2

003)

mod

el

Per

1ch

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nd

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e($

M)

(1)

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(2)

Indi

rect

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9(3

)To

tali

ndi

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932

069

51

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110

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11

360

088

4(4

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812

106

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341

961

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112

67P

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dva

lue

($)

(5)

Indi

rect

effe

ctfr

omn

ewm

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022

80

169

048

80

272

014

50

116

030

50

195

(6)

Indi

rect

effe

ctfr

omch

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inva

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ofex

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asse

ts0

193

014

60

383

022

20

138

011

40

269

018

0(7

)To

tali

ndi

rect

effe

ct0

421

031

50

871

049

40

283

023

00

574

037

5(8

)In

dire

ctd

irec

t2

581

935

353

031

741

413

522

30

Pan

elC

In

dire

ctin

cen

tive

ses

tim

ated

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the

GIR

mod

elau

gmen

ted

for

perf

orm

ance

-bas

edfl

ows

Per

1ch

ange

infu

nd

valu

e($

M)

(1)

Indi

rect

effe

ctfr

omn

ewm

oney

054

50

396

113

50

622

034

60

279

070

10

455

(2)

Indi

rect

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ctfr

omch

ange

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ng

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ts0

474

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50

892

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80

354

029

40

642

044

0(3

)To

tali

ndi

rect

effe

ct1

018

075

12

028

114

00

700

057

31

343

089

5(4

)In

dire

ctd

irec

t3

072

276

123

442

111

734

052

70P

er1$

chan

gein

fun

dva

lue

($)

(5)

Indi

rect

effe

ctfr

omn

ewm

oney

025

70

189

050

90

289

016

20

130

031

20

205

(6)

Indi

rect

effe

ctfr

omch

ange

inva

lue

ofex

isti

ng

asse

ts0

208

015

60

386

022

80

147

012

20

263

018

0(7

)To

tali

ndi

rect

effe

ct0

465

034

50

895

051

70

309

025

20

575

038

6(8

)In

dire

ctd

irec

t2

852

125

503

171

901

553

532

37

(Con

tin

ued

)


Tab

leII

ImdashC

onti

nu

ed

b=

068

5b

=0

8

α=

0α

=3

α=

0α

=3

δ+λ

=5

δ+λ

=10

δ+λ

=5

δ+λ

=10

δ+λ

=5

δ+λ

=10

δ+λ

=5

δ+λ

=10

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Pan

elD

In

dire

ctin

cen

tive

ses

tim

ated

from

the

Lan

Wan

gan

dYa

ng

(LW

Y20

13)

mod

el

Per

1ch

ange

infu

nd

valu

e($

M)

(1)

Indi

rect

effe

ctfr

omn

ewm

oney

055

40

366

133

70

691

042

50

315

072

50

454

(2)

Indi

rect

effe

ctfr

omch

ange

inva

lue

ofex

isti

ng

asse

ts0

583

036

11

245

067

30

551

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VI Conclusion

























nett =sumi


AUMtminus1minus 1

(A2)


S10 = X10 = AUM0

MS0 = 0

(A3)



i

i f eeit (A4)



Xit =




i

Sit + MSt

) (A6)













]

(B1)


(γ

η

)equiv

12σ




σ 2 (B2)
















(B3)


f (b) = 0 (B4)



π (w) =

min

αprime


π ψ (w) le 0 (B6)





(B7)





] (B8)



f (b) = 0 (B9)


(B10)







wnew =






f (w)baseold =

sumi


iSi (B12)








Appendix C Tables










3














investorspecific




i na na na 08










(1) (2)





REFERENCES
























































































II Hedge Fund Data




































)AUMitminus1

(2)















(1) (2)


Panel A Base-case





(Continued)



(1) (2)









(Continued)




(1) (2)



























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VI Conclusion

























nett =sumi


AUMtminus1minus 1

(A2)


S10 = X10 = AUM0

MS0 = 0

(A3)



i

i f eeit (A4)



Xit =




i

Sit + MSt

) (A6)













]

(B1)


(γ

η

)equiv

12σ




σ 2 (B2)
















(B3)


f (b) = 0 (B4)



π (w) =

min

αprime


π ψ (w) le 0 (B6)





(B7)





] (B8)



f (b) = 0 (B9)


(B10)







wnew =






f (w)baseold =

sumi


iSi (B12)








Appendix C Tables










3














investorspecific




i na na na 08










(1) (2)





REFERENCES


















































































II Hedge Fund Data




































)AUMitminus1

(2)















(1) (2)


Panel A Base-case





(Continued)



(1) (2)









(Continued)




(1) (2)



























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0






VI Conclusion

























nett =sumi


AUMtminus1minus 1

(A2)


S10 = X10 = AUM0

MS0 = 0

(A3)



i

i f eeit (A4)



Xit =




i

Sit + MSt

) (A6)













]

(B1)


(γ

η

)equiv

12σ




σ 2 (B2)
















(B3)


f (b) = 0 (B4)



π (w) =

min

αprime


π ψ (w) le 0 (B6)





(B7)





] (B8)



f (b) = 0 (B9)


(B10)







wnew =






f (w)baseold =

sumi


iSi (B12)








Appendix C Tables










3














investorspecific




i na na na 08










(1) (2)





REFERENCES











































































II Hedge Fund Data




































)AUMitminus1

(2)















(1) (2)


Panel A Base-case





(Continued)



(1) (2)









(Continued)




(1) (2)



























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leV

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0






VI Conclusion

























nett =sumi


AUMtminus1minus 1

(A2)


S10 = X10 = AUM0

MS0 = 0

(A3)



i

i f eeit (A4)



Xit =




i

Sit + MSt

) (A6)













]

(B1)


(γ

η

)equiv

12σ




σ 2 (B2)
















(B3)


f (b) = 0 (B4)



π (w) =

min

αprime


π ψ (w) le 0 (B6)





(B7)





] (B8)



f (b) = 0 (B9)


(B10)







wnew =






f (w)baseold =

sumi


iSi (B12)








Appendix C Tables










3














investorspecific




i na na na 08










(1) (2)





REFERENCES


































































II Hedge Fund Data




































)AUMitminus1

(2)















(1) (2)


Panel A Base-case





(Continued)



(1) (2)









(Continued)




(1) (2)



























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8

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VI Conclusion

























nett =sumi


AUMtminus1minus 1

(A2)


S10 = X10 = AUM0

MS0 = 0

(A3)



i

i f eeit (A4)



Xit =




i

Sit + MSt

) (A6)













]

(B1)


(γ

η

)equiv

12σ




σ 2 (B2)
















(B3)


f (b) = 0 (B4)



π (w) =

min

αprime


π ψ (w) le 0 (B6)





(B7)





] (B8)



f (b) = 0 (B9)


(B10)







wnew =






f (w)baseold =

sumi


iSi (B12)








Appendix C Tables










3














investorspecific




i na na na 08










(1) (2)





REFERENCES

























































II Hedge Fund Data




































)AUMitminus1

(2)















(1) (2)


Panel A Base-case





(Continued)



(1) (2)









(Continued)




(1) (2)



























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VI Conclusion

























nett =sumi


AUMtminus1minus 1

(A2)


S10 = X10 = AUM0

MS0 = 0

(A3)



i

i f eeit (A4)



Xit =




i

Sit + MSt

) (A6)













]

(B1)


(γ

η

)equiv

12σ




σ 2 (B2)
















(B3)


f (b) = 0 (B4)



π (w) =

min

αprime


π ψ (w) le 0 (B6)





(B7)





] (B8)



f (b) = 0 (B9)


(B10)







wnew =






f (w)baseold =

sumi


iSi (B12)








Appendix C Tables










3














investorspecific




i na na na 08










(1) (2)





REFERENCES




















































































)AUMitminus1

(2)















(1) (2)


Panel A Base-case





(Continued)



(1) (2)









(Continued)




(1) (2)



























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330

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0






VI Conclusion

























nett =sumi


AUMtminus1minus 1

(A2)


S10 = X10 = AUM0

MS0 = 0

(A3)



i

i f eeit (A4)



Xit =




i

Sit + MSt

) (A6)













]

(B1)


(γ

η

)equiv

12σ




σ 2 (B2)
















(B3)


f (b) = 0 (B4)



π (w) =

min

αprime


π ψ (w) le 0 (B6)





(B7)





] (B8)



f (b) = 0 (B9)


(B10)







wnew =






f (w)baseold =

sumi


iSi (B12)








Appendix C Tables










3














investorspecific




i na na na 08










(1) (2)





REFERENCES












































































)AUMitminus1

(2)















(1) (2)


Panel A Base-case





(Continued)



(1) (2)









(Continued)




(1) (2)



























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0






VI Conclusion

























nett =sumi


AUMtminus1minus 1

(A2)


S10 = X10 = AUM0

MS0 = 0

(A3)



i

i f eeit (A4)



Xit =




i

Sit + MSt

) (A6)













]

(B1)


(γ

η

)equiv

12σ




σ 2 (B2)
















(B3)


f (b) = 0 (B4)



π (w) =

min

αprime


π ψ (w) le 0 (B6)





(B7)





] (B8)



f (b) = 0 (B9)


(B10)







wnew =






f (w)baseold =

sumi


iSi (B12)








Appendix C Tables










3














investorspecific




i na na na 08










(1) (2)





REFERENCES



































































)AUMitminus1

(2)















(1) (2)


Panel A Base-case





(Continued)



(1) (2)









(Continued)




(1) (2)



























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VI Conclusion

























nett =sumi


AUMtminus1minus 1

(A2)


S10 = X10 = AUM0

MS0 = 0

(A3)



i

i f eeit (A4)



Xit =




i

Sit + MSt

) (A6)













]

(B1)


(γ

η

)equiv

12σ




σ 2 (B2)
















(B3)


f (b) = 0 (B4)



π (w) =

min

αprime


π ψ (w) le 0 (B6)





(B7)





] (B8)



f (b) = 0 (B9)


(B10)







wnew =






f (w)baseold =

sumi


iSi (B12)








Appendix C Tables










3














investorspecific




i na na na 08










(1) (2)





REFERENCES































































)AUMitminus1

(2)















(1) (2)


Panel A Base-case





(Continued)



(1) (2)









(Continued)




(1) (2)



























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VI Conclusion

























nett =sumi


AUMtminus1minus 1

(A2)


S10 = X10 = AUM0

MS0 = 0

(A3)



i

i f eeit (A4)



Xit =




i

Sit + MSt

) (A6)













]

(B1)


(γ

η

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12σ




σ 2 (B2)
















(B3)


f (b) = 0 (B4)



π (w) =

min

αprime


π ψ (w) le 0 (B6)





(B7)





] (B8)



f (b) = 0 (B9)


(B10)







wnew =






f (w)baseold =

sumi


iSi (B12)








Appendix C Tables










3














investorspecific




i na na na 08










(1) (2)





REFERENCES































































(1) (2)


Panel A Base-case





(Continued)



(1) (2)









(Continued)




(1) (2)



























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VI Conclusion

























nett =sumi


AUMtminus1minus 1

(A2)


S10 = X10 = AUM0

MS0 = 0

(A3)



i

i f eeit (A4)



Xit =




i

Sit + MSt

) (A6)













]

(B1)


(γ

η

)equiv

12σ




σ 2 (B2)
















(B3)


f (b) = 0 (B4)



π (w) =

min

αprime


π ψ (w) le 0 (B6)





(B7)





] (B8)



f (b) = 0 (B9)


(B10)







wnew =






f (w)baseold =

sumi


iSi (B12)








Appendix C Tables










3














investorspecific




i na na na 08










(1) (2)





REFERENCES























































(1) (2)


Panel A Base-case





(Continued)



(1) (2)









(Continued)




(1) (2)



























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δ+λ

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068

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8

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0α

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0α

=3

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=5

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4

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36

0

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28

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330

30

0






VI Conclusion

























nett =sumi


AUMtminus1minus 1

(A2)


S10 = X10 = AUM0

MS0 = 0

(A3)



i

i f eeit (A4)



Xit =




i

Sit + MSt

) (A6)













]

(B1)


(γ

η

)equiv

12σ




σ 2 (B2)
















(B3)


f (b) = 0 (B4)



π (w) =

min

αprime


π ψ (w) le 0 (B6)





(B7)





] (B8)



f (b) = 0 (B9)


(B10)







wnew =






f (w)baseold =

sumi


iSi (B12)








Appendix C Tables










3














investorspecific




i na na na 08










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(1) (2)









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330

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0






VI Conclusion

























nett =sumi


AUMtminus1minus 1

(A2)


S10 = X10 = AUM0

MS0 = 0

(A3)



i

i f eeit (A4)



Xit =




i

Sit + MSt

) (A6)













]

(B1)


(γ

η

)equiv

12σ




σ 2 (B2)
















(B3)


f (b) = 0 (B4)



π (w) =

min

αprime


π ψ (w) le 0 (B6)





(B7)





] (B8)



f (b) = 0 (B9)


(B10)







wnew =






f (w)baseold =

sumi


iSi (B12)








Appendix C Tables










3














investorspecific




i na na na 08










(1) (2)





REFERENCES























































(1) (2)



























Tab

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154

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288

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(2)

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leV

III

Ind

irec

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0

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330

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0






VI Conclusion

























nett =sumi


AUMtminus1minus 1

(A2)


S10 = X10 = AUM0

MS0 = 0

(A3)



i

i f eeit (A4)



Xit =




i

Sit + MSt

) (A6)













]

(B1)


(γ

η

)equiv

12σ




σ 2 (B2)
















(B3)


f (b) = 0 (B4)



π (w) =

min

αprime


π ψ (w) le 0 (B6)





(B7)





] (B8)



f (b) = 0 (B9)


(B10)







wnew =






f (w)baseold =

sumi


iSi (B12)








Appendix C Tables










3














investorspecific




i na na na 08










(1) (2)





REFERENCES








































































Tab

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VI Conclusion

























nett =sumi


AUMtminus1minus 1

(A2)


S10 = X10 = AUM0

MS0 = 0

(A3)



i

i f eeit (A4)



Xit =




i

Sit + MSt

) (A6)













]

(B1)


(γ

η

)equiv

12σ




σ 2 (B2)
















(B3)


f (b) = 0 (B4)



π (w) =

min

αprime


π ψ (w) le 0 (B6)





(B7)





] (B8)



f (b) = 0 (B9)


(B10)







wnew =






f (w)baseold =

sumi


iSi (B12)








Appendix C Tables










3














investorspecific




i na na na 08










(1) (2)





REFERENCES



































































Tab

leII

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VI Conclusion

























nett =sumi


AUMtminus1minus 1

(A2)


S10 = X10 = AUM0

MS0 = 0

(A3)



i

i f eeit (A4)



Xit =




i

Sit + MSt

) (A6)













]

(B1)


(γ

η

)equiv

12σ




σ 2 (B2)
















(B3)


f (b) = 0 (B4)



π (w) =

min

αprime


π ψ (w) le 0 (B6)





(B7)





] (B8)



f (b) = 0 (B9)


(B10)







wnew =






f (w)baseold =

sumi


iSi (B12)








Appendix C Tables










3














investorspecific




i na na na 08










(1) (2)





REFERENCES




























































Tab

leII

ID

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VI Conclusion

























nett =sumi


AUMtminus1minus 1

(A2)


S10 = X10 = AUM0

MS0 = 0

(A3)



i

i f eeit (A4)



Xit =




i

Sit + MSt

) (A6)













]

(B1)


(γ

η

)equiv

12σ




σ 2 (B2)
















(B3)


f (b) = 0 (B4)



π (w) =

min

αprime


π ψ (w) le 0 (B6)





(B7)





] (B8)



f (b) = 0 (B9)


(B10)







wnew =






f (w)baseold =

sumi


iSi (B12)








Appendix C Tables










3














investorspecific




i na na na 08










(1) (2)





REFERENCES





















































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0

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330

30

0






VI Conclusion

























nett =sumi


AUMtminus1minus 1

(A2)


S10 = X10 = AUM0

MS0 = 0

(A3)



i

i f eeit (A4)



Xit =




i

Sit + MSt

) (A6)













]

(B1)


(γ

η

)equiv

12σ




σ 2 (B2)
















(B3)


f (b) = 0 (B4)



π (w) =

min

αprime


π ψ (w) le 0 (B6)





(B7)





] (B8)



f (b) = 0 (B9)


(B10)







wnew =






f (w)baseold =

sumi


iSi (B12)








Appendix C Tables










3














investorspecific




i na na na 08










(1) (2)





REFERENCES





















































Tab

leII

ImdashC

onti

nu

ed

b=

068

5b

=0

8

α=

0α

=3

α=

0α

=3

δ+λ

=5

δ+λ

=10

δ+λ

=5

δ+λ

=10

δ+λ

=5

δ+λ

=10

δ+λ

=5

δ+λ

=10

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

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elB

In

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110

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Tab

leII

ImdashC

onti

nu

ed

b=

068

5b

=0

8

α=

0α

=3

α=

0α

=3

δ+λ

=5

δ+λ

=10

δ+λ

=5

δ+λ

=10

δ+λ

=5

δ+λ

=10

δ+λ

=5

δ+λ

=10

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

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bin

atio

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ram

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an

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ram

eter

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esar

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nd-

spec

ific

Rep

orte

dst

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tics

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aver

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nov

eral

lfu

nd-

quar

ters

(307

079

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the

data

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pute

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indi

rect

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ctfr

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tain

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olu

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CII

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fun

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omth

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odel

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odel

)

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=0

8

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0α

=3

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0α

=3

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=5

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=10

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=5

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=10

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=5

δ+λ

=10

δ+λ

=5

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=10

(1

)(2

)(3

)(4

)(5

)(6

)(7

)(8

)

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47

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406

37

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369

32

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(10)

of

the

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nd

esti

mat

esfr

omth

eLW

Ym

odel

435

46

4

378

36

0

277

28

8

330

30

0






VI Conclusion

























nett =sumi


AUMtminus1minus 1

(A2)


S10 = X10 = AUM0

MS0 = 0

(A3)



i

i f eeit (A4)



Xit =




i

Sit + MSt

) (A6)













]

(B1)


(γ

η

)equiv

12σ




σ 2 (B2)
















(B3)


f (b) = 0 (B4)



π (w) =

min

αprime


π ψ (w) le 0 (B6)





(B7)





] (B8)



f (b) = 0 (B9)


(B10)







wnew =






f (w)baseold =

sumi


iSi (B12)








Appendix C Tables










3














investorspecific




i na na na 08










(1) (2)





REFERENCES





















































Tab

leII

ImdashC

onti

nu

ed

b=

068

5b

=0

8

α=

0α

=3

α=

0α

=3

δ+λ

=5

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=10

δ+λ

=5

δ+λ

=10

δ+λ

=5

δ+λ

=10

δ+λ

=5

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=10

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

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Tab

leIV

Dir

ect

and

Ind

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pT

his

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isti

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agi

ven

age

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mat

esin

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leIV

mdashC

onti

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Tab

leV

III

Ind

irec

tP

ayfo

rP

erfo

rman

cefo

rM

utu

alF

un

ds

Th

ista

ble

pres

ents

esti

mat

esof

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rect

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for

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orm

ance

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utu

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equ

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ng

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alen

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ese

tup

for

mu

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ds)

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ich

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en

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na

nd

ther

efor

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atio

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ram

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odel

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Ym

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8

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0α

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(1

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)

Per

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nd

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e($

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fect

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ey0

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769

036

70

214

015

60

414

022

4(2

)In

dire

ctef

fect

from

chan

gein

valu

eof

exis

tin

gas

sets

022

10

146

043

30

206

012

00

088

023

30

126

(3)

Tota

lin

dire

ctef

fect

061

30

405

120

20

573

033

40

243

064

70

351

(4)

of

the

hed

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nd

esti

mat

esfr

omth

eG

IRm

odel

658

58

3

602

51

8

516

45

9

476

39

6

(5)

of

the

hed

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nd

esti

mat

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eLW

Ym

odel

539

55

7

465

42

0

343

34

7

419

35

9

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$1ch

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nd

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e($

)(6

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fect

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ey0

139

009

50

260

013

30

074

005

50

136

007

9(7

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dire

ctef

fect

from

chan

gein

valu

eof

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tin

gas

sets

007

80

053

014

60

075

004

10

031

007

60

044

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Tota

lin

dire

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fect

021

60

148

040

60

208

011

50

086

021

20

123

(9)

of

the

hed

gefu

nd

esti

mat

esfr

omth

eG

IRm

odel

514

47

1

466

42

1

406

37

5

369

32

7

(10)

of

the

hed

gefu

nd

esti

mat

esfr

omth

eLW

Ym

odel

435

46

4

378

36

0

277

28

8

330

30

0






VI Conclusion

























nett =sumi


AUMtminus1minus 1

(A2)


S10 = X10 = AUM0

MS0 = 0

(A3)



i

i f eeit (A4)



Xit =




i

Sit + MSt

) (A6)













]

(B1)


(γ

η

)equiv

12σ




σ 2 (B2)
















(B3)


f (b) = 0 (B4)



π (w) =

min

αprime


π ψ (w) le 0 (B6)





(B7)





] (B8)



f (b) = 0 (B9)


(B10)







wnew =






f (w)baseold =

sumi


iSi (B12)








Appendix C Tables










3














investorspecific




i na na na 08










(1) (2)





REFERENCES






































































Tab

leIV

Dir

ect

and

Ind

irec

tP

ayfo

rP

erfo

rman

ceb

yA

geG

rou

pT

his

tabl

epr

esen

tses

tim

ates

ofdi

rect

and

indi

rect

pay

for

perf

orm

ance

(in

cen

tive

s)at

aqu

arte

rly

freq

uen

cys

imil

arto

Tabl

eII

Ibu

tbr

oken

dow

nby

fun

dag

eR

epor

ted

stat

isti

csar

eav

erag

esta

ken

over

all

fun

dsof

agi

ven

age

All

esti

mat

esin

this

tabl

eu

seth

equ

arte

rly

equ

ival

ent

ofth

epa

ram

eter

sb

=0

685α

=3

an

dδ+λ

=10

t

hes

ear

eth

epa

ram

eter

sch

osen

byLW

Y

Fu

nd

age

(yea

rs)

01

23

45

67

89

1011

1213

14

15

Pan

elA

Dir

ect

ince

nti

ves

Per

1ch

ange

infu

nd

valu

e($

M)

(1)

Tota

ldir

ect

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ct0

100

150

180

230

290

380

460

510

580

720

851

031

321

281

221

04P

er1$

chan

gein

fun

dva

lue

($)

(2)

Tota

ldir

ect

effe

ct0

100

110

120

140

160

180

210

230

240

260

280

290

310

320

360

35

Pan

elB

In

dire

ctin

cen

tive

ses

tim

ated

from

the

GIR

mod

el

Per

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han

gein

fun

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lue

($M

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ndi

rect

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ct0

590

820

940

981

081

121

221

311

401

631

812

042

272

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261

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177

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valu

e($

)(1

)To

tali

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rect

effe

ct0

590

550

530

500

490

470

460

450

440

440

440

440

430

440

440

40(2

)In

dire

ctd

irec

t5

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004

253

483

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542

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961

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681

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511

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361

231

13

(Con

tin

ued

)


Tab

leIV

mdashC

onti

nu

ed

Fu

nd

age

(yea

rs)

01

23

45

67

89

1011

1213

14

15

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elD

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cen

tive

ses

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ated

from

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mod

el

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1ch

ange

infu

nd

valu

e($

M)

(1)

Tota

lin

dire

ctef

fect

071

100

116

119

133

140

154

172

186

203

225

253

288

262

228

172

(2)

Indi

rect

dir

ect

728

666

633

515

466

371

332

336

319

283

264

246

218

204

187

166

Per

1$ch

ange

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nd

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e($

)(1

)To

tali

ndi

rect

effe

ct0

650

610

590

560

550

530

520

520

510

500

500

510

500

500

500

45(2

)In

dire

ctd

irec

t6

305

504

743

883

432

882

472

262

071

891

771

741

601

541

411

27

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elE

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ated

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ance

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e($

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lin

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167

191

194

224

235

263

302

327

347

386

434

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382

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16

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98

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816

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614

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544

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lin

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098

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102

68

907

706

305

674

794

113

813

503

182

982

992

742

642

452

14















































































Tab

leV

III

Ind

irec

tP

ayfo

rP

erfo

rman

cefo

rM

utu

alF

un

ds

Th

ista

ble

pres

ents

esti

mat

esof

indi

rect

pay

for

perf

orm

ance

(in

cen

tive

s)fo

rm

utu

alfu

nds

ata

quar

terl

yfr

equ

ency

usi

ng

the

LWY

mod

el(e

quiv

alen

tto

the

GIR

mod

elu

nde

rth

ese

tup

for

mu

tual

fun

ds)

Wit

hin

this

fram

ewor

ka

mu

tual

fun

dis

asp

ecia

lca

sein

wh

ich

leve

rage

isn

otal

low

edan

dth

ein

cen

tive

fee

(k)

isze

roM

utu

alfu

nds

hav

en

oH

WM

prov

isio

na

nd

ther

efor

eth

eco

ntr

actu

alH

WM

grow

thra

te(g

)is

set

toze

roan

dth

era

tio

betw

een

anin

vest

orrsquos

wea

lth

and

HW

Mle

vel

(w)

ison

eat

all

tim

es

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imat

esof

indi

rect

ince

nti

ves

are

pres

ente

dfo

rei

ght

com

bin

atio

ns

ofth

epa

ram

eter

sbα

an

dδ+λ

All

oth

erpa

ram

eter

valu

esar

efu

nd-

spec

ific

Rep

orte

dst

atis

tics

are

aver

ages

take

nov

eral

lfu

nd-

quar

ters

(307

079

)in

the

data

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w-p

erfo

rman

cese

nsi

tivi

tyto

com

pute

the

indi

rect

effe

ctfr

omn

ewm

oney

isob

tain

edfr

omth

eco

effi

cien

tes

tim

ates

inC

olu

mn

(2)

ofTa

ble

CII

Iin

App

endi

xC

Row

s(4

)an

d(9

)(R

ows

(5)

and

(10)

)re

port

the

mag

nit

ude

ofin

dire

ctin

cen

tive

sfo

rm

utu

alfu

nds

inco

mpa

riso

nto

the

esti

mat

esfo

rh

edge

fun

dsfr

omth

eG

IRm

odel

(LW

Ym

odel

)

b=

068

5b

=0

8

α=

0α

=3

α=

0α

=3

δ+λ

=5

δ+λ

=10

δ+λ

=5

δ+λ

=10

δ+λ

=5

δ+λ

=10

δ+λ

=5

δ+λ

=10

(1

)(2

)(3

)(4

)(5

)(6

)(7

)(8

)

Per

1ch

ange

infu

nd

valu

e($

mil

lion

s)(1

)In

dire

ctef

fect

from

new

mon

ey0

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025

90

769

036

70

214

015

60

414

022

4(2

)In

dire

ctef

fect

from

chan

gein

valu

eof

exis

tin

gas

sets

022

10

146

043

30

206

012

00

088

023

30

126

(3)

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lin

dire

ctef

fect

061

30

405

120

20

573

033

40

243

064

70

351

(4)

of

the

hed

gefu

nd

esti

mat

esfr

omth

eG

IRm

odel

658

58

3

602

51

8

516

45

9

476

39

6

(5)

of

the

hed

gefu

nd

esti

mat

esfr

omth

eLW

Ym

odel

539

55

7

465

42

0

343

34

7

419

35

9

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$1ch

ange

infu

nd

valu

e($

)(6

)In

dire

ctef

fect

from

new

mon

ey0

139

009

50

260

013

30

074

005

50

136

007

9(7

)In

dire

ctef

fect

from

chan

gein

valu

eof

exis

tin

gas

sets

007

80

053

014

60

075

004

10

031

007

60

044

(8)

Tota

lin

dire

ctef

fect

021

60

148

040

60

208

011

50

086

021

20

123

(9)

of

the

hed

gefu

nd

esti

mat

esfr

omth

eG

IRm

odel

514

47

1

466

42

1

406

37

5

369

32

7

(10)

of

the

hed

gefu

nd

esti

mat

esfr

omth

eLW

Ym

odel

435

46

4

378

36

0

277

28

8

330

30

0






VI Conclusion

























nett =sumi


AUMtminus1minus 1

(A2)


S10 = X10 = AUM0

MS0 = 0

(A3)



i

i f eeit (A4)



Xit =




i

Sit + MSt

) (A6)













]

(B1)


(γ

η

)equiv

12σ




σ 2 (B2)
















(B3)


f (b) = 0 (B4)



π (w) =

min

αprime


π ψ (w) le 0 (B6)





(B7)





] (B8)



f (b) = 0 (B9)


(B10)







wnew =






f (w)baseold =

sumi


iSi (B12)








Appendix C Tables










3














investorspecific




i na na na 08










(1) (2)





REFERENCES






























































Tab

leIV

Dir

ect

and

Ind

irec

tP

ayfo

rP

erfo

rman

ceb

yA

geG

rou

pT

his

tabl

epr

esen

tses

tim

ates

ofdi

rect

and

indi

rect

pay

for

perf

orm

ance

(in

cen

tive

s)at

aqu

arte

rly

freq

uen

cys

imil

arto

Tabl

eII

Ibu

tbr

oken

dow

nby

fun

dag

eR

epor

ted

stat

isti

csar

eav

erag

esta

ken

over

all

fun

dsof

agi

ven

age

All

esti

mat

esin

this

tabl

eu

seth

equ

arte

rly

equ

ival

ent

ofth

epa

ram

eter

sb

=0

685α

=3

an

dδ+λ

=10

t

hes

ear

eth

epa

ram

eter

sch

osen

byLW

Y

Fu

nd

age

(yea

rs)

01

23

45

67

89

1011

1213

14

15

Pan

elA

Dir

ect

ince

nti

ves

Per

1ch

ange

infu

nd

valu

e($

M)

(1)

Tota

ldir

ect

effe

ct0

100

150

180

230

290

380

460

510

580

720

851

031

321

281

221

04P

er1$

chan

gein

fun

dva

lue

($)

(2)

Tota

ldir

ect

effe

ct0

100

110

120

140

160

180

210

230

240

260

280

290

310

320

360

35

Pan

elB

In

dire

ctin

cen

tive

ses

tim

ated

from

the

GIR

mod

el

Per

1C

han

gein

fun

dva

lue

($M

)(1

)To

tali

ndi

rect

effe

ct0

590

820

940

981

081

121

221

311

401

631

812

042

272

261

961

50(2

)In

dire

ctd

irec

t6

055

435

134

273

752

982

632

572

402

272

131

981

721

771

611

44P

er1$

chan

gein

fun

dva

lue

($)

(1)

Tota

lin

dire

ctef

fect

057

053

050

048

047

045

044

043

042

042

042

042

042

042

042

039

(2)

Indi

rect

dir

ect

551

478

407

333

290

242

209

187

172

159

150

146

134

130

117

110

Pan

elC

In

dire

ctin

cen

tive

ses

tim

ated

from

the

GIR

mod

elau

gmen

ted

for

perf

orm

ance

-bas

edfu

nd

flow

s

Per

1ch

ange

infu

nd

valu

e($

M)

(1)

Tota

lin

dire

ctef

fect

062

084

096

100

110

117

127

134

143

171

186

207

233

234

207

153

(2)

Indi

rect

dir

ect

631

560

524

435

385

311

273

261

245

239

219

201

177

183

170

147

Per

1$ch

ange

infu

nd

valu

e($

)(1

)To

tali

ndi

rect

effe

ct0

590

550

530

500

490

470

460

450

440

440

440

440

430

440

440

40(2

)In

dire

ctd

irec

t5

765

004

253

483

032

542

191

961

801

681

561

511

391

361

231

13

(Con

tin

ued

)


Tab

leIV

mdashC

onti

nu

ed

Fu

nd

age

(yea

rs)

01

23

45

67

89

1011

1213

14

15

Pan

elD

In

dire

ctin

cen

tive

ses

tim

ated

from

the

LWY

mod

el

Per

1ch

ange

infu

nd

valu

e($

M)

(1)

Tota

lin

dire

ctef

fect

071

100

116

119

133

140

154

172

186

203

225

253

288

262

228

172

(2)

Indi

rect

dir

ect

728

666

633

515

466

371

332

336

319

283

264

246

218

204

187

166

Per

1$ch

ange

infu

nd

valu

e($

)(1

)To

tali

ndi

rect

effe

ct0

650

610

590

560

550

530

520

520

510

500

500

510

500

500

500

45(2

)In

dire

ctd

irec

t6

305

504

743

883

432

882

472

262

071

891

771

741

601

541

411

27

Pan

elE

In

dire

ctin

cen

tive

ses

tim

ated

from

the

LWY

mod

elau

gmen

ted

for

perf

orm

ance

-bas

edfu

nd

flow

s

Per

1ch

ange

infu

nd

valu

e($

M)

(1)

Tota

lin

dire

ctef

fect

118

167

191

194

224

235

263

302

327

347

386

434

500

442

382

285

(2)

Indi

rect

dir

ect

120

811

16

104

98

447

816

265

685

915

614

854

544

213

793

453

132

74P

er1$

chan

gein

fun

dva

lue

($)

(1)

Tota

lin

dire

ctef

fect

106

098

095

091

091

088

086

087

086

084

084

087

085

085

087

075

(2)

Indi

rect

dir

ect

102

68

907

706

305

674

794

113

813

503

182

982

992

742

642

452

14















































































Tab

leV

III

Ind

irec

tP

ayfo

rP

erfo

rman

cefo

rM

utu

alF

un

ds

Th

ista

ble

pres

ents

esti

mat

esof

indi

rect

pay

for

perf

orm

ance

(in

cen

tive

s)fo

rm

utu

alfu

nds

ata

quar

terl

yfr

equ

ency

usi

ng

the

LWY

mod

el(e

quiv

alen

tto

the

GIR

mod

elu

nde

rth

ese

tup

for

mu

tual

fun

ds)

Wit

hin

this

fram

ewor

ka

mu

tual

fun

dis

asp

ecia

lca

sein

wh

ich

leve

rage

isn

otal

low

edan

dth

ein

cen

tive

fee

(k)

isze

roM

utu

alfu

nds

hav

en

oH

WM

prov

isio

na

nd

ther

efor

eth

eco

ntr

actu

alH

WM

grow

thra

te(g

)is

set

toze

roan

dth

era

tio

betw

een

anin

vest

orrsquos

wea

lth

and

HW

Mle

vel

(w)

ison

eat

all

tim

es

Est

imat

esof

indi

rect

ince

nti

ves

are

pres

ente

dfo

rei

ght

com

bin

atio

ns

ofth

epa

ram

eter

sbα

an

dδ+λ

All

oth

erpa

ram

eter

valu

esar

efu

nd-

spec

ific

Rep

orte

dst

atis

tics

are

aver

ages

take

nov

eral

lfu

nd-

quar

ters

(307

079

)in

the

data

Flo

w-p

erfo

rman

cese

nsi

tivi

tyto

com

pute

the

indi

rect

effe

ctfr

omn

ewm

oney

isob

tain

edfr

omth

eco

effi

cien

tes

tim

ates

inC

olu

mn

(2)

ofTa

ble

CII

Iin

App

endi

xC

Row

s(4

)an

d(9

)(R

ows

(5)

and

(10)

)re

port

the

mag

nit

ude

ofin

dire

ctin

cen

tive

sfo

rm

utu

alfu

nds

inco

mpa

riso

nto

the

esti

mat

esfo

rh

edge

fun

dsfr

omth

eG

IRm

odel

(LW

Ym

odel

)

b=

068

5b

=0

8

α=

0α

=3

α=

0α

=3

δ+λ

=5

δ+λ

=10

δ+λ

=5

δ+λ

=10

δ+λ

=5

δ+λ

=10

δ+λ

=5

δ+λ

=10

(1

)(2

)(3

)(4

)(5

)(6

)(7

)(8

)

Per

1ch

ange

infu

nd

valu

e($

mil

lion

s)(1

)In

dire

ctef

fect

from

new

mon

ey0

392

025

90

769

036

70

214

015

60

414

022

4(2

)In

dire

ctef

fect

from

chan

gein

valu

eof

exis

tin

gas

sets

022

10

146

043

30

206

012

00

088

023

30

126

(3)

Tota

lin

dire

ctef

fect

061

30

405

120

20

573

033

40

243

064

70

351

(4)

of

the

hed

gefu

nd

esti

mat

esfr

omth

eG

IRm

odel

658

58

3

602

51

8

516

45

9

476

39

6

(5)

of

the

hed

gefu

nd

esti

mat

esfr

omth

eLW

Ym

odel

539

55

7

465

42

0

343

34

7

419

35

9

Per

$1ch

ange

infu

nd

valu

e($

)(6

)In

dire

ctef

fect

from

new

mon

ey0

139

009

50

260

013

30

074

005

50

136

007

9(7

)In

dire

ctef

fect

from

chan

gein

valu

eof

exis

tin

gas

sets

007

80

053

014

60

075

004

10

031

007

60

044

(8)

Tota

lin

dire

ctef

fect

021

60

148

040

60

208

011

50

086

021

20

123

(9)

of

the

hed

gefu

nd

esti

mat

esfr

omth

eG

IRm

odel

514

47

1

466

42

1

406

37

5

369

32

7

(10)

of

the

hed

gefu

nd

esti

mat

esfr

omth

eLW

Ym

odel

435

46

4

378

36

0

277

28

8

330

30

0






VI Conclusion

























nett =sumi


AUMtminus1minus 1

(A2)


S10 = X10 = AUM0

MS0 = 0

(A3)



i

i f eeit (A4)



Xit =




i

Sit + MSt

) (A6)













]

(B1)


(γ

η

)equiv

12σ




σ 2 (B2)
















(B3)


f (b) = 0 (B4)



π (w) =

min

αprime


π ψ (w) le 0 (B6)





(B7)





] (B8)



f (b) = 0 (B9)


(B10)







wnew =






f (w)baseold =

sumi


iSi (B12)








Appendix C Tables










3














investorspecific




i na na na 08










(1) (2)





REFERENCES





















































Tab

leIV

Dir

ect

and

Ind

irec

tP

ayfo

rP

erfo

rman

ceb

yA

geG

rou

pT

his

tabl

epr

esen

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tim

ates

ofdi

rect

and

indi

rect

pay

for

perf

orm

ance

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cen

tive

s)at

aqu

arte

rly

freq

uen

cys

imil

arto

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eII

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tbr

oken

dow

nby

fun

dag

eR

epor

ted

stat

isti

csar

eav

erag

esta

ken

over

all

fun

dsof

agi

ven

age

All

esti

mat

esin

this

tabl

eu

seth

equ

arte

rly

equ

ival

ent

ofth

epa

ram

eter

sb

=0

685α

=3

an

dδ+λ

=10

t

hes

ear

eth

epa

ram

eter

sch

osen

byLW

Y

Fu

nd

age

(yea

rs)

01

23

45

67

89

1011

1213

14

15

Pan

elA

Dir

ect

ince

nti

ves

Per

1ch

ange

infu

nd

valu

e($

M)

(1)

Tota

ldir

ect

effe

ct0

100

150

180

230

290

380

460

510

580

720

851

031

321

281

221

04P

er1$

chan

gein

fun

dva

lue

($)

(2)

Tota

ldir

ect

effe

ct0

100

110

120

140

160

180

210

230

240

260

280

290

310

320

360

35

Pan

elB

In

dire

ctin

cen

tive

ses

tim

ated

from

the

GIR

mod

el

Per

1C

han

gein

fun

dva

lue

($M

)(1

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tali

ndi

rect

effe

ct0

590

820

940

981

081

121

221

311

401

631

812

042

272

261

961

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055

435

134

273

752

982

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272

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981

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611

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lin

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fect

057

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050

048

047

045

044

043

042

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042

042

042

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ect

551

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333

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242

209

187

172

159

150

146

134

130

117

110

Pan

elC

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ated

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mod

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e($

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lin

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084

096

100

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117

127

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143

171

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207

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rect

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ect

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524

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385

311

273

261

245

239

219

201

177

183

170

147

Per

1$ch

ange

infu

nd

valu

e($

)(1

)To

tali

ndi

rect

effe

ct0

590

550

530

500

490

470

460

450

440

440

440

440

430

440

440

40(2

)In

dire

ctd

irec

t5

765

004

253

483

032

542

191

961

801

681

561

511

391

361

231

13

(Con

tin

ued

)


Tab

leIV

mdashC

onti

nu

ed

Fu

nd

age

(yea

rs)

01

23

45

67

89

1011

1213

14

15

Pan

elD

In

dire

ctin

cen

tive

ses

tim

ated

from

the

LWY

mod

el

Per

1ch

ange

infu

nd

valu

e($

M)

(1)

Tota

lin

dire

ctef

fect

071

100

116

119

133

140

154

172

186

203

225

253

288

262

228

172

(2)

Indi

rect

dir

ect

728

666

633

515

466

371

332

336

319

283

264

246

218

204

187

166

Per

1$ch

ange

infu

nd

valu

e($

)(1

)To

tali

ndi

rect

effe

ct0

650

610

590

560

550

530

520

520

510

500

500

510

500

500

500

45(2

)In

dire

ctd

irec

t6

305

504

743

883

432

882

472

262

071

891

771

741

601

541

411

27

Pan

elE

In

dire

ctin

cen

tive

ses

tim

ated

from

the

LWY

mod

elau

gmen

ted

for

perf

orm

ance

-bas

edfu

nd

flow

s

Per

1ch

ange

infu

nd

valu

e($

M)

(1)

Tota

lin

dire

ctef

fect

118

167

191

194

224

235

263

302

327

347

386

434

500

442

382

285

(2)

Indi

rect

dir

ect

120

811

16

104

98

447

816

265

685

915

614

854

544

213

793

453

132

74P

er1$

chan

gein

fun

dva

lue

($)

(1)

Tota

lin

dire

ctef

fect

106

098

095

091

091

088

086

087

086

084

084

087

085

085

087

075

(2)

Indi

rect

dir

ect

102

68

907

706

305

674

794

113

813

503

182

982

992

742

642

452

14















































































Tab

leV

III

Ind

irec

tP

ayfo

rP

erfo

rman

cefo

rM

utu

alF

un

ds

Th

ista

ble

pres

ents

esti

mat

esof

indi

rect

pay

for

perf

orm

ance

(in

cen

tive

s)fo

rm

utu

alfu

nds

ata

quar

terl

yfr

equ

ency

usi

ng

the

LWY

mod

el(e

quiv

alen

tto

the

GIR

mod

elu

nde

rth

ese

tup

for

mu

tual

fun

ds)

Wit

hin

this

fram

ewor

ka

mu

tual

fun

dis

asp

ecia

lca

sein

wh

ich

leve

rage

isn

otal

low

edan

dth

ein

cen

tive

fee

(k)

isze

roM

utu

alfu

nds

hav

en

oH

WM

prov

isio

na

nd

ther

efor

eth

eco

ntr

actu

alH

WM

grow

thra

te(g

)is

set

toze

roan

dth

era

tio

betw

een

anin

vest

orrsquos

wea

lth

and

HW

Mle

vel

(w)

ison

eat

all

tim

es

Est

imat

esof

indi

rect

ince

nti

ves

are

pres

ente

dfo

rei

ght

com

bin

atio

ns

ofth

epa

ram

eter

sbα

an

dδ+λ

All

oth

erpa

ram

eter

valu

esar

efu

nd-

spec

ific

Rep

orte

dst

atis

tics

are

aver

ages

take

nov

eral

lfu

nd-

quar

ters

(307

079

)in

the

data

Flo

w-p

erfo

rman

cese

nsi

tivi

tyto

com

pute

the

indi

rect

effe

ctfr

omn

ewm

oney

isob

tain

edfr

omth

eco

effi

cien

tes

tim

ates

inC

olu

mn

(2)

ofTa

ble

CII

Iin

App

endi

xC

Row

s(4

)an

d(9

)(R

ows

(5)

and

(10)

)re

port

the

mag

nit

ude

ofin

dire

ctin

cen

tive

sfo

rm

utu

alfu

nds

inco

mpa

riso

nto

the

esti

mat

esfo

rh

edge

fun

dsfr

omth

eG

IRm

odel

(LW

Ym

odel

)

b=

068

5b

=0

8

α=

0α

=3

α=

0α

=3

δ+λ

=5

δ+λ

=10

δ+λ

=5

δ+λ

=10

δ+λ

=5

δ+λ

=10

δ+λ

=5

δ+λ

=10

(1

)(2

)(3

)(4

)(5

)(6

)(7

)(8

)

Per

1ch

ange

infu

nd

valu

e($

mil

lion

s)(1

)In

dire

ctef

fect

from

new

mon

ey0

392

025

90

769

036

70

214

015

60

414

022

4(2

)In

dire

ctef

fect

from

chan

gein

valu

eof

exis

tin

gas

sets

022

10

146

043

30

206

012

00

088

023

30

126

(3)

Tota

lin

dire

ctef

fect

061

30

405

120

20

573

033

40

243

064

70

351

(4)

of

the

hed

gefu

nd

esti

mat

esfr

omth

eG

IRm

odel

658

58

3

602

51

8

516

45

9

476

39

6

(5)

of

the

hed

gefu

nd

esti

mat

esfr

omth

eLW

Ym

odel

539

55

7

465

42

0

343

34

7

419

35

9

Per

$1ch

ange

infu

nd

valu

e($

)(6

)In

dire

ctef

fect

from

new

mon

ey0

139

009

50

260

013

30

074

005

50

136

007

9(7

)In

dire

ctef

fect

from

chan

gein

valu

eof

exis

tin

gas

sets

007

80

053

014

60

075

004

10

031

007

60

044

(8)

Tota

lin

dire

ctef

fect

021

60

148

040

60

208

011

50

086

021

20

123

(9)

of

the

hed

gefu

nd

esti

mat

esfr

omth

eG

IRm

odel

514

47

1

466

42

1

406

37

5

369

32

7

(10)

of

the

hed

gefu

nd

esti

mat

esfr

omth

eLW

Ym

odel

435

46

4

378

36

0

277

28

8

330

30

0






VI Conclusion

























nett =sumi


AUMtminus1minus 1

(A2)


S10 = X10 = AUM0

MS0 = 0

(A3)



i

i f eeit (A4)



Xit =




i

Sit + MSt

) (A6)













]

(B1)


(γ

η

)equiv

12σ




σ 2 (B2)
















(B3)


f (b) = 0 (B4)



π (w) =

min

αprime


π ψ (w) le 0 (B6)





(B7)





] (B8)



f (b) = 0 (B9)


(B10)







wnew =






f (w)baseold =

sumi


iSi (B12)








Appendix C Tables










3














investorspecific




i na na na 08










(1) (2)





REFERENCES





















































Tab

leIV

mdashC

onti

nu

ed

Fu

nd

age

(yea

rs)

01

23

45

67

89

1011

1213

14

15

Pan

elD

In

dire

ctin

cen

tive

ses

tim

ated

from

the

LWY

mod

el

Per

1ch

ange

infu

nd

valu

e($

M)

(1)

Tota

lin

dire

ctef

fect

071

100

116

119

133

140

154

172

186

203

225

253

288

262

228

172

(2)

Indi

rect

dir

ect

728

666

633

515

466

371

332

336

319

283

264

246

218

204

187

166

Per

1$ch

ange

infu

nd

valu

e($

)(1

)To

tali

ndi

rect

effe

ct0

650

610

590

560

550

530

520

520

510

500

500

510

500

500

500

45(2

)In

dire

ctd

irec

t6

305

504

743

883

432

882

472

262

071

891

771

741

601

541

411

27

Pan

elE

In

dire

ctin

cen

tive

ses

tim

ated

from

the

LWY

mod

elau

gmen

ted

for

perf

orm

ance

-bas

edfu

nd

flow

s

Per

1ch

ange

infu

nd

valu

e($

M)

(1)

Tota

lin

dire

ctef

fect

118

167

191

194

224

235

263

302

327

347

386

434

500

442

382

285

(2)

Indi

rect

dir

ect

120

811

16

104

98

447

816

265

685

915

614

854

544

213

793

453

132

74P

er1$

chan

gein

fun

dva

lue

($)

(1)

Tota

lin

dire

ctef

fect

106

098

095

091

091

088

086

087

086

084

084

087

085

085

087

075

(2)

Indi

rect

dir

ect

102

68

907

706

305

674

794

113

813

503

182

982

992

742

642

452

14















































































Tab

leV

III

Ind

irec

tP

ayfo

rP

erfo

rman

cefo

rM

utu

alF

un

ds

Th

ista

ble

pres

ents

esti

mat

esof

indi

rect

pay

for

perf

orm

ance

(in

cen

tive

s)fo

rm

utu

alfu

nds

ata

quar

terl

yfr

equ

ency

usi

ng

the

LWY

mod

el(e

quiv

alen

tto

the

GIR

mod

elu

nde

rth

ese

tup

for

mu

tual

fun

ds)

Wit

hin

this

fram

ewor

ka

mu

tual

fun

dis

asp

ecia

lca

sein

wh

ich

leve

rage

isn

otal

low

edan

dth

ein

cen

tive

fee

(k)

isze

roM

utu

alfu

nds

hav

en

oH

WM

prov

isio

na

nd

ther

efor

eth

eco

ntr

actu

alH

WM

grow

thra

te(g

)is

set

toze

roan

dth

era

tio

betw

een

anin

vest

orrsquos

wea

lth

and

HW

Mle

vel

(w)

ison

eat

all

tim

es

Est

imat

esof

indi

rect

ince

nti

ves

are

pres

ente

dfo

rei

ght

com

bin

atio

ns

ofth

epa

ram

eter

sbα

an

dδ+λ

All

oth

erpa

ram

eter

valu

esar

efu

nd-

spec

ific

Rep

orte

dst

atis

tics

are

aver

ages

take

nov

eral

lfu

nd-

quar

ters

(307

079

)in

the

data

Flo

w-p

erfo

rman

cese

nsi

tivi

tyto

com

pute

the

indi

rect

effe

ctfr

omn

ewm

oney

isob

tain

edfr

omth

eco

effi

cien

tes

tim

ates

inC

olu

mn

(2)

ofTa

ble

CII

Iin

App

endi

xC

Row

s(4

)an

d(9

)(R

ows

(5)

and

(10)

)re

port

the

mag

nit

ude

ofin

dire

ctin

cen

tive

sfo

rm

utu

alfu

nds

inco

mpa

riso

nto

the

esti

mat

esfo

rh

edge

fun

dsfr

omth

eG

IRm

odel

(LW

Ym

odel

)

b=

068

5b

=0

8

α=

0α

=3

α=

0α

=3

δ+λ

=5

δ+λ

=10

δ+λ

=5

δ+λ

=10

δ+λ

=5

δ+λ

=10

δ+λ

=5

δ+λ

=10

(1

)(2

)(3

)(4

)(5

)(6

)(7

)(8

)

Per

1ch

ange

infu

nd

valu

e($

mil

lion

s)(1

)In

dire

ctef

fect

from

new

mon

ey0

392

025

90

769

036

70

214

015

60

414

022

4(2

)In

dire

ctef

fect

from

chan

gein

valu

eof

exis

tin

gas

sets

022

10

146

043

30

206

012

00

088

023

30

126

(3)

Tota

lin

dire

ctef

fect

061

30

405

120

20

573

033

40

243

064

70

351

(4)

of

the

hed

gefu

nd

esti

mat

esfr

omth

eG

IRm

odel

658

58

3

602

51

8

516

45

9

476

39

6

(5)

of

the

hed

gefu

nd

esti

mat

esfr

omth

eLW

Ym

odel

539

55

7

465

42

0

343

34

7

419

35

9

Per

$1ch

ange

infu

nd

valu

e($

)(6

)In

dire

ctef

fect

from

new

mon

ey0

139

009

50

260

013

30

074

005

50

136

007

9(7

)In

dire

ctef

fect

from

chan

gein

valu

eof

exis

tin

gas

sets

007

80

053

014

60

075

004

10

031

007

60

044

(8)

Tota

lin

dire

ctef

fect

021

60

148

040

60

208

011

50

086

021

20

123

(9)

of

the

hed

gefu

nd

esti

mat

esfr

omth

eG

IRm

odel

514

47

1

466

42

1

406

37

5

369

32

7

(10)

of

the

hed

gefu

nd

esti

mat

esfr

omth

eLW

Ym

odel

435

46

4

378

36

0

277

28

8

330

30

0






VI Conclusion

























nett =sumi


AUMtminus1minus 1

(A2)


S10 = X10 = AUM0

MS0 = 0

(A3)



i

i f eeit (A4)



Xit =




i

Sit + MSt

) (A6)













]

(B1)


(γ

η

)equiv

12σ




σ 2 (B2)
















(B3)


f (b) = 0 (B4)



π (w) =

min

αprime


π ψ (w) le 0 (B6)





(B7)





] (B8)



f (b) = 0 (B9)


(B10)







wnew =






f (w)baseold =

sumi


iSi (B12)








Appendix C Tables










3














investorspecific




i na na na 08










(1) (2)





REFERENCES


































































































































Tab

leV

III

Ind

irec

tP

ayfo

rP

erfo

rman

cefo

rM

utu

alF

un

ds

Th

ista

ble

pres

ents

esti

mat

esof

indi

rect

pay

for

perf

orm

ance

(in

cen

tive

s)fo

rm

utu

alfu

nds

ata

quar

terl

yfr

equ

ency

usi

ng

the

LWY

mod

el(e

quiv

alen

tto

the

GIR

mod

elu

nde

rth

ese

tup

for

mu

tual

fun

ds)

Wit

hin

this

fram

ewor

ka

mu

tual

fun

dis

asp

ecia

lca

sein

wh

ich

leve

rage

isn

otal

low

edan

dth

ein

cen

tive

fee

(k)

isze

roM

utu

alfu

nds

hav

en

oH

WM

prov

isio

na

nd

ther

efor

eth

eco

ntr

actu

alH

WM

grow

thra

te(g

)is

set

toze

roan

dth

era

tio

betw

een

anin

vest

orrsquos

wea

lth

and

HW

Mle

vel

(w)

ison

eat

all

tim

es

Est

imat

esof

indi

rect

ince

nti

ves

are

pres

ente

dfo

rei

ght

com

bin

atio

ns

ofth

epa

ram

eter

sbα

an

dδ+λ

All

oth

erpa

ram

eter

valu

esar

efu

nd-

spec

ific

Rep

orte

dst

atis

tics

are

aver

ages

take

nov

eral

lfu

nd-

quar

ters

(307

079

)in

the

data

Flo

w-p

erfo

rman

cese

nsi

tivi

tyto

com

pute

the

indi

rect

effe

ctfr

omn

ewm

oney

isob

tain

edfr

omth

eco

effi

cien

tes

tim

ates

inC

olu

mn

(2)

ofTa

ble

CII

Iin

App

endi

xC

Row

s(4

)an

d(9

)(R

ows

(5)

and

(10)

)re

port

the

mag

nit

ude

ofin

dire

ctin

cen

tive

sfo

rm

utu

alfu

nds

inco

mpa

riso

nto

the

esti

mat

esfo

rh

edge

fun

dsfr

omth

eG

IRm

odel

(LW

Ym

odel

)

b=

068

5b

=0

8

α=

0α

=3

α=

0α

=3

δ+λ

=5

δ+λ

=10

δ+λ

=5

δ+λ

=10

δ+λ

=5

δ+λ

=10

δ+λ

=5

δ+λ

=10

(1

)(2

)(3

)(4

)(5

)(6

)(7

)(8

)

Per

1ch

ange

infu

nd

valu

e($

mil

lion

s)(1

)In

dire

ctef

fect

from

new

mon

ey0

392

025

90

769

036

70

214

015

60

414

022

4(2

)In

dire

ctef

fect

from

chan

gein

valu

eof

exis

tin

gas

sets

022

10

146

043

30

206

012

00

088

023

30

126

(3)

Tota

lin

dire

ctef

fect

061

30

405

120

20

573

033

40

243

064

70

351

(4)

of

the

hed

gefu

nd

esti

mat

esfr

omth

eG

IRm

odel

658

58

3

602

51

8

516

45

9

476

39

6

(5)

of

the

hed

gefu

nd

esti

mat

esfr

omth

eLW

Ym

odel

539

55

7

465

42

0

343

34

7

419

35

9

Per

$1ch

ange

infu

nd

valu

e($

)(6

)In

dire

ctef

fect

from

new

mon

ey0

139

009

50

260

013

30

074

005

50

136

007

9(7

)In

dire

ctef

fect

from

chan

gein

valu

eof

exis

tin

gas

sets

007

80

053

014

60

075

004

10

031

007

60

044

(8)

Tota

lin

dire

ctef

fect

021

60

148

040

60

208

011

50

086

021

20

123

(9)

of

the

hed

gefu

nd

esti

mat

esfr

omth

eG

IRm

odel

514

47

1

466

42

1

406

37

5

369

32

7

(10)

of

the

hed

gefu

nd

esti

mat

esfr

omth

eLW

Ym

odel

435

46

4

378

36

0

277

28

8

330

30

0






VI Conclusion

























nett =sumi


AUMtminus1minus 1

(A2)


S10 = X10 = AUM0

MS0 = 0

(A3)



i

i f eeit (A4)



Xit =




i

Sit + MSt

) (A6)













]

(B1)


(γ

η

)equiv

12σ




σ 2 (B2)
















(B3)


f (b) = 0 (B4)



π (w) =

min

αprime


π ψ (w) le 0 (B6)





(B7)





] (B8)



f (b) = 0 (B9)


(B10)







wnew =






f (w)baseold =

sumi


iSi (B12)








Appendix C Tables










3














investorspecific




i na na na 08










(1) (2)





REFERENCES



























































































































Tab

leV

III

Ind

irec

tP

ayfo

rP

erfo

rman

cefo

rM

utu

alF

un

ds

Th

ista

ble

pres

ents

esti

mat

esof

indi

rect

pay

for

perf

orm

ance

(in

cen

tive

s)fo

rm

utu

alfu

nds

ata

quar

terl

yfr

equ

ency

usi

ng

the

LWY

mod

el(e

quiv

alen

tto

the

GIR

mod

elu

nde

rth

ese

tup

for

mu

tual

fun

ds)

Wit

hin

this

fram

ewor

ka

mu

tual

fun

dis

asp

ecia

lca

sein

wh

ich

leve

rage

isn

otal

low

edan

dth

ein

cen

tive

fee

(k)

isze

roM

utu

alfu

nds

hav

en

oH

WM

prov

isio

na

nd

ther

efor

eth

eco

ntr

actu

alH

WM

grow

thra

te(g

)is

set

toze

roan

dth

era

tio

betw

een

anin

vest

orrsquos

wea

lth

and

HW

Mle

vel

(w)

ison

eat

all

tim

es

Est

imat

esof

indi

rect

ince

nti

ves

are

pres

ente

dfo

rei

ght

com

bin

atio

ns

ofth

epa

ram

eter

sbα

an

dδ+λ

All

oth

erpa

ram

eter

valu

esar

efu

nd-

spec

ific

Rep

orte

dst

atis

tics

are

aver

ages

take

nov

eral

lfu

nd-

quar

ters

(307

079

)in

the

data

Flo

w-p

erfo

rman

cese

nsi

tivi

tyto

com

pute

the

indi

rect

effe

ctfr

omn

ewm

oney

isob

tain

edfr

omth

eco

effi

cien

tes

tim

ates

inC

olu

mn

(2)

ofTa

ble

CII

Iin

App

endi

xC

Row

s(4

)an

d(9

)(R

ows

(5)

and

(10)

)re

port

the

mag

nit

ude

ofin

dire

ctin

cen

tive

sfo

rm

utu

alfu

nds

inco

mpa

riso

nto

the

esti

mat

esfo

rh

edge

fun

dsfr

omth

eG

IRm

odel

(LW

Ym

odel

)

b=

068

5b

=0

8

α=

0α

=3

α=

0α

=3

δ+λ

=5

δ+λ

=10

δ+λ

=5

δ+λ

=10

δ+λ

=5

δ+λ

=10

δ+λ

=5

δ+λ

=10

(1

)(2

)(3

)(4

)(5

)(6

)(7

)(8

)

Per

1ch

ange

infu

nd

valu

e($

mil

lion

s)(1

)In

dire

ctef

fect

from

new

mon

ey0

392

025

90

769

036

70

214

015

60

414

022

4(2

)In

dire

ctef

fect

from

chan

gein

valu

eof

exis

tin

gas

sets

022

10

146

043

30

206

012

00

088

023

30

126

(3)

Tota

lin

dire

ctef

fect

061

30

405

120

20

573

033

40

243

064

70

351

(4)

of

the

hed

gefu

nd

esti

mat

esfr

omth

eG

IRm

odel

658

58

3

602

51

8

516

45

9

476

39

6

(5)

of

the

hed

gefu

nd

esti

mat

esfr

omth

eLW

Ym

odel

539

55

7

465

42

0

343

34

7

419

35

9

Per

$1ch

ange

infu

nd

valu

e($

)(6

)In

dire

ctef

fect

from

new

mon

ey0

139

009

50

260

013

30

074

005

50

136

007

9(7

)In

dire

ctef

fect

from

chan

gein

valu

eof

exis

tin

gas

sets

007

80

053

014

60

075

004

10

031

007

60

044

(8)

Tota

lin

dire

ctef

fect

021

60

148

040

60

208

011

50

086

021

20

123

(9)

of

the

hed

gefu

nd

esti

mat

esfr

omth

eG

IRm

odel

514

47

1

466

42

1

406

37

5

369

32

7

(10)

of

the

hed

gefu

nd

esti

mat

esfr

omth

eLW

Ym

odel

435

46

4

378

36

0

277

28

8

330

30

0






VI Conclusion

























nett =sumi


AUMtminus1minus 1

(A2)


S10 = X10 = AUM0

MS0 = 0

(A3)



i

i f eeit (A4)



Xit =




i

Sit + MSt

) (A6)













]

(B1)


(γ

η

)equiv

12σ




σ 2 (B2)
















(B3)


f (b) = 0 (B4)



π (w) =

min

αprime


π ψ (w) le 0 (B6)





(B7)





] (B8)



f (b) = 0 (B9)


(B10)







wnew =






f (w)baseold =

sumi


iSi (B12)








Appendix C Tables










3














investorspecific




i na na na 08










(1) (2)





REFERENCES













































































































Tab

leV

III

Ind

irec

tP

ayfo

rP

erfo

rman

cefo

rM

utu

alF

un

ds

Th

ista

ble

pres

ents

esti

mat

esof

indi

rect

pay

for

perf

orm

ance

(in

cen

tive

s)fo

rm

utu

alfu

nds

ata

quar

terl

yfr

equ

ency

usi

ng

the

LWY

mod

el(e

quiv

alen

tto

the

GIR

mod

elu

nde

rth

ese

tup

for

mu

tual

fun

ds)

Wit

hin

this

fram

ewor

ka

mu

tual

fun

dis

asp

ecia

lca

sein

wh

ich

leve

rage

isn

otal

low

edan

dth

ein

cen

tive

fee

(k)

isze

roM

utu

alfu

nds

hav

en

oH

WM

prov

isio

na

nd

ther

efor

eth

eco

ntr

actu

alH

WM

grow

thra

te(g

)is

set

toze

roan

dth

era

tio

betw

een

anin

vest

orrsquos

wea

lth

and

HW

Mle

vel

(w)

ison

eat

all

tim

es

Est

imat

esof

indi

rect

ince

nti

ves

are

pres

ente

dfo

rei

ght

com

bin

atio

ns

ofth

epa

ram

eter

sbα

an

dδ+λ

All

oth

erpa

ram

eter

valu

esar

efu

nd-

spec

ific

Rep

orte

dst

atis

tics

are

aver

ages

take

nov

eral

lfu

nd-

quar

ters

(307

079

)in

the

data

Flo

w-p

erfo

rman

cese

nsi

tivi

tyto

com

pute

the

indi

rect

effe

ctfr

omn

ewm

oney

isob

tain

edfr

omth

eco

effi

cien

tes

tim

ates

inC

olu

mn

(2)

ofTa

ble

CII

Iin

App

endi

xC

Row

s(4

)an

d(9

)(R

ows

(5)

and

(10)

)re

port

the

mag

nit

ude

ofin

dire

ctin

cen

tive

sfo

rm

utu

alfu

nds

inco

mpa

riso

nto

the

esti

mat

esfo

rh

edge

fun

dsfr

omth

eG

IRm

odel

(LW

Ym

odel

)

b=

068

5b

=0

8

α=

0α

=3

α=

0α

=3

δ+λ

=5

δ+λ

=10

δ+λ

=5

δ+λ

=10

δ+λ

=5

δ+λ

=10

δ+λ

=5

δ+λ

=10

(1

)(2

)(3

)(4

)(5

)(6

)(7

)(8

)

Per

1ch

ange

infu

nd

valu

e($

mil

lion

s)(1

)In

dire

ctef

fect

from

new

mon

ey0

392

025

90

769

036

70

214

015

60

414

022

4(2

)In

dire

ctef

fect

from

chan

gein

valu

eof

exis

tin

gas

sets

022

10

146

043

30

206

012

00

088

023

30

126

(3)

Tota

lin

dire

ctef

fect

061

30

405

120

20

573

033

40

243

064

70

351

(4)

of

the

hed

gefu

nd

esti

mat

esfr

omth

eG

IRm

odel

658

58

3

602

51

8

516

45

9

476

39

6

(5)

of

the

hed

gefu

nd

esti

mat

esfr

omth

eLW

Ym

odel

539

55

7

465

42

0

343

34

7

419

35

9

Per

$1ch

ange

infu

nd

valu

e($

)(6

)In

dire

ctef

fect

from

new

mon

ey0

139

009

50

260

013

30

074

005

50

136

007

9(7

)In

dire

ctef

fect

from

chan

gein

valu

eof

exis

tin

gas

sets

007

80

053

014

60

075

004

10

031

007

60

044

(8)

Tota

lin

dire

ctef

fect

021

60

148

040

60

208

011

50

086

021

20

123

(9)

of

the

hed

gefu

nd

esti

mat

esfr

omth

eG

IRm

odel

514

47

1

466

42

1

406

37

5

369

32

7

(10)

of

the

hed

gefu

nd

esti

mat

esfr

omth

eLW

Ym

odel

435

46

4

378

36

0

277

28

8

330

30

0






VI Conclusion

























nett =sumi


AUMtminus1minus 1

(A2)


S10 = X10 = AUM0

MS0 = 0

(A3)



i

i f eeit (A4)



Xit =




i

Sit + MSt

) (A6)













]

(B1)


(γ

η

)equiv

12σ




σ 2 (B2)
















(B3)


f (b) = 0 (B4)



π (w) =

min

αprime


π ψ (w) le 0 (B6)





(B7)





] (B8)



f (b) = 0 (B9)


(B10)







wnew =






f (w)baseold =

sumi


iSi (B12)








Appendix C Tables










3














investorspecific




i na na na 08










(1) (2)





REFERENCES































































































Tab

leV

III

Ind

irec

tP

ayfo

rP

erfo

rman

cefo

rM

utu

alF

un

ds

Th

ista

ble

pres

ents

esti

mat

esof

indi

rect

pay

for

perf

orm

ance

(in

cen

tive

s)fo

rm

utu

alfu

nds

ata

quar

terl

yfr

equ

ency

usi

ng

the

LWY

mod

el(e

quiv

alen

tto

the

GIR

mod

elu

nde

rth

ese

tup

for

mu

tual

fun

ds)

Wit

hin

this

fram

ewor

ka

mu

tual

fun

dis

asp

ecia

lca

sein

wh

ich

leve

rage

isn

otal

low

edan

dth

ein

cen

tive

fee

(k)

isze

roM

utu

alfu

nds

hav

en

oH

WM

prov

isio

na

nd

ther

efor

eth

eco

ntr

actu

alH

WM

grow

thra

te(g

)is

set

toze

roan

dth

era

tio

betw

een

anin

vest

orrsquos

wea

lth

and

HW

Mle

vel

(w)

ison

eat

all

tim

es

Est

imat

esof

indi

rect

ince

nti

ves

are

pres

ente

dfo

rei

ght

com

bin

atio

ns

ofth

epa

ram

eter

sbα

an

dδ+λ

All

oth

erpa

ram

eter

valu

esar

efu

nd-

spec

ific

Rep

orte

dst

atis

tics

are

aver

ages

take

nov

eral

lfu

nd-

quar

ters

(307

079

)in

the

data

Flo

w-p

erfo

rman

cese

nsi

tivi

tyto

com

pute

the

indi

rect

effe

ctfr

omn

ewm

oney

isob

tain

edfr

omth

eco

effi

cien

tes

tim

ates

inC

olu

mn

(2)

ofTa

ble

CII

Iin

App

endi

xC

Row

s(4

)an

d(9

)(R

ows

(5)

and

(10)

)re

port

the

mag

nit

ude

ofin

dire

ctin

cen

tive

sfo

rm

utu

alfu

nds

inco

mpa

riso

nto

the

esti

mat

esfo

rh

edge

fun

dsfr

omth

eG

IRm

odel

(LW

Ym

odel

)

b=

068

5b

=0

8

α=

0α

=3

α=

0α

=3

δ+λ

=5

δ+λ

=10

δ+λ

=5

δ+λ

=10

δ+λ

=5

δ+λ

=10

δ+λ

=5

δ+λ

=10

(1

)(2

)(3

)(4

)(5

)(6

)(7

)(8

)

Per

1ch

ange

infu

nd

valu

e($

mil

lion

s)(1

)In

dire

ctef

fect

from

new

mon

ey0

392

025

90

769

036

70

214

015

60

414

022

4(2

)In

dire

ctef

fect

from

chan

gein

valu

eof

exis

tin

gas

sets

022

10

146

043

30

206

012

00

088

023

30

126

(3)

Tota

lin

dire

ctef

fect

061

30

405

120

20

573

033

40

243

064

70

351

(4)

of

the

hed

gefu

nd

esti

mat

esfr

omth

eG

IRm

odel

658

58

3

602

51

8

516

45

9

476

39

6

(5)

of

the

hed

gefu

nd

esti

mat

esfr

omth

eLW

Ym

odel

539

55

7

465

42

0

343

34

7

419

35

9

Per

$1ch

ange

infu

nd

valu

e($

)(6

)In

dire

ctef

fect

from

new

mon

ey0

139

009

50

260

013

30

074

005

50

136

007

9(7

)In

dire

ctef

fect

from

chan

gein

valu

eof

exis

tin

gas

sets

007

80

053

014

60

075

004

10

031

007

60

044

(8)

Tota

lin

dire

ctef

fect

021

60

148

040

60

208

011

50

086

021

20

123

(9)

of

the

hed

gefu

nd

esti

mat

esfr

omth

eG

IRm

odel

514

47

1

466

42

1

406

37

5

369

32

7

(10)

of

the

hed

gefu

nd

esti

mat

esfr

omth

eLW

Ym

odel

435

46

4

378

36

0

277

28

8

330

30

0






VI Conclusion

























nett =sumi


AUMtminus1minus 1

(A2)


S10 = X10 = AUM0

MS0 = 0

(A3)



i

i f eeit (A4)



Xit =




i

Sit + MSt

) (A6)













]

(B1)


(γ

η

)equiv

12σ




σ 2 (B2)
















(B3)


f (b) = 0 (B4)



π (w) =

min

αprime


π ψ (w) le 0 (B6)





(B7)





] (B8)



f (b) = 0 (B9)


(B10)







wnew =






f (w)baseold =

sumi


iSi (B12)








Appendix C Tables










3














investorspecific




i na na na 08










(1) (2)





REFERENCES





















































































Tab

leV

III

Ind

irec

tP

ayfo

rP

erfo

rman

cefo

rM

utu

alF

un

ds

Th

ista

ble

pres

ents

esti

mat

esof

indi

rect

pay

for

perf

orm

ance

(in

cen

tive

s)fo

rm

utu

alfu

nds

ata

quar

terl

yfr

equ

ency

usi

ng

the

LWY

mod

el(e

quiv

alen

tto

the

GIR

mod

elu

nde

rth

ese

tup

for

mu

tual

fun

ds)

Wit

hin

this

fram

ewor

ka

mu

tual

fun

dis

asp

ecia

lca

sein

wh

ich

leve

rage

isn

otal

low

edan

dth

ein

cen

tive

fee

(k)

isze

roM

utu

alfu

nds

hav

en

oH

WM

prov

isio

na

nd

ther

efor

eth

eco

ntr

actu

alH

WM

grow

thra

te(g

)is

set

toze

roan

dth

era

tio

betw

een

anin

vest

orrsquos

wea

lth

and

HW

Mle

vel

(w)

ison

eat

all

tim

es

Est

imat

esof

indi

rect

ince

nti

ves

are

pres

ente

dfo

rei

ght

com

bin

atio

ns

ofth

epa

ram

eter

sbα

an

dδ+λ

All

oth

erpa

ram

eter

valu

esar

efu

nd-

spec

ific

Rep

orte

dst

atis

tics

are

aver

ages

take

nov

eral

lfu

nd-

quar

ters

(307

079

)in

the

data

Flo

w-p

erfo

rman

cese

nsi

tivi

tyto

com

pute

the

indi

rect

effe

ctfr

omn

ewm

oney

isob

tain

edfr

omth

eco

effi

cien

tes

tim

ates

inC

olu

mn

(2)

ofTa

ble

CII

Iin

App

endi

xC

Row

s(4

)an

d(9

)(R

ows

(5)

and

(10)

)re

port

the

mag

nit

ude

ofin

dire

ctin

cen

tive

sfo

rm

utu

alfu

nds

inco

mpa

riso

nto

the

esti

mat

esfo

rh

edge

fun

dsfr

omth

eG

IRm

odel

(LW

Ym

odel

)

b=

068

5b

=0

8

α=

0α

=3

α=

0α

=3

δ+λ

=5

δ+λ

=10

δ+λ

=5

δ+λ

=10

δ+λ

=5

δ+λ

=10

δ+λ

=5

δ+λ

=10

(1

)(2

)(3

)(4

)(5

)(6

)(7

)(8

)

Per

1ch

ange

infu

nd

valu

e($

mil

lion

s)(1

)In

dire

ctef

fect

from

new

mon

ey0

392

025

90

769

036

70

214

015

60

414

022

4(2

)In

dire

ctef

fect

from

chan

gein

valu

eof

exis

tin

gas

sets

022

10

146

043

30

206

012

00

088

023

30

126

(3)

Tota

lin

dire

ctef

fect

061

30

405

120

20

573

033

40

243

064

70

351

(4)

of

the

hed

gefu

nd

esti

mat

esfr

omth

eG

IRm

odel

658

58

3

602

51

8

516

45

9

476

39

6

(5)

of

the

hed

gefu

nd

esti

mat

esfr

omth

eLW

Ym

odel

539

55

7

465

42

0

343

34

7

419

35

9

Per

$1ch

ange

infu

nd

valu

e($

)(6

)In

dire

ctef

fect

from

new

mon

ey0

139

009

50

260

013

30

074

005

50

136

007

9(7

)In

dire

ctef

fect

from

chan

gein

valu

eof

exis

tin

gas

sets

007

80

053

014

60

075

004

10

031

007

60

044

(8)

Tota

lin

dire

ctef

fect

021

60

148

040

60

208

011

50

086

021

20

123

(9)

of

the

hed

gefu

nd

esti

mat

esfr

omth

eG

IRm

odel

514

47

1

466

42

1

406

37

5

369

32

7

(10)

of

the

hed

gefu

nd

esti

mat

esfr

omth

eLW

Ym

odel

435

46

4

378

36

0

277

28

8

330

30

0






VI Conclusion

























nett =sumi


AUMtminus1minus 1

(A2)


S10 = X10 = AUM0

MS0 = 0

(A3)



i

i f eeit (A4)



Xit =




i

Sit + MSt

) (A6)













]

(B1)


(γ

η

)equiv

12σ




σ 2 (B2)
















(B3)


f (b) = 0 (B4)



π (w) =

min

αprime


π ψ (w) le 0 (B6)





(B7)





] (B8)



f (b) = 0 (B9)


(B10)







wnew =






f (w)baseold =

sumi


iSi (B12)








Appendix C Tables










3














investorspecific




i na na na 08










(1) (2)





REFERENCES












































































Tab

leV

III

Ind

irec

tP

ayfo

rP

erfo

rman

cefo

rM

utu

alF

un

ds

Th

ista

ble

pres

ents

esti

mat

esof

indi

rect

pay

for

perf

orm

ance

(in

cen

tive

s)fo

rm

utu

alfu

nds

ata

quar

terl

yfr

equ

ency

usi

ng

the

LWY

mod

el(e

quiv

alen

tto

the

GIR

mod

elu

nde

rth

ese

tup

for

mu

tual

fun

ds)

Wit

hin

this

fram

ewor

ka

mu

tual

fun

dis

asp

ecia

lca

sein

wh

ich

leve

rage

isn

otal

low

edan

dth

ein

cen

tive

fee

(k)

isze

roM

utu

alfu

nds

hav

en

oH

WM

prov

isio

na

nd

ther

efor

eth

eco

ntr

actu

alH

WM

grow

thra

te(g

)is

set

toze

roan

dth

era

tio

betw

een

anin

vest

orrsquos

wea

lth

and

HW

Mle

vel

(w)

ison

eat

all

tim

es

Est

imat

esof

indi

rect

ince

nti

ves

are

pres

ente

dfo

rei

ght

com

bin

atio

ns

ofth

epa

ram

eter

sbα

an

dδ+λ

All

oth

erpa

ram

eter

valu

esar

efu

nd-

spec

ific

Rep

orte

dst

atis

tics

are

aver

ages

take

nov

eral

lfu

nd-

quar

ters

(307

079

)in

the

data

Flo

w-p

erfo

rman

cese

nsi

tivi

tyto

com

pute

the

indi

rect

effe

ctfr

omn

ewm

oney

isob

tain

edfr

omth

eco

effi

cien

tes

tim

ates

inC

olu

mn

(2)

ofTa

ble

CII

Iin

App

endi

xC

Row

s(4

)an

d(9

)(R

ows

(5)

and

(10)

)re

port

the

mag

nit

ude

ofin

dire

ctin

cen

tive

sfo

rm

utu

alfu

nds

inco

mpa

riso

nto

the

esti

mat

esfo

rh

edge

fun

dsfr

omth

eG

IRm

odel

(LW

Ym

odel

)

b=

068

5b

=0

8

α=

0α

=3

α=

0α

=3

δ+λ

=5

δ+λ

=10

δ+λ

=5

δ+λ

=10

δ+λ

=5

δ+λ

=10

δ+λ

=5

δ+λ

=10

(1

)(2

)(3

)(4

)(5

)(6

)(7

)(8

)

Per

1ch

ange

infu

nd

valu

e($

mil

lion

s)(1

)In

dire

ctef

fect

from

new

mon

ey0

392

025

90

769

036

70

214

015

60

414

022

4(2

)In

dire

ctef

fect

from

chan

gein

valu

eof

exis

tin

gas

sets

022

10

146

043

30

206

012

00

088

023

30

126

(3)

Tota

lin

dire

ctef

fect

061

30

405

120

20

573

033

40

243

064

70

351

(4)

of

the

hed

gefu

nd

esti

mat

esfr

omth

eG

IRm

odel

658

58

3

602

51

8

516

45

9

476

39

6

(5)

of

the

hed

gefu

nd

esti

mat

esfr

omth

eLW

Ym

odel

539

55

7

465

42

0

343

34

7

419

35

9

Per

$1ch

ange

infu

nd

valu

e($

)(6

)In

dire

ctef

fect

from

new

mon

ey0

139

009

50

260

013

30

074

005

50

136

007

9(7

)In

dire

ctef

fect

from

chan

gein

valu

eof

exis

tin

gas

sets

007

80

053

014

60

075

004

10

031

007

60

044

(8)

Tota

lin

dire

ctef

fect

021

60

148

040

60

208

011

50

086

021

20

123

(9)

of

the

hed

gefu

nd

esti

mat

esfr

omth

eG

IRm

odel

514

47

1

466

42

1

406

37

5

369

32

7

(10)

of

the

hed

gefu

nd

esti

mat

esfr

omth

eLW

Ym

odel

435

46

4

378

36

0

277

28

8

330

30

0






VI Conclusion

























nett =sumi


AUMtminus1minus 1

(A2)


S10 = X10 = AUM0

MS0 = 0

(A3)



i

i f eeit (A4)



Xit =




i

Sit + MSt

) (A6)













]

(B1)


(γ

η

)equiv

12σ




σ 2 (B2)
















(B3)


f (b) = 0 (B4)



π (w) =

min

αprime


π ψ (w) le 0 (B6)





(B7)





] (B8)



f (b) = 0 (B9)


(B10)







wnew =






f (w)baseold =

sumi


iSi (B12)








Appendix C Tables










3














investorspecific




i na na na 08










(1) (2)





REFERENCES





































































Tab

leV

III

Ind

irec

tP

ayfo

rP

erfo

rman

cefo

rM

utu

alF

un

ds

Th

ista

ble

pres

ents

esti

mat

esof

indi

rect

pay

for

perf

orm

ance

(in

cen

tive

s)fo

rm

utu

alfu

nds

ata

quar

terl

yfr

equ

ency

usi

ng

the

LWY

mod

el(e

quiv

alen

tto

the

GIR

mod

elu

nde

rth

ese

tup

for

mu

tual

fun

ds)

Wit

hin

this

fram

ewor

ka

mu

tual

fun

dis

asp

ecia

lca

sein

wh

ich

leve

rage

isn

otal

low

edan

dth

ein

cen

tive

fee

(k)

isze

roM

utu

alfu

nds

hav

en

oH

WM

prov

isio

na

nd

ther

efor

eth

eco

ntr

actu

alH

WM

grow

thra

te(g

)is

set

toze

roan

dth

era

tio

betw

een

anin

vest

orrsquos

wea

lth

and

HW

Mle

vel

(w)

ison

eat

all

tim

es

Est

imat

esof

indi

rect

ince

nti

ves

are

pres

ente

dfo

rei

ght

com

bin

atio

ns

ofth

epa

ram

eter

sbα

an

dδ+λ

All

oth

erpa

ram

eter

valu

esar

efu

nd-

spec

ific

Rep

orte

dst

atis

tics

are

aver

ages

take

nov

eral

lfu

nd-

quar

ters

(307

079

)in

the

data

Flo

w-p

erfo

rman

cese

nsi

tivi

tyto

com

pute

the

indi

rect

effe

ctfr

omn

ewm

oney

isob

tain

edfr

omth

eco

effi

cien

tes

tim

ates

inC

olu

mn

(2)

ofTa

ble

CII

Iin

App

endi

xC

Row

s(4

)an

d(9

)(R

ows

(5)

and

(10)

)re

port

the

mag

nit

ude

ofin

dire

ctin

cen

tive

sfo

rm

utu

alfu

nds

inco

mpa

riso

nto

the

esti

mat

esfo

rh

edge

fun

dsfr

omth

eG

IRm

odel

(LW

Ym

odel

)

b=

068

5b

=0

8

α=

0α

=3

α=

0α

=3

δ+λ

=5

δ+λ

=10

δ+λ

=5

δ+λ

=10

δ+λ

=5

δ+λ

=10

δ+λ

=5

δ+λ

=10

(1

)(2

)(3

)(4

)(5

)(6

)(7

)(8

)

Per

1ch

ange

infu

nd

valu

e($

mil

lion

s)(1

)In

dire

ctef

fect

from

new

mon

ey0

392

025

90

769

036

70

214

015

60

414

022

4(2

)In

dire

ctef

fect

from

chan

gein

valu

eof

exis

tin

gas

sets

022

10

146

043

30

206

012

00

088

023

30

126

(3)

Tota

lin

dire

ctef

fect

061

30

405

120

20

573

033

40

243

064

70

351

(4)

of

the

hed

gefu

nd

esti

mat

esfr

omth

eG

IRm

odel

658

58

3

602

51

8

516

45

9

476

39

6

(5)

of

the

hed

gefu

nd

esti

mat

esfr

omth

eLW

Ym

odel

539

55

7

465

42

0

343

34

7

419

35

9

Per

$1ch

ange

infu

nd

valu

e($

)(6

)In

dire

ctef

fect

from

new

mon

ey0

139

009

50

260

013

30

074

005

50

136

007

9(7

)In

dire

ctef

fect

from

chan

gein

valu

eof

exis

tin

gas

sets

007

80

053

014

60

075

004

10

031

007

60

044

(8)

Tota

lin

dire

ctef

fect

021

60

148

040

60

208

011

50

086

021

20

123

(9)

of

the

hed

gefu

nd

esti

mat

esfr

omth

eG

IRm

odel

514

47

1

466

42

1

406

37

5

369

32

7

(10)

of

the

hed

gefu

nd

esti

mat

esfr

omth

eLW

Ym

odel

435

46

4

378

36

0

277

28

8

330

30

0






VI Conclusion

























nett =sumi


AUMtminus1minus 1

(A2)


S10 = X10 = AUM0

MS0 = 0

(A3)



i

i f eeit (A4)



Xit =




i

Sit + MSt

) (A6)













]

(B1)


(γ

η

)equiv

12σ




σ 2 (B2)
















(B3)


f (b) = 0 (B4)



π (w) =

min

αprime


π ψ (w) le 0 (B6)





(B7)





] (B8)



f (b) = 0 (B9)


(B10)







wnew =






f (w)baseold =

sumi


iSi (B12)








Appendix C Tables










3














investorspecific




i na na na 08










(1) (2)





REFERENCES




























































Tab

leV

III

Ind

irec

tP

ayfo

rP

erfo

rman

cefo

rM

utu

alF

un

ds

Th

ista

ble

pres

ents

esti

mat

esof

indi

rect

pay

for

perf

orm

ance

(in

cen

tive

s)fo

rm

utu

alfu

nds

ata

quar

terl

yfr

equ

ency

usi

ng

the

LWY

mod

el(e

quiv

alen

tto

the

GIR

mod

elu

nde

rth

ese

tup

for

mu

tual

fun

ds)

Wit

hin

this

fram

ewor

ka

mu

tual

fun

dis

asp

ecia

lca

sein

wh

ich

leve

rage

isn

otal

low

edan

dth

ein

cen

tive

fee

(k)

isze

roM

utu

alfu

nds

hav

en

oH

WM

prov

isio

na

nd

ther

efor

eth

eco

ntr

actu

alH

WM

grow

thra

te(g

)is

set

toze

roan

dth

era

tio

betw

een

anin

vest

orrsquos

wea

lth

and

HW

Mle

vel

(w)

ison

eat

all

tim

es

Est

imat

esof

indi

rect

ince

nti

ves

are

pres

ente

dfo

rei

ght

com

bin

atio

ns

ofth

epa

ram

eter

sbα

an

dδ+λ

All

oth

erpa

ram

eter

valu

esar

efu

nd-

spec

ific

Rep

orte

dst

atis

tics

are

aver

ages

take

nov

eral

lfu

nd-

quar

ters

(307

079

)in

the

data

Flo

w-p

erfo

rman

cese

nsi

tivi

tyto

com

pute

the

indi

rect

effe

ctfr

omn

ewm

oney

isob

tain

edfr

omth

eco

effi

cien

tes

tim

ates

inC

olu

mn

(2)

ofTa

ble

CII

Iin

App

endi

xC

Row

s(4

)an

d(9

)(R

ows

(5)

and

(10)

)re

port

the

mag

nit

ude

ofin

dire

ctin

cen

tive

sfo

rm

utu

alfu

nds

inco

mpa

riso

nto

the

esti

mat

esfo

rh

edge

fun

dsfr

omth

eG

IRm

odel

(LW

Ym

odel

)

b=

068

5b

=0

8

α=

0α

=3

α=

0α

=3

δ+λ

=5

δ+λ

=10

δ+λ

=5

δ+λ

=10

δ+λ

=5

δ+λ

=10

δ+λ

=5

δ+λ

=10

(1

)(2

)(3

)(4

)(5

)(6

)(7

)(8

)

Per

1ch

ange

infu

nd

valu

e($

mil

lion

s)(1

)In

dire

ctef

fect

from

new

mon

ey0

392

025

90

769

036

70

214

015

60

414

022

4(2

)In

dire

ctef

fect

from

chan

gein

valu

eof

exis

tin

gas

sets

022

10

146

043

30

206

012

00

088

023

30

126

(3)

Tota

lin

dire

ctef

fect

061

30

405

120

20

573

033

40

243

064

70

351

(4)

of

the

hed

gefu

nd

esti

mat

esfr

omth

eG

IRm

odel

658

58

3

602

51

8

516

45

9

476

39

6

(5)

of

the

hed

gefu

nd

esti

mat

esfr

omth

eLW

Ym

odel

539

55

7

465

42

0

343

34

7

419

35

9

Per

$1ch

ange

infu

nd

valu

e($

)(6

)In

dire

ctef

fect

from

new

mon

ey0

139

009

50

260

013

30

074

005

50

136

007

9(7

)In

dire

ctef

fect

from

chan

gein

valu

eof

exis

tin

gas

sets

007

80

053

014

60

075

004

10

031

007

60

044

(8)

Tota

lin

dire

ctef

fect

021

60

148

040

60

208

011

50

086

021

20

123

(9)

of

the

hed

gefu

nd

esti

mat

esfr

omth

eG

IRm

odel

514

47

1

466

42

1

406

37

5

369

32

7

(10)

of

the

hed

gefu

nd

esti

mat

esfr

omth

eLW

Ym

odel

435

46

4

378

36

0

277

28

8

330

30

0






VI Conclusion

























nett =sumi


AUMtminus1minus 1

(A2)


S10 = X10 = AUM0

MS0 = 0

(A3)



i

i f eeit (A4)



Xit =




i

Sit + MSt

) (A6)













]

(B1)


(γ

η

)equiv

12σ




σ 2 (B2)
















(B3)


f (b) = 0 (B4)



π (w) =

min

αprime


π ψ (w) le 0 (B6)





(B7)





] (B8)



f (b) = 0 (B9)


(B10)







wnew =






f (w)baseold =

sumi


iSi (B12)








Appendix C Tables










3














investorspecific




i na na na 08










(1) (2)





REFERENCES





















































Tab

leV

III

Ind

irec

tP

ayfo

rP

erfo

rman

cefo

rM

utu

alF

un

ds

Th

ista

ble

pres

ents

esti

mat

esof

indi

rect

pay

for

perf

orm

ance

(in

cen

tive

s)fo

rm

utu

alfu

nds

ata

quar

terl

yfr

equ

ency

usi

ng

the

LWY

mod

el(e

quiv

alen

tto

the

GIR

mod

elu

nde

rth

ese

tup

for

mu

tual

fun

ds)

Wit

hin

this

fram

ewor

ka

mu

tual

fun

dis

asp

ecia

lca

sein

wh

ich

leve

rage

isn

otal

low

edan

dth

ein

cen

tive

fee

(k)

isze

roM

utu

alfu

nds

hav

en

oH

WM

prov

isio

na

nd

ther

efor

eth

eco

ntr

actu

alH

WM

grow

thra

te(g

)is

set

toze

roan

dth

era

tio

betw

een

anin

vest

orrsquos

wea

lth

and

HW

Mle

vel

(w)

ison

eat

all

tim

es

Est

imat

esof

indi

rect

ince

nti

ves

are

pres

ente

dfo

rei

ght

com

bin

atio

ns

ofth

epa

ram

eter

sbα

an

dδ+λ

All

oth

erpa

ram

eter

valu

esar

efu

nd-

spec

ific

Rep

orte

dst

atis

tics

are

aver

ages

take

nov

eral

lfu

nd-

quar

ters

(307

079

)in

the

data

Flo

w-p

erfo

rman

cese

nsi

tivi

tyto

com

pute

the

indi

rect

effe

ctfr

omn

ewm

oney

isob

tain

edfr

omth

eco

effi

cien

tes

tim

ates

inC

olu

mn

(2)

ofTa

ble

CII

Iin

App

endi

xC

Row

s(4

)an

d(9

)(R

ows

(5)

and

(10)

)re

port

the

mag

nit

ude

ofin

dire

ctin

cen

tive

sfo

rm

utu

alfu

nds

inco

mpa

riso

nto

the

esti

mat

esfo

rh

edge

fun

dsfr

omth

eG

IRm

odel

(LW

Ym

odel

)

b=

068

5b

=0

8

α=

0α

=3

α=

0α

=3

δ+λ

=5

δ+λ

=10

δ+λ

=5

δ+λ

=10

δ+λ

=5

δ+λ

=10

δ+λ

=5

δ+λ

=10

(1

)(2

)(3

)(4

)(5

)(6

)(7

)(8

)

Per

1ch

ange

infu

nd

valu

e($

mil

lion

s)(1

)In

dire

ctef

fect

from

new

mon

ey0

392

025

90

769

036

70

214

015

60

414

022

4(2

)In

dire

ctef

fect

from

chan

gein

valu

eof

exis

tin

gas

sets

022

10

146

043

30

206

012

00

088

023

30

126

(3)

Tota

lin

dire

ctef

fect

061

30

405

120

20

573

033

40

243

064

70

351

(4)

of

the

hed

gefu

nd

esti

mat

esfr

omth

eG

IRm

odel

658

58

3

602

51

8

516

45

9

476

39

6

(5)

of

the

hed

gefu

nd

esti

mat

esfr

omth

eLW

Ym

odel

539

55

7

465

42

0

343

34

7

419

35

9

Per

$1ch

ange

infu

nd

valu

e($

)(6

)In

dire

ctef

fect

from

new

mon

ey0

139

009

50

260

013

30

074

005

50

136

007

9(7

)In

dire

ctef

fect

from

chan

gein

valu

eof

exis

tin

gas

sets

007

80

053

014

60

075

004

10

031

007

60

044

(8)

Tota

lin

dire

ctef

fect

021

60

148

040

60

208

011

50

086

021

20

123

(9)

of

the

hed

gefu

nd

esti

mat

esfr

omth

eG

IRm

odel

514

47

1

466

42

1

406

37

5

369

32

7

(10)

of

the

hed

gefu

nd

esti

mat

esfr

omth

eLW

Ym

odel

435

46

4

378

36

0

277

28

8

330

30

0






VI Conclusion

























nett =sumi


AUMtminus1minus 1

(A2)


S10 = X10 = AUM0

MS0 = 0

(A3)



i

i f eeit (A4)



Xit =




i

Sit + MSt

) (A6)













]

(B1)


(γ

η

)equiv

12σ




σ 2 (B2)
















(B3)


f (b) = 0 (B4)



π (w) =

min

αprime


π ψ (w) le 0 (B6)





(B7)





] (B8)



f (b) = 0 (B9)


(B10)







wnew =






f (w)baseold =

sumi


iSi (B12)








Appendix C Tables










3














investorspecific




i na na na 08










(1) (2)





REFERENCES

























































VI Conclusion

























nett =sumi


AUMtminus1minus 1

(A2)


S10 = X10 = AUM0

MS0 = 0

(A3)



i

i f eeit (A4)



Xit =




i

Sit + MSt

) (A6)













]

(B1)


(γ

η

)equiv

12σ




σ 2 (B2)
















(B3)


f (b) = 0 (B4)



π (w) =

min

αprime


π ψ (w) le 0 (B6)





(B7)





] (B8)



f (b) = 0 (B9)


(B10)







wnew =






f (w)baseold =

sumi


iSi (B12)








Appendix C Tables










3














investorspecific




i na na na 08










(1) (2)





REFERENCES










































































nett =sumi


AUMtminus1minus 1

(A2)


S10 = X10 = AUM0

MS0 = 0

(A3)



i

i f eeit (A4)



Xit =




i

Sit + MSt

) (A6)













]

(B1)


(γ

η

)equiv

12σ




σ 2 (B2)
















(B3)


f (b) = 0 (B4)



π (w) =

min

αprime


π ψ (w) le 0 (B6)





(B7)





] (B8)



f (b) = 0 (B9)


(B10)







wnew =






f (w)baseold =

sumi


iSi (B12)








Appendix C Tables










3














investorspecific




i na na na 08










(1) (2)





REFERENCES



































































nett =sumi


AUMtminus1minus 1

(A2)


S10 = X10 = AUM0

MS0 = 0

(A3)



i

i f eeit (A4)



Xit =




i

Sit + MSt

) (A6)













]

(B1)


(γ

η

)equiv

12σ




σ 2 (B2)
















(B3)


f (b) = 0 (B4)



π (w) =

min

αprime


π ψ (w) le 0 (B6)





(B7)





] (B8)



f (b) = 0 (B9)


(B10)







wnew =






f (w)baseold =

sumi


iSi (B12)








Appendix C Tables










3














investorspecific




i na na na 08










(1) (2)





REFERENCES

























































nett =sumi


AUMtminus1minus 1

(A2)


S10 = X10 = AUM0

MS0 = 0

(A3)



i

i f eeit (A4)



Xit =




i

Sit + MSt

) (A6)













]

(B1)


(γ

η

)equiv

12σ




σ 2 (B2)
















(B3)


f (b) = 0 (B4)



π (w) =

min

αprime


π ψ (w) le 0 (B6)





(B7)





] (B8)



f (b) = 0 (B9)


(B10)







wnew =






f (w)baseold =

sumi


iSi (B12)








Appendix C Tables










3














investorspecific




i na na na 08










(1) (2)





REFERENCES































































]

(B1)


(γ

η

)equiv

12σ




σ 2 (B2)
















(B3)


f (b) = 0 (B4)



π (w) =

min

αprime


π ψ (w) le 0 (B6)





(B7)





] (B8)



f (b) = 0 (B9)


(B10)







wnew =






f (w)baseold =

sumi


iSi (B12)








Appendix C Tables










3














investorspecific




i na na na 08










(1) (2)





REFERENCES


































































(B3)


f (b) = 0 (B4)



π (w) =

min

αprime


π ψ (w) le 0 (B6)





(B7)





] (B8)



f (b) = 0 (B9)


(B10)







wnew =






f (w)baseold =

sumi


iSi (B12)








Appendix C Tables










3














investorspecific




i na na na 08










(1) (2)





REFERENCES



























































(B3)


f (b) = 0 (B4)



π (w) =

min

αprime


π ψ (w) le 0 (B6)





(B7)





] (B8)



f (b) = 0 (B9)


(B10)







wnew =






f (w)baseold =

sumi


iSi (B12)








Appendix C Tables










3














investorspecific




i na na na 08










(1) (2)





REFERENCES























































(B7)





] (B8)



f (b) = 0 (B9)


(B10)







wnew =






f (w)baseold =

sumi


iSi (B12)








Appendix C Tables










3














investorspecific




i na na na 08










(1) (2)





REFERENCES
























































wnew =






f (w)baseold =

sumi


iSi (B12)








Appendix C Tables










3














investorspecific




i na na na 08










(1) (2)





REFERENCES
























































Appendix C Tables










3














investorspecific




i na na na 08










(1) (2)





REFERENCES





























































(1) (2)





REFERENCES























































(1) (2)





REFERENCES



















































































































Documents

Indirect Incentives of Hedge Fund Managers - … JOURNAL OF FINANCE •VOL. LXXI, NO. 2 APRIL 2016 Indirect Incentives of Hedge Fund Managers JONGHA LIM, BERK A. SENSOY, and MICHAEL