A variable impact neural network analysis of dividend policies and share prices of transportation and related companies

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ContentslistsavailableatSciVerseScienceDirect

Journal

of

International

Financial

Markets,

Institutions

&

Money

j o u r n a l h o m e p a g e :w w w . e l s e v i e r . c o m / l o c a t e / i n t f i n

A

variable

impact

neural

network

analysis

of

dividend

policies

and

share

prices

of

transportation

and

Hussein

A.

Abdou

a,∗

,

John

Pointon

b

,

Ahmed

El-Masry

b

,

Moji

Olugbode

b

,

Roger

J.

Lister

a

a_Salford_Business_School,_University_of_Salford,_Salford,_Greater_Manchester_M5_4WT,_UK b_Plymouth_School_of_Management,_University_of_Plymouth,_Plymouth,_Devon_PL4_8AA,_UK

a

r

t

i

c

l

e

i

n

f

o

Articlehistory:

Received1March2010 Accepted18April2012 Available online 1 May 2012

JELclassiﬁcation: C45 F23 G10 Keywords: Dividendyield Retention Market-to-bookvalue Neuralnetworks Transportation

a

b

s

t

r

a

c

t

The purposeof this research is toinvestigatedividend policy, includingitsimpactonsharepricesoftransportationproviders andrelatedservicecompanies,bycomparinggeneralized regres-sionneuralnetworkswithconventionalregressions.Ourresults usingregressionsrevealthatforEuropeandfortheUSandCanada themarket-to-book-value,asasurrogateforgrowthopportunities, fulﬁlsexpectationsofpressuresondividendsleadingtoanegative associationwithdividendyieldsinaccordancewiththepecking ordertheory.Neuralnetworkanalysisindicatesaclearrolefor growthopportunitiesfortheUSandCanadapointingtoan under-lyingconﬁdenceonthepartoftransportationcompaniesintheir owninternalpolicies.Finally,riskisrewardedespeciallyinEurope.

1. Introduction

Whatdoshareholdersoftransportationcompaniesgetfortheirmoney?Howdodividendsimpinge ontheirshareprices?Dodividendsplayastrongerrolethanretainedproﬁtsinvaluingthese compa-nies?Theseareimportantquestionsforthetransportationindustry,andneedtobeaddressed.Ifshare pricesinthissectorappreciate,theninvestorsﬁndthattheirreturnscomprisecapitalgainsaswellas

∗Correspondingauthor.Tel.:+441612953001;fax:+441612955022.

E-mailaddress:[email protected](H.A.Abdou).

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dividends.Ofcourse,ifpricesfall,capitallossesensueinstead.Butcapitalgainsorlossesarelikelyto bemorevolatilethandividends.Indeed,performanceofglobaltransportationﬁrmsiscloselylinked tovariationsinworldtrade(GoulielmosandPsiﬁa,2006)andglobalmacrofactors(Kavussanosand Marcoulis,2000,2005;GrammenosandArkoulis,2002).

Inviewofsuchexposuretofluctuationsinglobaltrade,anddifferencesinnationaleconomic cycles,goodplanningandappropriatestrategiclong-termdecision-makingareimportant(Bendall andStent,2003).Ofcourse,dividendsarepaidoutofearnings,leavingaresidualfortheretentions whichbecomeavailableforstrategicreinvestment,andsowhetherdividendsaremoreimportantthan retentionsisakeyissuethatmayalsoaffectthefutureofthetransportationindustry.Inaccounting termsretentionsproduceachangeinaccountingbookvalues,andindeeditisuponthenetworkassets oftheenterprisethatthecompaniescangeneratetheirreturns.Inusingthesenetworkassets,thereis evidencetosuggestthattheriskintermsoffreightvolatilitiescanbereducedbyoperatingsmall-sized vessels(Kavussanos,2003).Butdosharepricesoftransportationprovidersandrelatedservicefirms dulyreflectdividends,retentions,marketandbookvaluespershare?Ifso,whichelementsaremore significantinsuchvaluations?Thesearesomeoftheempiricalissues,whichthispaperattemptsto investigate.

Giventheinternationalnatureofthetransportationindustry,itissensible totakecognisance ofvariationsamongstcapitalmarketsacrosstheglobe.Regardingtherelativeimportanceof divi-dends,retainedprofits,marketandbookvalues,doEuropeantransportationprovidersandrelated service firmsdiffer fromthose in North America, theFar Eastand Australia? In the determina-tionofsharepricesofthesecompanies,ifaclearrolecanbeestablishedfordividends,thenthe nextstepisclearlytoevaluatethefactorsthatdrivedividendyields.Inthispaper,fromareview of someof the literatureon dividend policy,several factorswill be proposedthat are likely to beofpotentialimportancetodividendyielddetermination.Itemergesfromourliteraturereview thatpotentiallysignificantcontendersforsuchaninvestigationaregrowthprospects,asset back-ing,businessandfinancialrisks,size/stability,profitability,capitalexpenditureneeds,andcashflow generation.

Theaimsofthisresearcharetoidentifyrelevantvariables,andtotestthepredictiveabilitiesof themodelsusedfordeterminingbothsharepricesanddividendyieldsoftransportationcompanies whichtermweusetorefertotransportationprovidersandrelatedservicecompanies.Asfarasshare pricesareconcernedourfocusisonwhetherdividendsaremoreimportantthanretentions.Asfaras dividendyieldsareconcerned,wearemoreinterestedinidentifyingrelevantvariables,andassessing whichofthesearemoreimportantthanothers.Furthermore,wesuspectthattheremaybesome regionaldifferences,andifsotheninvestorsshouldbeawareofthem.Also,weconsiderhowfarour resultsaccordwithvariouseconomictheoriespertainingtodividendbehaviour,suchaspeckingorder, agencycostandtrade-offtheories.

Weﬁndthatthebookvaluepershareisthemostimportantdeterminantoftheshareprice.The variableimpactanalysisofsharepricedemonstratesthatdividendsaremoreimportantthan reten-tionsineachregionandoverall.Furthermore,themaindriversofdividendyieldsaredifferentin thethreeregions:market-to-book-valueintheUSandCanada(negativelyassociated);riskinEurope (positivelyassociated);andcashﬂowasapercentageofsales(negativelyassociated)inRestofthe World.

Withintheliterature,dividendsplayamoreimportantrolethanretentionsinexplainingshare prices,asevidencedinaUKstudybyRees(1997),inthespiritoftheOhlsonmodel(Ohlson,1995) onthevaluerelevanceofaccountinginformation.However,Gwilymetal.(2005)demonstratethat fortheUKthisisnottrueaftertheeffectsoftransactioncostsandriskhavebeentakenintoaccount. Barker(1999),fromsurveyevidence,findsthatanalyststendtousedividendyieldsintheutilities andfinancialsectors.Benartzietal.(1997)investigatetheinformationcontentofdividendpolicy pertainingtofutureearnings,followingtheclassicinvestigationbyLintner(1956).Theydonotfind anassociationbetweenacurrentdividendincreaseandfutureearnings’growth.Grullonetal.(2005) findthatdividendchangesbearnoinformation-contentregardingfuturechangesinearnings,after accountistakenofnon-linearitiesinearnings’behaviour.Riahi-BelkaouiandPicur(2001)arguethat theuseofthePEratioordividendyieldwilldependontheinvestmentopportunitysetopentothe firm.BenitoandYoung(2003)foundthatUKfirmswithgreatergrowthopportunities(higherTobin’s

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Q,whichissimilartothemarket-to-bookvaluemeasure)weremorepressurisedtoomitdividends. OmranandPointon(2004)foundanegativerelationshipbetweentheQ-ratioandthedividendpayout ratio.However,D’SouzaandSaxena(1999)cannotﬁndasigniﬁcantassociationbetweendividends andinvestmentopportunities(Q).

Inanagency-frameworkLie(2005)findsthatleverageisnegativelyassociatedwiththedividend payoutratio– buttherearedissentingvoices,e.g.TongandGreen(2005)andAdedeji(1998).Benito andYoung(2003)findthatfirmswithlowerprofitsaremorelikelytoomitdividends.Famaand French(2002)tendtosupportpeckingordertheorybutwithsomeevidencefortrade-offtheory,in thattheyfindapositiverelationshipbetweenprofitabilityandthedividendpayoutratio.Ifretentions, asthefirstpriority,areusedtofundcapitalexpenditurethenthereshouldbeanegativerelationship betweenthecapitalexpenditurerateanddividendyields.

BenitoandYoung(2003)statethatthereisanegativerelationshipbetween‘cash-flow’1_as_a pro-portionofthereplacementcostofcapitalstockandUKdividends.AstudyofGermanfirms,byAndres etal.(2008),indicatesthattheirdividendsaremorerelatedtocashflowsthantoearnings.

Someresearchershavefoundanegativerelationshipbetweenmarket-to-bookvaluesand divi-dendyield(Eaganetal.,1999;Lie,2005;Riahi-BelkaouiandPicur,2001;Gwilymetal.,2005).Benito andYoung(2003)findthatUKfirmswithgreatergrowthopportunitiesaremorepressurisedtoomit dividends.Wemightsensiblyexpectrisktobeisnegativelyrelatedtothedividendyield,because ahigherriskmakesdividendslesssustainable.Indeed,D’SouzaandSaxena(1999)findanegative relationshipbetweenmarketriskanddividendpayments.Furthermore,Lie(2005,p.10)finds neg-ativerelationshipsbetweendividendincreasesand(i)priorbeta,i.e.‘equitybetaestimatedusing dailyreturnsduringthefiscalyearpriortotheeventyear’,(ii)operatingincome(ratioof operat-ingincometototalassets)volatilitychangeand(iii)prioroperatingincomevolatility,i.e.‘standard deviationoftheratioofoperatingincometototalassetsfortheprevious5years’.Sizemightalso beafactorindividendyieldsonthebasisthatsmallerfirmsarerelativelyriskierleadingas men-tionedabovetoahigherdiscountratefortheincomeandaconsequentlyahighernumberforthe yield.

Therestofthispaperunfoldsasfollows:Section2setsthescenefortheempiricalanalysiscovering hypothesesandmodelsdesignedformultipleregressionanalysisandgeneralizedregressionneural networksanalysis;Section3describesthesourcesandcollectionofthedata;inSection4theresults andanalysisarediscussed,assessingboththeroleofdividendsinsharepricedetermination,andthe determinantsofdividendyieldsandﬁnallySection5comprisestheconclusion.

2. Methodology

Anumberofsigniﬁcanthypothesesemergefromtheabovereviewoftheliterature:

H1. Market-to-bookvalueisnegativelyrelatedtodividendyield(inaccordancewithpeckingorder). H2. Asset-backingispositivelyrelatedtodividendyield(sincewhenassetsaresufﬁcientagenerous

dividenddoesnotthreatentoputunduepressureonretentions).

H3. Totaldebttoequityisnegativelyrelatedtodividendyield(inaccordancewithagencytheory). H4. Sizeispositivelyrelatedtodividendyield(inviewofthepotentialassociationwithrisk). H5. Returnonequityispositivelyrelatedtodividendyield(inaccordancewithtrade-off). H6. Capitalexpenditurerateisnegativelyrelatedtodividendyield.

H7. Cashﬂowasapercentageofsalesispositivelyrelatedtodividendyield.

H8. Riskisnegativelyrelatedtodividendyield(onthebasisthatriskpotentiallypromptsretention).

1_The_Pacific_Basin_Shipping_Company₍_{www.pacificbasin.com}₎_announced_in_December_2004,_for_example,_that_its_interim dividendreflected,interalia,the‘levelofcashavailable’.

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Therearefourmaincomponentstothemethodology.Firstly,weundertakeamultipleregression analysisofthesharepriceofﬁrmiinyeartwithSPit,asthedependentvariable:

SP_it=a+b1DPSit+b2RPSit+b3BVPSit+eit (1)

where,DPSitisthedividendspershareofﬁrmiinyeart;RPSitistheretentionspersharebyﬁrmi

inyeart;BVPSitisthebookvaluepershare;aistheconstant;b1...b3aretherespectiveregression

coefﬁcientsfortheindependentvariablesandeitistheresidualforﬁrmiinyeart.Weruntheregression

globally,andagainforeachofthethreeregions.Eq.(1)isthestandardmodel,inthevaluerelevanceof accountingliterature,forassessingtheimpactofdividends,retentionsandbookvalueonshareprice. Inthispaperweareinterestedintheimpactofdividendsonactualstockprices,notonratesofreturn. WhilstusingSPasoureconomicvariable,weshouldindicatethatthecorrespondingregressionresults maybespuriousbecauseofthepossibilitythatSPisI(1).

Secondly,weconductamultipleregressionanalysisofthedividendyieldofﬁrmiinyeartwith DYit,asthedependentvariable:

DY_it =˛+ˇ1MTBVit+ˇ2ASSBKGit+ˇ3TDEit+ˇ4SIZEit+ˇ5ROEit+ˇ6CAPEXRATEit

+ˇ7CF%Sit+ˇ8STDEV(EB/TS)_it+εit (2)

where,MTBVitisthemarket-to-bookvalue;ASSBKGitistheasset-backingdeﬁnedasﬁxedassets/total

assets;TDEitisthetotaldebt/equity;SIZEitisthenaturallogarithmofmarketcapitalization;ROEitis

thereturnonequity;CAPEXRATEitisthecapitalexpenditureratedeﬁnedascapitalexpenditure/total

assets;CF%Sitisthecashﬂowasapercentageofsales;STDEV(EB/TS)itistheriskdeﬁnedasstandard

deviationoftheratioofearningsbeforeinterest,taxanddepreciation/totalassets;˛istheconstant; ˇ1...ˇ8aretherespectiveregressioncoefﬁcientsfortheindependentvariablesandεitisthewhite

noiseerrorterm.Theεitareindependentandnormallydistributedwithmeanzeroandvariance2.

Weruntheregressionglobally,andagainforeachofthethreeregions.

IntheFamaandFrench(2002)singlecountrystudy,dividendsarescaledbyassets,whereaswe scaledividendsbymarketvalue,toarriveatthedividendyield.Thisavoidsinadequaciesinfinancial reportingpractices,andinconsistenciesbetweenaccountingsystemsacrosstheglobethatwouldarise ifassetswereusedasthescalingfactor.Thepointisthatdifferentcountriesusedifferentvaluation approachesforaccountingpurposes.Butbyusingstockmarketvaluesforscaling,thisproblemis avoidedinthispaper.AlsointheFamaandFrench(2002)studyofdividendsanddebt,sizeisusedas aproxyforvolatility.Inourpaperweuseseparatevariablesforsizeandrisk.Indeedwelatershow thatfortransportationfirms,whensizeisahighlysignificantdeterminantofthedividendyield,the riskisnotsignificant,andviceversa.

Thirdly,werunageneralizedregressionneuralnetwork(GRNN)ofthedeterminantsoftheshare price.Weundertakethisfortrainingandtestingsamplesindividuallyandagainfortheoverallsample, globallyandforeachregion.Itshouldbeemphasisedthatthetrainingdataarethedatausedtobuild themodels,whilstthetestingdataplaynoroleinbuildingthemodels,butservetotestthepredictive capabilitiesofthemodel.Whenwereferinthispapertoanoverallsample,wemeanthatthewhole datasetisusedinbuildingthemodel.Wealsoprovideavariableimpactanalysisinordertoassess therelativeimportanceofeachdeterminantnamelyDPSit,RPSitandBVPSit.

Aneuralnetworkisasystemthattakesnumericinputs,performscomputationsontheseinputs, andcreatesoutputsforoneormorenumericvalues.Theinspirationforneuralnetworkscomesfrom thestructureofthehumanbrain.Abrainconsistsofalargenumberofcells,referredtoas‘neurons’ or‘nodes’.Aneuronreceivesimpulsesfromotherneuronsthroughanumberof‘dendrites’. Depend-ingontheimpulsesreceived,aneuronmaysendasignaltootherneurons,throughitssignal‘axon’, whichconnectstodendritesofotherneurons.Neuralnetworksprovideanalternativetomore tradi-tionalstatisticalmethods,suchaslinearregression(byuseoffunctionapproximations),discriminant analysisandlogisticregression(inclassiﬁcationproblems).Anadvantageofneuralnetworksisthat theyarecapableofmodellingextremelycomplexfunctions.Thisstandsincontrasttotraditional lin-eartechniques(seeforexampleMasters,1995).AparticularlypowerfuladvantageofGRNNwhichis appositetothepresentstudyisthatGRNNobviatestheneedforSPtobeI(1).

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Fig.1. Generalizedregressionneuralnetworkstructure.ThisﬁgureillustratesthestructureofaGRNNforanumberof inde-pendentnumericvariables.Theinputlayercontainsaneuronforeveryindependentvariableinthemodel.Thepatternlayer containsonenodeforeachtrainingcase.Eachneuroninthepatternlayercomputesitsdistancefromthepresentedcase.The nodesinthesummationlayersumitsinputs,whilsttheoutputnodedividesthemtogeneratetheprediction.

Source:Ownﬁgure.

Generalizedregressionneuralnetworkshavefourlayers,asdepictedinFig.1.Firstly,theinputlayer containsaneuronforeveryindependentvariableinthemodel.Secondly,inthepatternlayerthereis anodeforeachtrainingcase.Distancesfromthepresentedvaluefromthetrainingdatatothetarget valuearecomputedfollowingaradialbasis/kernel,andnormallyaGaussianfunctioninthesmoothing factorisappliedwithmeansquarederrorminimizationbeingachievedthroughtheconjugategradient descentoptimizationmethod.TheGRNNautomaticallyappliesthemeansquarederrorduringtraining intheutilizationofthesesmoothingfactors.Thirdly,inthesummationlayer,summationsaremadeof inputsfromthepatternlayeratnumeratoranddenominatornodes.Fourthly,andﬁnally,theoutput layertakesthecomputednumeratorvalueanddividesbytherespectivedenominatorvaluetoobtain theprediction(seeforexample,Master,1995;Specht,1991).Thesegeneralized regressionneural networksarerobusttooutliers,andshouldbewellsuitedtoourinternationaltransportationdata whichcomprisesawiderangeofdiversecompanies.Whilstprobabilisticneuralnetworks,forexample, areusedforclassiﬁcationpurposesofcategoricaldata,GRNNsareusedforaregressionanalysisofa continuousdependentvariable(s).Tomeasureandcomparetheoverallaccuracyofbothconventional regressionandGRNNmodelsweusequasi-quadraticrootmeansquareerror(RMSE)andlinearmean absoluteerror(MAE)asmeasuresofmodelaccuracy.Specht(1991)introducessucha‘memory-based network’thatcanbeusedforregressionproblems,andwhichisparticularlywellsuitedtosituations inwhichtheunderlyingrelationshipsmaybenon-linear.Hedemonstratedthatthealgorithmexhibits smoothlinksbetweenobservedvalues.TomandlandSchober(2001),buildingontheworkbySpecht, showthattheiralgorithmsarerobusttoparameter-sensitivity,thatthevectorsdonothavetobeequal, andtheyprovideadiscussioninteraliaoftheslopesoftheregressionsurfaces.Inparticular,Leung etal.(2000)applyGRNNstotheproblemofforecastingforeignexchangerates,makingcomparisons bothwithrandomwalksandmulti-layeredfeed-forwardnetworks,anddemonstratesuperiorityin predictions.

Fourthly,inordertoevaluatethesigniﬁcanceofdividendyield,werunaGRNNofits determi-nants,fortrainingandtestingsamplesindividuallyandagainfortheoverallsample,globallyandfor eachregion.Furthermore,weconductavariableimpactanalysistoassesstherelativeimportanceof eachdeterminant(MTBVit,ASSBKGit,TDEit,SIZEit,ROEit,CAPEXRATEit,CF%Sit,andSTDEV(EB/TS)it).

Eaganetal.(1999)usedaneuralnetworktoprovideapreliminarydataanalysisofdividendsofUS corporations,butdidnot ﬁndanystrongnon-linearrelationships. Inthispaper,wedonotusea neuralnetworkmodelfordata-miningpurposes,andneitherdoweattempttoidentifynon-linear relationships,insteadweonlyallowfortheirpossibilities.Thus,ourapproachistouseGRNNs,as

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Table1

Descriptivestatisticsfordifferentregionsandcountriesbasedonsize($millions).

Region Country Mean St.Dev Minimum Maximum No.companies

Europe 1351.313 6001.161 2.481 50,652.300 57 Denmark 7979.431 16,149.728 6.776 50,652.300 5 France 902.435 1174.763 3.991 5310.431 8 Germany 209.149 724.371 2.481 3433.362 4 Italy 250.053 137.436 91.461 657.233 2 Netherlands 687.457 713.221 42.876 3122.818 5 Norway 279.042 426.311 5.771 2127.103 20 Sweden 162.069 187.139 31.238 825.002 5 UK 862.940 1021.895 28.844 3555.251 8 USandCanada 494.700 602.120 3.704 2492.643 20 Canada 445.239 639.570 3.704 2492.643 5 US 519.864 586.397 5.161 2177.054 15

RestoftheWorld 654.068 1434.918 5.254 11,442.751 62

Australia 1169.485 1544.802 12.990 4918.361 3 China 514.391 534.747 71.214 2319.687 12 Hongkong 881.060 1325.247 5.254 9553.315 9 India 321.364 273.692 29.805 1072.987 5 Japan 690.324 1822.935 14.596 11,442.751 28 NewZealand 238.947 216.768 21.865 626.479 5 Total 139

Sizeismeasuredbymarketcapitalization.Thesampleconsistsof139transportationcompaniescovering16countriesfrom threeregions:NorthAmerica(CanadaandtheUS),Europe(Denmark,France,Germany,Italy,Netherlands,Norway,Sweden andtheUK)andRestoftheWorld(Australia,China,HongKong,India,Japan,andNewZealand).Thedataareextractedfrom Datastreamfor9yearsfrom1997to2005inclusive.

wellasmultipleregressions,inordertoassesstheimpactofvariablespre-speciﬁed,butwithoutthe restrictiveconstraintsoflinearities.

3. Data-set

Thisstudyisbaseduponaglobaldata-setof139transportationprovidersandrelatedservice com-panies.Theyincludeﬁrmsspecialisinginmarinetransportationandshipping,othertransportation services,oilequipmentandrelatedservicesandtravelandtourism.Withintheshippingcomponent are,interalia,deepseaforeigntransportationoffreight,watertransportationoffreight,freightand cargotransportationarrangement,deepseaforeigntransportation,generalstorage,ship-building andrepairing,towingandtugboatservicesandtransportationservicesandNECindustry.Thedata are extracted fromDatastream, across 16 countriesfor 9 years from1997 to2005 inclusive as showninTable1.Thesampleisinvestigatedasawholesetandagainindividuallyfortheregions: NorthAmerica(CanadaandtheUS),Europe(Denmark,France,Germany,Italy,Netherlands,Norway, Swedenand theUK)andRest oftheWorld (Australia,China,HongKong, India,Japan,andNew Zealand).

WehaveprovidedinTable1descriptivestatisticsfordifferentregionsandcountriesbasedonsize, namelymarketcapitalization.CompaniesintheRestoftheWorld,whichincludeahighproﬁleof Japanesecompanies,aresimilarinsizetothoseintheUSandCanada.InEurope,whichcomprisesa largerepresentationfromNorway,thecompaniesaregenerallylargerthanintheothertworegions.

4. Resultsandanalysis

4.1. Model1:shareprice

Theestimation methodsof Model1 are:ﬁrstlya multipleregression, usingshare priceasthe

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Table2

MultipleRegressionModel1:determinantsofshareprice.

USandCanada Europe RestofWorld Allregions

Coeff. P-value Coeff. P-value Coeff. P-value Coeff. P-value

Constant 6.95604 0.0011 99.2491 0.0000 32.1264 0.0027 73.876 0.0000

DPS 2.70456 0.0538 0.15046 0.8255 18.0691 0.0000 0.19485 0.7375

RPS 2.09344 0.0000 −0.0501 0.8264 1.69366 0.0040 0.25798 0.1768

BVPS 0.55230 0.0000 0.3098 0.0013 0.33844 0.0000 0.48838 0.0000

Furtheranalyticalresults

F-ratio 49.30*** 3.72** 104.80*** 70.31***

ANOVAP-value 0.0000 0.0117 0.0000 0.0000

R2_adj. _60.9064% _2.32678 _48.4741 _21.2848

RMSE 10.13501 181.367 135.451 159.554

MAE 7.61753 116.991 81.2094 99.6671

**and***denotesastatisticallysigniﬁcantdifferenceat5and1%level,respectively.

Thesampleconsistsof139transportationcompaniescovering16countriesfromthreeregions:NorthAmerica(Canadaand theUS),Europe(Denmark,France,Germany,Italy,Netherlands,Norway,SwedenandtheUK)andRestoftheWorld(Australia, China,HongKong,India,Japan,andNewZealand).ThedataareextractedfromDatastreamfortheyears1997–2005inclusive. Thetableshowstheresultsofestimating:

SPit=a+b1DPSit+b2RPSit+b3BVPSit+eit

Thedependentvariableisshareprice(SP).Theindependentvariablesaredividendpershare(DPS),retentionpershare(RPS) andbookvaluepershare(BVPS).Thetableshowsregressionmodelsforeachofthethreeregionsandforallregionscombined withrootmeansquareerror(RMSE)andmeanabsoluteerror(MAE),asmeasuresformodelaccuracy.

behindtheﬁrstmodel,asshowninTable2,istodeterminewhetherthesharepriceoftransportation companiescanbeexplainedintermsofdividendpershare(DPS),retentionspershare(RPS)and bookvaluepershare(BVPS).

FortransportationcompaniesintheUSandCanada,twoindependentvariablesarepositiveand significantatthe99%levelofconfidence(RPSandBVPS),andtheother(DPS)atthe90%levelof confidence.Thevariablesexplain60.9%(R2_adjusted)_of_the_variation_in_share_prices._The_message_of

theseﬁndingsfortheUSandCanadaisconsistentwithwhatagencytheorywouldleadustoexpect forarelativelytransparentandcompletemarketwhereaccountingnumbershaveasigniﬁcantdegree ofeconomiccredibilityandwheretransactioncostsarerelativelylow.

Turningtothefirstoftheabovefindings–importanceofdividendpershare–agenerousdividend reducesthefreecashflowavailabletomanagementandimposesmorefrequentrecoursetotheoutside worldforneededfunds.Suchrecourseimposesdisciplineasmanagershavetosubstantiatetheirquest forresources.Furthermoredividendscanbeanattractiontoinstitutionalshareholdersinsofarasthey needtorelyonanincomestreamtomeetregularcommitmentstoclientssuchaspensionersandin sofarasinstitutionsaresubjecttolowertaxratesthanindividuals.Thepresenceofinstitutionsis afurtherreassurancetoinvestorssincetheirexpertiseandlargeholdingswillactasadisciplineon managers.

Turningtothesecondoftheaboveresults–importanceofretentions–asophisticatedmarket wouldnotbefooledbydividendpaymentswithinadequateretentioncovernorbyaroundaboutin whichgenerousbutinadequatelycovereddividendswerepaidtotaxableinvestorsonlytobefollowed byanappealtothemarketforfunds.Thethirdresult–importanceofbookvaluepershare–likethe firstisconsistentwithagencytheory.Highbookvalueofassetsreassuresinvestorsinsofarasthey seeitasaproxyfortangibleassets.Deploymentoftangiblesislessatmanagers’discretionthan intan-giblesthusreducingthedangerofdiscretionaryredeploymentofresourcesfrom,forexample,safe projectstoriskyprojectsorintolow-yieldingprojectswhichwouldprovidemanagerswithshirking opportunities.Furthermoretangibleassetswillprovideaccesstocheaperdebtwithaccompanying taxrelief.Inshortthefindingsfordividendsandbookvalueareconsistentwithagencytheoryand thesefindingsareeconomicallyconsistentwiththefindingforretention.

For Europeantransportationcompanies,themodelperformsbadlyoverall(R2 _adjusted₌_2.3%),

althoughBVPSissignificantatthe99%levelofconfidence.Thenegativecoefficientforretentions underEuropeisnotsignificantlydifferentfromzerobecauseofitshighP-valueof0.8264,andcan

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thusbeignored.Itisparticularlyinterestingthattheinﬂuenceonsharepricewhichcarriesovermost stronglyintoEuropeisbookvaluepershare.Alesssophisticated,morenervousinvestorconfronted bylesstransparentaccountsinariskiermarketplacewillﬁndreassuranceinabackingoftangible assets.ThisisinaccordancewithagencytheorysinceasexplainedfortheUSandCanadaBVPSwill beperceivedbyinvestorsaslesssusceptibletomanagerialdiscretionandwhichwillattractmore economicborrowingandleasingopportunities.

For transportation companiesin Rest of the World themodel performs moderatelywell (R2

adjusted=48.5%)andallthreevariablesaresignificant(andpositive)atthe99%levelofconfidence. Liketheotherresultsthisisconsistentwithagencytheory.FortheRestoftheWorld,dividendsplay amoreimportantrolethanretentions,forthecoefficientforDPSis18.1comparedwith1.7forRPS. ForUSandCanadathecoefficientsareclose(2.7and2.1,respectively).Alesssophisticated,less trans-parentmarketcanbeexpectedtosethigherstorebydividendsthanretentions,retentionsbeingless trustedanddividendscarryingreassurance.

ForallregionscombinedonlyBVPSissigniﬁcantatthe99%levelofconﬁdence,withthemodel overallexplaining21.3%(R2_adjusted),_of_variation_in_the_share_prices_of_{international}_{transportation}

companies.2

Fromthepreviousanalysis,itisclearthattheinclusionofEuropeancompanieshascausedthis distortionandreflectstheinfluenceoftheuncertaintythatinvestorsinEuropeantransportationwere facingduringthisperiodoftime.Iftheinvestorcommunitydoesnotexpectdividendstobesustained, thenthedividendswouldshowupasbeingnotsignificantintheregressionmodels.Thisisinfactthe case.Nevertheless,theF-ratiosindicatethatallmodelsareperformingwell,sincetheyaresignificant ataconfidencelevelofatleast95%orabove.WecanalsoobservethatthemodelforUSandCanada generatesamuchlowerrootmeansquareerror(RMSE)of10.1andmeanabsoluteerror(MAE)of 7.6thanfortheothermodels.FortheRestoftheWorld,dividendsplayamoreimportantrolethan retentionssincethecoefficientforDPSis18.1comparedwith1.7forRPS.Interestingly,forUSand Canadathecoefficientsareclose(2.7and2.1).Encouragingastheseresultsarewemustacknowledge thepossibilitythat,asmentionedinourmethodologyforModel1,theregressionoutcomesmaybe

spuriousbecauseofthepossibilitythatSPisI(1).

WerundiagnostictestsforthemultipleregressionsinModel1.Theregressionresidualistestedfor

autocorrelationusingtheLjung–Box(Q)standardisedresiduals(for36lags)andtheBreusch–Godfrey LagrangeMultipliertest(7lags),asshowninTable3.Thetestsindicatethatautocorrelationisnot presentintheresiduals.Furthermore,thepresenceofheteroskedasticityischeckedusingthesquared (Q2₎_standardised_residuals₍₃₆_lags)_and_the_ARCH_test,_which_is_a_Lagrange_multiplier_(LM)_test_for

ARCH(7lags)intheresiduals.AgaintheresultsshowthattherearenoARCHeffectsinthe resid-uals.Furthermore,theaugmentedDickey–Fullertestsdemonstratethatforeachvariablethenull hypothesisofaunitrootisrejectedinfavourofstationarity(seeTable3).

Thegeneralizedregressionneuralnetworksapproachtowhich wenowturndoesnotrequire stationaritysothatstationaritytestsarenotneeded.Thiscommendsitasanattractivetechnique, andhasleddirectlytooneofthemostinformativeﬁndingsofourwork.AsshowninTable4,GRNN conﬁrmsthestronglysuggestiveresultoftheconventionalmultipleregressiontotheeffectthatfor eachandallregionsBVPSstandsoutasthemainandconsistentexplanatoryvariable.

WeobservethatintheUSandCanadabothDPSandRPShavearelativelyconsonantimpactof 27.9%and22.3%respectivelyonshareprice.Asarguedabove,wewouldexpectthatinvestorsin asophisticatedcapitalmarketwouldapproachclosertoindifferencebetweendividendandcapital gainsthaninlessdevelopedmarketswherethereassuranceofdividendsmightcarrygreaterweight. InEuropeDPShasamuchbiggerimpactonsharepricethanRPS(31.9%versus1.0%).FortheRest

2_We_re-ran_the_regression_using_additional_variables_for_regional_dummies,_namely,_DUM1US&CAN_and_DUM2EUROPE. Thesameconclusionswerefoundasthosewithoutthedummies.ThevariableswiththeirP-valueswereasfollows:constant (0.0000),BVPS(0.0000)DPS(0.9132),RPS(0.1941),DUM1US&CAN(0.0031)andDUM2EUROPE(0.0710).Thesestatisticsalso showsignificantlydifferentresultsforregions,whichagreewithourpreviousanalysis.TheoverallP-valueforthemodelwas 0.0000withanF-ratioof46.76,andanadjustedR2_adjusted_of_22.93%._Also,_we_re-ran_a_regression_for_Model1_using_dummies foryears,andwefoundthatthedummieswerenotsignificant.Furthermore,weappliedatestoflineartrendovertime,which showedthattimewasnotsignificantunderanyofourmodels.

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Table3

DiagnostictestsforModel1.

(a)Equation(Dependent=SP;Skewness=6.1650;andKurtosis=18.09840)

Test Q(7) Q(21) Q2₍₇₎ _Q2₍₂₁₎ _BG_LM _JB _ARCH

Statistic 0.0399 0.0189 0.0539 0.0314 0.8366 1264.90 0.00068

P-value 0.3078 0.5277 0.7929 0.9365 0.5863 0.0000 0.97555

(b)Stationaritytests

Variable ADFstatistic P-value

SP −20.3744 0.0000

DPS −18.3823 0.0000

RPS −15.3525 0.0000

BVPS −20.9820 0.0000

ThistableshowstheresultsofthestationaritytestsforvariablesinModel1.Theresultsshowthatourdataarestationary. Notation:Q,Ljung–Boxstandardisedresidualsforgivenlags;BGLM,Breusch–GodfreyLagrangeMultipliertestfor7lags; JB,Jarque–Beranormalitytest;ARCH,LagrangemultipliertestforARCHfor7lagsintheresiduals;andADF,augmented Dickey–Fullertest.

oftheWorldDPSismoreimportantthanRPS(22.9%versus9.9%).ForallregionsDPSwasmuch moreimportantthanRPS(25.6%versus11%).Theseresultssuggestthatinthetransportationsector thecapitalmarketismoredeveloped intheUSandCanadathanthatobservedforotherregions. However,acontributoryfactormaybethatinvestorsinotherregionsaremorenervousofthegrowth potentialassociatedwithretentionsthusplacinglessemphasisonretentionsandmoreondividends. TheGRNN1-Model1showslowerRMSEandMAEforUSandCanada.Totheextentthatalowererror

suggestsabettermodel,asshowninTable4,thisﬁndingisastepinestablishingGRNNasuseful practicalmodelfortransportationcompanies,particularlyinthatregion.

Since themultipleregressionanalysisprovidesalowadjustedR2 _for_European_{transportation}

companiesitmayhavebeenexpectedthatthealternativemethodology,usingGRNN,wouldhave producedapoorpredictionrateforEurope.Actually,theneuralnetworkapproach(GRNN1-Model1)

givesthelowestbadpredictionrate(100%−87.8%=12.2%),asshowninTable4.Intermsoferrors (RMSEandMAE)themultipleregression forEuropeistheworstmodel,butusingtheGRNNthe modelforEuropeisthesecondbestafterthatfortheUSandCanada.ThisimpliesthatGRNNhasa roleinsafeguardingagainstcursoryacceptanceoftheresultsofconventionalmultipleregression.

Table4

GRNN1(overallsample)Model1:determinantsofshareprice.

Modelanalysis USandCanada Europe RestofWorld Allregions

Diagnosticcriteria

Goodprediction% 79.7872% 87.7907% 34.3373% 26.2338%

RMSE 5.023 49.85 116.54 140.56

MAE 3.208 17.57 62.15 79.19

Std.Dev.ofabs.errors 3.865 46.65 98.58 116.13

Variablesimpactanalysis

DPS 27.8925% 31.9204% 22.8860% 25.5585%

RPS 22.2670% 1.0396% 9.8649% 11.0073%

BVPS 49.8405% 67.0400% 67.2491% 63.4341%

100.00% 100.00% 100.00% 100.00%

Thesampleconsistsof139transportationcompaniescovering16countriesfromthreeregions:NorthAmerica(Canadaandthe US),Europe(Denmark,France,Germany,Italy,Netherlands,Norway,SwedenandtheUK)andRestoftheWorld(Australia,China, HongKong,India,Japan,andNewZealand).ThedataareextractedfromDatastreamfortheyears1997–2005inclusive.The tableshowstheresultsofestimatingthedependentvariablenamelyshareprice(SP).Theindependentvariablesaredividend pershare(DPS),retentionpershare(RPS)andbookvaluepershare(BVPS).Thetableshowsourgeneralizedregressionneural networkmodels(GRNN1-Model1)foreachofthethreeregionsandforallregionscombinedwithrootmeansquareerror(RMSE) andmeanabsoluteerror(MAE),asmeasuresformodelaccuracy.

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Table5

GRNN2(training/testingsub-samples)Model1:determinantsofshareprice.

Train. Test. Train. Test. Train. Test. Train. Test.

Diagnosticcriteria Goodprediction % 93.056 36.364 92.857 29.487 39.916 28.723 81.771 21.134 RMSE 2.573 15.93 16.55 197.38 87.52 188.53 36.33 232.50 MAE 1.457 13.13 6.483 110.67 43.48 104.09 11.67 136.60 Std.Dev.ofabs. errors 2.121 9.014 15.23 163.44 75.96 157.19 34.40 188.14

DPS 25.6909 29.6928 33.4571 31.8871

RPS 22.8176 26.5715 1.0120 28.2423

BVPS 51.4915 43.7357 65.5309 39.8705

100.00 100.00 100.00 100.00

Thesampleconsistsof139transportationcompaniescovering16countriesfromthreeregions:NorthAmerica(Canadaand theUS),Europe(Denmark,France,Germany,Italy,Netherlands,Norway,SwedenandtheUK)andRestoftheWorld(Australia, China,HongKong,India,Japan,andNewZealand).ThedataareextractedfromDatastreamfortheyears1997–2005inclusive. Wedivideoursampleintodatafor1997–2003(trainingsub-set)anddatafor2004–2005(testingsub-set).Thetrainingdata isusedinbuildingtheneuralnetworkmodels,whilstthetestingdataisusedfortestingthepredictiveabilityoftheﬁtted model.Inthetestingcasethedataplaysnoroleinbuildingthemodels.Thetableshowstheresultsofestimatingthedependent variablenamelyshareprice(SP).Theindependentvariablesaredividendpershare(DPS),retentionpershare(RPS)andbook valuepershare(BVPS).Thetableshowsourgeneralizedregressionneuralnetworkmodels(GRNN2-Model1)foreachofthe threeregionsandforallregionscombinedwithrootmeansquareerror(RMSE)andmeanabsoluteerror(MAE),asmeasures formodelaccuracy.Train.denotestrainingsub-sampleandTest.denotestestingsub-sample.

Aswellastakingthewholedata-setforanalyticalpurposes,wedivideoursampleintodatafor 1997–2003anddatafor2004–2005.Theformersub-setofdataisusedinbuildingtheneuralnetwork models,whilstthelatterisusedfortestingthepredictiveabilityoftheﬁttedmodel.Inthetesting casethedataplaysnoroleinbuildingthemodels.Therefore,wehaveadata-setfortraininganda data-setfortesting(seeTable5).Thus,turningourattentiontotheGRNN2-Model1itcanbeobserved

thatthepredictiveabilityofthetrainingsampleineachregionisbetterthanthepredictiveabilityof therespectivetestingsample,whichistobeexpected.ForUSandCanadaagainthepredictiveability frombothtrainingandtestingsamplesisbetterthanthatfortheotherregions.Similarly,theirerrors (RMSEandMAE)aremuchlowerthanforotherregionsacrossbothtrainingandtestingsamples.

Astothetrainingsample(seeTable5),Europehasasimilarlygoodpredictionrate(92.9%)tothat ofUSandCanada(93.1%).FortheRestoftheWorld,thepredictionrateofthetrainingsampleispoor (39.9%).Neverthelessforallregionscombinedthegoodpredictionrateofthetrainingsampleishigh (81.8%).IntermsofminimumerrorsthemodelforUSandCanadaisexcellent(2.6%RMSE;1.5%MAE); andtheerrorsforEuropearesmallerthanfortheRestoftheWorld.Thevariableimpactanalysis revealsthatBVPSistheprimedeterminantofsharepriceacrossalldifferentregions.FortheRestof theWorldRPSismuchlessimportant(only1.0%impact).IndividuallyforUSandCanada,Europeand theRestoftheWorld,DPSismoreimportantthanRPS.However,combiningallregions,DPSandRPS exhibitsimilarimportance(28.2%DPS;31.9%RPS).

Itispossiblethattheremighthavebeenstructuralchangesinthesharepricedeterminationacross allglobalregionsin2004and2005combined.Buttheseyearsareusedfortestingpredictiveability andnotfortestingforstructuralchanges.3

4.2. Model2:dividendyield

AsinthecaseofModel1,theestimationmethodsforModel2aremultipleregressionandgeneralized

regressionneuralnetworks.ForModel2,themultipleregressionanalysisusesthedividendyieldas

3_Structural_change_tests_using_recursive_multiple_regressions_would_have_been_an_option,_but_here_we_are_using_neural networksinsteadwhichcanaccommodatechangesinstructuralrelationshipseitherlinearornon-linear.

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Table6

MultipleRegressionModel2:determinantsofdividendyield.

Coeff. P-value Coeff. P-value Coeff. P-value Coeff. P-value

Constant 0.20627 0.0079 0.00895 0.9517 0.04189 0.6164 0.09464 0.1797 MTBV −0.0293 0.0630 −0.0007 0.0007 −0.0001 0.6106 −0.0005 0.0002 ASSBKG 0.04957 0.5845 −0.1873 0.0244 −0.0125 0.8226 −0.1073 0.0297 TDE −0.0001 0.5338 −0.0001 0.3766 −0.0000 0.1091 −0.0000 0.1161 SIZE −0.0194 0.0062 0.01372 0.1483 0.00260 0.5182 0.00266 0.4727 ROE 0.00016 0.7903 0.00086 0.1323 0.00035 0.3338 0.00078 0.0176 CAPEXRATE −0.0485 0.5415 −0.0098 0.3090 −0.0078 0.2268 −0.0104 0.0704 CF%S 0.00267 0.0000 −0.0003 0.5797 −0.0009 0.0000 −0.0010 0.0000 STDEV(EB/TS) 0.17788 0.6258 1.59617 0.0000 0.03859 0.8786 1.17608 0.0000

Furtheranalyticalresults

F-ratio 6.78*** 6.96*** 99.43*** 48.18***

ANOVAP-value 0.0000 0.0000 0.0000 0.0000

R2_adj. _47.5626 _13.4865 _75.8291 _38.2229

RMSE 0.03789 0.23155 0.12079 0.18865

MAE 0.02543 0.126096 0.05289 0.08949

***denotesastatisticallysigniﬁcantdifference1%level.

Thesampleconsistsof139transportationcompaniescovering16countriesfromthreeregions:NorthAmerica(Canadaand theUS),Europe(Denmark,France,Germany,Italy,Netherlands,Norway,SwedenandtheUK)andRestoftheWorld(Australia, China,HongKong,India,Japan,andNewZealand).ThedataareextractedfromDatastreamfortheyears1997–2005inclusive. Thetableshowstheresultsofestimating:

DYit=˛+ˇ1MTBVit+ˇ2ASSBKGit+ˇ3TDEit+ˇ4SIZEit+ˇ5ROEit+ˇ6CAPEXRATEit+ˇ7CF%Sit+ˇ8STDEV(EB/TS)it+εit Thedependentvariableisdividendyield(DY).Theindependentvariablesaremarket-to-book-value(MTBV),asset-backing (ASSBKG)definedasfixedassets/totalassets,totaldebt/equity(TDE),sizedefinedasnaturallogarithmofmarketcapitalization (SIZE),returnonequity(ROE),capitalexpenditurerate(CAPEXRATE)definedascapitalexpenditure/totalassets,cashflowas apercentageofsales(CF%S)andrisk(STDEV(EB/TS))definedasstandarddeviationoftheratioofearningsbeforeinterestand taxanddepreciation/totalassets.Thetableshowsregressionmodelsforeachofthethreeregionsandforallregionscombined withrootmeansquareerror(RMSE)andmeanabsoluteerror(MAE),asmeasuresformodelaccuracy.

thedependentvariable.AsshowninTable6,theMultipleRegressionModel2setsoutthefactorsthat

helptoexplainthedividendyieldacrossglobalregions.

ThemodelforUSandCanadatransportationcompaniesyieldsthreesigniﬁcantresultsnamely market-to-book-value(MTBV),sizeandcashﬂowasapercentageofsales(CF%S).Thismodelshows that47.6%(R2_adjusted)_of_variations_in_yields_is_explained_by_the_independent_variables._MTBV₍₋₎

issignificantatthe90%levelofconfidencewiththerightsigninaccordancewithHypothesisH1. Thisaccordswiththetheoryofpeckingorderwhichhighlightsthepreferenceforretentionfunded investments.Intermsofourmodelhighermarket-to-bookvalues,whichareconsistentwithmore investmentopportunities,shouldbeassociatedwithgreaterretentionsandlowerdividendsanda consequentlystronglynegativerelationshipbetweenmarket-to-bookvalueanddividendyield.CF%S (+)issignificantatthe99%levelofconfidencewiththerightsigninaccordancewithHypothesisH7. ThemoresophisticatedinvestorsofNorthAmericaareparticularlylikelytoappreciatecashflowasa crediblebackingfordividends.

Sizeisnegativelyrelatedtodividendyieldatthe99%levelofconﬁdence,contrarytoHypothesis H4.Thisresultisconsistentwithsmallerﬁrmsbeingrelativelyriskyleadingtoahigherdiscountrate beingappliedtotheirincomestreamgivingalowermarketvalueandaconsequentlyhigherdividend yield.

ThemodelforEuropeantransportationcompaniesyieldsthreesignificantresultsnamelyMTBV, assetbacking(ASSBKG)and risk(STDEV[EB/TS]).Onlythefirstof thesesignificantlysupportsits HypothesisH1.Thismodelshowsthat13.5%(R2adjusted)ofvariationsinyieldsisexplainedbythe independentvariables.AsisthecaseforUSandCanada,MTBV(−)issignificantatthe99%levelof confidencewiththerightsigninaccordancewithHypothesisH1,asshowninTable6.

ContrarytoHypothesisH2 greaterassetbackingcoincideswithlowerdividend yield,andthis resultissigniﬁcantatthe95%conﬁdencelevel.ASSBKGlimitsriskinsofarasassetscanamountto

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avaluableputoptiontobreakupthecompanyifitscorporateplanfails.Secondly,inagencyterms, tangibleassetsarelessvulnerabletodiscretionarybehaviourbymanagement.Thesethingswould appearinthemarketasalowerdiscountbeingappliedtoassets,ahigherpresentvalueforthose assetsandconsequentlylowerdividendyield.ContrarytoHypothesisH8STDEV(EB/TS)isfoundto bepositivelyrelatedtodividendyieldatthe99%levelofconﬁdence.Thisisconsistentwithahigher marketdiscountbeingappliedtotheincomestreamofriskycompaniesleadingtolowerpresent valuesandhigherdividendyields.

However,overalltheR2_for_Europe_is_low._This_suggests_that_the_European_regression_analysis_is_less

usefulasawholemodel(whilstourneuralnetworksaremorecapableasdiscussedlater),although theabovehypothesistestingforindividualvariablesisstillvalid.Thisisconsistentwithourearlier discussionpertainingtoourﬁndingsonsharepricedeterminationinthatEuropeaninvestorsinthe transportationsectorappeartohavebeennervousaboutthesustainabilityofdividends.

ForRestoftheWorldthemodelprovidesahighR2_adjusted_of_75.8%._The_only_signiﬁcant_variable

affectingdividendyieldiscashflowasapercentageofsales(−),whichissignificantatthe99%levelof confidence,butthewrongsignforHypothesisH7(cashflow).GiventhehighR2andsincetheF-ratio fortheRestoftheWorldisveryhigh(99.43),andtheothervariablesarenotsignificant,thenthe cashflowasapercentageofsaleshasaverystronginfluenceinexplainingthedividendyieldforthe RestoftheWorld,asshowninTable6.Whyshouldhighcashflowasapercentageofsalesleadto lowdividendyield?Partofdividends’signallingvalueistoconveyinformationaboutimminentand futurecashflows.Ifthesearevisibleandapparentthereasonforthesignalisremovedanddividends nolongerneedtoservethispurpose.

Themodelforallregionswhichcombinealldatayieldssixsignificantresultsnamely market-to-book-value,assetbacking,returnonequity(ROE),capitalexpenditure(CAPEXRATE),cashflowas apercentageofsalesandrisk.ThreeoutofthemnamelyMTBV,ROEandCAPEXRATEsignificantly supporttheirHypothesesH1,H5andH6.Thismodelshowsthat48.2%(R2adjusted)ofvariationsin yieldsisexplainedbytheindependentvariables.Atthe99%levelofconfidence,keydeterminantsof thedividendyieldare:MTBV(−),CF%S(−)andSTDEV(EB/TS)(+);atthe95%levelofconfidence,the keydeterminantsareASSBKG(−)andROE(+);andatthe90%levelofconfidence,CAPEXRATE(−),as showninTable6.

WithinthethreesupportiveresultsthefindingforHypothesesH1MTBV(growth)issignificant andwiththecorrect sign.ThiscoincideswiththeindividualfindingsfortheUSand Canadaand forEurope.AlsosignificantandwiththecorrectsignarethefindingsforH5ROE(returnonequity) andH6CAPEXRATE(capitalexpenditure).ThecaseofROEreflectstherationalethatcompanieswith lowerprofitsaremorelikelytoomitorreducedividends.Thisshowsasthepositiverelationship betweenprofitabilityandthedividendpayoutratio.Thecaseofcapitalexpenditureisconsistentwith apreferenceforretentionsbeingthefirstportofcallforcapitalexpenditureandisreflectedina negativerelationshipbetweenthecapitalexpenditurerateanddividendyields.

WithintheresultsthatgoagainsttheirhypothesesHypothesisH2 ASSBKG(asset backing),H7 CF%S(cashﬂow)andH8STDEV[EB/TS](risk)arealsosigniﬁcantbutwithincorrectsigns.Contraryto HypothesisH2(ASSBKG)isfoundasforEuropetobenegativelyrelatedtodividendyield.Contrary toHypothesisH8 (STDEV[EB/TS])isfoundasforEuropetobepositivelyrelatedtodividendyield. ContrarytoHypothesisH7(CF%S)isfoundasforRestoftheWorldtobenegativelyrelatedtodividend yield.

FromMultipleRegressionModel2wecanalsoseethattheF-ratiosindicatethatallmodelsperform

well,sincetheyaresigniﬁcantataconﬁdencelevelof99%.4_We_can_also_observe_that_once_again_the dataforUSandCanadageneratemuchlowerRMSE(0.04)andMAE(0.03)thanfortheothermodels,as

4_We_re-ran_the_global_regression_using_additional_variables_for_regional_dummies_as_mentioned_in_the_previous_footnote. Similarconclusionswerefound,namely,thatMTBV,CF%SandSTDEV(EB/TS)weresignificantatthe99%confidencelevel. ThedummyforUSandCanadawasnotsignificant(P-value=0.8495),butthedummyforEuropewassignificantatthe99% confidencelevel(P-value=0.0071).ThisconfirmsouranalysisinTable6.TheoverallP-valueforthemodelwas0.0000withan

F-ratioof39.92,andanadjustedR2_of_38.95%._Also,_we_re-ran_a_regression_for_Model2_using_dummies_for_years,_and_we_found thatthedummieswerenotsigniﬁcant.Furthermore,weappliedatestoflineartrendovertime,whichshowedthattimewas notsigniﬁcantunderanyofourmodels.

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Table7

DiagnostictestsforModel2.

(a)Equation(Dependent=DY;Skewness=7.4932;andKurtosis=96.8786)

Test Q(7) Q(21) Q2₍₇₎ _Q2₍₂₁₎ _BG_LM _JB _ARCH

Statistic 1.5446 13.0030 0.1210 1.1932 0.0000 230,086.7 1.5502

P-value 0.9810 0.9090 1.0000 1.0000 1.0000 0.0000 0.2146

(b)Stationaritytests

Variable ADFstatistic P-value

DY −35.6887 0.0000 MTBV −18.0045 0.0000 ASSBKG −25.9986 0.0000 TDE −20.0953 0.0000 SIZE −11.5111 0.0000 ROE −3.7358 0.0047 CAPEXRATE −27.7887 0.0000 CF%S −21.2540 0.0000 STDEV(EB/TS) −26.9662 0.0000

ThistableshowstheresultsofthestationaritytestsforvariablesinModel2.Theresultsshowthatourdataarestationary. Notation:Q,Ljung–Boxstandardisedresidualsforgivenlags;BGLM,Breusch–GodfreyLagrangeMultipliertestfor7lags; JB,Jarque–Beranormalitytest;ARCH,LagrangemultipliertestforARCHfor7lagsintheresiduals;andADF,augmented Dickey–Fullertest.

showninTable6.ForModel2,theregressionresidualistestedforautocorrelationusingtheLjung–Box

(Q)standardisedresiduals(for36lags)andtheBreusch–GodfreyLagrangeMultipliertest(7lags),as showninTable7.Thetestsindicatethatautocorrelationisnotpresentintheresiduals.Astothe possiblepresenceofheteroskedasticity,weruntestsusingthesquared(Q2₎_standardised_residuals

(36lags)andtheARCHtest,whichisaLagrangemultiplier(LM)testforARCH(7lags)intheresiduals, andtheresultsshowedthattherearenoARCHeffectsintheresiduals.Foreachvariable,thenull hypothesisofaunitrootisrejectedinfavourofstationarity,aspertheaugmentedDickey–Fuller tests,asshowninTable7.

Beforeleavingourdiscussionoftheconventionalregression,itisworthnotingthattheprominent supportaffordedtoHypothesisH1bythesigniﬁcanceofmarket-to-book-valueisconsistentwiththe theoryofpeckingorderwhichhighlightsthepreferenceforretentionfundedinvestments.Accordingly highermarkettobookvalues,whichareconsistentwithmoreinvestmentopportunities,shouldbe associatedwithgreaterretentionsandlowerdividends.Thus,peckingorderbehavioursuggestsa stronglynegativerelationshipbetweenmarket-to-book-valueanddividendyield,whichisthecase fortransportationcompaniesintheUSandCanada,Europeandallregionscombined.Thediscussionof theGRNNwhichfollowsalsostressestheimportanceofmarket-to-book-valuesintheUSandCanada. We nowturntothegeneralizedregressionneuralnetworksapproachwhichdoesnotrequire stationaritytests.AsisthecaseforModel1 this commendsitasanattractivetechnique.GRNN1

-Model2,whichutilizesthewholedata-set,revealsinterestingresultsfortheregionalcomparisons,as

showninTable8.Intermsofvariableimpact,themaindeterminantsofdividendyieldvaryacross regions:market-to-book-value(37.0%)fortheUSandCanada;risk(29.8%)forEurope;andcashﬂow (83.9%)fortheRestoftheWorld.Forallregionscombined,themaindeterminantsofdividendyield arerisk(36.6%)andcashﬂow(36.0%).Thediagnosticsrevealexcellentpredictionrates:virtually100% fortheUSandCanada,and98.7%forEurope.EuropehasthelowestMAEoftheneuralnetworkmodels forindividualregionsandallregionscombined.InfactinouranalysistheRMSEandMAEareverylow acrossallregions.

ForUSandCanadatheimportanceofmarket-to-bookvaluesisstronglyconfirmedaswemight expectintransparentsophisticatedmarketswheregrowthisrelativelycredibleandwherea prefer-enceforfinancebyretentionwillleadtolowdividends.Thisfindingaccordswiththestrongindications fromourconventionalregressions.InEuropethemostimportantfactorindividendyield determina-tionisrisk.Thisagainaccordswithourearlierfindings.InRestoftheWorldcashflowisthemain

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Table8

GRNN1(overallsample)Model2:determinantsofdividendyield.

Diagnosticcriteria

Goodprediction% 100.00 98.6971 71.8254 86.5794

RMSE 0.0007357 0.0009050 0.02325 0.02508

MAE 0.0002801 0.0001011 0.009073 0.007415

Std.Dev.ofabs.errors 0.0006802 0.0008993 0.02140 0.02396

MTBV 37.0407 7.6942 0.3513 1.9131 ASSBKG 0.0207 13.7526 3.5072 0.1777 TDE 11.3337 8.9113 2.4831 4.6317 SIZE 13.6439 12.0018 5.2368 10.2069 ROE 11.8651 11.4896 3.4177 6.0667 CAPEXRATE 17.7083 6.9807 1.0597 4.3659 CF%S 8.3693 9.3313 83.9442 35.9980 STDEV(EB/TS) 0.0182 29.8385 0.0000 36.6402

100.00 100.00 100.00 100.00

Thesampleconsistsof139transportationcompaniescovering16countriesfromthreeregions:NorthAmerica(Canadaand theUS),Europe(Denmark,France,Germany,Italy,Netherlands,Norway,SwedenandtheUK)andRestoftheWorld(Australia, China,HongKong,India,Japan,andNewZealand).ThedataareextractedfromDatastreamfortheyears1997–2005inclusive. Thetableshowstheresultsofestimatingthedependentvariablenamelydividendyield(DY).Theindependentvariablesare market-to-book-value(MTBV),asset-backing(ASSBKG)definedasfixedassets/totalassets,totaldebt/equity(TDE),sizedefined asnaturallogarithmofmarketcapitalization(SIZE),returnonequity(ROE),capitalexpenditurerate(CAPEXRATE)definedas capitalexpenditure/totalassets,cashflowasapercentageofsales(CF%S)andrisk(STDEV(EB/TS))definedasstandarddeviation oftheratioofearningsbeforeinterestandtaxanddepreciation/totalassets.Thetableshowsourgeneralizedregressionneural networkmodels(GRNN1-Model2)foreachofthethreeregionsandforallregionscombinedwithrootmeansquareerror(RMSE) andmeanabsoluteerror(MAE),asmeasuresformodelaccuracy.

agendaitemfordividendyieldswhichisagainconsonantwithitshighsignificanceinour conven-tionalregressionmodels.Forthemodelofallregionscombined,riskandcashflowtogetheraccount formorethan72%ofvariableimpactondividendyieldagainmatchingthehighsignificanceofthese variablesinourearlierfindings.

GRNN2-Model2dividesthedata-setusing2004and2005datafortestingpurposesonly,asshown

inTable9.Onceagain,thepredictiveabilityforbothtrainingandtestingsamplesisbetterfortheUS andCanadathanfortheotherregions.Thepredictionratesofthetrainingsampleapproach100%for theUSandCanada,98.8%forEurope,and89.4%forRestoftheWorld.Forthetestingsamplesthegood predictionratesare35.7%(USandCanada),29.9%(Europe)and28.0%(RestoftheWorld).Theerrors (RMSEandMAE)arelowacrossallsamplesandexceptionallysointhetrainingsamplesfortheUS andCanadaandforEurope.Thevariableimpactanalysisrevealscashﬂowasapercentageofsalesas themaindeterminantofdividendyield(52.8%)fortheUSandCanada,andagain(86.1%)forRestof theWorld.ForEurope,themainsingledeterminantisrisk(19.8%).Combiningallregions,cashﬂow asapercentageofsaleshasthemainimpact(57.4%)andthenextfactorisrisk(24%).

BycomparingtheresultsofGRNN2-Model2withGRNN1-Model2,weseethatthereisastructural

changeinthedeterminantsofdividendyieldintheUSandCanada.Forthewholesampleperiod,from 1997to2005,market-to-book-valueandcashﬂowasapercentageofsalesaccountfor37.0%and8.4%, respectively;whereasfor1997–2003theyaccountfor16.2%and52.8%,respectively.

Finally,weturntodiagnosticcomparisonsacrossallmodelsandregions,asshowninTable10.For Model1,whichaddressesdeterminantsofshareprice,theUSandCanadaprovidethelowesterrors

(RMSEandMAE)acrossallourmodels(Regression,GRNN1,GRNN2-Training,andGRNN2-Testing).For

Model2,whichaddressesdeterminantsofdividendyield,theUSandCanadaalsoprovidethelowest

regressionerrors(RMSEandMAE),thelowestGRNN1RMSE,whilstEuropehasthelowestMAEfor

GRNN1.

However,whenthesampleperiodissplitintotraining(1997–2003)andtesting(2004–2005), EuropeprovidesthelowestRMSEforGRNN2(training)andthesameMAEasthatforUSandCanada

forGRNN2(training).Fortestingpurposes(using2004–2005)theUSandCanadaprovidethelowest

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H.A. Abdou et al. / Int. Fin. Markets, Inst. and Money 22 (2012) 796– 813 Table9

GRNN2(training/testingsub-samples)Model2:determinantsofdividendyield.

Train. Test. Train. Test. Train. Test. Train. Test.

Diagnosticcriteria

Goodprediction% 100.00 35.7143 98.7500 29.8507 89.4118 28.0488 71.4286 26.9939

RMSE 0.00003 0.05046 0.00002 0.1999 0.01006 0.1986 0.04250 0.3565

MAE 0.00000 0.02779 0.00000 0.08866 0.00412 0.0738 0.01568 0.1082

Std.Dev.ofabs.errors 0.00003 0.04212 0.00002 0.1792 0.00917 0.1844 0.03950 0.3396

MTBV 16.1800 9.1129 0.2341 1.7945 ASSBKG 2.8486 14.9576 2.1951 4.5945 TDE 3.5877 10.5542 1.5597 3.2760 SIZE 7.1966 12.6688 4.1272 5.4846 ROE 8.8076 12.6685 2.5103 0.4578 CAPEXRATE 4.4540 8.4586 0.5939 2.8489 CF%S 52.7748 11.8053 86.0799 57.4268 STDEV(EB/TS) 4.1507 19.7741 2.6998 24.1170

100.00 100.00 100.00 100.00

Thesampleconsistsof139transportationcompaniescovering16countriesfromthreeregions:NorthAmerica(CanadaandtheUS),Europe(Denmark,France,Germany,Italy,Netherlands, Norway,SwedenandtheUK)andRestoftheWorld(Australia,China,HongKong,India,Japan,andNewZealand).ThedataareextractedfromDatastreamfortheyears1997–2005inclusive. Wedivideoursampleintodatafor1997–2003(trainingsub-set)anddatafor2004–2005(testingsub-set).Thetrainingdataisusedinbuildingtheneuralnetworkmodels,whilstthe testingdataisusedfortestingthepredictiveabilityofthefittedmodel.Inthetestingcasethedataplaysnoroleinbuildingthemodels.Thetableshowstheresultsofestimatingthe dependentvariablenamelydividendyield(DY).Theindependentvariablesaremarket-to-book-value(MTBV),asset-backing(ASSBKG)definedasfixedassets/totalassets,totaldebt/equity (TDE),sizedefinedasnaturallogarithmofmarketcapitalization(SIZE),returnonequity(ROE),capitalexpenditurerate(CAPEXRATE)definedascapitalexpenditure/totalassets,cash flowasapercentageofsales(CF%S)andrisk(STDEV(EB/TS))definedasstandarddeviationoftheratioofearningsbeforeinterestandtaxanddepreciation/totalassets.Thetableshows ourgeneralizedregressionneuralnetworkmodels(GRNN2-Model2)foreachofthethreeregionsandforallregionscombinedwithrootmeansquareerror(RMSE)andmeanabsolute error(MAE),asmeasuresformodelaccuracy.Train.denotestrainingsub-sampleandTest.denotestestingsub-sample.

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Abdou et al. / Int. Fin. Markets, Inst. and Money 22 (2012) 796– 813 811

Diagnosticanalysiswithcomparisonsbetweenmodels.

Model1 Model2 Model1 Model2 Model1 Model2 Model1 Model2

RMSE Multipleregression 10.135 0.03789 181.367 0.23155 135.451 0.12079 159.554 0.18865 GRNN1 5.0230 0.00074 49.85 0.00091 116.54 0.02325 140.56 0.02508 GRNN2 Trainingsub-sample 2.573 0.00003 16.55 0.00002 87.52 0.01006 36.330 0.04250 Testingsub-sample 15.93 0.05046 197.38 0.1999 188.53 0.1986 232.50 0.35650 MAE Multipleregression 7.61753 0.02543 116.991 0.12610 81.2094 0.05289 99.6671 0.08949 GRNN1 3.2080 0.00028 17.570 0.00010 62.15 0.00907 79.190 0.00742 GRNN2 Trainingsub-sample 1.4570 0.00000 6.4830 0.00000 43.480 0.00412 11.670 0.01568 Testingsub-sample 13.130 0.02779 110.67 0.08866 104.09 0.07380 136.60 0.10820

Thesampleconsistsof139transportationcompaniescovering16countriesfromthreeregions:NorthAmerica(CanadaandtheUS),Europe(Denmark,France,Germany,Italy,Netherlands, Norway,SwedenandtheUK)andRestoftheWorld(Australia,China,HongKong,India,Japan,andNewZealand).ThedataareextractedfromDatastreamfortheyears1997–2005inclusive. SharepriceisthedependentvariableinModel1,whilstdividendyieldisthedependentvariableinModel2.Themultipleregressionsarebasedonthewholeperiodfrom1997to2005 inclusive,asisthecaseforgeneralizedregressionneuralnetwork(GRNN1)wherethewholedata-setisusedastrainingdata.GRNN2usesdatafrom1997to2003inclusiveasthetraining sub-set,andusesdatafrom2004to2005inclusiveasthetestingsub-set.Thetrainingdataisusedinbuildingtheneuralnetworkmodels,whilstthetestingdataisusedfortestingthe predictiveabilityoftheﬁttedmodel.Inthetestingcasethedataplaysnoroleinbuildingthemodels.Thetablecompareserrorratesnamelyrootmeansquareerror(RMSE)andmean absoluteerror(MAE)foreachofthemodelsandforeachofthethreeregionsandforallregionscombined,asmeasuresformodelaccuracy.

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Ourpurposehasbeentoinvestigatedividendpolicyusingmodelswhichfocusonsharepriceand dividendyieldrespectively.WemakeuseofGRNNandconventionalmultipleregressionasmutually supportivetechniques.Wehavebeenabletogeneratearangeofsignificantresults.Someofthese supportourhypothesesandsomechallengethembutinbothcasesithasbeenpossibletolinkour findingstosignificantaspectsofthetheoreticaldebateandthedecisionswhichconfrontinvestorsand corporatefinancialmanagementintheindustry.OurresultsfortheUSandCanadaarebroadly consis-tentwitharelativelytransparentandcompletemarketwhereaccountingnumbershaveasignificant degreeofeconomiccredibilityandwheretransactioncostsarerelativelylow.Othermarketsaround theworldappeartopenaliseretentionsandreflectamorepessimisticattitudetowardsthefuture oftransportationprovidersandrelatedservicecompanies.Partoftheoriginalityofourresearchhas beentodistinguishourfindingsacrosstheglobeandtocastlightonthisdistinctiveness.

5. Conclusion

FortheUSandCanada,thebookvaluepershareisthemaindeterminantofthesharepricefor transportationcompanies.Broadindifferencebetweendividendsandcapitalgainsembodiedinfuture growthexpressedasretainedearningsisalsoidentifiedforUSandCanada.Thisisconsistentwitha sophisticatedtransparentcapitalmarketinwhichdividendclientelesaresatisfied.ForEurope,thereis greateruncertaintyintermsofdividendsustainability.Indeed,forallregions,thereissomeinstability especiallyfor2004–2005aswefindinourtestingsample.HoweverdividendsappearinEuropeto havekeptsomeoftheirtraditionalinformativevalueatleastincomparisonwithretentions.The multipleregressionanalysisprovidesalowadjustedR2_for_Europe_whilst_neural_network_reveals_the

highestgoodpredictionrate.ThisillustrateshowGRNNcanguardagainstcursoryacceptanceofthe resultsofconventionalmultipleregression.Theneuralnetworksvariableimpactanalysisofshare pricedemonstratesthatdividendsaremoreimportantthanretentionsandthatbookvaluepershare isthemaindriverundereachoftheregionsandallregionscombined.Whendividendyieldbecomes thedependentvariable,theconventionalregressionoutputfortheUSandCanadashowsthatthe market-to-book-valueisnegativelyassociatedwithdividendyield.Inadditiongreatercashflowasa percentageofsalesandsmallermarketcapitalizationsarestronglyassociatedwithhigherdividend yields.ForEuropemarket-to-book-valueisstronglynegativelyassociatedwiththedividendyield, supportingthepeckingordertheory.However,higherriskisassociatedwithhigherdividendyields whilstlowerassetbackingisassociatedwithhigherdividendyield.ManyfirmsoutsideEurope,theUS andCanadaaremaintainingdividendswhilsttheircashflowsmaynotsupportsuchapolicy.However, itcouldbeinterpretedasasignaloftheirconfidenceintheprospectsforthisindustry.

ThediagnosticsrevealthattheGRNNsperformverywellintermsofminimizingerrorsandare superiorinthisrespecttotheconventionalregressions.Intermsofdividendyieldneuralnetworks variableimpactthemaindriversare(i)market-to-book-valueintheUSandCanada,whichis con-sistentwiththepeckingordertheory;(ii)risk,whichisconsistentwithhigherdividendyieldsduly compensatinginvestorsinthecaseofEurope;and(iii)cashflowinRestoftheWorld,whichis con-sistentwithconcernaboutfinancialmobility.Fortheglobalmodelforallregions,riskandcashflow asapercentageofsalesarethemostimportantvariablesandtogetheraccountformorethan two-thirdsofthetotalimpactonthedividendyield.Itisclearthat,throughthedividendyield,investorsin transportationcompanieshaveindeedbeenrewardedforriskonaglobalbasis.Theresultsofour var-iousmodelsfortheUSandCanadaareconsonantwitharelativelytransparent,wellinformedmarket peopledbyrelativelysophisticatedinvestors.Intermsofbothsharepriceanddividendyield,Europe hasbeenfoundtobedifferentfromthetwootherregions.Itfollowsthatfutureresearchmightbe directedatinvestigatingattributesspecifictoEuropeantransportationcompanies,andalso investi-gatingtrans-countrydifferencesintheprofilesofinvestorsandcompanies,insideandoutsideEurope, whetherduetofiscal,cultural,institutionalorindustrialfactors.

Acknowledgement

Theauthorswouldliketothanktheanonymousreferee(s)andeditor(s)forhelpfulcomments, whichhavebeenmostusefulinrevisingthemanuscript.

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