Market Momentum: Theory and Practice

451 Pages·2020·7.498 MB·English

by  Stephen Satchell, Andrew Grant

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Market Momentum
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Market Momentum
Theory and Practice
By
STEPHEN SATCHELL
and
(cid:2) ANDREW GRANT (cid:2)
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Thiseditionfirstpublished2021
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Names:Grant,AndrewRobert,1982-author.|Satchell,StephenEllwood,
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Title:Marketmomentum:theoryandpractice/AndrewRobertGrant,Stephen
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Abstracts
1AndrewGrant
This chapter examines the behavioural finance argument for the existence of
momentum profits. From a behavioural finance perspective, asset prices may deviate
from fundamental values, which can persist if market frictions prevent a prompt
correctiontomispricing.Asrisk,intheformofaFama-Frenchthree-factormodel,has
been shown to provide a poor explanation for momentum returns, academics have
sought psychology-inspired reasons for the phenomenon. We review the literature on
behavioural finance and momentum, starting with the theoretical studies of the late
1990s, which have become highly influential. Following on from this, we discuss the
recent empirical evidence supporting predictions such as slow information diffusion,
incorrect updating of beliefs, trading at the 52-week high, individual investor trading
and market-wide sentiment. Among the key insights is that behavioural finance can
helpprovideanexplanationforstatisticalpatternsthatgeneratemomentumportfolios
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(as in Chapter 2) and may help practitioners in identifying themes for enhancing their
investmentportfolios.
2SteveSatchell
Inthischapterwelookatdifferentmomentumstrategiesandtheirproperties.Many
of the empirical results of momentum strategies can be seen to result from the struc-
tureofreturnprocessesandthedesignofthestrategy.Inparticular,consideringsimple
cases,weinvestigatethereturndistributionsofwhatarethemajormomentumstrategies
which are cross-sectional momentum (CSM), time-series momentum (TSM), relative
strengthstrategies(RSS)andcross-assetmomentum.Forthecaseoftwoassets,wecan
sayagreatdealaboutthestructureandpropertiesofreturns.Fromastatisticalperspec-
tive,behaviouralanalysiswilldeterminethemagnitudeofmodelparametersalongwith,
insomecases,thespecificationofthemodelitself.Takingtheseasgiven,thestatistical
analysis will determine the properties of the strategy returns. In this form of analysis,
weareinterestedinthepopulationmomentssuchasthemean,variance,skewnessand
kurtosis but also the time-series moments of the momentum process. It is hoped that
therelativelysimpleanalysisinthischapterwillhelpthereaderinlaterchapters.
3NickBaltasandRobertKosowski
Motivated by studies of the impact of frictions on asset prices, we examine the
effect of key components of time-series momentum strategies on turnover and perfor-
mance.Weshowthatmoreefficientvolatilityestimationandprice-trenddetectioncan
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vi ABSTRACTS
significantlyreduceportfolioturnoverbymorethanone-third,withoutcausingastatis-
ticallysignificantperformancedegradation.Weproposeanovelimplementationofthe
strategythatincorporatesthepairwisesignedcorrelationsbymeansofadynamiclever-
agemechanism.Thecorrelation-adjustedvariantoutperformsthenaïveimplementation
of the strategy and the outperformance is more pronounced in the post-2008 period.
Finally,usingatransactioncostsmodelforfutures-basedstrategiesthatseparatescosts
into roll-over and rebalancing costs, we show that our findings remain robust to the
inclusionoftransactioncosts
4JoseMencheroandLeiJi
Inthischapter,westudytheriskandreturnofmomentumindevelopedequitymar-
kets.Weconstructfactorportfoliosbycross-sectionalregression.Univariateregression
resultsin‘simple’factorportfoliosthatcontain‘incidental’betsonotherfactors.Multi-
variateregressionresultsin‘pure’factorportfoliosthatareneutraltoallfactorsexcept
momentum. We compare performance of simple and pure momentum factors across
variousdevelopedmarkets.Wefindthatsimpleandpurefactorshavevirtuallyidentical
long-termperformancewithineachequitymarket.Thepurefactors,however,achieve
this performance with considerably lower volatility, resulting in higher risk-adjusted
performance.Wealsostudythevolatilitiesandcorrelationsofmomentumfactorsacross
time.WefindthatthesequantitiespeakedduringtheInternetBubbleandtheFinancial
Crisis.Finally,weshowthatformostperiods,momentumhasbeennegativelycorrelated
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withthemarket,thusofferingattractivediversificationopportunities.
5DandiBartolomeoandBillZieff
Pricemomentumexposureisafamiliarconcepttoanyonewhohasstudiedequity
return factors. Less addressed in the literature is how momentum effects in one asset
classmanifestinotherassetclasses.Theoriesofcorporatefinancethatlinkequityand
bond returns through their common dependence on the value of the firm have been
known for some time, but these theories do not address the dynamics between debt
and equity in a time-series context. Cross-Asset momentum provides a parsimonious
description of such dynamics. We describe theoretical underpinnings and empirical
resultsforseveralcross-assetclassmomentumeffects.Includedisadiscussionofequity
momentumimpactintofixedincome,selectedmomentumeffectsinilliquidassets,such
as real estate and private equity, and momentum results pertaining to commodities.
Implications for active management in asset allocation and active management within
assetclassesareexploredandhighlighted.
6KatharinaSchwaigerandMuhammadMassood
Momentum as an investment strategy is a well-known component of systematic
strategies. With the rise of exchange-traded funds (ETFs) and smart beta, momentum
strategies are available to a broad range of clients including retail via a fully transpar-
ent rules-based index strategy. The indices are built to weight and select stocks from
an equity universe with high momentum exposure or high past returns. Those ETFs
aim to offer momentum factor exposure at a competitive fee. In this chapter we shed
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Abstracts vii
lightonthemomentumETFlandscapeanddiscusshowmuchmomentumisinmomen-
tum ETFs. We find that momentum indices exhibit momentum characteristics despite
differencesintheunderlyingindexmethodologies,lackofleverageandinfrequentrebal-
ancing.
7OliverWilliams
Thischapterreviewstime-seriesmomentum(TSM)strategiesascommonlyusedby
CommodityTradingAdvisers(CTAs).WecompareTSMwithcross-sectionalmomen-
tum(CSM)anddescribeexamplesofvariousmethodsusedforTSMsignalgeneration,
showing that there are certain essential similarities in TSM approaches although the
degreeoftechnicalcomplexityvariesconsiderablyacrossmethods.Wenotevariousstyl-
izedfactsofCTAinvesting(e.g.relativelylowSharperatioandpositiveskewness)and
highlight practically relevant results from existing literature concerning characteristics
ofTSMreturns.Throughoutthechapterweusesimplemarketmodelstoillustrateour
pointsandwederiveanexpressionforthecorrelationbetweenTSMandCSMreturns
inthissetting,whichmaybeusefulwhencontemplatingassetallocationbetweenthese
strategies.
8AndersPettersonandOliverWilliams
Inthischapterweapplymomentumreturnsmodelsinanon-traditionalsetting:the
(cid:2) valuationofcontemporaryart.Specifically,weconsiderwhetheramomentummodelis (cid:2)
usefulatpredictingauctionoutcomesbasedonchangesinsubjectivevaluationopinions
observedoverashortperiodpriortotheauction.Weuseanoveldataset(suppliedby
ArtForecaster.com)thatcontainsmultiplevaluationsofthesamepieceofartprovided
byindividualforecastersatapproximatelythesametime.Combinedwithauctioneers’
estimatesandhammerpricesthisdatacanbeusedtoestimateasimpledynamicmodel
of valuation changes. We fail to find significant evidence of time-series momentum in
thesevaluationchanges,butaforecastingstrategybasedoncross-sectionalmomentum
appears to be effective. This implies that subjective opinion is more informative when
considered on a relative basis (one work of art versus another) rather than outright,
and we conjecture this may be due to a sub-conscious bias on the part of art market
participantstowardspositiveratherthannegativesentiment.Weconcludethatmomen-
tummodelscanprovideausefulframeworkforanalysisofinformationandbehaviour
outsidetraditionalfinancialmarkets.
9YangGao
Thischapterexplorestheinteractionbetweenmomentumandvolatility.Theliter-
aturehasshownthatmomentumstrategiesusingvolatilityastheconditioningvariable
strongly outperform plain momentum strategies by reporting higher alphas, increased
Sharperatiosandimprovedskewness.Thesevolatility-managedmomentumstrategies
benefitfromtheuseofvolatility,whichhasthepotentialtobetterforecastfuturereturns.
We empirically review the various volatility-managed momentum strategies that are
proposedintheacademicliterature.Ourfindingsshowstrongoutperformanceofboth
the time-series (TSVM) and cross-sectional volatility-managed momentum strategies
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viii ABSTRACTS
(CSVM)intheUSA,EuropeandAustralia.Wedocumentinterestingevidencethatthe
TSVM strategy seems to work in the Chinese and Japanese markets where traditional
momentumorCSVMdoesnotworkwell.Wefurtherdiscusstheroleofleveragecon-
straints,turnoverandshort-sellingconstraintsinthepracticaluseofvolatility-managed
momentum strategies. Last, we examine how volatility manages momentum risk and
suggestthatmomentumisnotonlyrelatedtoitsownvolatility.
10AndrewGrant,OhKangKwonandSteveSatchell
Theselectionofassetsbasedontherankingsofoneormoreoftheirattributesover
apriorperiodisastandardpracticeintheconstructionofportfoliosinbothacademic
andpractitionerfinance.Momentumisafamiliarexampleofthisactivity.Althoughthe
propertiesofreturnsfromtheseportfolioshavebeenthesubjectofconsiderableempir-
ical research, there is only limited literature investigating their theoretical properties.
In this chapter, we address this gap by deriving analytically the distributional proper-
ties of these portfolio returns under the assumption that asset attributes and returns
are jointly multivariate normal. We show that prior sorting of asset attributes induces
non-normality in the portfolio returns and that the returns depend on the order and
nature of sorting The analysis looks at two fundamental types of sorting, both com-
monlyusedbypractitioners,andtheauthorsshowthatthemethodofsortinginfluences
thepatternofreturnsundercommonlyencounteredcircumstances.
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11StefanoCavaglia,VadimMorozandLouisScott
The research presented in this chapter extends and corroborates the findings of
ScottandCavaglia(2016)whofirstillustratedthepotentialbenefitsofafactorpremia
overlaytoaninter-temporalwealthaccumulationstrategythatisfullyinvestedinequi-
ties.Weexamineapanelof5localfactorpremia(inclusiveofmomentum)ineachof21
developedequitymarketsand5regions.Innearlyallinstanceswefindthatwealthaccu-
mulationissignificantlyenhancedbyatimeinvariant,equalweightedallocationtothese
premia.Theenhancementisdriveninpartbythemeanreturnofthepremiabutmore
importantly by their generally positive payoff in adverse market environments. The
qualityandmomentumfactorpremiaarethelargestcontributorstothisresult.Apply-
ing conventional measures of risk aversion we quantify the importance of downside
protection to supporting the attainment of investors’ goals; the utility-based evidence
suggeststhatsomefactorpremiaarevaluedbyinvestorsevenwhentheirexpectedreturn
iszero.
12ChristopherPolk,MoHaghbinandAlessiodeLongis
Factorcyclicalitycanbeunderstoodinthecontextoffactorsensitivitytoaggregate
cash-flow news. Factors exhibit different sensitivities to macroeconomic risk, and this
heterogeneity can be exploited to motivate dynamic rotation strategies among estab-
lishedfactors:size,value,quality,lowvolatilityandmomentum.Atimelyandrealistic
identification of business cycle regimes, using leading economic indicators and global
risk appetite, can be used to construct long-only factor rotation strategies with infor-
mationratiosnearly70%higherthanstaticmultifactorstrategies.Therulesintroduced
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