av Vikas Agarwal, Narayan Y. Naik. 2002. Hedgefond er kjent for å vise ikke-lineære opsjonslignende eksponeringer til standardaktiva klasser, og den tradisjonelle lineære faktormodellen gir derfor begrenset hjelp til å ta vare på sine avkastningsavvik. Vi løser dette problemet ved å øke den tradisjonelle modellen med opsjonsbaserte risikofaktorer. O. Hedgefond er kjent for å vise ikke-lineære opsjonslignende eksponeringer til standardaktiva klasser, og den tradisjonelle lineære faktormodellen gir derfor begrenset hjelp til å ta vare på sine avkastningsavvik. Vi løser dette problemet ved å øke den tradisjonelle modellen med opsjonsbaserte risikofaktorer. Våre resultater viser at et stort antall aksjeorienterte sikringsfondsstrategier viser utbetalinger som ligner en kort posisjon i et put-alternativ på markedsindeksen, og har derfor betydelig risiko for venstre risiko, risiko som blir ignorert av den vanlige middelvariasjonsrammen . Ved å bruke en middels-betinget Value-at-Risk-rammeverk, demonstrerer vi i hvilken grad middel-variansrammen undervurderer hale-risikoen. Arbeid med den underliggende systematiske av Mila Getmansky, Andrew W. Lo, Igor Makarov - Journal of Financial Economics. 2004. Avkastningen til sikringsfond og andre alternative investeringer er ofte sterkt korrelert, i skarp kontrast til avkastningen på mer tradisjonelle investeringsvogner, for eksempel langvarige aksjeporteføljer og verdipapirfond. I dette papiret undersøker vi flere kilder til slik seriell korrelasjon og s. Avkastningen til sikringsfond og andre alternative investeringer er ofte sterkt korrelert, i skarp kontrast til avkastningen på mer tradisjonelle investeringsvogner, for eksempel langvarige aksjeporteføljer og verdipapirfond. I dette papiret undersøker vi flere kilder til slik seriell korrelasjon og viser at den mest sannsynlige forklaringen er eksponering for likviditet, dvs. investeringer i verdipapirer som ikke er aktivt handlet og hvilke markedspriser som ikke alltid er tilgjengelige. For porteføljer av illikvide verdipapirer vil rapporterte avkastninger ha en tendens til å være jevnere enn ekte økonomisk avkastning, noe som vil undergrave volatiliteten og øke risikojusterte ytelsesmålinger som Sharpe-forholdet. Vi foreslår en økonometrisk modell for illikviditetseksponering og utvikler estimatorer for utjevningsprofilen, samt et utjevningsjustert Sharpe-forhold. For et utvalg av 908 hedgefond hentet fra TASS-databasen, viser vi at våre estimerte utjevningskoeffisienter varierer vesentlig over hedgefonds stilkategorier og kan være en nyttig proxy for å kvantifisere eksponering for likviditet. av Markus K. Brunnermeier, Stefan Nagel - FINANSIELLTIDEN VOL. LIX, NO. 5. OKTOBER 2004. 2004. Dette dokumentet dokumenterer at hedgefondene ikke utøve en korrigerende kraft på aksjekursene under teknologiboblen. I stedet var de tungt investert i teknologibestandene. Dette ser ikke ut som følge av ubevissthet av boblen: Hedgefondene fanget oppgangen, men ved å redusere deres pos. Dette dokumentet dokumenterer at hedgefondene ikke utøve en korrigerende kraft på aksjekursene under teknologiboblen. I stedet var de tungt investert i teknologibestandene. Dette synes ikke å være et resultat av ubevissthet av boblen: Hedgefondene fanget oppgangen, men ved å redusere sine posisjoner i aksjer som var i ferd med å avta, unngikk mye av nedgangen. Våre funn spørsmålet om effektive markeder at rationelle spekulanter alltid stabiliserer prisene. De er konsistente med modeller der rasjonelle investorer kan foretrekke å ri bobler på grunn av forutsigbar investorstemning og begrensninger for arbitrage. være korrelert på denne måten. For det andre kan enkelte sikringsfond også anvende dynamiske handelsstrategier, som genererer ikke-lineær eksponering for aktivaklassefaktorer, noe som innebærer at en lineær modell ikke er spesifisert (-Fung og Hsieh 1997 - Agarwal og Naik 2000). Vi utførte en uformell kontroll av scatterplots og fant ikke mye ikke-linearitet i vår utvalg av sikringsfondets avkastning. Samlet tror vi at vår enkle modell 6 For midler. av Jennifer N. Carpenter - Journal of Finance. 2000. Dette papiret løser det dynamiske investeringsproblemet til en risikovillig leder kompensert med et anropsalternativ på de eiendelene han kontrollerer. Under lederens optimale policy, blir opsjonen enten dypt i eller dypt ut av pengene. Når aktivverdien går til null, går volatiliteten til uendelig. Imidlertid t. Dette papiret løser det dynamiske investeringsproblemet til en risikovillig leder kompensert med et anropsalternativ på de eiendelene han kontrollerer. Under lederens optimale policy, blir opsjonen enten dypt i eller dypt ut av pengene. Når aktivverdien går til null, går volatiliteten til uendelig. Alternativet kompensasjon fører imidlertid ikke strengt til økt risikosøking. Noen ganger er lederens optimale volatilitet mindre med alternativet enn det ville være hvis han handlet sin egen konto. Videre gir han sjefen flere alternativer, noe som gjør at han reduserer volatiliteten. Lederne med konverteringskompensasjonssystemer spiller en viktig rolle i finansmarkedene. Dette papiret løser for den optimale dynamiske investeringspolitikken for en risikoviktig leder betalt med et opsjonsalternativ på de eiendelene han kontrollerer. Papiret fokuserer på hvordan opsjonskompensasjonen påvirker lederne appetitt for risiko når han ikke kan sikre opsjonsposisjonen. På den ene siden gjør konveksiteten til opsjonen at lederen reduserer utbetalinger som sannsynligvis vil være nær pengene. Under den optimale politikken er lederen av Vikas Agarwal, Narayan Y. Naik, Elroy Dimson, William Goetzmann, David Hsieh, Frans De Roon, Henri Servaes - Journal of Financial and Quantitative Analysis. 2000. anonym dommer og deltakere på hedgefondskonferansen ved Duke University og Issues in. anonym dommer og deltakere på hedgefondskonferansen ved Duke University og Issues i av George O. Aragon - Journal of Financial Economics. 2007. Dette papiret finner en positiv, konkav relasjon mellom avkastningen og deling av re-strengelser av private investeringsfond, og viser at tidligere dokumenterte positive alfaer kan tolkes som kompensasjon for å holde illikvide fondaksjer. Den årlige avkastningen på midler med låseavsetninger er ca. Dette papiret finner en positiv, konkav relasjon mellom avkastningen og deling av re-strengelser av private investeringsfond, og viser at tidligere dokumenterte positive alfaer kan tolkes som kompensasjon for å holde illikvide fondaksjer. Den årlige avkastningen på midler med låseavsetninger er ca. 4 høyere enn for ikke-låsefonde, og alfasene av midler med de mest likvide aksjene er enten negative eller ubetydelige. Dette papiret finner også en positiv tilknytning mellom aksjebegrensninger og illikviditet i fondsmidler, noe som tyder på at midler som står overfor høye innløsningskostnader, bruker restriksjoner til skjerm for investorer med lav likviditetsbehov. Resultatene er i tråd med tidligere teorier som tyder på at likviditeten er priset, og at mindre likvide midler holdes av investorer med lengre investeringshorisonter. JEL-klassifisering: G11 G12 av Nicolas P. B. Bollen, Jeffrey A. Busse - FINANSIELLTIDEN VOL. LVI, NO. 3. JUNI 2001. Eksisterende studier av fondsbasert markedsundersøkelse analyserer månedlig avkastning og finner lite bevis på timing evne. Vi viser at daglige tester er kraftigere, og at verdipapirforetakene viser en betydelig timingsevne oftere i daglige tester enn i månedlige tester. Vi bygger et sett med syntetisk fond. Eksisterende studier av fondsbasert markedsundersøkelse analyserer månedlig avkastning og finner lite bevis på timing evne. Vi viser at daglige tester er kraftigere og at verdipapirforetakene viser en betydelig timing evne oftere i daglige tester enn i månedlige tester. Vi bygger et sett med syntetisk fond avkastning for å kontrollere for falske resultater. De daglige timingskoeffisientene for flertallet av midler er vesentlig forskjellig fra deres syntetiske kolleger. Disse resultatene tyder på at verdipapirforetak kan ha mer timing-evne enn tidligere dokumentert. av Gaurav S. Amin, Harry M. Kat. 2002Empiriske egenskaper av dynamiske handelsstrategier: Saken av sikringsfond Denne artikkelen presenterer noen nye resultater på et uutforsket datasett om sikringsfondets ytelse. Resultatene indikerer at hedgefondene følger strategier som er dramatisk forskjellig fra fond, og støtter påstanden om at disse strategiene er svært dynamiske. Artikkelen finner fem dominerende investeringsstiler i hedgefond, som når det legges til Sharpes (1992) eiendomsklassefaktormodell, kan gi et integrert rammeverk for stilanalyse av både buy-and-hold og dynamiske handelsstrategier. Artikkel utgitt av Oxford University Press på vegne av Society for Financial Studies i sin journal, The Review of Financial Studies. Vi vet at dette produktet ikke er tilgjengelig for nedlasting. For å finne ut om det er tilgjengelig, er det tre alternativer: 1. Sjekk under Relatert forskning om en annen versjon av denne artikkelen er tilgjengelig online. 2. Kontroller leverandørens nettside om den faktisk er tilgjengelig. 3. Utfør et søk etter en tilsvarende tittel som vil være tilgjengelig. Artikkel levert av Society for Financial Studies i sin journal Review of Financial Studies. Volum (År): 10 (1997) Utgave (Måned): 2 () Sider: 275-302Empiriske Kjennetegn ved Dynamiske Trading Strategier: Saken av HedgefondeneBecause hedgefondene er underlagt liten regulering, kan de bruke sitt skjønn til å redusere eller dra nytte av et likviditetsstøt. Som Fung og Hsieh (1997) viser, legger til fem dominerende investeringsstiler som brukes av hedgefond til Sharpex27s (1992) aktivklassefaktormodell, et integrert rammeverk for en stilanalyse av dynamiske handelsstrategier. Bevis viser også at dynamiske handelsstrategier påvirker sikringsfondets risikoeksponering. sitat Vis abstrakte Skjul abstrakt ABSTRAKT: Vi studerer høydemessig fordeling av sikringsfondets avkastning og identifiserer faktorer som kjører høytidsrisiko. Ved å bruke sikringsfondets månedlige avkastning, finner vi en sterk sammenheng mellom de første fire øyeblikkene av avkastning (dvs. gjennomsnittlig standardavvik (SD), skewness og kurtosis) og ulike karakteristika av midlene som løftestang, likviditet, insentiver og strategi - relaterte faktorer. Vi finner at etter å ha kontrollert for andre faktorer, har incentivrelaterte faktorer og en sikringsfonds27s spesifikke strategi størst innvirkning på fordelingen av fondets avkastning. Våre bevis tyder også på at investorer fordeler seg på tvers av hedgefondskarakteristikker samtidig som de legger større vekt på fondsstrategier og incentivfaktorer. Fulltekst Artikkel Okt 2016 H Kent Baker Imed Chkir Samir Saadi Ligang Zhong kvote Verdien av denne porteføljen av innkjøpsopsjoner øker med økningen av volatiliteten til HF-verdien og HFM-ene utøver disse alternativene dersom, på opsjonsperioden, Verdien av forvaltningskapitalen overstiger aksjekursen for innkjøpsmulighetene (IF). 7. Fung og Hsieh (1997) finner at når IFC er ute av pengene, dvs. den nåværende HF-verdien er under strekkprisen for den underliggende samtalen opsjoner, kontraktsbetingelser og omdømme bekymringer kan forhindre ledere i å øke risikoen. Det ser ut til at, når et godt omdømme er bygget, har HFM en tendens til å bevare den ved å følge mindre risikable styringsstrategier. sitat Vis abstrakt Skjul abstrakt ABSTRAKT: Dette papiret oppsummerer litteraturen om hedgefond (HF) utviklet de siste to tiårene, spesielt det som relaterer seg til lederegenskaper (en følgesvenn dekker risikostyringsegenskapene til HFs). Den klassifiserer den nåværende HF-litteraturen, og antyder hvilke kritiske problemer som er blitt løst, og hvilke problemer som ennå ikke er tilstrekkelig adressert. Det drøfter også virkningen av tidligere finansregulering og utsiktene for effekten av ny finansregulering på HF-industrien og dens prestasjons - og risikostyringspraksis, og foreslår nye veier for forskning. Videre fremhever det viktigheten av lederegenskaper for HF-ytelse, og suksessene og manglene til dato i utviklingen av mer sofistikerte HF-relaterte risikostyringsverktøy. JEL Klassifiseringskode: G20, G23 Fulltekst Artikkel Sep 2016 Internasjonal forretningsforskning Alcino Azevedo Izidin El Kalak-kvotePositive kurtose er preget av en toppet eller leptokurtisk distribusjon negativ kurtose indikerer en relativt flat fordeling. Fordeling med høye nivåer av kurtose er kjent som fettstert og er ikke-gaussisk (Fung amp Hsieh, 1997). sitat Vis abstrakt Skjul abstrakt ABSTRAKT: pgt Mange forsøk har blitt utført for å løse fremover-premiepuslespillet med liten eller ingen suksess. Det globale valutamarkedet betraktes som den mest informasjonseffektive og gjennomsiktige av alle finansmarkeder siden det viser en balanse mellom over og underreaksjon på informasjon med bemerkelsesverdig konsistens. Den effektive markedshypotesen sier at investorer ikke systematisk kan overgå et referanseindeks siden alle investorer har tilgang til samme informasjon. Derfor er forventet langsiktig avkastning for valutaer i hovedsak null. Arbitrage Pricing Theory hevder at investeringsavkastningen er tilfeldig. Som sådan kan handelsmenn ikke benytte seg av mispriced valutaer. Påstanden om avdekket renteparitetsparitet er at bi-nasjonal renteavvik er lik forventet differens i valutakurser. Dette papiret stiller følgende spørsmål: eksisterer alfa-persistens i valutabæringsfond eller er det meravkastningen bare en samling av atferdsmessig biasesltp Fulltekst Artikkel Aug 2016 Ian HudsonFung og Hsieh Document (1997) - Empiriske egenskaper. Dette er slutten av forhåndsvisningen. Registrer deg for å få tilgang til resten av dokumentet. Uformatert tekstforhåndsvisning: Empiriske egenskaper til dynamiske handelsstrategier: Saken av hedgefondene William Fung Paradigm, LDC David A. Hsieh Duke University Denne artikkelen presenterer noen nye resultater på et uutforsket datasett om hedgefondets ytelse. Resultatene indikerer at hedgefondene følger strategier som er dramatisk forskjellig fra fond, og støtter påstanden om at disse strategiene er svært dynamiske. Artikkelen er ikke dominerende investeringsstiler i hedgefond, som når den legges til Sharpes (1992) eiendomsklassefaktormodell, kan gi et integrert rammeverk for stilanalyse av både buy-and-hold og dynamiske handelsstrategier. Sharpe (1992) foreslo en eiendelklassefaktormodell for prestasjonsattribusjon og stilanalyse av fondforvaltere. Elegansen av Sharpes (1992) intuisjon ble demonstrert empirisk ved å vise at bare et begrenset antall store aktivaklasser var nødvendig for å kunne gjenta gjentakelsen av et omfattende univers av amerikanske fond. Basert på dette banebrytende arbeidet, er kommersielle programvarepakker nå allment tilgjengelige for investorer å analysere sine eiendomsfordelingsbeslutninger og stilblandingen av sine porteføljer. Innholdet i denne artikkelen er selve forfatterens meninger og kan ikke være representativ for de respektive institusjonene. Forfatterne er takknemlige for AIG Global Investors, Tass Management og Paradigm LDC for bruk av deres hedgefond og CTA pool databaser. Vi takker Max Baker, James Cui, Mark Unger og Guy Ingram for deres hjelp. Artikkelen benyttet også av kommentarer fra Michael Bradley, Ravi Jagannathan, Pete Kyle, Harry Markowitz, S. Viswanathan, prinsippene i Ivy Asset Management, og en anonym dommer. Adresse korrespondanse og forespørsler om data til David A. Hsieh, Fuqua School of Business, Duke University, Box 90120, Durham, NC 27708-0120. The Review of Financial Studies Summer 1997 Vol. 10, nr. 2, s. 275302 c 1997 Revisjonen av finansielle studier 0893-9454971.50 Revisjonen av finansielle studier v 10 n 2 1997 Suksessen til Sharpes (1992) tilnærming skyldes at de fleste fondforvaltere har investeringsmandat ligner på tradisjonelle kapitalforvaltere med relative returmål. De er vanligvis tvunget til å holde eiendeler i et godt betraktet antall aktivaklasser og er ofte begrenset til liten eller ingen innflytelse. Deres mandater er å møte eller overgå avkastningen på deres aktivaklasser. Derfor er det sannsynlig at de vil gi avkastning som har en tendens til å være svært korrelert med avkastningen på standard aktivaklasser.1 Følgelig er stilistiske forskjeller mellom ledere hovedsakelig på grunn av eiendelene i porteføljene, som lett blir tatt i Sharpes (1992) stilregressjoner. I denne artikkelen foreslår vi en utvidelse av Sharpes (1992) - modellen for å analysere investeringsstyringsstiler. Målet er å ha et integrert rammeverk for å analysere tradisjonelle ledere med relative returmål, samt alternative ledere med absolutte avkastningsmål. Disse alternative ledere har en tendens til å generere avkastning som er mindre korrelert med de som er av standardaktivaklasser. Følgelig må den opprinnelige Sharpe-modellen (1992) modieres for å fange de stilistiske forskjellene mellom disse alternative lederne. Spesielt fokuserer vi på hedgefondsledere og råvarehandlerådgivere. Dette er en viktig klasse ledere innenfor kategorien alternative ledere. Hedgefondsledere og CTA har vanligvis mandater for å gi et absolutt avkastningsmål uavhengig av markedsmiljøet.2 For å oppnå det absolutte avkastningsmålet, får de muligheten til å velge blant mange aktivaklasser og å benytte dynamiske handelsstrategier som ofte involverer kort salg, innflytelse og derivater. Følgelig utvider vi Sharpes (1992) aktivklassefaktormodell for å imøtekomme forskjellene mellom disse alternative lederne tilnærminger og de tradisjonelle fondforvaltere. Vårt arbeid er basert på intuisjonen som lederne returnerer kan karakteriseres mer generelt av tre sentrale determinanter: avkastningen fra eiendeler i lederporteføljene, deres handelsstrategier og deres bruk av innflytelse. I Sharpes (1992) - modellen var fokuset på den sentrale determinanten, plasseringen av avkastningskomponenten, som forteller oss hvilke aktivakategorier lederen investerer i. Vår modell utvider Sharpes-tilnærming ved å inkludere faktorer som reagerer hvordan en leder håndterer strategikomponenten avkastning og bruk av 1 Fondforvaltere kompenseres basert på mengden av forvaltningskapital. Siden fondinnsamlingen har gått til de topplasserte fondene, vurdert i henhold til deres respektive referanser, har ledere incitament til å overgå sine benchmarks. 2 Hedgefondsledere og CTAs utnytter mye av deres kompensasjon fra incitamentsavgifter, som kun utbetales når disse ledere gir en positiv avkastning. I tillegg krever en høy vannmerkefunksjon i sine incentivkontrakter at de skal gjøre opp alle tidligere tap før et incitamentsavgift betales. Dermed er disse alternative ledere kalt absolutte avkastningsforvaltere. 276 Empiriske egenskaper av dynamiske handelsstrategier utnytter kvantitetskomponenten til retur. Å legge til nye faktorer i Sharpes (1992) - modellen tillater oss å imøtekomme ledere som bruker dynamiske, leveraged trading strategier. Det er disse tilleggsfaktorene som gir innsikt i den strategiske forskjellen mellom relativ avkastning og absolutt avkastningsinvestering. Akkurat som Sharpes-modellen gir innsikt i eiendomsmiksavgjørelsen når bare relative returneringsstiler vurderes, gir den utvidede modellen et rammeverk for å analysere eiendomsmiksbeslutningen med et absolutt avkastningsmål. Vi bruker vår modell til 3,327 amerikanske fond fra Morningstar og 409 hedgefondCTA-bassenger fra en unik database som aldri har blitt analysert hittil. Som i Sharpe (1992), er vi at verdipapiravkastningen er høyt korrelert med standard aktivaklasser. I motsetning til at hedgefondsledere og CTAer genererer avkastning som har liten sammenheng med avkastningen på verdipapirfond og standard aktivaklasser. Videre er det mye ytelsesdiversitet innen hedgefond og CTA-bassenger. For å fange denne effekten foreslår vi tre ekstra stilfaktorer til Sharpes (1992) - modellen. Dette forbedrer modellens ytelse signifikant. Artikkelen er organisert som følger. I avsnitt 1 begynner vi med en åtte aktivaklassefaktormodell som ligner Sharpes (1992). Vi kaller disse aktivitets - eller plasseringsfaktorene. Oppdateringer til Sharpes (1992) resultater for amerikanske aksjefond er i del 2. Resultatene viser at den åtte-faktor lineære modellen gir tilfredsstillende estimater av aktivitetsmiks for en mye bredere utvalg av fondforvaltere, med bare mindre modikasjoner. I Seksjon 3 bruker vi Sharpes-stilregressjoner til sikring av fond og CTA-poolavkastning. Seksjon 4 diskuterer forskjellen mellom stedvalg og handelsstrategi. Seksjon 5 omhandler de vanlige stilene i hedgefond og CTA-bassenger. Seksjon 6 kommenterer problemene med ytelsesevaluering og overlevelsesforstyrrelser. Seksjon 7 omhandler implikasjonene til våre ndings og gir noen konklusjoner. 1. En Asset Class Factor Model Vi starter med avkastningen på en portefølje av eiendeler i periode t: Rt xj t rj t. (1) j hvor xj t er vekten på aktivet j i perioden t (fra t 1 til t), og rj t er avkastningen på aktivet j i periode t, j 0. J og j betegner summasjonsoperatøren over alle verdier av j. For enkelhets skyld er j 0-aktiva den risikofrie eiendelen. Ved forutsetning er låneopptaket og utlånsrenten de samme og lik risikofri avkastning. Antallet eiendeler (J) antas å være stort. For eksempel er det mer enn 2000 aksjer notert på New York Børs alene. Da vi inkluderer utenlandske aksjer, statsobligasjoner, bedriftsobligasjoner, boliglån, varer, valuta og så videre, er antall eiendeler i titusenvis. Det er uhåndterlig å jobbe med et stort antall eiendeler, spesielt når mange av dem er svært korrelert med hverandre. For å redusere oppgaven til et mer overkommelig nivå antar vi at det er en faktorstruktur for avkastning som i en standard arbitrage pricing theory (APT) modell: rj t j k Fkt jt. (2) k Det er K systematiske faktorer, Fkt. k 1. K er faktorbelastningen og er den idiosynkratiske avkastningen. Vi antar at de systematiske faktorene er eksogent spesielle og, etter Sharpe (1992), tolker vi faktorene som aktivaklasser. Ved hjelp av faktormodellen kan vi omskrive porteføljens retur som Rt wkt Fkt et. (3) k hvor wkt xj t j k. j et xj t jt. j I stedet for at porteføljene returnerer som et veid gjennomsnitt av et stort antall eiendomsavkastninger, er det nå et veid gjennomsnitt for et lite antall aktivaklasser. Således Sharpes (1992) stil regresjon, Rt bk Fkt ut. (4) k jobber godt med å ta imot stilene av åpne fond, hvis avkastning er høyt korrelert med standardkursene. Sharpe (1992) kaller dette en aktivaklassefaktormodell.3 I denne artikkelen bruker vi tre aksjeklasser: MSCI amerikanske aksjer, MSCI ikke-amerikanske aksjer og IFC-fremvoksende markedsaktier. Det er to obligasjonsklasser: JP Morgan amerikanske statsobligasjoner og JP Morgan ikke-amerikanske statsobligasjoner. 3 Sharpes valg av aktivaklasser er mer orientert mot amerikanske baserte midler, mens vi grupperer eiendeler i åtte klasser med global fokus. 278 Empiriske egenskaper av dynamiske handelsstrategier For kontanter bruker vi 1 måneders eurodollarinnskudd. For varer bruker vi prisen på gull. For valutaer bruker vi Federal Reserve Trade Weighted Dollar Index.4 Vi begynner med å oppdatere Sharpes (1992) resultater på amerikanske aksjer med åpne aksjer på en bredere utvalg. Det empiriske resultatet på verdipapirfond tjener som bakgrunn mot hvilken analysen av sikringsfond og CTA-avkastning kan sammenlignes. 2. Vederlagsberegning og stilanalyse for gjensidig fond Vi kjører Sharpes-stilregresjon for 3,327 åpne fond i Morningstar-databasen (oppdatert til desember 1995), som har minst 36 måneders avkastning. Figur 1 oppsummerer fordelingen av R 2 s av regresjonene. Det viser at 47 av fondene har 2 s over 75 og 92 har 2 s høyere enn 50. Figur 2 gir fordelingen av den (statistisk) mest signifikante aktivaklassen i disse regressene. Åttisju syv prosent av fondene er korrelert med to aktivaklasser: amerikanske aksjer og amerikanske statsobligasjoner. I 99 av midlene er koefcientene til de mest signifikante aktivaklassene positive, og 52 av dem er statistisk større enn null og ikke statistisk forskjellig fra en. Disse resultatene er veldig lik de i den opprinnelige Sharpe (1992) artikkelen. Den høye sammenhengen mellom fondet returnerer til standard avkastning av eiendomsklassen innebærer at valg av stilblanding blant verdipapirfondene ligner på å bestemme eiendomsmiks i porteføljen. Det gir også innfallet at fondets ytelse i stor grad er posisjonert drevet i den forstand at den underliggende strategien, gitt valg av markeder, ligner på kjøp og hold. Derfor, hvor de investerer, er mye mindre hvordan de investerer, nøkkelfaktoren for ytelse i verdipapirfond. Det er denne statiske karakteren av gjensidig fondsstiler som gjør Sharpes stilregresjon velegnet til å analysere gjensidig fondutvikling, og kanskje mer generelt tildeling av tradisjonelle ledere med en relativ avkastningsinvesteringsstil. Den høye korrelasjonen mellom fondsbeviser og aktivaklasser indikerer at fondsstiler er i utgangspunktet buy-and-hold-strategier som benytter ulike aktivaklasser. Det er to unntak. 4 De åtte aktivaklassene er forskjellige fra dem i Sharpe (1992). Sharpes-aktivaklassene er hovedsakelig veiet mot amerikanske verdipapirer. Han bruker flere amerikanske aksjeavkastninger, storkapitalvekst, storkapitalverdi og liten cap. Deres forskjeller er ganske små sammenlignet med bredere og mer globale aktivaklasser som gull, aksjemarkedskapital, etc. Siden disse aktivaklassene er viktige i hedgefondsuniverset, og siden vi må begrense antall aktivaklasser i regressene våre , vi har valgt de bredere, mer globale indeksene. I tillegg har vi utelatt eiendoms - og risikokapital fordi disse eiendelene ikke er viktige i fond, hedgefond og CTA. 279 Revisjonen av finansielle studier v 10 n 2 1997 Figur 1 Fordeling av R2 mot aktivaklasser Figur 2 Distribusjon av mest signifikante aktivaklasser Høyrentekapitalfond og kommunale obligasjonsfond har liten sammenheng med de åtte aktivaklassene. Med tanke på antall høyverdige bedriftsobligasjonsfond, og interessen i nødstilfeller av institusjonelle investorer, er inkludering av en høy yield-bedriftsindeksindekser berettiget. Gitt at kommunale obligasjoner har en lav 280 Empirical Characteristics of Dynamic Trading Strategies-korrelasjon med regjeringer, kan man vurdere å legge til en kommuneobligasjonsindeks for skattepliktige investorer for å regne for forskjellen mellom skattepliktig og skattefri avkastning. 3. Hedgefond Prestasjonsattribusjon Vi går nå til hedgefond og CTA-bassenger. Hedgefond er private investeringspartnerskapselskaper hvor administrerende partnerskap er gitt et bredt investeringsmandat. Disse kjøretøyene er begrenset til sofistikerte høyverdige investorer. En CTA er en individuell eller handelsorganisasjon, registrert hos Commodity Futures Trading Commission (CFTC) gjennom medlemskap i National Futures Association, gitt fullmakt til å foreta handelsbeslutninger på vegne av en kunde i futures, opsjoner og verdipapirer som er etablert utelukkende for kunden (administrert konto). Fram til adventen av diversierte futures-bassenger på 1980-tallet var CTA begrenset til hva de kunne handle (varer, råvare futures og futures alternativer). Globaliseringen og utvidelsen av alle markeder og reduksjonen i regulatoriske begrensninger har gitt GTA muligheten til å handle et økende antall instrumenter, for eksempel verdensrente-, valuta-, egenkapital - og fysiske råvaremarkeder. Derfor, mens historisk CTAs er blitt skilt fra hedgefondsforvaltere, har skillet mellom de to ti årene blitt uklart, da CTAs driver private investeringspartnerskap med brede mandater i nesten alle nasjonale markeder. Faktisk har flere ledere både hedgefond og CTA-bassenger. I denne artikkelen behandles hedgefond og CTA-bassenger som en enkelt gruppe midler, bare referert til som hedgefond. Vi kjører Sharpes stil regresjon på avkastningen på 409 hedgefond. Det er hensiktsmessig å kommentere omfanget av vår prøve. I motsetning til fondsmidler er det ikke nødvendig med sikringsfondforvaltere å offentliggjøre sine resultater og eiendeler under ledelse. Futures (februar 1995, s. 6264) anslår at det er mellom 1000 og 2000 hedgefond med 100160 milliarder kroner i forvaltningskapital ved utgangen av 1994.5 Selv om disse tallene ser ut til å være små i forhold til fondbransjen, oppover på 6000 midler og 2 billioner i eiendeler, på en leveransegrunnlag overstiger stillingene som et stort hedgefond tar, ofte over de største fondsmedlemmene. 5 Barron (20. februar 1995, s. 2326) oppnådde 277 hedgefond med 29,4 milliarder kroner i forvaltningskapital ved utgangen av 1993. Barroner (19. februar 1996, MW74MW75) opplistet 146 hedgefond med minst 20 millioner i forvaltede eiendeler og en 2-årig rekord i slutten av 1995. Disse midlene har totalt 25,1 milliarder kroner i forvaltningskapital. 281 Revisjonen av finansielle studier v 10 n 2 1997 Vårt univers består av ca 700 hedgefondsprogrammer og 240 CTA-bassenger, med forvaltningskapital på til sammen 80 milliarder kroner. En stor kilde til forskjell i å bygge dette universet er mangelen på ytelseshistorie. Dette er en naturlig konsekvens av at flertallet av midler ble startet på 1990-tallet, og mange fond har bare begrensede eiendeler for mye av deres eksistens. Også mange ledere har praktisk talt like tilbud som er oppført under forskjellige navn rettet mot offshore investorer. I tillegg er det midler av midler, som er porteføljer av hedgefond. Ved å komme til universet med 940 midler har vi ekskludert duplikatmidler og midler fra midler. Imidlertid er eiendelene til de dupliserte midlene (men ikke midler av midler) inkludert i de 80 milliarder kroner i forvaltningskapital. Det brukbare utvalg av midler faller til 409 fordi vi krever 3 års månedlig avkastning med minst 5 millioner i forvaltningskapital. Ytterligere detaljer er gitt i vedlegget. Figur 1 oppsummerer stilregresjonsresultatene. De er slående når de sammenlignes med verdipapirfondene. Mens mer enn halvparten av fondene har 2 s over 75, har nesten halvparten (48) av hedgefondene 2 s under 25. Figur 2 viser at ingen enkelt aktivklasse er dominerende i regressene. For hver aktivaklasse rapporterer vi separat fraksjonen av midler med positive koefcienter (solide sorte linjer) og negative koefcienter (tomme hvite stenger). Unlike mutual funds, a substantial fraction (25) of hedge funds are negatively correlated with the standard asset classes. In addition, in only 17 of hedge funds are the coefcients of the most signicant asset class statistically greater than zero and not statistically different from one. The evidence indicates that hedge funds are dramatically different from mutual funds. Mutual fund returns have high and positive correlation with asset class returns, which suggests that they behave as if deploying a buy-and-hold strategy. Hedge fund returns have low and sometimes negative correlation with asset class returns. In the next section we provide an explanation for the differences between the results of hedge funds versus those of mutual funds. 4. Two Dimensions of Style: Location Choice and Trading Strategy It is well publicized that most hedge funds use many of the same liquid asset classes as mutual funds. For example, George Soross Quantum Fund was long U. S. stocks and short Japanese stocks in the October 1987 stock market crash, short the British pound in September 1992, long precious metals in April 1993 (including a 13 stake in Newmont Mining), and long the U. S. dollarshort the Japanese yen in February 282 Empirical Characteristics of Dynamic Trading Strategies 1994.6 The fact that the Quantum Funds returns have low correlation to the returns of asset classes (R 2 40) must be due to its dynamic use of leverage and choice of asset exposure. To see this, compare the style regression in Equation (4) and the denition of returns in Equation (3). The style regression can attribute a managers returns to asset classes only if his returns are correlated to the asset class returns. Sharpe is clearly aware of this problem. He refers to the style regressions as nding an average of potentially changing styles over the period covered Sharpe (1992), p. 3 by the regression. From our earlier discussions, the concept of style should be thought of in two dimensions: location choice and trading strategy. Location choice refers to the asset classes, that is, the F s in Equation (3), used by the managers to generate returns. Trading strategy refers to the direction (longshort) and quantity (leverage), that is, the ws in Equation (3), applied to the assets to generate returns. The actual returns are therefore the products of location choice and trading strategy. To illustrate this point, consider a manager trading SampP futures contracts. Without leverage, a fully invested position of being consistently long one futures contract (i. e. buy and hold) will result in the style regression showing a coefcient of one on the SampP 500 index. If the manager leverages up to two futures contract, the regression coefcient will be two. Conversely, if he is short one futures contract, the regression coefcient will be 1. However, if he alternates between long and short each month, the regression coefcient will be close to zero. In this example, the location is the U. S. stock market in all cases. The returns, on the other hand, are very different depending on the trading strategy. In the rst two cases, the returns are positively correlated with U. S. stocks. In the third case, the returns are negatively correlated with U. S. stocks. In the fourth case, the returns are uncorrelated with U. S. stocks. This example illustrates how return is a function of the location choice as well as trading strategy. With the traditional managers (i. e. mutual fund managers), their emphasis centers on where to invest. Consequently, the observed returns on average resemble a buy-andhold strategy with limited leverage. In other words, the ws generally lie between zero and one, with perhaps a modest adjustment due to stock betas. Our empirical results also indicate that time variation of the ws have limited impact on the return characteristics of the dominant styles, which are highly correlated to the asset class returns. 6 See Barrons (November 2, 1987, pp. 3536), Forbes (November 9, 1992, pp. 4042), Barrons (May 17, 1993, p. 53), and Futures (April 1994, pp. 2428). 283 The Review of Financial Studies v 10 n 2 1997 This is not so with hedge funds. Their managers trading strategies have weights (w) that are not constrained to be between zero and one. In principal, the ws can be between negative innity and positive innity. In practice, the ws are usually between 10 and 10. In addition, the managers can be opportunistic, so that the ws can and do change quickly. Their returns are not likely to be correlated to the asset class returns. These are dynamic trading strategies. This helps to explain why Sharpes style regression, which is better suited to buyand-hold returns on asset classes, is not appropriate for performance attribution when applied to hedge fund managers who use dynamic trading strategies. 5. Hedge Funds Style Analysis In principle, Sharpes style regression can be extended by adding regressors to proxy the returns of dynamic trading strategies. In practice, this is impossible to implement on monthly returns because there is a nite number of monthly returns but an innite number of dynamic trading strategies. Instead we use factor analysis to determine the dominant styles in hedge funds. The idea is quite simple. If two managers use similar location choices and trading strategies, their returns should be correlated. Factor analysis can extract the dominant common styles, whether or not they are correlated to the asset classes. We factor analyze the 409 hedge funds as a single group and we are able to extract ve mutually orthogonal principal components, explaining approximately 43 of the cross-sectional return variance.7 Using the hedge funds most highly correlated with these principal components, we construct ve style factors whose returns are highly correlated to the principal components.8 7 We omitted funds specializing in emerging markets, since there is limited opportunity to employ dynamic trading strategies in emerging markets. Emerging markets do not have sufcient liquidity to allow managers to get in and out quickly, and many have prohibitions against short sales. Above all, available performance history is sketchy. Since our sample of hedge funds have returns over different time periods, the factor analysis was conducted on 297 funds that had returns over a common 36-month period. We standardized the returns for each fund so that they all had mean zero and variance one. This removes differences in variances caused by leverage differences. (For example, two funds empolying the exact same trading strategy but different leverage will have different return variances.) Principal components are performed on the standardized returns. The rst ve principal components explain, respectively, 11.87, 10.00, 9.42, 6.35, and 4.93 of the cross-sectional return variance. 8 We actually rotated the rst ve prinicipal components slightly to allow us to better interpret the data. The ve style factors represent investable returns on ve portfolios of hedge fund managers which closely replicate the ve rotated factors. This is done as follows. For each factor, we form a portfolio using hedge fundsCTA pools that are correlated only to that principal component. The portfolio weights are chosen so that the portfolio returns have maximal correlation with the corresponding principal component. Short sales constraints are imposed since it is not possible to sell short hedge funds and CTA pools. The correlations of the ve style factors to the corresponding principal components are all above 93. We use the maximal correlation portfolio, rather than 284 Empirical Characteristics of Dynamic Trading Strategies This quantitative method of dening investment styles should be contrasted with the qualitative method used by the hedge fund industry, which is based on the trading strategies described in the disclosure documents of hedge funds. By researching the disclosure documents of the funds in each style factor, we can associate our ve style factors with some of these commonly used qualitative style categories used by the hedge fund industry to describe trading strategies: SystemsOpportunistic, GlobalMacro, Value, SystemsTrend Following, and Distressed. In the absence of generally accepted and welldened style names, we have attempted to adhere to commonly used terms to describe hedge fund styles in the investment community. We acknowledge that the terminology is imprecise. To the best of our knowledge, there has not been formal statistical analysis of these loosely dened qualitative styles, nor do we have well-established sources such as Morningstar for reference as in the case of mutual fund styles. Indeed, various industry sources frequently publish a much wider range of style classications. Often, reported returns for the same style category will differ across sources and the same manager can appear in different style categories depending on the source. Data vendors frequently regard information on hedge fund styles to be proprietary. One of the objectives of this article is to see if there are indeed style categories that are consistent with return data. We are of the view that it is what fund managers do, not what they say they do, that determines stylistic differences. However, for labeling purposes, it is helpful to generally adhere to industry conventions where possible. The term systems traders is used to describe managers who use technical trading rules. Thus SystemsTrend Following refers to traders who use technical trading rules and are mostly trend followers, while SystemsOpportunistic refers to technically driven traders who also take occasional bets on market events relying on rule-based models. GlobalMacro refers to managers who primarily trade in the most liquid markets in the world, such as currencies and government bonds, typically betting on macroeconomic events such as changes in interest rate policies and currency devaluations and relying mostly on their assessments of economic fundamentals. Value refers to traders who buy securities of companies they perceive to be undervalued based on their microanalysis of the fundamentals. Distressed refers to managers who invest in companies near, in, or recently emerged from bankruptcycorporate restructuring.9 the optimal mean-variance tracking portfolio, because the principal components and the rotated factors are based on standardized returns, while the style factor portfolios are based on the actual returns. 9 We have investigated the stationarity of these style factors by dividing the data into two subperiods. 285 The Review of Financial Studies v 10 n 2 1997 In order to determine whether the ve style factors are location choices or dynamic trading strategies, we apply Sharpes style regression on the original eight asset classes plus high yield bonds to the ve style factors. Two style factors are each correlated with a single asset class. The Value style has an R 2 of 70 against the eight asset classes plus high yield corporate bonds and is strongly correlated to U. S. equities (with a coefcient of 0.95 and a t-statistic of 7.73). This is due to the fact that most Value managers have a long bias in U. S. equities. The Distressed style has an R 2 of 56 and is strongly correlated to high yield corporate bonds (with a coefcient of 0.89 and a t-statistic of 6.06). This is not surprising, since Distressed managers and high yield corporate bond funds both invest in companies with low or no credit ratings. Furthermore, it is common practice to price unrated, unlisted securities at a spread to the traded, high yield bonds, which explains the correlation between the Distressed style and high yield corporate bonds. The two Systems style factors (SystemsOpportunistic and SystemsTrend Following) have low R 2 s (29 and 17, respectively) and are not correlated to any of the asset classes. The GlobalMacro style is difcult to interpret. It has an R 2 of 55 and is correlated with U. S. bonds (coefcient: 0.84, t-statistic: 3.47), the U. S. dollar (coefcient: 0.46, t-statistic: 2.43), and the IFC emerging market index (coefcient: 0.15, t-statistic: 2.90). The correlation to U. S. bonds and the dollar are not surprising, given highly publicized reports regarding the bond and currency trades of the GlobalMacro managers in 1993 and 1994. However, the correlation with the IFC emerging market index could conceivably be a consequence of spurious cross-correlations with other major asset classes. A problem with the regression approach is that the results are very sensitive to outliers. The fact that the GlobalMacro style is statistically correlated with three asset markets does not necessarily mean that it is using a buy-and-hold strategy in these markets. A buy-and-hold strategy generates returns that have a linear relationship with those of an asset class, while a dynamic trading strategy does not. We resort to a different technique, similar to nonparametric regressions, to distinguish between these two trading strategies. In Table 1 we divide the monthly returns of each asset class (excluding cash) into ve states or environments of the world, ranging from severe declines to sharp rallies, by sorting the monthly returns into ve quintiles. The average returns (and the associated standard errors) of that asset class, as well as those of the ve style factors, are computed in each state of the world. Basically the principal components are unaffected. However, the style factors are somewhat affected, perhaps because traders have changed styles, or perhaps because of statistical variations. 286 Empirical Characteristics of Dynamic Trading Strategies Table 1 Returns of hedge fund style factors across different market environments: January 1991December 1995 (in percent per month) Environment MeanS. D. SysOpp MeanS. D. GlobalMac MeanS. D. Value MeanS. D. SysTrend MeanS. D. Distressed MeanS. D. Environment: US Eqty 1 2.820.29 1.620.99 2 0.050.19 0.211.08 3 1.590.11 1.561.09 4 3.040.12 0.311.36 5 5.130.59 1.511.91 0.820.62 2.140.42 1.870.69 1.420.29 1.670.44 1.980.61 0.170.54 1.580.51 3.740.88 5.190.80 1.451.26 1.710.82 0.770.51 1.911.70 0.501.55 1.560.38 2.080.72 1.720.47 1.560.36 1.860.53 Environment: Non-US Equity 1 5.160.42 1.601.29 2 1.770.22 1.051.29 3 0.810.15 0.820.89 4 3.350.19 1.491.25 5 6.990.50 2.281.73 0.500.55 1.250.75 0.900.42 1.850.54 1.930.66 0.921.02 1.840.70 1.880.70 2.420.81 3.431.17 2.451.59 1.190.93 0.000.70 0.400.56 3.821.58 1.520.45 1.510.31 2.330.62 0.960.34 2.360.58 Environment: US Bond 1 0.950.18 0.070.96 2 0.210.07 0.031.04 3 0.790.05 2.071.19 4 1.360.05 0.211.37 5 2.250.16 3.721.61 0.490.66 1.420.67 1.620.49 2.020.36 1.800.57 1.111.13 1.951.10 2.311.01 1.110.73 2.310.96 1.180.70 0.140.61 2.751.75 1.080.85 2.141.59 1.000.42 2.090.64 2.260.73 1.570.25 1.900.36 Environment: Non-US Bond 1 2.890.52 0.991.26 2 0.110.11 1.090.81 3 1.050.07 0.841.34 4 2.12 0.11 1.961.13 5 4.520.49 3.391.61 1.610.43 0.920.78 1.140.60 1.070.67 1.630.54 1.311.12 2.540.94 0.900.91 1.370.73 2.671.17 0.771.73 1.240.29 0.270.40 0.460.88 4.401.60 1.770.50 1.720.55 2.380.76 1.620.42 1.330.20 Environment: US Dollar 1 3.330.27 3.551.61 2 1.530.10 0.691.26 3 0.340.08 0.571.04 4 1.260.16 0.681.25 5 4.480.58 1.261.18 0.810.50 0.140.81 0.950.40 2.240.59 2.290.43 1.531.14 1.851.00 1.940.73 0.980.72 2.341.22 5.581.28 0.460.79 0.750.44 1.040.49 1.471.73 1.350.20 1.560.42 1.190.43 2.630.60 2.140.66 Environment: Gold 1 4.060.45 2 1.200.11 3 0.030.08 4 1.330.20 5 4.270.38 1.270.63 1.400.22 1.200.41 0.370.88 2.150.62 2.441.10 3.521.04 0.290.62 1.351.05 1.310.82 0.741.60 1.031.57 0.440.93 0.390.95 2.001.04 0.860.35 2.610.64 1.320.33 2.170.66 1.890.36 Environment: IFC Emerging Markets 1 4.800.71 1.291.32 0.380.82 2 1.590.19 1.770.77 0.810.55 3 0.560.14 1.141.02 1.170.42 4 2.760.22 0.701.48 1.470.41 5 8.521.33 0.371.84 2.560.59 0.340.94 1.011.07 2.230.77 1.570.74 3.451.12 1.250. 95 2.421.22 1.461.40 0.270.46 0.421.61 0.550.18 1.440.33 2.080.41 2.260.72 2.380.55 0.090.83 1.630.81 2.161.25 1.471.01 3.630.74 0.220.63 0.110.72 3.671.57 1.270.84 0.051.74 0.360.22 1.380.18 1.610.24 1.570.44 3.900.70 Environment: High 1 0.490.30 2 0.800.05 3 1.240.03 4 1.800.08 5 3.550.49 0.161.49 0.381.56 0.091.08 1.231.16 3.581.04 Yield Corporate Bonds 1.190.96 0.980.58 0.471.05 2.170.58 1.811.71 1.710.49 1.841.34 1.830.51 0.801.38 1.640.46 287 The Review of Financial Studies v 10 n 2 1997 If a style uses a buy-and-hold strategy in a given asset class, then its return in the ve states of the world should align with those in the asset class in a straight line. Using this method we identied that the Value style is akin to a buy-and-hold strategy in U. S. equities. The other four styles do not use buy-and-hold strategies in any of the asset classes. In particular, the Distressed style is not quite a buy-and-hold strategy in high yield corporate bonds, because its returns in states 4 and 5 for high yield corporates are out of line with those of the other states. For the same reason, the GlobalMacro style does not use buy-and-hold strategies in U. S. bonds, currencies, or emerging market equities. If a style uses a dynamic trading strategy in a given asset class, then its return should be large (positive or negative) when the underlying asset returns are at extremes (i. e. states 1 and 5). In the case of the SystemsOpportunistic style, it is most protable during rallies in U. S. bonds, non-U. S. bonds, and gold, and during declines in the U. S. dollar. The SystemsTrend Following style is most protable during rallies in non-U. S. equities and bonds, and during declines in the U. S. dollar. The GlobalMacro style is most protable during rallies in gold, the U. S. dollar, and emerging markets. The locations we have identied are consistent with the disclosure information provided by the traders. It is important to point out that this type of nonlinear, statedependent return tabulation is helpful only to infer the location of a trading style, but it is not very informative on the nature of the trading strategies employed. Based on the evidence, it is reasonable to conclude that the Value style is highly sensitive to the movements of the overall U. S. equity market. The Distressed style is also quite sensitive to the performance of the high yield corporate bond market. The other three styles are dynamic trading strategies in a variety of markets. They are not sensitive to the asset markets in the normal states (i. e. 2, 3, and 4), but can be sensitive to selective markets during extreme states. Given that we are measuring extreme or tail events, there is little hope of attaching statistical signicance. Indeed, we are making a much weaker statement. Table 1 shows that there exist nonlinear correlations between three style factors and some of the standard asset classes, which can give rise to optionlike payouts. Figures 3, 4, and 5 illustrate three of the most dramatic examples of optionlike payouts. Figure 3 shows that the SystemsTrend Following style has a return prole similar to a straddle (i. e. long a put and a call) on U. S. equities. Figure 4 shows that the SystemsOpportunistic style is like a call option on gold. Figure 5 shows that the GlobalMacro style behaves like a straddle on the U. S. dollar. 288 Empirical Characteristics of Dynamic Trading Strategies Figure 3 Systemstrend following style versus U. S. equity Figure 4 Systemsopportunistic style versus gold 289 The Review of Financial Studies v 10 n 2 1997 Figure 5 Globalmacro style versus dollar A few remarks are appropriate here. The terms Systems, Value, GlobalMacro, and Distressed are qualitative descriptors used by the hedge fund industry to describe the investment styles of hedge fund managers based on their disclosure documents. Here we are able to quantify the actual returns of these investment styles using factor analysis. It is important to remark that we are not advocating that it takes only ve style factors to completely characterize the myriad of strategies deployed by hedge fund managers. Contrary to the case of mutual funds where the statistically identied styles account for the lion share of performance variation, here the ve style factors can only account for 43 of the return variance of hedge funds. In the world of private investments, it is quite common to have a few niche arbitrageurs operating in illiquid markets where large hedge funds would nd it unsuitable given their size. Therefore the style factors represent the most popular trading strategies that can operate in asset markets with adequate depth and liquidity. Indeed, the lack of dominant style factors attests to the wealth of performance diversity available among these managers.10 10 We are aware of a number of trading strategies that are not captured by the ve dominant style factors. There are short sellers who only short equities. There are also traders who specialize in spread trading, such as (1) warrants versus stocks, (2) convertible securities versus stocks, (3) the short end versus the long end of the yield curve, (4) mortgage securities versus government securities, and (5) interbank swaps versus government securities. These are typically arbitrage 290 Empirical Characteristics of Dynamic Trading Strategies Lastly, a brief remark on what has come to be known as market neutral strategies is in order. There is a growing literature on what constitutes a market neutral strategy, its attractive characteristics and its potential pitfalls e. g. Lederman and Klein (1996). A detailed analysis of this category of trading styles, which often includes the Distressed style, is beyond the scope of this article. However, we note that return orthogonality to the traditional asset classes is a poor screening device for market neutral funds. As our example in Section 4 shows, a market timing strategy can appear to be uncorrelated to the very asset class it has directional exposure to, yet market timing strategies are generally not regarded as market neutral. A better screening criterion is to require a market neutral fund to be orthogonal to the ve hedge fund styles as well as the traditional asset classes. Our analysis shows that three hedge fund style factors (i. e. SystemsOpportunistic, SystemsTrend Following, and GlobalMacro) appear to use market timing strategies in various asset classes, so that they have directional exposure even if they are uncorrelated to the asset classes on average. Hedge funds correlated to these styles are not market neutral. In addition, two other hedge fund styles (Value and Distressed) are correlated to U. S. equity and high yield corporate bonds, respectively. Hedge funds correlated to these styles are also not market neutral. Beyond using correlation as a screening device, truly market neutral funds should not have excessive exposures to traditional asset classes in extreme moves. For example, a typical duration neutral xed income strategy may have no correlation to normal movements in interest rates, yet may have directional exposure to extreme movements see Fung and Hsieh (1996) for details. Limiting the amount of tail exposure, as is done in Table 1, is also a good device to screen for market neutral funds. 6. Insights on Performance Evaluation and Survivorship Bias for Hedge Funds Of the many differences between traditionally managed funds and hedge funds, two issues stand out: performance evaluation and survivorship bias, respectively. In this section, we contrast our ndings with the literature on these two important issues reported on mutual fund managers. In a simplistic setting, performance attribution and evaluation involve decomposing a managers returns into the part that can be replistrategies that have gained popularity over the last few years. The limited history, together with the diversity in the strategies employed, makes it less likely for their return characteristics to converge into identiable factors. 291 The Review of Financial Studies v 10 n 2 1997 cated by standard asset baskets, or market indices, and the residual that is attributed to the managers skill. The purpose of this decomposition rests on the assumption that investors are only willing to reward a manager for superior performance that cannot be easily replicated. Applying this concept to mutual funds, Jensen (1968) used a single-factor model, regressing a stock mutual funds returns (Rt ) on market returns (Rmt ) with being the constant term: Rt bRmt ut. (5) Sharpe (1992) extended this to a multiple-factor model for the general mutual fund: Rt bk Fkt ut. (6) k The slope coefcients of the regression tell us the replicating static mix of asset classes that would capture the funds performance. The constant term is used to measure the managers average ability to generate returns beyond this static mix of assets. In this decomposition, k bk Fkt was referred to as style, and ut as skill. The evidence in Figure 1, consistent with the mutual fund literature, shows that this regression works well for mutual funds, as indicated by the high R 2 values. However, this regression works very poorly for hedge funds because the R 2 values are very low. In the present context this would imply that mutual fund returns are generated primarily from static asset mix decisions, while hedge fund returns are generated primarily from skill. It is common practice to go beyond static asset class mixes in order to analyze the performance of mutual fund managers using simple trading strategies. This is achieved by further decomposing ut in Equation (5) into selectivity (which has its genesis from the equity world for describing the ability to pick stocks) and market timing (the ability to predict market direction). The identifying assumption is that selectivity consists of idiosyncratic, diversiable risks of individual stocks, while market timing consists of nondiversiable, nonlinear payouts of asset class returns based on trading strategies. Empirically the decomposition is implemented by adding proxies for market timing strategies to Equation (5). For example, Treynor and Mazuy (1966) used the square of the market return to proxy for market timing ability, while Merton and Henriksson (1981) used an option payout on the market return. Glosten and Jagannathan (1994) also provided some justication for using selected option-index portfolios as additional factors to proxy for dynamic trading strategies. The jury on the success of using a small number of proxies to pick up market timing abilities for mutual funds is still out. Jagannathan 292 Empirical Characteristics of Dynamic Trading Strategies and Korajczyk (1986) pointed out that a separation between selectivity and market timing is not in general possible when managers can follow dynamic trading strategies or use options. While this problem of identication may not be too severe in mutual funds, because managers do not use dynamic trading strategies or options extensively, it is likely to be very severe in hedge funds. Furthermore, with the exibility available to hedge fund managers, it is unclear whether the choice to bet on the currency market instead of stocks is to be interpreted as a selection decision or as a market timing decision. The only conclusive evidence we have is that the static asset mix component plays only a minor role in hedge fund performance in general. Consequently the important component of hedge fund performance is skill. In a sense, our model proposes a more detailed decomposition of the skill set to further characterize performance differences among hedge funds. A simplistic way of summarizing the difference between a manager that draws most of his return from the asset mix decision (the location decision) versus one that relies heavily on dynamic trading strategies is to think in terms of the intertemporal deltas to any given market. A manager that depends critically on the right location decision will have a slow-moving delta within a limited range (most mutual funds are limited in their use of short sales and leverage.) In contrast, a hedge fund manager can and will have deltas in orders of magnitude greater that can shift dramatically over very short intervals of time. A case in point is George Soross Quantum Fund. It is well known that Quantum gained 25.5 in September 1992 by betting on the devaluation of the British pound. Using monthly returns, the regression of Quantum against the pound has an R 2 of only 23. Using daily returns for the month of September 1992, the R 2 is only 10 The bet appeared to have been put on around September 11 and taken off around September 22. This can be seen from Figure 6, which plots Quantums daily net asset value per share versus the British poundU. S. dollar exchange rate (measured in pounds per U. S. dollar). The inability of simple statistical procedures in picking up the correlation between Quantum and the pound means that the number of proxies needed to pick up very short-term dynamic trading strategies is virtually innite. In the spirit of the present discussion, it is unclear whether this type of event return should be classied as selectivity or market timing. On the face of it, it appears to be market timing, but then why not bet on the other currencies Simply put, hedge fund returns are much harder to explain or replicate using simple trading rules. It is the recognition of these difculties that led us to add hedge fund styles to Sharpes asset class factor model. These new styles are 293 The Review of Financial Studies v 10 n 2 1997 Figure 6 Quantum net asset value versus GBPUSD exchange rate, September 1992 analogous to the market timing proxies in the mutual fund performance evaluation literature. The good news is that these new styles are uncorrelated to asset class returns. The bad news is that they are correlated with market returns during extreme moves or tail events.11 The exposure to tail events in asset markets is not diversiable, which substantially complicates risk management. Furthermore, we emphasize the limitations in using these new styles in performance attribution. The factor analysis indicates that there are many niche styles in the hedge fund universe still unaccounted for. It is conceivable that, with such a heterogeneous population, performance attribution may ultimately require in-depth due diligence on a case-by-case basis. Next we turn to the effect of survivorship bias on our empirical results. Here we need an estimate of the attrition rate in hedge funds. This turns out to be an exceedingly difcult task. Unlike mutual funds, hedge funds need not register with the Securities and Exchange Commission, nor does a hedge fund industry association exist that can document the entry and exit of funds. In short, it is almost impossible to know exactly how many funds existed as of a given point in time. Given that the population of hedge funds is unknown, there are two ways to estimate an attrition rate. The rst method takes a sample 11 Some of the more dramatic losses in the so-called market neutral funds occurred during large event moves in the asset markets. This can be attributed partially to a failure of their risk management system to cope with the abrupt increase in the correlation between their positions in the market. 294 Empirical Characteristics of Dynamic Trading Strategies of currently existing hedge funds and tracks them going forward in time. This prospective method of estimating attrition rate can only be done as a future research project. The second method to estimate the attrition rate is to go back in time to nd all funds that existed at a given point in time, say December 1994, and determine how many did not survive until a later point in time, say December 1995. This retrospective method of determining the attrition rate is appropriate in mutual funds, since the population of mutual funds on both dates is known. As the population of hedge funds at any given date is unknown, one is tempted to estimate a retrospective attrition rate by taking the funds in a database with returns in December 1994 and see how many of them dropped out by December 1995. This procedure would yield a downward bias in the attrition rate. To understand the bias of the retrospective attrition rate in a hedge fund database, one must understand the process and objectives in creating and maintaining a hedge fund database. Suppose there are N funds in the hedge fund population on December 1994 and A funds are in our database. Assume that there are no new funds coming into the population. The attrition rate is d per year. At the end of 1995, N d funds have exited the population and Ad funds have exited our database. If no funds were added into the database during 1995, the retrospective attrition rate would have been d (Ad)A. However, database vendors have an incentive to add quality funds into the database. In 1995 there are still (N A)(1 d) funds which were not in the database. Suppose B of them are added to the database, along with their past returns. At the end of 1995, there are A B funds in the database with returns in December 1994, but Ad of them had dropped out by December 1995. The retrospective attrition rate would be given by (Ad)(A B), which is a downward biased estimate of d by the factor A(A B). If we multiply the retrospective attrition rate by the adjustment factor (AB)A, we will have an unbiased estimate of the true attrition rate. Unfortunately we cannot calculate the adjustment factor (A B)A because we do not know when a given fund was added to the database. But we can obtain an upper bound for the adjustment factor. It is reasonable to assume that the sampling rate of the surviving funds in 1995 is the same as that of the original sample in 1994, that is, B(N A)(1 d) AN. This means B (N A)(1 d)(AN ). The adjustment factor, (AB)A, now becomes 1(1-AN)(1-d). As the adjustment factor is decreasing in AN and d, its maximum is two, when AN 0 and d 0. Thus doubling the retrospective attrition rate gives an upper bound of the true attrition rate. 295 The Review of Financial Studies v 10 n 2 1997 A further complication arises when new hedge funds enter the population. Unlike the mutual fund industry, in which new entrants arrive without return histories, it is common practice in the hedge fund industry to expect new funds to come with a track record accumulated either over an incubation period prior to launching the fund or from their previous trading history with a nancial institution. Typically, new funds are added to a database with a performance history. This will further bias downward the retrospective attrition rate. In estimating the retrospective attrition rate, we dene the population of hedge funds to be those that have operated for at least 3 years to avoid picking up new funds whose incubation period is typically less than 3 years. We examined 139 funds in the Paradigm database with returns in December 1994. To the best of our knowledge, at most, six funds had ceased operation by the end of 1995.12 That represents a retrospective attrition rate of 4.3 in 1 year and a maximum upper bound of 8.6 for the true attrition rate.13 This estimate of the attrition rate in hedge funds is comparable to that in mutual funds. Grinblatt and Titman (1989) found an average attrition rate of 4.3 per year between 1974 and 1984 for mutual funds. Brown et al. (1992) found the average attrition rate to be 4.8 between 1977 and 1985, ranging from 2.6 in 1985 to 8.5 in 1977. The low attrition rate in hedge funds means that survivorship bias is unlikely to affect the result that hedge fund returns are uncorrelated with those of asset classes. Even if we added back the 8.6 of hedge funds that had exited the sample, and even if their style regression R 2 s were 1.00, it would not dramatically change the distribution graphed in Figure 1. Survivorship bias is unlikely to impact the number of hedge fund styles in the factor analysis. It is conceivable that survivorship bias in funds can result in survivorship bias in our style estimates, if the funds that exited the sample had the same style and no surviving funds had that style. We were able to determine that this did not occur by examining the funds that ceased operation in 1995. Based on their returns and their disclosure documents, we determined that the exiting funds did not come from the same style. Some were systems traders, while others were niche funds that fell outside the ve dominant styles. The broader and more interesting question is to what extent survivorship biases the returns of the styles extracted from factor analysis based on a sample of surviving funds. Grinblatt and Titman (1989) 12 Four have ceased operations and the status of two more are unknown. 13 The authors are pursuing a project with Tass Management to study entry and exit in the Tass databases in conjunction with the behavior of assets under management going back a few years. Preliminary results on CTA funds indicate that the survivorship bias is similar to that in mutual funds. 296 Empirical Characteristics of Dynamic Trading Strategies found that survivorship biased upward mutual fund returns by 0.50 per year. For hedge funds, it is unclear if survivorship biases their returns upward or downward. The reason has to do with the life cycle of hedge funds when assets under management interact with performance. A small fund that has good performance attracts assets. Unlike mutual funds, hedge fund strategies have limited capacity. This means that, over any given time period, performance may well decline when a funds size gets too large. If it subsequently experiences poor performance, assets begin to ow out. In some cases the fund can return to some equilibrium level of assets under management and the fund survives. However, there will be other cases where assets shrink so much that it is no longer economical to cover the funds xed overhead and the manager closes it down and the fund exits. This can occur even if the returns during the latter stage are above the surviving funds average, but compares poorly to its peers in the same trading style. In other words, funds exiting the sample can easily have returns higher than the population average of the survivors. There are less common, but nonetheless anecdotal, examples where an exiting fund has better performance than the population average. It is frequently the case that with private investment pools like hedge funds, acceptable performing funds can go unnoticed for prolonged periods of time. After all, one would hardly expect marketing to be high on these traders list of skills. In these cases the managers can get impatient and simply close down the business and return to trade for a nancial institution. Another example is with successful funds. There are successful funds that have reached their perceived capacity and have stopped accepting new investments.14 At this stage, there is no incentive to report their performance to third parties outside of their own investor base. In other words, funds can drop out of a data vendors universe simply because they have chosen not to report their otherwise stellar performance. Other reasons unrelated to poor performance may cause a data vendor to cease reporting a funds performance. Tass Management, for example, delists a fund to avoid any liability in potential reporting errors. This can happen to funds with above average returns as well as below average returns. Ultimately one must recognize that hedge fund managers are a heterogeneous lot, thus survivorship bias needs careful interpretation. It is unclear to us that survivorship necessarily puts an upward bias on observed mean returns. More carefully conducted empirical work is needed. 14 The fact that George Soross Quantum Fund is closed to new investors and has been distributing assets to investors since 1992 illustrates our point that even large macro funds must limit their size in order to continue to turn in a good performance. 297 The Review of Financial Studies v 10 n 2 1997 7. Implications In this article we analyze investment styles using mutual fund returns from Morningstar and hedge fund returns from a dataset that has never been subjected to formal analysis. We have shown that there are 12 important investment styles buy-and-hold in nine asset classes (our eight original asset classes plus high yield corporate bonds) and three dynamic trading strategies. There are a number of implications. In terms of performance attribution and style analysis, we provide an extension to Sharpes style factor model. A style regression using these 12 variables should produce reasonably high R 2 values in at least 85 of mutual funds and perhaps 40 of hedge funds. We believe that this provides a good starting point in performance attribution and style analysis that can cope with both relative as well as absolute return managers.15 The results of our article also have implications for portfolio construction. An investor can now allocate across both location choices and trading strategies. There are, however, complications arising from the use of dynamic trading strategies that do not exist under a static buy-and-hold type of trading strategy. For the portfolio that includes dynamic trading strategies, portfolio construction and risk management are potentially more complex, depending on the investors risk preferences. Suppose an investor has quadratic preferences. Here, standard mean-variance tools are appropriate for asset allocation and risk management. We can show that the dynamic trading strategies can improve the performance of a traditional stock-bond portfolio without substantially increasing its risk. For example, a portfolio of 60 U. S. equities and 40 U. S. bonds has an annualized mean return of 11.55 and an annualized standard deviation of 7.97 between 1990 and 1995. By shifting 50 of the portfolio into the three dynamic trading strategies with equal weights, the annualized mean return increases to 15.92 and the annualized standard deviation decreases to 7.10. This is an economically significant benet. For investors with nonquadratic preferences, it is unclear whether mean-variance tools are appropriate for portfolio construction and 15 Since the three dynamic trading strategies exhibit nonlinear correlation with the eight noncash asset classes, it is picking up some of the Jensens alphas when only the buy-and-hold strategies are used. See, for example, Glosten and Jagannathan (1994). The main difference between our approach and that of Glosten and Jagannathan (1994) is that the factor analysis does not prespecify the underlying assets to which the dynamic trading strategies are related. The factor analysis could have picked up an important hedge fundCTA investment style using an asset class that is statistically independent of the eight noncash asset classes. The fact that the important hedge fund styles are either linearly or nonlinearly correlated to the eight noncash assets indicates that this is not so. We could not have known this before the factor analysis was performed. 298 Empirical Characteristics of Dynamic Trading Strategies risk management because some of the style factors involving dynamic trading strategies exhibit nonnormal distributions.16 Furthermore, they may have nonlinear correlation with those of the nine buy-and-hold styles. Portfolio construction and risk management must take into account investor preferences and the joint distribution of the 12 investment styles. The proper technique for portfolio construction when investors have nonquadratic preferences is a subject beyond the scope of this article.17 We can, however, illustrate how it may differ from the meanvariance approach. Suppose an investor is willing to give up some of the gains in a strongly rising stock market in order to reduce the downside risk in a rapidly falling one. This type of optionlike payout prole (similar to that of a portfolio insurance strategy) is generally not available from traditional managers. For example, consider Table 1 under the column SystemsOpportunistic. This particular style underperformed seven of the eight noncash asset classes during major rallies or extreme positive states. However, it delivered positive performance in the states when extreme negative outcomes were recorded in equities and bonds, which constitute the core of most institutional portfolios. An equally weighted portfolio of the three dynamic trading strategies can deliver superior performance in the states when extreme negative outcomes were recorded in the four equity and bond asset classes. Thus blending the three dynamic trading strategies to traditional managers can provide some downside protection. For example, take an investor who is highly averse to negative returns. The traditional 60 stock40 bond portfolio suffered a maximum monthly loss of 5.93 during the 19901995 period. If 50 of that portfolio is replaced by an equally weighted portfolio of the three dynamic trading strategies, the maximum monthly loss would be reduced to 2.87. For this investor, the latter portfolio would strongly dominate the traditional 60 stock40 bond portfolio. In other words, it is possible to achieve an optionlike return prole (relative to standard bench marks) with direct investment into existing hedge funds. Risk management in the presence of dynamic trading strategies is also more complex. Hedge fund managers have a great deal of 16 The ve hedge fund style factors have kurtosis of 3.22, 4.29, 2.64, 6.66, and 7.32, with a standard error of 0.63. This indicates that at least three of the ve style factors are not normally distributed. 17 In a recent article Hlawitschka (1996) extended the Levy and Markowitz (1979) article to examine the use of mean-variance models when options are present in the opportunity set. Although the results generally favor the mean variance approximation, the dataset used is limited. Given that historical returns from a wide cross section of dynamically managed portfolios were generally unavailable to these previous studies, the present dataset could provide useful input to address the question of portfolio selection with nonquadratic preferences. 299 The Review of Financial Studies v 10 n 2 1997 freedom to generate returns that are uncorrelated with those of asset classes and traditional fund managers. This style diversication comes at a cost. Care must be taken to ensure that proper infrastructure is in place to operate broad investment mandates involving a wide range of nancial instruments. Another important element of risk is that periodically the portfolio can become overly concentrated in a small number of markets. As an example, take a portfolio with exposure in three markets: U. S. equities, U. S. bonds, and non-U. S. bonds. A part of the portfolio is managed traditionally, using buy-and-hold strategies. The remainder is in hedge funds allocated in the three styles with dynamic trading strategies. Suppose a steady trend develops in the international bond markets, as was the case in 1993. The GlobalMacro traders would have been long and leveraged. The SystemsTrend Following and SystemsOpportunistic traders would have been long as well, to take advantage of the trend. By December 1993 the portfolio could have been highly concentrated in non-U. S. bonds. It would have made a lot of money in 1993. But when the world bond market declined sharply in 1994, the portfolio would have lost a lot of money. We refer to this phenomenon as diversication implosion. The intuition here is that, although style exposures are still diverse, market exposures can converge. Overall the empirical results show that style diversication can be achieved by blending the traditional relative return investment approach to the absolute return investment styles. However, there is also an implicit cost. Conceptually it is the exibility in the absolute return managers investment mandate that allows them to deliver an uncorrelated set of returns. But freedom has its price. It is important for an investor using managers with dynamic trading strategies to take extra steps to reduce the chance of diversication implosion and exposure to extreme or tail events. This calls for greater efforts in due diligence, portfolio construction, and risk monitoring. In this article we outlined some tools to extend traditional style analysis to alternative managers employing dynamic trading strategies. Hopefully this will provide an analytical framework for managing portfolios with a better diversity of styles.18 18 A diskette containing the monthly returns of the 409 hedge funds used in this study will be made available for academic research purposes for a nominal fee of 15.00 U. S. from Duke University. Please send all requests to David A. Hsieh. Each academic researcher should write, on the letterhead of hisher academic institution, a statement stating that the data will be used only for academic purposes, that the data will not be redistributed to other parties, and that the work will acknowledge The Review of Financial Studies, AIG, Tass, and Paradigm LDC for making the data available. Updates of the data, which came from Tass Management, can be purchased 300 Empirical Characteristics of Dynamic Trading Strategies Data Appendix Generally hedge funds are private investment pools structured in such a way as to minimize regulatory and tax impediments in operating the strategy. Consistent with this objective, most funds have adopted a low prole and often secretive posture. This is especially so with some of the offshore funds catering to non-U. S. domiciled investors. Not only are performance statistics not readily given out, periodic returns are only legally released via the offshore administrators, even for investors in the funds. Similarly, marketing materials are only available on a very restricted basis. This is particularly so because some of the largest fund managers have no interest in increasing the assets under management. In contrast, data on CTAs who are regulated by the CFTC are much more readily available. Unfortunately pools of capital managed by CTAs are much smaller in comparison to hedge funds. For example, one of the largest CTAs is John W. Henry amp Co. managing a little under 2 billion. In comparison, George Soross Quantum Fund controls well over 8 billion in assets. The hedge fund universe is where a much wider range of dynamic trading strategies are used, as opposed to the CTA universe which mostly consists of technical traders operating in the commodity and nancial futures markets. Consequently the more interesting set of the data is also the harder set to assemble. Our universe of hedge funds and CTA pools consists of 250 hedge funds from Paradigm LDC (with assets under management of 44.6 billion), 451 hedge funds from Tass Management (with assets under management of 27.7 billion), and 239 CTA pools from Tass Management (with assets under management of 6.7 billion). Paradigm LDC is the general partner to Paradigm LP, a Cayman Islandbased consulting rm specializing in hedge fund portfolios. Paradigms database has been assembled through information on investments made by its clients, as well as direct contacts with hedge fund managers it follows as potential investments. Tass Management is one of the few database vendors specializing in supplying data on hedge funds and CTAs. Tass obtains its data directly from fund managers. To construct the universe of funds used in this article we carefully excluded similar funds offered by the same management company. Some of these are created for regulatory reasons, while others are created because of investor demand. Most of these funds within the same family are based on similar strategies with highly correlated returns. Without ltering out such duplications, they would overweigh directly from Tass. However, Paradigm LDC will not be able to supply updates. 301 The Review of Financial Studies v 10 n 2 1997 certain style participation and bias our analysis. Excluded also are funds of funds, which invest in other hedge funds and are not central to our style analysis. From this universe we extracted funds that have at least 3 years of monthly returns and at least 5 million in assets under management. Excluding the small funds is important. Frequently CTA databases include funds that manage as little as a few hundred thousand dollars employing very high leverage with wildly volatile returns. These funds are, for all practical purposes, not viable investment targets for professional investors. As a result, the usable database has 409 funds consisting of 168 hedge funds and 89 CTA pools from Tass and 152 hedge funds from Paradigm LDC. Each fund is identied by a fund number, followed by its latest 36 monthly returns. Another point to note is that nearly all of these returns are adjusted for ex post audit changes. Frequently a funds monthly returns are revised after yearend audit. We have made all of the adjustments known to us to date. References Brown, S. J. W. Goetzmann, R. G. Ibbotson, and S. A. Ross, 1992, Survivorship Bias in Performance Studies, Review of Financial Studies, 5, 553580. Fung, W. and D. Hsieh, 1996, Global Yield Curve Risk, Journal of Fixed Income, 6, 3748. Glosten, L. and R. Jagannathan, 1994, A Contingent Claim Approach to Performance Evaluation, Journal of Empirical Finance, 1, 133160. Grinblatt, M. and S. Titman, 1989, Mutual Fund Performance: An Analysis of Quarterly Portfolio Holdings, Journal of Business, 62, 393416. Hlawitschka, W. 1996, The Empirical Nature of Taylor-Series Approximations to Expected Utility, working paper, School of Business, Faireld University forthcoming in American Economic Review. Jagannathan, R. and R. A. Korajczyk, 1986, Assessing the Market Timing Performance of Managed Portfolios, Journal of Business, 59, 217236. Jensen, M. C. 1968, The Performance of Mutual Funds in the Period 19451964, Journal of Finance, 23, 389416. Lederman, J. and R. A. Klein, 1996, Market Neutral: State of the Art Strategies for Every Market Environment, Irwin Professional Publishing, Chicago. Levy, H. and H. M. Markowitz, 1979, Approximating Expected Utility by a Function of Mean and Variance, American Economic Review, 69, 308317. Merton, R. C. and R. D. Henriksson, 1981, On Market Timing and Investment Performance II: Statistical Procedures for Evaluating Forecasting Skills, Journal of Business, 41, 867887. Sharpe, W. F. 1992, Asset Allocation: Management Style and Performance Measurement, Journal of Portfolio Management, 18, 719. Treynor, J. and K. Mazuy, 1966, Can Mutual Funds Outguess the Market Harvard Business Review, 44, 131136. 302. View Full Document This note was uploaded on 06252015 for the course FINA 410 taught by Professor Blimma during the Winter 03913 term at Concordia Canada. 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