three essays on corporate disclosure

ThreeEssaysonCorporateDisclosure

Dissertation

AttheFrankfurtSchoolofFinanceandManagement

Supervisedby(alphabetically)

Prof.Dr.YupingJiaProf.Dr.ChristopherKochProf.Dr.JörgR.Werner

Submittedby

ElisabethPaulineKläsDisputationdate:12.04.2018

FrankfurtamMain,October2017

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TableofContent

Acknowledgments...........................................................................................................................3

Introduction......................................................................................................................................4

StrategicNewsDisclosurebeforeIndexRecompositions...............................................10

ExtendedAuditorReportingandPrivateInformationDisclosure..............................42

MandatoryFinancialReportingandCompetition.............................................................77

StatementofCertification.......................................................................................................109

CurriculumVitae........................................................................................................................110

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Acknowledgments

Firstandforemost,IwouldliketoexpressmyspecialappreciationandthankstoProfessorDr.

JörgR.Wernerforhiscontinuoussupportandencouragementofmyresearch.Iappreciateall

hisideas,insights,andcomments.

Furthermore, I would especially like to thank Professor Dr. Yuping Jia for her guidance. My

researchgreatlybenefittedfromhervaluablecommentsandsuggestions.

Mysincerestthanksalsogotomyco‐author,Dr.ChristianWilk,forhistremendouseffortand

dedication.Ithoroughlyenjoyedourcollaboration.

IamgratefultoProfessorDr.NicoleV.S.Ratzinger‐Sakelforhervaluablefeedback.Moreover,I

would to thank Professor Dr. Christopher Koch for taking the time to serve as my external

supervisor.

Lastbutbynomeansleast,Iwouldliketothankmyfamily,friendsandfellowPhDstudentsfor

alwayssupportingandencouragingme.

Thankyouverymuch.

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Introduction

This cumulative dissertation contributes to the empirical literature on corporate disclosure.

Corporatedisclosureisacentralthemeintheaccountingliterature.Itsimportancestemsfrom

thefact thattheavailabilityof information iscrucial fortheefficientallocationofresources in

capital markets (see, e.g., Healy and Palepu [2001]). Capital‐market participants need

information to decide whether to buy, hold, or sell a firm’s equity and debt instruments, or

whether to provide loans.With everynewpieceof public information, investorsupdate their

currentbeliefs,thusenablingthemtomakeinformedinvestmentdecisions.

Informationasymmetriesandagencycosts,however,impedethemarket’sefficientfunctioning

(Healy and Palepu [2001]). In his seminal paper, Akerlof [1970] describes how information

asymmetriesandconflicting incentivesbetweenbuyersandsellerscan lead tomarket failure.

Whenevercapital‐marketparticipantsareunabletoex‐anteassessthetruthfulnessofmanagers’

informationdisclosure,theywillundervaluegoodfirmsandovervaluebadfirms.Agencycosts

are a consequence of the separation of ownership and control (Jensen andMeckling [1976]).

Investorsoftendonothavethetimeorresourcestobeactively involved inacompany’sdaily

operations.Actingastheprincipal,theydelegatethecontroltoautility‐maximizingagent,who

actsontheirbehalfbutnotnecessarilyintheprincipals’bestinterests.

Corporatedisclosureplaysanessential role incapitalmarketsbecause it reduces information

asymmetries and agency costs. For example, accounting information allows capital‐market

participantstoassessafirm’sperformanceandthusmakeinformedinvestmentdecisions.After

the investment, accounting information can be used to monitor the firm’s management and

assess whether it handles the firm’s resources responsibly (Beyer, Cohen, Lys, and Walther

[2010]).

Economic theorysuggests thepresenceof informationasymmetries increasesa firm’sbid‐ask

spreadanddecreasesits liquidity(GlostenandMilgrom[1985]).Firmscanmitigatethiseffect

bydisclosingprivateinformation,whichreducesinformationasymmetriesandthusincreasesa

firm’s market capitalization via a decrease in its cost of capital (Diamond and Verrecchia

[1991]).Severalstudiesprovideempiricalsupportforthisnotionbydocumentingthepositive

capital‐marketeffectsofmandatoryandvoluntarydisclosure(e.g.,LeuzandVerrecchia[2000];

Balakrishnan, Billings, Kelly, and Ljungqvist [2014]). However, prior literature further shows

that firms—beingawareof thismechanism—exploit the timingand contentof information to

influence stock prices, such as in the context of stock repurchases (Brockman, Khurana, and

Martin[2008])orM&Atransactions(AhernandSosyura[2014]).

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The first paperofmydissertation (“StrategicNewsDisclosurebefore IndexRecompositions”)

contributes to the research on corporate disclosure by examiningwhether firms strategically

disseminate information before index recompositions in order to boost their chances of

favorably switching indexes. The economic intuition is thatmembership in the Russell Index

familyisbasedonafirm’smarketcapitalizationonly.Favorablyimpactingindexrecompositions

canthusbestbeachievedbynewsdisclosure.Wefindthatfirmsmovingfromthelower‐ranked

Russell 2000 to the higher‐ranked Russell 1000 disclose significantly more firm‐initiated,

discretionary news prior to index recompositions, as compared to a control group of non‐

movingfirms.Wefurthershowthisdisclosurestrategyallowsfirmstotemporarilyrunupstock

prices.Eachadditionalnewsreleaseincreasesafirm’sprobabilityoffavorablyswitchingindexes

byapproximately1%.

Tothebestofourknowledge,ourpaperisthefirstcomprehensivestudyofstrategiccorporate

news disclosure in the context of index recompositions. Our study offers economic and

methodologicalcontributions.First, itcontributestotheliteratureonstrategiccorporatenews

disclosure by identifying a novel setting in which managers use discretionary news to

temporarily run up stock prices. Our findings are interesting for researchers and investors

because they serve as an indicator to ex‐ante identify potential index movers. Second, we

contributetoarecentstringofliteraturethatusesthesettingofRussellIndexrecompositionsas

aquasi‐naturalexperimentforregressiondiscontinuitydesign(e.g.,Chang,Hong,andLiskovich

[2015];BirdandKarolyi[2017]).Thesestudiesassumefirmsaroundtheindexcutoffarelocally

randomized,andthushavesimilarfirmcharacteristicspriortoindexrecompositionsanddiffer

only in terms of their index membership. Our findings, however, suggest differences in the

disclosurebehaviorofmovingandnon‐movingfirmslocatedjustbelowtheRussell1000Index

cutoff.

The external verification of annual reports promotes confidence in the credibility of firms’

mandatory financial reporting. The audit is thus an additional mechanism to mitigate

information asymmetries between a firm and capital‐market participants. The primary tool

through which financial‐statement users obtain information about the audit is the auditor’s

report.However,usershave increasinglyraisedconcernsaboutthereport’s informativevalue

due to its standardized nature. The UK was the first country to implement measures that

respondtousers’callsformoreinformativeauditor’sreports.

The second paper of my dissertation (“Extended Auditor Reporting and Private Information

Disclosure”)examinestheinformationcontentoftheextendedUKauditor’sreportbyanalyzing

investor reactions to management forecasts before and after the implementation of the new

auditing standard. The intuition is that an informative auditor’s report increases investors’

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understanding of and trust in the audit’s verification role and therefore the credibility of

managers’ ex‐ante unverifiable private information disclosures. We document a decrease in

information asymmetries and an increase in absolute cumulative abnormal returns around

managementforecastsfollowingthedisclosureofthenewaudit‐relatedinformation.Thiseffect

isconcentratedamongfirmswithmoredetailedauditor’sreports,ahighernumberofrisks,and

lowergroupmaterialitythresholds.

Our paper contributes to the mixed empirical evidence on the information content of the

enhancedauditor’sreportpresentedinconcurrentstudiesbyexaminingitspotentiallyindirect

effects (Lennox, Schmidt, and Thompson [2017]; Reid, Carcello, Li, and Neal [2015a]). We

furthercontributetotheliteratureonthecomplementaryroleofauditedfinancialinformation

andvoluntarydisclosures(Ball,Jayaraman,andShivakumar[2012])byshowinghowinvestors

usedifferenttypesofaudit‐relatedinformationwhenassessingthecredibilityofmanagement‐

earningsforecasts.Inlightofinternationalinstitutionsandnationalregulatorsannouncingand

implementing similar requirements, understanding how the new disclosures add to the

informationsetthatisavailabletofinancial‐statementusersisessential.

Corporatedisclosurecomesatthecostofrevealingproprietaryinformationtothepublic,thus

potentially impairing a firm’s competitiveness (Verrecchia [1983];Wagenhofer [1990]). Prior

literature has documented that firms’ concerns about proprietary costs shape their voluntary

andmandatorydisclosurebehavior (e.g.,Ellis,Fee,andThomas [2012];BotosanandStanford

[2005]). However, little is known about the extent to which mandatory financial reporting

actuallyrevealsproprietaryinformation.

UsingthemandatoryIFRSadoptioninEuropeasanexogenousshocktomandatorydisclosure,

the third paper of my dissertation (“Mandatory Financial Reporting and Competition”)

investigates the effect of mandatory financial reporting on a firm’s competitiveness. The

economicintuitionisthatIFRSisconsideredtobeofhigherqualitythanlocalGAAP.Afterthe

adoptionofIFRS,afirm’sfinancialstatementrevealsmoreinformationthatmaybevaluableto

itspeers,thuspotentiallyimpairingthefirm’scompetitiveness.Ifindmandatoryadopterssuffer

competitively relative to voluntary adopters following the introduction of IFRS. This effect

occurs through the mechanisms of increased disclosures and enhanced financial‐statement

comparability. Additional analyses reveal not all types of accounting information are equally

importanttocompetitors.

Mystudyaddstothescarceliteratureontheproprietarycostsofmandatorydisclosure(Zhou

[2014])byshowingthatseveralIFRSaccountingstandardsrevealproprietaryinformationand

thus impacta firm’scompetitiveness.Moreover, Icontribute to the literatureontheeconomic

effects of themandatory IFRS adoption (e.g., Li [2010]; Chen, Young, and Zhuang [2013]) by

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beingoneofa fewpapers thathighlight its firm‐specific costs.My findingsare interesting for

regulators and standard setters because they inform about the potential externalities of

mandatoryfinancialreporting.

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References

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AKERLOF,G.A."TheMarketof'Lemons':QualityUncertaintyandtheMarketMechanism."QuarterlyJournalofEconomics84(1970):488‐500.

BALAKRISHNAN,K.;M.B.BILLINGS;B.KELLYandA.LJUNGQVIST."ShapingLiquidity:OntheCausalEffectsofVoluntaryDisclosure."TheJournalofFinance69(2014):2237‐2278.

BALL,R.;S.JAYARAMANandL.SHIVAKUMAR."AuditedFinancialReportingandVoluntaryDisclosureasComplements:ATestoftheConfirmationHypothesis."JournalofAccountingandEconomics53(2012):136‐166.

BEYER,A.;D.A.COHEN;T.Z.LYSandB.R.WALTHER."TheFinancialReportingEnvironment:ReviewoftheRecentLiterature."JournalofAccountingandEconomics50(2010):296‐343.

BIRD,A.andS.A.KAROLYI."GovernanceandTaxes:EvidencefromRegressionDiscontinuity."TheAccountingReview92(2017):29–50.

BOTOSAN,C.A.andM.STANFORD."Managers'MotivestoWithholdSegmentDisclosuresandtheEffectofSFASNo.131onAnalysts'InformationEnvironment."AccountingReview80(2005):751‐771.

BROCKMAN,P.;I.K.KHURANAandX.MARTIN."VoluntaryDisclosuresAroundShareRepurchases."JournalofFinancialEconomics89(2008):175‐191.

CHANG,Y.‐C.;H.HONGandI.LISKOVICH."RegressionDiscontinuityandthePriceEffectsofStockMarketIndexing."ReviewofFinancialStudies28(2015):212‐246.

CHEN,C.;D.YOUNGandZ.ZHUANG."ExternalitiesofMandatoryIFRSAdoption:EvidencefromCross‐BorderSpilloverEffectsofFinancialInformationonInvestmentEfficiency."TheAccountingReview88(2013):881‐914.

DIAMOND,D.W.andR.E.VERRECCHIA."Disclosure,Liquidity,andtheCostofCapital."JournalofFinance46(1991):1325‐1359.

ELLIS,J.A.;C.E.FEEandS.E.THOMAS."ProprietaryCostsandtheDisclosureofInformationAboutCustomers."JournalofAccountingResearch50(2012):685‐727.

GLOSTEN,L.R.andP.R.MILGROM."Bid,AskandTransactionPricesinaSpecialistMarketwithHeterogeneouslyInformedTraders."JournalofFinancialEconomics14(1985):71‐100.

HEALY,P.M.andK.G.PALEPU."InformationAsymmetry,CorporateDisclosure,andtheCapitalMarkets:AReviewoftheEmpiricalDisclosureLiterature."JournalofAccounting&Economics31(2001):405‐440.

JENSEN,M.C.andW.H.MECKLING."TheoryoftheFirm:ManagerialBehavior,AgencyCostsandOwnershipStructure."JournalofFinancialEconomics3(1976):305‐360.

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LENNOX,C.S.;J.J.SCHMIDTandA.M.THOMPSON."IstheExpandedModelofAuditReportingInformativetoInvestors?EvidencefromtheUK."UnpublishedPaper,UniversityofSouthernCalifornia,UniversityofTexasatAustinandUniversityofIllinoisatUrbana‐Champaign,2017.Availableathttps://papers.ssrn.com/sol3/papers.cfm?abstract_id=2619785.

LEUZ,C.andR.E.VERRECCHIA."TheEconomicConsequencesofIncreasedDisclosure."JournalofAccountingResearch38(2000):91‐124.

LI,S."DoesMandatoryAdoptionofInternationalFinancialReportingStandardsintheEuropeanUnionReducetheCostofEquityCapital?"TheAccountingReview85(2010):607–636.

REID,L.C.;J.V.CARCELLO;C.LIandT.L.NEAL."AreAuditorandAuditCommitteeReportChangesUsefultoInvestors?EvidencefromtheUnitedKingdom."UnpublishedPaper,UniversityofPittsburghandUniversityofTennessee,2015a.Availableathttps://papers.ssrn.com/sol3/papers.cfm?abstract_id=2637880.

VERRECCHIA,R.E."DiscretionaryDisclosure."JournalofAccountingandEconomics5(1983):179‐194.

WAGENHOFER,A."VoluntaryDisclosureWithaStrategicOpponent."JournalofAccountingandEconomics12(1990):341‐363.

ZHOU,Y."DisclosureRegulationandtheCompetitionbetweenPublicandPrivateFirms:TheCaseofSegmentReporting."UnpublishedPaper,UniversityofFlorida,2014.Availabelathttp://zicklin.baruch.cuny.edu/faculty/accountancy/Downloads/Paper_Ying‐Zhou.pdf.

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StrategicNewsDisclosure

beforeIndexRecompositions*

ElisabethKläs‡,JörgR.Werner†,andChristianR.Wilk‡

October2017

Abstract: Weinvestigatefirms’disclosurebehavioraroundindexrecompositions.Ourevidence

shows firmsmovingup to theRussell1000disclosesignificantlymorepositive firm‐initiated,

discretionarynewspriortoindexrecompositions,comparedtoacontrolgroupofnon‐moving

firms. The disclosure strategy carries positive value implications for firms’ market

capitalization.Eachadditionalnewsreleaseincreasestheprobabilityofsuccessfullyswitching

indexesbyapproximately1%.

JEL‐Classification:G14,G23,M41Keywords:

StrategicCorporateDisclosure,IndexRecomposition,EfficientMarkets.

*WethankYakovAmihud,RonMasulis,ZachariasSautner,MartinArtz,andtheparticipantsofthe2017Financial Accounting and Reporting Section Midyear Meeting, the European Financial ManagementAssociation’s2016AnnualMeeting, theEuropeanAccountingAssociation’s39thAnnualCongress2016,theFinancePhDSeminaratSternSchoolofBusiness,NewYorkUniversity,andtheBrownbagSeminaratFrankfurtSchoolofFinance&Managementfortheirhelpfulcomments.Moreover,wewouldliketothankJasonChaofromRussellInvestmentsforprovidinguswiththedata.Allremainingerrorsareours.Thispaper was written, in part, when Christian Wilk was visiting Stern School of Business, New YorkUniversity,andElisabethKläswasvisitingLancasterUniversityManagementSchool.‡FrankfurtSchoolofFinance&Management.†FrankfurtSchoolofFinance&Management(correspondingauthor).

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I. Introduction

The strategic use of corporate disclosure is a central theme in the accounting and finance

literature.Prior researchhas identified several settings inwhich firmsexploit the timingand

content of information to influence stock prices. Such settings include, among others, M&A

transactions(AhernandSosyura [2014]), stockrepurchases(Brockman,Khurana,andMartin

[2008])seasonedequityofferings(LangandLundholm[2000]),andCEOcompensationawards

(AboodyandKasznik[2000];Edmans,Goncalves‐Pinto,Groen‐Xu,andWang[2017]).Relatively

little, however, is known about firms’ disclosure behavior in the context of index

recompositions.

In2016,2.6trillionUSDoftotalnetassetswereinvestedinindexfunds,withtheaverageindex

equity fund being almost four times larger than the average actively managed equity fund

(Investment Company Institute [2017]). The growing popularity of passive funds has turned

indexrecompositionsintoimportantcorporateevents.Beingamemberofahighlyrankedindex

means more visibility and a broader investor base; thus, due to the prospect of increasing

shareholdervalue,firmshaveincentivestomovefromalower‐toahigher‐rankedindex.This

paper investigateswhether firms favorably impact their position in an index by strategically

disclosingmorenewspriortoindexrecompositions.

Ouranalysisfocusesonthedisclosurebehavioroffirmsmovingfromthelower‐rankedRussell

2000 to the higher‐ranked Russell 1000. We provide evidence that firms moving up to the

Russell1000disclosesignificantlymorepositive firm‐initiated,discretionarynewsduringthe

100daysleadinguptotheindexrecomposition,comparedtoacontrolgroupoffirmsthatfailto

switch indexes.We further show this disclosure strategy allows firms to temporarily run up

stockprices.Eachadditionalnewsreleaseincreasesafirm’sprobabilityoffavorablyswitching

indexesbyapproximately1%evenaftercontrollingforafirm’sgrowthinmarketcapitalization.

The economic intuition behind our results is as follows: Becausemembership in the Russell

Index family is based on a firm’s market capitalization only, favorably impacting index

recompositionscanbestbeachievedbynewsdisclosure.Economictheoryshowsthepresence

of information asymmetries increases a firm’s bid‐ask spread and consequently decreases its

liquidity(GlostenandMilgrom[1985]).Thecorrespondingcountermeasureisthedisclosureof

privateinformation,whichreducesinformationasymmetriesandthusincreasesafirm’smarket

capitalizationviaadecreaseinitscostofcapital(DiamondandVerrecchia[1991]).

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Thekeydifferencefromotherindexes,suchastheS&P500,isthatthedeterminationofRussell

Indexmembershipisbasedononesinglevariable,afirm’smarketcapitalization.Eachyearon

the last trading day in May, Russell Investments ranks all eligible securities based on their

market capitalization. The largest 1,000 firmsbecomemembers of theRussell 1000, and the

next2,000largestjointheRussell2000.TheRussell3000Eincludesthelargest4,000firms.By

contrast,thedeterminationofindexweightsandtheactualindexreconstitutiontakeplaceon

thelastFridayinJune.Becauseofthisconstructionmethodology,firmsareabletoinfluencethe

Russell Index recomposition process by initiating a disclosure‐driven temporary run‐up in

marketcapitalization.

Tothebestofourknowledge,thispaperisthefirstcomprehensivestudyonstrategiccorporate

news disclosure in the context of index recompositions. Our paper offers economic and

methodologicalcontributions.First,itcontributestotheliteratureonstrategiccorporatenews

disclosure by identifying a novel setting in which managers use discretionary news to

temporarily run up stock prices. Our findings are interesting for researchers and investors

because they serve as an indicator to ex‐ante identify potential index movers. Second, we

contributetoarecentstringofliteraturethatusesthesettingofRussellIndexrecompositions

asaquasi‐naturalexperimentforregressiondiscontinuitydesign(RDD)(e.g.,Chang,Hong,and

Liskovich [2015]; Boone and White [2015]; Fich, Harford, and Tran [2015]). These studies

assume firms around the index cutoff are locally randomized, thus showing similar firm

characteristics prior to index recompositions and differing only in terms of their index

membership. Our results show differences in the disclosure behavior of moving and non‐

movingfirmslocatedjustbelowtheRussell1000Indexcutoff.

Theremainderofthispaperisorganizedasfollows.SectionIIreviewsthepriorliteratureand

developsourhypotheses.OurdataandvariablesaredescribedinsectionIII.SectionIVpresents

ourresearchapproachandempiricalresults.Analyses for theRussell1000Down‐Moversare

showninsectionV,andsectionVIdiscussesimplicationsforregressiondiscontinuitydesigns.

SectionVIIconcludes.

II. RelatedLiteratureandHypotheses

Severalstudiesexaminefirms’disclosurebehavioraroundmajorcorporateevents.Ahernand

Sosyura [2014] find that acquirers in M&A transactions with stock payments increase the

number of press releases during private merger negotiations to benefit from a temporary

increaseintheirstockpricewhenthestockexchangeratioisfixed.Brockmanetal.[2008]show

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thatfirmsmanipulatevoluntarydisclosurestodecreasestockpricespriortostockrepurchasing

activities.Overandaboveincreasingthefrequencyofbad‐newsdisclosures,firmsactivelybias

earningsannouncementsdownwards.LangandLundholm[2000]provideevidencethatfirms

significantly increase their discretionary news disclosure during the six months prior to the

announcementofseasonedequityofferings,withtheintentionofloweringtheircostofequity

capital. Dimitrov and Jain [2011] further show managers attempt to reduce shareholder

discontent at annual meetings by strategically disclosing positive news during the 40 days

leadinguptothemeeting.

Strategicdisclosurebehaviorhas alsobeendocumented in the contextofCEOcompensation.

Edmansetal.[2017]findCEOsrelease5%morediscretionarynewsinequity‐vestingmonthsin

ordertotemporarilyincreasestockpricesandthusprofitfromahighercompensation.Aboody

andKasznik [2000]provideevidence thatCEOsdelaygoodnewsandrushbadnewsprior to

fixedscheduledstockoptionawardsinordertolowertheoptionstrikeprice.Similarly,Cheng

andLo[2006]documentapositiveassociationbetweenincreasesininsiders’stockpurchases

andthefrequencyofbad‐newsforecasts.

A recent strand of literature in finance and accounting uses the setting of Russell Index

recompositionsforregressiondiscontinuitydesigns(RDD).Theargumentisthatfirmsaround

theindexcutoffaremechanicallyplacedintoindexes;thus,theyexperienceanexogenousshock

in the demand of index‐tracking funds, which leads to a change in passive institutional

ownership.BecauseRussell Indexes arevalueweighted, firmsat the topof the lower‐ranked

Russell2000receiveconsiderablymore index‐fundbuyingthando firmsat thebottomof the

higher‐rankedRussell1000.Changetal.[2015]firstidentifiedthesettingintheirinvestigation

of the price effects of indexing. Later studies use this setting to analyze the impact of

institutional ownership on firm transparency and information production (Boone andWhite

[2015]), corporate tax avoidance (Bird and Karolyi [2017]), the quality and quantity of

corporatedisclosures(BirdandKarolyi[2016]),monitoringactivitiesofinstitutionalinvestors

inthecontextofacquisitions(Fichetal.[2015]),payoutpolicy(Crane,Michenaud,andWeston

[2016]), and corporate governance (Appel, Gormley, and Keim [2016]; Schmidt and

Fahlenbrach[2017]).Althoughthemethodologiesdifferacrossthestudies,thekeyassumption

isthesame:“smallandrandom”(Changetal.[2015,p.215])changesintheend‐of‐Maymarket

capitalizationoffirmslocatedaroundtheindexcutoffdetermineindexassignments,withfirms

having “imprecise control onwhich sideof the cutoff they endupon” (Chang et al. [2015, p.

218]). Inotherwords, firmsare locally randomizedaround the indexcutoffpointanddonot

self‐selectintoindexes.

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AspartoftheireffortstojustifytheassumptionoflocalrandomizationaroundtheRussell1000

cutoff,Changetal.[2015]expressconcernthatfirmsmaydecreasetheirmarketcapitalization

inordertoavoidmovinguptothebottomoftheRussell1000.Theintuitionisthatfirmslocated

at the top of the Russell 2000 experience more benefits from index tracking than do firms

locatedatthebottomoftheRussell1000.However,afirm’sultimategoalisshareholderwealth

maximization, which this strategy does not achieve. Rather, it can be accomplished via

membership in the higher‐ranked Russell 1000. Market indexes are used for a variety of

purposes, including benchmarking, and are thus observed by a large number of market

participants. Becoming a member in a higher‐ranked index is consequently accompanied by

morevisibilityandaccesstoabroader investorbase. Inthe longrun, firmsstrive forgrowth,

thereby increasing their index weight and reducing the initial disadvantage of lower index

tracking.Thus,firmshaveincentivestobecomemembersofhigher‐rankedindexes.Anecdotal

evidence further corroborates our economic line of argumentation. For example, when Isis

Pharmaceuticals,Inc.wasaddedtotheRussell1000in2015,itsmanagementannounceditwas

“pleasedtobeaddedto this important index,whichhelpstoraiseawarenessofourcompany

evenmorebroadlyamonginvestors”(http://goo.gl/X1wfq5).

Before index recompositions, firms are able to estimate their daily index rank in the Russell

3000Ebasedonpubliclyavailablemarket‐capitalizationdata.Thisabilityallowsfirmstoassess

the likelihood of an index switch and whether strategically running up their market

capitalization is worthwhile. One concern may be that the market‐capitalization measure

RussellInvestmentsusestodetermineindexmembershipisproprietary,potentiallyhampering

firms’ ability to predict their chances of switching indexes. However, although Russell

Investments uses a proprietary float‐adjusted market‐capitalization measure to determine

indexweights,itdoesnotadjustitsproprietarymarket‐capitalizationmeasurewhenassigning

firms to indexes at the end of May. Moreover, Russell Investments advertises its indexes as

“transparent andpredictable” (Russell Investments [2015]). Chang et al. [2015, p. 215]point

outthat“itiseasytopredictmembershipusingmarketcapitalizationscalculatedfrompublicly

available data”. Thus, the proprietary nature of Russell Investment’s index‐recomposition

variableisunlikelytobeofconcerninoursetting.

Totemporarilyboosttheirmarketcapitalizationandthusincreasetheirchancesofabeneficial

index assignment, firms must increase their stock price. In this context, strategic news

disclosure presents an attractive option. Economic theory suggests information asymmetries

reduce a security’s liquidity via larger bid‐ask spreads (Glosten and Milgrom [1985]). The

disclosure of private information can mitigate this effect, which leads to a decrease in the

existentinformationasymmetriesandconsequentlyincreasesafirm’smarketcapitalizationvia

15

a decrease in its cost of capital (Diamond and Verrecchia [1991]). Consistent with this

prediction,LeuzandVerrecchia[2000]showempiricallythatfirmscommittingtohigherlevels

of disclosure quality experience lower bid‐ask spreads and higher share turnover.Moreover,

voluntary disclosure increases liquidity and firm value (Balakrishnan, Billings, Kelly, and

Ljungqvist [2014]). As Edmans et al. [2017] point out, an alternative channel is additional

disclosures that attract the attentionof individual investors (Barber andOdean [2008]). This

temporaryincreaseininvestorattentionleadstoasignificantshort‐termincreaseinprices(Da,

Engelberg,andGao[2011]).Consequently,firmshavetheincentiveandtheabilitytoinfluence

indexrecompositions.

Sofar,wehavearguedthatfirmscaninfluencetheirmarketcapitalizationvianewsdisclosure.

Wenowelaborateonthestrategiccomponent.Managersareunlikelytofalsify informationin

order to increase their chances of joining a higher‐ranked index. Rather, we expect them to

exploitthetimingofcorporatenews,whichisalesscostlystrategy(DimitrovandJain[2011]).

For example, a firm could move its latest product announcement shortly before the index

recomposition date to positively affect its share price. Although US federal law places strict

requirements on the disclosure of periodic filings andmaterial corporate events, firms enjoy

considerable flexibility regarding the timing and content of other news releases (Ahern and

Sosyura[2014]).Inoursetting,thetimingofmandatory,non‐discretionarynewsisunlikelyto

bethemaintoolofstrategic‐disclosureactivities,becausethetargetdateinouranalysisisthe

sameforeachfirm.Ifallfirmsweretoboosttheirmarketcapitalizationjustafewdaysbefore

the index recomposition, the effects would cancel out. Instead, we expect firms to start

disclosing news severalweeks before the index recomposition. In this context, firm‐initiated,

discretionarynewsreleasesarethemosteffectivetooltotemporarilyrunupstockprices.Here,

firm‐initiated news refers to corporate press releases and filings rather than to publications

fromexternalsources,anddiscretionarymeansmanagementhasthemostdiscretioninterms

of the timing and content of news releases. Given that firms thatmove to the higher‐ranked

indexhaveachievedtheirgoaloffavorablyswitchingindexes,weexpectthesefirmstodisclose

morepositive firm‐initiated,discretionarynewspriorto indexrecompositions,comparedtoa

controlgroupofnon‐switchingfirms.

Hypothesis1:FirmsmovingupfromtheRussell2000totheRussell1000disclosemorepositive

firm‐initiated,discretionarynewsbeforeindexrecompositions,comparedtoacontrolgroupof

non‐movingfirms.

A crucial question in our setting is whether strategic disclosure leads to an economically

significant increase in a firm’s probability of switching indexes. Fundamental firm

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characteristicsareunarguablythedominantdriverof indexswitches.However,giventhatthe

difference in market capitalization of two firms located at neighboring index positions is

marginal, small changes in market capitalization can determine whether a firm switches

indexes. For example, on the last trading day inMay 2013, the relative difference inmarket

capitalization of the firms ranked 1,000 and 1,001 was only 1.13 percent. Therefore, small

changesinmarketcapitalizationcandetermineindexassignment.

Hypothesis2:Strategicnewsdisclosurepriortoindexrecompositionsincreasesfirms’probability

ofsuccessfullyswitchingindexes.

III. DataandVariables

RussellIndexMembership

Russell Investments provides us with the monthly index constituents and the proprietary

market‐capitalizationmeasure.Oursampleperiodspanseightyears,from2007through2014.

Prior to 2007, index recompositions were based on a hard cutoff point. With the newly

introduced+/‐2.5%bandaroundtheindexcutoffpoints,RussellInvestmentsintendstoreduce

indexturnover,thushamperingfirm’sabilitytoanticipateindexswitches.

OurpaperfocusesontheindexcutoffbetweentheRussell1000andtheRussell2000,that is,

the index rank 1,000.We investigate our hypotheses by analyzing the disclosure behavior of

two groups:Up‐Movers andUp‐Candidates. Firms that successfully switched from the lower‐

rankedRussell 2000 to the higher‐rankedRussell 1000are labeledUp‐Movers. Firms located

justbelowtheRussell1000cutoffarelabeledUp‐Candidates.Thisgroupconsistsof firmsthat

seemedlikelytomoveuptothehigher‐rankedindexbut failedbya fewranks.Thegroupsof

Up‐MoversandUp‐Candidatesaremutuallyexclusive.

Weconstructan indicatorvariable foreachgroup.Up‐Movers areex‐post identifiedbasedon

the index‐constituents listprovidedbyRussell Investments, that is,basedon theactual index

assignments.ThegroupofUp‐Candidatesisconstructedbasedonafirm’sindexrank,thatis,the

distancetotheRussell1000cutoffpoint.Morespecifically,wecalculateafirm’srankbysorting

all Russell 3000Emembers on the last trading day in February of each year based on their

publicly available CRSP market capitalization. This approach allows us to identify the most

promising candidates for an index switch ex ante, that is, 100 days before the index

recomposition. We define Up‐Candidates ex ante because we expect the strategic corporate

disclosure activities to start several weeks or even months before the date of the index

17

recomposition. This approach allows us to observe how the disclosure activities of actual

moversdevelopincomparisontothoseofpotentialmovers.Wedonotchoosealaterdate,such

as March, because we may not capture an important portion of the firms’ strategic news

activities, the yearly reporting period. In addition, we use publicly available market‐

capitalizationdatainsteadoftheproprietaryRussellmarketcapitalization,becausethelatteris

notobservable toUp‐Movers andUp‐Candidates.Thisapproachallowsus to identifypotential

candidatesfromtheperspectiveofthefirm.

Next,weapplytheconceptofbandwidthsusedinRDDtodefinetherangeinwhichafirm’srank

must be located for it to be defined as an Up‐Candidate. In our main analyses, we apply a

bandwidthof300,whichimplieschoosing150firmslocatedjustaboveand150firmslocated

justbelowtheindexcutoff.OurcontrolgroupforUp‐MoversconsistsofUp‐Candidates,namely,

the 150 firms located just below the Russell 1000 cutoff. For robustness purposes, we re‐

estimate our analyses using a bandwidth of 100.Untabulated statistics showall results hold.

Figure1portraystheconstructionprocessofourcontrolgroup.

[InsertFigure1abouthere]

StrategicNewsDisclosure

News data are obtained from the S&P Capital IQ Key Developments database. The database

contains structured and summarized corporate news releases compiled from over 20,000

sources. The key advantage of Capital IQ Key Developments is that it allows us to cleanly

categorizethesourceandtypeofeachnewsitem.

As a first step,we retain firm‐initiatednewsonly. In a second step,we split our sample into

discretionaryandnon‐discretionarynewsitemsinordertoidentifynewstypesoverwhichthe

managementhasthemostdiscretionandcanthusbestexploitinastrategicmanner.Next,we

countthenumberofdiscretionaryandnon‐discretionarynewsitemsdisclosedonagivenday.

Moreover,wecomputethecumulativeabnormalreturnsandabnormaltradingvolumearound

eachnewsdisclosureevent.

18

ControlVariables

WeobtainaccountingdatafromCompustatNorthAmerica,marketdatafromCRSP,andanalyst

forecast data from I/B/E/S. We define Firm Size as the log of book value of total assets;

Cumulative RelativeMarket Value Growth as a firm’s cumulative relative growth in market

capitalizationovertheentireRussellyear, that is, fromJuneof thepreviousyearuntilMayof

the current year;ReturnonAssets(ROA) as operating income before depreciation divided by

total assets; andBookLeverage as the sumof current liabilitiesand long‐termdebt scaledby

totalassets.Tobin’sQisthesumofcommonequityandmarketequityminustotalassetsscaled

bytotalassets;#Analystsisthelogof1plusthenumberofanalystsfollowingaparticularstock;

andwemeasureStockTurnoverasthedailytradingvolumedividedbytheaveragenumberof

sharesoutstandingovertheRussellyear.EADay,AGMDayandBoardMeetingDayareindicator

variablestakingthevalueof1wheneveranearningsannouncement,anannualgeneralmeeting,

oraboardmeetingtakesplace,andzerootherwise.

IV. EvidenceonFirms’StrategicDisclosureBehavior

SummaryStatistics

Summarystatistics,asasnapshotonthe last tradingday inMay,arereported inTable1.Up‐

Movers(3.360billionUSD)andUp‐Candidates(1.944billionUSD)differsignificantlyintermsof

theirmarketcapitalizationatthedayoftheindexrecomposition.

Unreported statistics show that firms leaving the Russell 1000 (Down‐Movers) have a lower

marketcapitalization(1.094billionUSD)comparedto firmsatriskofswitchingtothe lower‐

rankedRussell2000(Down‐Candidates)(2.558billionUSD).Theaveragemarketcapitalization

of all candidates located within a bandwidth of 300 around the Russell 1000 cutoff (2.251

billionUSD)isslightlyhigherthanthatofallmovers(2.227billionUSD)andiscomparableto

the averagemarket capitalization of firms locatedwithin a bandwidth of 200 as reported in

BooneandWhite[2015](1.9billionUSD).ThesummarystatisticsfurthershowUp‐Movershave

asignificantlyhighercumulativerelativegrowthinmarketvalue.Thisfindingisnotsurprising,

giventhatUp‐Moverssuccessfullyswitchindexes.

Up‐MoversandUp‐Candidatesdonotexhibitasignificantdifferenceintheirmedianfirmsizeas

measuredbybookvalueoftotalassets.Thus,althoughthemedianmarketvalueofUp‐Moversis

largerthanthatofUp‐Candidates,thegroupsdonotsignificantlydifferintermsoftheirmedian

bookvalue.Onaverage, comparedwithUp‐Candidates,Up‐Movers aremoreprofitable,havea

19

higher Tobin’s Q, have more analysts following their stock, and experience higher stock

turnover. This observation is in line with index recompositions being driven primarily by

fundamental firm characteristics. However, during the 100 days before the index

recomposition, Up‐Movers disclose, on average, more news than Up‐Candidates release. This

finding provides a first indication that the disclosure behavior of both groups differs before

indexrecompositionsandmaythusexplainthesignificantgapinmarketvalueundcumulative

relativemarket‐valuegrowthatthelasttradingdayinMay.

[InsertTable1abouthere]

CumulativeAbnormalDiscretionaryNewsDisclosure

Throughoutthepaper,wedifferentiatebetweenthreetimeperiods:before,between,andafter.

Beforecapturesthe100daysleadinguptotheindexrecompositiononthelasttradingdayin

May. Between refers to the time period between the index recomposition and the index

reconstitution on the last Friday in June.After consists of the 100 days following the index‐

reconstitution date. If Up‐Movers strategically disclose news to favorably impact index

assignments, we expect to observe a slowdown in news publications following index

recompositions.Theintuitionisthatmanagerscanstrategicallyexploitthetimingandcontent

ofdiscretionarynews.Assuch,morefavorablenewswillbepublishedinthebeforeperiod.Less

newsandless‐favorablenewswillbereleasedinthebetweenandafterperiods.

Figure 2 plots the cumulative, abnormal discretionary news production of Up‐Movers (solid

line)andUp‐Candidates (dotted line)before indexrecompositions.Wecalculatetheabnormal

componentinnewsdisclosurerelativetothesamefirm’sdisclosurebehaviorintheprioryear

(seeAhernandSosyura [2014]).Foreaseof comparison,wenormalizeournewsmeasure to

zero100daysbeforetheindexrecomposition.Figure2providesgraphicalevidenceforourfirst

hypothesis.Up‐MoversdisclosemorediscretionarynewsthanUp‐Candidatesdiscloseduringthe

before period. The difference in disclosure behavior starts approximately 80 days before the

indexrecompositionandgradually increasesuntil the last tradingday inMay.Thepattern in

disclosure behavior also supports our choice of ex‐ante defining Up‐Candidates because our

figurefullycapturesthedivergingpattern.

20


Figure3plotsbothgroups’cumulative,abnormalnewsproductionoverourthreetimeperiods:

before,between, andafter. The graphical analysis indicates that after indexmemberships are

assigned on the last trading day in May, the news production of Up‐Movers approximately

parallelsthatofUp‐Candidates.Adrasticdivergenceinnewsdisclosure,asobservedduringthe

beforeperiod, isnotvisibleinthebetweenandafterperiods.Approachingtheendoftheafter

period, the news‐disclosure activities of both groups converge. The graphical analysis thus

indicatesUp‐Moversslowdowntheirnews‐disclosureactivitiesfollowingindexrecompositions.


DiscretionaryNewsDisclosure

To assess whether the visually observed difference in disclosure behavior is statistically

significant,weperformunivariatet‐tests.TheanalysisconfirmsthepatternobservedinFigures

2and3.Table2reportsthegroups’averagenewspublicationduringthethreetimeperiodsand

the respective t‐tests (Panel A). Prior to the index‐recomposition date, Up‐Movers disclose

significantlymorenewsthanUp‐Candidates.Althoughthedifferenceinnewsdisclosureremains

statistically significant during thebetween period, themagnitude of the difference decreases.

The difference in news disclosure is insignificant during the after period. Both groups

experience a slowdown in news publication during the between and after periods. This

slowdownislargerforUp‐Moverswhencomparingthebeforeandafterperiods.Although4.276

news releases during the before period may seem small, one must keep in mind that this

number reflects the average firm‐initiated news only. Our news measure does not include

newspaper articles and publications in other external sources.Moreover, one disclosure can

includemultiplepiecesofinformation.

In Panels B and C of Table 2, we differentiate between discretionary and non‐discretionary

newsitems.Discretionarydisclosuresdrivethemajorityofnewsreleases—Up‐Movershave,on

average,3.603discretionaryversusonly0.673non‐discretionarydisclosures.Consistentwith

PanelA,Up‐Moversdisclosesignificantlymorediscretionarynewsduringthebeforeperiodthan

21

Up‐Candidates.Again,thedifferenceremainsstatisticallysignificantinthebetweenperiod,but

decreases in magnitude. The difference is insignificant in the after‐period. Both groups

experience a slowdown in news publications during the between andafter periods,with the

slowdownbeingsignificantlylargerforUp‐Movers.However,nosignificantdifferenceexistsin

terms of non‐discretionary news during all three periods. Although both groups significantly

reduce their non‐discretionary news disclosure, the difference in non‐discretionary news

reduction between groups is insignificant. Overall, the graphical and univariate analyses

support our first hypothesis:Up‐Movers disclose significantlymore news thanUp‐Candidates

disclose prior to index recompositions. The majority of news releases are firm‐initiated,

discretionarydisclosures,whichslowdownimmediatelyafterindexmembershipisassigned.


AbnormalReturnsandTradingVolume

This section analyzes the stock market effects of news publications around index

recompositions. We hypothesize that firms strategically disclose positive news in order to

favorably impact their index assignment. Hence, the disclosure activities should have an

increasingeffectonfirms’marketcapitalization.Wecomputethecumulativeabnormalreturns

(CAR)andabnormaltradingvolume(AV)aroundeachnewsitem.CARiscalculatedbyapplying

themarketmodel,athree‐dayeventwindow,andanestimationwindowof255daysendingon

the 91st day before index recomposition.We define AV as the excess volume relative to the

averagetradingvolumeoveranestimationwindowof40days.Again,weuseathree‐dayevent

window.

Table 3 reports the event study’s results for all news. The benchmark group consists of the

remainingfirmsintheRussell2000.Duringthe100daysbeforetheindexrecomposition,Up‐

Movers experience a significantly positive three‐day CAR of, on average, 55 basis points per

newsdisclosure.TheCARpernewsforUp‐Candidatesisnotstatisticallydifferentfromzero.The

differenceinCARbetweenthetwogroupsis56basispointsandhighlystatisticallysignificant.

ConsistentwiththeslowdowninnewsdisclosurereportedinFigure3andTable2,Up‐Movers

experience CARs that are not significantly different from zero during the between and after

periods.ThedifferenceinCARbetweenbothgroupsisnolongersignificantinthesubsequent

timeperiods.Becausethebeforeperiodcoincideswiththereportingseasonformostfirms,we

22

re‐estimateouranalysis, controlling forkeyreportingeventssuchasearningsannouncement

dates(EADay),annualgeneralmeetings(AGMDay),andboardmeetings(BoardDay).PanelB

shows the results are robust. A back‐of‐the‐envelope calculation shows that an additional

discretionarynewsitemcan,onaverage, increasethemarketcapitalizationofthefirmranked

1,001byapproximately44.2millionUSD.Thisincreaseallowsthefirmtomoveupmorethan

tenranksandswitchfromtheRussell2000totheRussell1000Index.

Table3PanelAfurthershowsbothgroupsexperiencesignificantlypositiveAVduringthe100

daysleadinguptotheindexrecomposition,withtheAVofUp‐Moversbeingsignificantlylarger

thanthatofUp‐Candidates.TheaverageAVpernewsisinsignificantforbothgroupsduringthe

betweenperiodand,again,significantlypositiveduringtheafterperiod.Theresultsarerobust

whencontrollingforkeyreportingevents(PanelB).


Table4furtherextendsourCARanalysisandreportstheevent‐studyresultsbydiscretionary

and non‐discretionary news disclosures. We do not use control variables in this analysis,

because EA Day, AGM Day, and Board Day are non‐discretionary news. Prior to the index

recomposition, the CARs of Up‐Movers are significantly positive for discretionary and non‐

discretionarynews.Up‐CandidatesexperienceinsignificantCARsthataresubstantiallysmaller

than thoseofUp‐Movers.During thebetween period,Up‐Movers exhibit a decline inCAR.The

CARsfordiscretionaryandnon‐discretionarynewsareinsignificantandnolongerdifferfrom

those of Up‐Candidates. Also in the after period, both groups exhibit CARs that do not

significantly differ from each other. Overall, the event study shows Up‐Movers experience

significantly positive CAR, and supports our first hypothesis that Up‐Movers disclose

significantlymorepositive firm‐initiated, discretionarynews, compared to a control groupof

non‐movingfirms.


23

ProbitAnalysis

We have provided evidence that Up‐Movers strategically disclose news prior to index

recompositions and that this strategy is associatedwith an increase inmarket capitalization.

Thissection investigateswhether thedisclosurebehavior leads toaneconomicallysignificant

increaseintheprobabilityofindexswitches.WeestimatetheprobitmodelP(Mover=1|News

Disclosure)overthebeforeperiod.Model(1)regressesallnewsitemsonindexadditionstothe

Russell1000(Mover).Fundamental firmcharacteristics, includingafirm’scumulativerelative

growthinmarketcapitalization,areaddedintomodel(2).Models(3)and(4)analyzetheeffects

separatelyfordiscretionaryandnon‐discretionarynewsitems.

Table5showsthatonlydiscretionarynewsitemssignificantlyimpacttheprobabilityofindex

switches.Thepublicationofonediscretionarynewsitemincreasestheprobabilityofanindex

switchby1.1%.GiventhatUp‐Moversdisclose,onaverage,3.6discretionarynewsitemsbefore

indexrecompositions,firmscanincreasetheirprobabilityofmovingtotheRussell1000by,on

average,approximately4.0%.


News‐TypeAnalysis

In this section,wegraphicallyanalyze the typesofnews itemsdisclosedduring the100days

before the index recomposition. Figures4 and5plot thenews typesby frequency andmean

CAR forUp‐Movers andUp‐Candidates, respectively.We focus on firm‐initiated, discretionary

newswith ameanCARof greater than50basis points inbothdirections and a frequencyof

larger than five. Figure 4 shows the news itemsmost frequently disclosed byUp‐Movers are

confirmed corporate guidance, as well as information about business expansions and debt

financing. M&A calls, corporate guidance, as well as information about discontinued

operations/downsizings,strategicalliances,andbusinessexpansionsareamongthenewsitems

thattriggerthelargestmeanCAR.Interestingly,thefivemostfrequentlydisclosednewsitems

byUp‐Moversalltriggerapositivemarketreaction,furtherindicatingUp‐Moversaresuccessful

intemporarilyrunningupstockpricespriortoindexrecompositions.

24

The most frequently disclosed news items by Up‐Candidates refer to M&A transaction

announcements,seekingacquisitions/investments,andfollow‐onequityofferings.Newsitems

triggering the largest positive market reactions are M&A‐related information and raised

corporateguidance.Onlyfourofthefivemostfrequentlydisclosednewsitemsexhibitapositive

meanCAR.Moreover,themarketreactiontothefivemostfrequentlydisclosednewsitemsis—

with the exception of M&A transaction announcements—smaller compared to that of Up‐

Movers,thusindicatingthedisclosurestrategyofUp‐Candidatesislesssuccessful.

[InsertFigure4and5abouthere]

Figure6plotsthemeandifferencesinnewsfrequencyandmeanCARbetweenUp‐Moversand

Up‐Candidates. The full‐sample graph consists of four quadrants. We are interested in firm‐

initiated, discretionary news items that Up‐Movers disclose more frequently and more

successfullythanUp‐Candidates,namely,theupperrightquadrant.Newsitemsmorefrequently

disclosedbyUp‐Movers relative toUp‐Candidates includeproduct‐relatedannouncementsand

M&Atransactionclosings.NewsitemsmoresuccessfullydisclosedbyUp‐MoversrelativetoUp‐

Candidates include information about discontinued operations and downsizings, strategic

alliances, and business expansion. Overall, the figures show the news types disclosed byUp‐

Moversduringthebeforeperiodareinformationthatislikelytopleaseinvestorsandencourage

themtoinvestinthecompany.


V. Russell1000Down‐Movers

Thissectionre‐estimatesouranalysesforfirmsthatleavetheRussell1000(Down‐Movers)or

areatriskofmovingdown(Down‐Candidates)fromtheRussell1000totheRussell2000.Given

thatDown‐Movers fail to stay in thehigher‐ranked index,wewouldnotexpect these firms to

successfully engage in strategic news disclosure. All results are untabulated. In univariate

25

analyses,wefindthedifferenceindiscretionarydisclosurebehaviorbetweenDown‐Moversand

Down‐Candidates is insignificant in thebeforeandbetweenperiods. In theafterperiod,Down‐

Movers disclose significantly less firm‐initiated, discretionary news compared to Down‐

Candidates.Theresultsfurthershowdiscretionarynewsdisclosurebybothgroupssignificantly

slows down after the index recomposition; the difference in discretionary news reduction

between groups is insignificant. The intuition behind our results is as follows: index

recompositionsaredrivenprimarilybyfundamentalfactors.Giventhatbothgroupsareleaving

or are at risk of leaving the Russell 1000, they are unlikely to perform well. Consequently,

comparedtoUp‐Movers,Down‐Moversinparticularhavefewerpositivenewsreleasesthatcan

beplacedstrategically.Moreover,basedonacost‐benefitanalysis,engaging instrategicnews

disclosuremaysimplynotbeworthwhile forsuch firms,because theyaredirectlycompeting

with Up‐Movers and Up‐Candidates for positions in the Russell 1000. Unreported summary

statistics support our intuition and show Down‐Movers are less profitable and have higher

leverageandalowerTobin’sQthanUp‐Movers. Inlinewithourexpectation,anunreportedt‐

testrevealsUp‐Moverspublishsignificantlymorefirm‐initiated,discretionarynewsthanDown‐

Movers(0.271,p‐value:0.000).

TheeventstudyshowstheCARsforDown‐MoversandDown‐Candidatesareinsignificantbefore

the index recomposition. The difference between the two groups is insignificant aswell. The

resultsholdwhenadding control variables.Moreover, additional analyses show theCARs for

Down‐Movers and Down‐Candidates are insignificant for discretionary and non‐discretionary

firm‐initiatednewsinthebeforeperiod.

Finally, we re‐estimate our probit analysis and find that neither discretionary nor non‐

discretionary news disclosure significantly impacts the probability of staying in the higher‐

ranked index. This finding is not surprising, given that the news‐disclosure activities of both

groups are rather low. Consequently, Down‐Movers have fewer opportunities to disclose

positivenews, leaving littlespace forstrategicnewsdisclosure.Overall,ourresultsarenot in

linewiththeargumentthatfirmshaveincentivestomovetothelower‐rankedindexbecauseof

increased benefits from index tracking (Chang et al. [2015]). If thiswere the case,wewould

expecttoseeDown‐Moversdisclosingsignificantlynegativeand, ingeneral,morenewsthana

controlgroup.

26

VI. ImplicationsforRegressionDiscontinuityDesigns

Inthissection,webrieflydiscusstheimplicationsofourfindingsforRDDinthecontextofthe

RussellIndexrecomposition.Thekeyassumptionofthesestudiesisthelocalrandomizationof

firmsaroundtheindexcutoff.Thevalidityofthisassumptiondependsonthedegreetowhich

firmsareabletomanipulatetheassignmentvariable,thatis,theirmarketcapitalization.

Our results show Up‐Movers disclose significantly more firm‐initiated, discretionary news

duringthe100days leadingupto the indexrecomposition,comparedtoacontrolgroup,and

thisdisclosurebehaviorhaspositivevalueimplications.Consequently,ourstudyhighlightsthe

problem of choosing an appropriate bandwidth in these RDD studies. The challenge is to

balance the trade‐off between estimation precision and bias. Although a smaller bandwidth

capturesonlythosefirmsthatareverycloselylocatedaroundtheindexcutoff,thatis,firmsto

which the local randomization ismost likely to apply, it comes at the price of restricting the

sample to relatively fewer observations, potentially reducing the results’ external validity.

Hence,anumberofresearchershaveincreasedthebandwidthinordertocovermorefirmsin

their analysis. Yet this bandwidth increase also comes at a price. The larger the chosen

bandwidth, themore firms that are included in the analysiswill have some, but not precise,

control over the assignment variable (i.e., Up‐Movers), which may introduce a bias to the

analysis. Therefore, our results highlight the importance of the bandwidth selection in RDD

aroundtheRussellIndexcutoffafter2007.

VII. Conclusion

This paper investigates strategic news disclosure around Russell Index recompositions. We

provideevidencethatcomparedtoacontrolgroupofnon‐movingfirms,firmsthatsuccessfully

switch indexes release significantly more positive firm‐initiated, discretionary news prior to

indexrecompositions.Thisdisclosurestrategyhaspositivevalueimplications,enablingmoving

firmstotemporarilyboosttheirmarketcapitalization.Moreover,weshoweachadditionalnews

publication increases the probability of switching indexes by approximately 1%. Additional

analysesunderlinethestrategicnatureofmovers’disclosurebehavior.

Note,however,thatourfindingsarelimitedtoindexesthatarerecomposedbasedonsecurities’

market capitalization. We cannot make any statement about indexes, such as the S&P 500,

whosereconstitutioncriteriaarebasedonadditionalorothercriteria.

27

AppendixA:RussellIndexConstruction

ThissectiondescribestheannualreconstructionprocessoftheRussellindexes.Allinformation

depicted here is obtained from the Russell Investments Guide (Russell Investments [2015]).

Each year on the last tradingday ofMay,Russell Investments ranks all eligibleUS securities

basedonaproprietarymeasureoftheirmarketcapitalization.TheRussell3000Eiscomposed

of the largest 4,000 securities (or of all eligible securities if the total number of eligible

securitiesisbelow4,000).Securitieswitharankbetween1and1,000becomemembersofthe

Russell1000,securitieswitharankbetween1,001and3,000jointheRussell2000.TheRussell

3000consistsofthe3,000largestsecurities.In2007,RussellInvestmentsintroducedabanding

policy,whichaimsatreducingindexturnover.Thedeterminationofindexmembersistherefore

nolongerbasedontheclearcutoffpointsatthe1,000thrankintheRussell1000andthe3,000th

rank in the Russell 2000. Instead, a cumulative market capitalization range of +/‐ 2.5% is

defined around each cutoff point. If a security is locatedwithin this band, it does not switch

indexes.Inotherwords,afirmmustexceedthis5%bandinordertomovetothehigher‐ranked

indexandmustfallbelowtherangeinordertoberemovedfromthehigher‐rankedindex.The

bandingpolicydoesnotapplytotheRussell3000and3000Ecutoffpoints.

Afterthesecuritiesareassignedtotheindexes,RussellInvestmentsdeterminesafirm’sindex

weight by adjusting its market‐capitalization measure for free float. Again, this measure is

proprietary information. Index weights are assigned based on the ranking of the free‐float‐

adjusted market capitalization within each index. Although the membership determination

occurs on the last trading day in May, the actual index reconstitution and the index weight

assignments take place on the last Friday in June. For our study, only the membership

determination based on the end‐of‐May market capitalization is relevant. Moreover, Russell

Investmentsappliesastrictno‐replacementrule,whichmeansthatsecuritiesleavingtheindex

overtheyeararenotreplaced.Thenumberofsecuritieswithineachindexcanthusvary.IPOs

are,however,addedquarterly,andcorporateactionsaretakenintoaccountonadailybasis.

28

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Figure1:ConstructionofControlGroup

Thisfigureillustratestheconstructionofourcontrolgroup,Up‐Candidates, foraRussell1000inclusion.FollowingtheliteratureonRDDaroundtheRussell1000indexcutoff,candidatesaredefinedbasedonbandwidths,i.e.,thedistancefromtherank1,000.Candidatesthatarelocatedbelow the rank 1,000 are labeledUp‐Candidates. These firms were likely to move up to theRussell 1000 but failed. By contrast, candidates located above the rank 1,000 are labeled asDown‐Candidates.ThesefirmswereatriskofleavingtheRussell1000butsucceededinstaying.Weuseabandwidthof300inourmainanalyses.Forrobustnesspurposes,wereplicatealltestsapplyingabandwidthof100.Note that thedefinitionofmovers isnotbasedonranksbutexpostontheRussellIndex‐constituentslist.Candidates,however,areex‐anteclassified,basedontheirmarket‐capitalizationrankonthelasttradingdayinFebruary.

31

Figure2:CumulativeAbnormalDiscretionaryNews

DisclosurebeforeIndexRecompositions

Thisgraphplots thecumulative,abnormaldiscretionarynewsproductionofUp‐Movers (solidline)andUp‐Candidates(dottedline)duringthe100daysbeforetheindexrecompositiononthelasttradingdayinMay.WedefineUp‐MoversasfirmsthatmovefromtheRussell2000totheRussell1000.AfirmiscategorizedasanUp‐Candidateifitsrankiswithinabandwidthof300below theRussell 1000 cutoff.We cumulate theabnormalnumberof newsover time (Ahernand Sosyura [2014]). The abnormal component in news releases ismeasured relative to thesame firm’s news production over the same time period in the previous year. For ease ofcomparison,wenormalizeournewsmeasuretozero100daysbeforetheindexrecomposition.Firm‐initiatednewsreleases refer tonews itemspublishedby therespective firmandnotanexternalsource.Discretionarynewsreleasesarenews itemsoverwhichthemanagementhasdiscretion in terms of content and timing (Edmans et al. [2017]). We winsorize our newsmeasureatthe1%level.Thesampleperiodisfrom2007through2014.

32

Figure3:CumulativeAbnormalDiscretionaryNews

DisclosurearoundIndexRecompositions

Thisgraphplots thecumulative,abnormaldiscretionarynewsproductionofUp‐Movers (solidline)andUp‐Candidates(dottedline)overthreetimeperiods.WedefineUp‐MoversasfirmsthatmovefromtheRussell2000totheRussell1000.AfirmiscategorizedasanUp‐Candidateifitsrankiswithinabandwidthof300belowtheRussell1000cutoff.Beforereferstothe100daysbefore the index recomposition on the last trading day in May. Between covers the 30 daysbetweentheindexrecompositionandtheindexreconstitutiononthelastFridayinJune.Afterspans the100days following the index reconstitution.Wecumulate theabnormalnumberofnewsitemsovertime(AhernandSosyura[2014]).Theabnormalcomponentinnewsreleasesismeasured relative to the same firm’s news production over the same time period in thepreviousyear.Foreaseofcomparison,wenormalizeournewsmeasuretozero100daysbeforethe index recomposition. Firm‐initiated news releases refer to news items published by therespective firmandnot an external source.Discretionarynews releases are news itemsoverwhichthemanagementhasdiscretionintermsofcontentandtiming(Edmansetal.[2017]).Wewinsorizeournewsmeasureatthe1%level.Thesampleperiodisfrom2007through2014.

33

Figure4:NewsTypesDisclosedbyUp‐Movers

This graph plots the frequency and mean CAR of firm‐initiated, discretionary news itemsdisclosedbyUp‐Moversduringthe100dayspriortotheindexrecomposition.Notethisfigureonlycapturesnews itemswithameanCARofgreater than50basispoints inbothdirectionsand a frequency of larger than five. CAR is calculated using themarketmodel, an estimationperiodof [‐346,‐91]days, and an eventwindowof [‐1,1] days.WedefineUp‐Movers as firmsthatmovefromtheRussell2000totheRussell1000.Firm‐initiatednewsreleasesrefertonewsitemspublishedbytherespectivefirmandnotanexternalsource.Discretionarynewsreleasesare news items over which the management has discretion in terms of content and timing(Edmansetal.[2017]).Thesampleperiodisfrom2007through2014.

Newsitems:

id21:DiscontinuedOperations/Downsizings,id22:StrategicAlliances,id31:BusinessExpansion,id50:Shareholder/Analyst Calls, id 73: Impairments/Write Offs, id 77: Changes in Company Bylaws/Rules,id 83: Private Placements, id 87: Fixed Income Offerings, id 138: Announcements of Sales/TradingStatement,id231:UpdateEquityBuyback,id232:AnnouncementEquityBuyback.

34

Figure5:NewsTypesDisclosedbyUp‐Candidates

This graph plots the frequency and mean CAR of firm‐initiated, discretionary news itemsdisclosed byUp‐Candidatesduring the 100 days prior to the index recomposition. Note thisfigure only captures news items with a mean CAR of greater than 50 basis points in bothdirections and a frequency of larger than five. CAR is calculated using themarketmodel, anestimationperiodof[‐346,‐91]days,andaneventwindowof[‐1,1]days.AfirmiscategorizedasanUp‐Candidateifitsrankiswithinabandwidthof300belowtheRussell1000cutoff.Firm‐initiatednewsreleasesrefertonewsitemspublishedbytherespectivefirmandnotanexternalsource.Discretionarynewsreleasesarenewsitemsoverwhichthemanagementhasdiscretionintermsofcontentandtiming(Edmansetal.[2017]).Thesampleperiodisfrom2007through2014.

Newsitems:

id 3: Seeking Acquisitions/Investments, id 21: Discontinued Operations/Downsizings, id 22: StrategicAlliances,id25:Lawsuits&LegalIssues,id73:Impairments/WriteOffs,id83:PrivatePlacements,id87:FixedIncomeOfferings,id101:ExecutiveChanges‐CEO,id102:ExecutiveChanges‐CFO,id157:ActivistLetter to Target, id 160: Communication (Letter etc.) to Employees by Target, id 163: Declaration ofVotingResults‐10Q/13D/AnySECform,id187:SupportingstatementtoTargetbyThirdParty.

35

Figure6:Mover‐CandidateDifferencesinNewsDisclosure

ThisgraphplotsthedifferencesinnewsfrequencyandmeanCARbetweenUp‐MoversandUp‐Candidates. Note this figure only captures the upper right quadrant of the full‐sample graph,that is, firm‐initiated, discretionary news items thatUp‐Movers disclosemore frequently andmore successfully relative to Up‐Candidates during the 100 days prior to the indexrecomposition. CAR is calculated using themarketmodel, an estimation period of [‐346,‐91]days, and an eventwindowof [‐1,1] days.WedefineUp‐Movers as firms thatmove from theRussell2000totheRussell1000.AfirmiscategorizedasanUp‐Candidateifitsrankiswithinabandwidth of 300 below the Russell 1000 cutoff. Firm‐initiated news releases refer to newsitemspublishedbytherespectivefirmandnotanexternalsource.Discretionarynewsreleasesare news items over which the management has discretion in terms of content and timing(Edmansetal.[2017]).Thesampleperiodisfrom2007through2014.

Newsitems:

id21:DiscontinuedOperations/Downsizings,id22:StrategicAlliances,id31:BusinessExpansion,id77:ChangesinCompanyBylaws/Rules,id138:AnnouncementsofSales/TradingStatement.

36

Table1:SummaryStatistics

This table reports the summary statistics for Up‐Movers and Up‐Candidates around the Russell 1000 cutoff. The summary statistics present asnapshotofthevariablesontheindexrecompositiondate,i.e.,thelasttradingdayinMay.WedefineUp‐MoversasfirmsthatmovefromtheRussell2000totheRussell1000.AfirmiscategorizedasanUp‐CandidateifitsrankisbelowtheRussell1000cutoff.Abandwidthof300applies;i.e.,wehaveselectedthe150firmsclosesttotherank1000.WedefineMarketValueasthemarketcapitalizationmeasuredinbillons,CumulativeRelativeMarketValueGrowthasthecumulativerelativegrowthinmarketcapitalizationovertheentireRussellyear,thatis,fromJuneofthepreviousyearuntilMayof the current year,FirmSize as the book value of total assetsmeasured in billions, andReturnonAssets as operating income beforedepreciation divided by total assets.We calculateBookLeverage as the sum of current liabilities and long‐termdebt scaled by total assets, andTobin’sQasthesumofcommonequityandmarketequityminustotalassets,scaledbytotalassets.#Analystsisthenumberofanalystsfollowingaparticularstock,andwecomputeStockTurnoverasthedailytradingvolumedividedbytheaveragenumberofsharesoutstandingovertheRussellyear.CumulativeAbnormalDiscretionaryNews iscalculatedfollowingAhernandSosyura[2014].Discretionarynewsreleasesarenewsitemsoverwhichthemanagementhasdiscretionintermsoncontentandtiming(Edmansetal.[2017]).Allcontinuousvariablesarewinsorizedatthe1%level.Thesampleperiodisfrom2007through2014.

Mean Median SD Mean Median SD

MarketValue 3.360 3.256 1.142 1.944 1.984 0.473 1.416 *** 1.272 ***CumulativeRelativeMarketValueGrowth 69.819 66.851 46.499 34.497 27.437 38.616 35.322 *** 39.415 ***FirmSize 3.611 1.796 5.453 2.894 1.741 3.838 0.718 ** 0.055ReturnonAssets 3.917 3.748 3.706 3.325 3.255 3.233 0.592 ** 0.493 **BookLeverage 24.054 19.419 21.349 22.605 21.249 18.553 1.450 ‐1.830Tobin'sQ 2.966 2.094 2.124 2.179 1.608 1.582 0.787 *** 0.486 ***#Analysts 10.814 10.000 5.606 9.126 8.000 4.720 1.688 *** 2.000 ***StockTurnover 19.055 15.355 12.471 12.978 9.908 9.369 6.077 *** 5.448 ***CumulativeAbnormalDiscretionaryNews 8.678 7.817 5.695 7.046 6.000 4.873 1.632 *** 1.817 ***

Up‐Movers Up‐Candidates DifferencesMedian Diff.Mean Diff.

36

37

Table2:DiscretionaryNewsDisclosure

Thistablereportsunivariatet‐testsofUp‐Candidates’andUp‐Movers’averagenewsdisclosureoverourthreetimeperiods.WedefineUp‐MoversasfirmsthatmovefromtheRussell2000totheRussell1000.AfirmiscategorizedasanUp‐Candidate if itsrankiswithinabandwidthof300belowtheRussell1000cutoff.Beforereferstothe100daysbeforetheindexrecompositionon the last tradingday inMay.Between covers the30daysbetween the indexrecompositionandtheindexreconstitutiononthelastFridayin June.Afterspansthe100days followingtheindex reconstitution. News disclosure is measured as the count of daily firm‐initiated newsitemsaveragedover the threetimeperiods.Discretionarynewsreleasesarenews itemsoverwhich themanagementhasdiscretion in termsof contentand timing (Edmanset al. [2017]).PanelAreferstoallfirm‐initiatednewsitems.PanelsBandCrefertodiscretionary(Dis.)andnon‐discretionary (Non‐Dis.) firm‐initiated news items, respectively. All continuous variablesarewinsorized at the 1% level. The sample period is from 2007 through 2014.p‐values arepresented inparentheses.Significanceat the0.01,0.05,and0.10 levels is indicatedby***,**,and*.

(1) (2) (3)

PanelA:AllNewsItems

Up‐Candidates(C) 3.628 2.639 2.867 ‐0.989 *** ‐0.762 ***0.000 0.000

Up‐Movers(M) 4.276 3.201 2.950 ‐1.075 *** ‐1.326 ***0.000 0.000

(M)‐(C) 0.648 *** 0.562 *** 0.084 ‐0.086 ‐0.564 ***0.000 0.007 0.344 0.729 0.000

PanelB:Dis.NewsItems

Up‐Candidates(C) 2.973 2.391 2.498 ‐0.582 *** ‐0.475 ***0.000 0.000

Up‐Movers(M) 3.603 2.912 2.602 ‐0.690 *** ‐1.001 ***0.002 0.000

(M)‐(C) 0.630 *** 0.522 *** 0.104 ‐0.108 ‐0.525 ***0.000 0.008 0.208 0.641 0.000

Up‐Candidates(C) 0.655 0.248 0.369 ‐0.407 *** ‐0.286 ***0.000 0.000

Up‐Movers(M) 0.673 0.289 0.348 ‐0.384 *** ‐0.325 ***0.000 0.000

(M)‐(C) 0.018 0.041 ‐0.021 0.022 ‐0.0390.463 0.338 0.325 0.651 0.231

PanelC:Non‐Dis.NewsItems

DifferencesBefore Between After

(2)‐(1) (3)‐(1)

38

Table3:AbnormalReturnsandVolumes(AllNews)

This table reports event‐study results around index recompositions. Cumulative abnormalreturns (CAR) and abnormal trading volume (AV) are computed for each firm‐initiatednewsreleaseemployinganeventwindowof[‐1,1]days.Cumulativeabnormalreturnsarecalculatedusingthemarketmodelandanestimationperiodof[‐346,‐91]days.Wecomputeafirm’sdailyabnormaltradingvolumeasthedailytradingvolumeminustheaveragetradingvolumeduringan estimation window of 40 days, divided by the firm’s number of shares outstanding. ThesampleiscomposedofUp‐Movers,Up‐Candidates,andallremainingfirmsintheRussell2000.WedefineUp‐Movers as firms thatmove fromtheRussell2000 to theRussell1000.A firm iscategorizedasanUp‐Candidateifitsrankiswithinabandwidthof300belowtheRussell1000cutoff.Beforereferstothe100daysbeforetheindexrecompositiononthelasttradingdayinMay.Betweencoversthe30daysbetweentheindexrecompositionandtheindexreconstitutiononthelastFridayinJune.Afterspansthe100daysfollowingtheindexreconstitution.EADay,AGMDayandBoardDayareindicatorvariablestakingthevalueof1atthedayoftheearningsannouncement, the annual general meeting, and the board meeting, and zero otherwise.Discretionary news releases are news items over which the management has discretion intermsofcontentandtiming(Edmansetal.[2017]).Wewinsorizeallcontinuousvariablesatthe1% level and use robust standard errors. Panel A shows the results for the univariateregressions, Panel B shows the results for the multivariate regressions controlling for keyreporting events. The sample period is from 2007 through 2014. p‐values are presented inparentheses.Significanceatthe0.01,0.05,and0.10levelsisindicatedby***,**,and*.

39

Table3:AbnormalReturnsandVolumes(AllNews)

(continued)

PanelA:WithoutControls Before Between After Before Between After

Up‐Movers 54.896*** ‐52.173 48.825 2.664*** 0.517 5.911***(0.000) (0.384) (0.334) (0.000) (0.667) (0.010)

Up‐Candidates ‐1.349 1.264 ‐16.195* 0.766*** ‐0.704 1.014*(0.858) (0.925) (0.076) (0.006) (0.501) (0.081)

Constant 4.053* 14.114*** 2.895 2.413*** 3.651*** 2.155***(0.064) (0.002) (0.257) (0.000) (0.000) (0.000)

R2 0.00013 0.00003 0.00003 0.00028 0.00003 0.00019Observations 118176 19154 83574 81743 13730 56588Test:M‐C=0 56.24*** ‐53.44 65.02 1.90*** 1.22 4.90**p‐value 0.000 0.381 0.204 0.001 0.433 0.038PanelB:WithControls

Up‐Movers 54.452*** ‐52.226 47.820 2.658*** 0.363 6.023***(0.000) (0.383) (0.344) (0.000) (0.765) (0.008)

Up‐Candidates ‐1.680 1.234 ‐16.610* 0.785*** ‐0.702 1.030*(0.823) (0.926) (0.069) (0.005) (0.502) (0.076)

EADay ‐14.154 4.839 ‐14.986 1.280*** 3.021** 1.708***(0.123) (0.916) (0.184) (0.000) (0.026) (0.000)

AGMDay 3.086 ‐4.362 ‐27.008* ‐2.102*** ‐1.549** ‐2.108***(0.603) (0.859) (0.076) (0.000) (0.027) (0.000)

BoardDay ‐19.773 ‐17.599 16.564 ‐1.606*** ‐1.753 ‐0.734(0.353) (0.800) (0.605) (0.000) (0.122) (0.131)

Constant 5.328** 14.144*** 4.510* 2.455*** 3.617*** 2.022***(0.023) (0.003) (0.083) (0.000) (0.000) (0.000)

R2 0.00017 0.00004 0.00008 0.00134 0.00039 0.00091Observations 118176 19154 83574 81743 13730 56588Test:M‐C=0 56.13*** ‐53.46 64.43 1.87*** 1.07 4.99**p‐value 0.000 0.381 0.209 0.001 0.496 0.034

CAR AV

40

Table4:AbnormalReturns(byNewsType)

This table reports event‐study results around index recompositions differentiating betweendiscretionary (Dis.) and non‐disrectionary (Non‐Dis.) firm‐initiated news items. Cumulativeabnormalreturns(CAR)arecomputedforeachfirm‐initiatednewsreleaseemployinganeventwindowof [‐1,1]days.CAR is calculatedusing themarketmodelandanestimationperiodof[‐346,‐91]days.ThesampleiscomposedofUp‐Movers,Up‐Candidates,andallremainingfirmsin the Russell 2000. We defineUp‐Movers as firms that move from the Russell 2000 to theRussell1000.AfirmiscategorizedasanUp‐Candidateifitsrankiswithinabandwidthof300belowtheRussell1000cutoff.Beforereferstothe100daysbeforetheindexrecompositiononthelasttradingdayinMay.Betweencoversthe30daysbetweentheindexrecompositionandtheindexreconstitutiononthelastFridayinJune.Afterspansthe100daysfollowingtheindexreconstitution. Discretionary news releases are news items overwhich themanagement hasdiscretionintermsofcontentandtiming(Edmansetal. [2017]).Wewinsorizeallcontinuousvariables at the 1% level and use robust standard errors. The sample period is from 2007through 2014.p‐values are presented in parentheses. Significance at the 0.01, 0.05, and0.10levelsisindicatedby***,**,and*.

CAR(Dis.) CAR(Non‐Dis.) CAR(Dis.) CAR(Non‐Dis.) CAR(Dis.) CAR(Non‐Dis.)

Up‐Movers 45.247*** 109.086*** ‐38.560 ‐148.213 45.217 91.115(0.002) (0.002) (0.553) (0.288) (0.378) (0.748)

Up‐Candidates ‐4.525 12.343 5.880 ‐94.882 ‐20.197** 17.294(0.575) (0.541) (0.659) (0.265) (0.027) (0.679)

Constant 6.784*** ‐7.278 15.365*** ‐2.813 5.502** ‐16.783*(0.004) (0.182) (0.001) (0.885) (0.036) (0.065)

R2 0.00010 0.00031 0.00002 0.00098 0.00004 0.00002Observations 95563 22613 17849 1305 73900 9674Test:M‐C=0 49.77*** 96.74** ‐44.44 ‐53.33 65.41 73.82p‐value 0.003 0.016 0.501 0.741 0.208 0.796

Before Between After

41

Table5:ProbabilityofIndexSwitching

This table reports the marginal effects for our probit regressions. Our dependent variablemeasures index switches from theRussell 2000 to theRussell 1000. Thenewsmeasures arecalculatedover thebeforeperiod, that is, the100daysbefore the indexrecompositiononthelasttradingdayinMay.ThesampleiscomposedofUp‐MoversandUp‐Candidates.WedefineUp‐MoversasfirmsthatmovefromtheRussell2000totheRussell1000.AfirmiscategorizedasanUp‐Candidateifitsrankiswithinabandwidthof300belowtheRussell1000cutoff.WedefineFirmSizeasthelogofbookvalueoftotalassets,CumulativeRelativeMarketValueGrowthasthecumulative relativegrowth inmarket capitalizationover theentireRussell year, that is, fromJuneofthepreviousyearuntilMayofthecurrentyear,andReturnonAssetsasoperatingincomebeforedepreciationdividedby totalassets.WecalculateBookLeverage as thesumofcurrentliabilitiesandlong‐termdebtscaledbytotalassets,andTobin’sQasthesumofcommonequityandmarketequityminustotalassets,scaledbytotalassets.#Analystsis the logof1plusthenumber of analysts following a particular stock, andwe computeStockTurnover as the dailytrading volume divided by the average number of shares outstanding over the Russell year.Discretionary news releases are news items over which the management has discretion intermsofcontentandtiming(Edmansetal.[2017]).Wewinsorizeallcontinuousvariablesatthe1% level and use robust standard errors. All models include year fixed effects. The sampleperiod is from2007 through2014.p‐valuesarepresented inparentheses. Significanceat the0.01,0.05,and0.10levelsisindicatedby***,**,and*.

DisrectionaryNews

Non‐Discretionary

News(1) (2) (3) (4)

AllNewsItems 0.014*** 0.009***(0.000) (0.000)

Dis.NewsItems 0.011***(0.000)

Non‐Dis.NewsItems ‐0.006(0.476)

FirmSize 0.148*** 0.148*** 0.148***(0.000) (0.000) (0.000)

CumulativeRelativeMarketValueGrowth 0.003*** 0.003*** 0.003***(0.000) (0.000) (0.000)

ReturnonAssets 0.017*** 0.017*** 0.016***(0.000) (0.000) (0.000)

BookLeverage 0.001* 0.001* 0.001**(0.072) (0.067) (0.046)

TobinsQ 0.089*** 0.089*** 0.089***(0.000) (0.000) (0.000)

#Analysts 0.062*** 0.061*** 0.065***(0.000) (0.000) (0.000)

StockTurnover ‐0.000 0.000 0.000(0.999) (0.973) (0.534)

McFaddenR2 0.01 0.31 0.31 0.30Observations 4370 3720 3720 3720

AllNews

42

ExtendedAuditorReportingand

PrivateInformationDisclosure*

ElisabethKläs†,NicoleV.S.Ratzinger‐Sakel‡,andJörgR.Werner§

October2017

Abstract:WeexaminetheinformativevalueoftheenhancedUKauditor’sreportbyfocusingon

theverificationroleofauditedfinancialstatements.Theintuitionisthataninformativeauditor’s

report increases investors’ understanding of and trust in the audit’s verification role and

therefore the perceived credibility of managers’ ex‐ante unverifiable private‐information

disclosures.Wedocumentadecrease in informationasymmetriesandan increase inabsolute

cumulative abnormal returns aroundmanagement forecasts following the implementation of

theenhancedUKauditor’sreport.Wefurthershowtheeffectisconcentratedamongfirmswith

moredetailedauditor’sreports,ahighernumberofdisclosedkeyauditrisks,andlowergroup

materialitythresholds.

JEL‐Classification:M41,M42,M48

Keywords:

Auditor Reporting Model, Informative Value, Management Earnings Forecasts, Audit

Verification.

*We thank the participants of the 2017 American Accounting Association International AccountingSection Midyear Meeting, the 2017 European Accounting Association Annual Congress, and the 2015AmericanAccountingAssociationAnnualMeeting.Moreover,wethankTobiaTassinariforhisassistanceincollectingthedata.Allremainingerrorsareours.PartofthisworkwasdevelopedwhenElisabethKläswasvisitingLancasterUniversityManagementSchool.†PhDstudent,AccountingDepartment,FrankfurtSchoolofFinance&Management.‡ProfessorofAuditingandCorporateAccounting,Chair,HBSHamburgBusinessSchool,UniversityofHamburg.§ProfessorofAccounting,FrankfurtSchoolofFinance&Management,Tel.+4969154008838,[email protected](correspondingauthor).

43

I. Introduction

Recent initiatives by international institutions and regulators to enhance the auditor’s report

triggered a debate about the new report’s informative value. The reforms aim at providing

financial‐statementuserswithmoreinformationonhowtheauditwasconducted.In2013,the

UKFinancialReportingCouncil(FRC)wasthefirstregulatortorequireadditionaldisclosuresin

the auditor’s report (FRC [2013a]). Shortly after, the European Commission (European

Commission [2014]), the IAASB (IAASB [2015a]), and thePCAOB (PCAOB [2017]) announced

andimplementedsimilarinitiatives.Concurrentstudieslargelyfocusondirectreactionstothe

newUKauditor’sreportsandprovidemixedevidenceonthenewdisclosures’informativevalue

(Gutierrez,Minutti‐Meza,Tatum,andVulcheva[2016];Lennox,Schmidt,andThompson[2017];

Reid,Carcello,Li,andNeal[2015a]).

Thispapercontributestothecurrentdebatebyfocusingonanalternative, indirectchannelto

assess the informative value of the enhanced UK auditor’s reports. Specifically, we examine

investorreactionsaroundmanagementearningsforecastsbeforeandaftertheimplementation

ofthenewauditor’sreport.Theeconomicintuitionisasfollows:agreaterlevelofindependent

financial‐statement verification enables managers to more credibly commit to the truthful

disclosureofprivateinformation,becauseinvestorsuseauditedfinancialinformationtoassess

thecredibilityofmanagers’pastforecasts(Ball,Jayaraman,andShivakumar[2012]).Weargue

theenhancedauditorreportingrequirementsincreaseinvestors’trustintheaudit’sverification

role. As a consequence, we expect to observe an increase in the perceived credibility of

managers’earningsforecasts.

Ourresultsprovideevidenceinfavorofthenewdisclosures’informativevalue.Wedocumenta

decreaseininformationasymmetriesandanincreaseinabsolutecumulativeabnormalreturns

(CARs) around management forecasts after the implementation of the revised UK auditor’s

report.We further show this effect is concentrated among firmswithmoredetailed auditor’s

reports,ahighernumberofdisclosedkeyauditrisks,andlowergroupmaterialitythresholds.

We contribute to the mixed evidence on the informative value of the UK auditor’s report

presentedinconcurrentstudies(Amiram,Chircop,Landsman,andPeasnell[2017];Gutierrezet

al.[2016];Lennoxetal.[2017];Reidetal.[2015a];WilliamsSmith[2017]).Focusingsolelyon

directmarketreactionstotheextendeddisclosuresoverlooksthefactthatthenewsetofaudit‐

relatedinformationmaybeusefulatothertimes.Thus,wefurthercontributetotheliteratureon

the complementary roleof audited financial informationandvoluntarydisclosures (Ball et al.

[2012]) by showing how investors use different types of audit‐related information when

44

assessingthecredibilityofmanagementearningsforecasts.Inlightofinternationalinstitutions

and national regulators announcing and implementing similar requirements, understanding

how the new disclosures contribute to the information set that is available to financial‐

statementusersisessential.Ourfindingsmaythereforebeinterestingforregulators,auditors,

andmanagers.

The remainder of this paper is organized as follows: Section II describes the institutional

background. Section III reviews prior literature and develops our hypothesis. Our research

designanddataaredescribedinsectionIV.SectionsVandVIpresentourresultsandsensitivity

analyses.SectionVIIconcludes.

II. InstitutionalBackground

Audits are designed to promote confidence in the credibility of firms’ financial information,

mitigating information asymmetries between a firm’s management and its stakeholders. The

auditor’sreportistheprimarytoolthroughwhichfinancial‐statementusersobtaininformation

abouttheaudit.Usershave,however,increasinglyraisedtheirconcernsabouttheauditreport’s

lack of informative value, particularly due to its standardized language (e.g., FRC [2013b];

PCAOB[2013]).

TheUKFinancialReportingCouncil (FRC)was the first regulator to implementmeasures that

respond to users’ calls for more informative auditor’s reports. ISA 700 (UK and Ireland)

(Revised June2013)mandates auditors of firmswith a Premium listing on the London Stock

Exchangetodiscloseinformationon(1)theassessedrisksofmaterialmisstatements,(2)how

theconceptofmaterialitywasapplied,includingthematerialitythreshold,and(3)thescopeof

the audit (FRC [2013a]).1The revised standard became effective for audits of financial

statementsendingonorafterSeptember30,2013.Togetherwiththeconcurrentchangesinthe

UKCorporateGovernanceCode(FRC[2012]),theFRChastakensubstantialmeasurestoreduce

informationasymmetriesbetweenthefirm,theauditor,andthefinancial‐statementusers.The

revisedUKCorporateGovernanceCoderequirestheAuditCommitteetodiscuss,amongother

aspects,“significantissuesthatthecommitteeconsideredinrelationtothefinancialstatements,

andhowtheseissueswereaddressed”(FRC[2012,p.20]).

1Risksofmaterialmisstatementsaredefinedasthoserisks“whichhadthegreatesteffecton:theoverallauditstrategy;theallocationofresourcesintheaudit;anddirectingtheeffortsoftheengagementteam”FRC[2013a,p.6].Forsimplicity,werefer to ISA700(UKand Ireland) (Revised June2013)as ISA700throughoutthisstudy.

45

Notablechanges to theauditor’s reportwerealso implementedby theEuropeanCommission,

the IAASB, and the PCAOB. In 2014, the European Commission passed a new regulation

requiringauditorsofpublic‐interestentitiesto,interalia,discussthemostsignificantassessed

risks ofmaterialmisstatements in their auditor’s report (European Commission [2014]). The

regulationisapplicablefromJune17,2016,onwards.InJanuary2015,theIAASBissuedthenew

InternationalStandardonAuditingISA701CommunicatingKeyAuditMattersintheIndependent

Auditor’sReportandrevisedISA700ForminganOpinionandReportingonFinancialStatements

(IAASB[2015a]).Themostsignificantchangeisthatauditorsoflistedcompaniesmustdescribe

keyauditmatters(KAMs)intheirauditor’sreportsandexplainhowtheyaddressedthoserisks

duringtheauditprocedure(IAASB[2015b]).2Thenewandrevisedstandardsbecameeffective

forauditsoffinancialstatementsendingonorafterDecember15,2016.

Asaconsequence, theFRCrevised the InternationalStandardsofAuditing(UK) to implement

theEuropeanUnion’sAuditReformandtheIAASB’sprojectsondisclosures,auditorreporting,

and the auditor’s responsibility for other information accompanying financial statements

(Deloitte [2016]).3Inter alia, theFRCupdated ISA700 (UK) (Revised June2016)Formingand

OpinionandReportingonFinancialStatements, and issued ISA 701 (UK) CommunicatingKey

AuditMattersintheIndependentAuditor’sReport. The standards adopt the IAASB’s concept of

KAMs but extend it to include a discussion of themost significant assessed risks ofmaterial

misstatements.Moreover,theFRCretainstheexistentdisclosurerequirementsontheconcept

of materiality and the audit scope. The revised auditor reporting requirements apply to all

public interestentitiesandcompaniesthatarerequiredto,orvoluntarilychooseto,reporton

how they comply with the UK Corporate Governance Code and are effective for audits of

financialstatementsstartingonorafterJune17,2016.4

Enhancementstotheauditor’sreportarealsobeingimplementedintheUnitedStates.InJune

2017,thePCAOBadoptedanewauditorreportingstandard,which,interalia,requiresauditors

to communicate critical auditmatters (CAMs) in their auditor’s reports (PCAOB [2017]).5The

PCAOB expects that the expanded auditor’s report “will reduce the information asymmetry

between investors and auditors, which should in turn reduce the information asymmetry

betweeninvestorsandmanagementaboutthecompany'sfinancialperformance”(PCAOB[2017,

p. 66]). In light of the various international initiatives to enhance the auditor’s report,

2Key auditmatters are defined as “thosematters that, in the auditor’s professional judgment,were ofmost significance in the audit of the financial statement of the current period. Key audit matters areselectedfrommatterscommunicatedwiththosechargedwithgovernance”(IAASB[2015d,paragraph8]).3SinceJune17,2016,theFRCisnolongertheresponsibleauditstandardsetterinIreland(IAASA[2016]).4OursampleperiodendsbeforethesecondrevisionofISA700(UK)becameeffective.5Acriticalauditmatterisa“matterthatwascommunicatedorrequiredtobecommunicatedtotheAuditCommitteeandthat(1)relatestoaccountsordisclosuresthatarematerialtothefinancialstatementsand(2)involvedespeciallychallenging,subjective,orcomplexauditorjudgement”(PCAOB[2017,p.11]).

46

understanding whether the new requirements indeed fulfill their goals of better informing

investors about the audit process is essential. The UK setting offers researchers the unique

opportunity to provide early evidence on the informative value of the new audit‐related

information.Ourpaperisthefirsttoshedlightonanypotentialindirectcapitalmarketeffects.

III. RelatedLiteratureandHypothesis

During the ISA 700 consultation process, investors argued the new audit‐related disclosures

couldprovideinsightsintohowtheauditingstandardswereappliedonthecompanylevel.The

enhancedauditor’sreportwouldallowthemtobetterunderstandtheauditprocessandthusbe

in a better position to assess the audit’s quality (FRC [2013b]). Indeed, descriptive evidence

suggeststhenewdisclosuresarenotjustboilerplate.Comparingpre‐andpost‐ISA700auditor’s

reports, Williams Smith [2017] documents an increase in readability and a stronger use of

negativeanduncertaintone.

Opponents of the enhanced auditor‐reporting model, however, question its usefulness. In

particular, some auditors argued the new disclosures would be too complex and short

descriptions that do not capture the overall contextmight cause unnecessary concerns (FRC

[2013b]).Somepreparersfurtherbelieve“thebinaryauditor’sopinionshouldprovidethemost

importantinformationthatanauditorcanconveytousersoffinancialstatements”(FRC[2013b,

p. 6]). Prior literature on the information content of modified, non‐standard audit opinions

showsexplanatorylanguageintheauditor’sreporthasinformativevalueforfinancial‐statement

users (e.g., Chen, He, Ma, and Stice [2016]; Menon and Williams [2010]). Although the new

disclosures required by ISA 700 are not equivalent to modified audit opinions, the findings

suggest investors may consider the new audit‐related disclosures useful. Whether the new

auditor’s report fulfills its intendedpurpose is, however, anopenquestion. So far, concurrent

empiricalstudiesprovidemixedevidenceonthenewdisclosures’informativevalue.

Some studies examine direct reactions to the new auditor’s reports by analyzing investor

reactions around the annual report’s release date. The intuition is that UK firms typically

disclose earnings announcements several weeks before the actual annual reports. These

earnings announcements contain essentially all relevant financial reporting information, and

thus researcher argue the annual report only contributes two newpieces of information: the

auditor’s reportand theAuditCommittee report (Gutierrez et al. [2016];Reidet al. [2015a]).

Gutierrez et al. [2016] findno significant change in investor reactions aroundannual reports’

release dates following the revision of ISA 700. Moreover, cross‐sectional analyses show no

significantassociationbetweenthecontentofauditor’sreportsandinvestorreactions.Lennox

47

et al. [2017] conclude investors do not consider the material risks of misstatements to be

incrementallyinformative,becausethemajorityofriskswerealreadyknownbeforetheywere

disclosed in the auditor’s report. However, the authors also show the new auditor’s reports

capture the relevant financial‐statement risks. By contrast, Reid et al. [2015a] report a

significant increase in abnormal trading volume around the annual report’s publication

following the implementation of ISA 700. Additional analyses reveal the change in abnormal

tradingvolume is concentratedamong firmswithaweak informationenvironmentand those

withmoredetailedauditor’sreports.

Using a valuation model, Amiram et al. [2017] show firms with low materiality thresholds

experience an increase in their financial statements’ perceived relative credibility, thus

benefittingfromthenewdisclosuresonmateriality.Theauthorsfurtherfindfirmswithgreater

reliance on debt financing and more inside shareholders have lower materiality thresholds.

Williams Smith [2017] documents another consequence of the new audit‐related disclosures:

She finds analyst forecast dispersion decreases after implementation of the revised standard,

suggestinganalystsbenefitfromthenewdisclosures.6

Prior literature has not considered any indirect effects of the new disclosure regime. To the

extentthattherevisedauditor’sreportsenhanceinvestors’understandingoftheauditprocess

and thus potentially increase their trust in the audit, the new audit‐related informationmay

impactmarketparticipants’decisionsnotonly—ornotprimarily—around theannual report’s

publicationdate,butalsoatothertimes.Balletal.[2012,p.138]pointoutthat“auditedfinancial

reportingindirectlyaffects informationreleasedatothertimesandthroughothermedia.”The

authorsarguethatagreaterlevelofindependentfinancial‐statementverification,thatis,higher‐

quality audits, enables managers to credibly commit to the truthful disclosure of private

information.The intuition is that investorscanuseaudited financial informationtoassess the

credibilityofmanagers’pastdisclosures.Themanagerknowsthefirm’sactualperformancewill

beverifiedbyan independentauditorexpost,and thereforediscloseshisprivate information

more truthfully ex ante. Specifically, Ball et al. [2012] find a positive associationbetween the

extentofmanagementearningsforecastingactivityandtheresourcesinvestedin independent

financial‐statement verification, as proxied by excess audit fees. Investor reactions to those

forecasts increase in the level of financial‐statement verification. The latter finding implies

6Concurrent studies also use experimental designs to investigate the effect of KAMs/CAMs on auditorliability (Gimbar,Hansen,andOzlanski [2016];Brasel,Doxey,Grenier, andReffett [2016];Kachelmeier,Schmidt, and Valentine [2017]; Backof, Bowlin, and Goodson [2017]), investors’ investment decisions(Christensen,Glover, andWolfe [2014]), and investors’ information‐acquisitionprocess (Sirois,Bédard,and Bera [2017]). Köhler, Ratzinger‐Sakel, and Theis [2016] assess the informative value of KAMs forprofessional and non‐professional investors. See Bédard, Coram, Espahbodi, and Mock [2016] for anoverviewofthecurrentliterature.

48

investors consider the credibility ofmanagers’ disclosures tobe an increasing functionof the

amountofresourcesspentontheaudit(Balletal.[2012]).

Weargue thatabetterunderstandingof and thus trust in theauditprocessmay increase the

perceived credibility of managers’ private‐information disclosures. In other words, a lack of

investorreactionsaroundtheannualreports’releasedatesdoesnotnecessarilyimplythenew

auditor’sreportisnotinformative.Forexample,considerrisksofmaterialmisstatements:these

risks are the ones the auditor considered to be of great significanceduring the audit process

(FRC [2013a]). Although the disclosed risks are not an indication of audit quality per se, the

auditor’s professional reaction to the material risks of misstatements may affect investors’

assessmentsofthecredibilityofmanagers’earningsforecasts.Forecastsmaybeconsideredto

bemorecredibleinthepost‐ISA‐700‐periodif investorslearnhowauditorsreacttothefirm’s

risky areas. Previously, they only knew the majority of risks existed, but did not have any

informationonwhetherandhowtheauditordealtwiththoserisks(Lennoxetal.[2017]).

Ourargumentregardingthematerialitythresholdissimilar: theauditor’staskistodetermine

whether financial statements have beenprepared in accordancewith the relevant accounting

standards and whether they are free of material misstatements. The materiality threshold

informsinvestorsabouttheamounttheauditorconsidersquantitativelymaterial.Thus,alower

materiality threshold does not necessarily imply the level of assurance is higher. It does,

however, inform investors about theamount theauditor considersmaterial.This information

maybeuseful to investorswhenevaluatingthecredibilityofmanagement forecasts. Investors

nowbetterunderstandtheextenttowhichtheauditedfinancialstatementswillbecapableof

restrictingmanagers’discretion.Tosummarize,abetterunderstandingofandthereforetrustin

theauditprocess—thekeyobjectiveofthenewregulation—isnotequallyimportantatalltimes

and for all decisions. Based on the new audit‐related disclosures, investorsmay update their

beliefsabout theverification roleof financial statementsand thus change their assessmentof

thecredibilityofmanagers’private‐informationdisclosures.

OursettingdiffersfromBalletal.[2012]intwocriticalrespects:First,thenewdisclosuresare

not designed to increase audit quality but rather to inform investors about how financial‐

statementverificationisachieved.7Second,thedecisioninBalletal.[2012]toinvestresources

inmanagementforecastsandfinancial‐statementverificationismadejointly;thatis,thelevelof

voluntary disclosure is endogenous to the level of audit fees. In our setting, the disclosure of

audit‐related information is exogenous. It is mandated by an auditing standard. If the new

information improvesinvestors’understandingoftheauditprocess, itshouldstrengthentheir

7The empirical evidence on whether the new auditor’s reports increase audit quality is inconclusive(Reid,Carcello,Li,andNeal[2015b];Gutierrezetal.[2016]).

49

trust in itsverificationroleandwewouldexpect investors toconsider this informationwhen

evaluating managers’ voluntary disclosures. Consequently, we expect to observe a change in

investorreactionstomanagementforecastsaftertherevisionofISA700.

Hypothesis:

Investorsconsiderthenewaudit‐relateddisclosureswhenassessingthecredibilityofmanagers’ex‐

anteunverifiablevoluntarydisclosures.

IV. DesignandData

ResearchDesign

Totestourhypothesis,weuseshort‐windoweventstudiestoexaminemarketreactionsaround

management earnings forecasts. We use bid‐ask spreads (SPREAD) and absolute CARs

(ABS_CAR) to measure investor reactions. Bid‐ask spreads are commonly used as a direct

measureofinformationasymmetries(e.g.,LeuzandVerrecchia[2000]).Theeconomicintuition

is that the bid‐ask spread reflects the costs of adverse selection that arise from information

asymmetries and reduces a firm’s liquidity (Glosten andMilgrom [1985]). Firms canmitigate

thiseffectbyvoluntarilydisclosingprivate information,which increasesa firm’s liquidityand

thus decreases its cost of capital via a reduction in information asymmetries (Diamond and

Verrecchia [1991]). Consistent with economic theory, Coller and Yohn [1997] show

managementearningsforecastsreduceinformationasymmetriesaboutthefirm.8Therefore,to

theextentthatthenewauditor’sreportstrengthensinvestors’understandingofandtrustinthe

auditprocess,weexpecttoobserveadecrease inspreadsaroundmanagement forecastsafter

therevisionofISA700.Pricereactions,ascapturedbytheCAR,reflectchangesinthemarket’s

expectation (Beaver [1968]). Consequently, if the new audit‐related disclosures improve the

perceivedcredibilityofmanagementearningsforecastsbyenhancinginvestors’understanding

of the verification role of audited financial statements,we expect to observe a stronger stock

marketreactiontoforecastsaftertherevisionofISA700.

Westartouranalysisbyestimating the change in investor reactions tomanagementearnings

forecastsemployingthefollowingpre‐postmodel:

InvestorReactions=α+β1Post+β2Size+β3Leverage+β4ROA+β5MB+β6Fees

+β7#Analysts+β8GoodNews+IndustryFE+ε.

8SeeHirst,Koonce,andVenkataraman[2008]foranoverviewoftheliteratureonmanagementforecasts.

50

We employ an eventwindow of [‐1;1] to compute the average bid‐ask spread (SPREAD) and

absoluteCAR(ABS_CAR)aroundeachforecast.SPREAD iscomputedasthedifferencebetween

dailyaskandbidpricesdividedby themidpoint.CARsarecomputedusing themarketmodel

andanestimationwindowof[‐45;10].WefollowBalletal.[2012]andusetheabsolutevalueof

CAR.Theintuitionisthatweareinterestedinthemagnitudeofthemarketreactionsratherthan

thedirectionofthestockpricemovement.

Postisanindicatorvariabletakingthevalueof1 ifthemanagementforecastispublishedafter

therevisionofISA700,andzerootherwise.9Notethepost‐periodmayvaryonthefirmleveldue

todifferentfiscalyear‐ends.Tofallwithinthepost‐period,werequireaforecasttobepublished

after the company released its first annual report containing the new audit information.

Following prior literature,we control for several forecast and accounting characteristics that

may be associated with investor reactions, and match lagged accounting and auditing

information to each forecast. The intuition is that investors take into account the audit

information disclosed in the most recent annual report when assessing the credibility of

managementforecaststhataresubsequentlyreleased.

WedefineSize as the logofbookvalueof total assets,Leverage as totaldebtdividedby total

assets,andROAasnetincomedividedbytotalassets.MBiscalculatedasmarketvalueofequity

divided by book value of equity, Fees as the log of the auditor’s total remuneration, and #

Analystsasthelogof1plusthenumberofanalystsfollowingthefirm.GoodNewsisanindicator

variable taking the value of 1 if the CAR around amanagement forecast is positive, and zero

otherwise.WefurtherincludeindicatorvariablesforeachindustryusingtheFamaandFrench

[1997]17‐industryclassificationtocontrolforindustrycharacteristics.Ifinvestorsconsiderthe

enhancedauditor’sreporttobeusefulwhenassessingthecredibilityofmanagementforecasts,

weexpectthecoefficientonPosttobesignificantlynegative(positive)wheninvestorreactions

areproxiedbythebid‐askspread(absoluteCAR).

Any change inmarket reaction in the post‐period identified by ourmodelmay be driven by

factors other than the new auditor’s report per se. In a next step, we therefore investigate

whetherthechangeinmarketreactionsinthepost‐perioddiffersdependingontheinformation

includedintheauditor’sreports.Weemploythefollowingproxiestomeasuretheinformation

content of the auditor’s reports: # Risks is the number of risks of material misstatements

disclosedbytheauditor,andGroupMaterialityisthegroupmaterialitythresholdexpressedasa

percentageoftotalassets.Wedefine#Words(All)asthenumberofwordsusedtodescribethe

scope, risks, and materiality. #Words (Risks) is the number of words used to describe the

materialrisksofmisstatements,and#Words(Materiality+Scope) isthenumberofwordsused

9SeeAppendixAforamoredetaileddescriptionandanexplanatoryexample.

51

to describe the audit’s scope and the applied materiality threshold.10ACMateriality is the

threshold for reportablemisstatements to the Audit Committee expressed as a percentage of

totalassets.#ACRisks isdefinedasthenumberofrisksdisclosedbytheAuditCommittee.We

winsorizeallcontinuousregressionvariablesat1%tominimizetheeffectsofoutliers.

In contrast toReid et al. [2015a] andLennox et al. [2017],wedo not use firms listed on the

LondonStockExchange’sAlternativeInvestmentMarket(AIM)orUSfirmsascontrolgroups.As

Gutierrezetal. [2016]pointout, thetwogroupsexhibitsignificantdifferencesfromPremium‐

listed firms in terms of their compliance and listing requirements as well as their litigation

environments.11Instead, we focus on the structural breaks in market reactions to forecasts

beforeandaftertherevisionofISA700.Weaddresspotentialconcernsregardingthecausality

ofourfindingsbypartitioningoursamplebasedondifferenttypesofauditinformation.

SampleConstructionandData

ISA700(UKandIreland)(RevisedJune2013)appliestoallfirmswithaPremiumlistingonthe

LondonStockExchange.Our sampleperiod spansover fouryears, starting twoyearsprior to

andendingtwoyearsaftertherevisedauditingstandardbecameeffective.Weobtainhistorical

companylistsfromtheLondonStockExchange’swebsiteandexcludeallfinancialcompanies.12

We further require our sample firms to be listed on the Premium segment during the entire

sampleperiodaswellastohaveavailablemarket,accounting,andanalystdataforallyears.To

compareacompanywithitselfovertime,werequirefirmstodiscloseatleastonemanagement

earningsforecastperyear.Ourfinalsampleconsistsof1,394forecastsfrom143firmsoverfour

years.Table1describesoursample‐selectionprocess.


WeobtainmanagementearningsforecastsfromtheCapitalIQS&PKeyDevelopmentsdatabase.

OurdatasetendsonJune30,2016,becausedata forsubsequentperiodswerenotavailableat

the date of our sample collection.We follow the approach of Li and Yang [2016] to identify

management earnings forecasts in Capital IQ S&P Key Developments using textual analysis.

10We combine the number of words used to describe the audit’s scope and the applied materialitythresholdsbecausetheyarefrequentlyexplainedinthesectionoftheauditor’sreport.11Comparing untabulated descriptive statistics shows AIM and Premium firms are very different. Forexample, firms with a Premium listing are significantly larger, more profitable, and have lowerinformationasymmetries.12http://www.londonstockexchange.com/statistics/historic/company‐files/company‐files.htm.

52

Appendix A provides a detailed description of our coding procedure.Market, accounting, and

audit‐fee data (denoted in millions) are obtained from FactSet. We hand‐collected missing

accounting data in FactSet from the firms’ annual reports to avoid a further reduction of our

samplesize.AnalystdataareobtainedfromI/B/E/S.Audit‐relatedinformationishand‐collected

fromeach firm’sannualreport,whichweobtain fromthePerfect Informationdatabaseorthe

respectivecorporatewebsites.

V. EvidenceontheReport’sInformationContent

SummaryStatistics

ThesummarystatisticsreportedinTable2provideafirstindicationthattheenhancedauditor’s

reportmayhavesomeinformativevalue.Inthepost‐period,firmsexperienceasmalldecrease

ininformationasymmetriesaroundmanagementearningsforecasts.Themedianbid‐askspread

is stable. Both the mean and median CAR increase in the post‐period. Untabulated statistics

furthershowtheforecastfrequencyperfirmdoesnotchangesignificantlyinthepost‐period(p‐

value:0.5596).Afirmdisclosesonaverage4.9forecastsduringthepre‐periodand4.8forecasts

duringthepost‐period.

In both periods, approximately half the management forecasts trigger a positive market

reaction.Thetotalauditfeespaidbyoursamplefirmsincreaseinthepost‐period;however,this

increaseisnotstatisticallysignificant.Moreover,wheninterpretingthevariableFees,onemust

keep in mind that it measures the auditor’s total remuneration and does not differentiate

betweenfeespaidforaudit‐relatedandfeespaidfornon‐audit‐relatedservices.

We further providedescriptive information about the enhanced auditor’s reports in thepost‐

period:thethreenewsectionsintheenhancedauditor’sreportcontainonaverage1,532words,

ofwhichapproximately70%describetherisksofmaterialmisstatements.Theauditorsapplya

materiality thresholdof on average0.64%of total assets anddiscloseon average4.2 risks of

material misstatements. This finding is in line with Gutierrez et al. [2016], who report an

averagematerialitythresholdof0.61%oftotalassetsandanaverageof3.9risks.Thehighest

materialitythresholdappliedinoursampleis6.5%oftotalassets,whereasthelowestthreshold

is0.1%oftotalassets.Thehighestnumberofrisksdisclosedinanauditor’sreportinoursample

is 10,whereas the lowest number is one. The auditor informs theAudit Committee about all

auditdifferencesinexcessofonaverage0.03%oftotalassets.TheAuditCommitteediscloseson

average4.8risks,whichisslightlymorethantherisksofmaterialmisstatementsidentifiedby

theauditor.

53


Table3reportsthesummarystatisticsofallauditinformationbyauditfirm.Thevastmajorityof

oursamplefirms—97%—areauditedbyaBig4auditor.ThisfindingisinlinewithGutierrezet

al. [2016]andLennoxetal. [2017],whoreportaBig4quotaofapproximately94%and90%,

respectively.PricewaterhouseCoopers(PwC)auditsthelargestshareofsamplefirms,with32%,

followed by KPMG and Deloitte Touche with 26% each. Our audit‐firm distribution is very

similar to Lennox et al. [2017]. Grant Thornton has the most detailed auditor’s report, with

1,773words, closely followed by PwC,with 1,738words. BDO and Deloitte Touche have the

shortestdescriptionsofaudit‐related information,with996and1,377words,respectively.On

average,PwCusesalmosttwiceasmanywordstodescribetherisksofmaterialmisstatements

comparedtoErnst&Young,althoughErnst&Younghasbyfarthemostdetaileddescriptionof

theaudit’sscopeandtheappliedmaterialitythreshold.WiththeexceptionofGrantThornton,all

audit firms apply a relatively similar averagemateriality threshold of approximately 0.5% to

0.7%oftotalassets.NotethelargeaverageforGrantThorntonisdrivenbyone(outlier)client

and thus does not indicate a significantly different audit approach: the untabulated median

materiality threshold of 0.6% of total assets is similar to the thresholds applied by the other

auditfirms.Mostauditorsidentifyonaveragefourrisksofmaterialmisstatements,withKPMG

disclosingthefewest(3.6risks).Again,one(outlier)clientdrivesthecomparablylargernumber

ofaveragerisksdisclosedbyGrantThornton.Theuntabulatedmedianisfourrisksandisagain

comparabletotheotherauditfirms.


Pre‐PostAnalysis

Table 4 reports the results of our pre‐post analysis. The coefficient on Post is negative and

statistically significant when using SPREAD as our dependent variable. In other words,

informationasymmetriesaroundmanagementforecastsdecreaseaftertherevisionofISA700.

Moreover, firmsthatare larger,moreprofitable,andhavemoreanalysts followingexperience

lowerinformationasymmetries.ThecoefficientonFeesisinsignificant.NotethatFeesmeasures

the auditor’s total remuneration. A larger amount of Fees may therefore either stem from a

higherdegreeoffinancial‐statementverificationoralargerproportionofnon‐auditfees,which

54

are generally believed to harm the auditor’s independence. UsingABS_CAR as our dependent

variable, the coefficient on Post is significantly positive. Thus, the absolute CAR around

management forecasts is higher after the revision of ISA 700. The analysis further shows the

marketreactionislesspronouncedforlargerandmoreprofitablefirmswithahigherleverage

andmarket‐to‐bookratio.Wedonotcontrolforthemanagementforecasts’informationcontent

when using ABS_CARas our dependent variable, because the indicator variable GoodNews is

basedon theCARaroundeachmanagement forecast.13Overall, thepre‐postanalysis supports

our hypothesis that investors consider the new audit‐related information to be useful when

assessingthecredibilityofmanagers’earningsforecasts.


Pre‐PostAnalysisbyAuditor’sReportInformation

Thechangeinmarketreactionsinthepost‐periodmaybedrivenbyfactorsotherthanthenew

auditor’sreportperse.Toalleviatethisconcern,weanalyzewhetherthemarketreactionvaries

dependingonthenewdisclosures’informationcontent.TheresultsarereportedinTables5to

10.

Westartouranalysisbypartitioningthesamplebasedonwhetherthenumberofwordsusedto

describethenewinformationintheauditor’sreportisaboveorbelowthesamplemedian.Thus,

the number of words is a proxy for how detailed the auditor’s description of his work is.

Consequently,weexpectthechangeininvestorreactionsinthepost‐periodtobeconcentrated

inthesubsamplewithmoredetaileddescriptions.Table5showsthebid‐askspreadsdecrease

inthepost‐periodforfirmswithmoredetailedauditor’sreports.Bycontrast,thecoefficienton

Post for firms with less detailed reports is negative but insignificant. The results are similar

whenusingABS_CARasourdependentvariable.Investorsreactmorestronglytomanagement

forecasts in thepost‐periodwhentheauditor’s reportcontainsmoredetailed information.No

significantchangeinabsoluteCARcanbeobservedinthesubsamplewithlessdetailedauditor’s

reports.Overall,theresultsprovideevidencethatinvestorsconsiderdetaileddescriptionsinthe

auditor’s report to be informative when evaluating the credibility of management earnings

forecasts.

13OurresultsholdwhenweincludethevariableGoodNewsinourregressionmodel.

55


Toobtainabetterunderstandingofthedifferentsectionsintheauditor’sreport,were‐estimate

ouranalysis,differentiatingbetweenthenumberofwordsusedtodescribetherisksofmaterial

misstatements and the number of words used in the sections on the audit’s scope and the

applied group materiality threshold. The risk section typically describes the identified risks,

explainshowtheauditordealtwiththerisks,anddiscussestheconclusions.Thesectiononthe

materialitythresholdiscomparablyshorterandincludesthequantitativematerialitythreshold,

the base, and the percentage used to determine the threshold as well as the threshold for

reportablemisstatementstotheAuditCommittee.Thirty‐twopercentand15%oftheauditor’s

reportsinoursamplealsostatethecomponentandperformancemateriality,respectively.The

section on the audit scope encompasses a mostly short description on how the auditor

structuredtheaudit.

Table6reportsourresults.Firmswithamoredetailedriskdescriptionexperienceasignificant

decrease in bid‐ask spreads and a significant increase in absolute CAR around management

forecastsfollowingtheimplementationoftherevisedauditingstandard.Firmswithlessdetailed

explanationsdonotexhibitanysignificantchange inmarket reactionsduring thepost‐period.

Theresultsfortheaudit’sscopeandappliedmaterialitythresholdaremixed.Whiletheabsolute

CARincreasesinthepost‐periodforfirmswithmoredetailedexplanations,thebid‐askspread

doesnotchangesignificantly following therevisionof ISA700.Apossibleexplanation for the

mixed results is that investors consider the quantitative materiality threshold to be more

importantthanitsqualitativedescription.


We thereforeanalyze the information contentof thequantitative groupmateriality threshold.

We partition the sample based on whether the materiality threshold is above or below the

median.Thematerialitythresholdisherebyexpressedasapercentageoftotalassets.Weexpect

the market reaction in the post‐period to be stronger for firms with lower materiality

thresholds,becausealowermaterialitythresholdrestrictstheamountuptowhichmanagement

canexercisediscretion.Table7showsthatfirmswithmaterialitythresholdsbelowthemedian

experience a significant decrease in information asymmetries and a significant increase in

56

absolute CAR following the revision of ISA 700. Firms with higher materiality levels do not

exhibit a significant change in SPREADorABS_CARduring the post‐period. The results show

investors take into account the quantitative materiality thresholds when evaluating the

credibilityofmanagementearningsforecasts.


The auditor’s report also contains the threshold for reportable misstatements to the Audit

Committee. The threshold for reportable misstatements in our sample is on average

approximately 4% of the applied group materiality threshold. Untabulated statistics further

show the two thresholds have a correlation of 0.8735.We re‐estimate our pre‐post analysis,

partitioningthesamplebasedonwhetherthethresholdofreportablemisstatements(expressed

as a percentage of total assets) is above or below the sample median. Given that the two

thresholds are interrelated, we expect to observe results similar to those of our group

materiality analysis. The results in Table 8 confirm our expectation. Firms with thresholds

below themedian for reportablemisstatements experience a statistically significant decrease

(increase)inbid‐askspreads(absoluteCAR)inthepost‐period.


Next,weexaminetheinformativevalueofthenumberofrisksidentifiedbytheauditor.Wedo

notformanexpectationforthisanalysis,becausedifferentscenariosarepossible:First,thetotal

numberofriskspersemaynotbeinformativetoinvestors;rather,explanationsmightmatter.

Second,investorsmayfindmanagementforecastsoffirmswithmoreriskstobelesscrediblein

thepost‐ISA‐700‐periodbecausetheyconsidertheauditedfinancialstatementtoberiskierand

the audit’s verification role thusweaker expost.However, this scenario seemsunlikely given

thatthemajorityofriskswerealreadyknowntoinvestorsbeforetheIAS700revision(Lennox

et al. [2017]). In this case, the bid‐ask spreads would increase and the absolute CAR would

decrease for firms with more audit risks in the post‐period. Third, investors might consider

managementforecastsissuedbyfirmswithahighernumberofriskstobemorecredibleinthe

post‐period.The intuition is that investorswere already informedabout themajority of risks

57

beforetheyweredisclosedintheauditor’sreport(Lennoxetal.[2017]).However,theydidnot

know whether and how the auditor addressed those risks, and thus consider the audit’s

verificationroletobestrongerexpost.Inthiscase,thebid‐askspreadswoulddecreaseandthe

absoluteCARwouldincreaseforfirmswithmorerisksaftertherevisionofISA700.Consistent

with the third scenario, Table 8 shows a significantly negative (positive) coefficient on Post

when we use SPREAD (ABS_CAR) as our dependent variable. Taken together, our analyses

provide evidence that investors consider the new auditor’s report to be informative when

assessingthecredibilityofmanagementearningsforecasts.


Pre‐PostAnalysisbyAuditCommitteeReportInformation

When it implemented the enhanced auditor’s report, the FRC also revised its UK Corporate

GovernanceCode.ThenewcoderequirestheAuditCommitteetodisclose“significantissuesthat

the committee considered in relation to the financial statements, and how these issueswere

addressed” (FRC [2012,p. 20]).Although the risksofmaterialmisstatements reportedby the

auditorandthesignificantissuesidentifiedbytheAuditCommitteearenotnecessarilyidentical,

theFRCreportsanoverlapof74%in the firstyearand85%inthesecondyear followingthe

revisionofISA700(FRC[2016a]).Bothrisktypeshaveacorrelationof0.5484inoursample.

ThisfindingisconsistentwithGutierrezetal.[2016],whoreportacorrelationof0.54.

We assess the information content of theAudit Committee’s significant issues bypartitioning

our sample based onwhether the number of significant issues is above or below the sample

median.The results are reported inTable9. In linewithourprevious findings, firmswithan

above‐the‐median number of significant issues experience a statistically significant decrease

(increase) inbid‐askspreads (absoluteCAR)aroundmanagementearnings forecastsafter the

revisionofISA700.AlthoughtheabsoluteCARinthepost‐perioddoesnotchangesignificantly

forfirmsbelowthemedian,theincreaseinbid‐askspreadsisstatisticallysignificantatthe10%

level.


58

VI. SensitivityAnalyses

Weperformseveral (untabulated)sensitivityanalyses toassess therobustnessofour results.

The sample used in our main analysis consists of firms that regularly issue management

earningsforecasts.Weconsiderthefocusonregularforecasterstobeadesignchoice.However,

firmsmightnon‐randomlyselect intothegroupofregular forecasters,whichmayinturnbias

ourregressionresults(Lennox,Francis,andWang[2012]).WethereforeemploytheHeckman

[1979]two‐stageregressionmodelasourfirstsensitivityanalysis.Inthefirststage,wemodel

thedecisiontopublishatleastoneforecastperyear,byestimatingaprobitmodel.Werelyon

Hirstetal.'s[2008]studytoidentifydeterminantsofbeingafirmthatregularlyissuesforecasts.

Inthesecondstage,weincludetheinversemillsratiofromthefirststageandreplicateourpre‐

postanalysis.Theresultsarequalitativelysimilarandthecoefficientontheinversemillsratiois

statisticallyinsignificant.

Second,werelaxoursamplingrequirementsandnolongerrequirefirmstopublishatleastone

forecastperyear.Ouradjustedsampleconsistsof2,042forecastspublishedby313firms.Again,

ourresultsarequalitativelysimilar.

Third,ourdependentvariablesarenon‐negativebyconstruction.Wethereforere‐estimateour

analysesandapplyaTobitregressionmodel.Theresultsarerobust,withthecoefficientsbeing

almostidenticaltoourmainregressionmodel.

Fourth, to control for time‐invariant differences between the auditors, such as the auditor’s

individual style,were‐estimateouranalysesand includeauditor‐fixedeffects.Theresultsare

qualitativelysimilar.

Fifth,were‐estimateouranalyses,employingtheFamaandFrench[1997]classificationwith12

and30industries.Ourresultsarerobust.Theonlynoteworthyexceptionisthatfirmswithan

above‐the‐median number of words used to describe the audit’s scope and the applied

materialitythresholddonotexperienceasignificantincreaseinabsoluteCARinthepost‐period.

ThecoefficientonPostis0.006(p‐value:0.109)whenweuse12‐industryclustersand0.006(p‐

value: 0.104) when we use 30‐industry clusters. The sensitivity analysis further indicates

investors consider the quantitative materiality threshold to be the most useful piece of

informationintheauditreport’ssectiononscopeandmateriality.

Last,weexcludeallforecaststhatareamereconfirmationofpreviouslyannouncedforecasts.14

We perform two versions of this sensitivity analysis. We first simply exclude all 157

14SeeAppendixAformoreinformationonhowweidentifiedforecastconfirmationsintheCapitalIQS&PKeyDevelopmentsdatabase.

59

confirmatory forecasts from our current sample. During the pre‐ and post‐periods, firms

disclosed71and86confirmationforecasts,respectively.Theresultsarequalitativelysimilar.In

thealternativeversion,wesamplefirmsbasedonnon‐confirmativeforecasts,andyieldatotal

sample of 1,134 forecasts from 124 firms. Again, our results are robust to this alternative

specification.

VII. Conclusion

Respondingtoinvestors’callsformoreinformativeauditor’sreports,theUKFinancialReporting

Council implemented substantial changes to the auditor’s report in 2013. Understanding the

newdisclosures’informativevalueforfinancial‐statementusersisessential,asotherregulators

have recently applied similar requirements (PCAOB [2016]; IAASB [2015b]; European

Commission[2014]).Incontrasttoconcurrentstudies(e.g.,Gutierrezetal.[2016];Lennoxetal.

[2017];Reidetal.[2015a]),whichprimarilyfocusondirectreactionstothenewaudit‐related

information,we consider the premise that audited financial informationmay indirectly affect

informationreleasedatothertimes(Balletal.[2012]),andanalyzewhetherinvestorsconsider

the new disclosures to be useful when evaluating the credibility of management earnings

forecasts.

Weprovideevidence in favorof theenhancedreport’s informativevalue.Specifically,we find

thatinformationasymmetriesaroundforecastsdecreaseaftertherevisionofISA700,whilethe

absolute cumulative abnormal returns increase. Taking into account the new reports’

informationcontent,weshowthechangeinmarketreactionisconcentratedamongfirmswith

more detailed auditor’s reports, a higher number of risks, and lower group materiality

thresholds.

Ourstudy issubject tocertaincaveats.First,oursamplecontainsarelativelysmallnumberof

UK firms, which could limit the generalizability of our results. Further analyses with US and

Europeandatamaybehelpfultocorroborateourfindings.Moreover,wesuggestfuturestudies

shouldexamine the typesof risks investorsconsider importantwhenevaluatingmanagement

forecasts.Second,wecannotruleoutthatotherunidentifiedfactorscoulddriveourresults.We

attempt tominimize thispossibilitybyanalyzinghow investors react to thedifferent typesof

informationcontainedintheauditor’sreport.

60

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64

AppendixA:IdentificationandAssignmentofForecasts

Thissectiondescribestheidentificationandassignmentofmanagementforecastsobtainedfrom

theCapitalIQS&PKeyDevelopmentsdatabase.WefollowtheapproachinLiandYang[2016]to

identifymanagementearningsguidance.WecollectallnewsitemsoftheKeyDevelopmentType

“Corporate Guidance.” A corporate guidance is identified as an earnings guidance if the news

headline or text includes the word “Earning,” “earning,” or “EPS.” A corporate guidance is

identified as “Confirmed” if its Key Development Type is “Corporate Guidance –

New/Confirmed”anditsheadlineincludestheword“affirm,”“Affirm,”“reiterate,”or“Reiterate.”

Similarly,acorporateguidanceisclassifiedas“New”ifitsKeyDevelopmentTypeis“Corporate

Guidance–New/Confirmed”anditsheadlinedoesnotcontainanyoftheaforementionedwords.

Whenmultiplenewsitemsarereleasedononeday,weexcludeduplicates.

Akeyaspectofourresearchdesignistocorrectlyassignmanagers’forecaststotherespective

fiscalyear.CapitalIQS&PKeyDevelopmentsprovidesuswiththeforecast’spublicationdatebut

does not contain a separate column with the fiscal year to which the forecast relates. This

information isonly included in thenews item’sheadlineand text.Weassigna forecast to the

fiscalyeartifitispublishedonoraftertheannualearningsannouncementforthefiscalyeart‐1

andbeforetheannualearningsannouncementforthefiscalyeart.Weobtainannualearnings

announcementdates fromI/B/E/SandCapital IQS&PKeyDevelopments,and identifyannual

earnings announcements in Capital IQ using text analysis. See Appendix B for the list of the

wordsusedforourtextualanalysis.

Next,weassignlaggedaccountingandauditinformationtoeachforecast.Forexample,Aggreko

plcprovidesearningsguidanceforthefullyear2013onOctober28,2013.Thefirm’sfiscalyear‐

endisonDecember31,2013.ItsearningsannouncementdatesareMarch7,2013,andMarch6,

2014. We thus assign the accounting and auditing variables of the fiscal year 2012 to this

forecast. The intuition is that investors take into account the information obtained from the

independent auditor’s report of 2012 to assess the forecasts released during the subsequent

fiscalyear.Consequently,theindicatorvariablePosttakesavalueofzerobecausethenewaudit‐

related information is not yet available.Wemanually check our coding procedure for a large

subsampleoffirms.

65

AppendixB:IdentificationofEarningsAnnouncements

Weperformawordsearchintheheadlineofnewsitemstoidentifythedateofafirm'sannualearnings announcement in Capital IQ S&PKeyDevelopments. A news item is classified as anannualearningsannouncementifitsKeyDevelopmentTypeis“AnnouncementofEarnings”anditsheadlinecontainsoneofthefollowingwordcombinations:

financialresultsfortheyearended FinancialResultsfortheYearEndedearningsresultsfortheyearended EarningsResultsfortheYearEndedearningsfortheyearended EarningsfortheYearEndedfinancialresultsfortheyearending FinancialResultsfortheYearEndingearningsresultsfortheyearending EarningsResultsfortheYearEndingearningsforyearending EarningsfortheYearEndingfinancialresultsforthefiftytwoweek FinancialResultsfortheFiftyTwoWeekearningsresultsforthefiftytwoweek EarningsResultsfortheFiftyTwoWeekearningsforthefiftytwoweek EarningsfortheFiftyTwoWeekfinancialresultsforthe52week FinancialResultsforthe52Weekearningsresultsforthe52week EarningsResultsforthe52Weekearningsforthe52week Earningsforthe52Weekfinancialresultsforthefiftythreeweek FinancialResultsfortheFiftyThreeWeekearningsresultsforthefiftythreeweek EarningsResultsfortheFiftyThreeWeekearningsforthefiftythreeweek EarningsfortheFiftyThreeWeekfinancialresultsforthe53week FinancialResultsforthe53Weekearningsresultsforthe53week EarningsResultsforthe53Weekearningsforthe53week Earningsforthe53Weekfinancialresultsforthetwelvemonth FinancialResultsfortheTwelveMonthearningsresultsforthetwelvemonth EarningsResultsfortheTwelveMonthearningsforthetwelvemonth EarningsfortheTwelveMonthfinancialresultsforthe12month FinancialResultsforthe12Monthearningsresultsforthe12month EarningsResultsforthe12Monthearningsforthe12month Earningsforthe12Monthfinancialresultsforthethirteenmonth FinancialResultsfortheThirteenMonthearningsresultsforthethirteenmonth EarningsResultsfortheThirteenMonthearningsforthethirteenmonth EarningsfortheThirteenMonthfinancialresultsforthe13month FinancialResultsforthe13Monthearningsresultsforthe13month EarningsResultsforthe13Monthearningsforthe13month Earningsforthe13Monthfinancialresultsfortheyear FinancialResultsfortheYearearningsresultsfortheyear EarningsResultsfortheYearearningsfortheyear EarningsfortheYearfinancialresultsforthefullyear FinancialResultsfortheFullYearearningsresultsforthefullyear EarningsResultsfortheFullYear)earningsforthefullyear EarningsfortheFullYear"earnings"and"andyear ended" "Earnings" and "and "Year Ended""earningsresult"and"andyearended" "Earnings Result" and "and YearEnded""financialresult"and"andyearended" "Financial Result" and "and YearEnded""earnings"and"andyear ending" "Earnings" and "and Year Ending""earningsresult"and"andyearending" "Earnings Result" and "and YearEnding""financialresult"and"andyearending" "Financial Result" and "and YearEnding"

66

Table1:SampleSelection

Thistabledescribesoursamplingapproach.Oursample‐selectionprocessstartswithhistoricalcompanylistsretrievedfromtheLondonStockExchange’swebsite.Market,forecast,accounting,analyst, andauditdataareobtained fromFactSet,Capital IQS&PKeyDevelopments, I/B/E/S,Perfect Information, and by hand‐collection. Our final sample consists of 1,394 forecastspublishedby143companiesover4years.

UKfirmswithpremiumlistingontheMainMarketofLondonStockExchangebetweenSeptember2013andSeptember2015

1,074

‐FirmsnotincorporatedintheUK 199

‐Financialfirms 438

‐Duplicatesduetonamechanges 22

‐FirmsnotavailableinFactSet 6

‐Firmsswitchinglistingsegmentduringsampleperiod 10

‐FirmsnotavailableinCapitalIQS&PKeyDevelopmentsorI/B/E/S 47

‐Firmswithlessthanoneforecastperyear 207

‐Firmswithmissingdata 2

=Numberoffirmsinsample 143

=Numberofforecastsinsample 1,394

67


Thistablereportsthesummarystatisticsofallvariablesforthepre‐andpost‐period.SPREADistheaveragebid‐askspreadaroundeachforecastandiscalculatedasthedifferencebetweenthedailyaskandbidpricedividedbythemidpoint.ABS_CAR is theabsolutecumulativeabnormalreturn computed using the market model and an estimation window of [‐45;10]. An eventwindowof[‐1;1]isusedforbothmarketvariables.GoodNewsisanindicatorvariabletakingthevalueof1iftheCARaroundamanagementforecastispositive,andzerootherwise.WedefineSizeas the logofbookvalueof totalassets,Leverageas totaldebtdividedbytotalassets,andROAasnetincomedividedbytotalassets.MBismarketvalueofequitydividedbybookvalueofequity,#Analysts isthelogof1plusthenumberofanalystsfollowingthefirm,andFees isthelogoftheauditor'stotalremuneration.Wedefine#Words(All)asthenumberofwordsusedtodescribe the audit’s scope, risks, andmateriality threshold.#Words (Risks) is the number ofwordsusedtodescribetherisksofmaterialmisstatements,and#Words(Materiality+Scope)isthenumberofwordsusedtodescribetheaudit’sscopeandtheappliedmaterialitythreshold.#Risksdenotesthenumberofrisksofmaterialmisstatementsdisclosedbytheauditor,andGroupMaterialitydenotesthegroupmaterialitythresholdexpressedasapercentageoftotalassets.ACMaterialityisthethresholdforreportablemisstatementstotheAuditCommitteeexpressedasapercentageoftotalassets.#ACRisksisdefinedasthenumberofsignificantissuesdisclosedbytheAuditCommittee.All continuousvariableswith theexceptionof theaudit informationarewinsorizedat1%.Thesampleperiodspansfouryears.

Mean Median SD N Mean Median SD N

SPREAD 0.0020 0.0012 0.0040 707 0.0019 0.0012 0.0030 687ABS_CAR 0.047 0.033 0.048 707 0.053 0.034 0.059 687

GoodNews 0.542 1.000 0.499 707 0.525 1.000 0.500 687

Size 7.315 7.313 1.614 286 7.396 7.427 1.585 286Leverage 0.207 0.193 0.156 286 0.224 0.200 0.165 286ROA 0.071 0.065 0.063 286 0.054 0.059 0.082 286MB 3.328 2.298 5.864 286 3.805 2.937 6.639 286#Analysts 2.590 2.773 0.613 286 2.526 2.708 0.610 286

Fees 0.099 0.000 1.194 286 0.144 0.052 1.229 286#Words(All) 1531.552 1361.500 674.932 286#Words(Risks) 1084.535 947.000 628.453 286#Words(Materiality+Scope) 447.017 420.500 175.387 286GroupMateriality 0.640 0.516 0.502 286ACMateriality 0.027 0.021 0.025 284#Risks 4.245 4 1.485 286#ACRisks 4.833 5 1.981 282

Pre‐Period Post‐Period

MarketData

AccountingData

AuditData

ForecastData

68

Table3:AuditInformationbyAuditFirm

Thistablereportstheinformationintheauditor’sreportbyaudit firm.Wedefine#Words(All)asthenumberofwordsusedtodescribetheaudit’sscope, risks, andmateriality threshold.#Words (Risks) is the number of words used to describe the risks of material misstatements, and#Words(Materiality+Scope) is thenumberofwordsusedtodescribetheaudit’sscopeandtheappliedmateriality threshold.GroupMateriality is thegroupmaterialitythresholdexpressedasapercentageoftotalassets,andACMaterialityisthethresholdforreportablemisstatementstotheAuditCommitteeexpressed as apercentageof total assets.#Risks denotes thenumberof risksofmaterialmisstatementsdisclosedby theauditor, and#ACRisks isdefinedasthenumberofsignificantissuesdisclosedbytheAuditCommittee.Thesampleperiodspansfouryears.

#Firm‐Years

%ofSample

#Words(All)

#Words(Risks)

#Words(Materiality+Scope)

GroupMateriality

ACMateriality #Risks #ACRisks

Ernst&Young 39 0.136 1442 738 704 0.682 0.0334 4.128 4.921KPMG 74 0.259 1459 1136 323 0.684 0.0325 3.581 4.514DeloitteTouche 73 0.255 1377 924 453 0.638 0.0133 4.466 4.915PricewaterhouseCoopers 92 0.322 1738 1313 426 0.529 0.0271 4.565 5.054GrantThornton 7 0.024 1773 1197 576 1.416 0.0802 5.286 3.833BDO 1 0.003 996 754 242 0.587 0.0107 5 5Total 286 1.000

68

69

Table4:Pre‐PostAnalysis

This table reports the results of our pre‐post analysis. SPREAD is the average bid‐ask spreadaround each forecast and is calculated as the difference between the daily ask and bid pricedividedbythemidpoint.ABS_CAR istheabsolutecumulativeabnormalreturncomputedusingthemarketmodelandanestimationwindowof[‐45;10].Aneventwindowof[‐1;1]isusedforbothmarket variables. Post is an indicator variable taking the value of 1 if themanagementforecastispublishedaftertherevisionofISA700,andzerootherwise.GoodNewsisanindicatorvariable taking the value of 1 if the CAR around amanagement forecast is positive, and zerootherwise.WedefineSizeasthelogofbookvalueoftotalassets,Leverageastotaldebtdividedby total assets, andROA as net income divided by total assets.MB ismarket value of equitydivided by book value of equity, Fees is the log of the auditor's total remuneration, and #Analysts is the log of 1 plus the number of analysts following the firm. We winsorize allcontinuousvariablesat1%anduserobuststandarderrors.Thesampleperiodspansfouryears.p‐valuesarepresentedinparentheses.Significanceatthe0.01,0.05,and0.10levelsisindicatedby***,**,and*.

SPREAD ABS_CAR

Post ‐0.0003 * 0.006 **(0.053) (0.031)

Size ‐0.0005 *** ‐0.010 ***(0.000) (0.000)

Leverage 0.0013 ‐0.020 *(0.114) (0.061)

ROA ‐0.0130 *** ‐0.060 **(0.000) (0.029)

MB 0.0000 ‐0.001 ***(0.888) (0.007)

Fees 0.0000 0.002(0.812) (0.376)

#Analysts ‐0.0006 * 0.003(0.056) (0.470)

GoodNews 0.0001(0.639)

Intercept 0.0079 *** 0.128 ***(0.000) (0.000)

Industry‐FE Yes YesR2 0.249 0.097N(Forecasts) 1394 1394N(Firms) 143 143

FullSample

70

Table5:Pre‐PostAnalysisbyNumberofWords

This table reports the results of our pre‐post analysis. We partition the sample based onwhether the number of words used to describe the audit’s scope, risks, and materialitythreshold,#Words(All), isaboveorbelow themedian.SPREAD is theaveragebid‐askspreadaround each forecast and is calculated as the difference between the daily ask and bid pricedividedbythemidpoint.ABS_CAR istheabsolutecumulativeabnormalreturncomputedusingthemarketmodelandanestimationwindowof[‐45;10].Aneventwindowof[‐1;1]isusedforbothmarket variables. Post is an indicator variable taking the value of 1 if themanagementforecastispublishedaftertherevisionofISA700,andzerootherwise.GoodNewsisanindicatorvariable taking the value of 1 if the CAR around amanagement forecast is positive, and zerootherwise.WedefineSizeasthelogofbookvalueoftotalassets,Leverageastotaldebtdividedby total assets, andROA as net income divided by total assets.MB ismarket value of equitydivided by book value of equity. Fees is the log of the auditor's total remuneration, and #Analysts is the log of 1 plus the number of analysts following the firm. We winsorize allcontinuousvariablesat1%anduserobuststandarderrors.Thesampleperiodspansfouryears.p‐valuesarepresentedinparentheses.Significanceatthe0.01,0.05,and0.10levelsisindicatedby***,**,and*.

High Low High LowSPREAD SPREAD ABS_CAR ABS_CAR

Post ‐0.0005 ** ‐0.0002 0.011 *** 0.001(0.044) (0.328) (0.008) (0.759)

Size ‐0.0007 *** 0.0000 ‐0.012 *** ‐0.010 ***(0.000) (0.979) (0.000) (0.004)

Leverage ‐0.0013 0.0023 *** ‐0.048 *** ‐0.002(0.380) (0.005) (0.008) (0.910)

ROA ‐0.0175 *** ‐0.0036 * ‐0.051 ‐0.049(0.000) (0.096) (0.198) (0.230)

MB 0.0000 0.0000 0.000 * ‐0.001 *(0.130) (0.237) (0.088) (0.075)

Fees 0.0001 ‐0.0002 0.009 *** ‐0.005 *(0.586) (0.235) (0.007) (0.063)

#Analysts 0.0002 ‐0.0015 *** 0.004 0.006(0.602) (0.000) (0.557) (0.351)

GoodNews ‐0.0002 0.0004 *(0.507) (0.055)

Intercept 0.0097 *** 0.0055 *** 0.146 *** 0.124 ***(0.000) (0.000) (0.000) (0.000)

Industry‐FE Yes Yes Yes YesR2 0.295 0.302 0.123 0.121N(Forecasts) 715 670 715 670N(Firms) 71 71 71 71

#Words(All)

71

Table6:Pre‐PostAnalysisbyNumberofWordsinEachSection

Thistablereportstheresultsofourpre‐postanalysis.Wepartitionthesamplebasedonwhetherthenumberofwordsusedtodescribetherisksofmaterialmisstatements,#Words(Risks),andtheaudit’sscopeandtheappliedmaterialitythreshold,#Words(Materiality+Scope),isaboveorbelowthemedian.SPREADistheaveragebid‐askspreadaroundeachforecastandiscalculatedasthedifferencebetweenthedailyaskandbidpricedividedbythemidpoint.ABS_CARistheabsolutecumulativeabnormalreturncomputedusingthemarketmodelandanestimationwindowof[‐45;10].Aneventwindowof[‐1;1]isusedforbothmarketvariables.Postisanindicatorvariabletakingthevalueof1ifthemanagementforecastispublishedaftertherevisionofISA700,andzerootherwise.GoodNewsisanindicatorvariabletakingthevalueof1iftheCARaroundamanagementforecastispositive,andzerootherwise.WedefineSizeasthelogofbookvalueoftotalassets,Leverageastotaldebtdividedbytotalassets,andROAasnetincomedividedbytotalassets.MBismarketvalueofequitydividedbybookvalueofequity,Feesisthelogoftheauditor'stotalremuneration,and#Analystsisthelogof1plusthenumberofanalysts followingthefirm.Wewinsorizeallcontinuousvariablesat1%anduserobuststandarderrors.Thesampleperiodspansfouryears.p‐valuesarepresentedinparentheses.Significanceatthe0.01,0.05,and0.10levelsisindicatedby***,**,and*.

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72

Table6:Pre‐PostAnalysisbyNumberofWordsinEachSection(continued)

High Low High Low High Low High LowSPREAD SPREAD ABS_CAR ABS_CAR SPREAD SPREAD ABS_CAR ABS_CAR

Post ‐0.0006 ** 0.0000 0.013 *** ‐0.001 ‐0.0001 ‐0.0004 0.007 * 0.004(0.024) (0.862) (0.002) (0.781) (0.724) (0.116) (0.058) (0.293)

Size ‐0.0009 *** ‐0.0002 ** ‐0.014 *** ‐0.006 ** ‐0.0003 ** ‐0.0006 ** ‐0.008 *** ‐0.013 ***(0.001) (0.039) (0.000) (0.015) (0.026) (0.014) (0.002) (0.000)

Leverage ‐0.0005 0.0018 ** ‐0.044 ** 0.006 ‐0.0003 0.0021 ** ‐0.011 ‐0.026 *(0.732) (0.026) (0.012) (0.655) (0.819) (0.013) (0.526) (0.061)

ROA ‐0.0189 *** ‐0.0056 *** ‐0.050 ‐0.037 ‐0.0131 *** ‐0.0138 *** ‐0.068 * ‐0.053(0.000) (0.005) (0.214) (0.299) (0.003) (0.000) (0.065) (0.280)

MB 0.0000 0.0000 0.000 ‐0.001 ** 0.0000 0.0000 ‐0.001 ** ‐0.001(0.549) (0.302) (0.164) (0.013) (0.914) (0.885) (0.037) (0.103)

Fees 0.0001 ‐0.0002 * 0.008 ** ‐0.006 *** ‐0.0004 * 0.0003 0.001 0.003(0.616) (0.097) (0.027) (0.007) (0.065) (0.143) (0.709) (0.232)

#Analysts 0.0003 ‐0.0009 *** 0.006 0.003 ‐0.0007 ‐0.0006 0.008 0.003(0.569) (0.000) (0.347) (0.553) (0.109) (0.138) (0.162) (0.661)

GoodNews ‐0.0003 0.0003 * 0.0001 0.0000(0.251) (0.074) (0.519) (1.000)

Intercept 0.0108 *** 0.0054 *** 0.158 *** 0.098 *** 0.0071 *** 0.0095 *** 0.104 *** 0.151 ***(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Industry‐FE Yes Yes Yes Yes Yes Yes Yes YesR2 0.410 0.210 0.129 0.105 0.268 0.303 0.108 0.101N(Forecasts) 697 692 697 692 700 685 700 685N(Firms) 71 71 71 71 71 71 71 71

#Words in Risk Section # Words in Scope and Materiality Section

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Table7:Pre‐PostAnalysisbyGroupMaterialityThreshold

This table reports the results of our pre‐post analysis. We partition the sample based onwhetherthegroupmaterialitythresholdexpressedasapercentageofassets,GroupMateriality,isaboveorbelowthemedian.SPREADistheaveragebid‐askspreadaroundeachforecastandiscalculated as the difference between the daily ask and bid price divided by the midpoint.ABS_CARistheabsolutecumulativeabnormalreturncomputedusingthemarketmodelandanestimationwindowof[‐45;10].Aneventwindowof[‐1;1]isusedforbothmarketvariables.Postisanindicatorvariabletakingthevalueof1 if themanagement forecast ispublishedaftertherevisionofISA700,andzerootherwise.GoodNewsisanindicatorvariabletakingthevalueof1iftheCARaroundamanagementforecastispositive,andzerootherwise.WedefineSizeasthelogofbookvalueoftotalassets,Leverageastotaldebtdividedbytotalassets,andROAasnetincomedividedbytotalassets.MBismarketvalueofequitydividedbybookvalueofequity,Feesisthelogoftheauditor'stotalremuneration,and#Analysts isthelogof1plusthenumberofanalysts following the firm. We winsorize all continuous variables at 1% and use robuststandard errors. The sample period spans four years.p‐values are presented in parentheses.Significanceatthe0.01,0.05,and0.10levelsisindicatedby***,**,and*.


Post 0.0001 ‐0.0008 *** 0.004 0.009 **(0.565) (0.008) (0.363) (0.018)

Size ‐0.0008 *** ‐0.0001 ‐0.008 ** ‐0.013 ***(0.000) (0.697) (0.016) (0.000)

Leverage 0.0024 * ‐0.0005 ‐0.003 ‐0.031 *(0.070) (0.405) (0.824) (0.051)

ROA ‐0.0123 *** ‐0.0179 *** ‐0.035 ‐0.047(0.002) (0.000) (0.372) (0.341)

MB 0.0000 ** ‐0.0001 *** ‐0.001 * 0.000 *(0.037) (0.000) (0.063) (0.062)

Fees ‐0.0001 ‐0.0002 ‐0.003 0.008 **(0.407) (0.258) (0.313) (0.014)

#Analysts 0.0002 ‐0.0017 *** 0.003 0.005(0.638) (0.002) (0.557) (0.486)

GoodNews 0.0000 0.0001(0.851) (0.574)

Intercept 0.0064 *** 0.0098 *** 0.124 *** 0.141 ***(0.000) (0.000) (0.000) (0.000)


GroupMateriality

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Table8:Pre‐PostAnalysisby

ReportableDifferencestoAuditCommittee

This table reports the results of our pre‐post analysis. We partition the sample based onwhether the threshold for reportable misstatements to the Audit Committee expressed as apercentageoftotalassets,ACMateriality, isaboveorbelowthemedian.SPREAD istheaveragebid‐ask spreadaroundeach forecastand is calculatedas thedifferencebetween thedailyaskand bid price divided by the midpoint.ABS_CAR is the absolute cumulative abnormal returncomputedusingthemarketmodelandanestimationwindowof[‐45;10].Aneventwindowof[‐1;1]isusedforbothmarketvariables.Postisanindicatorvariabletakingthevalueof1ifthemanagementforecastispublishedaftertherevisionofISA700,andzerootherwise.GoodNewsis an indicator variable taking the value of 1 if the CAR around a management forecast ispositive,andzerootherwise.WedefineSizeasthelogofbookvalueoftotalassets,Leverageastotaldebtdividedbytotalassets,andROAasnetincomedividedbytotalassets.MB ismarketvalue of equity divided by book value of equity, Fees is the log of the auditor's totalremuneration,and#Analystsisthelogof1plusthenumberofanalystsfollowingthefirm.Wewinsorize all continuous variables at 1% and use robust standard errors. The sample periodspansfouryears.p‐valuesarepresentedinparentheses.Significanceatthe0.01,0.05,and0.10levelsisindicatedby***,**,and*.


Post 0.0001 ‐0.0007 ** 0.006 0.007 *(0.616) (0.016) (0.169) (0.082)

Size ‐0.0010 *** ‐0.0002 ‐0.009 *** ‐0.011 ***(0.000) (0.142) (0.005) (0.000)

Leverage 0.0018 0.0019 ** ‐0.010 ‐0.032 **(0.212) (0.020) (0.565) (0.021)

ROA ‐0.0118 *** ‐0.0134 *** ‐0.026 ‐0.090 **(0.007) (0.000) (0.524) (0.027)

MB 0.0000 0.0000 ‐0.001 * ‐0.001 **(0.421) (0.392) (0.097) (0.030)

Fees 0.0000 0.0000 0.000 0.003(0.882) (0.911) (0.973) (0.395)

#Analysts 0.0005 ‐0.0015 *** 0.000 0.010(0.206) (0.000) (0.962) (0.111)

GoodNews 0.0000 0.0001(0.983) (0.775)

Intercept 0.0072 *** 0.0099 *** 0.136 *** 0.110 ***(0.000) (0.000) (0.000) (0.000)


ACMateriality

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Table9:Pre‐PostAnalysisbyNumberofRisks

This table reports the results of our pre‐post analysis. We partition the sample based onwhether the number of risks of material misstatements disclosed by the auditor, #Risks, isaboveorbelowthemedian.SPREAD istheaveragebid‐askspreadaroundeachforecastandiscalculated as the difference between the daily ask and bid price divided by the midpoint.ABS_CARistheabsolutecumulativeabnormalreturncomputedusingthemarketmodelandanestimationwindowof[‐45;10].Aneventwindowof[‐1;1]isusedforbothmarketvariables.Postisanindicatorvariabletakingthevalueof1 if themanagement forecast ispublishedaftertherevisionofISA700,andzerootherwise.GoodNewsisanindicatorvariabletakingthevalueof1iftheCARaroundamanagementforecastispositive,andzerootherwise.WedefineSizeasthelogofbookvalueoftotalassets,Leverageastotaldebtdividedbytotalassets,andROAasnetincomedividedbytotalassets.MBismarketvalueofequitydividedbybookvalueofequity,Feesisthelogoftheauditor'stotalremuneration,and#Analysts isthelogof1plusthenumberofanalysts following the firm. We winsorize all continuous variables at 1% and use robuststandard errors. The sample period spans four years.p‐values are presented in parentheses.Significanceatthe0.01,0.05,and0.10levelsisindicatedby***,**,and*.


Post ‐0.0007 *** ‐0.0002 0.012 *** ‐0.002(0.008) (0.503) (0.002) (0.615)

Size ‐0.0005 *** ‐0.0001 ‐0.011 *** ‐0.007 **(0.005) (0.668) (0.000) (0.045)

Leverage ‐0.0034 ** 0.0059 *** ‐0.032 * 0.037 *(0.033) (0.000) (0.071) (0.080)

ROA ‐0.0170 *** ‐0.0088 *** ‐0.070 * ‐0.062(0.000) (0.003) (0.087) (0.241)

MB 0.0000 ‐0.0001 ** ‐0.001 ** ‐0.001(0.297) (0.015) (0.012) (0.360)

Fees 0.0002 ‐0.0003 * 0.007 ** ‐0.007 **(0.387) (0.083) (0.046) (0.031)

#Analysts ‐0.0002 ‐0.0014 *** 0.011 * 0.003(0.596) (0.002) (0.098) (0.585)

GoodNews ‐0.0001 0.0002(0.658) (0.401)

Intercept 0.010 *** 0.006 *** 0.126 *** 0.111 ***(0.000) (0.000) (0.000) (0.000)


#Risks

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Table10:Pre‐PostAnalysis

byNumberofAuditCommitteeRisks

This table reports the results of our pre‐post analysis. We partition the sample based onwhetherthenumberofsignificantissuesdisclosedbytheAuditCommittee,#ACRisks,isaboveor below the median. SPREAD is the average bid‐ask spread around each forecast and iscalculated as the difference between the daily ask and bid price divided by the midpoint.ABS_CARistheabsolutecumulativeabnormalreturncomputedusingthemarketmodelandanestimationwindowof[‐45;10].Aneventwindowof[‐1;1]isusedforbothmarketvariables.Postisanindicatorvariabletakingthevalueof1 if themanagement forecast ispublishedaftertherevisionofISA700,andzerootherwise.GoodNewsisanindicatorvariabletakingthevalueof1iftheCARaroundamanagementforecastispositive,andzerootherwise.WedefineSizeasthelogofbookvalueoftotalassets,Leverageastotaldebtdividedbytotalassets,andROAasnetincomedividedbytotalassets.MBismarketvalueofequitydividedbybookvalueofequity,Feesisthelogoftheauditor'stotalremuneration,and#Analysts isthelogof1plusthenumberofanalysts following the firm. We winsorize all continuous variables at 1% and use robuststandard errors. The sample period spans four years. p‐values are presented in parentheses.Significanceatthe0.01,0.05,and0.10levelsisindicatedby***,**,and*.


Post ‐0.0008 ** 0.0003 * 0.009 ** 0.003(0.017) (0.067) (0.031) (0.526)

Size ‐0.0005 ** ‐0.0004 *** ‐0.009 *** ‐0.008 **(0.041) (0.007) (0.007) (0.046)

Leverage 0.0006 0.0012 ‐0.019 0.005(0.598) (0.328) (0.225) (0.822)

ROA ‐0.0150 *** ‐0.0127 *** ‐0.072 * ‐0.033(0.002) (0.000) (0.070) (0.543)

MB 0.0000 * 0.0001 *** ‐0.001 *** 0.000(0.097) (0.006) (0.005) (0.320)

Fees 0.0000 ‐0.0002 0.003 ‐0.003(0.955) (0.200) (0.395) (0.361)

#Analysts ‐0.0007 ‐0.0004 0.003 0.001(0.147) (0.167) (0.647) (0.840)

GoodNews ‐0.0001 0.0001(0.584) (0.696)

Intercept 0.0098 *** 0.0066 *** 0.128 *** 0.118 ***(0.000) (0.000) (0.000) (0.000)


#ACRisks

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MandatoryFinancialReportingandCompetition*

ElisabethKläs†

October2017

Abstract: This paper investigates the effect of mandatory financial reporting on a firm’s

competitiveness. Although an extant literature documents the capital‐market benefits of

mandatory financial reporting, little is known about its proprietary costs. Using the IFRS

adoptioninEuropeasanexogenousshocktomandatorydisclosure,Ifindmandatoryadopters

suffer competitively relative to voluntary adopters following the introduction of IFRS. Two

mechanisms are responsible for this effect: increased disclosure and enhanced financial‐

statementcomparability.Additionalanalysesrevealnotalltypesofaccountinginformationare

equallyrelevanttocompetitors.

JEL‐Classification:M41,M48,M49Keywords: Mandatory Financial Reporting, International Financial Reporting Standards,

Competition,ProprietaryCosts.

*I thank Yuping Jia, JörgWerner, Igor Goncharov, Frank Ecker, Martin Arzt, and Edgar Löw for theirhelpfulcommentsandsuggestions.Allremainingerrorsaremine.Thispaperwaswritten,inpart,whenIwasvisitingLancasterUniversityManagementSchool.†FrankfurtSchoolofFinance&Management.

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I. Introduction

Mandatory financial reporting isdesigned to reduce informationasymmetriesbetweena firm

and capital‐market participants. Yet prior literature suggests firms’ concerns about the

proprietarycostsofmandatorydisclosuresshapetheirreportingdecisions(e.g.,Schneiderand

Scholze [2015]; Dhaliwal, Huang, Khurana, and Pereira [2014]). Although the capital‐market

benefits of financial reporting are well documented (e.g., Leuz and Verrecchia [2000]; Li

[2010a]), little is known about the extent towhichmandatory disclosure reveals proprietary

informationandthusadverselyaffectsafirm’scompetitiveness.

This paper adds to the scarce evidence on the proprietary costs of mandatory financial

reporting.UsingthemandatoryIFRSadoptioninEuropeasanexogenousshocktomandatory

disclosure requirements, I examine how mandatory financial reporting affects a firm’s

competitiveness. The intuition is that increased disclosure and enhanced financial‐statement

comparability following the adoption of IFRS render a firm’s financial statement more

informative.Asaconsequence,peerscanprofitfromthisinformation,thuspotentiallyharming

thefirm’scompetitiveness.

Myresultssuggestfirms’concernsabouttheproprietarycostsofmandatoryfinancialreporting

arejustified.Focusingonafirm’syearlyrevenuegrowth,Idocumentthatmandatoryadopters

suffercompetitivelyrelativetovoluntaryadoptersfollowingtheintroductionofIFRS.Ifurther

show that both increased disclosure and enhanced financial‐statement comparability are

responsibleforthiseffect.Additionalanalysesprovideevidencethatnotalltypesofaccounting

information are equally important to a firm’s competitors. Specifically, I focus on the IFRS

accounting standards on revenue recognition, intangibles, leases, inventory, and treatment of

events after the reporting period. The findings indicate disclosure pursuant to the first four

accounting standards reveals valuable information about a firm; information about the

treatmentofeventsafterthereportingperiodisconsideredlessrelevant.

To the best ofmyknowledge, only one empirical study investigates the impact ofmandatory

disclosure on competition. Zhou [2014] shows that publicUS companies lobbying against the

newstandardonsegmentreportinglosepartoftheirmarketsharetoprivatefirmsrelativeto

firms in non‐lobbying industries after the introduction of SFAS No. 131. I contribute to this

literature by providing evidence that several IFRS accounting standards reveal proprietary

informationandthusaffectafirm’scompetitiveness.Ifurthercontributetotheliteratureonthe

economic effects of themandatory IFRS adoption (e.g., Li [2010a]; Chen, Young, and Zhuang

[2013])bybeingoneofafewstudiesthathighlightitsfirm‐specificcosts.Myfindingsarethus

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interestingforregulatorsandstandardsettersastheyprovideinformationontheexternalities

ofmandatoryfinancialreporting.

The remainder of this paper is organized as follows. Section II discusses prior literature and

developsthehypothesis.SectionIIIdescribesthesetting,theresearchdesign,anddata.Sections

IVandVpresenttheempiricalresultsandsensitivityanalyses.SectionVIconcludes.

II. RelatedLiteratureandHypothesis

BenefitsandCostsofMandatoryDisclosure

Theunravelingresultsuggestsamanagervoluntarilydisclosesallprivateinformationprovided

that(1)disclosure iscostless,(2) investorsareawarethe firmhasprivate information,(3)all

investorsinterpretthefirm’sdisclosuresinthesameway,and(4)thefirmcancrediblydisclose

itsprivateinformation.1Theintuitionisthatrationalinvestorsinterpretlessthanfulldisclosure

asabadsignal,andconsequentlydiscount the firmvalueto theextent that the firm isalways

better off revealing all information (Grossman [1981]; Milgrom [1981]). When the model

includesproprietarycosts,apartial‐disclosureequilibriumexists inwhichthe firmchoosesto

withhold some information (see, e.g., Verrecchia [1983]; Wagenhofer [1990]). The firm

considers the trade‐off between the benefits and costs of disclosure. A partial‐disclosure

equilibriumcanexistbecausearational investornolongerknowswhetherthefirmwithholds

informationbecauseofbadnewsorbecauseofproprietarycoststhatoutweighthebenefitsof

disclosure. In other words, proprietary costs are a key motive preventing firms from full

disclosure.

The capital‐marketbenefits ofmandatorydisclosure arewell documented. For example, Leuz

andVerrecchia[2000]showthatfirmscommittingtoincreasedlevelsofdisclosureexperience

lowerbid‐askspreadsandhighertradingvolume.TheextantliteratureonthemandatoryIFRS

adoption documents positive capital‐market effects, such as lower costs of capital, decreased

crashrisk,andanimprovedanalysts’informationenvironment,whenthenewsetofaccounting

standardsiscrediblyimplemented(e.g.,Daske,Hail,Leuz,andVerdi[2008];Li[2010a];DeFond,

Hung,Li,andLi[2015];Byard,Li,andYu[2011]).However,surprisingly little isknownabout

the extent to which mandatory financial reporting reveals proprietary information and thus

influencesafirm’scompetitiveness.

1SeeDye[2001,p.17].

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Prior literature primarily focuses onhow firms’ concerns about proprietary costs shape their

voluntary andmandatory disclosure decisions.2To the best ofmy knowledge, only one study

investigates how proprietary information revealed by mandatory financial reporting affects

competition. Zhou [2014] provides evidence of public US companies in industries that lobby

againstthenewaccountingstandardonsegmentreportinglosingpartoftheirmarketshareto

private firmsrelative topublic firms innon‐lobbying industriesafter the introductionofSFAS

No.131.However,moreresearchinthisareaisneededtoobtainabetterunderstandingofthe

economic consequences of mandatory disclosure in the context of competition. Zhou [2014]

focusesontheinformationrevealedbyUSGAAPsegmentreporting,whichisonlyoneelement

ofmandatoryfinancialreporting.

StrategicMandatoryDisclosureinthePresenceofCompetition

Theory suggests competition incentivizes firms to engage in strategic mandatory financial

reporting. For example, Schneider and Scholze [2015] analyze a setting in which internally

generated segment informationmustbedisclosed to thepublic, as is the casewith IFRS8or

SFAS131.Theyfindaggregatinginternalinformation,andthusinefficientlyallocatingresources,

in order to avoid a rival’smarket entrymight be optimal for an incumbent firm.Aggregating

informationeven if the incumbentcannotpreventa rival’smarketentrymayalsobeoptimal,

because aggregating information reduces the competition’s intensity. Empirical evidence

supports this notion. Botosan and Stanford [2005] provide evidence that proprietary‐cost

considerations, rather than the intention to hide poor performance, drove the decision to

aggregatesegmentsbeforeSFASNo.131.Dhaliwaletal.[2014]documentapositiveassociation

between product market competition and conditional conservatism. The intuition is that

reporting losses ina timeliermanner thangainsdiscouragesnewentrants inan industryand

encouragesexistingrivalstounder‐produce.Similarly,Datta,Iskandar‐Datta,andSingh[2013]

show that firms with weak product market power are more likely to engage in earnings

management, implyingcompetitivepressuremotivates firmsto limit the informationavailable

totheircompetitors.Note,however,thattheextenttowhichfirmsengageinstrategicfinancial

reporting is limited to the discretion allowed in GAAP. Thus, strategic financial reporting is

unlikelytofullymitigatetheimpactofmandatorydisclosureoncompetition—ifanyexists.

2A large body of literature analyzes how the presence of propriety costs shape voluntary disclosureactivities (e.g., Li [2010b]; Ellis, Fee, andThomas [2012];Huang, Jennings, andYu [2017]). Providing areviewonthistopicgoesbeyondthescopeofthisstudybecausethefocusisonmandatorydisclosure.

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LearningfromCompetitors’FinancialStatements

Verrecchia [1983,p.181]argues that “the releaseof avarietyof accounting statisticsabout a

firm(e.g.,sales,netincome,costsofoperation,etc.)maybeusefultocompetitors,shareholders,

oremployeesinawaywhichisharmfultoafirm’sprospects.”Indeed,todevelopandmaintaina

competitiveadvantage,andthusbeprofitable,firmsmustcloselywatchandquicklyrespondto

their competitors’ actions. The management accounting literature on competitor accounting

focusesonthisaspect.3HeinenandHoffjan[2005]definecompetitoraccountingas“theanalysis

ofaccounting informationrelatingtocompetitors.” Itspurpose is to“providedetailed insights

into a competitor’s present cost and financial situation; determine one’s own competitive

positionandpredictfuturecompetitivebehaviour.”

Guilding, Cravens, and Tayles [2000] identify three elements of competitor accounting: (1)

competitor cost assessment, (2) competitive position monitoring, and (3) competitor

performanceappraisalbasedonpublishedfinancialstatements,withthelatterbeingdefinedas

the “numerical analysis of a competitor’s published statements as part of an assessment of a

competitor’s key sources of competitive advantage.”4Although financial statements represent

the key information source for the third element, they are also an important source of

informationfortheothertwoelements.Indeed,SubramanianandIsHak[1998]reportthatUS

firms consider the annual report to be the second most important information source for

competitoranalysisnexttoinformationreceivedfromtheirownsalespeople.Inaddition,using

asurveyoflargecompaniesinNewZealand,theUK,andtheUnitedStates,Guildingetal.[2000]

find competitor accounting is oneof themostwidelyused elements of strategicmanagement

accounting. Moon and Bates [1993] provide an analytical framework of how to evaluate a

competitorbasedonitsfinancialstatement.Theyarguethatimportantinsightscanbederived

fromanalyzingtrendsinthemovementofsalesandprofits,andchangesinassetsandliabilities.

Financial‐ratioanalysiscanfurtherbeusedtoevaluatetheextenttowhichafirmhasachieved

itsstrategicobjectives.

Empiricalevidencesupportsthenotionthatfirmsrelyontheircompetitors’financialstatements

when formulating strategies. Beatty, Liao, and Yu [2013] analyze the effects of fraudulent

3Bromwich [1990]defines strategicmanagementaccountingas “theprovisionandanalysisof financialinformationonthefirm’sproductmarketsandcompetitors’costandcoststructuresandthemonitoringoftheenterprise’sstrategiesandthoseifitscompetitorsinthesemarketsoveranumberofperiods.“4Competitorcostassessmentisdefinedasthe“provisionofregularlyupdatedestimatesofacompetitor’scostsbasedon,forexample,appraisaloffacilities,technology,economiesofscale.Sourcesincludedirectobservation,mutualsuppliers,mutualcustomersandex‐employees”(Guildingetal.[2000]).Competitivepositionmonitoringreferstothe“analysisofcompetitorpositionswithinthe industrybyassessingandmonitoring trends in competitor sales, market share, volume, unit costs and return on sales. Thisinformation can provide a basis for the assessment of competitors’ market strategy” (Guilding et al.[2000]).

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reporting on industry peers’ investments, and find peer firms have significantly higher

investmentsduringperiodsinwhichtheindustryleaderengagesinfraudulentreporting.Chen

et al. [2013] find firms’ investment efficiency improves following themandatory adoption of

IFRS in Europe. The intuition is that improved comparability and increased disclosure across

Europe allow managers to more efficiently use information in their foreign peers’ financial

reports.

HypothesesDevelopment

It follows from the above discussion that firms use information contained in their peers’

financial statementswhen formulating strategies.Competitors are awareof thisbehavior and

thustakeproprietarycostsintoaccountwhendisclosinginformation.Althoughmanagershave

considerable discretion over voluntary disclosures, financial‐reporting rules prescribe the

qualityandquantityofmandatorydisclosures, thereby limitingmanagers’discretionover the

firm’s reporting. Consequently, mandatory financial reporting might enable competitors to

extractusefulinformationfromtheirpeers’financialstatementsandmaythusadverselyaffecta

firm’scompetitiveness.

However,someargumentsareagainstthenotionthatmandatoryfinancialreportingadversely

affects a firm’s competitiveness. Firms spenda considerable amountof time and resources to

analyze the competition. As stated above, the financial statement is only one out of many

informationsources.Themajorityofrelevantinformationmaythusalreadybeavailabletothe

firm without or before being disclosed in its peers’ annual reports. Indeed, the Institute of

CharteredAccountants in England andWales states that “some commentators argue that the

competitive element of proprietary costs (‘competitive costs’) is largely fictional because

competitorsusuallyknow,throughgossip,tradesourcesandillicitleaksofprivateinformation,

howwelltheirrivals’variousprojectsaredoing”(ICAEW[2013,p.13]).Yettheinstitutegoeson

toarguethat“theremayoftenbesometruthinthis,butcompaniesgotoconsiderablelengthsto

trytokeepsuchinformationprivate,andtheydonotgenerallybehaveasthoughitisalreadyin

their competitors’ hands. So the existence of competitive costs for public disclosures is real,

although the actual costs may often be exaggerated” (ICAEW [2013, p. 13]). Based on the

competingargumentsabove,Iformulatethenullhypothesis:

Hypothesis:Mandatoryfinancialreportingdoesnotadverselyaffectafirm’scompetitiveness.

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III. SettingandResearchDesign

Setting

ThemandatoryadoptionofIFRSinEuropeprovidesapowerfulsettingtoinvestigatetheeffect

ofmandatoryfinancialreportingonafirm’scompetitiveness.In2002,theEuropeanUnion(EU)

published EC Regulation No. 1606/2002, requiring all publicly traded companies to prepare

theirconsolidatedaccountswithfiscalyearsstartingonorafterJanuary1,2005,inaccordance

withIFRS.Thenewreportingregimewasexpectedtoincreasetransparencyandcomparability,

andthustheoverallqualityoffinancialstatements(EuropeanCommission[2002]).

Withtheinternationalizationofmarkets,firmsincreasinglyfacecompetitionfromforeignpeers.

Althoughlocalcompetitorsmostlypreparetheirfinancialstatementsusingthesameaccounting

rules,collectingandanalyzingfinancialinformationofforeignpeersiscostlybecausedifferent

accounting standards apply. Moreover, some information cannot be analyzed, because it is

simplynotdisclosedbyforeignpeers(Chenetal.[2013]).Priorstudiesontheeconomiceffects

of IFRSprovideevidencethatthenewaccountingregimeimprovestheinformationcontentof

financial statements via increased disclosure and enhanced financial‐statement comparability

(e.g.,Li[2010a];Byardetal.[2011];Chenetal.[2013];Tan,Wang,andWelker[2011];Yipand

Young[2012];Brochet, Jagolinzer,andRiedl [2013]).Asaconsequence, thecostsofanalyzing

foreign peers’ financial statements are lower following the adoption of IFRS. For example,

different rules on revenue recognition make comparing the firm’s revenues with those of a

foreign competitor difficult for managers. Similarly, more disclosures on product lines and

geographicareasbyforeigncompetitorsenablethefirmtobetterunderstanditspeergroup.In

addition, Lang and Stice‐Lawrence [2015] document an increase in the annual report textual

disclosure quality ofmandatory adopters relative to a control group after the introduction of

IFRS.

The IFRS adoption unlikely impacts all firms equally. I expect private firms to be the main

beneficiaries,becausetheyobtainmorerelevantinformationabouttheircompetitorswhilenot

beingsubjecttothesamedisclosurerules.Voluntaryadopters,thatis,firmsfollowingIFRSorUS

GAAPpriorto2005,appliedhigher‐qualityaccountingstandardspriortotheIFRSadoptionand

are thus not impacted by the exogenous shock to mandatory disclosure. Although voluntary

adopters learnmore about their peers following the introduction of IFRS, they are alsomore

comparable, which allows competitors to more efficiently exploit their financial‐statement

information.IthusexpecttheneteffectofIFRSonthecompetitivenessofvoluntaryadoptersto

be zero. In fact, the IFRS adoption is likely to adversely affect only the competitiveness of

mandatoryadopters,thatis,firmsapplyinglocalGAAPpriorto2005.BecauseIFRSandUSGAAP

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areconsideredtobeofhigherqualitythanlocalGAAP—formanycountries,themainargument

for switching to IFRS—mandatory adopters were subject to lower financial‐reporting

requirements prior to 2005. Althoughmandatory adopters also learnmore about their peers

after the introduction of IFRS, I expect the costs ofmore informative financial statements to

outweighthiseffect.

The choice of Europe as my setting has two additional advantages. First, the EU is a single

marketplace, and the adoption of IFRS is a further step towards strengthening its internal

market(EuropeanCommission[2002]).Thus,itispowerfulsettingtomeasurethedynamicsof

competition. Second,EU countrieshave relatively strong legal environments andenforcement

regimes,whichallowforstrongerIFRSadoptioneffects(Li[2010a]).

My setting offers contributions over and above Zhou [2014], who analyzes the disclosure of

proprietary information under segment reporting at the industry level in the United States. I

focus on the change in firm‐level competitiveness in a multi‐country market following the

introductionofanewsetofaccountingstandards.Mysetting is thusnotrestrictedtoasingle

accounting standard. Rather, the heterogeneity in local GAAP allowsme to examine different

accountingstandardsthatarelikelytorequireacompanytorevealvaluableinformationtoits

peergroupandthuspotentiallyharmitscompetitiveness.

SampleConstruction

The sample and all data used to construct my regression variables are obtained from the

CompustatGlobaldatabase.Correctly identifying theaccountingstandardsappliedat the firm

leveliscrucialformyanalysis.Thisfeatisnottrivial,becauseCompustatGlobalcontainscoding

errors in its accounting standards classification (Covring, DeFond, and Hung [2007]). Solely

relying on other databases, such as FactSet or Worldscope, does not alleviate the problem,

becausethecodingsystemofWorldscopeissubjecttoinconsistenciesaswell(seeDaske,Hail,

Leuz, andVerdi [2013]OnlineData andCodingAppendix). I therefore rely on threedifferent

sourcestoidentifytheaccountingstandardappliedatthefirmlevel:CompustatGlobal,FactSet

Fundamentals, and the data set of voluntary IFRS adopters provided byDaske et al. [2013].5

Appendix A provides a detailed description of the coding procedure. I include a firm in my

sample if it has available data for the entire sample period and is not active in the financial

sector.Thefinalsampleconsistsof10,596firm‐yearobservationsfrom15Europeancountries

overatimeperiodsixyears.6

5http://research.chicagobooth.edu/arc/journal/onlineappendices.aspx.6I do not include countries that became EU members in 2004, because the consequences of EUmembershipmayinterferewiththeIFRSadoptioneffects.

85

ResearchDesign

Totestmyhypothesis,Iemploythefollowingdifference‐in‐differencesdesign:

RevenueGrowth=α+β1MandatoryAdopter+β2Post

+β3MandatoryAdopter×Post+Controls

+CountryFixedEffects+IndustryFixedEffects+YearFixedEffects+ε.

Iuseannualfirm‐levelrevenuegrowthasmyproxyforafirm’scompetitiveness.Theintuition

behindmyproxy is that revenue growth reflects a firm’s competitiveness.The larger a firm’s

competitive advantage, thehigher andmore stable its revenuegrowth. If proprietary costsof

disclosureadverselyaffectafirm’scompetitiveness,thiseffectshouldbedirectlyreflectedinits

revenue growth. My proxy has two distinct advantages over an industry‐wide measure. Ali,

Klasa, and Yeung [2009] point out that—due to the lack of private firms—industry‐

concentrationmeasures based on US public firms in Compustat can be a poor proxy for the

actual industry concentration and may thus lead to incorrect inferences. The Herfindahl‐

Hirschman Index (HHI) andotherpopular industry‐wideproxies for competition, such as the

four‐firm concentration ratio or product market size, are typically constructed using firms’

revenues. My proxy is thus based on the same underlying variable but is not subject to the

potentialproblemsassociatedwithalackofprivatefirms.Unreportedtestsshowasignificantly

positive correlation between revenue growth and the Herfindahl Index (0.1***). I further

perform a linear regression with revenue growth as dependent variable and the Herfindahl

Indexasindependentvariable.Controllingforkeyfirmcharacteristicsthatarelikelytoimpact

revenue growth (i.e., size, debt, intangible assets and lagged capex) the results still show a

significantlypositive association between revenue growth and the Herfindahl Index (p‐value:

0.019).AnotheradvantageofmyproxyisthatIdonotexpectIFRStoaffectallfirmsequally.A

firm‐specific proxy thus allows me to assess the effect of the mandatory IFRS adoption on

differentgroupsoffirms.

MandatoryAdopter isabinaryvariabletakingthevalueof1ifafirmfollowslocalGAAPinthe

pre‐periodandIFRSinthepost‐period.MandatoryAdopterisequaltozeroifafirmadoptsIFRS

orUSGAAP in thepre‐periodand IFRS in thepost‐period.7I chooseVoluntaryAdopters asmy

control group because I assume the net effect of IFRS on the competitiveness of Voluntary

7“In two limited cases, member States may exempt certain companies temporarily from the IFRSrequirement–butonlyuntil2007:1)companiesthatarelistedbothintheEUandonanon‐EUexchangeandthatcurrentlyuseUSGAAPastheirprimaryaccountingstandardsand(2)companiesthathaveonlypublicly traded debt securities. Non‐EU companies listed on EU exchanges can continue to use theirnationalGAAPsuntil2007”(Deloitte[2005,p.14]).IexcludecompaniesthatswitchfromlocalGAAPtoIFRSlaterthan2005.

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Adopterstobezero.ThecontrolgroupalsocomprisesfirmsthatvoluntarilyadoptedUSGAAP,

basedon thenotion thatbothUSGAAPand IFRSareofhigherquality relative to localGAAP.

DoingsoallowsmetoincreasethesamplesizeofVoluntaryAdopters.Anadditionaladvantageof

choosing Voluntary Adopters as the control group is that they are subject to the same EU

regulationsasMandatoryAdoptersandthusanyconcurrentregulatoryandeconomicchangesin

theEUinfluencethem.Postisanindicatorvariabletakingthevalueof1forfiscalyearsending

on or after the IFRS adoption date, that is, December 31, 2005, and zero otherwise. If the

adoption of IFRS adversely affects the competitiveness ofMandatoryAdopters, I expect the

coefficientontheinteractiontermMandatoryAdopter×Post‐Periodtobenegative.

Inaddition,Icontrolforthefollowingfirmcharacteristics:Sizeisthelogofbookvalueoftotal

assets. I expect larger firms to have lower revenue growth, because they are likely to be at a

moremature stage and thus have limited growthpotential. Intangibles is the book value of a

firm’sintangiblesscaledbytotalassets.Icontrolforfirms’intangiblesbecausetheamountand

typesof intangibles(e.g., intellectualproperty)reflectafirm’sgrowthpotential.Capex(Lag) is

defined as theone‐year lag of capital expenditure scaledby total assets.8The intuition is that

investments in fixed assets allow a firm to increase its ability to generate revenues.Debt is

measured as current liabilities plus long‐term debt scaled by total assets and controls for a

firm’srisk.Ifurtherincludecountry,industry,andyearfixedeffects,andclusterrobuststandard

errorsatthefirmlevel.9Allcontinuousvariablesarewinsorizedatthe1%leveltominimizethe

effectsofoutliers.

IV. EvidenceonProprietaryCostsofMandatoryDisclosure

SummaryStatistics

Table1showsthesampledistributionbycountry.TheUKhasthelargestnumberoffirm‐year

observations (2,652), followed by Germany (2,076) and France (1,968). Luxembourg has the

lowest number of firm‐year observations (48). Overall, 22% of all sample firms voluntarily

adopted IFRS orUS GAAP in the pre‐period. The number ofVoluntaryAdopters varies across

countries,withnoneinPortugalandSpain,andalmost100%inAustria.10

8The results remainqualitatively unchangedwhen scaling capital expenditures byproperty, plant, andequipment.9Idonotseparatelycontrolforthecompetitivenesswithinanindustry,becausetheindustryfixedeffectscapture it. However, for robustness purposes, I replicate all analyses including the HHI as a separatecontrolinmyregressionspecification.Theresultsarerobust.10Notethetabledoesnotdepicttheentiremarketbutratherasubsetofit,namely,firmsthatareincludedinCompustatGlobalandcomplywithmydatarequirements.Moreover,thenumberofvoluntaryadoptersmaydiffercomparedtootherstudies,because(1)mydefinitionofvoluntaryadoptersincludesfirmsthat

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Table2 reports thesummarystatistics for thepre‐andpost‐period.BothMandatoryAdopters

andVoluntaryAdoptersexperienceastatisticallysignificantincreaseintheirmeanandmedian

revenuegrowthduring thepost‐period,with the increasebeing larger forVoluntaryAdopters.

ThesummarystatisticsfurthershowMandatoryAdoptersexhibithighergrowthratesinthepre‐

period, whereas VoluntaryAdopters experience higher growth rates in the post‐period. The

differencebetweenthetwogroups is,however, statistically insignificant inbothperiods.Both

groupshaverelativelysimilarfirmcharacteristicsanddonotdiffersignificantlyintermsoftheir

size,intangibles,andcapitalexpenditureinbothperiods.However,MandatoryAdoptershaveon

averageasignificantlyhigherleverageratiocomparedtoVoluntaryAdoptersinthepre‐aswell

aspost‐period.


Full‐SampleAnalysis

Istartmyanalysiswithadifference‐in‐differencesregressionforthefullsample.Theresultsare

reportedinTable3.Thecoefficientontheinteractiontermissignificantlynegative,suggesting

theadoptionof IFRSadverselyaffects thecompetitivenessofMandatoryAdopters.Thecontrol

variablesareinlinewithmyexpectation.Firmsthatarelargerandhaveahigherleverageratio

experience lower growth rates, whereas a higher proportion of intangible assets and lagged

capitalexpenditurepositivelyaffectrevenuegrowth.Overall, thefull‐sampleanalysisprovides

evidenceinfavorofthenotionthatmandatoryfinancialreportingadverselyinfluencesafirm’s

competitiveness.Note,however,thattheIFRSadoptionunlikelyaffectsallMandatoryAdopters

equally.Theextentoftheimpactmaydependonvariousfactors,withtheheterogeneityinlocal

GAAPbeingoneofthemostinterestingaspectstotakeintoaccount.


voluntarily adopted US GAAP in the pre‐period, and (2) I do not exclusively rely on the accountingstandardsclassificationinCompustatGlobal.

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AnalysisofUnderlyingMechanisms

ToobtainabetterunderstandingofhowIFRSaffectsthecompetivenessofMandatoryAdopters,

I exploit cross‐country differences in local GAAP. Prior literature identifies two mechanisms

through which the mandatory IFRS adoption increases the information content of financial

statements: increased disclosure and enhanced financial‐statement comparability (e.g., Li

[2010a]; Byard et al. [2011]; Chen et al. [2013]; Tan et al. [2011]; Yip and Young [2012]). I

expecttheeffectoftheIFRSadoptiontobestrongerforMandatoryAdopterslocatedincountries

withgreaterdifferencesbetweenlocalGAAPandIFRSandfewerdisclosurerequirementsunder

local GAAP. The intuition is that an IFRS financial statement reveals more information to

competitorsinthepost‐period,whenthedifferencebetweenlocalGAAPandIFRSisgreater.

To test my prediction, I follow Li [2010a] and differentiate between improved financial

disclosure and increased financial‐statement comparability, using a survey by Nobes [2001].

Improvedfinancialdisclosureismeasuredasthenumberofadditionaldisclosuresrequiredby

IFRSas compared to localGAAP.Comparability ismeasuredas thenumberof inconsistencies

between local GAAP and IFRS. In addition, I use two alternative measures for local GAAP

differences: I follow Chen, Ng, and Tsang [2015] and construct the Absence Index and the

DivergenceIndexcreatedbyDing,Hope, Jeanjean,andStolowy[2007].Basedonthesurveyby

Nobes[2001],theAbsenceIndexmeasurestheextenttowhichaccountingrulesonrecognition,

measurement,anddisclosuresare included in IFRSbutmissing in localGAAP.TheDivergence

Indexmeasurestheextenttowhichrulesregardingthesameaccountingissuesdifferbetween

localGAAPandIFRS.Table4reportsthecountry‐levelvariables.


I partition the sample into two groups based onwhether firms are located in countrieswith

above‐orbelow‐the‐medianvaluesof(1)additionaldisclosures,(2)inconsistencies,(3)Absence

Index, and (4) Divergence Index. The results are displayed in Table 5. The coefficient on the

interactiontermissignificantlynegativeforthesubsampleoffirmsdomiciledincountrieswith

a greater increase in financial‐statement comparability and a higher Divergence Index.

MandatoryAdopterslocatedintherespectivebelow‐the‐mediansubsamplesdonotexperiencea

statistically significant change in revenue growth relative to VoluntaryAdopters in the post‐

period.Similarly,theinteractiontermisnegativeandstatisticallysignificantforthesubsample

offirmsdomiciledincountrieswithanabove‐the‐medianvalueofAbsenceIndex.Thecoefficient

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is insignificant for the respective below‐the‐median sample. Mandatory Adopters located in

countrieswithmore disclosures required by IFRS do not experience a statistically significant

changeinrevenuegrowthrelativetovoluntaryadoptersinthepost‐period.Thecoefficientfor

therespectivebelow‐the‐mediansubsamplesisinsignificantaswell.

Theresultscanbeinterpretedasenhancedfinancial‐statementcomparabilitybeingtheprimary

channel responsible for the adverse effect of IFRS on the competitiveness of Mandatory

Adopters. Missing disclosure rules in local GAAP do not seem to play a role. An alternative

explanation is that the extent of the effect ofmissingdisclosure rulesdependson the typeof

(proprietary)informationdisclosed.Thus,increaseddisclosuremaystillplayanimportantrole,

butonlywithrespecttospecificaccountingstandards.Thesamemaybethecaseforenhanced

financial‐statementcomparability.Thenextsectioninvestigatesthispossibility.


AnalysisofAccountingStandards

Not all information revealed by a firm’s financial statement is equally relevant to that firm’s

competitors. Exploiting the cross‐country heterogeneity in local GAAP, this section examines

whether the extent to which the IFRS adoption adversely affects the competitiveness of

MandatoryAdoptersdependsonspecificIFRSaccountingrulesandthusthetypeofinformation

disclosed in the financial statement. Based on the survey by Nobes [2001], I identify the

respectiveIFRSaccountingstandardsforwhichnodisclosurerulesexistunderlocalGAAPorfor

which inconsistencies exist between local GAAP and IFRS.11Table 6 reports the descriptive

statisticsfortheaccountingstandardsIinvestigateinthesubsequentanalyses.


11A country is classified as having no disclosure rules under local GAAP regarding a specific IFRSaccounting standard if the accounting standard is listed in the category “No specific rules requiringdisclosures.“Similarly,acountryisclassifiedashavinginconsistenciesbetweenaspecificIFRSaccountingstandard and its local GAAP counterpart if the specific accounting standard is listed in the category“Inconsistenciesthatcouldleadtodifferencesformanyenterprises.”

90

First,Ifocusonthemechanismofimprovedfinancial‐statementcomparabilityandidentifyfive

accountingaspectswhoseharmonizationislikelytorevealrelevantinformationaboutafirmto

its competitors.12Again, I partition the sample into two groups based on whether firms are

located in countries with inconsistencies between local GAAP and IFRS regarding a specific

accountingissue.ThefirstsetofaccountingstandardsIexamineisrevenuerecognition(IAS11

and IAS18). Six countries inmysampleexhibit inconsistenciesbetween localGAAPand IFRS

regardingthisspecificaccounting issue.Harmonizingaccountingrulesonrevenuerecognition

reveals valuable information aboutMandatoryAdopters, because the new information allows

competitors to better understand a firm’s revenue structure and compare revenues across

competitors.Thecomparabilityisparticularlyrelevantforconstructioncontractsforwhichthe

timing of revenue recognition can differ significantly across countries. For example, under

French GAAP, the percentage‐of‐completion method is the preferred treatment, but it is not

requiredforconstructioncontracts(Nobes[2001]).InItaly,thecompleted‐contractmethodcan

beappliedfortherevenuerecognitiononconstructioncontractsandservices(Nobes[2001]).

Table 7PanelA shows the coefficient on the interaction term is significantlynegative for the

subsampleof firmsdomiciled incountrieswith inconsistenciesregardingrevenuerecognition.

MandatoryAdopters locatedinthesubsamplewithnomajorinconsistenciesregardingrevenue

recognition do not experience a significant change in revenue growth relative to voluntary

adoptersfollowingtheintroductionofIFRS.

Another accounting standardwhose harmonization is likely to reveal valuable information to

competitorsisthestandardonintangibleassets(IAS38).Eightcountriesinmysampleexhibit

inconsistenciesbetween localGAAPand IFRSregarding theaccountingof intangibles. Inmost

industries,intangibles,suchasintellectualpropertyordevelopmentcosts,buildthebasisfora

firm’s current and future competitiveness, and are thus the firms’ most valuable assets. The

adoptionof IFRSenables competitors to learnmoreabout and compare firms’ key sourcesof

success.Inthepre‐period,thetreatmentofintangibleassetsvariesgreatlyacrosscountries.For

example, under Italian GAAP, companies are allowed to capitalize deferred costs such as

advertisingcostsrelatedtonewbusinessesorproductsandcertainincorporationcosts(Nobes

[2001]). Similarly, Greek GAAP allows the capitalization of research costs and pre‐operating

costs,whereasGermanGAAPdoesnotrequirerecognizinginternallygeneratedintangiblesthat

areexpectedtoprovideongoingservicetothefirm(Nobes[2001]).Again,thecoefficientonthe

interactiontermissignificantlynegativeforthesubsampleoffirmsdomiciledincountrieswith

12Note that I can only examine the effect of accounting issues for which a sufficient cross‐countryvariationexists.Forexample,12outof15countriesinmysamplehavenospecificdisclosurerulesunderlocal GAAP regarding investment property (IAS 40). Similarly, inconsistencies between IFRS and localGAAP in terms of the recognition and measurement of financial instruments (IAS 39) exist for all 15countries. My analysis thus focuses on accounting issues that (1) exhibit a sufficient cross‐countryvariationand(2)arelikelytorevealvaluableinformationaboutafirm.

91

inconsistencies regarding intangible assets.MandatoryAdopters located in countries with no

major inconsistencies do not experience a significant change in revenue growth relative to

voluntaryadoptersfollowingtheintroductionofIFRS.

In untabulated analyses, I further investigate the effect of three other accounting standards,

whicharelikelytorevealvaluableinformationtocompetitors:leases(IAS17),inventories(IAS

2), and segment reporting (IAS14). Firmshave the choice to eitherbuyor lease fixedassets.

Whether the leased asset shows up in the firm’s balance sheet depends on the specific

accountingrules.Byharmonizingtherulesonoff‐andon‐balance‐sheetleasearrangements,the

standardsetterenablescompaniestocompareitsowninvestmentsinfixedassetswiththatof

itscompetitors.ThetreatmentofleasecontractsunderlocalGAAPcanvarysignificantlyacross

countries.Forexample,underGermanGAAP, leasesare typicallyclassifiedbasedontaxrules,

and thus are seldom recognized as finance leases (Nobes [2001]).According toFrenchGAAP,

capitalizing finance leases is preferred but not required,whereas under Greek GAAP, finance

leasesarenotcapitalizedandleasepaymentsnotnecessarilyrecognizedonastraight‐linebasis

(Nobes[2001]).Sixcountriesinmysampleexhibit inconsistenciesregardingleaseaccounting.

Untabulated statistics show the coefficient of the interaction term is significantly negative

(coefficient: ‐0.040; p‐value: 0.057) for the subsample of firms located in countries with

inconsistencies regarding lease accounting. The interaction term is negative but insignificant

(coefficient: ‐0.015; p‐value: 0.679) for the other subsample, indicating peers consider

accountinginformationaboutleasearrangementstobevaluable.

Comparableinformationaboutafirm’sinventories,includingfinishedgoods,workinprogress,

andrawmaterials,maybeusefultothefirm’scompetitorsbecauseithelpsthemunderstandthe

firm’s inventory levels and cost structure. Themeasurement of inventories under local GAAP

varies significantly across countries. For example, under Austrian GAAP, excluding overheads

from the calculation of costs for inventory and self‐constructed assets is possible (Nobes

[2001]).AccordingtoItalianGAAP,inventories,withtheexceptionoffinishedgoods,arevalued

at the lower of cost and replacement cost (Nobes [2001]). Nine European countries have

inconsistencies between local GAAP and IFRS regarding the treatment of inventories.

Untabulated results show the interaction term is significantly negative (coefficient: ‐0.051;p‐

value: 0.051) for the subsample of firms experiencing an increase in comparability regarding

inventoryaccounting.MandatoryAdoptersdomiciledincountrieswithnoinconsistenciesdonot

suffer competitively relative to voluntary adopters after the introduction of IFRS (coefficient:

‐0.030,p‐value:0.573).Thisfindingisconsistentwiththeargumentthataccountinginformation

aboutinventorylevelsrevealsvaluableinformationtocompetitors.

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Last,Zhou[2014]documentstheproprietarycostsofsegmentreporting.Ithuspredicttheeffect

ofIFRSonthecompetitivenessofMandatoryAdoptersisgreaterforfirmsdomiciledincountries

with inconsistencies between local GAAP and IAS 14. For example, according to Irish andUK

GAAP,segmentreportingshowsnetassetsrather thanassetsand liabilitiesseparately(Nobes

[2001]). Untabulated results are not in line with prior literature. The interaction term is

insignificant for the subsample of firms located in countrieswith inconsistencies (coefficient:

0.099; p‐value: 0.452), whereas the coefficient on the interaction term for the subsample

without inconsistencies is significantly negative (coefficient: ‐0.036; p‐value: 0.059). Note,

however,thatthecross‐countryvariationinthisanalysisisratherlow,withonlythreecountries

experiencing inconsistencies regarding local GAAP and IAS 14. In addition, the subsample

without inconsistencies includes countries for which segment reporting significantly changes

aftertheIFRSadoption.Forexample,GermanGAAPallowsparentcompaniesthatarerequired

to issue consolidated accounts to report segment information on a voluntary basis.With the

mandatory adoption of IFRS, all firms being required to disclose their consolidated accounts

using IFRSarenowmandatedtoreportsegment information.Yet thesurveybyNobes[2001]

classifiesGermanyashavingneitherinconsistenciesbetweenlocalGAAPandIAS14thatcould

lead to differences formany enterprises nor inconsistencies that could lead to differences in

certainenterprises.Thus,oneshouldinterprettheresultsofthisanalysiswithcaution.


Next, I turn to additional disclosures required by different IFRS accounting standards. The

resultsarereportedinTable7PanelB.Followingtheargumentationlaidoutabove,Ifocuson

theaccountingstandardsoninventories(IAS2)andsegmentreporting(IAS14).Replicatingthe

analysisfortheotheraccountingstandardsexaminedaboveisnotpossible,becauseallsample

countries have specific disclosure rules under local GAAP regarding revenue recognition,

intangibles, and leasing. Seven countries in my sample have no specific disclosure rules

regardingcertainaspectsofinventoryaccounting.Forexample,GermanandFrenchGAAPhave

nospecificrulesthatrequiredisclosuresoftheFIFOorcurrentcostofinventorywhenvaluedon

theLIFObasis(Nobes[2001]).Thecoefficientontheinteractiontermforthesubsampleoffirms

located incountrieswithadditionaldisclosuresrequiredby IFRS issignificantlynegative.The

coefficientontheinteractiontermfortheothersubsampleisinsignificant.Theresultsareinline

with the prediction that additional disclosures regarding inventories reveal valuable

information to competitors.Again,my resultson segment reportingarenot in linewithprior

93

literature. The interaction term is negative but insignificant for both subsamples. Overall, the

findings suggest the mechanism of increased disclosure also appears to play a role in

disseminatingproprietaryinformationaboutMandatoryAdopters.

PlaceboAnalysis

In this section, I perform a placebo analysis by examining an accounting standard whose

informationislikelytobelessrelevanttocompetitors.Becausealargercross‐countryvariation

exists regarding accounting‐standard inconsistencies as compared to additional disclosures

requiredbyIFRS,Ifocusonthemechanismofenhancedcomparability.Specifically,Ipredictthe

informationrevealedbyharmonizingthetreatmentofeventsafterthereportingperiod(IAS10)

is generally less relevant to competitors. Table 8 reports the results. Consistent with my

prediction,theinteractiontermisinsignificantforbothsubsamples.Theplacebotestshowsthat

not all types of accounting information are equally important in adversely affecting a firm’s

competitiveness.


V. SensitivityAnalyses

Iperformseveral(untabulated)sensitivityanalysestoassesstherobustnessofmymainresults

ontheaccounting‐standardlevel.First, Iusethereturnonassetsasanalternativeproxyfora

firm’scompetitiveness.Itiscalculatedasoperatingincomedividedbytotalassets.Theintuition

isthatindustrycompetitionisanimportantfactorthatdeterminesafirm’sprofitability.Thus,if

themandatory adoption of IFRS adversely affects the competitivenessofMandatoryAdopters,

their profitabilitymay suffer in the post‐period. The results are robust. The only noteworthy

exceptionoccursinthesubsampleoffirmsdomiciledincountrieswithinconsistenciesinterms

ofrevenuerecognitionbetweenlocalGAAPandIFRS.Thecoefficientontheinteractiontermis

borderlineinsignificant(coefficient:‐0.014,p‐value:0.107).

Second,Ireplicatetheanalysisusingfirmandyearfixedeffectstoensurenounobservablefirm

characteristics drive my results. In this specification, I also separately control for the

competitivenesswithinanindustry,usingtheHHI.Theresultsarequalitativelysimilar.

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Third,firmsmayusethetransitionyearof2005forearnings‐managementpurposes.Toensure

anypotentialearningsmanagementinthetransitionyeardoesnotdrivemyresults,Ireplicate

theanalysesexcludingthetransitionyear.Theresultsarerobust.

Fourth,toensurecountrieswithoutvoluntaryadoptersdonotdrivemyresults,Ireplicatethe

mainanalysisexcludingPortugalandSpain.Again,theresultshold.

Last,Ire‐estimatetheanalysisincludingNorwayandSwitzerlandinmysample.Neithercountry

isamemberoftheEU.However,NorwayisamemberoftheEuropeanEconomicArea,withfull

access to theEUsinglemarket,whereasSwitzerland isamemberof theEuropeanFreeTrade

AreaandhassignedseveralbilateralagreementswiththeEU.Theresultsremainqualitatively

unchanged.

VI. Conclusion

This paper analyzes the impact ofmandatory financial reporting on a firm’s competitiveness.

Although the capital‐marketbenefitsofmandatorydisclosurearewelldocumented (e.g., Leuz

and Verrecchia [2000]; Li [2010a]), little is known about the proprietary costs ofmandatory

disclosure.UsingthemandatoryIFRSadoptioninEuropeasanexogenousshocktomandatory

financialreporting, I findthatfirmsmandatorilyadoptingIFRSsuffercompetitivelyrelativeto

voluntary adopters in the post‐period. Additional analyses reveal two mechanisms are

responsibleforthiseffect:increaseddisclosureandenhancedfinancial‐statementcomparability.

I further show not all types of accounting information are equally important to competitors.

Overall,myresultssuggest firms’concernsabout theproprietarycostsofmandatory financial

reportingarejustified.

This study is subject to several caveats.My sample does not include private firms and I thus

cannot make any statements about the potentially positive effects of the mandatory IFRS

adoption on the competitiveness of private firms.Moreover, themandatory adoption of IFRS

does not affect all mandatory adopters equally. My study takes this aspect into account by

differentiatingbetweenthetypesofinformationdisclosed.Isuggestthatfuturestudiesfocuson

firm‐andindustry‐levelcharacteristics.

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AppendixA:IdentificationofAccountingStandards

This section describes the coding of accounting standards. Identifying voluntary IFRS andUS

GAAP adopters prior to 2005 is not trivial, because the accounting‐standards classification of

Compustat Global contains coding errors (Covring et al. [2007]). Solely relying on other

databases such as FactSet orWorldscope does not alleviate this problem, because the coding

systemofWorldscopeissubjecttoinconsistenciesaswell(seeDaskeetal.[2013]OnlineData

and Coding Appendix).13Tominimize the probability of incorrectly classifying the accounting

standardappliedineachfirm‐year,Iusethreesources:CompustatGlobal,FactSet,andthedata

setofvoluntaryIFRSadoptersprovidedbyDaskeetal.[2013](hereafterDHLV).

I primarily rely on theDHVL classification. The authors identify voluntary IFRS andUSGAAP

adopters using data from Worldscope, supplemented with extensive hand collection. Their

classificationsystemendsin2005,theyearofthemandatoryIFRSadoptioninEurope.Whena

samplefirmcannotbematchedtotheDHVLdataset,Icomparetheaccounting‐standardcodes

in FactSet and Compustat Global. If both databases report the same accounting standard, I

proceed with this classification. This approach covers approximately 90% of all firm‐year

observations inmy initial sample of all Compustat Global firms. If the twodatabases provide

contradictingaccountingstandards,IproceedwiththeCompustatGlobalclassification.

When classifying accounting standards in FactSet and Compustat Global, I closely follow the

codingsystempresentedinDHLV.Afirm‐yearisclassifiedasIFRSifitsFactSetlabelis2,6,8,

18,19,or23anditsCompustatGobalclassificationis“DA”,“DI,”or“DT”(seealsoCovringetal.

[2007];Li[2010a]).Notethelabels12and16donotoccurinmysample.Iclassifyafirm‐yearas

USGAAPifitsFactSetlabelis3,13,or20anditsCompustatGlobalclassificationis“US”or“DU.”

Afirm‐yearisclassifiedaslocalGAAPifitsFactSetlabelis1,10,17,or21.Notethelabels11,14,

and15donotoccurinmysample,whereasallfirm‐yearslabeledas5arecoveredbytheDHLV

dataset.Only4 firm‐yearobservationsare labeled4or7. In thesecases, I relyonCompustat

Global. The Compustat Global classification for local GAAP is “DS”, “DO,” or ”DD.”

13http://research.chicagobooth.edu/arc/journal/onlineappendices.aspx.

96

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Table1:Country‐LevelSampleDistribution

Thistablereportsthesampledistributionbycountry.Thefinalsamplecomprises10,596firm‐yearobservationsfrom15Europeancountriesoveratimeperiodofsixyears.Afirmisincludedinthesampleifithasavailabledatafortheentiresampleperiodandisnotactiveinthefinancialsector.AfirmisclassifiedasaMandatoryAdopterifitadoptslocalGAAPinthepre‐periodandIFRSinthepost‐period.AfirmisdefinedasaVoluntaryAdopterifitadoptsIFRSorUSGAAPinthepre‐periodandIFRSinthepost‐period.

Country AllMandatoryAdopters

VoluntaryAdopters

Austria 246 6 240

Belgium 180 102 78

Denmark 390 324 66

Finland 462 444 18

France 1,968 1,890 78

Germany 2,076 636 1,440

Greece 162 150 12

Ireland 138 126 12

Italy 378 174 204

Luxembourg 48 6 42

TheNetherlands 570 450 120

Portugal 150 150 0

Spain 204 204 0

Sweden 972 960 12

UnitedKingdom 2,652 2,604 48

Total 10,596 8,226 2,370

100


Thistablereportsthesummarystatisticsofallvariables inthepre‐andpost‐period.A firmisclassifiedasaMandatoryAdopterifitadoptslocalGAAPinthepre‐periodandIFRSinthepost‐period.AfirmisdefinedasaVoluntaryAdopter if itadoptsIFRSorUSGAAPinthepre‐periodandIFRSinthepost‐period.RevenueGrowthisafirm’syearlygrowthinrevenues.Sizeisdefinedas the log of book value of total assets; Intangibles, as the book value of a firm’s intangiblesscaled by total assets;Capex(Lag), as the one‐year lag of capital expenditure scaled by totalassets;andDebt,ascurrentliabilitiespluslong‐termdebtscaledbytotalassets.Allcontinuousvariablesarewinsorizedatthe1%level.Thesampleperiodspanssixyears.

Mean Median SD N Mean Median SD N

AllSampleFirmsRevenueGrowth 0.086 0.029 0.450 5,298 0.142 0.085 0.408 5,298Size 5.695 5.414 2.127 5,298 5.983 5.740 2.139 5,298Intangibles 0.146 0.086 0.164 5,298 0.181 0.123 0.177 5,298Capex(Lag) 0.052 0.039 0.049 5,298 0.046 0.034 0.044 5,298Debt 0.501 0.502 0.206 5,298 0.498 0.497 0.196 5,298

RevenueGrowth 0.069 0.030 0.418 1,185 0.152 0.097 0.424 1,185Size 5.769 5.327 2.317 1,185 5.995 5.625 2.370 1,185Intangibles 0.142 0.091 0.147 1,185 0.174 0.124 0.163 1,185Capex(Lag) 0.051 0.039 0.046 1,185 0.046 0.034 0.044 1,185Debt 0.470 0.468 0.204 1,185 0.469 0.468 0.191 1,185

RevenueGrowth 0.091 0.029 0.458 4,113 0.140 0.082 0.403 4,113Size 5.674 5.436 2.069 4,113 5.979 5.768 2.068 4,113Intangibles 0.147 0.084 0.168 4,113 0.183 0.122 0.181 4,113Capex(Lag) 0.053 0.039 0.050 4,113 0.046 0.034 0.045 4,113Debt 0.510 0.511 0.206 4,113 0.506 0.506 0.196 4,113

Pre‐Period Post‐Period

VoluntaryAdopters

MandatoryAdopters

101

Table3:Full‐SampleAnalysis

This table reports the results of the full‐sample difference‐in‐differences analysis. ThedependentvariableisRevenueGrowth.MandatoryAdopter isabinaryvariabletakingthevalueof1ifafirmadoptslocalGAAPinthepre‐periodandIFRSinthepost‐period.MandatoryAdopterisequaltozeroifafirmadoptsIFRSorUSGAAPinthepre‐periodandIFRSinthepost‐period.Post isabinaryvariableequal to1 for fiscalyearsendingonorafterDecember31,2005,andzerootherwise.Size isdefinedas the logofbookvalueof totalassets;Intangibles,as thebookvalue of a firm’s intangibles scaled by total assets;Capex(Lag), as the one‐year lag of capitalexpenditurescaledbytotalassets;andDebt,ascurrentliabilitiespluslong‐termdebtscaledbytotal assets. The Fama and French [1997] 17‐industry classification applies. Robust standarderrorsareclusteredat the firm level.All continuousvariablesarewinsorizedat the1% level.The sample period spans six years.p‐values are presented in parentheses. Significance at the0.01,0.05,and0.10levelsisindicatedby***,**,and*.

MandatoryAdopter ‐0.014(0.474)

Post 0.031(0.176)

MandatoryAdopter×Post ‐0.031 *(0.093)

Size ‐0.011 ***(0.000)

Intangibles 0.209 ***(0.000)

Capex(Lag) 0.237 *(0.091)

Debt ‐0.093 ***(0.002)

Intercept 0.100(0.111)

Country‐FE YesIndustry‐FE YesYear‐FE YesR2 0.043N 10,596

Full sample

102

Table4:Country‐LevelVariablesonMechanism

This table reports the summary statistics for the country‐level variables capturing theunderlyingmechanisms. All variables are constructed using the survey byNobes [2001]. Thenumber of inconsistencies captures the increase in comparability across countries after theintroductionof IFRS (Li [2010a]). Thenumberof additional disclosuresmeasuresdisclosuresthataremissinginlocalGAAPbutarerequiredbyIFRS(Li[2010a]).TheAbsenceIndexcapturestheextenttowhichcertainaccountingrulesareincludedinIFRSbutmissinginlocalGAAP(Dinget al. [2007]). The Divergence Index measures the extent to which rules regarding the sameaccountingissuedifferbetweenlocalGAAPandIFRS(Dingetal.[2007]).

CountryNumberof

Inconsistencies

NumberofAdditionalDisclosuresRequired

byIFRSDivergenceIndex AbsenceIndex

Austria 20 8 36 34Belgium 15 7 32 22Denmark 13 5 21 31Finland 19 8 31 22France 19 6 34 21Germany 20 7 38 18Greece 20 9 28 40Ireland 15 0 34 0Italy 19 6 37 27Luxembourg 16 8TheNetherlands 5 2 25 10Portugal 12 7 22 29Spain 22 9 29 28Sweden 11 4 26 10UnitedKingdom 15 0 35 0

Mean 16 6 31 21Median 16 7 32 22Std.Dev. 4 3 5 12

103

Table5:AnalysisofUnderlyingMechanism

This table reports the results of the difference‐in‐differences analyses investigating the underlyingmechanism. The sample is partitioned into twogroupsbasedonwhetherafirmisdomiciledinacountrywithanabove‐orbelow‐median(1)numberofadditionaldisclosuresrequiredbyIFRS(Li[2010a]), (2) number of inconsistencies between local GAAP and IFRS (Li [2010a]), (3) value ofAbsenceIndex (Ding et al. [2007]), or (4) value ofDivergenceIndex(Dingetal.[2007]).TheinformationisobtainedfromNobes[2001].ThedependentvariableisRevenueGrowth.MandatoryAdopterisabinaryvariabletakingthevalueof1ifafirmadoptslocalGAAPinthepre‐periodandIFRSinthepost‐period.MandatoryAdopterisequaltozeroifafirm adopts IFRS orUS GAAP in the pre‐period and IFRS in the post‐period.Post is a binary variable equal to 1 for fiscal years ending on or afterDecember31,2005, andzerootherwise.Size isdefinedas the logofbookvalueof total assets;Intangibles, as thebookvalueof a firm’s intangiblesscaledbytotalassets;Capex(Lag),astheone‐yearlagofcapitalexpenditurescaledbytotalassets;andDebt,ascurrentliabilitiespluslong‐termdebtscaled by total assets. The Fama and French [1997] 17‐industry classification applies. Robust standard errors are clustered at the firm level. Allcontinuousvariablesarewinsorizedatthe1%level.Thesampleperiodspanssixyears.p‐valuesarepresentedinparentheses.Significanceatthe0.01,0.05,and0.10levelsisindicatedby***,**,and*.

103

104

Table5:AnalysisofUnderlyingMechanism(continued)

Abovemedian Belowmedian Abovemedian Belowmedian Abovemedian Belowmedian Abovemedian Belowmedian

Mandatory 0.093 ‐0.033 0.007 ‐0.053 ‐0.011 ‐0.046 0.005 ‐0.029(0.142) (0.346) (0.740) (0.225) (0.630) (0.325) (0.918) (0.183)

Post 0.000 0.029 0.054 ** ‐0.004 0.041 * 0.078 0.009 0.032(0.997) (0.450) (0.035) (0.946) (0.097) (0.242) (0.835) (0.233)

MandatoryAdopter×Post ‐0.057 ‐0.037 ‐0.048 ** ‐0.004 ‐0.045 ** ‐0.040 ‐0.067 * ‐0.024(0.250) (0.326) (0.018) (0.940) (0.020) (0.522) (0.082) (0.274)

Size 0.003 ‐0.017 *** ‐0.004 ‐0.018 *** ‐0.009 *** ‐0.021 *** 0.001 ‐0.013 ***(0.780) (0.000) (0.240) (0.000) (0.004) (0.003) (0.892) (0.000)

Intangibles 0.134 0.242 *** 0.177 *** 0.238 *** 0.194 *** 0.306 *** 0.153 0.204 ***(0.461) (0.000) (0.003) (0.000) (0.000) (0.003) (0.255) (0.000)

Capex(Lag) 0.124 0.189 0.193 0.232 0.164 0.491 0.283 0.084(0.178) (0.276) (0.268) (0.299) (0.250) (0.167) (0.414) (0.567)

Debt 0.018 ‐0.078 ** ‐0.062 ‐0.110 ** ‐0.116 *** ‐0.014 ‐0.068 ‐0.098 ***(0.887) (0.037) (0.107) (0.012) (0.001) (0.843) (0.481) (0.002)

Intercept ‐0.106 0.187 *** 0.067 0.147 ** 0.133 ** 0.096 ‐0.041 0.091 **(0.484) (0.002) (0.342) (0.033) (0.046) (0.303) (0.750) (0.041)

Country‐FE Yes Yes Yes Yes Yes Yes Yes YesIndustry‐FE Yes Yes Yes Yes Yes Yes Yes YesYear‐FE Yes Yes Yes Yes Yes Yes Yes YesR2 0.063 0.055 0.029 0.057 0.044 0.055 0.038 0.052N 1,122 7,068 5,496 5,052 7,458 2,910 1,530 8,376

AdditionalDisclosures Increase in Comparability Divergence Index Absence Index

104

105

Table6:AccountingStandardVariables

This table reports thedescriptive statistics for theheterogeneity in localGAAPby accountingstandards.Acountryislabeledasoneif(1)inconsistenciesexistbetweenlocalGAAPandIFRSregarding a specific accounting standard or (2) a specific IFRS accounting standard requiresadditionaldisclosuresoverlocalGAAP.Allaccounting‐standardvariablesareconstructedusingthesurveyofNobes[2001].

Country

IAS2 IAS10 IAS11&18 IAS14 IAS17 IAS38 IAS2 IAS14

Austria 1 1 1Belgium 1 1 1 1Denmark 1 1 1Finland 1 1 1 1 1 1France 1 1 1 1 1Germany 1 1 1 1Greece 1 1 1 1 1 1 1Ireland 1 1Italy 1 1 1Luxembourg 1 1 1 1 1TheNetherlands 1Portugal 1 1Spain 1 1 1 1 1Sweden 1UnitedKingdom 1 1

Total 9 6 6 3 6 8 7 8

InconsistenciesBetweenLocalGAAPandIFRS AdditionalDisclosuresRequiredbyIFRS

106

Table7:AnalysisofAccountingInformation

This table reports the results of the difference‐in‐differences analyses exploiting theheterogeneity in local GAAP. In Panel A, the sample is partitioned into two groups based onwhether a firm is domiciled in a country with inconsistencies between local GAAP and IFRSregardingaspecificaccountingstandard.InPanelB,thesampleispartitionedintotwogroupsbasedonwhetherafirmisdomiciledinacountrywithadditionaldisclosurerulesrequiredbyIFRS regarding a specific accounting issue. All accounting‐standard variables are constructedusing the survey of Nobes [2001]. The dependent variable is Revenue Growth. MandatoryAdopter isabinaryvariabletakingthevalueof1ifa firmadopts localGAAPinthepre‐periodandIFRSinthepost‐period.MandatoryAdopterisequaltozeroifafirmadoptsIFRSorUSGAAPinthepre‐periodandIFRSinthepost‐period.Postisabinaryvariableequalto1forfiscalyearsendingonor afterDecember31, 2005, and zerootherwise.Size is defined as the logof bookvalueoftotalassets;Intangibles,asthebookvalueofafirm’sintangiblesscaledbytotalassets;Capex (Lag), as the one‐year lag of capital expenditure scaled by total assets; and Debt, ascurrent liabilitiesplus long‐termdebtscaledby totalassets.TheFamaandFrench [1997]17‐industry classification applies. Robust standard errors are clustered at the firm level. Allcontinuousvariablesarewinsorizedatthe1%level.Thesampleperiodspanssixyears.p‐valuesarepresentedinparentheses.Significanceatthe0.01,0.05,and0.10levelsisindicatedby***,**,and*.

PanelA:EnhancedFinancial‐StatementComparability

Incon‐sistencies

No Incon‐sistencies

Incon‐sistencies

NoIncon‐sistencies

MandatoryAdopter 0.008 ‐0.027 0.011 ‐0.074 *(0.846) (0.194) (0.602) (0.077)

Post 0.044 0.027 0.055 ** 0.012(0.253) (0.315) (0.039) (0.772)

MandatoryAdopter×Post ‐0.069 ** ‐0.015 ‐0.041 * ‐0.026(0.048) (0.497) (0.051) (0.520)

Size ‐0.006 ‐0.012 *** ‐0.002 ‐0.019 ***(0.199) (0.001) (0.562) (0.000)

Intangibles 0.211 *** 0.203 *** 0.151 *** 0.259 ***(0.004) (0.000) (0.009) (0.000)

Capex(Lag) 0.351 0.151 0.192 0.226(0.144) (0.366) (0.268) (0.310)

Debt ‐0.009 ‐0.128 *** ‐0.038 ‐0.128 ***(0.853) (0.000) (0.297) (0.005)

Intercept 0.051 0.112 * 0.011 0.145 *(0.523) (0.100) (0.838) (0.068)

Country‐FE Yes Yes Yes YesIndustry‐FE Yes Yes Yes YesYear‐FE Yes Yes Yes YesR2 0.037 0.050 0.028 0.059N 3,408 7,188 5,478 5,118

IAS11& 18 ‐ Revenues IAS 38‐Intangibles

107

Table7:AnalysisofAccountingInformation(continued)

PanelB:IncreasedDisclosure

Additionaldisclosures

No additionaldisclosures

Additionaldisclosures

Noadditionaldisclosures

MandatoryAdopter ‐0.003 ‐0.029 0.015 ‐0.021(0.908) (0.382) (0.716) (0.323)

Post 0.059 ** 0.011 0.032 0.025(0.039) (0.740) (0.354) (0.380)

MandatoryAdopter×Post ‐0.039 * ‐0.023 ‐0.031 ‐0.028(0.077) (0.464) (0.290) (0.223)

Size ‐0.003 ‐0.017 *** ‐0.002 ‐0.013 ***(0.372) (0.000) (0.699) (0.000)

Intangibles 0.157 *** 0.245 *** 0.135 * 0.231 ***(0.008) (0.000) (0.075) (0.000)

Capex(Lag) 0.174 0.249 0.302 0.142(0.325) (0.245) (0.179) (0.406)

Debt ‐0.060 * ‐0.109 ** ‐0.066 ‐0.102 ***(0.100) (0.014) (0.171) (0.004)

Intercept 0.054 0.123 0.070 0.019(0.298) (0.108) (0.412) (0.660)

Country‐FE Yes Yes Yes YesIndustry‐FE Yes Yes Yes YesYear‐FE Yes Yes Yes YesR2 0.031 0.054 0.034 0.053N 5,070 5,526 3,660 6,936

IAS2 ‐ Inventories IAS 14 ‐ SegmentReporting

108

Table8:PlaceboAnalysis

Thistablereportstheresultsoftheplaceboanalysis.ThesampleispartitionedintotwogroupsbasedonwhetherafirmisdomiciledinacountrywithinconsistenciesbetweenlocalGAAPandIFRS regarding the treatment of events after the reporting period (Nobes [2001]). Thedependentvariable isRevenueGrowth.MandatoryAdopter isabinaryvariabletakingthevalueof1ifafirmadoptslocalGAAPinthepre‐periodandIFRSinthepost‐period.MandatoryAdopterisequaltozeroifafirmadoptsIFRSorUSGAAPinthepre‐periodandIFRSinthepost‐period.Post isabinaryvariableequal to1 for fiscalyearsendingonorafterDecember31,2005,andzerootherwise.Size isdefinedas the logofbookvalueof totalassets;Intangibles,as thebookvalue of a firm’s intangibles scaled by total assets;Capex(Lag), as the one‐year lag of capitalexpenditurescaledbytotalassets;andDebt,ascurrentliabilitiespluslong‐termdebtscaledbytotal assets. The Fama and French [1997] 17‐industry classification applies. Robust standarderrorsareclusteredat the firm level.All continuousvariablesarewinsorizedat the1% level.The sample period spans six years.p‐values are presented in parentheses. Significance at the0.01,0.05,and0.10levelsisindicatedby***,**,and*.

Incon‐sistencies

NoIncon‐sistencies

MandatoryAdopter ‐0.050 ‐0.004(0.252) (0.859)

Post ‐0.015 0.061 **(0.780) (0.015)

MandatoryAdopter×Post ‐0.019 ‐0.031(0.724) (0.129)

Size ‐0.013 ** ‐0.010 ***(0.013) (0.003)

Intangibles 0.207 *** 0.215 ***(0.001) (0.000)

Capex(Lag) 0.354 * 0.158(0.099) (0.394)

Debt ‐0.118 *** ‐0.078 **(0.006) (0.044)

Intercept 0.103 0.113(0.148) (0.103)

Country‐FE Yes YesIndustry‐FE Yes YesYear‐FE Yes YesR2 0.051 0.044N 4,092 6,504

IAS10‐EventsAfterReportingPeriod

109

StatementofCertification

I hereby confirm that this dissertation constitutes my own work, produced without aid and

supportfrompersonsand/ormaterialsotherthantheoneslisted.Allusedsourcesareindicated

asdirectorindirectquotations.Quotationmarksindicatedirectlanguagefromanotherauthor.

AppropriatecreditisgivenwhereIhaveusedideas,expressionsortextfromanotherpublicor

non‐public source.The thesis in this formor in anyother formhasnotbeen submitted to an

examinationbody.

ElisabethKläs

October30,2017

110

CurriculumVitae

EDUCATION

Since ‐ 09/2013 DoctoralProgramme(Dr.rer.pol.)FrankfurtSchoolofFinance&Management,GermanyExchange:LancasterUniversityManagementSchool,UK

03/2012 ‐ 10/2013 MasterofFinance(M.Sc.)FrankfurtSchoolofFinance&Management,GermanyExchange:UniversitàCommercialeLuigiBocconi,Italy

09/2008 ‐ 02/2012 BachelorinInternationalBusinessStudies(B.Sc.)FrankfurtSchoolofFinance&Management,GermanyExchange:CassBusinessSchool,CityUniversityLondon,UK

09/2006 ‐ 09/2008 A‐LevelKing’sCollegeTaunton,UK

WORKINGEXPERIENCE

10/2013

10/2017

ResearchAssociateAccountingDepartmentFrankfurtSchoolofFinance&Management,Germany

01/2012

‐

06/2012

StudentTraineeCorporate&InvestmentBanking/FixedIncomeSalesBNPParibas,Germany

08/201101/2011

‐‐

09/201102/2011

InternshipAssurancePricewaterhouseCoopersSàrl,Luxembourg

08/2010 ‐ 09/2010 InternshipFinancialMarketStabilisationFund(SoFFin),Germany

SCHOLARSHIPS

Short‐termscholarship2016,GermanAcademicExchangeService

FrankfurtamMain,October2017