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DoInstitutionalInvestorsInfluenceCapitalStructureDecisions?
RoniMichaelyandChristopherVincent*
January2013
ABSTRACTThispaperempirically investigates therelationshipbetween institutionalholdingsandcapital structure. Institutionsmayaffect capital structure through theirmoni‐toringandinformation‐gatheringroles.Atthesametime,institutionsmaygravitatetoward firms with specific capital structures, forming leverage‐based investmentclienteles.Usingimpliedtradesgeneratedfrommutualfundoutflowsasaninstru‐mentforinstitutionalholdings,andasemi‐naturalexperimentinwhichadditiontotheS&P500 Indexprovidesanexogenous shock to institutionalholdings,we findthatinstitutionalholdingsareasignificantdeterminantoffirms’capitalstructures:Achangeininstitutionalholdingscausesanoppositechangeinleverage.Moreover,usingdynamicpanelestimation,wefindthatwhileinstitutionsaffectcapitalstruc‐turedecisions,changesinleveragedonotaffectinstitutionalholdings,andthereisnoevidenceofaclienteleeffect.Finally,we find that firms lower their leverage inresponsetoincreasedinstitutionalholdingsbybecomingmorelikelytoissueequi‐ty,andlesslikelytoincreasedebt.Whileourfindingsareconsistentwithmodelsinwhich institutions substitute for debt by monitoring and reducing informationasymmetry problems, further evidence suggests that the effect on asymmetric in‐formationdominates.
*RoniMichaelyisfromtheJohnsonSchoolofManagement,CornellUniversityandtheIDC.ChristopherVincentisfromtheJohnsonSchoolofManagement,CornellUniversity.Emails:[email protected];[email protected]‐preciatethecommentsofJacobBoudoukh,DavidDeAngelis,MarkFlannery,ItayGoldstein,VidhanGoyal,YanivGrinstein,OhadKadan,AndrewKarolyi,ShimonKogan,YelenaLarkin,Mark Leary, Kai Li, Michelle Lowry, Jillian Popadak, Gideon Saar, Denis Sosyura, LukasRoth,FengZhang,andseminarparticipantsatCornellUniversity,GeorgetownUniversity,Nanyang Technological University, National University of Singapore, Singapore Manage‐mentUniversity,UniversityofAlberta,UniversityofBritishColumbia,UniversityofMichi‐gan, University of Syracuse, University of Toronto, University of Utah, the Corporate Fi‐nanceConferenceatHKUST,theCorporateFinanceConferenceattheUniversityofWash‐ingtonatSt.Louis,andtheSEIsummerconferenceatIDC.
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Institutionsarearguablythemostimportantandpowerfulclassofinvestors. Theiraver‐
ageequityownershipinU.S.firmshasincreasedeight‐foldoverthepastthirtyyears,and
by theendof2009theyheld70%of theaggregateU.S.marketcap.1 Forboth largeand
smallfirms,institutionsarenow,moreoftenthannot,themajorityinvestorgroup.Inthis
paperweexaminehowinstitutionalinvestorsaffectfirms’capitalstructure,andhowfirms’
financialleverageaffectsinstitutionalinvestors’decisionstoholdtheirequity.
Through the effects of institutional holdings on agency costs, asymmetric infor‐
mation,andtaxes,institutionsmayaffectthewayinwhichfirmsstructuretheircapital.As
weelaborateinthenextsection,thereareseveralreasonswhythisislikelytooccur.First,
bymonitoringmanagementthroughvoiceor(threatof)exit,institutionsasequityholders
reduce conflicts of interests between management and shareholders (e.g., Shleifer and
Vishny(1986),AdmatiandPfleiderer(2009),andLevit(2012)).Second,institutionsgath‐
er informationandmaketradesbasedonthat information,and indoingsoreduce infor‐
mation asymmetry problems associated with equity (e.g., Sias (2004), and Bushee and
Goodman(2007)). Finally,asequityholders, institutionshave,onaverage,arelative tax
advantageoverindividualinvestors.
While institutional investors interactwiththeaforementionedfrictionsunderlying
many capital structuremodels, the resulting nature of the relationship between institu‐
tionalholdingsandcapitalstructureisnotclear. Ononehand, institutionalholdingsand
debtcouldbesubstitutes.If,forexample,organizationalinefficienciesmustbecontrolled
asinJensen(1986),threateningtosellsharesorunseatmanagersiftheyengagein“empire
building”maybeaseffectiveascommittingmanagerstopledgefundstocreditors. Or,in
thecontextofMyersandMajluf(1984), if institutionalinformation‐gatheringandtrading
producesinformation(Sias(2004)),theadverseselectioncostsofequitymaydecline,lead‐
ingfirmstotilttowardahigherpercentageofequityfinancingintheircapitalstructures.
Inbothcases,institutionalholdingsanddebtwouldbesubstitutes.
Ontheotherhand,institutionalholdingsanddebtcouldactascomplements.Inan
agencysetting,itmaybethatbyincreasinginvestorprotectioninstitutionalinvestorsena‐
bleoutsideshareholderstoimplementdevicessuchasdebtthatlimitmanagementdiscre‐
1Thisfigureiscalculatedusing13FdataasfiledwiththeSEC,includesonlypubliclytradedequity,andex‐cludesADR’sandforeignincorporatedfirms.
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tionandbetteraligntheobjectivesofmanagersandshareholders(e.g.the“outcomemod‐
el” in La Porta, Lopez‐de‐Silanes, Shleifer, and Vishny (2000)). Or in an information‐
asymmetry setting, itmightbe that institutionshelp solve futureunderinvestmentprob‐
lems caused by the adverse selection costs of equity (again, through their role as infor‐
mationproducers), thusallowingfirmstoforgoissuingequitytoday(Viswanath(1993)).
In either case, the relationshipbetween institutionalholdingsanddebtwouldbea com‐
plementaryone.
While it isperhapsmostnatural toconjecture thatmanagers takeall information,
includingtheextentofinstitutionalequityholdings,intoconsiderationbeforemakingcapi‐
talstructuredecisions,institutionsmayalsotakefirms’leverageintoaccountwhenmaking
theirowninvestmentdecisions. Assuch,theeffectmaygointheoppositedirection,and
capitalstructuredecisionsmayaffect the levelof institutionalholdings ina firm. Recent
workhasshowninstitutionalinvestorstoforminvestmentclientelesbasedonfirmcharac‐
teristicssuchassizeandliquidity(GompersandMetrick(2001)),payoutpolicy(Grinstein
andMichaely(2005)),andcorporategovernance(McCahery,Sautner,andStarks(2010)),
whichtogethersuggestthatinstitutionsmayalsoformcapitalstructureinvestmentclien‐
teles.
Predicatedon the testable implicationsofextantcapital structure theories,weex‐
aminetheinteractionbetweeninstitutionalholdingsandcapitalstructureusingannualda‐
tafromtheperiod1979to2009.Ourfirstfindingisastrongandrobustnegativerelation‐
ship between institutional holdings and leverage:more institutional holdings in period t
areassociatedwithlessleverageinperiod(t+1).Theeconomiceffectofinstitutionalhold‐
ingson leverage is comparable inmagnitude toknowndeterminantsof leverage suchas
profitabilityandindustrymedianleverage,andtherelationshipholdsaftercontrollingfor
importantfactorsthatcovarywithinstitutionalholdings,suchasinsiderholdingsandana‐
lystcoverage,aswellasforknowndeterminantsofleverage,includinginitialfirmleverage
(Lemmon,Roberts,andZender(2008)).
Althoughwemakenopriorclaimsregardingthedirectionofcausality,weareinter‐
ested in whether the effect of institutional holdings on leverage is of a causal nature.
Hence,weemploythreetechniquestoremovethepotentialfeedbackeffectofleverageon
holdings:twoseparateinstrumentalvariablesapproachesandasemi‐naturalexperiment.
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OurfirsttechniqueisanArellano‐Bonddynamicpanelestimationinwhichweinstrument
forchangesininstitutionalholdingswithlaggedholdings.
Oursecondtechniqueisalineartwo‐stageleastsquaresestimationinwhichweuse
impliedmutualfundtradinginducedbyinvestoroutflowsasaninstrumentforinstitution‐
alholdings(Edmans,Goldstein,andJiang(2011)). Impliedchanges inmutual fundhold‐
ingsaffectaggregateinstitutionalholdings,butnotforreasonsassociatedwithfirmcharac‐
teristicssuchasleverage.Thatis,whenamutualfundexperienceslargeoutflowsitmust
sellaportionofitsholdingstorepayinvestors.Byusingimpliedtradesbasedonprevious‐
lydisclosedmutualfundholdings,wehaveaninstrumentthatcorrelateswithinstitutional
holdingsformechanicalreasonsratherthaninformationalreasons,andhencesatisfiesthe
exclusionrestriction.
OurfinaltechniquelooksatthechangeinleveragesurroundingadditiontotheS&P
500Index.Standard&Poor’sexplicitlyaimstomakeindexinclusionaninformation‐free
event,andassuchweuseitastreatmentintwodifference‐in‐differencesestimationsthat
testwhetherleveragechangesdifferentiallyfortreatedfirms–thoseaddedtotheS&P500
–relativetoamatched‐sampleofcontrolfirms–thosethatdonotexperiencetheshockto
institutionalholdingscausedbyindexaddition.
Even though the sample sizes vary dramatically across techniques, with all three
approacheswefindthatleveragedecreasesinresponsetoincreasesininstitutionalhold‐
ings.Withthe2SLSestimation,wefindthatthedecreaseinleveragestemmingfromanin‐
creaseininstitutionalholdingsisabout2.8timeslargerthanwhatwefindwithoutinstru‐
mentation, and represents an8.0% change in leverageper standarddeviation change in
holdings. Correspondingly, inournaturalexperiment,weshowthatafterbeingaddedto
theS&P500Index,firmsdecreasetheir leverageby1.5%(7.7%ofthetreatmentgroup’s
average leverage) relative to a groupof similar, propensity‐score‐matched control firms.
Takentogether,theseresultsareconsistentwithawiderangeofagency‐andasymmetric
information‐basedmodelsof capital structure thatpredict a relationshipadvancing from
agencycostsorinformationasymmetrytoleverage.
Atthesametime,wefindtheeffectofcapitalstructureoninstitutionalholdingsto
bemoreambiguous. Inboth level and first‐difference regressions,we find that leverage
hasanegativeeffecton institutionalholdings. However, inanArellano‐Bondestimation
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featuring (internal) instrumental variables,we find no effect of leverage on institutional
holdings. In accordance with most capital structure theories, our findings point to a
strongereffectadvancingfrominstitutionalholdingstocapitalstructurethanfromcapital
structuretoinstitutionalholdings.
Next, we investigate how firms change their capital structure in response to a
changeininstitutionalholdings. Wefindthatanincreaseininstitutionalholdingssignifi‐
cantlyraisesthesubsequentprobabilityofafirmbeinganetequityissuer,withoutaffect‐
ingtheprobabilityofdebtissuance.Furthermore,thesizeofequityissuancesalsoincreas‐
eswith positive changes in institutional holdings. Consistentwith direction of causality
frominstitutionstoleverage,andnottheotherwayaround,wedonotfindthatanincrease
inequity issuances inyear t results inan increase in institutionalholdings inyear (t+1).
Together,theseresultssuggestthatequityissuancesarethemainchannelthroughwhich
firmsadjusttheircapitalstructureinresponsetoincreasedinstitutionalholdings.
Althoughourresultssupportbothagency‐andinformation‐basedmodelsthatimply
institutional investorshaveanegative influenceonfirms’ leverage,wefindevidencethat
information asymmetry is the friction through which they most strongly impact capital
structure. Firmsparticularlyprone tohighagencycostsare, ingeneral, large,profitable,
cash‐richfirmswithrelativelyfewgrowthopportunities.Ontheotherhand,firmsparticu‐
larlypronetoinformationasymmetryandhighadverseselectioncostsaregenerallysmall,
young,cash‐strappedfirmswithrelativelymanygrowthopportunities.Wefindtheeffect
ofinstitutionalholdingsonleveragetobestrongerinsmallfirmswithmanygrowthoppor‐
tunities(“asymmetricinformationfirms”)thaninlargefirmswithfewgrowthopportuni‐
ties(“agencyfirms),althoughinpracticeinformationasymmetryandagencycostsarenot
mutuallyexclusivefrictions.
Furthermore,wefindnoevidencethattaxconsiderationsplayaroleintherelation‐
shipbetweeninstitutionalholdingsandleverage.WeutilizetheJobsandGrowthTaxRe‐
liefReconciliationActof2003(whichlowerstherelativetaxadvantageonequitybyinsti‐
tutions) to test whether the relationship between institutional holdings and leverage
changed.Wefindnodifferenceintheeffectofinstitutionalholdingsonleverageinthepe‐
riodbeforethetaxchangecomparedtotheperiodafter.
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Finally,wefindtheeffectofinstitutionalholdingsonleveragetobequiterobust.In
additiontoestimatingtheeffectduringvarioussubperiods,weredefineleverageinanum‐
berofdifferentways–usinga logit transformation,usingonly long termdebt,using the
marketvalueofassetsratherthanthebookvalue,usingdebtplusequityinthedenomina‐
torratherthantotalassets,andbydeductingexcesscashfromtotaldebt–andourresults
holdregardlessofthespecification.Inaddition,wecontrolforpotentialcovariatessuchas
market timing considerations, liquidity, and the corporate governance environment, and
findthatinstitutionalholdingsmaintainsitsstrongeffectonfirms’leverage.Lastly,wefind
ourresultstoberobusttousingquarterlydataratherthanannualdata.
Whilepriorliteraturehasnotfocusedontherelationshipbetweencapitalstructure
and institutional investors, the ideathat institutionsmayaffectcorporate financialpolicy
hasnotbeenignored.Specifically,recentworkhasshowninstitutionalinvestorstoplaya
role inmergersandacquisitions (Gaspar,Massa, andMatos (2005)), analyst activityand
accuracy(Ljungqvist,Marston,Starks,Wei,andYan(2007)),payoutpolicy(Grinsteinand
Michaely (2005)), executive compensation (Hartzell and Starks (2003)), CEO turnover
(Huson,Parrino,andStarks(2001)),andcorporategovernance(GillanandStarks(2000)).
Along these lines, Cronqvist and Fahlenbach (2009) find strong evidence of blockholder
fixed effects in firms’ investment and financial policies, as well as in firm performance
measures.Ourpapercontributestothisliterature.
Therestofthepaperisorganizedasfollows.InSectionIwereviewthetheoretical
determinantsforcapitalstructureanddiscusshowinstitutionalinvestorsimpactthesede‐
terminants,andbyextension,capitalstructure.InSectionIIwedescribeourdataandcon‐
structseveralimportantvariables.InSectionIIIwetesttherelationbetweeninstitutional
holdingsandleverage.InSectionIVweconclude.
I.Hypothesisdevelopmentandrelatedliterature
Manycapitalstructuretheoriessuggestthatagencycosts,asymmetricinformation,
andtaxesareimportantfrictionsthatenablethechoiceofleveragetoaffectfirmvalue.In‐
stitutional investors,however,oftenpossesscommoncharacteristicsthatmayimpactthe
severitytowhichfirmsareaffectedbythesefrictions,therebyalteringtheprocessthrough
whichcapitalstructureisdetermined.Inthissection,webrieflyreviewseveralofthemost
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relevant theories of capital structure, discuss some common institutional characteristics
thatsuggestinstitutionalinvestorsinteractwiththesefrictions,anddetailtheimplications
oftheseinteractionsforfirms’capitalstructures.
Onesetofcapitalstructuremodelsposits that theseverityofconflictsof interests
between managers and equity holders leads to a particular leverage ratio. Jensen and
Meckling(1976)arguethatthisconflictiscostlybecauseitincentivizesmanagerstoover‐
indulgeinactivitiesbeneficialonlytothem,astheybeartheentirecostofrefrainingfrom
theseactivitieswhilereapingonlyaportionofthegains.Debtalleviatesthisinefficiencyby
increasing themanager’s (relative) share of equity, thus increasinghis ownership of the
residual claim. In a similar vein, Jensen (1986) argues that debtmitigates shareholder‐
manager agency conflictsby committingmanagers topledge funds to repay creditors, in
turnlesseningtheamountof“freecash”availablefororganizationalinefficienciessuchas
consumingperquisitesandempirebuilding.
Institutions,however,canaffecttheseverityoffirms’agencycosts,becauseinstitu‐
tions engage in monitoring (e.g., Shleifer and Vishny (1986), Maug (1998), and Huson,
Parrino,andStarks(2001)). Inthepresenceofineffectivecorporatecontrolmechanisms
(Jensen (1993)), “monitoring” activities are value‐enhancing undertakings ranging from
minimalinterventioninacompany’saffairstomoreaggressivetechniquesofshareholder
activism,includingthe“WallStreetWalk,”initiatingpressurecampaigns,andjockeyingfor
a seat on the board of directors (e.g., Parrino, Sias, and Starks (2003), Gillan and Starks
(2007),AdmatiandPfleiderer(2009),andMcCaheryetal. (2010)). Onestrandof litera‐
ture suggests that blockholders and investorswith large holdings and long horizons are
particularlyeffectivemonitors (Edmans (2009)andChen,Harford, andLi (2007)),while
anotherstrandsuggeststhatentirefundfamiliesvoteinagreementduetocentralizedvot‐
ing departments or ISS guidelines for proxy voting (e.g., Matvos and Ostrovsky (2010),
Morgan,Poulsen,Wolf,andYang(2011),andIliev,Lins,Miller,andRoth(2012)).Thus,the
implicationisthatallinstitutions,tosomeextent,canbeeffectivemonitors.Forexample,
FennandRobinson (2009) find that even sponsorsof indexedETFsdevote timeand re‐
sources tomonitoring firms’management,andareactivebyvotingstrategically inproxy
contests.Thegoalofsuchactivitiesistoreducetheconflictsofinterestthatexistbetween
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shareholdersandmanagers, and indoing so, institutional investors canpotentially influ‐
encecapitalstructuredecisions.
Whilemanymodelssuggestthatshareholder‐manageragencyconflictsdrivecapital
structuredecisions(e.g.,Stulz(1990),Zwiebel(1996),andGomes(2000)),themannerin
which institutionsaffect this relationship isnot clear. Thedivergent theoretical implica‐
tionsarewell illustratedintwomodelsputforthbyLaPortaetal.(2000):thesubstitute
modelandtheoutcomemodel. In thecontextof thesubstitutemodel, institutionalhold‐
ings provide investor protection while acting as an alternative bonding mechanism for
debt,suggestingthat,allelseequal, firmswithahighproportionof institutionalholdings
needlessdebtinequilibrium.Ontheotherhand,intheoutcomemodeloutsideinvestors
musthaveadequatemechanismsinplace,perhapsintheformoflawsandregulations,be‐
foretheycanforcemanagementtoincreasedebtandtherebylimitpotentialwealthexpro‐
priation. Because institutional investors increase investorempowermentandprotection,
theoutcomemodelimpliesthatfirmswithastronginstitutionalshareholderbasewillhave
moredebt.
A second set of capital structure models suggests that firms choose their capital
structure in response to the information environment in which they operate. In these
models, insiders (managers) possess information regarding their firm’s future return
streamor investmentopportunities (i.e. their firm’s intrinsicvalue) that isnotknown to
outsideinvestors.Ingeneral,debtispreferabletoequityinfirmswithhigherlevelsofin‐
formationasymmetrymainlybecauseitcarrieslessadverseselectioncoststhanequity.
Institutionalinvestorscanplayanimportantroleinasymmetricinformationmodels
ofcapitalstructurebecausetheydevoteconsiderableresourcestocollecting information.
Manyinstitutionsaresubjecttostateorfederalfiduciaryandprudencystandards(seeDel
Guercio(1996))incentivizingthemtocollectinformationaboutthefirmstheyinvestinin
ordertodecreasethelikelihoodofbeingsuedforviolatingthesestandards(Allen,Bernar‐
do,andWelch(2000)). Thisprocessleadsthemtobemoreinformedthanothertypesof
investors, but also affects the adverse selection costs of equity by decreasing the infor‐
mationgapbetweenoutsideandinsideshareholders,becauseatleastaportionofthein‐
formation theycollect is reflected in their tradingpatterns (Sias (2004), andBusheeand
Goodman(2007)).
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Aswithagencymodels,asymmetricinformationmodelsofcapitalstructuredonot
unilaterally imply thesamerelationshipbetween institutionalholdingsand leverage. On
onehand,thetwocanbeinverselyrelated.InMyers(1984)andMyersandMajluf(1984),
due to information asymmetry andmispriced equity, firmsmay under‐invest in positive
NPVprojects.Asaresult,firmsfundinvestmentsbychoosingfinancingsourcesthatmini‐
mizeadverseselectioncosts,preferringretainedearningsoverriskydebtoverequity(the
peckingorder).Institutionalinvestorsmitigatetheadverseselectioncostsofequity,soas
equity financing becomes comparatively cheaper, firms with high institutional holdings
shouldhavelowerleveragethanthosewithlowinstitutionalholdings.2
On theotherhand, institutionalholdings canhaveapositive effect on leverage in
asymmetricinformationmodelsofcapitalstructure. Viswanath(1993)buildsontheMy‐
ersandMajluf(1984)intuitionthatthedilutivecostsofissuingequitymaycausefirmsto
rejectworthwhileprojects,andconstructsamulti‐periodmodel inwhich firmsoptimally
issuestocktoday,wheninformationasymmetryislow,toavoidthepotentiallossofaposi‐
tiveNPVprojectinthefutureduetoinformationasymmetryproblemsatthattime.How‐
ever, as institutions narrow the information gap betweenmanagers and (future) share‐
holders,theydecreasethedilutivecostsoffutureequityissuances.Assuch,astronginsti‐
tutionalshareholderbasemayallowfirmstoavoid issuingequitytoday, implyingacom‐
plementaryrelationshipbetweeninstitutionalholdingsandleverage.
Thetaxstatusofinstitutionalinvestorsisathirdcharacteristicthatsuggestsinstitu‐
tionsmayaffectcapitalstructure.Asequityholders,institutionsgenerallyhavearelative
tax advantage over individual investors. Many institutions are tax‐exempt, while many
otherspay taxesononlya smallportionof thedividends they receive from their invest‐
ments.Thus,allelseequal,afirmwithalargerbaseofinstitutionalequityholdersshould
issue comparatively more equity than a firm owned by (higher‐tax‐paying) individuals.
This implication is consistentwith theAllen,Bernardo,andWelch (2000)argument that
managerscanappealtothetaxadvantageonequityincomeenjoyedbyinstitutions,leading
institutional investors to formtaxclientelesconditionalonthedividendpoliciesof firms.
2InsignalingmodelssuchasRoss(1977),institutionalholdingscansubstitutefordebtinnarrowingthein‐formationgapbetweenoutsideandinsideshareholders, leaving firmswithlessneedtosignaltheirqualitywithcostlydebt.Thesemodels,however,havenotreceivedmuchempiricalsupport.
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Thus, tax considerations suggest that higher institutional holdings should be associated
withahigherportionofequityinfirms’capitalstructure.
Inadditiontotheoriesrelatedtoagency,information,ortaxes,arecentstrandofthe
capital structure literature examines the dynamics of firms’ capital structures as deter‐
mined by the costs of making adjustments (e.g. Leary and Roberts (2005), Strebulaev
(2007),andByoun(2008)),andtherelationshipbetweentheseadjustmentsandfirmchar‐
acteristicssuchascashflowsandinvestmentpolicies(e.g.,Faulkender,Flannery,Hankins,
and Smith (2012)). If institutions, as a group, have amitigating effect on firms’ agency
costsandinformationasymmetryproblems,costlyadjustmentmodels implythat, ingen‐
eral,firmswithgreaterinstitutionalholdingswilladjusttheircapitalstructuremorequick‐
ly. Whilethis is interestinginitsownright,thefocusofthispaperisontherelationship
betweeninstitutionalinvestorsandthedocumentedheterogeneityin leverageratios,and
not on the relationbetweenownership structure and the speedof adjustment of capital
structure.
Throughoutthissectionwehavediscussedcapitalstructuretheoriesinconjunction
with the premise that institutional investors can affect the capital structure policies of
firms.Thesetheories,however,donotexcludethepossibilitythatinstitutionsmayalsobe
affectedbyfirms’capitalstructures.Andindeed,priorstudieshaveshowninstitutionalin‐
vestors to exhibit preferences for certain firm characteristics. In particular, Del Guercio
(1996)findsthatinstitutionstendtoinvestinlowerriskfirms,whileGompersandMetrick
(2001) find that institutions prefer to hold large, liquid firms. Grinstein and Michaely
(2005)andDesaiandJin(2011)studyinstitutionalpreferencesforpayoutpolicy,withthe
former finding that institutions prefer dividend‐paying stocks over non‐dividend‐paying
stocks,andthelatterfindingthatinstitutionsformtax‐basedclientelesinrelationtopay‐
outpolicy.Intermsofcorporategovernance,McCaheryetal.(2010)findthat,ingeneral,
corporategovernanceisimportanttotheinstitutionalinvestmentdecision,whileHartzell
andStarks(2003)findthat,morespecifically,institutionsrevealapreferenceforfirmsin
whichexecutivecompensationexhibitshighpay‐for‐performancesensitivity. Thus,while
itmay bemost intuitive to think of the relationship between institutional investors and
capitalstructuretoadvancefrominstitutionstofirms,wealsoconsiderthealternativethat
institutionschoosetheirinvestmentsatleastinpartbecauseoffirms’leveragepolicies.
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II.Data,constructionofvariables,andsummarystatistics
Our data consists of all firm‐year observations for U.S. incorporated firms in the
CRSP–Compustatmergeddatabasebetween1979and2009, excludingAmericanDeposi‐
tary Receipts, utility companies (SIC codes 4900 – 4999), and financial firms (SIC codes
6000–6999).3 Thesample iscomprisedof firms listedontheNYSE,Amex,andNasdaq,
withtherequirementthatallfirm‐yearshavenon‐missingandnon‐negativedatafortotal
assets, total debt, and market capitalization.4 Following Lemmon et al. (2008), we
winsorizeratiovariablesattheonepercentlevelinanattempttolessentheeffectsofout‐
liers,andwerestrictleverageratiostotheclosedunitinterval.
InstitutionalholdingsdatacomesfromtheThomson‐ReutersInstitutionalHoldings
Database, as reported on Form 13F filed with the Securities and Exchange Commission
(SEC).Allinstitutionalmanagerswithmorethan$100millioninassetsundermanagement
arerequiredtofile,detailingallequityholdingsofmorethan$200,000or10,000shares.
Following Grinstein and Michaely (2005) we set institutional holdings equal to 0% for
firmsnotappearinginthe13Fdatabase(potentiallybecausetheinstitutionalmanagersdo
notmeettheentire13Ffilingrequirements),andwerestrict institutionalholdingstothe
closedunit interval. Ourresultsareunchanged,however, ifweremovetheobservations
notappearinginthe13Fdatabaseorallowinstitutionalholdingstoexceed100%(asocca‐
sionallyhappensduetofiscal/calendaryeardifferences).
After merging the institutional holdings data with the CRSP‐Compustat database
andremovingobservationsnotmeetingtheaforementioneddatarequirements,weareleft
with130,202firmyearobservationsacross31years. Oursamplecontainsanaverageof
4,200firmsperyear,andfluctuatesbetween3,228firmsin1979and5,741firmsin1997.
Becauseweonlyrequirepositiveassets,apositivemarketcap,andnon‐missingdebt,near‐
lyallofthefirmsintheNYSEarerepresentedeachyear.
3ThistimeframewaschosenbecausetheThomson‐Reutersinstitutionalholdingsdatabeginsin1979.4Ourresultsarerobusttoalternatespecificationsinwhichwerequirestockpricetobeatleast$5andassetstobeatleast$1million.Furthermore,multivariableanalysesimplicitlyrequirenon‐missingdataforallrele‐vantvariables,sothenumberoffirm‐yearsusedinthevarioustestsfluctuatesdependingonthespecificationchosen.
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WeimposeoneadditionaldatarequirementforourArellano‐Bond(1991)estima‐
tions. Since we use lagged levels to instrument for each contemporaneous, differenced
equation,werequireallfirmstoappearconsecutivelyforatleastfouryearsatsomepoint
duringoursampleperiod.Thisexcludesfirmsleavingoursamplebetween1979and1981,
andfirmsenteringoursamplebetween2007and2009.Afterimposingthisadditionaldata
requirement,weareleftwith122,859firmyearobservationsforourArellano‐Bondtests,
foranaverageof3,963firmsperyear.
Weconstructourvariablesinaccordancewithseveralimportantpriorstudies.The
fullsetofvariabledefinitionscanbefoundintheAppendix,butwehighlightourkeyvaria‐
bleshere.Wedefineinstitutionalholdings(Hold)astheratiooftotalsharesheldbyinstitu‐
tionsattheendofeachcalendaryeartothetotalnumberofsharesoutstandingattheend
ofthelastfiscalyear.Wesimilarlydefineinstitutionalownershipbytheinstitutionshold‐
ingthefivelargestpositions(Hold5)asaproxyforownershipconcentration.Whilecoor‐
dinationmechanismsexistforinstitutionstoactcollaboratively,itstillmightbethatlarger,
moreconcentrated institutional investorsaremoreeffectiveatmitigatingagencyand in‐
formationasymmetryconcerns(ShleiferandVishny(1986)).Inmostcasesourresultsdo
notchangewhenswitchingbetweenHoldandHold5,butwhentheeffectisnon‐negligible,
wespecificallyaddressit.
Ourotherprimary variable of interest is firm leverage (TDA),whichwedefine as
perFrankandGoyal(2009)astheratiooftotaldebttothebookvalueofassets. Forro‐
bustnesswerepeatour testswithseveralalternativemeasuresof leverage: totaldebt to
themarketvalueofassets,long‐termdebttothebookvalueofassets,long‐termdebttothe
marketvalueofassets,totaldebtdividedbytotaldebtplusstockholder’sequity,andtotal
debtlessexcesscashdividedbytotalassets.5 Ourresultsholdwhenusingthesealterna‐
tivemeasures,andarealsorobusttotheinclusionofoffbalancesheetoperatingleasesinto
ourdefinitionofleverage(TDLA,asperGraham,Lemmon,andSchallheim(1998)).
TheremainingvariablesaredefinedintheAppendix.There,wedefinenetdebtand
equityissuancesandissuersasperFamaandFrench(2005);adjustedreturn,beta,sales,
andadividendpayerdummyasperGrinsteinandMichaely(2005);cashcowasperBrav,
5Wedefineexcesscashascashholdingsminusrevenue*.02ifcashholdingsexceed2%ofrevenue,andzerootherwise.
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Graham,Harvey,andMichaely (2005); initial firm leverageasperLemmonetal. (2008);
and assets, change in assets, market cap, profitability, market‐to‐book, capital expendi‐
tures, tangibility, intangible assets, collateral, total payout, a lossmaking dummy, and a
uniquenessdummyasperFrankandGoyal(2009).
Table Ipresentssummarystatistics forboth theentiresampleperiodand for two
nearlyequallengthsubperiods(theearliersubperiodcontainssixteenyearsofdatarather
than fifteen). Non‐ratiomeasures (market cap, log(assets), and log(sales)) are inflation‐
adjustedto2009dollars,andincreaseovertime.Table1showsthattheaveragepublicly
tradedfirmissignificantlylargernowthanitwas30yearsago.
Theratiomeasuresinthetablealsochangesubstantiallyovertime,includingaggre‐
gate institutionalholdings(Hold)andtop‐5 institutionalholdings(Hold5). Averagehold‐
ingsmorethandoublefromthefirsthalfofthesampletothesecond(19%to40%),while
top‐5institutionalholdingsnearlydouble(11%to20%).Eventhesefigures,however,un‐
derstatethecurrentimportanceofinstitutionalequityinvestments. In2009,meanhold‐
ings increased to 55% overall, but jumped to 76% for firms in the fourth size quintile
($1,169millionaveragemarketcap),and77%forfirmsinthefifthandlargestsizequintile
($15,279millionaveragemarketcap).Invalue‐weightedterms,byyear‐end2009,institu‐
tionsheld71%ofthe$10.6trilliondollarmarketcapforallfirmsinoursample.
Averageleverage,ontheotherhand,fallsfrom25%inthefirsthalfofthesampleto
22%inthesecondhalf,whileleverageincludingoff‐balance‐sheet(OBS)operatingleases
falls from 34% to 29%. This slightly smaller drop (in percentage terms) illustrates the
growthinoperatingleasesfoundinFranzenetal.(2009).
III.Results
A.Theeffectofinstitutionalholdingsonleverage
The implication of traditional capital structuremodels is that by interactingwith
frictionssuchasagencycostsandasymmetric information, institutional investors’equity
holdingscanplayaroleinthedeterminationofleverageratios.
To characterize the relationship between institutional holdings and leverage, we
mustcontrolforotherfirmcharacteristicsthataffectleverageandmaybecorrelatedwith
institutional holdings. To this end,webeginour empirical studywith amultivariate re‐
14
gressionwithleverage(timet)asthedependentvariable.Sinceoneofourgoalsistoex‐
amine thecausaleffectof institutionalholdingson leverage,weexaminehowthe lagged
valueofinstitutionalholdingsaffectsleverage(whilethisisonlyapartialsolution,welater
examinetheissuemorethoroughly). Ouradditionalindependentvariablesaremeasured
at time (t – 1), and, aside from institutional holdings, are commonly used in the capital
structureliterature(see,e.g.,FrankandGoyal(2009)):market‐to‐book,logofassets,prof‐
itability, tangibility,median industry leverage, adividendpayerdummy, capital expendi‐
tures,collateral,intangibleassets,totalpayout,changeinassets,andindustryuniqueness.
Wealsocontrolforinitialfirmleverage,asLemmonetal.(2008)findleverageratiostobe
surprisinglystableovertime. Lastly,weincludeyeardummyvariablesinallofourtests,
andincertainspecifications,weincludefirmfixedeffects.Weoptnottoincludeindustry
fixed effects in our regressions because Lemmon et al. (2008) find that industrymedian
leveragevitiatesthepoweroftheindustryfixedeffecttoexplainleverageratios,andsub‐
stantiallyincreasestheadjustedR‐squaredintheprocess.Noneoftheresultsinthispaper
change,however,ifweincludeindustryfixedeffects.TableIIpresentstheresultsofthese
panel regressions,with t‐statisticsandsignificance levels reflectingstandarderrorsclus‐
teredatthefirmleveltoallowtheerrortermtobeheteroskedasticandcorrelatedwithin
firms.
Thedata incolumn1showthat firmswithhighinstitutionalholdings,onaverage,
havelowleverageevenaftercontrollingforotherfirmcharacteristics.Withonlyyearfixed
effectsascontrols,thecoefficientonHoldis‐0.028(unreported),andafteraddingcontrol
variables known to explain the variation in leverage, such as initial firm leverage (Ini‐
tial_TDA)andthemedianwithin‐industryleverageratio(Industry_TDA),theabsolutemag‐
nitudeincreasesto‐0.100(column1). Theresultissignificantatthe1%level,andisro‐
busttochangesinempiricalspecification,suchasusingtop‐5institutionalholdingsorre‐
strictingthesampletofirmswithleverage>10%.Infacttheresultisevenstrongerwhen
weusemarketleverageratherthanbookleverage,orifweexcludefirmswith0%institu‐
tionalholdings(generallyfirmsthatdonotappearonany13Fforms).
Incolumn2weincludeafirmfixedeffecttocontrolforpotentialfirm‐specificomit‐
tedvariables. Whiletheeffectof institutionalholdingsonleveragesomewhatabates,the
15
coefficientof‐0.068remainssignificantatthe1%level.6Ineconomicterms,theregression
estimateincolumn1(2)suggeststhatanincreaseininstitutionalholdingsbyonestandard
deviationisassociatedwithaleverageratio2.9%(2.0%)lower,onaverage,inthefollow‐
ingyear. Toput thatnumber inperspective,2.9%representsapproximately12%of the
averageleverageratio(23.6%)duringoursampleperiod.Infact,afterfirststandardizing
allvariables tohaveameanof0andastandarddeviationof1, theeffectof institutional
holdings(‐0.133)iscomparableinmagnitudetothatofprofitability(‐0.147)andindustry
medianleverage(0.127),andistwiceasstrongastangibility(0.068). Theonlyvariables
with a considerably stronger effect are assets (0.207) and initial leverage (0.316). Fur‐
thermore,wefindthiseffecttobeapermanentone.Thatis,wedonotfindevidenceofa
reversal after regressing leverageon the second lagor third lag of institutional holdings
(thecoefficientsremainnegative),nordowefindthatthesecond/thirdlagsaffectleverage
atallwhenthefirstlagisincluded(notreported).
Giventhatpriorresearchhasfoundbothinstitutionalholdingsandleveragetovary
strongly by firm size (e.g. Gompers and Metrick (2001) and Bennett, Sias, and Starks
(2003)),inournextspecificationweinteractholdingswithadummyvariablerepresenting
each firm’s annualmarket capquintile to seewhether the relationbetween institutional
holdingsandleveragevariesbyfirmsize.Asreportedincolumn3,wefindthisinteraction
tobemeaningfuleveninthepresenceofafirmfixedeffect.Thenegativerelationshipper‐
sistsinallmarketcapquintiles,althoughtherelationshipisonlymarginallysignificantin
the largestsizequintile. Intermsofeconomicmagnitude, institutionalholdingshavethe
largesteffecton leverageamongstmedium‐sizedfirms:those inquintiles2,3and4. We
findthat theaverageeffecton leverageofa (quintile‐specific)onestandarddeviation in‐
crease in institutionalholdingsvariesbetween ‐0.5%for the largest firms,and ‐1.1%for
the firms in quintile 3. By allowing the relationship between leverage and institutional
6Lemmonetal.(2008)notethatwhilemostcoefficientsremainsignificantinaleverageregressionwithfirmfixedeffects,themagnitudesofthecoefficientsshrinkconsiderably(TableVintheirpaper).We,however,donotfindsuchstarkdifferencesbetweencoefficientsinourspecificationwithfixedeffectsandourspecifica‐tionwithout.Thedifferencebetweenourfindingsandtheirsstemsfromtreatmentoftheerrorterm.Lem‐monetal.imposeafirst‐orderautoregressivestructureontheirerrorterm,andwedonot.Wedo,however,testforserialcorrelationintheerrorsusingavariantoftheDurbin‐Watsontestderivedforusewithanun‐balancedpanel(Bhargava,Franzini,andNarendranathan(1982)),anddonotfindconclusiveevidenceinfa‐vorofanautoregressiveerrorstructure.
16
holdingstovarybyfirmsizeweexplainalargerportionofthevariationinleverageratios,
increasing our adjusted R2 from 32.3% to 35.0% without a firm fixed effect, and from
63.9%to64.6%withafixedeffect.Furthermore,ourresultscomparefavorablytosimilar
testsencounteredintheliterature,whereadjustedR2sintheabsenceofafirmfixedeffect
peakataround30%.7
Inunreportedanalysis,were‐estimatecolumns1through3aftersubjectingourde‐
pendentvariableTDAtoalogittransformation.Sinceleverageisaproportionboundedbe‐
tween zero and one, the regressions in columns 1 through 3might bemisspecified, and
performing a logit transformation alleviates any concerns this may cause. Here, and
throughoutthepaper,ourresultsareunchangedwhenusingthetransformed,unbounded
dependentvariable. Consequently, intheremainingtableswereportresultsonlyforour
boundedleverageandinstitutionalholdingsvariables,asthecoefficientsontheindepend‐
ent variables have straightforward interpretations. Finally, althoughwe cluster standard
errorsatthefirmlevel,were‐runthetestsincolumns1through3accordingtothetech‐
niqueoutlinedinFamaandMacBeth(1973).Ourconclusionsdonotchange.
One factor thatmay affect capital structure ismarket timing (Baker andWurgler
(2002)).Inaddition,equitymarkettimingmayinteractwithinstitutionalholdingsaswell.
For example,Alti andSulaeman (2012) show that firms issue equity afterhigh stock re‐
turnsonlywheninstitutionaldemandisstrong.Theirfindingmightimplythattherelation‐
shipwefindbetweenleverageandinstitutionalholdingsisnotonlybecauseoftheeffectof
institutionson the frictionsunderlying capital structuremodels, but ratheranartifact of
firmstimingthemarketwithequityissuances.
Wecontrolforthispossibilityintwoways.First,weincludetheBakerandWurgler
(2002) external finance weighted average (EFWA)market‐to‐book ratio (defined in the
Appendix)inaregressionmirroringtheoneinColumn1,andourresultholds(notreport‐
ed). Second, tospecificallyaddress the findingsofAltiandSulaeman(2012),we include
laggedabnormalstockreturn(realizedreturnrelativetotheCAPMprediction–definedin
theAppendix)ontherighthandsideoftheestimationinColumn4.Bycontrollingforab‐
7Insimilarregressions,Lemmonetal.(2008)produceanadjustedR2of30%withdatafrom1965to2003,while Frank and Goyal (2009) produce adjustedR2s of 30%, 14%, 14%, and 21% for the 1970’s, 1980’s,1990’s,and2000to2003,respectively.
17
normalstockreturn,weestimatetheeffectofinstitutionalholdingsonleverageoutsideof
thecontextstudiedinAltiandSulaeman(2012),andwecontinuetofindthatinstitutional
holdingshaveanegativeeffectonleverage.Infact,themagnitudeofthecoefficientactual‐
lyincreasesfrom‐0.068inColumn2to‐0.070inColumn4,althoughthesampleisslightly
smaller(laggedreturnrequirestwoprioryearsofpricingdata,asopposedtotheoneprior
yearrequiredbythevariablesinColumn2).Furthermore,ifwerestrictthesampletoin‐
cludeonlyfirmsthatdidnothavealargeabnormalreturnintheprioryear(e.g.ifwein‐
cludeonlyfirmswithreturnsinthe25thto75thpercentile),ourresultshold.Basedonthe‐
seresults,weconcludethatourfindingsarenotamanifestationoffirmstimingthemarket
withequityissuances.
Aprimarymotivationforthispaperisthatinstitutionalinvestorsaffecttheconflict
ofinterestbetweenoutsideequityholdersandmanagers. However,asecondconflictex‐
ists between equity holders and debt holders (e.g., Jensen and Meckling (1976), Myers
(1977),andDiamondandHe(2011)),anditisunclearwhetherinstitutionalequityinves‐
torshaveaneffectonthisconflict.Ononehand,greaterinformation‐gatheringandeffec‐
tivemonitoringbyinstitutionsreducestheopacityoffirm’assets.Indeed,Edmans(2009)
shows that blockholders attenuatemanagers’myopic behavior,making bonds less risky,
whileJiang,LiandShao(2010)findthatwheninstitutionsarebothequityholdersanddebt
holders,thecostofborrowingfalls.Ontheotherhand,greateralignmentbetweenmanag‐
ers and shareholders can lead to more managerial risk‐taking and higher costs of debt
(Ortiz‐Molina(2006)),particularly in theabsenceoftakeoverprotections(Cremers,Nair,
andWei(2007)).
Inourempiricalspecificationswecontrolforthesepotentialeffects inavarietyof
differentways.Mostbroadly,wecontrolforfirms’creditratingstoproxyfortheeffectof
institutionalholdingsonthecostofdebt.Inaddition,wecontrolforthelevelofmanagerial
equityholdingsbecauseDatta,Iskandar‐Datta,andRaman(2005)showthatmanagersare
more likely tomakecapital structuredecisions inalignmentwith thepreferencesofout‐
sideequityholdersiftheirownshareholdingsarehigh.Andfinally,giventhattheconflict
of interestbetweenshareholdersandbondholdersdependson thecorporategovernance
environmentofa firm(Cremersetal. (2007)),wecontrol for theGompers, Ishii,Metrick
(2003)GIMIndexandthenumberofanalyststhatcovereachfirm.
18
Wefindthattheeffectofinstitutionalholdingsonleveragepersistsaftercontrolling
foreachofthesemeasures. Controllingforcreditratingsdoesnotmateriallychangeany
result,andcontrollingfortheGIMIndexdoesnotchangeourresultsandsignificantlyre‐
ducesthesamplesize,soweonlyreporttheestimatesaftercontrollingforinsiderholdings
(column 5) and analyst coverage (column 6). We find that after controlling for insider
holdings theeffectof institutionalholdingson leverageremainsnegativeandsignificant,
eveninaregressionwithfirmfixedeffects. Thecoefficientof‐0.044issmallerinmagni‐
tude than the ‐0.068 found in column 2, but the samples are very different since
ExecuCompinsiderholdingsdataisonlyavailableafter1991,andonlyforS&P1500firms.
Overall,wefindtheeffectofinstitutionalholdingsonleveragetobequiterobusttothepo‐
tentialeffectofinstitutionalholdingsontheconflictofinterestbetweenequityholdersand
debtholders.
Next,weexaminethepossibilitythattherelationshipbetweeninstitutionalholdings
andleveragestemsfromimportantcovariatesofinstitutionalholdings–analystcoverage
andequityliquidity–ratherthanfromtheholdingsthemselves.Chang,Dasgputa,andHil‐
ary(2006) find that firmswithmoreanalystcoveragehave lower targetdebtratios,and
thatfirmswithlessanalystcoveragearelesslikelytoissueequityandmorelikelytotime
themarketwhen they do issue. Furthermore,Hennessey andWhited (2005) show that
leverageispersistentandnegativelycorrelatedwithlaggedliquidity.
Toaccountforthecorrelationbetweeninstitutionalholdingsandanalystcoverage,
wefollowChangetal.(2006)andusedatafromI/B/E/StodefineAnalystCoverageasthe
maximumnumberofanalystswhomakeannualearningsforecastsinanymonthduringthe
twelvemonthspriortofiscalyearend,whileassumingthatfirmsnotcoveredbyI/B/E/S
have no analyst coverage. And to account for equity liquidity we include the Amihud
(2002)illiquiditymeasure(definedintheAppendix). Wefindthatinstitutionalholdings
remainasignificantdeterminantofleverageaftercontrollingforthenegativerelationship
betweenanalystcoverageand leverage(column6),andthat therelationship isrobust to
theinclusionofAmihud’sliquiditymeasure(unreported).
Tofurtherextendouranalysis,wemodifyourdefinitionofleveragetoincludeoff‐
balance‐sheet(OBS)operatingleasesincolumn7.Franzenetal.(2009)provideevidence
ofadramaticincreaseintheuseofOBSoperatingleasesoverthelastpastdecades,advo‐
19
catingthatamorecompletemeasureofleveragewouldincludeOBSleasesonbothsidesof
the balance sheet. Furthermore, Rampini and Viswanathan (2010) and Eisfeldt and
Rampini (2009) suggest, respectively, that leasing increases firms’debt capacitybecause
“leasingtangibleassetsrequireslessnetworthperunitofcapital”and“repossessionofa
leasedassetiseasierthanforeclosureonthecollateralofasecuredloan.”Assuch,operat‐
ingleasesmayplayaroleintherelationbetweeninstitutionalholdingsandleverage.
As per Franzen et al. (2009) andGrahamet al. (1998),we calculate leases as the
capitalizedvalueofnon‐cancellableOBSoperatingleases,andincorporatethatvalueinto
bothtotalassetsandtotaldebt.Wefindthatwhiletherelationbetweenholdingsandlev‐
eragebecomesslightlylessnegativeineachsizequintile(e.g.from‐0.060forfirmsinthe
thirdsizequintileincolumn3to‐0.050forthesamefirmsincolumn7),ourresultsarero‐
busttotheinclusionofOBSleases.8Infact,ouradjustedR2sincreasesignificantlywhenwe
includeleases.Withoutleases,thespecificationincolumn1yieldsanadjustedR2of32.3%,
butwhenweaddOBSleasesintodebtandassetstheadjustedR2increasesto37.1%(not
reported). Similarly, comparing columns 3 and 7,we see the adjustedR2 increase from
64.6%to69.0%whenleasesareincluded.
Finally,priorliteraturesuggeststhattherelationbetweeninstitutionsandfrictions
suchasagencycostsandasymmetricinformationmaydifferbyinstitutiontypeorinvest‐
menthorizon(Changetal.(2007)),althoughitisnotclearapriorihowthisshouldaffect
firms’leverageintheaggregate.Toexaminewhethertherelationshipbetweeninstitution‐
al holdings and leverage differs by these institutional characteristicswe utilize type and
horizonclassificationsascalculatedandprovidedbyBrianBusheeonhiswebsite.9While
wedonotreporttheresultsofthesetests,wefindthattherelationbetweeninstitutional
holdingsandleveragedoesnotparticularlyvarybyinstitutiontype. Investmentadvisers
(mutual funds, hedge funds, etc.) have the strongest effect, and the largest holdings, but
pensionfunds,insurancecompanies,andbanksallhaveanegativeandsignificanteffectas
well.Universityandfoundationendowments,aswellas“miscellaneous”institutions,have
aninsignificanteffect.Asforinvestmenthorizon,wefindthatlong‐terminvestors(“dedi‐ 8Thesampleincolumn6isdifferentfromthatincolumn3,sowecannotdirectlycomparecoefficients.How‐ever,whenwere‐run theTDA regressionsusing theTDLAsample, theHold coefficients in theTDA regres‐sionsarestillmeaningfullylargerthantheHoldcoefficientsintheTDLAregressions.9http://acct3.wharton.upenn.edu/faculty/bushee/
20
cated” institutions)andquasi‐indexershaveanegativeandsignificanteffecton leverage,
whileshort‐terminvestors(“transient”institutions)haveanegativeeffectthatisonlysig‐
nificantinaregressionwithfirmfixedeffects. Quasi‐indexershavethelargesteffect,and
alsohavethelargestholdings.
Sofarwehavecontrolledfordifferencesinfirmcharacteristicsbyincludinglagged
levelsofimportantvariablesinourregressions,andhavealleviatedconcernsoveromitted
variablesby including firm fixedeffects. Estimating theeffectof changes in institutional
holdings on changes in leverage is anotherway to control for unobservedheterogeneity
and omitted variables, but also allows us to estimate the effect of holdings on leverage
whilecontrollingforlaggedchangesinleverage.Tothisend,TableIIIpresentstheeffects
oflaggedchangesininstitutionalholdingsonchangesinleverage.Theoutputincolumn1
showsthatachangeininstitutionalholdingsresultsinasubsequentchangeinleverageto
theoppositedirection. The coefficientof ‐0.014 is significantat the1% level, evenafter
controllingforlaggedchangesinleverage.
ThesecondcolumnofTableIIIpresentstheeffectofchangesinstitutionalholdings
on subsequent changes in leverage,withOBS leasesadded tobothdebtandassets. The
magnitudeoftheeffectisover50%largerthaninthecasewithoutOBSleases,andtheco‐
efficientremainssignificantatthe1%levelevenaftercontrollingforlaggedchangesinlev‐
erage.Aswasthecaseinlevels(TableII),theeffectofchangesininstitutionalholdingson
changesinleverageisrobusttotheinclusionofOBSleases.
Overall,ourfindingsthusfarlendsupporttothehypothesisthatinstitutionalhold‐
ings and debt exist as partial substitutes, rather than complements, in themitigation of
problemsstemmingfromagencycostsorinformationasymmetry.Firmsinwhichinstitu‐
tions have a strong presence tend toward low leverage, and firms in which institutions
strengthentheirpresenceareassociatedwithlowerleverage.What’smore,ourresultsin
TablesIIandIIIsuggestthattherelationshipmaybeofacausalnatureratherthansimply
anassociation, aswe find that firmsdecrease (increase) their leverage inresponse to in‐
creases(decreases)ininstitutionalholdings.Wetestthisindetaillaterinthepaper.
B.Theeffectofleverageoninstitutionalholdings
21
Whilemostcapital structure theories implicitlysuggestcausality from institutions
tofirms’leverage,onecanthinkofscenarios,notinconsistentwiththesetheories,inwhich
causalitywouldgointhereversedirection.Infact,severalrecentpapersshowinstitution‐
alinvestorstoexhibitpreferencesoverfirmcharacteristics,establishingaprecedentsug‐
gesting that firms’ capital structuremay influence institutional investors’ equity holding
decisions.Inparticular,institutionshaveshownpreferencesoverfirmcharacteristicssuch
asvisibility,transactioncosts,andvolatility(Falkenstein(1996)),payoutpolicy(Grinstein
andMichaely(2005);DesaiandJin(2011)),andcorporategovernance(FerreiraandMatos
(2008),andMcCaheryetal. (2010)). Furthermore,DelGuercio(1996) findsthat institu‐
tionstilttheirportfoliostoward“prudent”stocks,whichinthecontextofthispapermight
implylow‐leveragefirms.
Totesttheeffectof leverageoninstitutionalholdingswestartwithalevelregres‐
sionwherethedependentvariableisinstitutionalholdings,measuredattimet.Theinde‐
pendentvariables,measuredat time(t–1),are leverage, logsales,beta,annualadjusted
return,totalpayout,market‐to‐book,adividendpayerdummy(equalto1intheeventthe
firm pays a dividend), and year dummy variables. Following Grinstein and Michaely
(2005),weselectlogsalesandbetatoaccountforrisk,themarket‐to‐bookratiotocontrol
forgrowthopportunitiesandasymmetricinformation,andourtwopayoutpolicyvariables
becausetheauthorsfindarelationshipbetweendividendpayersandholdings,aswellas
betweenrepurchaseactivityandinstitutionalholdings.TableIVpresentstheeffectoflev‐
erageoninstitutionalholdingsinPanelA,andtheeffectofchangesinleverageonchanges
ininstitutionalholdingsinPanelB.
In Panel A we find that, on average, institutions have higher holdings in low‐
leveragefirmsthaninhigh‐leveragefirms.Withoutafirmfixedeffect(column1),there‐
sultissignificantandsuggeststhataonestandarddeviationincreaseinlaggedleverageis
associatedwitha2.5%decreaseininstitutionalholdings,afigurecorrespondingto10%of
theaverage full‐period institutionalholdings (24%). Witha firmfixedeffect (column2),
theeffectabatesbyaround30%,althoughitremainssignificant.Inaddition,weverifythat
ourresultsarerobusttotheinclusionofOBSoperatingleasesintoourdefinitionoflever‐
age,arenotdrivenbyatimetrend(werepeatourtestsforseveralsubperiods),andarenot
22
duetothepossiblemisspecificationofinstitutionalholdings(were‐runthetestsaftersub‐
jectingHoldtoalogittransformation).
Asanalternativetoafixedeffectsregression,wetesttherelationbetweeninstitu‐
tional holdings and leverage in first differences rather than in levels, and include lagged
changesininstitutionalholdingsasacontrol.TheresultsarereportedinPanelB.Wefind
that,onaverage,institutionalholdingsdecreaseafterfirmsincreasetheirleverage,regard‐
less if leverage is calculatedwithorwithoutOBSoperating leases (wedonot report the
testthatincludesOBSleases).Evenaftercontrollingforthelaggedchangeininstitutional
holdings,theΔTDA coefficientof‐0.037issignificantatthe1%level.
C.Addressingtheissueofendogeneity
Sofarwehavefoundthatinstitutionalholdingsnegativelyaffectleverage,andthat
leveragenegativelyaffectsinstitutionalholdings.Whileregressionsonlagged,differenced
variables arean important step towardaddressing the issueofendogeneity, theydonot
solve the issue. In this subsectionwe employ three techniques to remove the potential
feedback effect of leverage on holdings: two separate instrumental variables (IV) ap‐
proachesandasemi‐naturalexperiment.OurfirstIVapproachisadynamicpanelestima‐
tionsimilar to theonedevelopedbyHoltz‐Eakin,Newey,andRosen(1988)andArellano
andBond(1991),whileoursecondIVapproachisaclassicaltwo‐stageleastsquaresesti‐
mationusingmutual fundflowinducedtradingasan instrumentfor firm‐specific institu‐
tionalholdings.Finally,ourthirdapproachisasemi‐naturalexperimentinwhichaddition
totheS&P500Indexprovidesanexogenousshocktoinstitutionalholdings.
C.1.DifferenceGMM
TheArellanoandBond(1991)“difference”generalizedmethodofmomentsestima‐
torfitslinearmodelswithonedynamicvariableandadditionalcontrolsonshort,wideNx
Tpanels.Itisdesignedtohandlefixedeffectsandendogeneityofregressorsbyusingpast
valuesofendogenousvariablestoinstrumentforcurrentchangesinthosevariables. The
procedure avoids dynamic panel bias (Nickell (1981)), and deals with potential biases
causedbythecorrelationofinstitutionalholdingsandleverageovertime.
23
Weruntwoseparateestimations,onewithleverageasthedependent,dynamicvar‐
iable,andonewithchanges in institutionalholdingsas thedependent,dynamicvariable.
Themodelsarespecifiedasfollows:
Leverageit , InstHoldingsi,t‐1 xi,t‐1' εit
InstHoldingsit , Leveragei,t‐1 xi,t‐1' πit
and
E E E 0andE E E 0
(1)
whereiindexesthefirm,tindexestime,xisavectorofcontrols,µiandφiarethefirmfixed
effects,andνitandωitare the idiosyncraticshocks,whichareorthogonal toµiandφi, re‐
spectively. The vectorx can contain variables that are endogenous, predetermined, and
exogenous,aswellasdeeperlagsofthedependent,dynamicvariable.Theestimationisa
two‐step procedure: first, variables are differenced to remove firm fixed effects, and se‐
cond,aGMMestimatorinstrumentsendogenousvariableswiththeiravailablelagsinlev‐
els.
TableVpresentstheestimatesfromourArellano‐Bondestimationswithleverageas
thedependent,dynamicvariable inPanelA, and institutionalholdingsas thedependent,
dynamicvariableinPanelB.Inbothpanels,theinternalinstrumentsbeginatthethirdlag
toavoidproblemsstemmingfromautocorrelatederrorterms,andwecollapseourinstru‐
mentmatricestoeliminateproblemsassociatedwithexcessiveinstruments.
TheresultsinPanelAshowastrong,negativeeffectofinstitutionalholdingsonlev‐
erage.ThecoefficientonHoldissignificantatthe1%level(column1),evenaftercontrol‐
lingforotherimportantdeterminantsofleverage,autocorrelationininstitutionalholdings
andleverage,timevaryingidiosyncraticshocks,andtimetrends.Inaddition,theresultis
robusttotheinclusionofOBSoperatingleasesintoourdefinitionofleverage(notreported
inthetable).
Inthespecificationestimatedincolumn2,wesubstituteHold5,theholdingsofthe
fivelargestinstitutionalinvestors,fortotalholdings(Hold).Theresultisagainsignificant
andisevenstrongerthantheonepresentedincolumn1,withtheabsolutemagnitudeof
24
thecoefficientincreasingfrom‐0.148to‐0.183.Becausewearetestingacausalrelation‐
ship, itmakes intuitivesense that the top five institutional investorshaveamoresignifi‐
cant impactonleveragethanall institutionscombined,astheylikelyhavethehighest in‐
centivestomonitor,collectinformation,andengageinactivism.
We test thestrengthofour instrumentswith theprocedureoutlined inStockand
Yogo(2005)andreturnaKleibergen‐PaapF‐statisticof68.0,whichexceedsallStockand
Yogo(2005)instrumentalvariablecriticalvaluesforweakidentification. Bothspecifica‐
tionspresentedinPanelApasstheArellano‐Bondtestforsecond(third)orderautocorre‐
lationinlevels(differences).Insum,ourinstrumentspasstestsforbothinstrumentvalidi‐
tyandstrength.Finally,whilethespecificationpresentedincolumn1failstheHansentest
for joint validity of the full instrument set, the J‐test, the specification in column2using
top‐5holdingspasseseasily.
PanelB,however,tellsadifferentstory.Whileourpreviousregressiontestsinlev‐
elsandindifferencesshowleveragetohaveanegativeeffectoninstitutionalholdings,this
effectdisappearsonceweremovetheeffectofholdingsonleverage.Withtheinstitutional
holdings as the dependent, dynamic variable in column 1, the coefficient onTDA is, at ‐
0.012, insignificantly different from zero. Similarly, after substituting top‐5 institutional
holdingsfortotalholdings,wefindincolumn2thattheeffectofleverageisstillinsignifi‐
cant.BothspecificationspasstheHansenJ‐test,suggestingthevalidityofthefullsetofin‐
struments,aswellastheStockandYogotestsforinstrumentweakness.10
Alltold,theresultsinTableVsuggestthatafterremovingtheendogenouscompo‐
nents of the relationship between institutional holdings and leverage, institutions affect
firms’ leveragethroughtheirholdings,butchangesin leveragedonot leadinstitutionsto
altertheirholdings.
C.2.Two‐StageLeastSquares
Next,weestimatetheextenttowhichinstitutionalholdingsaffectcapitalstructure
usinganinstrumentalvariableandlineartwo‐stageleastsquares.Toremovethepotential
10InanalternativespecificationtoPanelB,column1,werestrictourinstrumentmatrixtoincludeonlyonelaggedchangeininstitutionalholdings,andalsoincorporateourinstrumentalvariablefromthenextsection,MFFlow,intotheestimation.Leveragecontinuestohaveaninsignificanteffectoninstitutionalholdings.
25
feedbackeffectofleverageoninstitutionalholdingsweneedaninstrumentforinstitutional
holdings that is unrelated to the unobserved factors that affect firms’ financial leverage.
Our instrument, implied trades induced bymutual fund outflows (MFFlow), is borrowed
fromEdmans,Goldstein,andJiang(2011).11
The ideabehind the instrument is that outflowsbymutual fund investors lead to
changesinmutualfundportfolioholdings,asthefundsmustsellaportionoftheirholdings
torepayinvestorswhowishtoredeemtheirinvestment.If,however,inresponsetoinves‐
toroutflows,mutualfundstradebasedoninformationrelatedtofirms’capitalstructures,
actualtradesmaynotbeavalidinstrumentforinstitutionalholdings.Toalleviatethiscon‐
cern,weuseimpliedtradesconstructedfrominvestorflowsandpreviouslydisclosedport‐
folioholdings.Impliedtradesarelikelytosatisfytheexclusionrestrictionbecausemutual
fundinvestors’decisionstoaccumulateordivestsharesinafundareunrelatedtothecapi‐
tal structure policies of the individual firms held by the fund. After all, if the investors
caredto,theycouldtradethestocksoftheindividualfirmsratherthantrademutualfund
shares. Thus, impliedtradesconstructedusingmutual fundflowswill leadtochanges in
institutionalholdingsthatmayaffectcapitalstructure,butthatarenotmotivatedbyit.
Onepotentialconcernwithusingfundflowinducedtradingasaninstrumentforin‐
stitutionalholdingsisthatthepricepressureresultingfromexcessivefundflowsmaylead
firmstotimethemarketwithdebtandequityissuances,thuscreatingaspuriousrelation‐
shipbetweeninstitutionalholdingsandleverage.GaoandLou(2011)investigatethisissue
andfindsomesupport formarket timingconsiderations,predominately in firmsmore fi‐
nanciallyconstrained.Themagnitudeoftheeffecttheyfind,however,isverysmall.Specif‐
ically,GaoandLoufindthataonestandarddeviationincreaseinflowinducedpricepres‐
sureisassociatedwithadecreaseinthesubsequentdebttoequityratioofapproximately
5.6basispoints. We,ontheotherhand,findinTableIIthataonestandarddeviationin‐
crease in institutionalholdings isassociatedwitha228basispointdecrease in leverage,
andifweruntheregressionusingthedebttoequityratioinplaceofleverage,wefindthe
negativeassociationtobegreaterthan1,100basispoints.Withtheeconomiceffectofin‐
stitutionalholdingsoncapital structure tobenearly200 times larger than theeconomic
11Edmansetal.(2011)studytheimpactofstockpricesontakeoverprobabilities,andnotethatpricemaybeendogenous,necessitatinganinstrumentalvariablesapproach.
26
effectofflowinducedpricepressure,theconcernthatmarkettimingconsiderationsgener‐
ateaspuriousrelationshipbetweeninstitutionalholdingsandleverageappearstobemi‐
nor.
Theinstrumentweuse,MFFlow,isthefirm‐specificannualdollarchangeinholdings
impliedbymutualfundinvestoroutflowsandpreviouslydisclosedmutualfundholdings.
Specifically,forfirmiinquartert,
MFFlowi,t, x , , x ,
, x ,, (2)
whereFj,tisthetotaloutflowexperiencedbyfundjinquartert,TAj,t‐1isfundj’stotalassets
attheendofthepreviousquarter,SHARESi,j,txPRCi,t‐1isthedollarvalueoffundj’sholdings
ofstocki,andVOLi,tisthetotaldollartradingvolumeofstockiinquartert.Thesummation
isover funds j forwhichquarterly investoroutflowsequalorexceed5%of fund j’s total
assets(i.e.‐Fj,t/TAj,t‐1≥5%).Edmansetal.(2011)arguethattheimpactofoutflowonpric‐
eswilldependonastock’sliquidity,andassuchscaleMFFlowbytradingvolume.Although
thisconcernislessrelevantforourpaper,weproceedinkind.Finally,tocompletethecal‐
culation,wesumMFFlowacrossthefourquartersinagivencalendaryear.
TableVIpresentstheeffectofinstitutionalholdingsonleverage,estimatedviatwo‐
stageleastsquares.12Wefindthatafterremovingthefeedbackeffectofleverageoninsti‐
tutional holdings, the effect of holdings on leverage increases substantially. Our results
showthataonestandarddeviationincreaseininstitutionalholdingsleadstoastatistically
andeconomically significantdecrease in leverageof8.0%, as compared to2.9%without
instrumentation(column1,TableII).TheresultissupportedbyafirststageF‐statisticof
358.21,whichhiseasilylargeenoughtorejectthehypothesisthattheinstrumentisweak
(StockandYogo(2005)).
12Duetodataconstraints,thesampleinTableVIdiffersslightlyfromthatinTableII.MFFlow,obtainedfromAlexEdmans’swebsite,iscalculatedannuallybetween1980and2007,inclusive,andassuch,2008and2009areexcludedfromthetestsinTableVII.Repeatingcolumn1inTableIIIusingthissample,however,yieldsaverysimilarcoefficientforinstitutionalholdingsof‐0.080.
27
C.3.S&P500IndexInclusion
Asourfinalmethodofcontrollingforthepotentialfeedbackeffectofleverageonin‐
stitutional holdings, we conduct a semi‐natural experiment that utilizes the exogenous
shocktoinstitutionalholdingsassociatedwiththeinclusionofafirmintotheS&P500In‐
dex(PruittandWei(1989)).Aghion,VanReenen,andZingales(2008),whoalsouseaddi‐
tion to the S&P 500 Index as an instrument for institutional holdings, mention three
sourcesforthisincrease:indexfunds,non‐indexersthatbenchmarkagainsttheIndex,and
institutionsappealingtotheimpliedendorsementofbroadindexingtosatisfyprudentman
restrictions.
S&P500inclusionisavalidinstrumentforinstitutionalholdingsbecauseitiscon‐
sideredbyStandardandPoor’stobeaninformation‐freeevent.Whenachangeismadeto
anS&Pequityindex,Standard&Poor’sstatesinitspressreleasethat“companyadditions
toanddeletionsfromanS&Pequityindexdonotinanywayreflectanopiniononthein‐
vestmentmeritsofthecompany.”Furthermore,Neubert(1985)describesthecriteriafor
S&P 500 selection to be: “size, industry classification, capitalization, trading vol‐
ume/turnover, emerging companies/industries, and responsivenessof themovementsof
stockpricetochangesinindustryaffairs,”allofwhicharepublicinformationalreadyfac‐
toredintofirmvaluationsandmetrics.
Aswithmutualfundflowinducedtrading,markettimingconsiderationscanpoten‐
tiallyrenderS&P500inclusionaninvalidinstrument.Thatis,asStandard&Poor’saimsto
constructanindexthatisbothrepresentativeandstable,itoftenchooseslargefirmswith
goodpastperformance.Giventhis,firmsthatareaddedtotheindexoftenexperiencelarge
stockreturnsinthequartersprecedinginclusion.Itcouldbethecase,then,thatfirmstake
advantageofthelargereturnsandissueequitywhilestockpricesarehigh,inturnlowering
leverageandcreatingaspuriousnegativerelationshipbetweeninstitutionalholdingsand
leverage.Infact,AltiandSulaeman(2012)findthatfirmstimethemarkettoissueequity
onlywhentheyhavebotha favorablestockpriceandhighinstitutionaldemandfortheir
shares.
Whileweareunabletocompletelyeliminatethispossibility,weemployapropensi‐
tyscorematchingtechnique(RosenbaumandRubin(1983,1985),andHeckman,Ichimura,
andTodd(1997))inwhichwematcheachfirmthatenterstheS&P500toacontrolfirm
28
withtheclosestpropensityscorebasedonthecumulativeCAPM‐adjustedstockreturnfor
thequarterbeforeinclusionandthequarterofaddition.Wealsomatchonsize,market‐to‐
book,leverage,institutionalholdings,assetgrowth,andliquidity,andweforcematchesto
beinthesameindustryatthesametimeperiod(year‐quarter).
For our S&P500natural experiment,weuse quarterlydata fromCompustat, and
count 526 first‐time additions to the Index between 1979 and 2009 (excluding financial
firmsandutilities).Aswerequirenon‐missingleverageforfourquartersafteraddition,we
lose75observationsofthe526(including24fromfirmsthatwereaddedin2009–thefi‐
nalyear inoursample). Furthermore, sincewematchon laggedreturn,werequire two
quartersofnon‐missingpricespriortoaddition,decreasingoursamplebythe8firmsthat
wereaddedinthefirsttwoquartersof1979–thefirstyearinoursample.Finally,welose
28observationsdue tomissingdata required forourmatchingprocedure (inparticular,
thecomponentsofAmihud’s illiquiditymeasure), leavinguswith415S&P500additions
betweenthethirdquarterof1979and2008.
TobeeligibleforthecontrolgroupwerequirethatafirmhasneverbeenintheS&P
500Index,that ithasbeendroppedfromtheIndexat leastoneyearpriortobecominga
match,orthatithasbeenintheIndexforatleastoneyearbeforebecomingamatch.Ex‐
cluding firms in the S&P 500 from the control group would make finding appropriate
matchesmoredifficult,butitwouldalsomakeiteasiertogenerateresultsinadifference‐
in‐difference framework. Because finding qualitymatches is important,we include S&P
500firmsinthecontrolgroupandindoingsosubjectourselvestoadifficulttestingenvi‐
ronment.Thismatchingprocedureyieldsacontrolgroupinwhich209firmsaremembers
oftheS&P500Index,and206firmsarenot.However,inanalternateprocedure,weforce
allmatchestobemembersoftheS&P500IndextocontrolforapossibleS&P500fixedef‐
fect,andourresultshold. Withour treatmentandcontrol samplesnowconstructed,we
conductbothadifference‐in‐differencepairedt‐testandadifference‐in‐differenceregres‐
siontest.TheresultsarepresentedinTableVII.
PanelAofTableVIIpresentssummarystatisticsonequarterbeforeS&P500addi‐
tionforboththetreatmentandcontrolgroups.Thegroupsareverysimilar.Weseethat
thetreatmentgroupisslightlylargerintermsofmarketcapitalizationandtotalassets,alt‐
houghthegroupshaveequalmarket‐to‐bookratios.Furthermore,thetreatmentgrouphas
29
marginallyhigher institutionalholdingsandleveragebeforeadditiontotheIndex. While
notreported,wefindthatbetweenthequarterbeforeadditionandthequarterofaddition,
institutionalholdingsincreaseby3.4%,onaverage,forthetreatedfirms,andonly0.6%,on
average,forthecontrolfirms.Thedifferencebetweenthesechangesissignificantlydiffer‐
entfromzero. Importantly,thecumulativeabnormalreturninthequarterpriortoaddi‐
tionandthequarterofadditionisactuallyhigherforthecontrolgroupthanforthetreat‐
mentgroup,helpingtoassuageconcernsthattheleveragedecreasewefindstemsfromthe
markettimingconsiderationsstudiedinAltiandSulaeman(2012).
In Panel B we present the results of a difference‐in‐difference paired t‐test. We
comparethemeandifferenceinleveragebetweenthetreatmentandcontrolgroupsinthe
quarterpriortoadditiontothemeandifferenceoneyearafteraddition,whichshouldpro‐
videfirmswithenoughtimetoaltertheircapitalstructurepolicies inresponsetothein‐
creaseininstitutionalholdingscausedbyS&P500addition.Wefindthatthemeandiffer‐
ence between treatment and control groups shrinks by (a statistically significant) 0.015,
from 0.017 before addition to 0.002 after addition. The mean difference produces a t‐
statisticof ‐2.061, indicatinga change in leveragestemming fromthe increase in institu‐
tionalholdingsthatresultsfromindexinclusion.13
Finally,inPanelCwepresenttheresultsofourregressionanalysis.Forthistestwe
create two new dummy variables, one indicating whether the firm joins the S&P 500
(Treatment=1inthiscase),andtheotherindicatingwhetherthetimeperiodisbeforeor
afterinclusion(Post=1afterinclusion,forboththetreatmentfirmsandthematchedsam‐
ple). The difference‐in‐difference estimator of interest is the interaction of these two
dummyvariables,Treatment*Post. Inorder to isolate theeffectofS&P500addition,we
restrictoursample to theperiodoneyearbefore inclusiontooneyearafter inclusion(9
quartersperfirm)forboththetreatmentandcontrolgroups.Intheregressionwecontrol
forthedeterminantsof leverageusedinTableII,aswellaslaggedandcontemporaneous
adjustedreturnand(Amihud’s)illiquiditytomitigateconcernsrelatedtomarkettiming. 13Tofurtherensurethatthedifferentialchangeinleveragebetweenthetreatmentandcontrolgroupisin‐deedduetotheinclusionintotheS&P500,weusethefollowingplacebotest:WelookbacktwoyearsbeforeadditiontotheS&P500,andrepeatourdifference‐in‐differencepairedt‐testfromPanelB(usingthesamefirmsinthetreatmentandcontrolgroups).Wefindthemeandifferenceinleverage(andinstitutionalhold‐ings)betweenthetreatmentandcontrolgroupsbeforeandafterthisparticularquartertobeindistinguisha‐blefromzero.
30
WefindtheeffectofTreatment*Postonleveragetobenegativeandsignificant,with
acoefficientof‐0.015andt‐statisticof‐2.00.TheresultcomplementsourfindingsinPanel
B, andpoints toa significantdifference in the change in leveragebetween the treatment
andcontrolgroupsafter index inclusion,evenaftercontrolling forotherknowndetermi‐
nantsofleverageandmarkettimingforces.The1.5%changeinleverageiseconomically,as
wellasstatisticallysignificant,as it represents7.7%of theaverage treatment firms’pre‐
event leverage (19.4%), and is already beingmeasured above‐and‐beyond the “natural”
changeinleverageinthecontrolfirms.
Takentogether,wehavepresentedthreedifferenttests:Arellano‐Bond,2SLS,anda
semi‐naturalexperimentusinginclusiontotheS&P500Index.Thosetestsdifferdramati‐
callyintheirsamplesize,andthenatureoftheteststhemselvesdiffersacrossexperiments.
Nonetheless,allthethreetestssupportthesameconclusion:institutionalholdingshavea
causalnegativeinfluenceoncapitalstructure.Inaddition,ourdynamicpanelresultssug‐
gestthatinstitutionsdonotunequivocallychangetheirholdingsinresponsetochangesin
leverage.Weinterpretthisasevidencethatinstitutionscarryoutactivitiesthateitherdi‐
rectlyorindirectlymitigateagencyorinformationalfrictionsregardlessoftheexistingin‐
ternalmechanismsfordoingdo.Institutionsmonitor,forexample,fortheirownpurposes,
anddonotdecreasetheirinvestmentinafirmjustbecausethefirmincreasesitsleverage
andhencefurtherbondsmanagerialactivities.Itisuptofirms,then,todecreasecostlyin‐
ternalinvestorprotectionmechanismswhenlargeinstitutionalinvestorsprovideanalter‐
nativeexternalinvestorprotectionmechanism.
D.AgencyCosts,AsymmetricInformation,orTaxes?
Wepositedthatinstitutionalinvestorsmayhaveaneffectonfirms’capitalstructure
because theyareuniquelyqualified to interactwith frictions that, at least inpart, deter‐
minefirms’ leverageratios. Ourresultsthusfarsupportbothagencyandasymmetricin‐
formation models that predict changes in institutional holdings will results in opposite
changesinleverage.Thisevidence,however,isalsoconsistentwithtaxconsiderationsbe‐
causeoftherelativetaxadvantageenjoyedbyinstitutionalinvestorsoverindividualinves‐
torsonequityincome.Inthissection,wetrytoshedsomelightontherelativeimportance
31
ofthesefrictions(asymmetricinformation,agency,andrelativetaxation)forthenegative
relationbetweeninstitutionalholdingsandleverage.
Tothisend,weidentifyfirmslikelytofacehighlevelsofagencycostsandfirmslike‐
ly tosuffer fromhighdegreesof informationasymmetry. We thencompare theeffectof
institutional holdings on leverage between the groups. Firms particularly prone to high
agencycostsare,ingeneral,large,profitablefirms,withfewgrowthopportunitiesrelative
to their cash holdings. On the other hand, firms commonly prone to information asym‐
metry problems are generally small, young, cash‐strapped firms with relatively many
growthopportunities.
Wesort firmsannually intomarket capquintilesandmarket‐to‐bookquintiles, as
themarket‐to‐bookratioproxiesforgrowthopportunities.Wethenclassify“Small”firms
asfirmsinmarketcapquintiles1and2,and“Large”firmsasfirmsinmarketcapquintiles
4and5.Similarly,weclassify“Low”growthopportunityfirmsasfirmsinmarket‐to‐book
quintiles1and2,and“High”growthopportunityfirmsasfirmsinmarket‐to‐bookquintiles
4and5.Usingtheseclassifications,wedefineagencyfirmsasfirmsinboththe“Large”and
“Low”categories,andasymmetricinformationfirmsasfirmsinboththe“Small”and“High”
categories.Separatingfirmsinthismannerallowsustotestwhethertherelationshipbe‐
tweeninstitutionalholdingsandleverageisdrivenbyonefrictioninparticular.
PanelAofTableVIIIreportstheresultsofthistest.Whilewefindinstitutionalhold‐
ingstobeanegativeandsignificantdeterminantofleverageforbothtypesoffirms,there‐
lation isconsiderablystronger forasymmetric information firms(column1). Thecoeffi‐
cientsof‐0.147and‐0.050forasymmetricinformationandagencyfirms,respectively,rep‐
resentchangesof‐2.1%and‐1.4%relativetoaonestandarddeviationincreaseinlagged
institutionalholdings. Togivethosenumberssomeperspective,2.1%represents9.0%of
average leverage forasymmetric information firms(23.3%),while1.2%represents5.1%
ofaverageleverageforagencyfirms(27.4%).
Thespecificationsincolumns1and2includeafirmfixedeffect,andtheresultsare
robusttoalternatedefinitionsofLarge‐LowandSmall‐High,aswellastoalternatecatego‐
rizationsinwhichwereplaceSmall/Largewitha“cashcow”indicator(notreportedinthe
table). FollowingBravetal. (2005),wedefinecashcow firmsasprofitable firmswitha
32
strongcreditrating(Aorbetter)andaP/EratiolowerthanthemedianP/Eratioamong
profitablefirmswithastrongcreditrating.
Next,weextend this analysisbyestimating theeffectofholdingson leverage ina
dynamicpanelseparatelyforagencyandasymmetricinformationfirms.Theresultsincol‐
umns3and4supportandextendthoseincolumns1and2. Thatis, inadynamicpanel,
institutionalholdingsonlyaffectfirmspronetoasymmetricinformationproblems(column
3),astheeffectofinstitutionalholdingsonleverageisnegativebutinsignificantforfirms
prone to agency costs (column4). For robustness,wealso run the specifications in col‐
umns1and2 in first‐differences,andalternativelycombinethetests intooneregression
with a dummy variable designating agency or asymmetric information firms, and obtain
complementaryresults(notreportedinthetable).
TheresultsinPanelAsuggestthatinformationasymmetryisthedominantfriction
behindthenegativerelationshipbetweeninstitutionalholdingsandleverage.Thetaxad‐
vantage of institutional investors, however, implies a relationship in the same direction.
Consequently, we employ a semi‐natural experiment to address whether tax considera‐
tionsplayaroleintherelationshipbetweeninstitutionalholdingsandleverage.Specifical‐
ly,wetestwhetherthetaxadvantageoverequity incomefor institutional investorscom‐
pared to individual investors drives the negative relation between institutional holdings
andleverage.TheJobsandGrowthTaxReliefReconciliationActof2003dramaticallylow‐
eredthetaxrateonequityincomeforindividualinvestors,anddiminishedtherelativetax
advantageenjoyedbyinstitutionalinvestors.Iftaxconsiderationsarethedrivingforcebe‐
hindourresultsthusfar,weshouldseeaweakereffectofinstitutionalholdingsonleverage
after2003thanbefore.
Totestthishypothesiswerestrictoursampletotheyears2000to2006,excluding
2003,andconstructtwonewinstitutionalholdingsvariables,Hold_00‐02andHold_04‐06,
to test the relationship between institutional holdings and leverage pre‐ and post‐tax
break.TheresultsofthistestarereportedinPanelB.Bothcoefficientsarenegativeand
significant,‐0.077forthepre‐2003periodand‐0.086forthepost‐2003period,andanF‐
testwithap‐valueof0.401revealsthattheyareinsignificantlydifferentfromoneanother.
TheresultsinPanelBsuggestthattaxconsiderationsarenotbehindthenegativerelation‐
ship between institutional holdings and leverage. In unreported analysis,we re‐run the
33
testinPanelBusingfirstdifferencesasopposedtolevels,andweconfirmthatwecannot
rejecttheequalityofthecoefficientsbeforeandafterthe2003taxbreakswereintroduced.
Furthermore,theresultisrobusttotheinclusionofleasesintothedefinitionofleverage.
E.Howdofirmschangetheirleverage?
Wehaveestablishedthatchangesininstitutionalownershipnegativelyaffectlever‐
ageratios. Next,we investigatethenatureof thisprocess. That is,howdofirmschange
theircapitalstructureinresponseto,say,anincreaseininstitutionalownership?Isitdone
byissuingmoreequity,repurchasingfewershares,orbydecreasingnetdebt?Weexamine
therelationintermsoftheprobabilityofanissuanceaswellasthesizeofissuances.
Wedefineequity issuanceanddebt issuance inaccordancewithFamaandFrench
(2005)andLemmonetal.(2008),amongothers.Equityissuance(%ChangeEquity)isthe
productof thesplit‐adjustedchange insharesoutstandingandthesplit‐adjustedaverage
stockprice,normalizedbytotalassetsattime(t–1).Consequentially,afirmisanequity
issuer(EquityIssue=1)if%ChangeEquity>1%,andanequityrepurchaser(EquityRepo=
1)if%ChangeEquity<‐1%.Similarly,debtissuance(%ChangeDebt)isthechangeintotal
debt (long‐term+short‐term)normalizedby totalassetsat time (t–1). Finally, thenet
changeincapitalstructure(%ChangeNetDebt)issimply%ChangeDebt‐%ChangeEqui‐
ty,andafirmisanetdebtissuer(NetDebtIssue=1)if%ChangeNetDebt>0.
Totesttheeffectofinstitutionalholdingsonissuancedecisions,werunlogisticre‐
gressionsseparatelyforequityissuances,equityrepurchases,andnetdebtissuances,with
lagged changes in institutional holdings as our primary independent variable. In these
testsweincludethecontrolvariables,infirstdifferences,fromTableIV,aswellaslagged
annualadjustedreturnandbetatocapturepossiblemarkettimingeffects. Theresultsof
ourlogisticregressionsarepresentedinthefirstthreecolumnsofTableIX.
Wefindthatanincreaseininstitutionalholdingsisassociatedwithahigherproba‐
bilityofafirmbeinganequityissuerinthefollowingyear(column1),andalowerproba‐
bilityofafirmbeinganequityrepurchaserinthefollowingyear(column2).Bothresults
are statistically significant at the 1% level after clustering standard errors by firm. The
logitcoefficientof1.409incolumn1impliesthataonestandarddeviationincreaseinthe
change in institutionalholdings isassociatedwitha14.8%increase intheoddsofa firm
34
beinganequity issuer in the followingyear. In termsofprobabilities,nearly39%of the
firm‐yearobservationsincolumn1haveequityissuances.Thus,increasingtheoddsofeq‐
uityissuanceby14.8%translatestoa3.3%increaseintheprobabilityoftheaveragefirm
inoursamplebeinganetequityissuer.Similarly,thelogitcoefficientof‐0.368incolumn2
impliesthataonestandarddeviationincreaseinthechangeininstitutionalholdingleads
toa0.4%decreaseintheprobabilityoftheaveragefirminoursamplebeinganequityre‐
purchaserduring the followingyear. Our results complement thoseofChang,Chen, and
Dasgupta(2011),whoreportthatthelikelihoodofequityissuancesincreaseswiththeper‐
centageofsharesheldbyshort‐terminstitutionalinvestors.
Column3presentstheeffectofchangesininstitutionalholdingsontheprobability
ofafirmchangingitscapitalstructurebyissuingmoredebtthanequityinagivenyear(Net
Debt Issue =1). 40%of the firm‐years inour sample experienceanet change in capital
structure in thisdirection. Incolumn3wefindthatan increase in institutionalholdings
leadstoalowerprobabilityofafirmissuingmoredebtthanequity.Specifically,thecoeffi‐
cientof‐0.353suggeststhataonestandarddeviationincreaseinthechangeofinstitutional
holdingsleadstoa0.8%decreaseintheprobabilityoftheaveragefirmchangingitscapital
structurebyissuingdebtaboveandbeyonditsequityissuances.
For robustness,we redefine equity issuances using a 5% cutoff rather than a 1%
cutoff in order to exclude minor fluctuations due to stock option plans, conversion of
bonds,etc.Althoughtheseresultsarenotreported,wefindanevenlargereffectofinstitu‐
tional holdings on equity issuances and repurchases, and the effect remains statistically
significant.Inaddition,were‐estimatetheregressionsincolumns1through3usingtop‐5
institutionalholdingsrather thanaggregateholdings,andourresultsareunchanged. Fi‐
nally,we estimate an alternate specification inwhichwe define equity issuance using a
deal‐baseddummyvariableconstructedusingissuancedatafromSDC,andourresultsare
qualitativelyunchanged.
Next,weexaminetheeffectofchangesininstitutionalholdingsonthesizeofequity
andnetdebtissuances(columns4and5,respectively).Thereisnocolumnspecificallyfor
repurchasesbecausethedependentvariablesinthispanelaresimplynetchanges,andas
suchcanbepositiveornegative.)Column4complimentsourfindingsincolumn1,aswe
findthatchangesininstitutionalholdingsarerelatednotonlytotheprobabilityofanequi‐
35
tyissuance,butalsotothesizeofequityissuancesasapercentageoftotalassets.Thecoef‐
ficientof0.061issignificantatthe1%level.
Whilewetesttheeffectofchangesininstitutionalholdingsonchangesinleverage
inTableII(andfindanegativerelation),thetestsinTableIXaredifferent.Here,wefixthe
denominator(totalassets)andlookexplicitlyatchangesinequityandnetdebtoutstanding
ratherthanatthechangeintheleverageratio.Whereasbeforeourtestsallowedustosee
whetherornotfirmschangetheirleverageinresponsetochangesininstitutionalholdings,
thetestsinTableIXallowustoseehowfirmschangetheirleverage.Thus,incolumn5we
estimatetheimpactofachangeininstitutionalholdingsonthenetchangeincapitalstruc‐
ture(%ChangeDebt‐%ChangeEquity).Wefindinstitutionalholdingstobestronglyneg‐
atively related to ourmeasure of net debt issuance, indicating thatwhen firmsdecrease
theirleverageinresponsetoanincreaseininstitutionalholdings,theydosoprimarilyby
issuingequityratherthanbydecreasingdebt.
Wealsofindthatincreasesanddecreasesininstitutionalholdingshavesymmetric
effectsonequityandnetdebtissuances.Forexample,ourresultsindicatethatfirmsboth
decrease theirnetdebt issuancesafteran increase in institutionalholdings,and increase
their net debt issuances after a decrease in institutional holdings. (These results are not
tabulated).
Insum,inthissectionweshowthatchangesininstitutionalholdingshaveapositive
and significant effect on theprobability and size of subsequent equity issuances, both in
gross termsandnetofdebt issuances. However, inunreportedresults,wealsoexamine
whether changes indebtorequity issuances inyear t affect institutionalholding inyear
t+1.Wefindnoevidencethatinstitutionalholdingsareaffectedbychangesinequity,debt,
ornetdebt,reinforcingthedirectionofcausalityfrominstitutionstofirmsasfarascapital
structureisconcerned.
IV.Conclusion
In thispaperweanalyzewhether institutionalholdings influencecapitalstructure
decisions,andwhetherfirms’financialleverageaffectsinstitutionalinvestors’decisionsto
hold theirequity. Theoriesof capital structure imply that firmschoose their leverage in
responsetomarketfrictionssuchasagencycostsandasymmetricinformation.Meanwhile,
36
institutional monitoring and information‐gathering affect firms’ agency costs and infor‐
mation environment, opening channels through which institutions may influence firms’
choicesof leverage. Intheagencyframework,institutionalinvestorsserveasanexternal
disciplinary mechanism for management, lessening the need for internal disciplinary
mechanismssuchasdebt. Intheasymmetric informationframework, institutional inves‐
tors decrease information asymmetry and reduce the adverse selection costs of equity,
servingtodecreasetheamountofdebtneededforsignalingequilibriums,andtolowerthe
costofequityrelativetodebt.
Wefindanegativerelationshipbetweenleverageandinstitutionalholdings.Thatis,
firmswithalargepercentageoftheirsharesheldbyinstitutions,onaverage,haverelative‐
ly low leverage ratios. Wealso find that changes in institutionalholdingsareassociated
withchangesinleverageintheoppositedirection(andviceversa).Toestablishcausality,
weusethreetechniquestoadjustfortheendogenouscomponentsoftherelationshipbe‐
tweeninstitutionalholdingsandleverage.InanArellano‐Bonddynamicpanelestimation
thatutilizesinternalinstruments(laggedlevels)forbothchangesinleverageandchanges
inholdings,wefindthatanincreaseininstitutionalholdingsleadstoadecreaseinlever‐
age,butthatachangeinleveragedoesnotleadtoachangeinholdings.Inacomplemen‐
tarytestusingtwo‐stageleastsquaresandimpliedtradesinducedbymutualfundoutflows
asaninstrumentforinstitutionalholdings(Edmansetal.(2011)),weisolatetheeffectof
institutionalholdingsonleverageratiosandagainfindasignificantlynegativerelationship.
Finally,weconfirmthenegativeeffectofholdingsonleverageusingthepositiveshockto
institutional holdings that results from S&P 500 Index inclusion as treatment in a semi‐
naturalexperiment.Whilethetechniquesaredifferent,andthesamplesizesvarydramati‐
callyacrossexperiments,theresultsconsistentlysuggestthatinstitutionalholdingsnega‐
tivelyaffectfirms’financialleverage,butthatfirms’leveragedoesnotunequivocallyaffect
institutionalholdings.
Withteststhatallowustocharacterizehowfirmslowertheirleverageinresponse
tochangesininstitutionalinvestment,wefindthatasinstitutionalholdingsincrease,firms
becomemore likely to issue equity,while the probability of debt issuance is unchanged.
Furthermore,wefindthatthesizeofequityissuancesarerelatedtochangesininstitution‐
alholdings,both ingross termsandnetofdebt issuances,suggesting that inresponseto
37
changesininstitutionalholdings,firmsaltertheircapitalstructurepredominantlythrough
equityissuances.
Ourresultsarestronglysupportiveofcapitalstructuretheoriesthatpredictasub‐
stitutiverelationshipbetweenleverageandinstitutionalholdings. Usingteststhatdiffer‐
entiatebetween firmsprone toagencycostsand firmsprone toasymmetric information
problems,weprovideevidence that institutionsmoststrongly influencecapital structure
through their effect on information asymmetry. Furthermore, the preferential tax treat‐
mentofinstitutionalinvestorsasequityholdersalsosuggestsanincreaseinequityrelative
todebtasinstitutionalholdingsincrease.However,wefindnoevidencethattaxconsidera‐
tionsplayaroleinthisrelationship.
Finally,ourresultssuggestthatsignificantlydifferentmotivesunderlietheinterac‐
tionbetweeninstitutionsanddividends(GrinsteinandMichaely(2005))andthe interac‐
tionbetweeninstitutionsandleverage(thispaper). GrinsteinandMichaely(2005)show
thatinstitutionsdonotaffectfirms’payoutpolicies,butthatchangesinpayoutpoliciesaf‐
fectinstitutionalholdings.Moreover,theyshowthattheinteractionismostpronouncedin
largefirmsandfirmsthatarecashcows,consistentwiththemonitoringroleofinstitutions.
We,ontheotherhand,findthatinstitutionsdoaffectleverage,butthatchangesinleverage
donotunambiguouslyaffectinstitutionalholdings.Furthermore,wefindthattheinterac‐
tionismostpronouncedinsmall,growthfirms,inlinewiththenotionthatinstitutionalin‐
vestorsreduceasymmetric information, inturninducingfirmstoreduceleverage. These
contrastingfindingsmay,perhaps,pointnotonlytodifferentinteractionsbetweeninstitu‐
tionalholdingsandvariousfinancialpolicies,butalsotodifferingrolesplayedbydividends
and leverage inresolvingasymmetric informationandagencyconflictsbetweenmanage‐
mentandoutsideequityholders.
38
Appendix
A.VariableDefinitions
Leverage(TDA)istheratiooftotaldebt(DLTT+DLC)toassets(AT)
Leverageincludingleases(TDLA)istheratiototaldebt(DLTT+DLC)plusthepresentvalue
of off balance sheet (OBS) non‐cancelable operating leases (Oplease) to assets
(AT+Oplease).Opleaseiscalculatedas:
RentExp0 1
5
1
,
whereRentExp0 is thecurrentyearrentalexpense(XRENT),MLPt is theminimum
leasepaymentsforyearst=1,…,5(MRC1,MRC2,MRC3,MRC4,MRC5),andKdisthe
costofdebtcapital(setto10%followingGraham,Lemmon,andSchallhem(1998)).
Institutional holdings (Hold) is the ratio of total shares held by institutions to the total
numberofsharesoutstanding(CSHO)
Institutionalholdings,top‐5(Hold5)istheratioofthetotalsharesheldbytheinstitutional
investorswith the five largestpositions to the totalnumberofsharesoutstanding
(CSHO)
Logofassets(Assets)isthenaturallogarithmofassets(AT)
Changeinlogassets(ChgAssets)istheannualchangeinlogassets(AT)
Marketcap (MktCap) is theprice (PRCC_F) times the totalnumberof sharesoutstanding
(CSHO)
Logofsales(Sales)isthenaturallogarithmofsales(SALE)
Profitability(Profit)istheratioofoperatingincomebeforedepreciation(OIBDP)toassets
(AT)
Market‐to‐bookratio (MktBk) is the ratioofmarketvalueof assets (MVA) tobookassets
(AT). MVA isthesumofthemarketvalueofequity(PRCC_F*CSHPRI)plusdebtin
currentliabilities(DLC)pluslong‐termdebt(DLTT)pluspreferred‐liquidationval‐
ue(PSTKL)minusdeferredtaxesandinvestmenttaxcredit(TXDITC).
Capitalexpenditure(Capex)istheratioofcapitalexpenditure(CAPX)toassets(AT)
Tangibility(Tang)istheratioofnetproperty,plant,andequipment(PPENT)toassets(AT)
Intangibleassets(Intang)istheratioofintangibles(INTAN)toassets(AT)
39
Collateral(Colltrl)istheratioofinventory(INVT)plusnetproperty,plant,andequipment
(PPENT)toassets(AT)
Dividendpayingdummy(Dividend)isadummyvariabletakingthevalueofoneifcommon
dividends(DVC)ispositive,andzerootherwise
Payout (Payout) is the ratio of commondividends (DVC)plus purchases of common and
preferredstock(PRSTKC)toassets(AT)
Lossmakingdummy(Losses)isadummyvariabletakingthevalueofoneifProfit isnega‐
tive,andzerootherwise
Uniquenessdummy(Unique)isadummyvariabletakingthevalueofoneiftheSICcodeof
thefirmisbetween3400and4000,andzerootherwise
Cashcow(CashCow)isadummyvariabletakingthevalueofoneifafirmisprofitable(Prof‐
it>0),hasa credit ratingofAorbetter (SPLTICRMbetween2and8), andaP/E
lowerthanthemedianP/EamongprofitablefirmswithacreditratingofAorhigh‐
er,andzerootherwise
Medianindustryleverage(Industry_TDA)isthemedianratiooftotaldebttoassetsbyindus‐
trybyyear,withthe49industriesdefinedasperFamaandFrench
Initialfirmleverage(Initial_TDA)isthefirstnon‐missingvalueofLeverage
Insiderholdings(Hold_Insider) is thetotalpercentageofsharesownedby insiders,asper
Execucomp
Amihud’sIlliquidityisthemonthlyratioofabsolutestockreturntoitsdollarvolume,aver‐
agedoverthepriortwelvemonths
“EFWA”Market‐to‐BookisdefinedasperBakerandWurgler(2002)as:
∑∗ ,
wheresummationsbeginateithertheIPOyearorthefirstyearofCompustatdata,
andeandddenotenetequity(SSTK‐PRSTKC)andnetdebt(DLTIS‐DLTR+DLCCH)
issues
Equity issuance (%ChangeEquity) is the ratioof thesplit‐adjustedchange in sharesout‐
standing (CSHOt – CSHOt‐1*(AJEXt‐1/AJEXt)) times the split‐adjusted average stock
price(PRCC_Ft+PRCC_Ft‐1*(AJEXt/AJEXt‐1))toendofyeart‐1assets(AT)
40
Equityissuer(EquityIssue)isadummyvariabletakingthevalueofoneif%ChangeEquity>
1.0%,andzerootherwise
EquityRepurchaser(EquityRepo)isadummyvariabletakingthevalueofoneif%Change
Equity<‐1.0%,andzerootherwise
Debtissuance(%ChangeDebt)isratioofthechangeintotaldebt(DLTT+DLC)fromyeart‐
1toyearttoendofyeart‐1assets(AT)
Netdebtissuance(%ChangeNetDebt)isthe%ChangeDebt‐%ChangeEquity
Netdebtissuer(NetDebtIssue)isadummyvariabletakingthevalueofoneif%ChangeNet
Debt>0,andzerootherwise
Beta(Beta)istheannualCAPMbetaofeachfirm,asreportedinCRSP
Pastabnormalreturn(AdjRet)iseachfirm’sannualreturnadjustedbytheCAPMreturnus‐
ingthefirm’sbeta,the10‐yearTreasurybondyield,andtherealizedreturnonthe
S&P500index
41
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45
TableISummaryStatistics
This table reports simplemeans foranumberofcorporate finance. MarketCap isdefinedas InstitutionalHoldings(Hold)isdefinedastotalinstitutionalholdingsrelativetototalsharesoutstanding.Similarly,Top5institutionalholdingsisthepercentoftotalsharesoutstandingheldbythefivelargestinstitutionalinvestors.Leverage(TDA)isdefinedastotaldebtscaledbybookassets.Log(Assets)(Assets)isthenaturallogarithmoftotalassets.MarkettoBook(Mkt/Bk)isdefinedasthemarketvalueofassetsscaledbythebookvalueofas‐sets. Profitability (Profit) is defined as operating income before depreciation scaled by book assets.Log(Sales) (Sales) is thenatural logarithmof total sales. CapitalExpenditures (Capex) isdefinedascapitalexpenditurescaledbybookassets. IntangibleAssets(Intang)isdefinedasintangibleassetsscaledbybookassets. Tangibility (Tang) is defined as the tangible assets (net PP&E) scaled by book assets. Collateral(Colltrl)isdefinedastotalcollateral(thesumofinventoryandnetproperty,plant,andequipment)scaledbybookassets.ThesampleconsistsofallU.S.incorporatedfirms,excludingfinancialfirmsandutilities,withapositive market cap, positive assets, and non‐missing total debt. Accounting data is obtained from theCompustat‐CRSPmergeddatabase,andinstitutionalholdingsdataisobtainedfromThomson‐Reutersvia13FSECfilings.
FullSample SubsamplesVariable 1979to2009 1979to1994 1995to2009MarketCap($MM) 1,757.99 872.47 2,654.70InstitutionalHoldings 0.29 0.19 0.40InstitutionalHoldings,Top5 0.15 0.11 0.20Leverage 0.24 0.25 0.22Log(Assets) 5.03 4.68 5.38MarkettoBook 1.72 1.57 1.89Profitability 0.04 0.07 0.02Log(Sales) 4.95 4.70 5.20CapitalExpenditures 0.07 0.08 0.06IntangibleAssets 0.09 0.05 0.13Tangibility 0.29 0.32 0.25Collateral 0.44 0.51 0.38FirmYears 130,202 65,510 64,692
46
TableIITheEffectofInstitutionalHoldingsonLeverage
Thistablereportsestimatesfrompanelregressionsof leverageoninstitutionalholdings.Thedependentvar‐iablesaremeasuredattheendoffiscalyeart,andtheindependentvariables,describedinTableIanddefinedintheAppendix,aremeasuredat(t‐1). TDLA isdefinedastotaldebt includingoff‐balance‐sheetoperatingleases scaled by (total assets plus off‐balance‐sheet operating leases). Cap1‐Cap5 are annualmarket capquintiledummyvariablescorrespondingtothesmallestandlargestfirms,ascending.Industry_TDAisdefinedas theannualmedian leverageperFama‐French industrygroup. Initial_TDA isdefinedaseach firm’s firstnon‐missingleverageratio. Dividend isadummyvariableequaltooneifa firmpaysadividendandzerootherwise.Allspecificationscontainanintercept,andthefollowingindependentvariables,omittedforbrevi‐tyanddefinedintheAppendix:Intang,Payout,Unique,Losses,andChgAssets. Columns3and6alsocontaindummyvariablesCap1–Cap4.Column4includesannualstockreturnadjustedforthepredictedCAPMre‐turn. Column5 includes insider holdings (fromExecuComp) scaled by total shares outstanding,with andwithoutafixedeffect,respectively,andreflectdataforS&P1500firmsfrom1992to2009.Incolumn6,Ana‐lystCoverageiscalculatedusingdatafromtheI/B/E/SHistoricalSummaryFileasthemaximumnumberofanalystswhomakeannualearningsforecastsinanymonthduringthetwelvemonthspriortofiscalyearend,whileassumingthatfirmsnotcoveredbyI/B/E/Shavenoanalystcoverage.Incolumn7independentvaria‐blesarenormalizedby(assets+OBSoperatingleases)ratherthan(assets),asourmeasurefor leverageinthiscolumn,TDLA,includesOBSoperatingleases.ThedataconsistofallU.S.incorporatedfirmswithCRSP,Compustat,andinstitutionalholdingsdatabetween1979and2009(withtheexceptionofcolumns4and5),excludingfinancialfirmsandutilities.InstitutionalholdingsdataisobtainedfromThomson‐Reutersvia13FSEC filings. ***, **, and * represent statistical significanceat the1%,5%,and10% level, respectively, andstandarderrors,reportedinparentheses,areclusteredatthefirmlevel.
47
TableII–Continued
(1) (2) (3) (4) (5) (6) (7)TDA (t ) TDA (t ) TDA (t ) TDA (t ) TDA (t ) TDA (t ) TDLA (t )
Hold (t ‐1) ‐0.100*** ‐0.068*** ‐0.070*** ‐0.044*** ‐0.057***(0.0069) (0.0080) (0.0085) (0.0158) (0.0079)
HoldxCap1 (t ‐1) ‐0.108*** ‐0.085***(0.0184) (0.0191)
HoldxCap2 (t ‐1) ‐0.082*** ‐0.073***(0.0120) (0.0123)
HoldxCap3 (t ‐1) ‐0.060*** ‐0.050***(0.0099) (0.0099)
HoldxCap4 (t ‐1) ‐0.040*** ‐0.035***(0.0094) (0.0097)
HoldxCap5 (t ‐1) ‐0.020* ‐0.019*(0.0105) (0.0106)
AdjustedRet. (t ‐1) ‐0.012***(0.0014)
InsiderHold. (t ‐1) 0.009(0.0378)
AnalystCov. (t ‐1) ‐0.003***(0.0004)
Assets (t ‐1) 0.022*** 0.031*** 0.053*** 0.030*** 0.020*** 0.037*** 0.039***(0.0011) (0.0027) (0.0030) (0.0029) (0.0065) (0.0028) (0.0031)
Profit (t ‐1) ‐0.138*** ‐0.120*** ‐0.103*** ‐0.109*** ‐0.203*** ‐0.123*** ‐0.106***(0.0079) (0.0091) (0.0091) (0.0101) (0.0465) (0.0091) (0.0113)
Mkt/Bk(t ‐1) ‐0.001 0.001 0.009*** 0.003** 0.000 0.001 0.007***(0.0008) (0.0009) (0.0009) (0.0011) (0.0032) (0.0009) (0.0012)
Industry_TDA (t ‐1) 0.282*** 0.315*** 0.286*** 0.304*** 0.288*** 0.311*** 0.226***(0.0189) (0.0336) (0.0325) (0.0358) (0.0652) (0.0334) (0.0356)
Initial_TDA (t ‐1) 0.346***(0.0101)
Dividend (t ‐1) ‐0.060*** ‐0.024*** ‐0.018*** ‐0.023*** ‐0.012 ‐0.023*** ‐0.013***(0.0034) (0.0038) (0.0037) (0.0041) (0.0080) (0.0038) (0.0039)
Capex (t ‐1) 0.035* 0.021 0.062*** 0.022 0.028 0.022 ‐0.010(0.0177) (0.0154) (0.0152) (0.0170) (0.0481) (0.0153) (0.0202)
Tang (t ‐1) 0.065*** 0.094*** 0.079*** 0.091*** ‐0.004 0.099*** 0.295***(0.0119) (0.0225) (0.0219) (0.0245) (0.0773) (0.0224) (0.0196)
Observations 88,444 88,444 88,444 77,250 15,795 88,444 66,285FirmFixedEffect No Yes Yes Yes Yes Yes YesYearFixedEffect Yes Yes Yes Yes Yes Yes YesAdj.R ‐squared 0.323 0.639 0.646 0.648 0.699 0.640 0.690
Leverageinlevels
48
TableIIITheEffectofChangesinInstitutionalHoldingsonChangesinLeverage
Thistablereportsestimatesfrompanelregressionsof changes inleverageonchangesininstitutionalhold‐ings.Thefirst‐differenceddependentvariablesarecalculatedasYt‐Yt‐1,andthefirst‐differencedindepend‐entvariables,describedinTableIanddefinedintheAppendix,arecalculatedasXt‐1‐Xt‐2.Incolumn2,inde‐pendentvariablesarenormalizedby(assets+operatingleases)ratherthan(assets),asthemeasureoflever‐ageusedincludesleases. ThedataconsistofallU.S. incorporatedfirmswithCRSP,Compustat,andinstitu‐tionalholdingsdatabetween1979and2009, excluding financial firmsandutilities. InstitutionalholdingsdataisobtainedfromThomson‐Reutersvia13FSECfilings.***,**,and*representstatisticalsignificanceatthe1%,5%,and10%level,respectively,andstandarderrors,reportedinparentheses,areclusteredatthefirmlevel.
(1) (2)ΔTDA (t ) ΔTDLA (t )
ΔHold (t ‐1) ‐0.014*** ‐0.022***(0.0047) (0.0047)
ΔTDA (t ‐1) ‐0.153***(0.0070)
ΔTDLA (t ‐1) ‐0.143***(0.0079)
ΔAssets (t ‐1) 0.026*** 0.027***(0.0024) (0.0025)
ΔProfit (t ‐1) ‐0.045*** ‐0.039***(0.0064) (0.0083)
ΔMkt/Bk(t ‐1) ‐0.002*** ‐0.004***(0.0006) (0.0007)
ΔIndustry_TDA (t ‐1) 0.065*** 0.069***(0.0204) (0.0229)
ΔDividend (t ‐1) 0.002 0.003(0.0021) (0.0022)
ΔCapex (t ‐1) 0.059*** 0.036**(0.0111) (0.0149)
ΔTang (t ‐1) 0.020 0.051***(0.0172) (0.0165)
ΔColltrl (t ‐1) 0.058*** 0.045***(0.0133) (0.0134)
ΔIntang (t ‐1) 0.021** 0.008(0.0102) (0.0104)
ΔPayout (t ‐1) 0.033*** 0.039***(0.0103) (0.0113)
Observations 72,208 52,127YearFixedEffect Yes YesAdj.R ‐squared 0.033 0.036
49
TableIVTheEffectofLeverageonInstitutionalHoldings–LevelsandDifferences
Thistablereportsestimatesfrompanelregressionsofchangesininstitutionalholdingsonchangesinlever‐age.InPanelA,thedependentvariablesaremeasuredattheendoffiscalyeart,andtheindependentvaria‐blesaremeasuredat(t‐1). InPanelB,thefirst‐differenceddependentvariableiscalculatedasYt‐Yt‐1,andthefirst‐differencedindependentvariablesarecalculatedasXt‐1‐Xt‐2.Payoutisdefinedasannualdividendsplus repurchases scaledbybookassets. AdjustedRet. is definedasa firms’ annual returnadjustedby theCAPMreturn. Beta isa firm’sannualCAPMbeta. TDA,TDLA,Sales,Dividend,andMkt/Bkaredescribed inTable I, and all variables are defined in theAppendix. The data consist of all U.S. incorporated firmswithCRSP,Compustat,andinstitutionalholdingsdatabetween1979and2009,excludingfinancialfirmsandutili‐ties. InstitutionalholdingsdataisobtainedfromThomson‐Reutersvia13FSECfilings. ***,**,and*repre‐sentstatisticalsignificanceatthe1%,5%,and10%level,respectively,andstandarderrors,reportedinpa‐rentheses,areclusteredatthefirmlevel.
(1) (2) (1)Hold (t ) Hold (t ) ΔHold (t )
TDA (t ‐1) ‐0.123*** ‐0.087*** ΔTDA (t ‐1) ‐0.037***(0.0080) (0.0079) (0.0042)
ΔHold (t ‐1) ‐0.125***(0.0071)
Mkt/Bk(t ‐1) 0.016*** 0.011*** ΔMkt/Bk(t ‐1) 0.003***(0.0010) (0.0008) (0.0004)
Sales (t ‐1) 0.067*** 0.039*** ΔSales (t ‐1) 0.007***(0.0011) (0.0019) (0.0009)
Dividend (t ‐1) 0.010** 0.011** ΔDividend (t ‐1) 0.004*(0.0047) (0.0047) (0.0021)
Payout (t ‐1) 0.192*** 0.049** ΔPayout (t ‐1) ‐0.005(0.0361) (0.0217) (0.0096)
Beta (t ‐1) 0.049*** 0.013*** ΔBeta (t ‐1) ‐0.001(0.0020) (0.0013) (0.0006)
AdjustedRet. (t ‐1) 0.006*** 0.002*** ΔAdjustedRet (t ‐1) 0.001***(0.0004) (0.0003) (0.0001)
Observations 84,764 84,764 Observations 71,093FirmFixedEffect No Yes FirmFixedEffect NoYearFixedEffect Yes Yes YearFixedEffect YesAdj.R ‐squared 0.521 0.816 Adj.R ‐squared 0.036
PanelA‐Inst.HoldingsinLevels PanelB‐Inst.HoldingsinDifferences
50
TableVArellano‐Bond–InstitutionalHoldingsandLeverage
ThistablereportsestimatesfromArellano‐Bondautoregressions ofleverageoninstitutionalholdings(PanelA),andinstitutionalholdingsonleverage(PanelB).Thedependentvariablesaremeasuredattheendofyeart,andtheindependentvariables,describedinTablesIandIVanddefinedintheAppendix,aremeasuredasoftime(t‐1).InPanelA,wealsocontrolforColltrl,Intang,Payout,Unique,andLosses.Inbothpanels,internalinstrumentsbeginwithlag3. Accordingly,theArellano‐BondAR(3)test ispresentedinfirstdifferencestoruleout secondorder serial correlationbetween the first‐differencederror termat time t and the level attimet‐3.Thenullhypothesisisnoserialcorrelation.ThedataconsistofallU.S.incorporatedfirmswithCRSP,Compustat,andinstitutionalholdingsdataforaminimumoffourconsecutiveyearsbetween1979and2009,excludingfinancialfirmsandutilities.InstitutionalholdingsdataisobtainedfromThomson‐Reutersvia13FSEC filings. ***, **, and * represent statistical significanceat the1%,5%,and10% level, respectively, andstandarderrors,reportedinparentheses,arerobusttoanypatternofheteroskedasticityandautocorrelationwithinpanels.
PanelA‐LeverageRegression PanelB‐InstitutionalHoldingsRegression(1) (2) (1) (2)
TDA (t ) TDA (t ) Hold (t ) Hold5 (t )
Hold (t ‐1) ‐0.148*** Hold (t ‐1) 0.924***(0.0260) (0.0335)
Hold5 (t ‐1) ‐0.183*** Hold5 (t ‐1) 0.872***(0.0363) (0.0221)
TDA (t ‐1) 0.635*** 0.650*** TDA (t ‐1) 0.012 0.003(0.0222) (0.0258) (0.0174) (0.0107)
Assets (t ‐1) 0.021*** 0.015*** Mkt/Bk(t ‐1) ‐0.007*** 0.000(0.0027) (0.0018) (0.0008) (0.0004)
Profit (t ‐1) ‐0.031*** 0.055 Sales (t ‐1) ‐0.011*** 0.000(0.0072) (0.0580) (0.0013) (0.0008)
Mkt/Bk(t ‐1) 0.001 0.000 Dividend (t ‐1) ‐0.005 0.001(0.0007) (0.0009) (0.0031) (0.0020)
Industry_TDA (t ‐1) 0.068*** 0.072*** Payout (t ‐1) ‐0.020 ‐0.031***(0.0190) (0.0187) (0.0175) (0.0104)
Dividend (t ‐1) 0.004* 0.004 Beta (t ‐1) ‐0.006*** ‐0.001(0.0022) (0.0076) (0.0010) (0.0006)
Capex (t ‐1) 0.127*** 0.117*** AdjustedRet. (t ‐1) 0.000* 0.000(0.0114) (0.0119) (0.0002) (0.0002)
Tang (t ‐1) ‐0.008 ‐0.012(0.0134) (0.0135)
Observations 71,493 71,493 Observations 71,096 71,093Numberoffirms 9,244 9,244 Numberoffirms 9,396 9,395AR(3)Pr>z 0.844 0.985 AR(3)Pr>z 0.603 0.486HansenJ ‐statistic 75.16 52.28 HansenJ ‐statistic 16.24 9.239Hansenp‐value 0.030 0.463 Hansenp‐value 0.181 0.682
51
TableVIInstrumentalVariables–TheEffectofInstitutionalHoldingsonLeverage
Thistablereportsestimatesfromlineartwo‐stageleastsquarespanelregressionsofleverageoninstitutionalholdings.Thedependentvariable,TDA,ismeasuredattheendoffiscalyeart,andtheindependentvariables,describedinTableIandIIanddefinedintheAppendix,aremeasuredasoftime(t–1). TheinstrumentforHold incolumn1,MFFlow, isthehypotheticalannualchangeinmutualfundholdingsimpliedbypreviouslydisclosedholdingsandquarterlymutualfundoutflows≥5%oftotalfundassets.Allspecificationscontainanintercept, and the following independent variables omitted for brevity:Unique,Losses andChgAssets. Thedata consist of all U.S. incorporated firmswith CRSP, Compustat, and institutional holdings data between1980and2007(duetotheavailabilityofMFFlowdata),excludingfinancialfirmsandutilities. InstitutionalholdingsdataisobtainedfromThomson‐Reutersvia13FSECfilings,whileMFFlowdataisobtainedfromAlexEdmans’swebsite.***,**,and*representstatisticalsignificanceatthe1%,5%,and10%level,respectively,andstandarderrors,reportedinparentheses,areclusteredatthefirmlevel.
(1)2SLS:TDA (t )
Instrument MFFlow
Hold (t ‐1) ‐0.292***(0.0378)
Assets (t ‐1) 0.037***(0.0033)
Profit (t ‐1) ‐0.123***(0.0086)
Mkt/Bk(t ‐1) 0.002(0.0010)
Industry_TDA (t ‐1) 0.258***(0.0204)
Initial_TDA (t ‐1) 0.332***(0.0106)
Dividend (t ‐1) ‐0.061***(0.0036)
Capex (t ‐1) 0.061***(0.0192)
Tang (t ‐1) 0.051***(0.0125)
Colltrl (t ‐1) 0.158***(0.0104)
Intang (t ‐1) 0.301***(0.0121)
Payout (t ‐1) ‐0.017(0.0268)
FirstStageAdj.R ‐squared 0.548FirstStageF‐Statistic 354.63Observations 80,611YearFixedEffect YesAdj.R ‐squared 0.291
52
TableVIIS&P500Inclusion–TheEffectofaShocktoInstitutional HoldingsonLeverage
This table reports leverage difference‐in‐difference tests surrounding addition to the S&P500 Index. ThetreatmentgroupconsistsofasampleofU.S.incorporatedfirmsthatwereaddedtotheS&P500betweenthethirdquarterof1979and2008,whilethecontrolgroupconsistsofapropensityscorematchedsampleofU.S.incorporatedfirms. Financial firmsandutilitiesareexcluded. PanelApresentssummarystatistics forthetwogroupsof firms in thequarterbefore indexaddition. InPanelBweconductadifference‐in‐differencepairedt‐testcomparingthemeandifferenceinleverage(TDA)inthequarterbeforeadditiontothemeandif‐ference four quarters after addition. Panel C reports estimates from a panel regression of leverage on atreatment/controldummyvariable (Treatment=1 if in the treatmentgroup,0otherwise),aPre/PostS&P500additiondummyvariable (Post =1 if after addition,0otherwise), their interactionTreatment*Post, aswellasothercontrolvariables.Thedependentvariable,TDA,ismeasuredattheendofquartert.TomitigateconcernsovermarkettimingwecontrolforlaggedandcontemporaneousCAPM‐adjustedreturn,aswellaslaggedandcontemporaneousilliquidity(Amihud(2002)).Furthermore,wecontrolfortheindependentvar‐iablesusedinTableIIcolumn1.Thesevariables,describedinTableIanddefinedintheAppendix,aremeas‐uredasofquarter(t–1).ToisolatetheeffectoftheshockininstitutionalholdingsstemmingfromS&P500inclusion,werestrictoursampleto9quartersperfirm:theperiod4quartersbeforeinclusionto4quartersafterinclusion.InstitutionalholdingsdataisobtainedfromThomson‐Reutersvia13FSECfilings.***,**,and*representstatisticalsignificanceatthe1%,5%,and10%level,respectively,andstandarderrors,reportedinparentheses,areclusteredatthefirmlevel.
PanelA–SummaryStatsinQuarter(t‐1) PanelC– Difference‐in‐DifferenceRegression
Treatment ControlMarketCap 5,962.6 5,657.9Ln(Assets) 7.3 7.2Mkt/Bk 2.9 2.9Inst.Hold 44.4% 39.4%Leverage 19.4% 17.7%CAR(‐1,0) 1.9% 2.6%Observations 415 415
(1)
TDA (t )
Treatment*Post ‐0.015**(0.0075)
PostDummy 0.006(0.0058)
TreatmentDummy 0.010(0.0091)
Assets (t ‐1) 0.036***(0.0042)
Profit (t ‐1) ‐0.677***(0.1211)
Mkt/Bk(t ‐1) 0.002(0.0022)
Industry_TDA (t ‐1) 0.379***(0.0599)
Initial_TDA (t ‐1) 0.205***(0.0287)
Dividend (t ‐1) ‐0.011(0.0115)
Tang (t ‐1) 0.114***(0.0363)
Observations 5,980Year/QtrFixedEffect YesAdj.R ‐squared 0.387
PanelB–Difference‐in‐Difference
pairedt‐test
(TDA treat‐TDA cont)(t ‐1) 0.017(TDA treat‐TDA cont)(t +4) 0.002(Diff(t +4)‐Diff(t ‐1)) ‐0.015
t ‐statistic ‐2.061p‐value 0.040
Quarter(t ‐1)vs.Quarter(t +4)
53
TableVIIITheEffectofInstitutionalHoldingsonLeverage– TestsofUnderlyingFrictions
Thistablereportsestimatesfrompanelregressionsof leverageon institutionalholdings. Thedependentvariable,TDA, ismeasuredattheendoffiscalyeart,andtheindependentvariables,describedinTableIanddefinedintheAppendix,aremeasuredasoftime(t–1). InPanelA,wetestwhetheragencycostsorasymmetricinformationisbehindtherelationshipbetweeninstitutionalholdingsandleverage.Asymmet‐ricInformation(Agency)firmsaresmall(large)firmswithmany(few)growthopportunities.Columns1and2containthefollowingindependentvariables,omittedforbrevity:Intang,Payout,Unique,Losses,andChgAssets.ThedataconsistofallU.S.incorporatedfirmswithCRSP,Compustat,andinstitutionalholdingsdata between1979 and2009, excluding financial firms andutilities,with the additional requirement incolumns3and4ofatleastfourconsecutiveyearsofnon‐missingdata. InPanelB,weusedatabetween2000and2006,excluding2003,totesttheimpactofinstitutionalholdingsonleverageconditionalonthesize of the tax advantage enjoyed by institutions over individuals. The new variablesHold_00‐02 andHold_04‐06reflectthis.TheFtestteststheequalityofHold_00‐02andHold_04‐06,withthenullhypothesisbeingthattheyareequal.Thefollowingindependentvariablesareincludedbutomittedforbrevity:Intang,Payout,Unique,Losses, andChgAssets. Institutionalholdingsdata isobtained fromThomson‐Reutersvia13FSECfilings.***,**,and*representstatisticalsignificanceatthe1%,5%,and10%level,respectively,andstandarderrors,reportedinparentheses,areclusteredatthefirmlevel.
(1) (2) (3) (4) (1)Asym.Info.:TDA (t )
Agency:TDA (t )
Asym.Info.:TDA (t )
Agency:TDA (t ) TDA (t )
Hold (t ‐1) ‐0.147*** ‐0.050*** ‐0.220*** ‐0.026 Hold_00‐02 (t ‐1) ‐0.077***(0.0526) (0.0158) (0.0787) (0.0189) (0.0113)
TDA (t ‐1) 0.592*** 0.639*** Hold_04‐06 (t ‐1) ‐0.086***(0.0372) (0.0310) (0.0121)
Assets (t ‐1) 0.022* 0.037*** 0.033*** 0.005* Assets (t ‐1) 0.022***(0.0119) (0.0076) (0.0058) (0.0029) (0.0019)
Profit (t ‐1) ‐0.089*** ‐0.297*** ‐0.058*** ‐0.029 Profit (t ‐1) ‐0.150***(0.0190) (0.0476) (0.0087) (0.0222) (0.0164)
Mkt/Bk(t ‐1) 0.003 0.015* 0.000 ‐0.002 Mkt/Bk(t ‐1) 0.004***(0.0020) (0.0077) (0.0011) (0.0023) (0.0014)
Industry_TDA (t ‐1) 0.333** 0.293*** 0.097 0.062** Industry_TDA (t ‐1) 0.246***(0.1561) (0.0643) (0.0693) (0.0292) (0.0291)
Dividend (t ‐1) ‐0.029 0.002 ‐0.004 0.010*** Dividend (t ‐1) ‐0.048***(0.0234) (0.0076) (0.0101) (0.0040) (0.0057)
Capex (t ‐1) 0.030 0.048 0.055* 0.146*** Capex (t ‐1) ‐0.190***(0.0554) (0.0394) (0.0335) (0.0230) (0.0456)
Tang (t ‐1) 0.130* 0.092 ‐0.007 ‐0.049** Tang (t ‐1) 0.171***(0.0664) (0.0562) (0.0356) (0.0226) (0.0213)
Colltrl (t ‐1) 0.129*** 0.038 0.043 0.040** Colltrl (t ‐1) 0.139***(0.0486) (0.0513) (0.0275) (0.0186) (0.0174)
Observations 10,120 11,206 9,814 12,368 Observations 17,312FirmFixedEffect Yes Yes Yes Yes FirmFixedEffect NoYearFixedEffect Yes Yes Yes Yes YearFixedEffect YesAdj.R ‐squared 0.598 0.737 Pr>F 0.401HansenJ ‐statistic 62.04 57.79 Adj.R ‐squared 0.331Hansenp‐value 0.270 0.409
PanelA‐AsymmetricInformationorAgency PanelB‐TaxesLeverageinlevels(cols1‐2),anddifferenceGMM(cols3‐4) Leverageinlevels
54
TableIXTheEffectofChangesinInstitutionalHoldingsonEquityandDebtIssuances
Thistablepresentsestimatesfromlogitandlinearregressionsofequityissuancesonlaggedchangesininsti‐tutionalholdings.EquityIssue(EquityRepo)isadummy=1ifthechangeinsplit‐adjustedsharesoutstandingnormalizedbytotalassets,%ChangeEquity,>1%(<‐1%),and0otherwise.%ChangeDebtisthechangeindebtnormalizedbytotalassets,whileNetDebtIssue=1if%ChangeDebt–%ChangeEquity>0,and0oth‐erwise.Allspecificationscontainanintercept.Thecontrolvariables,datasources,andsamplearedescribedinTablesIandIV.***,**,and*representstatisticalsignificanceatthe1%,5%,and10%level,respectively,andstandarderrors,reportedinparentheses,areclusteredatthefirmlevel.
(1) (2) (3) (4) (5)Equity
Issue (t )=1Equity
Repo (t )=1NetDebtIssue (t )=1
%ChangeEquity (t )
%ChangeNetDebt (t )
ΔHold (t ‐1) 1.409*** ‐0.368*** ‐0.353*** 0.061*** ‐0.028**(0.0947) (0.1173) (0.0906) (0.0097) (0.0129)
ΔTDA (t ‐1) ‐0.265*** ‐0.960*** ‐1.048*** 0.095*** ‐0.318***(0.0917) (0.0959) (0.0808) (0.0177) (0.0207)
ΔAssets (t ‐1) 0.777*** ‐0.122*** 0.368*** 0.006 0.066***(0.0417) (0.0387) (0.0331) (0.0066) (0.0081)
ΔProfit (t ‐1) ‐0.055 0.073 ‐0.114* ‐0.061*** 0.035*(0.0756) (0.0657) (0.0640) (0.0167) (0.0190)
ΔMkt/Bk(t ‐1) 0.128*** ‐0.031*** ‐0.013* 0.031*** ‐0.027***(0.0091) (0.0087) (0.0074) (0.0021) (0.0024)
ΔIndustry_TDA (t ‐1) ‐2.033*** 0.594 1.610*** ‐0.097** 0.181***(0.3525) (0.4901) (0.3623) (0.0391) (0.0496)
ΔDividend (t ‐1) 0.056 0.237*** 0.210*** 0.001 0.016***(0.0416) (0.0613) (0.0458) (0.0029) (0.0046)
ΔCapex (t ‐1) ‐0.030 0.511*** 1.509*** ‐0.035 0.144***(0.1506) (0.1766) (0.1582) (0.0238) (0.0311)
ΔTang (t ‐1) ‐0.304 0.536** 0.412* ‐0.095** 0.151***(0.2452) (0.2636) (0.2243) (0.0410) (0.0493)
ΔColltrl (t ‐1) 0.494*** ‐1.105*** 0.565*** 0.078** ‐0.005(0.1916) (0.2075) (0.1710) (0.0320) (0.0375)
ΔIntang (t ‐1) 0.357** ‐0.265 ‐0.287** 0.089*** ‐0.061*(0.1596) (0.1871) (0.1450) (0.0284) (0.0344)
ΔPayout (t ‐1) ‐0.893*** 4.693*** 1.921*** ‐0.095*** 0.143***(0.1876) (0.3194) (0.2116) (0.0207) (0.0276)
ΔAdjustedRet (t ‐1) 0.007** ‐0.007** ‐0.007** 0.001 ‐0.001(0.0031) (0.0031) (0.0028) (0.0006) (0.0007)
ΔBeta (t ‐1) 0.036*** ‐0.077*** ‐0.018 0.002 ‐0.004*(0.0120) (0.0139) (0.0119) (0.0018) (0.0022)
YearFixedEffect Yes Yes Yes Yes YesObservations 58,885 58,878 58,878 58,878 58,878PseudoR ‐squared 0.045 0.036 0.022Adj.R ‐squared 0.039 0.041
LinearRegressionsLogitRegressions