Working Paper Series Brasília n. 184 Apr p. 160


 Godwin Alexander
 3 years ago
 Views:
Transcription
1
2 ISSN CGC.38.66/5 Working Papr Sris rasília n. 84 Apr. 9 p. 6
3 Working Papr Sris Editd by sarch Dpartmnt Dpp Editor: njamin Miranda Tabak Editorial Assistnt: Jan Soia Moita Had o sarch Dpartmnt: Carlos Hamilton Vasconclos Araújo Th anco Cntral do rasil Working Paprs ar all valuatd in doubl blind rr procss. production is prmittd only i sourc is statd as ollows: Working Papr n. 84. Authorizd by Mário Msquita, Dputy Govrnor or Economic Policy. Gnral Control o Publications anco Cntral do rasil Scr/Surl/Dimp SS Quadra 3 loco EdiícioSd º andar Caixa Postal rasília DF razil Phons: and Fax: Th viws xprssd in this work ar thos o th authors and do not ncssarily rlct thos o th anco Cntral or its mmbrs. Although ths Working Paprs otn rprsnt prliminary work, citation o sourc is rquird whn usd or rproducd. As opiniõs xprssas nst trabalho são xclusivamnt dos autors não rltm, ncssariamnt, a visão do anco Cntral do rasil. Ainda qu st artigo rprsnt trabalho prliminar, citação da ont é rqurida msmo quando rproduzido parcialmnt. Consumr Complaints and Public Enquiris Cntr anco Cntral do rasil Scr/Surl/Diat SS Quadra 3 loco EdiícioSd º subsolo rasília DF razil Fax: Intrnt: http//
4 havior Financ and Estimation isk in Stochastic Portolio Optimization José Luiz arros Frnands * Juan Ignacio Pña ** njamin Miranda Tabak *** Th Working Paprs should not b rportd as rprsnting th viws o th anco Cntral do rasil. Th viws xprssd in th paprs ar thos o th authors and do not ncssarily rlct thos o th anco Cntral do rasil. Abstract Th objctiv o this papr is twoold. Th irst is to incorporat mntal accounting, lossavrsion, asymmtric risktaking bhavior, and probability wighting in a multipriod portolio optimization or individual invstors. Whil ths bhavioral biass hav prviously bn idntiid in th litratur, thir ovrall impact during th dtrmination o optimal asst allocation in a multipriod analysis is still missing. Th scond objctiv is to account or th stimation risk in th analysis. Considring 6 daily indx stock data ovr th priod rom 995 to 7, w mpirically valuat our modl ATE havior sampl Adjustd Tchniqu against th traditional Markowitz modl. Kywords: havior, Portolio Optimization, sampling JEL Classiication: G, G. * Univrsidad Católica d rasília and anco Cntral do rasil GrênciaExcutiva d isco da Ára d Política Montária ** Dpartamnto d Economía d la Emprsa, Univrsidad Carlos III d Madrid, España. *** Univrsidad Católica d rasília and anco Cntral do rasil Dpartamnto d Estudos Psquisas 3
5 In a standard asst allocation procdur, onc th risk tolranc, constraints, and inancial goals ar st, th output is givn by a manvarianc optimization Markowitz, 95; Fldman and isman,. Unortunatly this procdur is likly to ail or individuals, who ar suscptibl to bhavioral biass. For instanc, in rspons to shorttrm markt movmnts and to th dtrimnt o th longtrm invstmnt plan, th individual invstor may rquir his asst allocation to b changd. Frnands t al. [7] suggst that arly liquidation o a long trm invstmnt may b th caus o momntum. In trms o motional biass, svral mpirical studis Tvrsky and Kahnman, 99 hav shown that, whn daling with gains, agnts ar riskavrs, but whn choics involv losss, agnts ar risksking asymmtric risktaking bhavior. Morovr, in a wid varity o domains, popl ar signiicantly mor avrs to losss than thy ar attractd to samsizd gains. Lossavrsion Schmidt and Zank, 5 is a rlvant psychological concpt that has bn importd to inancial and conomic analysis, and it rprsnts th oundation o prospct thory. Th currnt paradigm o individual bhavior in inanc thory is basd on xpctd utility maximization and riskavrsion, which has bn undr attack in rcnt yars du to its dscriptiv inaccuracy. Exprimntal psychologists hav dmonstratd that popl systmatically dviat rom th choic prdictions th classical paradigm implis as individuals ar typically biasd. havioral biass can roughly b groupd in two catgoris: cognitiv and motional, though both typs yild irrational dcisions. caus cognitiv biass huristics lik anchoring, availability, and rprsntativ biass stm rom aulty rasoning, bttr inormation and advic can otn corrct thm. Convrsly, motional biass, such as rgrt and lossavrsion, originat rom impulsiv lings or intuition, rathr than conscious rasoning, and ar hardly possibl to corrct. Lo t al. [5] invstigatd svral possibl links btwn psychological actors and trading prormanc, inding that subjcts whos motional raction to montary gains and losss was mor intns on both th positiv and ngativ sid xhibitd signiicantly wors trading prormanc. Shrin [5] posits that th portolios slctd by invstors whos choics conorm to prospct thory will dir in ky aspcts rom th portolios slctd by invstors whos choics conorm to xpctd utility thory. Th gnral charactr o bhavioral portolios is that thy atur a combination o scuritis that ar vry sa 4
6 with scuritis that ar vry risky, with th ovrall portolio ailing to b wll divrsiid. In this sns, an optimal solution to th asst allocation problm should guid invstors to mak dcisions that srv thir bst intrst. This could b th rcommndation o an asst allocation that suits th invstor s natural psychological prrncs motional biass, vn though it may not maximiz xpctd rturn or a givn lvl o risk. Mor simply, a clint s bst practical allocation may b a slightly undrprorming longtrm invstmnt program to which th invstor can comortably adhr. From a manvarianc optimization prspctiv, bhavioral invstors slct portolios that ar stochastically dominatd. This dos not man that th individual invstors ar irrational in any sns: it is not irrational or popl to anticipat motional ractions and tak thm into account whn making dcisions that try to adjust thir choics to thir prrncs. Howvr, portolio managrs lack th guidlins ncssary or incorporating ths biass during th procss o dtrmining asst allocation. W addrss this issu by valuating whthr managrs should modrat th way clints naturally bhav to countract th cts o bhavioral biass so that thy can it a prdtrmind asst allocation or thy should crat an asst allocation that adapt to clints biass, so that clints can comortabl adhr to th und. In gnral trms, prospct thory and its lattr vrsion cumulativ prospct thory Kahnman and Tvrsky, 979, 99 posits our novl concpts in th ramwork o individuals risk prrncs. First, invstors valuat assts according to gains and losss and not according to inal walth mntal accounting. Scond, individuals ar mor avrs to losss than thy ar attractd to gains lossavrsion. Third, individuals ar risksking in th domain o losss and riskavrs in th domain o gains asymmtric risk prrnc. Finally, individuals valuat xtrm probabilitis in a way that ovrstimats low probabilitis and undrstimats high probabilitis probability wighting unction. This study, as ar as w know, is th irst to considr all thos aspcts in th ramwork o portolio choic. Thr ar conlicting rsults in th inanc litratur on how prior outcoms act th risktaking bhavior o invstors in subsqunt priods. Lossavrsion would prdict that tradrs with proitabl mornings would rduc thir xposur to atrnoon risk, trying to avoid losss and thus guaranting th prvious gains Wbr and Zuchl, 3. Odan [998] and Wbr and Camrr [998] hav shown that invstors ar mor willing to sll stocks that trad abov th purchas pric winnrs than stocks that trad blow purchas pric losrs a phnomnon trmd th disposition ct 5
7 Schrin and Statman, 985. oth works intrprtd this bhavior as vidnc o dcrasd riskavrsion atr a loss, and incrasd riskavrsion atr a gain. Th standard xplanation or th prvious bhavior is basd on prospct thory, and particularly on th act that individuals ar risksking in th domain o losss and riskavrs in th domain o gains asymmtric risk prrnc. Howvr, anothr stram o th litratur ound th opposit bhavior. Thalr and Johnson [99] nam th housmony ct, th bhavior o incrasing risk apptit atr a gain. arbris t al. [] prsnt a modl whr invstors ar lss lossavrs atr a gain whil thy bcom mor lossavrs atr prior losss. Our proposd modl addrsss and clariis th prvious contradiction btwn housmony and disposition ct. Dspit th vast litratur conirming th bhavioral biass associatd with prospct thory, th considration o all thos biass in an asst allocation ramwork is still missing. arbris and Huang [] and arbris t al [] us lossavrsion and mntal accounting Thalr, 999 to xplain aspcts o stock pric bhavior, but do not mploy th ull prospct thory ramwork and don t xamin optimal asst allocation. nartzi and Thalr [995] considr prospct thory to solv th quity prmium puzzl whn invstors ar lossavrs and valuat thir portolios myopically with a horizon o approximatly on yar. Thy also suggst an optimal allocation in quitis rom 3% to 55%. Magi [5] uss bhavioral prrncs to numrically solv a simpl modl o intrnational portolio choic, providing a possibl xplanation or th quity hom bias puzzl, th tndncy o individual invstors to prr its homcountry stocks dspit th gratr prormanc o orign stocks. Davis and Satchll [4] provid a solution or th optimal quity allocation, and xplor mor thoroughly th cumulativ prospct thory paramtr spac that is consistnt with obsrvd quity allocations givn a inancial markt s rturns distributions ovr a onmonth horizon. Shrin [5] considrs htrognous invstors to s th impact o bhavioral concpts in th ramwork o asst pricing. Th irst main goal o this study is to incorporat mntal accounting, lossavrsion, asymmtric risktaking, disposition ct, and probability wighting in portolio optimization in a multipriod stting or individual invstors. W provid a solution or th asst allocation problm, taking into account all bhavioral biass associatd with prospct thory and using a utility unction suggstd in Giorgi t. al., 4 consistnt with both th xprimntal rsults o Tvrsky and Kahnman, and also 6
8 with th xistnc o quilibrium. W also shd mor light on th issu o how prior outcoms act subsqunt risktaking bhavior, invstigating th invstor s risktaking bhavior ollowing a ris, or a all, in th pric o th risky asst. In lin with prospct thory, invstors driv utility rom luctuations in th valu o thir inal walth. In our ramwork, thr is a inancial markt on which two assts ar tradd. A risklss asst, also calld a bond, and a risky asst, also calld a stock undr th assumption o normally distributd rturns or th risky asst. As w ar modling th dcision making procss o an individual invstor, shortslling is not allowd. In ach priod w considr two priods, th invstor chooss th wight o his ndowmnt to b invstd in th risky asst, in ordr to maximiz his utility prospct thory basd. W assum that th invstor acts myopically in a sns that h dosn t discount longtrm wlar whn valuating his utility, and that th rrnc point rlativ to which h masurs his gains and losss or th irst priod is his initial ndowmnt. Although all agnts solv th sam maximization problm in th irst priod, th scond priod dcision dpnds on th rrnc point rlativ to which th agnt masurs th scond priod outcoms gains or losss. W considr two possibl rrnc points: th initial walth or th currnt walth, and analyz both cass. St Amour [6] valuats houshold portolios and his rsults rval that rrncs ar strongly rlvant and statdpndnt. Anothr wllknown issu in asst allocation problms, using Markowitz optimization, is that th output is strongly drivn by th risk/rturn stimation, which usually gnrats vry unstabl portolios. Th most amous problm with this tchniqu is th substitution problm, whr two assts with th sam risk but slightly dirnt xpctd rturns. Th optimizr would giv all th wight to th asst with th highr xpctd rturn, lading to a vry unstabl asst allocation. Th scond goal o this chaptr is to incorporat stimation risk in th portolio allocation bhavioral problm. cnt litratur has trid to ovrcom th prvious problm o lading to unasibl portolios. Th main ocus o thos modls is to ind out how to crat ralistic portolios considring that th valus usd or risk and rturn ar not dtrministic but instad just stimats thy ar stochastic. It should b notd that th misspciication o xpctd rturns is much mor critical than that o variancs Zimmr and Nidrhausr, 3. 7
9 Jorion [986] ors a simpl mpirical ays stimator that should outprorm th sampl man in th contxt o a portolio. His main ida is to slct an stimator with avrag minimizing proprtis rlativ to th loss unction th loss du to stimation risk. Instad o th sampl man, an stimator obtaind by shrinking th mans toward a common valu is proposd th avrag rturn or th minimum varianc portolio, which should lad to dcrasd stimation rror. Similar to Jorion, Kmp t al [] assums that th prior man is idntical across all risky assts. Howvr, Kmp s modl considrs stimation risk as a scond sourc o risk, dtrmind by th htrognity o th markt and givn by th standard dviation o th xpctd rturns across risky assts. lack and Littrman [99] postulat that th considration o th global CAPM Capital Asst Pricing Modl quilibrium can signiicantly improv th usulnss o asst allocation modls, as it can provid a nutral starting point or stimating th st o xpctd xcss rturns rquird to driv th portolio optimization procss. Horst t al. [] propos a nw adjustmnt in manvarianc portolio wights to incorporat th stimation risk. Th adjustmnt amounts to using a psudo riskavrsion, rathr than th actual riskavrsion, which dpnds on th sampl siz, th numbr o assts in th portolio, and th curvatur o th manvarianc rontir. Th psudo riskavrsion is always highr than th actual on and this dirnc incrass with th uncrtainty in th xpctd rturn stimations. Manhout [4] also considrs an adjustmnt in th coicint o riskavrsion to insur th invstor against som ndognous worst cas. Finally, Michaud [998] suggsts portolio sampling as a way to allow an analyst to visualiz th stimation rror in traditional portolio optimization mthods, and Shrr [] posits that sampling rom a multivariat normal distribution a paramtric mthod trmd Mont Carlo simulation is a way to captur th stimation rror. Markowitz and Usmn [3] compard th traditional approach to rsampling and thir rsults support th lattr. Frnands t al. [8] valuat svral asst allocation modls and suggst that rsampling mthods typically or th bst rsults. This study prsnts a novl approach ATE havioral sampl Adjustd Tchniqu to incorporat bhavioral biass and stimation risk into manvarianc portolio slction. In a papr clos to ours, Vlck [6] proposs a modl to valuat portolio choic with lossavrsion, asymmtric risktaking bhavior, and sgrgation o risklss opportunitis. His indings suggst that th changs in portolio wights crucially dpnd on th rrnc point and th ratio btwn th rrnc point and th 8
10 currnt walth, and thus indirctly on th prormanc o th risky asst. Our work dirs rom his study as w xplicitly considr all novl aspcts o prospct thory: mntal accounting, lossavrsion, asymmtric risktaking bhavior, and probability wighting unction. W also valuat th inicincy cost o th bhavioral biass and considr a mor gnral orm or th risky asst rturn procss, including stimation risk in th analysis. Considring daily quity data rom th priod rom 995 to 7, w mpirically valuat our modl in comparison to th traditional Markowitz modl. Our rsults support th us o ATE as an altrnativ or dining optimal asst allocation and posit that a portolio optimization modl may b adaptd to th individual biass implid in prospct thory. Th rmaindr o this papr contains th ollowing sctions. Sction A discusss th bhavioral biass considrd and dscribs our modl proposing th bhavioral rsampling adjustd tchniqu ATE. Sction prsnts th mpirical study, dscribing th data and implmntation, and providing th rsults. Sction C concluds th rsarch by rviwing th main achivmnts. A Th havioral Modl W prsnt a two priod s modl or portolio choic in a stylizd inancial markt with only two assts, whr th invstor s prrncs ar dscribd by cumulativ prospct thory as suggstd by Kahnman and Tvrsky [979] and Tvrsky and Kahnman [99]. In our ramwork, thr is a inancial markt in which two assts ar tradd. A risklss asst, also calld th bond, and a risky asst, th stock. Lt us considr th rturn o th stock in ach priod givn by th ollowing procss: n, with n ~ N,. Th riskr bond yilds a sur rturn o. W assum that th tim valu o th mony is positiv, i.. that intrst rats ar nonngativ. Th prrncs o th invstor ar basd on changs in walth and ar dscribd by prospct thory. W assum that h owns an initial ndowmnt, W normalizd to montary unit, and that h arns no othr incom. Th agnt invsts a proportion o his walth in th stock and  in th bond. Sinc w want to modl th individual invstor s bhavior, w assum that short slling is not allowd. W also assum that th invstor acts myopically, and th rrnc point rlativ to which h 9
11 masurs his gains and losss in th irst priod is his initial walth. Thn, th prcivd gain or loss in th nd o th irst priod is givn by: x ΔW x x [ W W ] n Eq. W As pointd out in Vlck [6] th choic procss undr prospct thory starts with th diting phas, ollowd by th valuation o ditd prospcts, and inally th altrnativ with th highst valu is chosn. During th diting phas, agnts discriminat gains and losss. Thy also prorm additional mntal adjustmnts in th original probability unction p x, dining th probability wighting unction p. asd on xprimntal vidnc, individuals adjust th liklihood o outcoms such that small probabilitis ar ovrwightd and larg probabilitis ar undrwightd. W will considr th probability wighting unction, as in Giorgi t al. [4] givn by: p, Eq. γ p γ γ p p γ whr γ is th adjustmnt actor. Th ollowing graph compars th valus o p and p, considring γ Figur In th valuing phas, th agnts attach a subjctiv valu to th gambl. Lt us assum th valu unction proposd by Giorgi t al. [4], as ollows: v x x, x, i i x x < Eq. 3 whr is th coicint o absolut risk prrnc, > > maks th valu unction stpr in th ngativ sid lossavrsion, and x is th chang in walth or wlar, rathr than inal stats mntal accounting, as proposd by Kahnman and Tvrsky [979]. Also, th valu unction is concav abov th rrnc point and convx blow it asymmtric risk prrnc. It is usul to considr th prvious orm or th valu unction bcaus o th xistnc o a CAPM quilibrium 3 and th ability to rach constant coicints o risk prrnc. Th prvious ormulation is also
12 supportd by th laboratory rsults rom oshdomènch and Silvstr [3]. Th ollowing graph indicats vx whn.88,.5 and Kahnman and Tvrsky suggstd valus Figur In our twopriod modl or portolio choic, th invstor chooss a wight in th risky asst to maximiz his xpctd utility V. His prrncs ar basd on changs in his walth x and ar dscribd by prospct thory. Th total xpctd valu h addrsss to a givn choic o is givn by: d V v x x dx Eq. 4 dx whr v x is th prospct valu o th outcom x, and x is th wightd cumulativ probability associatd with that outcom. Prospct thory is a dscriptiv thory, postulating that, in comparing altrnativs, th invstor will choos th altrnativ that maks V as high as possibl. Lt us thn valuat th invstor s problm in ach priod. A. First Priod In th irst priod, th agnt s problm consists o dining th allocation o his initial walth btwn th two assts tradd in th inancial markt. H maximizs his utility in t by allocating a raction,, o his initial walth 4, W, in th risky asst and  in th riskr asst. W considr that th invstor is a myopic optimizr in th sns that h taks into account only th irst priod rsult. For multipriod horizons, th choics at arlir dats impact th rrnc points at latr dats. This atur allows or complx modling. Howvr, as pointd out in Shrin [5], prospct thory is a thory about invstors who ovrsimpliy, and so, assuming that individuals ar sophisticatd nough to prciv th link btwn thir currnt choics and utur rrnc points is somthing unrasonabl. W also constrain short slling, as it is common or individual invstors modls. Thus, his problm can b givn by d maxv v x x dx dx Eq. 5
13 Lt us mak th ollowing drivation: n x. arranging th trms in x, w gt n x. W call and C. Thn, Cn x, and so x > implis C n >. Thn, 6 Eq. ˆ C C C C C V n d n d C V n d n d C C V n d n d V x d x d V dx x dx d x v V C C nc C nc C nc C nc C Cn C Cn x x Whr, or th last stp, w usd 5 : z x z x d φ φ ˆ Obsrv that, i w wr considring a standard utility unction riskavrsion ovr all possibl outcoms, th valu would b givn by: C S V Eq. 7 Morovr, th partial drivativs o V Eq. 6 ar:
14 3 [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] Eq. 9 } ] [ { Eq. 8 ]} ˆ [ { V V As a consqunc, th ollowing proprtis hold 6, i > V ; ii V or or ; iii < V or >. Equations 6 and 7 clarly yild dirnt wights or th risky asst, considring th rmaining paramtrs ixd. Thus, it is possibl to valuat th cost o inicincy associatd with th bhavioral biass as compard to th standard utility solution. [ ] [ ] Eq. Cost PT PT S S whr S is th risky asst wight givn by th standard utility maximization problm, and PT is th stock wight as dind in our modl. Proposition. Th optimal asst allocation in t, or th risky asst * is such that maximizs th valu unction givn by: C C C C C V C whr: [ ] * * and * C. I w wr considring a standard utility unction, th optimal allocation in t, or th risky asst would thn b givn by:
15 * Lt us irst considr standard valus or th modl s paramtrs 7. Th riskr rat quals th historical annual rturn o th US thrmonth Trasury ill.73%. Th quity xpctd rturn and volatility quals th historical avrag o th MSCI global quity indx and its standard dviation 7.6% and.98%. Th adjustmnt actor in th probability wighting unction quals γ.9. Th coicint o riskavrsion quals 3. Also, as suggstd by Kahnman and Tvrsky,.5 and. Th individual s valus prospct thory and standard as a unction o th prcntag o his walth invstd in th risky asst ar givn in Figur 3. Th individual invstor is xpctd to choos th allocation in th risky asst which maximizs his xpctd valu Figur As can b obsrvd rom th graph, using a standard utility unction, th allocation in th risky asst approachs % thta or which th valu unction rachs its maximum, whil using prospct thory utility, th invstor should allocat 8% o his walth in th stock 8. Th shaps o th graphs ar dirnt, notably or larg allocations in th stock. Th valu unction using standard utility is qual to or gratr than th on or prospct utility. Th rason or this dirnc coms rom th act that in prospct thory, ngativ outcoms ar pnalizd mor as ar risky portolios bcaus individuals ar lossavrs >. In th lossavrsion litratur vidnc suggsts that individuals ar around twic mor snsitiv to losss than thy ar attractd to sam siz gains. For small allocations in stocks, th prospct o losss bcoms lss likly and th valu unctions tnd to coincid. latd to th ct o probability wighting, i w st γ, thus cancling out its ct, w rach th ollowing Figur rprsnting th valu unction: Figur
16 Not that th amount optimally invstd by th bhavioral invstor in th risky asst dcrass to 48%, and so probability wighting tnds to incras th risk apptit. Kahnman and Tvrsky [979] suggst that th ovrwighting o low probabilitis has an ambiguous ct on risktaking, as it can induc riskavrsion in th domain o losss, and risksking in th domain o gains. In our cas, th ovrstimation o th xtrm positiv outcoms probabilitis, shown in Figur 3, is inducing invstors to tak mor risk. Howvr, dspit th cts o lossavrsion and probability wighting, vn i w considr and γ, kping constant th rmaining paramtrs, th valu unctions wouldn t coincid, as can b sn in Figur 5: Figur oth modls would prdict that th invstor should allocat % o his ndowmnts in th stock. Howvr, th valu unctions ar dirnt bcaus, in prospct thory, individuals ar risksking in th loss domain asymmtric risk prrnc. Thus, thy would b mor comortabl in allocating a gratr part o thir walth in th risky asst. Th prospct valu unction is gratr than th standard utility unction. Obsrv that th ct o th asymmtric risk prrnc gos in th opposit dirction o lossavrsion and probability wighting. Whn w diminish th coicint o risk prrnc.5 in both utility unctions, w rduc th ct o asymmtry, and so th valu unctions ar much closr, as can b sn in th ollowing igur Figur Th cts o th bhavioral biass can thus b summarizd as ollows: lossavrsion rducs risktaking, and asymmtric risktaking bhavior inducs risky attituds. Probability wighting has an ambiguous ct on risk. Our intuition is that, in th long run, as th valu unction paramtrs ar changing, ths biass tnd to cancl out, liminating th icincy loss originatd by ach bias. That is why w argu that human biass do not nd to b modratd to rach an icint invstmnt stratgy. Th 5
17 xprimntal rsults o lavatskyy and Pogrbna [6] rval that th ct o lossavrsion is largly nutralizd by th ovrwighting o small probabilitis and undrwighting o modrat and high probabilitis. In ordr to vriy proprty i, Lt us valuat V whil changing and kping constant th othr paramtrs considring 5%. Figur 7 prsnts th graph which indicats that ovr all positiv valus o, th slop o V is positiv. Th valu unction is incrasing in. Thus, whn th risky asst has a highr xpctd rturn, ctris paribus implis a highr valu or th invstor: Figur Considring proprtis ii and iii, Lt us valuat V whil changing and kping constant th othr paramtrs considring 5%. Figur 8 prsnts th graph indicating that ovr all positiv valus o, th slop o V is ngativ, whil or, th slop is null. Whn tnds to ininity, th slop tnds to null. Th valu unction is dcrasing in. Th intuition is that, i th volatility o th risky asst is highr, or th sam allocation, this implis a highr probability o losss rducing th valu o th prospct. In lin with traditional rational invstor, bhavioral individuals also prr highr rturn and lowr risk; mainly bcaus thy ar riskavrs in th gain domain and also lossavrs Figur Now lt us valuat th valus o whn w chang th riskr rat and th xpctd rturn o th risky asst. Sinc many paramtrs ar involvd, it is not possibl to ind closd orm solutions or. Thror, w prsnt numrical rsults or th optimal allocation o walth in t. Figur 9 prsnts th rsults or % < < 5% and < 6%. Th rmaining paramtrs ar ixd.98%, 3,.5, < and Figur 9 6
18 As xpctd, whn th risky asst ors mor attractiv rturns, th agnt gradually invsts mor in th stock. Whn th stock is vry attractiv, th invstor chooss to allocat his ntir walth in th risky asst. Thus, w obsrv that is incrasing in and dcrasing in. Also, whn is highr, th changs in du to a variation in ar smoothr, bcaus in ths cass losss ar lss likly and w approach th standard utility solution. Whn is lowr, th changs in du to a variation in ar mor abrupt, giving ris to xtrm portolio allocations. I w considr that is not known with crtainty, th rsulting portolio would b vry unstabl. Goms [3], in a modl with lossavrs invstors, has ound that individuals will not hold stocks unlss th quity prmium is quit high. W can valuat th xpctd cost o inicincy rlatd to th bhavioral biass associatd to th prospct thory unction, or th sam paramtrs considrd in th prvious analysis, using quation. Th rsult is prsntd in Figur, and its orm is du to th act that, in standard utility unction, th invstor is willing to tak mor risk than with th lossavrs prospct utility. Th cost is du to th act that th xpctd rturn o th stock is gratr than th bond, and th standard utility invstor is allocating a gratr part o his walth in th risky asst than th prospct utility individual. Thus, th cost is incrasing in. Howvr, it is worth noting that th prvious cost is basd on xpctd rturns, which ar stochastic in practic. Th ral cost can just b obsrvd at th nd o th irst priod with th ralization o th stock s rturn. An important insight can b mad rom Figur in trms o th bst practic or asst allocation. As long as th riskr rat is lowr and th xpctd rturn o th stock is highr, th optimal allocation should modrat th invstor s biass in ordr to rach a bttr prormanc. On th othr hand, i th risk prmium is lowr, th modration is lss rlvant, and th optimal allocation may adapt to th individual s biass Figur
19 W can also analyz th chang in th allocation o th stock whn w vary th lossavrsion in th risktaking bhavior. Th rsult is shown in Figur, or < < 4. Obsrv that, as long as th invstor is much mor avrs to losss than h is attractd to gains, th allocation in th risky asst is lowr. Whn. 5, th allocation in th risky asst corrsponds to 8%, as prviously mntiond Figur Dimmock[5] has alrady shown that a highr lvl o lossavrsion lads to lowr quity xposur, and htrognity in th coicint o lossavrsion has th ability to xplain puzzling aturs o houshold inancial bhavior. A. Scond Priod In ordr to valuat th scond priod allocation choic o th invstor, Lt us kp som paramtrs ixd:.98, 3,.5 and. Atr th invstor has mad his irst priod dcision in t, th stat o natur ralizs in t, whn h is acd with his scond priod problm. Again, h must allocat his walth in th two possibl assts in th inancial markt, bond and stock, to maximiz his utility. Lt us considr th sam normal distribution or th rturn o th risky asst. Th invstor s walth position at t quals his position in t plus th rturn o his portolio in th scond priod. Whil all agnts solv th sam maximization problm in th irst priod, in th scond priod, it will dpnd on th rrnc point to which h masurs his gains and losss in th ramwork o prospct thory. In our modl, thr ar two candidats or th invstor s rrnc point at t : his initial walth at t W or his walth at th nd o th irst priod, t W. I h masurs his gains and losss rlativ to his walth at t his currnt walth, h trats ach gain and loss sparatly. On th othr hand, i h considrs his initial walth as th rrnc point, h adds up th outcoms gains and losss, that is, h nts his positions. Th prvious distinction is rlvant in prospct thory. Th valu unction is concav in th domain o gains and convx in th loss domain asymmtric risk bhavior. First, Lt us considr as th invstor s rrnc point his currnt walth at t. In this cas, th maximization problm h will solv in th scond priod is th sam as th on or th irst priod. Thus, w can stat th ollowing proposition. 8
20 * Proposition. Th optimal asst allocation in t, or th risky asst, i th agnt masurs his gains and losss rlativ to his currnt walth, is such that maximizs th * sam valu unction o th irst priod. * W can obsrv that an individual who masurs his gains and losss rlativ to his currnt walth is actually solving th sam maximization problm in ach priod. That is why th allocation in th risky asst might b th sam. This is not surprising; as h is not using past inormation to updat his blis about th assts, his prrncs ar similarly unactd. Nxt, lt us analyz th invstor s maximization problm i h valuats his gains and losss rlativ to his initial walth. I h has an initial walth position o W and his walth riss in th irst priod to W and alls in th nxt priod to W 5, h valus his position at t as a gain o 5, and not as a gain o ollowd by a loss o 5. In th scond priod, th agnt s problm consists o dining th allocation o his walth W btwn th two assts tradd in th inancial markt. H maximizs his utility in t by allocating a raction,, o his walth W in th risky asst and  in th risklss asst. As w did in th irst priod analysis, w also constrain short slling. d maxv v x x dx dx Lt us mak th ollowing drivation: x W [ n ] W and W W [ ], whr priod. So x W [ ] is th rturn o th stock in th irst [ n ] arranging th trms in x and considring W, w gt x [ n ] Lt us call [ ] [ ] ] [. 9
Adverse Selection and Moral Hazard in a Model With 2 States of the World
Advrs Slction and Moral Hazard in a Modl With 2 Stats of th World A modl of a risky situation with two discrt stats of th world has th advantag that it can b natly rprsntd using indiffrnc curv diagrams,
More informationEcon 371: Answer Key for Problem Set 1 (Chapter 1213)
con 37: Answr Ky for Problm St (Chaptr 23) Instructor: Kanda Naknoi Sptmbr 4, 2005. (2 points) Is it possibl for a country to hav a currnt account dficit at th sam tim and has a surplus in its balanc
More informationLong run: Law of one price Purchasing Power Parity. Short run: Market for foreign exchange Factors affecting the market for foreign exchange
Lctur 6: Th Forign xchang Markt xchang Rats in th long run CON 34 Mony and Banking Profssor Yamin Ahmad xchang Rats in th Short Run Intrst Parity Big Concpts Long run: Law of on pric Purchasing Powr Parity
More informationGold versus stock investment: An econometric analysis
Intrnational Journal of Dvlopmnt and Sustainability Onlin ISSN: 2688662 www.isdsnt.com/ijds Volum Numbr, Jun 202, Pag 7 ISDS Articl ID: IJDS20300 Gold vrsus stock invstmnt: An conomtric analysis Martin
More informationQUANTITATIVE METHODS CLASSES WEEK SEVEN
QUANTITATIVE METHODS CLASSES WEEK SEVEN Th rgrssion modls studid in prvious classs assum that th rspons variabl is quantitativ. Oftn, howvr, w wish to study social procsss that lad to two diffrnt outcoms.
More informationby John Donald, Lecturer, School of Accounting, Economics and Finance, Deakin University, Australia
Studnt Nots Cost Volum Profit Analysis by John Donald, Lcturr, School of Accounting, Economics and Financ, Dakin Univrsity, Australia As mntiond in th last st of Studnt Nots, th ability to catgoris costs
More informationIntermediate Macroeconomic Theory / Macroeconomic Analysis (ECON 3560/5040) Final Exam (Answers)
Intrmdiat Macroconomic Thory / Macroconomic Analysis (ECON 3560/5040) Final Exam (Answrs) Part A (5 points) Stat whthr you think ach of th following qustions is tru (T), fals (F), or uncrtain (U) and brifly
More informationEFFECT OF GEOMETRICAL PARAMETERS ON HEAT TRANSFER PERFORMACE OF RECTANGULAR CIRCUMFERENTIAL FINS
25 Vol. 3 () JanuaryMarch, pp.375/tripathi EFFECT OF GEOMETRICAL PARAMETERS ON HEAT TRANSFER PERFORMACE OF RECTANGULAR CIRCUMFERENTIAL FINS *Shilpa Tripathi Dpartmnt of Chmical Enginring, Indor Institut
More information(Analytic Formula for the European Normal Black Scholes Formula)
(Analytic Formula for th Europan Normal Black Schols Formula) by Kazuhiro Iwasawa Dcmbr 2, 2001 In this short summary papr, a brif summary of Black Schols typ formula for Normal modl will b givn. Usually
More informationTraffic Flow Analysis (2)
Traffic Flow Analysis () Statistical Proprtis. Flow rat distributions. Hadway distributions. Spd distributions by Dr. GangLn Chang, Profssor Dirctor of Traffic safty and Oprations Lab. Univrsity of Maryland,
More informationTheoretical aspects of investment demand for gold
Victor Sazonov (Russia), Dmitry Nikolav (Russia) Thortical aspcts of invstmnt dmand for gold Abstract Th main objctiv of this articl is construction of a thortical modl of invstmnt in gold. Our modl is
More informationForeign Exchange Markets and Exchange Rates
Microconomics Topic 1: Explain why xchang rats indicat th pric of intrnational currncis and how xchang rats ar dtrmind by supply and dmand for currncis in intrnational markts. Rfrnc: Grgory Mankiw s Principls
More informationLecture notes: 160B revised 9/28/06 Lecture 1: Exchange Rates and the Foreign Exchange Market FT chapter 13
Lctur nots: 160B rvisd 9/28/06 Lctur 1: xchang Rats and th Forign xchang Markt FT chaptr 13 Topics: xchang Rats Forign xchang markt Asst approach to xchang rats Intrst Rat Parity Conditions 1) Dfinitions
More informationQuestion 3: How do you find the relative extrema of a function?
ustion 3: How do you find th rlativ trma of a function? Th stratgy for tracking th sign of th drivativ is usful for mor than dtrmining whr a function is incrasing or dcrasing. It is also usful for locating
More informationBasis risk. When speaking about forward or futures contracts, basis risk is the market
Basis risk Whn spaking about forward or futurs contracts, basis risk is th markt risk mismatch btwn a position in th spot asst and th corrsponding futurs contract. Mor broadly spaking, basis risk (also
More information14.3 Area Between Curves
14. Ara Btwn Curvs Qustion 1: How is th ara btwn two functions calculatd? Qustion : What ar consumrs and producrs surplus? Earlir in this chaptr, w usd dfinit intgrals to find th ara undr a function and
More informationModern Portfolio Theory (MPT) Statistics
Modrn Portfolio Thory (MPT) Statistics Morningstar Mthodology Papr May 9, 009 009 Morningstar, Inc. All rights rsrvd. Th information in this documnt is th proprty of Morningstar, Inc. Rproduction or transcription
More informationExponential Growth and Decay; Modeling Data
Exponntial Growth and Dcay; Modling Data In this sction, w will study som of th applications of xponntial and logarithmic functions. Logarithms wr invntd by John Napir. Originally, thy wr usd to liminat
More informationthe socalled KOBOS system. 1 with the exception of a very small group of the most active stocks which also trade continuously through
Liquidity and InformationBasd Trading on th Ordr Drivn Capital Markt: Th Cas of th Pragu tock Exchang Libor 1ÀPH³HN Cntr for Economic Rsarch and Graduat Education, Charls Univrsity and Th Economic Institut
More informationPerformance Evaluation
Prformanc Evaluation ( ) Contnts lists availabl at ScincDirct Prformanc Evaluation journal hompag: www.lsvir.com/locat/pva Modling Baylik rputation systms: Analysis, charactrization and insuranc mchanism
More informationA Note on Approximating. the Normal Distribution Function
Applid Mathmatical Scincs, Vol, 00, no 9, 4549 A Not on Approimating th Normal Distribution Function K M Aludaat and M T Alodat Dpartmnt of Statistics Yarmouk Univrsity, Jordan Aludaatkm@hotmailcom and
More informationThe Normal Distribution: A derivation from basic principles
Th Normal Distribution: A drivation from basic principls Introduction Dan Tagu Th North Carolina School of Scinc and Mathmatics Studnts in lmntary calculus, statistics, and finit mathmatics classs oftn
More informationNonHomogeneous Systems, Euler s Method, and Exponential Matrix
NonHomognous Systms, Eulr s Mthod, and Exponntial Matrix W carry on nonhomognous firstordr linar systm of diffrntial quations. W will show how Eulr s mthod gnralizs to systms, giving us a numrical approach
More informationIMES DISCUSSION PAPER SERIES
IMES DISCUSSIN PAPER SERIES Th Choic of Invoic Currncy in Intrnational Trad: Implications for th Intrnationalization of th Yn Hiroyuki I, Akira TANI, and Toyoichirou SHIRTA Discussion Papr No. 003E13
More informationA Derivation of Bill James Pythagorean WonLoss Formula
A Drivation of Bill Jams Pythagoran WonLoss Formula Ths nots wr compild by John Paul Cook from a papr by Dr. Stphn J. Millr, an Assistant Profssor of Mathmatics at Williams Collg, for a talk givn to th
More informationThe example is taken from Sect. 1.2 of Vol. 1 of the CPN book.
Rsourc Allocation Abstract This is a small toy xampl which is wllsuitd as a first introduction to Cnts. Th CN modl is dscribd in grat dtail, xplaining th basic concpts of Cnts. Hnc, it can b rad by popl
More informationRelationship between Cost of Equity Capital And Voluntary Corporate Disclosures
Rlationship btwn Cost of Equity Capital And Voluntary Corporat Disclosurs Elna Ptrova Eli Lilly & Co, Sofia, Bulgaria Email: ptrova.lnaa@gmail.com Gorgios Gorgakopoulos (Corrsponding author) Amstrdam
More information14.02 Principles of Macroeconomics Problem Set 4 Solutions Fall 2004
art I. Tru/Fals/Uncrtain Justify your answr with a short argumnt. 4.02 rincipls of Macroconomics roblm St 4 Solutions Fall 2004. High unmploymnt implis that th labor markt is sclrotic. Uncrtain. Th unmploymnt
More informationAP Calculus AB 2008 Scoring Guidelines
AP Calculus AB 8 Scoring Guidlins Th Collg Board: Conncting Studnts to Collg Succss Th Collg Board is a notforprofit mmbrship association whos mission is to connct studnts to collg succss and opportunity.
More informationRural and Remote Broadband Access: Issues and Solutions in Australia
Rural and Rmot Broadband Accss: Issus and Solutions in Australia Dr Tony Warrn Group Managr Rgulatory Stratgy Tlstra Corp Pag 1 Tlstra in confidnc Ovrviw Australia s gographical siz and population dnsity
More informationSimulated Radioactive Decay Using Dice Nuclei
Purpos: In a radioactiv sourc containing a vry larg numbr of radioactiv nucli, it is not possibl to prdict whn any on of th nucli will dcay. Although th dcay tim for any on particular nuclus cannot b prdictd,
More informationSUBATOMIC PARTICLES AND ANTIPARTICLES AS DIFFERENT STATES OF THE SAME MICROCOSM OBJECT. Eduard N. Klenov* RostovonDon. Russia
SUBATOMIC PARTICLES AND ANTIPARTICLES AS DIFFERENT STATES OF THE SAME MICROCOSM OBJECT Eduard N. Klnov* RostovonDon. Russia Th distribution law for th valus of pairs of th consrvd additiv quantum numbrs
More informationCategory 7: Employee Commuting
7 Catgory 7: Employ Commuting Catgory dscription This catgory includs missions from th transportation of mploys 4 btwn thir homs and thir worksits. Emissions from mploy commuting may aris from: Automobil
More informationAsset set Liability Management for
KSD larning and rfrnc products for th global financ profssional Highlights Library of 29 Courss Availabl Products Upcoming Products Rply Form Asst st Liability Managmnt for Insuranc Companis A comprhnsiv
More informationStatistical Machine Translation
Statistical Machin Translation Sophi Arnoult, Gidon Mailltt d Buy Wnnigr and Andra Schuch Dcmbr 7, 2010 1 Introduction All th IBM modls, and Statistical Machin Translation (SMT) in gnral, modl th problm
More information7 Timetable test 1 The Combing Chart
7 Timtabl tst 1 Th Combing Chart 7.1 Introduction 7.2 Tachr tams two workd xampls 7.3 Th Principl of Compatibility 7.4 Choosing tachr tams workd xampl 7.5 Ruls for drawing a Combing Chart 7.6 Th Combing
More informationIncomplete 2Port Vector Network Analyzer Calibration Methods
Incomplt Port Vctor Ntwork nalyzr Calibration Mthods. Hnz, N. Tmpon, G. Monastrios, H. ilva 4 RF Mtrology Laboratory Instituto Nacional d Tcnología Industrial (INTI) Bunos irs, rgntina ahnz@inti.gov.ar
More informationFACULTY SALARIES FALL 2004. NKU CUPA Data Compared To Published National Data
FACULTY SALARIES FALL 2004 NKU CUPA Data Compard To Publishd National Data May 2005 Fall 2004 NKU Faculty Salaris Compard To Fall 2004 Publishd CUPA Data In th fall 2004 Northrn Kntucky Univrsity was among
More informationParallel and Distributed Programming. Performance Metrics
Paralll and Distributd Programming Prformanc! wo main goals to b achivd with th dsign of aralll alications ar:! Prformanc: th caacity to rduc th tim to solv th roblm whn th comuting rsourcs incras;! Scalability:
More information5 2 index. e e. Prime numbers. Prime factors and factor trees. Powers. worked example 10. base. power
Prim numbrs W giv spcial nams to numbrs dpnding on how many factors thy hav. A prim numbr has xactly two factors: itslf and 1. A composit numbr has mor than two factors. 1 is a spcial numbr nithr prim
More informationImproving Managerial Accounting and Calculation of Labor Costs in the Context of Using Standard Cost
Economy Transdisciplinarity Cognition www.ugb.ro/tc Vol. 16, Issu 1/2013 5054 Improving Managrial Accounting and Calculation of Labor Costs in th Contxt of Using Standard Cost Lucian OCNEANU, Constantin
More informationArchitecture of the proposed standard
Architctur of th proposd standard Introduction Th goal of th nw standardisation projct is th dvlopmnt of a standard dscribing building srvics (.g.hvac) product catalogus basd on th xprincs mad with th
More informationSTATEMENT OF INSOLVENCY PRACTICE 3.2
STATEMENT OF INSOLVENCY PRACTICE 3.2 COMPANY VOLUNTARY ARRANGEMENTS INTRODUCTION 1 A Company Voluntary Arrangmnt (CVA) is a statutory contract twn a company and its crditors undr which an insolvncy practitionr
More informationPrinciples of Humidity Dalton s law
Principls of Humidity Dalton s law Air is a mixtur of diffrnt gass. Th main gas componnts ar: Gas componnt volum [%] wight [%] Nitrogn N 2 78,03 75,47 Oxygn O 2 20,99 23,20 Argon Ar 0,93 1,28 Carbon dioxid
More informationHigh Interest Rates In Ghana,
NO. 27 IEA MONOGRAPH High Intrst Rats In Ghana, A Critical Analysis IEA Ghana THE INSTITUTE OF ECONOMIC AFFAIRS A Public Policy Institut High Intrst Rats In Ghana, A Critical Analysis 1 by DR. J. K. KWAKYE
More informationPolicies for Simultaneous Estimation and Optimization
Policis for Simultanous Estimation and Optimization Migul Sousa Lobo Stphn Boyd Abstract Policis for th joint idntification and control of uncrtain systms ar prsntd h discussion focuss on th cas of a multipl
More informationIn the first years of the millennium, Americans flocked to Paris to enjoy French
14 chaptr Exchang Rats and th Forign Exchang Markt: An Asst Approach 320 In th first yars of th millnnium, Amricans flockd to Paris to njoy Frnch cuisin whil shopping for dsignr clothing and othr spcialtis.
More informationMONEY ILLUSION IN THE STOCK MARKET: THE MODIGLIANICOHN HYPOTHESIS*
MONEY ILLUSION IN THE STOCK MARKET: THE MODIGLIANICOHN HYPOTHESIS* RANDOLPH B. COHEN CHRISTOPHER POLK TUOMO VUOLTEENAHO Modigliani and Cohn hypothsiz that th stock markt suffrs from mony illusion, discounting
More informationunion scholars program APPLICATION DEADLINE: FEBRUARY 28 YOU CAN CHANGE THE WORLD... AND EARN MONEY FOR COLLEGE AT THE SAME TIME!
union scholars YOU CAN CHANGE THE WORLD... program AND EARN MONEY FOR COLLEGE AT THE SAME TIME! AFSCME Unitd Ngro Collg Fund Harvard Univrsity Labor and Worklif Program APPLICATION DEADLINE: FEBRUARY 28
More informationGlobal Sourcing: lessons from lean companies to improve supply chain performances
3 rd Intrnational Confrnc on Industrial Enginring and Industrial Managmnt XIII Congrso d Ingniría d Organización BarclonaTrrassa, Sptmbr 2nd4th 2009 Global Sourcing: lssons from lan companis to improv
More informationCHAPTER 4c. ROOTS OF EQUATIONS
CHAPTER c. ROOTS OF EQUATIONS A. J. Clark School o Enginring Dpartmnt o Civil and Environmntal Enginring by Dr. Ibrahim A. Aakka Spring 00 ENCE 03  Computation Mthod in Civil Enginring II Dpartmnt o Civil
More informationWORKERS' COMPENSATION ANALYST, 1774 SENIOR WORKERS' COMPENSATION ANALYST, 1769
081685 WORKERS' COMPENSATION ANALYST, 1774 SENIOR WORKERS' COMPENSATION ANALYST, 1769 Summary of Dutis : Dtrmins City accptanc of workrs' compnsation cass for injurd mploys; authorizs appropriat tratmnt
More informationKeywords Cloud Computing, Service level agreement, cloud provider, business level policies, performance objectives.
Volum 3, Issu 6, Jun 2013 ISSN: 2277 128X Intrnational Journal of Advancd Rsarch in Computr Scinc and Softwar Enginring Rsarch Papr Availabl onlin at: wwwijarcsscom Dynamic Ranking and Slction of Cloud
More informationUpper Bounding the Price of Anarchy in Atomic Splittable Selfish Routing
Uppr Bounding th Pric of Anarchy in Atomic Splittabl Slfish Routing Kamyar Khodamoradi 1, Mhrdad Mahdavi, and Mohammad Ghodsi 3 1 Sharif Univrsity of Tchnology, Thran, Iran, khodamoradi@c.sharif.du Sharif
More informationExpertMediated Search
ExprtMdiatd Sarch Mnal Chhabra Rnsslar Polytchnic Inst. Dpt. of Computr Scinc Troy, NY, USA chhabm@cs.rpi.du Sanmay Das Rnsslar Polytchnic Inst. Dpt. of Computr Scinc Troy, NY, USA sanmay@cs.rpi.du David
More informationHSBC Bank International Expat Explorer Survey 08
HSBC Bank Intrnational Expat Explorr Survy 08 Rport On: Expat Existnc Th Survy Th Expat Explorr survy qustiond 2,155 xpatriats across four continnts about th opportunitis and challngs thy fac. Th survy
More informationA Theoretical Model of Public Response to the Homeland Security Advisory System
A Thortical Modl of Public Rspons to th Homland Scurity Advisory Systm Amy (Wnxuan) Ding Dpartmnt of Information and Dcision Scincs Univrsity of Illinois Chicago, IL 60607 wxding@uicdu Using a diffrntial
More informationNoble gas configuration. Atoms of other elements seek to attain a noble gas electron configuration. Electron configuration of ions
Valnc lctron configuration dtrmins th charactristics of lmnts in a group Nobl gas configuration Th nobl gass (last column in th priodic tabl) ar charactrizd by compltly filld s and p orbitals this is a
More informationAnalyzing Product Attributes using Logical Framework of Quality Function Deployment (Phase I): Concept and Application
Intrnational Journal Enginring Rsarch & Tchnology (IJERT) Vol. 2 Issu 10, Octobr  2013 Analyzing Product Attributs using Logical Framwork Quality Function Dploymnt (Phas I): Concpt and Application Dvndra
More informationThe Matrix Exponential
Th Matrix Exponntial (with xrciss) 92.222  Linar Algbra II  Spring 2006 by D. Klain prliminary vrsion Corrctions and commnts ar wlcom! Th Matrix Exponntial For ach n n complx matrix A, dfin th xponntial
More informationDeer: Predation or Starvation
: Prdation or Starvation National Scinc Contnt Standards: Lif Scinc: s and cosystms Rgulation and Bhavior Scinc in Prsonal and Social Prspctiv s, rsourcs and nvironmnts Unifying Concpts and Procsss Systms,
More informationAnalyzing the Economic Efficiency of ebaylike Online Reputation Reporting Mechanisms
A rsarch and ducation initiativ at th MIT Sloan School of Managmnt Analyzing th Economic Efficincy of Baylik Onlin Rputation Rporting Mchanisms Papr Chrysanthos Dllarocas July For mor information, plas
More informationElectronic Commerce. and. Competitive FirstDegree Price Discrimination
Elctronic Commrc and Comptitiv FirstDgr Pric Discrimination David Ulph* and Nir Vulkan ** Fbruary 000 * ESRC Cntr for Economic arning and Social Evolution (ESE), Dpartmnt of Economics, Univrsity Collg
More informationAbstract. Introduction. Statistical Approach for Analyzing Cell Phone Handoff Behavior. Volume 3, Issue 1, 2009
Volum 3, Issu 1, 29 Statistical Approach for Analyzing Cll Phon Handoff Bhavior Shalini Saxna, Florida Atlantic Univrsity, Boca Raton, FL, shalinisaxna1@gmail.com Sad A. Rajput, Farquhar Collg of Arts
More informationEssays on Adverse Selection and Moral Hazard in Insurance Market
Gorgia Stat Univrsity ScholarWorks @ Gorgia Stat Univrsity Risk Managmnt and Insuranc Dissrtations Dpartmnt of Risk Managmnt and Insuranc 800 Essays on Advrs Slction and Moral Hazard in Insuranc Markt
More informationSPREAD OPTION VALUATION AND THE FAST FOURIER TRANSFORM
RESEARCH PAPERS IN MANAGEMENT STUDIES SPREAD OPTION VALUATION AND THE FAST FOURIER TRANSFORM M.A.H. Dmpstr & S.S.G. Hong WP 26/2000 Th Judg Institut of Managmnt Trumpington Strt Cambridg CB2 1AG Ths paprs
More informationMETHODS FOR HANDLING TIED EVENTS IN THE COX PROPORTIONAL HAZARD MODEL
STUDIA OECONOMICA POSNANIENSIA 204, vol. 2, no. 2 (263 Jadwiga Borucka Warsaw School of Economics, Institut of Statistics and Dmography, Evnt History and Multilvl Analysis Unit jadwiga.borucka@gmail.com
More informationAlgorithmic Trading, Market Efficiency and The Momentum Effect. Rafael Gamzo
Algorithmic Trading, Markt Efficincy and Th Momntum Effct Rafal Gamzo Studnt Numbr: 323979 A rsarch rport submittd to th Faculty of Commrc, Law and Managmnt, Univrsity of th Witwatrsrand, in partial fulfilmnt
More informationDefining Retirement Success for Defined Contribution Plan Sponsors: Begin with the End in Mind
Dfining Rtirmnt Succss for Dfind Contribution Plan Sponsors: Bgin with th End in Mind David Blanchtt, CFA, CFP, AIFA Had of Rtirmnt Rsarch Morningstar Invstmnt Managmnt david.blanchtt@morningstar.com Nathan
More informationFree ACA SOLUTION (IRS 1094&1095 Reporting)
Fr ACA SOLUTION (IRS 1094&1095 Rporting) Th Insuranc Exchang (301) 2791062 ACA Srvics Transmit IRS Form 1094 C for mployrs Print & mail IRS Form 1095C to mploys HR Assist 360 will gnrat th 1095 s for
More informationME 612 Metal Forming and Theory of Plasticity. 6. Strain
Mtal Forming and Thory of Plasticity mail: azsnalp@gyt.du.tr Makin Mühndisliği Bölümü Gbz Yüksk Tknoloji Enstitüsü 6.1. Uniaxial Strain Figur 6.1 Dfinition of th uniaxial strain (a) Tnsil and (b) Comprssiv.
More informationConstraintBased Analysis of Gene Deletion in a Metabolic Network
ConstraintBasd Analysis of Gn Dltion in a Mtabolic Ntwork Abdlhalim Larhlimi and Alxandr Bockmayr DFGRsarch Cntr Mathon, FB Mathmatik und Informatik, Fri Univrsität Brlin, Arnimall, 3, 14195 Brlin, Grmany
More informationFraud, Investments and Liability Regimes in Payment. Platforms
Fraud, Invstmnts and Liability Rgims in Paymnt Platforms Anna Crti and Mariann Vrdir y ptmbr 25, 2011 Abstract In this papr, w discuss how fraud liability rgims impact th pric structur that is chosn by
More informationLecture 3: Diffusion: Fick s first law
Lctur 3: Diffusion: Fick s first law Today s topics What is diffusion? What drivs diffusion to occur? Undrstand why diffusion can surprisingly occur against th concntration gradint? Larn how to dduc th
More informationCostVolumeProfit Analysis
ch03.qxd 9/7/04 4:06 PM Pag 86 CHAPTER CostVolumProfit Analysis In Brif Managrs nd to stimat futur rvnus, costs, and profits to hlp thm plan and monitor oprations. Thy us costvolumprofit (CVP) analysis
More informationEconomic Insecurity, Individual Behavior and Social Policy
Economic Inscurity, Individual Bhavior and Social Policy By Indrmit S. Gill igill@worldbank.org and Nadm Ilahi nilahi@worldbank.org Th World Bank Washington, DC 20433 First Draft: March 27, 2000 Papr writtn
More informationRemember you can apply online. It s quick and easy. Go to www.gov.uk/advancedlearningloans. Title. Forename(s) Surname. Sex. Male Date of birth D
24+ Advancd Larning Loan Application form Rmmbr you can apply onlin. It s quick and asy. Go to www.gov.uk/advancdlarningloans About this form Complt this form if: you r studying an ligibl cours at an approvd
More informationVoice Biometrics: How does it work? Konstantin Simonchik
Voic Biomtrics: How dos it work? Konstantin Simonchik Lappnranta, 4 Octobr 2012 Voicprint Makup Fingrprint Facprint Lik a ingrprint or acprint, a voicprint also has availabl paramtrs that provid uniqu
More informationLogo Design/Development 1on1
Logo Dsign/Dvlopmnt 1on1 If your company is looking to mak an imprssion and grow in th marktplac, you ll nd a logo. Fortunatly, a good graphic dsignr can crat on for you. Whil th pric tags for thos famous
More informationThe price of liquidity in constant leverage strategies. Marcos Escobar, Andreas Kiechle, Luis Seco and Rudi Zagst
RACSAM Rv. R. Acad. Cin. Sri A. Mat. VO. 103 2, 2009, pp. 373 385 Matmática Aplicada / Applid Mathmatics Th pric of liquidity in constant lvrag stratgis Marcos Escobar, Andras Kichl, uis Sco and Rudi Zagst
More informationAn Broad outline of Redundant Array of Inexpensive Disks Shaifali Shrivastava 1 Department of Computer Science and Engineering AITR, Indore
Intrnational Journal of mrging Tchnology and dvancd nginring Wbsit: www.ijta.com (ISSN 22502459, Volum 2, Issu 4, pril 2012) n road outlin of Rdundant rray of Inxpnsiv isks Shaifali Shrivastava 1 partmnt
More informationOnline Price Competition within and between Heterogeneous Retailer Groups
Onlin Pric Comptition within and btwn Htrognous Rtailr Groups Cnk Kocas Dpartmnt of Markting and Supply Chain Managmnt, Michigan Stat Univrsity kocas@msu.du Abstract This study prsnts a modl of pric comptition
More informationGenetic Drift and Gene Flow Illustration
Gntic Drift and Gn Flow Illustration This is a mor dtaild dscription of Activity Ida 4, Chaptr 3, If Not Rac, How do W Explain Biological Diffrncs? in: How Ral is Rac? A Sourcbook on Rac, Cultur, and Biology.
More informationRealTime Evaluation of Email Campaign Performance
Singapor Managmnt Univrsity Institutional Knowldg at Singapor Managmnt Univrsity Rsarch Collction L Kong Chian School Of Businss L Kong Chian School of Businss 102008 RalTim Evaluation of Email Campaign
More informationUse a highlevel conceptual data model (ER Model). Identify objects of interest (entities) and relationships between these objects
Chaptr 3: Entity Rlationship Modl Databas Dsign Procss Us a highlvl concptual data modl (ER Modl). Idntify objcts of intrst (ntitis) and rlationships btwn ths objcts Idntify constraints (conditions) End
More informationCategory 11: Use of Sold Products
11 Catgory 11: Us of Sold Products Catgory dscription T his catgory includs missions from th us of goods and srvics sold by th rporting company in th rporting yar. A rporting company s scop 3 missions
More informationDehumidifiers: A Major Consumer of Residential Electricity
Dhumidifirs: A Major Consumr of Rsidntial Elctricity Laurn Mattison and Dav Korn, Th Cadmus Group, Inc. ABSTRACT An stimatd 19% of U.S. homs hav dhumidifirs, and thy can account for a substantial portion
More informationAn Adaptive Clustering MAP Algorithm to Filter Speckle in Multilook SAR Images
An Adaptiv Clustring MAP Algorithm to Filtr Spckl in Multilook SAR Imags FÁTIMA N. S. MEDEIROS 1,3 NELSON D. A. MASCARENHAS LUCIANO DA F. COSTA 1 1 Cybrntic Vision Group IFSC Univrsity of São Paulo Caia
More informationTheoretical approach to algorithm for metrological comparison of two photothermal methods for measuring of the properties of materials
Rvista Invstigación Cintífica, ol. 4, No. 3, Nuva época, sptimbr dicimbr 8, IN 187 8196 Thortical approach to algorithm for mtrological comparison of two photothrmal mthods for masuring of th proprtis
More informationPlanning and Managing Copper Cable Maintenance through Cost Benefit Modeling
Planning and Managing Coppr Cabl Maintnanc through Cost Bnfit Modling Jason W. Rup U S WEST Advancd Tchnologis Bouldr Ky Words: Maintnanc, Managmnt Stratgy, Rhabilitation, Costbnfit Analysis, Rliability
More informationOverinvestment of free cash flow
Rv Acc Stud (2006) 11:159 189 DOI 10.1007/s1114200690121 Ovrinvstmnt of fr cash flow Scott Richardson Publishd onlin: 23 Jun 2006 Ó Springr Scinc+Businss Mdia, LLC 2006 Abstract This papr xamins th
More informationB285141. April 21, 2000. The Honorable Charles B. Rangel Ranking Minority Member Committee on Ways and Means House of Representatives
Unit Stats Gnral Accounting Offic Washington, DC 20548 Halth, Eucation, an Human Srvics Division B285141 April 21, 2000 Th Honorabl Charls B. Rangl Ranking Minority Mmbr Committ on Ways an Mans Hous of
More informationNew Basis Functions. Section 8. Complex Fourier Series
Nw Basis Functions Sction 8 Complx Fourir Sris Th complx Fourir sris is prsntd first with priod 2, thn with gnral priod. Th connction with th ralvalud Fourir sris is xplaind and formula ar givn for convrting
More informationWORKLOAD STANDARD DEPARTMENT OF CIVIL ENGINEERING. for the. Workload Committee : P.N. Gaskin (Chair) J.W. Kamphuis K. Van Dalen. September 24, 1997
WORKLOAD STANDARD for th DEPARTMENT OF CIVIL ENGINEERING Sptmbr 24, 1997 Workload Committ : P.N. Gaskin (Chair) J.W. Kamphuis K. Van Daln 9 2 TABLE OF CONTENTS l. INTRODUCTION... 3 2. DEFINITION OF WORKLOAD
More informationC H A P T E R 1 Writing Reports with SAS
C H A P T E R 1 Writing Rports with SAS Prsnting information in a way that s undrstood by th audinc is fundamntally important to anyon s job. Onc you collct your data and undrstand its structur, you nd
More informationEntityRelationship Model
EntityRlationship Modl Kuanghua Chn Dpartmnt of Library and Information Scinc National Taiwan Univrsity A Company Databas Kps track of a company s mploys, dpartmnts and projcts Aftr th rquirmnts collction
More informationThe international Internet site of the geoviticulture MCC system Le site Internet international du système CCM géoviticole
Th intrnational Intrnt sit of th goviticultur MCC systm L sit Intrnt intrnational du systèm CCM géoviticol Flávio BELLO FIALHO 1 and Jorg TONIETTO 1 1 Rsarchr, Embrapa Uva Vinho, Caixa Postal 130, 95700000
More informationOPTIONS AND FUTURES: A TECHNICAL APPRAISAL
Pag 15 OPTIONS AND FUTURES: A TECHNICAL APPRAISAL by David J.S. Rutldg Papr prsntd to Sminar on Trading in Options: Opportunitis in th Intrnational Markt sponsord by Th Sydny Stock Exchang and Th Scuritis
More informationModelling and Solving TwoStep Equations: ax + b = c
Modlling and Solving ToStp Equations: a + b c Focus on Aftr this lsson, you ill b abl to φ modl problms φ ith tostp linar quations solv tostp linar quations and sho ho you ord out th ansr Cali borrod
More informationEthanolic Extraction of Soybean Oil: Oil Solubility Equilibria and Kinetic Studies
Ethanolic Extraction of Soyban Oil: Oil Solubility Equilibria and Kintic Studis Christiann E. C. Rodrigus*, Natália M. Longo, Cibl C. Silva, Kila K.. Aracava, Bruna R. Garavazo Sparation Enginring Laboratory
More information