Manager Skills Of Long/short Equity Hedge Funds : The .

ger skills of long/short equity hedge funds : the factor model dependencyAuteur : Claes, MaximePromoteur(s) : Lambert, MarieFaculté : HEC-Ecole de gestion de l'Université de LiègeDiplôme : Master en ingénieur de gestion, à finalité spécialisée en Financial EngineeringAnnée académique : 2017-2018URI/URL : http://hdl.handle.net/2268.2/4804Avertissement à l'attention des usagers :Tous les documents placés en accès ouvert sur le site le site MatheO sont protégés par le droit d'auteur. Conformémentaux principes énoncés par la "Budapest Open Access Initiative"(BOAI, 2002), l'utilisateur du site peut lire, télécharger,copier, transmettre, imprimer, chercher ou faire un lien vers le texte intégral de ces documents, les disséquer pour lesindexer, s'en servir de données pour un logiciel, ou s'en servir à toute autre fin légale (ou prévue par la réglementationrelative au droit d'auteur). Toute utilisation du document à des fins commerciales est strictement interdite.Par ailleurs, l'utilisateur s'engage à respecter les droits moraux de l'auteur, principalement le droit à l'intégrité de l'oeuvreet le droit de paternité et ce dans toute utilisation que l'utilisateur entreprend. Ainsi, à titre d'exemple, lorsqu'il reproduiraun document par extrait ou dans son intégralité, l'utilisateur citera de manière complète les sources telles quementionnées ci-dessus. Toute utilisation non explicitement autorisée ci-avant (telle que par exemple, la modification dudocument ou son résumé) nécessite l'autorisation préalable et expresse des auteurs ou de leurs ayants droit.

APPENDICESAppendix 1This table presents a list of academic papers which used Morningstar as commercial hedge funddatabase, considering five main financial economics journals: the Journal of Finance (JF) theJournal of Financial Economics (JFE) the Review of Financial Studies (RFS) the Journal ofFinancial & Quantitative Analysis (JFQA) and the Financial Analysts Journal (FAJ).AuthorsTitleYearJournalAgarwal V, Daniel ND& Naik NYDo hedge funds manage their reported returns?2011RFSAgarwal V, Daniel ND& Naik NYRole of managerial incentives and discretion inhedge fund performance.2009JFAgarwal V & Naik NYHedge funds for retail investors? An examinationof hedged mutual funds.2009JFQAAmin GS & Kat HMHedge fund performance 1990-2000: Do the «Money Machines » really add value?2003JFQAAng A, Gorovyy S& van Inwegen GBHedge fund leverage.2011JFE2011JFEAvramov D, Kosowski R,Naik NY & Teo MHedgefunds,managerialmacroeconomic variables.skill,andBollen NPB & Pool VKSuspicious patterns in hedge fund returns and therisk of fraud.2012RFSBollen NPB & Pool VKConditional return smoothing in the hedge fundindustry.2008JFQABollen NPB & Pool VKDo hedge fund managers misreport returns?Evidence from the pooled distribution.2009JFBollen NPB& Whaley REHedge fund risk dynamics: Implications forperformance appraisal.2009JFThe returns to hedge fund activism.2008FAJBrown SJ, GoetzmannWN, Liang B& Schwarz CTrust and delegation.2012JFEBrown SJ, GoetzmannWN & Park JCareers and survival: Competition and risk in thehedge fund and CTA industry.2001JFCassar G & Gerakos JHedge funds: Pricing controls and the smoothingof self-reported returns.2011RFSBrav A, Jiang W, Frank P& Randall ST

Chen Y & Liang BDo market timing hedge funds time the market?2007JFQAChoi D, Getmansky M,Henderson B& Trookes HConvertible bond arbitrageurs as suppliers ofcapital.2010RFSDeuskar P, Pollet JM,Wang ZJ & Zheng LThe good or the bad? Which mutual fundmanagers join hedge funds?2011RFSDichev ID & Yu FHigher risk, lower returns: What investors reallyearn.2011JFEDudley E& Nimalendran MMargins and hedge fund contagion.2011JFQAEling MDoes the measure matter in the mutual fundindustry?2008FAJFung W & Hsieh DAEmpirical characteristics of dynamic tradingstrategies: The case of hedge funds.1997RFSFung W & Hsieh DAMeasurement biases in hedge fund performancedata: An update.2009FAJFung W & Hsieh DAHedge fund benchmarks: A risk-based approach.2004FAJFung W, Hsieh DA, NaikNY & Ramadorai THedge funds: Performance, risk, and capitalformation.2008JFFung H-G, Xu XE& Yau JGlobal hedge funds: Risk, return, and markettiming.2002FAJInvestor activism and takeovers.2009JFEGriffin JM & Xu JHow smart are the smart guys? A unique viewfrom hedge fund stock holdings.2009RFSKosowski R, Naik NY& Teo MDo hedge funds deliver alpha? A Bayesian andbootstrap analysis.2007JFEMassoud N, Nandy D,Saunders A & Song KDo hedge funds trade on private information?Evidence from syndicated lending and shortselling.2011JFERamadorai TThe secondary market for hedge funds and theclosed hedge fund premium.2012JFGreenwood R & Schor MRetrieved from Joenväärä, Kosowski and Tolonen (2016)

Appendix 2The table below summarizes and describes all the factors used in the four different multifactormodels. The Fama and French factors (MKT, SMB, HML, RMW, CMA) and the momentumfactor computed by Carhart (1997) are available on the website of K. French1. The Fung andHsieh (2001) factors (PTFSBD, PTFSFX, PTFSCOM) are available on their website 2. Theequity market factor (SP) and the size spread factor (SIZE) are both available on Bloomberg.The bond market factor (BOND) and the credit spread factor (CRED) are, for their part,available on the website of the Federal Reserve Bank of St. Louis. Finally, the Agarwal andNaik (2004) optional factors (ATMP, OTMP, ATMC, ITMC) are computed from the S&P 500index and the equity factor (RUS) is the Russel 3000 index monthly total return.Risk Factors DenominationMKTMarket factorSMBSmall Minus Big factorHMLHigh Minus Low factorMOMMomentum factorRMWRobust Minus Weak factorCMAConservative Minus Aggressive factorRUSRussell 3000 index monthly total returnPTFSBDMonthly return of the PTFS Bond Lookback StraddlePTFSFXMonthly return of the PTFS Currency Lookback StraddlePTFSCOMSPSIZEBONDCREDMonthly return of the PTFS Commodity Lookback StraddleStandard & Poor's 500 index monthly total returnRussell 2000 index monthly total return – Standard & Poor’s 500 monthly totalindexMonthly change in the 10-year Treasury constant maturity yield (month end-tomonth end)The monthly change in the Moody's Baa yield less 10-year Treasury constantmaturity yield (month end-to-month end)ATMPAt-the-money European Put option on the Standard & Poor’s 500OTMPOut-of-the-money European Put option on the Standard & Poor’s 500ATMCAt-the-money European Call option on the Standard & Poor’s 500OTMCOut-of-the-money European Call option on the Standard & Poor’s 500The data are retrieved from the Fama and French’s /ken.french/data library.html2The data are retrieved from the Fama and French’s website https://faculty.fuqua.duke.edu/ dah7/HFRFData.htm1

Appendix 3In order to obtain their three factors, Fama and French made use of 6 value-weighted portfoliosbased on size and book-to-market. The description of these factors is available and monthlyupdated on their website3.SMB (Small Minus Big) is constructed by taking the difference between the mean return on thethree small portfolios minus the mean return on the three big portfolios:SMB 1 (Small Value Small Neutral Small Growth)31 (Big Value Big Neutral Big Growth)3HML (High Minus Low) is constructed by taking the difference between the mean return onthe two value portfolios and the mean return on the two growth portfolios:HML 11 (Small Value Big Value) (Small Growth Big Growth)22𝐑 𝐌 𝐑 𝐟 is the excess return on the market, obtained by computing a value-weighted return ofall CRSP firms incorporated in the US and listed on the NYSE, AMEX, or NASDAQ. On topof that, these CRSP companies need to fulfil other criteria: A CRSP share of 10 or 11 at the beginning of month t; Good shares and price data at the beginning of month t; Good return data for t minus the one-month Treasury bill rate from Ibbotson Associates.The factors were retrieved from the Fama and French’s y/ken.french/Data Library/f-f factors.html3

Appendix 4In order to obtain their five factors, Fama and French made use of 6 value-weighted portfoliosbased on size and book-to-market, 6 value-weighted portfolios based on size and profitabilityand 6 value-weighted portfolios based on size and investment. The description of these factorsis available and monthly updated on their website4.SMB (Small Minus Big) is constructed by taking the difference between the mean return on thenine small stock portfolios and the mean return on the nine big stock portfolios:SMB 1 (SMB B SMB(OP) SMB(INV) )( )3Mwhere 13SMB( B ) (Small Value Small Neutral Small Growth)M1 3 (Big Value Big Neutral Big Growth) 1SMB(OP) 3 (Small Robust Small Neutral Small Weak )1 3 (Big Robust Big Neutral Big Weak) 1SMB(INV) 3 (Small Conservative Small Neutral Small Aggressive)1 3 (Big Conservative Big Neutral Big Aggressive)HML (High Minus Low) is constructed by taking the difference between the mean return onthe two value portfolios and the mean return on the two growth portfolios:HML 11 (Small Value Big Value) (Small Growth Big Growth)22RMW (Robust Minus Weak) is constructed by taking the difference between the mean returnon the two robust operating profitability portfolios and the mean return on the two weakoperating profitability portfolios:RMW 11 (Small Robust Big Robust) (Small Weak Big Weak)22The factors were retrieved from the Fama and French’s y/ken.french/Data Library/f-f 5 factors 2x3.html4

CMA (Conservative Minus Aggressive) is constructed by taking the difference between themean return on the two conservative investment portfolios and the mean return on the twoaggressive investment portfolios:CMA 11 (Small Conservative Big Conservative) (Small Aggressive Big Aggressive)22𝐑 𝐌 𝐑 𝐟 is the excess return on the market, obtained by computing a value-weighted return ofall CRSP firms incorporated in the US and listed on the NYSE, AMEX, or NASDAQ. On topof that, these CRSP companies need to fulfil other criteria: A CRSP share of 10 or 11 at the beginning of month t; Good shares and price data at the beginning of month t; Good return data for t minus the one-month Treasury bill rate from Ibbotson Associates.

Appendix 5Computation procedureIn order to synthetically recreate the optional factors constructed by Agarwal and Naik, theBlack-Scholes model was used. The inputs of the model and the underlying assumptions aredescribed below. Let 𝑆𝑡 be the spot price of the underlying asset at time t. The period under study extendsfrom January 1998 to December 2017 and options on the S&P500 index trading on theChicago Mercantile Exchange are considered; K be the exercise price of the option; σ be the volatility of returns of the underlying asset. The VIX index was used since thisvolatility index depicts how volatile are the prices of options on the S&P 500; t be the time in years. As the options have a maturity of one month, the variable t willbe set equal to 0.083 (1/12); 𝑟𝑓 is the annualized risk-free interest rate, continuously compounded. The risk-free rateavailable on the website of Fama and French for each month was reused in this case andtransformed to obtain the corresponding annualized rate.Once all the inputs collected, the subsequent step was simply to apply the formula of the modeluntil reaching the put/call price.Stσ2d1 [ln ( ) (rf ) t]K2σ t1d2 d1 σ tC(S, t) N(d1 ) St N(d2 ) Ke rtP(S, t) N( d2 ) Ke rt N( d1 ) StWith N(.) being the cumulative distribution function of the standard normal distribution, P theput price and C the call price. Once all the put prices are computed (for each month), the returnon the option (not exercised) can be calculated. Then, it is interesting to compute the descriptivestatistics (mean, standard deviation, median, etc.) of each series of returns to observe theircharacteristics and afterwards to plot it against the S&P 500 return to observe a potentialrelationship between the two series of return.