DO FUNDS OF HEDGE FUNDS REALLY ADD VALUE? A Post Crisis Analysis 1

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1 DO FUNDS OF HEDGE FUNDS REALLY ADD VALUE? A Pos Crisis Analysis 1 Absrac: In spie of a somewha disappoining performance hroughou he crisis, and a series of high profile scandals, invesors are showing ineres in hedge funds. Sill, funds of hedge funds keep on experiencing ou-flows. Can his phenomenon be explained by he failure of funds of hedge funds managers o deliver on heir promise o add value hrough acive managemen, or is i sympomaic of a move oward greaer disinermediaion in he hedge fund indusry? Lile aenion has been paid so far o he added-value, and he sources of he added-value, of funds of hedge funds. The lack of ransparency ha is characerisic of he hedge funds arena and makes he performance aribuion exercise paricularly challenging is probably an explanaion. The objecive of his aricle is o fill in he gap. We inroduce o his end a reurnbased aribuion model allowing for a full decomposiion of funds of hedge funds performance. The resuls of our empirical sudy sugges ha funds of hedge funds are funds of funds like ohers. Sraegic Allocaion urns ou o be a crucial sep in he invesmen process, in ha i no only adds value over he long-erm, bu mos imporanly, i brings resilience precisely when invesors need i he mos. Fund Picking, on he oher hand, urns ou o be a double-edged sword. Overall, funds of hedge funds appear o succeed in overcoming heir double fee srucure, and add value across marke regimes, alhough o varying degrees and in differen forms. Serge Darolles, Ph.D. is a Research Fellow a CREST and Depuy Head of Lyxor Asse Managemen Research and Developmen Deparmen. serge.darolles@lyxor.com Mahieu Vaissié, Ph.D. is a Research Associae wih he EDHEC-Risk Insiue and a Senior Porfolio Manager a Lyxor Asse Managemen. mahieu.vaissie@lyxor.com Lyxor Asse Managemen Tours Sociéé Générale 17, cours Valmy Paris La Défense Cedex Key words: Funds of Hedge Funds Performance, Performance Aribuion Model, Sraegic Allocaion, Tacical Allocaion, Fund Picking, Acive Managemen, Added-Value, Sae-space Models, Kalman Filer. 1 We would like o express our graiude o Emmanuelle Jay for superb compuer assisance. 1 Elecronic copy available a: hp://ssrn.com/absrac=

2 Posiive inflows since he hird quarer of 09 and a number of indusry surveys (see for example Preqin Global Hedge Fund Invesor Review) sugges ha in spie of a somewha disappoining performance hroughou he crisis, and a series of high profile scandals, invesors and especially insiuional invesors, are sill showing ineres in hedge funds. Agains his backdrop, funds of hedge funds, which used o be he favorie roue for radiional invesors o gain exposure o hedge funds sraegies, keep on experiencing ouflows. Can his phenomenon be explained by he failure of funds of hedge funds managers o deliver on heir promise o add value hrough acive managemen, or is i sympomaic of a move oward greaer disinermediaion in he hedge funds indusry? Exhibi 1: Year-o-Dae Esimaed Change in Asses (in $ Bn), as of he end of Q2 $30 $25 $ $15 $ $5 $0 -$5 -$ -$15 -$ Toal Indusry $23.29 $24.24 Ne Asse Flows Funds of Hedge Funds $6.62 -$13.90 Performance-based Asse Change Source: HFR Global Hedge Fund Indusry Repor, Q2, The debae on acive versus passive managemen is no a pey local quarrel. I has been agiaing he invesmen communiy and challenging one of he cenral assumpions of economic heory, namely marke efficiency, for decades. In his respec, a large body of empirical lieraure has documened he performance of muual funds, and mos sudies do no seem o suppor he proposiion ha professional money managers succeed in adding value hrough acive managemen (see Sharpe (1966), Treynor (1966), Jensen (1968), Grinbla and Timan (1992), Hendricks e al. (1993), Elon e al. (1996), Carhar (1997), or Blake e al. (1999), among oher examples). Bu, despie radiional invesors significan exposure o funds of hedge funds, lile aenion has been paid so far o he added value of hese 2 Elecronic copy available a: hp://ssrn.com/absrac=

3 Fund Performance invesmen vehicles. This is all he more surprising in ha funds of hedge funds inves in funds ha show hemselves a persisence ha appears o be a bes shorer-erm han he ypical Fund Selecion process (see Agarwal and Naik (00), Amenc e al. (03), Baquero e al.. (05), Capocci e al. (05), Capocci and Hübner (04), Eling (09), Herzberg and Mozes (03), Ka and Menexe (03), Kosowski e al. (07), Malkiel and Saha (05)). The lack of ransparency ha is characerisic of he hedge fund arena and ha makes he performance aribuion exercise paricularly challenging is probably an explanaion. The objecive of his sudy is o fill in he gap. Our conribuion in his aricle is wofold. On he one hand, we propose a performance aribuion model incorporaing sae-space models, ha makes i possible o disenangle he value semming from Sraegic Allocaion decisions (i.e., saic beas), from Tacical Allocaion bes (i.e., dynamic beas), and from he Fund Selecion (i.e., alpha). The meri of his performance aribuion model is herefore o allow for a full decomposiion of he performance, i.e., like wih porfolio-based approaches (see for example Brinson e al. (1986, 1991)), bu in a reurn-based seing. On he oher hand, our observaion period covers he recen sysemic crisis. We can herefore es he exen o which he value added by funds of hedge funds managers is regime-dependen; we can also analyze more specifically he behaviour of funds of hedge funds while hey experience - for he firs ime on records - a period of significan capial ou-flows. Unsurprisingly, asse allocaion and risk managemen being wo sides of he same coin, we find ha he value added a he Sraegic Allocaion level is significanly posiive, especially during Sressed Marke Condiions. The resuls are more mixed when i comes o Tacical Allocaion and Fund Picking. Exhibi 2: Decomposiion of he Performance of a Funds of Hedge Funds Value added hrough Fund/sock Picking Value added hrough Tacical Allocaion Alpha Benefis Dynamic Beas Benefis Value added hrough Sraegic Allocaion Saic Beas Benefis Reurn on he Neural Porfolio Performance of an Uninformed Invesor 3

4 The remainder of his aricle is organized as follows. In he firs secion, we will propose a performance aribuion model allowing for a full decomposiion of funds of hedge funds reurns. We will hen ry in he second secion o figure ou wheher Sraegic Allocaion really maers in he case of funds of hedge funds. In he hird secion, we will dig furher and ry o ge a beer undersanding of he sources of funds of hedge funds managers added value, and assess he exen o which i varies across marke regimes. We will subsequenly evaluae he impac of various exogenous variables on funds of hedge funds managers added value. We will finally end his aricle wih some concluding remarks and suggesions for fuure research. 4

5 I. A PERFORMANCE ATTRIBUTION MODEL FOR ACTIVELY MANAGED PORTFOLIOS Mos performance sudies, consider Sraegic Allocaion as an exogenous variable, as if fund managers had no impac on his crucial par of he invesmen process. They herefore only consider he value added by he fund manager hrough Tacical Allocaion, and Sock or Fund Picking. However, as evidenced in he lieraure, Sraegic Allocaion appears o be he main deerminan of a fund s performance (see Brinson e al. (1986, 1991), or Ibboson and Kapplan (00), among oher examples). I is herefore inconsisen o ignore he value added a he Sraegic Allocaion level. To address his issue, we sugges exending he approach inroduced in Bailey e al. (1990) and consider ha he performance (P) of a fund of hedge funds is made up of four disinc componens: i/ he performance of an uninformed invesor (N), ii/ he value added by he porfolio manager hrough he Sraegic Allocaion process (S), iii/ he value added by he porfolio manager hrough he Tacical Allocaion process (T), iv/ he value added by he porfolio manager hrough he Fund Selecion process (F). By doing so, we can decompose he performance of a fund of hedge funds as follows: (1) P N S T F, or alernaively N S T F R R R Neural Porfolio Sraegy Benchmark Tacical Benchmark P R R R Tacical Benchmark Neural Porfolio Sraegy Benchmark Le us now develop he inuiion beyond he differen benchmarks involved in his decomposiion. The impac of any invesmen decision can be measured by comparing is oucome wih he one of an alernaive decision (i.e., Neural Porfolio). As highlighed in Hensel e al. (1991), he resuls of he performance aribuion process srongly depend on he 5

6 choice of his alernaive decision; here is however no consensus on is definiion. One could choose he risk-free rae or he minimum risk porfolio. Bu, i is highly quesionable ha his would be an appropriae benchmark for an uninformed invesor. Anoher opion would be o follow a liabiliy-driven logic. Bu, since invesors have specific liabiliy consrains, such a benchmark would no fi hem all equally; nowihsanding he fac ha designing a liabiliy maching porfolio is no sraighforward when i is made up of alernaive sraegies. We hus ook anoher roue and oped for he equilibrium logic, by selecing he marke porfolio, or more specifically, an indusry composie index 2 as Neural Porfolio. The Sraegic Allocaion of a fund of hedge funds reflecs he long-erm bes made by he porfolio manager. We assume in he following ha hese bes remain unchanged over he whole observaion period. This is a crucial assumpion for our sequenial reurn decomposiion, as any misspecificaion a his sage could induce spurious effecs on he following erms of he decomposiion 3. From a pracical sandpoin, he Sraegy Benchmark is obained hrough a classical reurn-based syle analysis, i.e., wih a consrained regression (please refer o Sharpe (1992) for greaer deails on he benefis of his approach). The performance of all he funds of hedge funds of our sample is firs regressed on he same se of risk facors: ' R Fund, RF,, where he error erm is an independenly idenically disribued (iid) Gaussian whie noise ~ N(0, ). The inercep erm in he regression is se o zero, and he facor loadings are consrained o be posiive and sum up o one. The cusomized Sraegy Benchmark of every single fund of hedge funds is compued as he linear combinaion ' R F, of he saisically significan facors in he regression and heir 2 A series of Hedge Fund Indices buil from various daabases of individual Hedge Fund reurns are available on he marke. Please refer o Amenc e al. (04) for greaer deails on he characerisics of hose indices. For he sake of his sudy, we seleced he asse-weighed composie Index provided by HFR Research; he HFR daabase is among he mos represenaive of he indusry and his asse-weighed Composie Index is, as a resul, commonly used by marke paricipans as a proxy for he marke porfolio. 3 One could include a srucural break analysis o consider several Sraegic Allocaions, bu such an analysis is ou of he scope of his paper and lef for furher research. 6

7 respecive performance. The value added by he Sraegic Allocaion (S) is hen defined as he reurn difference beween he Sraegy Benchmark and he Neural Porfolio. While Sraegic Allocaion shifs are expeced o occur occasionally, Tacical bes are liable o be aken on a coninuous basis. Despie his obvious difference in erms of ime horizon, Sraegic and Tacical Allocaion decisions have one poin in common. They boh rely on - respecively long-erm and shor-erm - forecass of risk premiums (i.e., bes on sysemaic risk), and can as a resul be capured using a se of risk facors. In his respec, we assume ha funds of hedge funds managers only make acical bes on he risk facors enering ino he composiion of he Sraegy Benchmark. Two argumens suppor his assumpion. On he one hand, porfolio managers are no immune o he so-called familiariy bias (see Heah and Tversky (1991) for greaer deails on his behavioral bias). They will herefore be inclined o focus, boh a Sraegic and Tacical Allocaion levels, on he same sub-se of sraegies (i.e., hose sraegies hey are mos familiar wih). On he oher hand, limiing he number of facors simplifies he saisical modeling of he ime-varying coefficiens used o compue he Tacical Benchmark, and improves in urn he robusness of he resuls. Since informaion arrives randomly 4, and Tacical bes are assumed o be responses o new informaion, we expec he exposure o risk facors o evolve randomly over ime. Unforunaely, risk facor exposures canno be direcly observed in he case of funds of hedge funds. The reason is wofold. Firsly, alhough he rend is very clearly owards more ransparency, invesors do no sysemaically have access o he full composiion of funds of hedge funds, and is evoluion over ime. Secondly, funds of hedge funds managers hemselves do no always have a complee view of he risk facor exposures of he underlying hedge funds, and as a resul, of he bes hey implicily ake a he fund of funds level. This is all he more rue when he rading frequency of he underlying funds is significanly higher han heir reporing frequency (i.e., embedded risks can be dramaically differen from hose showed a a specific dae), or when he number and he diversiy of posiions makes i difficul o come up wih accurae aggregaed facor exposures. Tacical bes explicily (i.e., a he sraegy level) and implicily (i.e., a he underlying fund level) aken by he fund of hedge funds manager can alernaively add up or cancel each ohers. Using a reurn-based syle analysis herefore allows us o miigae one of he main shorcomings of holding-based approaches, by capuring and assessing boh effecs concomianly. 4 I should be noed ha our objecive here is no o model he invesor s informaion se or his decision making process bu raher is impac on risk facor exposures. 7

8 Time-varying rik facors exposures are esimaed using a sae-space model (see Hamilon (1994) for a deailed discussion on sae-space models). One of he advanages of his approach is o deermine an opimal weighing scheme from he daa. As a resul, here is no need o specify an arbirary window size, as is he case for regressions wih rolling windows. Building on he growing lieraure (see Bogue (1973), Sunder (1980), Alexander e al. (1982), Annaer and Van Campenhou (02), Swinkels and Van der Sluis (02) among oher examples) we model he evoluion of k-dimensional risk facor exposures equaion) wih a 1 s order Markov process. (he ransiion A 1, where A is a kxk marix and N(0, H ) ~ H denoes he kxk variance covariance marix of he independenly idenically disribued (iid) error erms. We hus link hese dynamics o he observed fund reurns wih he following measuremen equaion: ' R Fund, RF,, where he error erm is an independenly idenically disribued (iid) Gaussian whie noise ~ N (0, g ). Therefore, correponds o he kx1-vecor of non observable facor exposures. I is generally referred o as he sae vecor. A is he kxk- ransiion marix. g is he unexplained variance of he regression model indicaing he covariance srucure of he sae variables a ime. In wha follows, he parameers in g and H will be assumed consan over ime and esimaed by 5 : g H ˆ ˆ 2 2 ( R ( R F Fund ' R F ) 1 R F ˆ OLS )( R T k Fund R F ˆ OLS )' where ˆ OLS is he uncondiional leas squares esimae and R F he Txk-marix conaining he reurns of he k facors over T observaions. 5 Condiional parameers modeled by GARCH or facor analysis models could be aken ino consideraion bu would, o our mind, add oo much complexiy compared o he expeced benefis. Condiioning regressors is independen from condiioning he residuals. 8

9 We denoe P he ime-condiional kxk-covariance marix of he sae vecor following wo-sep approach. The firs sep corresponds o he forecasing sysem: and apply he P 1 1 A AP A' H where and P are he bes predicors of and 1 1 P, condiionally on he informaion se available a ime -1. and P are respecively he updaed values obained by he following updaing equaions: P P 1 1 K v K S K ' wih v R Fund RF, ) he innovaion of he process and: (, 1 S R P R g K P R S. ' ' F, 1 F, 1 F, 1 K is called he Kalman-Gain a ime and deermines he impac of he innovaion on he esimaed sae parameers. This procedure, known as he Kalman Filer, is applied for any given =1 T. We iniialize he filer wih and P as he parameers semming from a leas square esimaion over he whole sample period: ˆ 1 0 OLS and P 1 0 H. We use he maximum likelihood mehod o esimae he ransiion marix A from an iniial poin se o he ideniy marix. The Kalman Filer hen gives he esimaed ime series of facor exposures for =1 T. The resuling facor loadings are hen used o consruc he 9

10 Tacical Benchmark ' R F,. The value added by he Tacical Allocaion (T) corresponds o he reurn difference beween he Tacical and he Sraegic Benchmarks. Finally, he residual erm can be inerpreed as he par of he performance ha canno be explained by saic and/or dynamic exposures o risk facors. In he conex of funds of funds managemen, his erm can be inerpreed as he value added hrough he Fund Selecion process (F). I measures he abiliy of he porfolio manager o selec he bes alpha providers, i.e., hose funds ha are able o produce value wihou any saic or dynamic exposure o risk facors.

11 II. DOES STRATEGIC ALLOCATION MATER? Running a reurn-based syle analysis requires a significan number of observaions so ha saisical inferences migh be seen as meaningful. We hus merged wo of he larges commercial daa bases, namely HFR (hps:// and Lipper TASS (hp://ass.lipperweb.com), in an aemp o dispose of a represenaive sample; we exraced he 229 funds of hedge funds showing a coninuous rack record from January 00 hrough July 09 (i.e., ou of 15 funds of hedge funds available), so ha we ended up wih 115 monhly observaions. For he sake of our analysis, we will subsequenly spli his observaion period in wo subperiods, namely he Normal Marke Condiions, ranging from January 00 hrough June 07, and he Sressed Marke Condiions, ranging from July 07 hrough July 09. Finally, in order o avoid any double couning, we eliminaed 45 funds presening similar names and a correlaion higher han 0.95 wih anoher fund. The resuls of he empirical sudy herefore res on a sample made up of 184 funds of hedge funds. Reurn daa series are denominaed in USD and ne of all fees. Exhibi 3: Disribuion of Funds of Hedge Funds by Age More Age (in monhs) from incepion o July 09 11

12 The selecion of he inpus is a crucial sep in a reurn-based analysis. As sressed in Sharpe (1992), syle facors mus be collecively exhausive and muually exclusive. For his reason we seleced he series of hedge fund indices published by EDHEC. Indeed, as evidenced in Amenc e al. (04), hese indices of hedge fund indices have he meri o offer grea qualiies concerning boh he represenaiviy and puriy dimensions. In an aemp o miigae he impac of performance measuremen biases, we applied an adjusmen facor consisen wih Fung and Hsieh (00, 02), i.e., an average annual survivorship bias of 3%, plus an average annual insan hisory bias of 1.4%. We finally charged an index calculaion fee in line wih marke pracice (i.e., 60 bps p.a.). Las bu no leas, i is worh sressing ha a major weakness of he reurn-based analysis is o ignore he degree of significance of he exposures o syle indices, which in urn may have significan consequences on he resuls of he performance aribuion process. To circumven his issue, and reduce saisical noise, we performed a sepwise regression and removed all he facors showing -saisics lower han As evidenced in Oen and Bams (00), cases of misclassificaion migh be reduced by 50% when he significance of facor loadings is aken ino accoun. As a resul, we expec he resuling models o be more accurae and robus, and in urn beer suied for performance measuremen purposes. By doing so, we end up wih a limied se of facors, ranging from 2 o 4 depending on he funds of hedge funds. Unsurprisingly, we find ha he differen funds of hedge funds are no exposed o he same facors, suggesing ha hey follow differen invesmen policies. As one could have expeced, equiy-oriened sraegies, such as Long/Shor Equiy, Shor Selling, Equiy Marke Neural or Merger Arbirage show he highes rae of occurrence. We finally used he mehodology presened in he firs secion o cusomize a Sraegy and a Tacical Benchmarks for every single fund of hedge funds in our sample. 12

13 Exhibi 4: Resuls of he Facor Analysis Hedge Funds sraegy Long/Shor Equiy Equiy Marke Neural Shor Selling Merger Arbirage Global Macro CTA Global Converible Arbirage Disressed Securiies Fixed Income Arbirage Emerging Markes Numerous sudies have been conduced on he sources of he variabiliy of muual fund reurns. They all led o he same conclusion: Sraegic Allocaion accouns for a large par of muual fund reurn variabiliy. This is no surprising as muual funds follow buy-and-hold sraegies. Since funds of hedge funds are acively managed, we expec acive managemen (i.e., Tacical Allocaion and/or Fund Picking) o accoun for a subsanial par of he variabiliy of heir reurns. As a resul, Sraegic Allocaion should mechanically accoun for a significanly lower par of funds of hedge funds reurn variabiliy. To measure he impac of Sraegic Allocaion on he variabiliy of funds of hedge funds reurns, we regressed hese reurns on he hisorical reurns of he corresponding Sraegy Benchmark. As can be seen from Exhibi 5, around 68% of funds of hedge funds reurn variabiliy are explained by heir invesmen policy. This number has o be conrased wih he one obained by muual funds, namely 88% (see Ibboson and Kaplan (00)). The firs observaion we can make is ha Sraegic Allocaion plays a cenral role in funds of hedge funds reurn variabiliy. Funds of hedge funds herefore appear no o be as differen from muual funds as one could have expeced. The second observaion we can make is ha Sraegic Allocaion is a key driver of funds of hedge funds reurn variabiliy whaever he marke regime. In his respec, i is ineresing o poin ou ha i appears o be all he more rue during Sressed Marke Condiions, alhough resuls mus be inerpreed wih care, par of he increase in he coefficien of deerminaion being aribued o he smaller sample size. 13

14 Exhibi 5: Range of Time Series Regression R 2 Values Perceniles Full observaion Normal marke Sressed marke period condiions condiions Muual Funds* 5% 37.8% 37.3% 45.1% 46.9% 25% 55.8% 58.0% 69.7% 79.8% 50% 68.3% 74.3% 83.1% 87.6% 75% 79.9% 88.7% 98.9% 91.4% 95% 4.3% 7.2% 126.4% 94.1% * Resuls are aken from Ibboson and Kaplan (00) To measure he impac of Sraegic Allocaion on he level of funds of hedge funds reurns, we hen compued he raio of he Sraegy Benchmark reurns o he funds of hedge funds oal reurns. As can be seen from Exhibi 6, around 45% of funds of hedge funds reurn are explained by heir invesmen policy. Here again, he Sraegic Allocaion process urns ou o play an imporan role; his ime around, however, i does no appear o be he main driver, excep during Sressed Marke Condiions, whereby i accouns for close o 80% of funds of hedge funds reurn. Exhibi 6: Range of Percenage of Toal Reurn Level Explained by Policy Reurn Perceniles Full observaion Normal marke Sressed marke period condiions condiions Muual Funds* 5% 11.5% 30.1% 29.8% 82% 25% 28.1% 38.6% 45.4% 94% 50% 44.8% 46.7% 78.6% 0% 75% 67.0% 62.2% 111.5% 112% 95% 156.0% 111.0% 240.4% 132% * Resuls are aken from Ibboson and Kaplan (00) So, does Sraegic Allocaion maers? The answer is clearly yes. We find ha despie common percepion, muual funds and funds of hedge funds are acually no ha differen. In boh cases, Sraegic Allocaion urns ou o play a cenral role in he reurn variabiliy (i.e., coefficien of deerminaion ranging from roughly 70% o 80%), and i also appears o accoun for a subsanial porion of oal reurn. In he laer case, however, benefis of Sraegic Allocaion seem o be clearly higher during Sressed Marke Condiions (i.e., 78.6% versus 44.8% for he full observaion period). Can his regime-dependency be explained by he behavior of he Neural Porfolio or is i due o a significan change in he naure of he value added by funds of hedge funds managers? This is wha will ry o find ou in he nex secion. 14

15 III. THE ALPHA AND OMEGA OF FUNDS OF HEDGE FUNDS ADDED VALUE Our objecive in his secion is wofold. On he one hand, we wan o beer undersand he sources of funds of hedge funds managers added-value. On he oher hand, we wan o assess he exen o which he added-value of funds of hedge funds managers, and is sources, is regime-dependen. To address he firs poin, we applied he performance aribuion model inroduced in he firs secion o he differen funds of hedge funds of our sample, on he whole observaion period (i.e., from January 00 hrough July 09). As can be seen from Exhibi 8, funds of hedge funds urn ou o add on average 3.45% p.a. (i.e., 3.45% = 4.50% %) over he performance of he Neural Porfolio. Ineresingly, mos funds of hedge funds appear o creae some value compared o an uninformed invesor over he long erm. The firs driver of his added-value appears o be Fund Picking, which is posiive in 92% of he cases and amouns on average o 2.66% p.a. There is however one cavea. Firsly, he esimaion of he value added hrough Fund Picking very much depends on he adjusmens we made o ake ino accoun performance measuremen biases and oher index fees; should hese adjusmens have been lower, he value added would have decreased proporionally, and would evenually have urned negaive. We mus herefore inerpre his resul wih care. I is however worh poining ou ha Fund Picking appears o be a double-edged sword in ha hose funds of hedge funds failing o add value a his sage desroy on average as much as 2.50% p.a. The second driver of funds of hedge funds managers added-value appears o be Sraegic Allocaion, which is posiive in 83% of he cases and amouns on average o 1.08% p.a. And his ime around, he esimaion is no dependen on he performance adjusmens we made (i.e., adjusmens made a he level of he Neural Porfolio and he Sraegy Benchmark are virually he same). Moreover, Sraegic Allocaion does no appear o be a double-edged sword (i.e., disribuion is posiively skewed and negaive oucomes average o -0.31% p.a.). No surprisingly given he limied liquidiy of heir underlying asses, 66% of he funds of hedge funds in our sample desroyed 1.00% p.a. hrough Tacical Allocaion decisions. 15

16 Exhibi 7: Value Added over he Full Period Toal Sraegic Tacical Fund picking Exhibi 8: Value Added over he Full Period Perceniles Toal Sraegic Tacical Funds picking Mean 4.50% 1.08% -0.29% 2.66% Volailiy 2.39% 1.16% 1.72% 2.47% Skewness Kurosis % > % 82.6% 33.7% 92.4% Mean (> 0) 4.61% 1.37% 1.03% 3.11% % < % 17.4% 66.3% 7.6% Mean (< 0) -2.38% -0.31% -1.00% -2.50% To address he second poin, we applied he performance aribuion model inroduced in he firs secion o he differen funds of hedge funds of our sample, over wo differen observaion periods, i.e., Normal Marke Condiions, ranging from January 00 hrough June 07, and Sressed Marke Condiions, running from June 07 o July 09. As can be seen from Exhibi & 12, while funds of hedge funds appear o add on average 4.21% p.a. (i.e., 4.21% = 0.41% % %)) during Normal Marke Condiions, hey urn our o add on average 0.73% p.a. (i.e., 0.73% = 2.50% % %) during Sressed Marke Condiions. When drilling down o he sources of funds of hedge funds managers, we have he confirmaion ha Fund Picking is a double-edged sword. I can be rewarding, during Normal 16

17 Marke Condiions, and provided ha he funds of hedge funds manager proves o be able o idenify he bes single hedge funds (i.e., +3.89% p.a. on average), bu i is cosly when he funds of hedge funds manager is no an exper a picking he righ funds, especially during Sressed Marke Condiions (i.e., -4.30% p.a.). In his respec, i is worh poining ou ha while 93% of he funds of hedge funds in our sample succeeded in adding value a he Fund Picking level during Normal Marke Condiions, only 48% proved o be able o do so during Sressed Marke Condiions. I is hus precisely when one needs Fund Picking o bring resiliency ha i seems o add o downside risk. And here again, he esimaed value added by funds of hedge funds managers a he Fund Picking level very much depends on he performance adjusmens deailed in he second secion of his aricle. Fund Picking could herefore prove o be an even riskier game o play ha he resuls presened in Exhibi and 12 sugges. In oher words, as far as he value added by funds of hedge funds managers a he Fund Picking level is concerned, here is no only a wide cross secional dispersion, bu here is also a srong - and unfavorable regime-dependency. The picure is very differen when i comes o he value added by funds of hedge funds manager a he Sraegic Allocaion level. Firsly, as can be seen from Exhibi and 12, cross secional dispersion is fairly limied and he disribuion of he added-value is posiively skewed whaever he marke regime. As a maer of fac, funds of hedge funds ha failed o creae value (only) desroyed on average 0.64% p.a. during Normal Marke Condiions, and 0.99% p.a. during Sressed Marke Condiions. Those funds of hedge funds ha proved o be more successful added respecively 1.54% p.a. and 3.50% p.a. Secondly, alhough he value added a he Sraegic Allocaion level shows a cerain regime-dependency, his ime around i urns ou o be very favorable. While funds of hedge funds in our sample added on average 0.41% p.a. during Normal Marke Condiions (48% of were posiive), hey added on average 2.50% p.a. during Sressed Marke Condiions (77% of were posiive). As opposed o Fund Picking, i is precisely when i is needed he mos ha benefis from Sraegic Allocaion are he sronges. This posiive asymmery is no surprising in ha Sraegy Allocaion and Risk Managemen are wo sides of he coin. In line wih he resuls found over he whole observaion period, Tacical Allocaion appears o accoun for a limied porion of he oal value added by funds of hedge funds managers. Moreover, i shows a similar paern as Fund Picking when he environmen changes. Indeed, when funds of hedge funds add on average 0.32% p.a. a his sage during Normal Marke 17

18 Condiions, hey derac on average 1.69% p.a. when marke condiions deeriorae. On op of ha, while 61% of he funds of hedge funds in our sample showed a cerain skill o ime heir sraegy exposures during Normal Marke Condiions, only 31% proved o succeed in doing so during Sressed Marke Condiions. This does no come as a big surprise given he liquidiy of he underlying funds, and heir behavior hroughou he recen crisis. Exhibi 9: Value Added over Normal Marke Condiions 25 Toal Tacical Sraegic Fund picking Exhibi : Value Added over Normal Marke Condiions Perceniles Toal Sraegic Tacical Funds picking Mean 7.76% 0.41% 0.32% 3.48% Volailiy 2.77% 1.51% 2.24% 2.50% Skewness Kurosis % > 0 0% 48.4% 60.9% 92.9% Mean (> 0) 7.76% 1.54% 1.24% 3.89% % < 0 0% 51.6% 39.1% 7.1% Mean (< 0) -0.64% -1.08% -2.11% 18

19 Exhibi 11: Value Added over Sressed Marke Condiions Toal Tacical Sraegic Fund picking Exhibi 12: Value Added over Sressed Marke Condiions Perceniles Toal Sraegic Tacical Funds picking Mean -7.25% 2.50% -1.69% -0.09% Volailiy 7.31% 3.60% 3.31% 6.79% Skewness Kurosis % > 0 9.8% 77.7% 30.9% 48.4% Mean (> 0) 5.64% 3.50% 1.86% 4.19% % < % 22.3% 69.1% 51.6% Mean (< 0) -8.65% -0.99% -3.13% -4.30% From he above we can conclude ha Sraegic Allocaion is he only sep of he invesmen process where funds of hedge funds managers consisenly bring value, and more imporanly, ha helps miigaing he downside risk during Sressed Marke Condiions. I herefore clearly appears as a key driver of he value added by funds of hedge funds managers. The quesion is o find ou which sep of he invesmen process bes explains he cross secional dispersion of funds of hedge funds reurns. To address his poin, we simply regressed he value added by he differen funds of hedge funds a he differen seps of he invesmen process, on heir oal reurn. Resuls are presened below. 19

20 Toal Toal Toal Exhibi 13: Cross Secional Dispersion of Funds of Hedge Funds Reurns 15% 11% 7% 3% -1% -5% -3% 0% Sraegic 3% 6% 15% 11% 7% 3% -1% -5% -5% -3% -1% Tacical 1% 3% 5% 15% 11% 7% 3% -1% -5% -5% -2% 1% 4% 7% % Fund Picking As can be seen from Exhibi 13, Fund Picking urns ou o be a discriminaing facor for funds of hedge funds performance, boh on he upside, and on he downside, while Sraegic Allocaion shows a posiive asymmery. And, in line wih our previous conclusions, Tacical Allocaion does no seem o be of a grea help o explain he performance discrepancy beween he differen funds of hedge funds of our sample. These resuls sugges ha invesmen companies, wih limied resources and/or no specific Fund Picking skill, would clearly be beer off focusing heir effors on Sraegic Allocaion. In he nex secion, we ll ry o find ou wheher some funds of hedge funds characerisics, such as he asses under managemen, he liquidiy erms, or he flows, may also impac heir abiliy o add value hroughou he invesmen process.

21 IV. SHEDDING MORE LIGHT ON THE ADDED VALUE OF FUNDS OF HEDGE FUNDS As we have seen in he previous secion, he value added by funds of hedge funds, and he sources of his added-value largely depend on he marke environmen. Our objecive in his secion is o assess he exen o which i may also depend on funds characerisics. We sared by soring funds of hedge funds by size and formed hree groups wih asses ranging from 0 o $250mio, from $250mio o $1bn, and wih asses above $1bn. We hen calculaed he average value added by he hree groups a he differen sages of he invesmen process. The resuls presened in Exhibi 14 confirm he inuiion ha we had in he previous secion, in ha he larges funds of hedge funds appear o be beer equipped han smaller ones o add value hrough Fund Picking; Fund Picking even shows up as he main deerminan of he ouperformance of larges funds of hedge funds. The reason for ha migh be a privileged access for sar hedge fund managers or simply a wider coverage of he invesmen universe. I is however difficul o draw a definiive conclusion given he sensiiviy of his resul o he adjusmens made o accoun for performance measuremen biases. Ineresingly, he larger he fund of hedge funds, he smaller he value added a he Sraegic Allocaion sage. This suggess ha when asses grow, funds of hedge funds ge - a a cerain poin - cumbersome, and hey lose he flexibiliy required o gain exposure o niche sraegies. They herefore end up wih a porfolio ha is closer o he marke porfolio han he one of more nimble managers. Exhibi 14: Impac of Funds of hedge Funds Asses Under Managemen (in $ mio) All Over 00 Toal Mean 4.50% 4.39% 4.64% 4.93% Vol 2.39% 2.64% 1.82% 1.51% Sraegic Mean 1.08% 1.14% 1.05% 0.72% Vol 1.16% 1.24% 0.91% 1.01% Tacical Mean -0.29% -0.44% -0.26% -0.03% Vol 1.72% 1.24% 0.93% 0.86% Funds picking Mean 2.66% 2.64% 2.81% 3.% Vol 2.47% 2.40% 1.61% 1.40% 21

22 The liquidiy of fund of hedge funds is also liable o have a maerial impac on heir flexibiliy, and in urn, on heir capaciy o add value. Funds of hedge funds redempion frequency can a priori be used as a proxy for he weighed average liquidiy of he underlying hedge funds. The mos liquid funds of hedge funds are herefore supposed o be hose benefiing from he greaes capaciy o adjus heir Sraegy Allocaion o changing marke condiions. One can consequenly expec ha hese funds of hedge funds will end o add more value han heir less liquid compeiors a he Tacical Allocaion level. Conversely, less liquid funds of hedge funds being less consrained han heir mos liquid compeiors, hey should be able o creae more value a he Sraegic Allocaion and evenually a he Fund Picking levels. To es hese hypoheses, we sored he funds of hedge funds by liquidiy erms, and conrased he average value added by he corresponding funds of hedge funds a he Sraegic Allocaion, Tacical Allocaion, and Fund Picking levels. As can be seen from Exhibi 15, value added a he Tacical Allocaion level declines when liquidiy erms deeriorae, bu only o a limied exen (i.e., an average of -0.30% p.a. for he monhly liquidiy bucke vs % p.a. for he annual liquidiy bucke). The impac a he Sraegic Allocaion and Fund Picking levels appears o be somewha higher. Value a he Sraegic Allocaion level surprisingly increases when we go up he liquidiy ladder (i.e., an average of 1.15% p.a. for he monhly liquidiy bucke vs. 0.87% p.a. for he annual liquidiy bucke). As expeced, value a he Fund Picking level is significanly higher when we go down he liquidiy ladder (i.e., an average of 3.74% p.a. for he annual liquidiy bucke vs. 2.37% p.a. for he monhly liquidiy bucke). Exhibi 15: Impac of Funds of Hedge Funds Liquidiy Toal Sraegic Tacical Fund picking All Mean 4.50% 1.08% -0.29% 2.66% Vol 2.39% 1.16% 1.72% 2.47% Monhly Mean 4.26% 1.15% -0.30% 2.37% Vol 2.80% 1.22% 0.97% 2.29% Quarerly Mean 4.42% 1.13% -0.11% 2.35% Vol 2.32% 1.24% 2.61% 3.04% Annual Mean 5.% 0.87% -0.55% 3.74% Vol 1.33% 0.88% 0.99% 1.28% 22

23 Bu, as clearly evidenced during he recen crisis, he liquidiy of he asses and he liabiliies of funds of hedge funds is no always perfecly aligned. Resuls presened in Exhibi 15 may herefore give a biased picure of he relaionship beween liquidiy and he capaciy of funds of hedge funds managers o creae value a he differen sages of he invesmen process. We proceeded as follows o address his issue and esimae he liquidiy of heir asses, and in urn, he exen o which i affecs heir capaciy o creae value hroughou he invesmen process. Firsly, we classified hedge fund sraegies in wo groups, referred o as liquid (i.e., Long/Shor Equiy, Equiy Marke Neural, Shor Selling, Merger Arbirage, CTA, and Global Macro) and illiquid (i.e., Converible Bond Arbirage, Fixed Income Arbirage, Emerging Markes). Secondly, using heir cusomized Sraegy Benchmarks we esimaed he exposure of funds of hedge funds o liquid/illiquid sraegies. Finally, we formed hree groups, wih varying exposures o liquid/illiquid sraegies. We considered ha hose funds of hedge funds made up of a leas wo hird of liquid sraegies were liquid, whereas hose wih less han a hird of liquid sraegies were illiquid; he remaining funds of hedge funds fall ino he socalled Average caegory. As can be seen from Exhibi 16, we ge a very differen picure from he one obained in he previous experimen. While he value desroyed a he Tacical Allocaion level is also minimal for he mos liquid funds of hedge funds, value added hrough Sraegic Allocaion and Fund Picking evolves in he opposie way. I improves when we go down he liquidiy ladder for he Sraegic Allocaion (i.e., an average of 2.29% p.a. for he illiquid caegory vs. 0.69% p.a. for liquid one), bu deerioraes a bi in he case of Fund Picking (i.e., an average of 2.99% p.a. for he liquid caegory vs. 2.27% p.a. for he illiquid one). This ime around, mos of he illiquidiy premium appears o be capured, as one could have expeced, a he Sraegic Allocaion level. Exhibi 16: Impac of Funds of hedge Funds Invesmen Policy Toal Sraegic Tacical Fund picking All Mean 4.50% 1.08% -0.29% 2.66% Vol 2.39% 1.16% 1.72% 2.47% Liquid Mean 4.67% 0.69% -0.06% 2.99% Vol 2.46% 1.04% 1.01% 2.18% Average Mean 3.88% 1.03% -0.75% 2.56% Vol 2.46% 0.71% 0.84% 2.18% Illiquid Mean 5.01% 2.29% -0.60% 2.27% Vol 2.63% 1.30% 1.65% 2.31% 23

24 Finally, as a resul of a poenially significan mismach beween he liquidiy of he asses and he liabiliies, he capaciy of funds of hedge funds managers o add value a he differen seps of he invesmen process may be sensiive o flows, especially ou-flows. In an aemp o es his hypohesis, we sored he funds of hedge funds based on he flows hey have experienced over he period corresponding o Sressed Marke Condiions, and conrased he average value added by he corresponding funds of hedge funds a he Sraegic Allocaion, Tacical Allocaion, and Fund Picking levels. As can be seen from Exhibi 17, here is a high correlaion beween funds of hedge funds oal performance and flows, bu i is on Fund Picking ha he impac of flows appears o be he larges (i.e., an average of 4.34% p.a. for funds experiencing in-flows vs % p.a. for hose experiencing ou flows). I is however difficul o draw a definiive conclusion due o he endogeneiy beween flows and performance. Wha is more ineresing o observe is he fac ha funds of hedge funds add value a he Sraegic Allocaion level even in he case of massive ou-flows, alhough o a lesser exen (i.e., an average of 4.08% p.a. for funds experiencing in-flows vs. 1.92% p.a. for hose experiencing ou-flows). This confirms ha Sraegic Allocaion brings resilience during marke urmoil. Exhibi 17: Impac of Funds of Hedge Funds Ou-Flows over he Sressed Marke Condiions Period Toal Sraegic Tacical Fund picking All Mean -7.25% 2.50% -1.69% -0.09% Vol 7.31% 3.60% 3.31% 6.79% >0% Mean -1.60% 4.08% -2.04% 4.34% Vol 7.89% 4.58% 3.73% 8.54% [-50%;0%] Mean -6.90% 2.24% -1.72% 0.55% Vol 4.85% 3.32% 3.12% 4.42% <-50% Mean % 1.92% -1.41% -3.84% Vol 7.72% 3.06% 3.35% 6.75% 24

25 V. CONCLUDING REMARKS AND EXTENSIONS Funds of hedge funds have long been he favourie roue for radiional invesors ha look for alernaive invesmens, bu lack he experience and more generally he resources o inernalize he whole invesmen process. The flipside of funds of hedge funds appealing value proposiion, however, is a double fee srucure. Our objecive in his aricle was o find ou wheher funds of hedge funds managers succeed in overcoming his seback hrough acive managemen, or if, like muual funds, hey end o fail o add value. We proposed o his end a reurn-based aribuion model incorporaing sae-space models ha allows for a full decomposiion of funds of hedge funds reurns. The resuls of our empirical sudy sugges ha funds of hedge funds are funds of funds like ohers. Sraegic Allocaion urns ou o explain a significan porion of boh he variabiliy and he level of reurn of funds of hedge funds (respecively 68% and 45%). Moreover, i adds value over he long-erm, and mos imporanly, i brings resilience precisely when invesors need i he mos. Sraegic Allocaion herefore is a key driver of funds of hedge funds performance. Fund picking also urns ou o be a poenial source of enhanced reurns hough more difficul o capure, as shown by a significan cross secion dispersion; Fund Picking clearly is a double-edged sword ha requires exensive resources and a seasoned experise o be masered properly. Tacical Allocaion, on he oher hand, has a marginal impac on he performance of funds of hedge funds. In sum, invesors wih limied resources and/or experise would probably be beer off focusing on Sraegic Allocaion. Overall, funds of hedge funds - unlike muual funds - succeed in overcoming heir double fee srucure, and add value across marke regimes, alhough o varying degrees and in differen forms. We can herefore conclude ha he ou-flows from funds of hedge funds ha we keep on observing canno be aribued o a collecive failure of funds of hedge funds managers o deliver on heir promises. These resuls end o corroborae he findings of a recen indusry survey which concludes ha [ ] he rend for going direc is a resul of an indusry mauriy raher han ha of an individual player (please refer o I Takes Three for a Tango, Barclays Capial Asse Managemen Soluions Group, April ). 25

26 There is however one cavea o hese resuls. As menioned in he firs secion, we made he assumpion ha funds of hedge funds mainained he same Sraegic Allocaion over he whole observaion period. Bu marke condiions have changed maerially, and some managers may have adjused heir Sraegic Allocaion accordingly. Furher research needs o be done o ake his phenomenon ino accoun in he design of he Sraegy. As highlighed by Meron (1981), Admai and Ross (1985), or Dybvig and Ross (1985), when a fund is iming he marke, is exposure o he marke will no be linear, generally leading o a biased esimae of is sock picking abiliy. The same remark probably holds rue when considering changes in he Sraegic Allocaion of a fund of hedge funds, and he esimaion of he value added a he Tacical Allocaion and Fund Picking levels. One soluion o address his issue would be o include a srucural break analysis o consider several sraegic allocaions. From a pracical sandpoin, his aricle provides invesors wih a pragmaic approach o gain an in-deph undersanding of he added value and he sources of he added value of funds of hedge funds. I can herefore help smaller invesors separaing he whea from he chaff and miigae he so-called selecion risk when hey are looking for a parner. I can also help invesors wih more resources o deermine wheher hey would be beer off going on heir own, or via a dedicaed fund of hedge funds srucure. Should hey op for he laer soluion, i could help hem monioring he hidden coss ha ypically come hand in hand wih agency relaionships. Finally, our empirical sudy clearly evidenced ha he conribuion of Tacical Allocaion o funds of hedge funds overall performance is limied, and more ofen han no negaive. This can probably be parly explained by he poor level of liquidiy offered by hedge funds in he pas. Bu now ha liquid producs are available on he marke, furher research needs o be conduced on dynamic porfolio consrucion approaches, in order o ake ino accoun he dynamics of hedge fund sraegies, and in urn, beer conrol for funds of hedge funds downside risk.. 26

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