THE2016U.S.ELECTIONUPSETANDITSIMPLICATIONSFORU.S.CLEANCOMPANIES:ANEVENTSTUDY

MajorResearchPaper–M.Sc.EnvironmentalSustainability

PreparedbyFrédéricSéguinResearchSupervisedbyProfessorAnthonyHeyes,Ph.D

InstituteoftheEnvironmentUniversityofOttawa

April23rd,2018

ABSTRACT This research investigates the stock market reactions of major publicly-tradedenvironmentally-focusedcompaniesfollowingthesurpriseoutcomeofthe2016U.S.presidentialelectionusingevent studymethodology.Furthermore, threesamplesof cleancompaniesareselected based on market valuation (small-capitalization, mid-capitalization, and large-capitalization)totestwhetherfirmvalueplaysaroleinasecurity’sreaction.Thisresearchthenproceeds with testing whether environmentally-focused companies experienced statisticallysignificantnegativeabnormalreturnsfollowingthesurpriseoutcomeofthe2016U.S.election.Results indicate that small-sizedcompanies sufferednegativeabnormal returnsoneventdaythat quickly recovered post-event.Mid-sized companies also experienced negative abnormalreturns on event day but drastically recovered to finish in strong positive territory. Largecapitalizationcompaniesalsohadnegativeabnormalreturnsoneventday,whichpersistedforat leasttenpost-eventdays.Theresultingevidence,althoughstatistically fragile, leadstotherejectionofthestatedhypothesis.

TABLEOFCONTENTS1.Introduction 1

2.EventofInterest:2016U.S.Election 2

3.Methodology:ABasicEventStudy 5 3.1.KeyConcept&Assumptions:EventStudiesinEconomicsandFinance 5 3.2.StructureandProcedure 5

4.ModelDesign,Data&StatisticalTesting 9 4.1.ElectionResultsandEventWindow 9 4.2.CompanySelection 11 4.3.DataSources 15 4.4.NormalReturnModel 15 4.5.EstimationWindow 16 4.6.MarketModelParameterEstimation 17 4.7.StatisticalTestingFramework 18

5.Results 20 5.1.StatisticalSignificance 22

6.Discussion&Conclusion 25

7.References 28

8.AppendixA–CleanCompanyList 33

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1.INTRODUCTION Therearevariousinstancesinfinanceandeconomicswhereitcanbecomequiteuseful

to quantify the effects of a specific event on the valuation of publicly-traded securities.

Unanticipatedeventscansometimesshockfinancialassetmarketsandderivingavalueofthis

impactcanyieldinterestinginsightsforindustriesorcompaniesofinterest.Althoughthistask

mightappearintimidatingatfirst,ameasurementcanbeconstructedrelativelyeasilyusingevent

study methodology. In this research paper, an event study is carried out to analyse the

implicationsofthestunning2016U.S.electionoutcomeforcleancompaniesthatoperatewith

theenvironmentanditssustainabilityaspriorities.Thestudylooksathowthesesmall-to-large-

capitalization enterprises’ shares reacted to theNovember 2016 surprise andwhether those

reactionsweresignificantandsustained.Thehypothesisbeing tested is that, considering the

Republican candidate’s pre-election position on climate change and the fact that the global

consensus had projected a Democrat victory, clean companies were expected to suffer

statisticallysignificantnegativereturnsrelativetothebroadermarketintheunlikelyadventofa

DonaldTrumpvictory.

Thisresearchfollowsthegeneraltemplateofaneventstudyascarriedout inA.Craig

MacKinlay’s1997papertitled“EventStudiesinEconomicsandFinance”.Thefollowingresearch

paperstartswithadescriptionoftheeventofinterestanditssignificanceinSection2.Thebasic

methodologyandprocedureofeventstudiesusedinthepaperarethenpresentedinSection3.

Nextcomestheeventstudy’sselectedmodelanddesigninSection4.Section5thenproceeds

withdetailingtheresultsofthestudyandtotestingthemforstatisticalsignificance.Following

thiscomesadiscussionoftheresultsinSection6andconcludingremarksinSection7.

2

2.EVENTOFINTEREST:2016U.S.PRESIDENTIALELECTION The2016U.S.presidentialelectionoutcomesentshockwavesaroundtheglobe,ushering

modernglobalpoliticsintounchartedterritory.Thewinner,DonaldJ.Trump,isabillionairereal-

estateandbrandmanagerwithzeropriorpoliticalexperience.Notonlywasheseenbymostas

unfittoserveinthiscriticalrole,hemanagedtobeatoutaveterancareerpoliticianinHillary

Clintonwhowasfarmorequalifiedandwhohadthechancetobethefirstwomentoholdoffice

inUnitedStateshistory.Whatseemedahighly likely roadto thepresidencyandachanceto

reachahistoricalmilestoneforgenderequalityallcamecrashingdownwhentheRepublican

candidatepulledoffthesurprisingupsetontheeveningofNovember8th,2016.12345

Andquitetheupsetitwas.Almostallthepollsaroundthecountryseemedtoindicate

thattheDemocraticcandidatewasthefavoritetowin.Pollaggregatorsandelectionsimulations

alsoshowedthatHillaryClintonshouldcomfortablywinenoughstatestoobtainthe270electoral

college votes necessary to take the presidency. On the eve of the election, predictions of a

Democratvictoryreachedashighas98%attheHuffingtonPost,84%attheNewYorkTimes,and

71%atFiveThirtyEight.678910

NotonlywastheAmericanpopulationexpressingtheirpreferenceforClinton,evidence

suggeststhatthestockmarketalsoseemedtodisliketheideaofapotentialTrumppresidency.

WolfersandZitzewitz(2016)presentedsomeevidencethatdestabilizingevents leadingupto

theelectionproducedasset reactions in linewithamarketexpectingaDemocratwin.These

unanticipatedeventsincludethefirstdebatewonbyClintononSept.26th,thereleaseofDonald

Trump’s“AccessHollywood”tapes,andthere-openingandsubsequentclosingoftheClinton

1CNNNews.(2016).Whowonthetownhalldebate?2USAToday.(2016).USAToday’sEditorialBoard:Trumpis“unfitforthepresidency.”3Miller,J.(2016).BobGates:DonaldTrump“unfittobecommander-in-chief.”4ColumbusDispatch.(2016).Editorial:Forpresident|Trumpunfit,Clintonisqualified.5Grim,R.,Terkel,A.,&Date,S.V.(2016).TrumpMadeItClear-Again-ThatHe’sUnfitForThePresidency.6Grenier,E.(2016).HillaryClintonhastheleadforthefinalweekend,butwillitholdthroughTuesday?7Zurcher,A.(2016).USelection:IsTrumporClintongoingtowin?8Agiesta,J.(2016).Poll:MostseeaHillaryClintonvictoryandafaircountahead.9Jackson,N.,&Hooper,A.(2016).Election2016Forecast-President&Senate.10FiveThirtyEight.(2016).Whowillwinthepresidency?-2016ElectionForecast.

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emailinvestigationdaysbeforetheelection.Theauthors’predictivemodelshowsthatifTrump

weretopullanupsetvictory,marketswouldsufferalargedeclineofnear10%intheshort-term.

Althoughthisisonlyonestudyanalysingtheimpactofsmallerisolatedevents,itisinlinewith

what the overwhelming popular consensus was at time; Clinton is going to win. Upon

considerationofwhatmostnationalpollswere indicating,the incessantscandalssurrounding

theRepublicancandidate,thefactthattheDemocraticcandidatewasmuchmorequalifiedfor

thejob,andtheapparentpreferenceoffinancialmarkets,itisdifficulttoargueagainstthefact

thattheelectionresultswerehighlysurprisingtomost.1112

Unexpectedeventsofsuchimportancearequiteinterestingtostudyusingfinancialasset

dataastheycanyieldinsightsintohowspecificindustrialsectorsorassetclassesarereactingat

or surrounding the timeofdisclosure. Forexample,onewouldexpect theelection results to

influence investors’ earnings forecasts for companies or sectors championed by thewinning

candidate. Seeing how likely Clinton would win, it is reasonable to assume that issues she

supported,includinghealth-care,freetrade,andenvironmentalconcerns,wouldhavebenefited

fromherpresidency.Now,withtheresultsoftheelectionknown,onecanstudytheopposite

sideofthespectrumtofindinterestinginsights.1314

Duringthecampaign,DonaldTrumpandHillaryClintonwereopposedonmany issues

rangingfromimmigrationpolicytotradeagreements.Anotherissueofstrongcontentionwas

thatofclimatechangeandtheroleofenvironmentalsustainabilityintheUnitedStates’energy

mixdiscussion.Companiesthatoperateinthesesectorsarethemainsubjectsofinterestinthis

study.TheClintonplatformcampaignedfor50%ofU.S.electricitygenerationtocomefromclean

sources by 2026, for 500million solar panels to be installed by 2020, and for reducing the

country’soilconsumptionbyonethird.Thispointofviewisdiametricallydifferentfromtheone

pushedbytheTrumpcampaign.TheRepublicanisfamouslyknowntohavestatedthat“climate

changeisahoaxcreatedbyChinatoundermineU.S.manufacturingcompetitiveness”.Hence,

11Wolfers,J.,&Zitzewitz,E.(2016).Whatdofinancialmarketsthinkofthe2016election?12Zitzewitz,E.(2016).Column:Thestockmarketdoesn’tliketheideaofaTrumppresidency.13MacKinlay,A.C.(1997).EventStudiesinEconomicsandFinance.JournalofEconomicLiterature,35(1),13–39.14Ghosh,I.(2016).Trumpvs.Clinton:Wheretheystandontheissues.

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hisdisbeliefofclimatechangescienceandtheneedforcleanersourcesofenergyshouldn’tcome

as a shock. In linewith his position on globalwarming, Trump also campaigned strongly on

dismantling Obama’s 2013 Climate Action Plan, withdrawing from the 2015 Paris Climate

Agreement,andrevitalizingtheincreasinglyfadingAmericancoalindustry.1516171819

Havingbothcandidatesonsuchdifferentstandingsregardingtheenvironmentbeforethe

electionmeantthatenvironmentally-consciouscompanieswereexpectedtobeaffectedinone

oftwodistinctways.Simplyput,cleancompaniesweredestinedtothriveunderClintonorto

sufferunderTrump.Thisresearchteststhishypothesisusinganeventstudymethodologybased

onfinancialassetdatainthedayssurroundingtheelection’soutcome.Broadly,thestudytests

whether in the advent of a surprise Trump presidency, environmentally-focused companies’

stockwouldsufferstatisticallysignificantnegativereturnsrelativetothebroadermarketdueto

hisadversepositionsontheissueofclimatechange.Furthermore,thestudylooksatwhethera

studiedcompany’smarketcapitalizationhasanimpactontheamplitudeoftheirstock’sreaction

followingthesurpriseelectionoutcome.Inbrief,thehypothesisbeingtestedusingthisstatistical

method is whether a Donald Trump win in the 2016 U.S. election resulted in statistically

significantnegativereturnsforcleancompanies.

15Ghosh,I.(2016).Trumpvs.Clinton:Wheretheystandontheissues.16Baker,D.R.(2016).ClintonandTrumppolaroppositesonglobalwarmingandenergy.17TrefisTeam.(2016).Clinton,TrumpAndTheFutureOfTheU.S.SolarIndustry.18Geiling,N.(2016).Inatiradeagainstrenewables,Trumpclaimswindpower“killsallthebirds.”19CBSNews.(2016).WheredoesDonaldTrumpstand?

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3.METHODOLOGY:ABASICEVENTSTUDY3.1.KEYCONCEPT&ASSUMPTIONS:EVENTSTUDIESINECONOMICSANDFINANCE

Thefollowingresearchutilizesthewell-acceptedconceptofeventstudiestoanalysethe

impactofaspecificeventonthemarketvalueofcompanies.Thissectiondescribesthebasicsof

this concept as well as the general structure of this statistical analysis technique. Used in

economics, finance, lawandvariousotherdisciplines, thisanalysis toolyieldsameasureofa

specific event’s economic impact on the market capitalization of a company using financial

marketdata.Byaggregatingthevaluesoftheseimpactsforatargetedportfolioofsecurities,one

canpaintaquantitativepictureofthefalloutthatasurpriseeventhadonvariousaspectsofthe

marketplace.Thismethodderivesitspracticalityfromtheassumptionthat,giventhefreeflow

of information and rationality of markets, the effect of unforeseen events is reflected

immediatelyinthepriceofpubliclytradedsecurities.Inotherwords,thisstatisticaltechnique

presumesthatmarketsareefficientatincorporatingallpublicinformationintosecurityprices.

EventstudiesofthiskindwerepioneeredbyFama,Fisher,Jensen,andRollintheirseminal1969

paper“TheAdjustmentofStockPricestoNewInformation”andwerelaterupdatedbyBrown

andWarnerintheir1980paper“MeasuringSecurityPricePerformance”.Inthecontextofthis

specificresearchpaper,theemployedeventstudymethodologyfollowsthetemplateofCraig

MacKinlay’s 1997 paper “Event Studies in Economics and Finance” inwhich he analyses this

statisticalapproachfurtheranddevisesastandardizedprocessforcarryingitout.20212223

3.2.STRUCTUREANDPROCEDURE Eventstudiescanbedesignedandconstructedinavarietyofways.Nonetheless,they

follow a similar flow when it comes to execution. Ideally, event studies begin with a clear

definitionoftheeventorseriesofeventsbeingstudied.Thisdeterminationwilldictatethedesign

oftheentirestudy.Forexample,theeventstudyillustratedinMacKinlay’s1997paperlookedat

20EventStudyTools.(2018).EventStudyAssumptions.21MacKinlay,A.C.(1997).EventStudiesinEconomicsandFinance.22Fama,Fisher,Jensen,&R.(1969).TheAdjustmentofStockPricestoNewInformation.23Brown,S.J.,&Warner,J.B.(1980).Measuringsecuritypriceperformance.

6

theaveragestockmarketreturnsonthedayofquarterlyearningsannouncementsforthe30

companieslistedintheDowJonesIndustrialIndexforafive-yearperiodspanningfrom1989to

1993.Inthiscase,havingfourquarterlyannouncementspercompanyperyearyields600data

points (30firmswith20datapointseach)onwhichtobasethestudy’sstatisticalanalysis. In

contrast,aneventstudycouldjustaseasily lookatonesingleeventanditseffectonasingle

company.Inanenvironmentaleconomicssetting,thenotoriousBritishPetroleumGulfofMexico

oilspillin2010isonesucheventthathasbeenanalysedusingeventstudymethods.Allinall,

thiswiderangeofpossiblestudydesignsmakesthistechniquehighlyflexibleandadaptableto

one’sspecificsubjectofinquiry.2425

Once an event or a series of events is selected, the next step is to define the event

window; the period surrounding the event in which stock returns are scrutinized. In the

MacKinlayexampleofquarterlyearningsdisclosures,thisannouncementismadeonagivenday

everyquarter.Toexamineitsimpactonshareprices,dailyreturnsatandsurroundingthedayof

theannouncementareobserved.Usually,theeventwindowissettocoveranamountofdays

before and after event day to capture any other stock reactions or events that could have

incidenceonreturns.Forinstance,informationontheearningsannouncementcouldleakdays

prior to event day, pre-emptively impacting the concerned company’s stock. Furthermore, a

stock’s return could suffer a reversal in the days following the announcement oncemarket

participantsreassesstheimpactofthisnewpieceofinformation.Regardless,havingawiderview

ofhowacompany’ssharesreactaroundaneventwillhelpyieldabetterunderstanding if its

overallresponseandwillleadtohigherqualityinsights.26

Afterdeterminationoftheeventandtheeventwindowofthestudy,thenextstepisto

defineaselectioncriteriaforchoosingwhichcompanieswillbesubjecttoanalysis.Leftsolelyto

theresearcher’sdiscretion,thisselectionprocessneedstoberelevanttotheeventofstudyand

keepinmindanybiasesthatmaybeinplay.Factorssuchasgeographicallocationofasecurity’s

24MacKinlay,A.C.(1997).EventStudiesinEconomicsandFinance.25Boudreaux,D.,Rao,S.,Das,P.,&Rumore,N.(2013).HowMuchDidTheGulfOilSpillActuallyCostBritishPetroleumShareholders?26MacKinlay,A.C.(1997).EventStudiesinEconomicsandFinance.

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listing, firmsize, industrial sectorand/or financialperformancemetricsareconsideredat this

point.Althoughmoresubjective,thisstepiscriticalasitselectsthecompaniesthroughwhich

the event’s hypothesised effect is expected to be reflected. If done incorrectly, the selected

companies might not yield any discernable effect due to lack of relevance and render the

researcher’sconclusionsdefenceless.27

Nextcomesthemostcriticalcomponentoftheeventstudy;themeasureofabnormal

returns.Simplyput,abnormalreturnsmeasuretheproportionofastock’sreturnsthatcanonly

beattributedtotheeventinquestion.Itisisolatedbytakingtherealreturnofastockduringthe

eventwindowandsubtractingfromititsnormalreturn,thereturnthatwasexpectedforthis

securityhadtheeventneveroccurred.28

Mathematically:

(1) AR#$ = R#$ − E R#$ X$)

Where: AR#$ =Abnormalreturnforsecurity“i”attime"t"

R#$ =Real(observed)returnforsecurity“i”attime"t"

E R#$ X$) =Normal(expected)returnforsecurity“i”attime"t" giventheconditioninginformation“X”ofthechosennormal returnmodelattime“t” Variousmodelsexisttoestimatethenormalreturnsofastockovertheeventwindow.

Thetwomostcommonaretheconstantmeanreturnmodel,whichimpliesthatthatthemean

realreturnofastockstaysconstantthroughtime,andthemarketreturnmodel,whichassumes

astablelinearrelationshipbetweenthesecurity’sreturnandthemarket’sreturn.29

27MacKinlay,A.C.(1997).EventStudiesinEconomicsandFinance.28Ibid.29Ibid.

Realreturn: Actualreturnobservedduringtheeventwindow(assetpricedata)Normalreturn: Aprioriexpectedreturnfortheeventwindow(modelestimations)AbnormalReturn: Measureofrealreturnsolelyattributabletotheevent

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Onceanormalreturnmodelischosen,anestimationwindowneedstobeoutlined.This

timewindowisusedtocalculatethestatisticalparametersofeachstockthatarelaterusedto

estimatenormalreturnsforeacheventwindowday.Usually,itlastsforasignificantlylongperiod

oftime,withMacKinlay’s1997paperclaimingthatabasiceventstudyusingdailystockmarket

returnswitha120-trading-daysestimationwindowissufficient.Regardlessofthemodelchosen,

thepricedataofasecurityinthisperiodisusedtoestimatethenormalreturnsofthatsecurity

forthedurationoftheeventwindow.Ingeneral,theestimationwindowwillstopthedaybefore

theeventwindowbegins.Avoidingthisoverlapofbothtimeperiodspreventstheeventorthe

dayssurroundingitfrominfluencingthenormalreturnparameterestimates.30 Withthepreviouselementscompleted,thenormalreturnparameterscanbeestimated

andthenormalreturnscalculated.Bysubtractingthemfromtherealreturnsobserved,weobtain

theabnormalreturnsforeachsecurityoneachgivendayoftheeventwindow.Thesubsequent

stepistolaydownthedesignofthetestingframeworkthatwillbeusedtoderiveinsightsfrom

abnormal returns.This includes the formulationanddefinitionof thenullhypothesisandthe

choiceofmethodforaggregatingindividualstocks’abnormalreturns.31 Followingtestingdesigncomesthepresentationofempiricalresults.Atthissection,the

findingsofthestudyarepresentedandcancontaintables,graphics,anddiagnosticsofwhatthe

data seems to be showing. Preferably, these results lead to insights enabling a better

understandingoftherootsandcausesoftheeffect(orlackthereof)thatstockssustainedatand

aroundtheevent.Analysiscanbepushedfurtherinanefforttoclarifysomeoftheresultsorto

speculateontheirmeaningbeforeconcludingthestudy.32

SUMMARYPROCEDURE 1–Eventdefinition 2–Eventwindow 3–Companyselectioncriteria 4–Normalreturnmodelselection 5–Estimationwindow

30MacKinlay,A.C.(1997).EventStudiesinEconomicsandFinance.31Ibid.32Ibid.

6–Normalreturncalculation7–Abnormalreturncalculation8–Statisticaltestingframework9–Empiricalresultspresentation10–Discussionandconclusion

9

4.MODELDESIGN,DATA&STATISTICALTESTING

Nowthattheconceptofeventstudiesanditsmethodologyareknown,modeldesigncan

proceedinrelationtotheinquiryofinterest;theunexpectedoutcomeofthe2016U.S.election.

4.1.ELECTIONRESULTSANDEVENTWINDOW

Theeventofinterestbeingthe2016U.S.electionresults,theperiodsoftimetobeused

inthiseventstudyneedtobecarefullyconsidered.Unlikeadividenddeclarationorapositive

earningsreportpublishedduringtradinghours,theresultsoftheelectionweremadeofficialby

the Associated Press around 2:30 am Eastern Time (ET) during the night between Tuesday

November8thandWednesdayNovember9th.WithU.S.stockmarkettradinghoursstartingat

9:30 am ET, equities only felt the consequences of these results during the trading day of

November9th.Thisdateisthereforeretainedaseventdayforthisstudy.Also,consideringdaily

stockpricesserveastherawdatainputtothisresearch,dailystockreturnsareusedtocalculate

normalreturns.33

November9thbeingselectedastheeventday,thequestionofchoosinganeventwindow

isnowaddressed.Thismatterneedscontemplationbecauseofthevaryingwaysinwhichother

factorscaninfluencestockmovementsbeforeorafterthetargeteventhastakenplace.Indeed,

eventsthatmovemarketscandosopre-emptivelyandinvariousdegreesofshockamplitude.

Forinstance,theBritishPetroleumOilSpillof2010caughteveryonebyabsolutesurprise.Noone

hadanyinclinationthatitwasabouttooccur,ascanbeattestedbythefactthat11workersdied

intheexplosionthatcausedoneoftheworstoilspillseverandthattheoperatorundercontract,

Transocean,saidithadnowarningofwhatwasabouttocome.Hadtherebeenanyindication

thatthisenvironmentaldisasterwasimminent,itissafetoassumethatactionswouldcertainly

havebeentakenbyBritishPetroleumtoremedythispotentiallyexplosivesituation.Incontrast,

othertypesofevents,suchasquarterlyearningsdisclosures,canbeanticipatedtosomedegree

inthatmarketplayerstrytoforecastcompanyearningsinthehopeofprofitingfromthem.3435

33Berenson,T.(2016).DonaldTrumpWinsthe2016Election.34BBCNews.(2010).Timeline:BPoilspill.35Griffin,D.,Black,N.,&Devine,C.(2015).5yearsaftertheGulfoilspill:Whatwedo(anddon’t)know.

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AsEfficientMarketsTheorydictates,investorsvaluethepriceofastockbyforecasting

thenetpresentvalueofacompany’sfuturecashflows.AccordingtoEMT,marketsareefficient

at incorporatingnew information almost immediately into securities prices. Ifmajor changes

occurinthatcompany’senvironment,suchasasurprisingpresidentialelectionoutcome,future

cash flow forecasts are bound to change after this new information becomes public. In the

hypothetical scenario where an investor forecasts that a company will disclose better-than-

expectedearnings,itisexpectedthatshewillbuyapositioninthatcompany’sstockpriortothe

earningsreportinthehopethatonthedayofdisclosure,thestockperformspositively.Similarly,

ifsheanticipatesnegativeearnings’newsonthatday,itisexpectedthatshewillsellthestock

shortpriortodisclosureinthehopeofrebuyingitlateratacheaperpriceandsimultaneously

securingaprofit.Althoughearningsdisclosuresareanticipatedtosomedegree(theirdateof

disclosure and the overall market’s general expectations are usually known before they’re

published)theystillhavethepotentialtoshockmarketsinverysignificantwaysifthepublicized

numbersarecontrarytotheoverallmarket’sexpectations.Thesescenarios,inadditiontoother

factorssuchasinsiderinformationleaksormarketmanipulation,canaffectthepriceofasecurity

in the days surrounding a surprise event.Due to this feature of financial assetmarkets, it is

important to analyse the abnormal returns that occur in pre-event and post-event days, in

additiontotheimmediatereactiontotheeventitself.3637383940

Inthespecificcaseofthispresentresearch,theeventofinterestismuchmoreanalogous

totheearningsdisclosurescenariothantheBPOilSpillof2010,whilestillcontainingastrong

elementofsurprise.Forstarters,noonewassurprisedbytheelectionitselfsinceallknewthe

timehadcometoelectanewpresidentandtheelectiondatewasknownbyall.Thesurprise

cameinthedirectionoftheresultsthemselves,whichmostexpectedtobeinfavorofaHillary

Clintonpresidency.Second,althoughhighlyexpected,noonehadpeggeda100%chancethat

theDemocratwouldwin.Aspreviouslymentioned,Trumphadashighascloseto30%chances

36Ong,H.(2017).Howdoquarterlyearningsreportsaffectstockprices?37Blau,B.M.,&Pinegar,J.M.(2013).Areshortsellersincrementallyinformedpriortoearningsannouncements?38Christophe,S.E.,Ferri,M.G.,&Angel,J.J.(2004).Short-SellingPriortoEarningsAnnouncements.39MacKinlay,A.C.(1997).EventStudiesinEconomicsandFinance.40Investopedia.(n.d.).ShortSelling.

11

ofwinningtheWhiteHouseaccordingtosomemembersofthemedia.Althoughrelativelysmall,

somearoundtheworldstillbelievedthatTrumphadachancetocomeoutontop.4142

For thisevent study, it is critical to see theeventof interestunder this light.Yes, the

victoryofDonaldTrumpcameasashocktomost,butitwasn’tentirelyunexpected.Thestudy’s

designneedstokeepthisinmindwhenanalysingstockmovementssurroundingNovember9th.

Examiningabnormalreturnsduringthosenon-eventdayscouldyieldinsightsintowhyasecurity

reactedthewayitdid.Ingeneral,eventstudiesanalyseabnormalreturnsforeachdayincluded

intheeventwindow.Theirlengthusuallyrangesfrom3-daywindows(-1day,eventday,+1day)

to41-daywindows(-20days,eventday,+20days).Thisstudyoptsfora21-dayeventwindow(-

10days,eventday,+10days)whichcoversthetradingdaysfromOctober26thtoNovember23rd,

2016.43

4.2.COMPANYSELECTION Thecompaniesretainedforthisstudywerescreenedusingthreeselectionmetrics.First,

ameasureofU.S.exposurewasdevised.ToanalysetheimpactofaDonaldTrumpU.S.election

victory, it is essential to observe this impact in securities that are expected to be decidedly

impactedbythisoutcome,i.e.companieswhoaregreatlyexposedtotheUnitedStates.Second,

companies considered to have environmentally-friendly purposes or environmentally-

sustainableoperationswereselected.Finally,ameasureofcompanies’marketvaluewasused

toscreenoutcompaniesthatareverysmallorextremelylarge.Inaggregate,theobjectiveofthis

selectionistoisolateU.S.exposedcompaniescoveringasubstantialrangeofvaluationsthatare

consideredclean.Forthepurposesofthisresearch,thisportfolioofsecuritieswillbereferredto

as“cleancompanies”or“cleanstocks”movingforward.

41Zurcher,A.(2016).USelection:IsTrumporClintongoingtowin?42FiveThirtyEight.(2016).Whowillwinthepresidency?-2016ElectionForecast.43MacKinlay,A.C.(1997).EventStudiesinEconomicsandFinance.

12

1.UnitedStatesExposure Toassessacompany’slevelofexposuretotheUnitedStates,U.S.stocklistingswasfirst

employed.CompanieslistedintheU.S.typicallyhavesignificantoperationsandinterestsinthe

country,itseconomy,anditsgeo-politicallandscape.Forthisreason,onlystockslistedonthe

NewYorkStockExchange(NYSE)andtheNASDAQwereanalysed.Pushingthisevenfurther,only

companiesthatarealsoheadquarteredintheU.S.werekeptinplay.Thiswasdonetogetridof

non-U.S.companiesthatlistsharesinthecountrybutthathavemostoftheiroperationsoutside

ofit.Thesecompanies,althoughexposedsomewhattotheUnitedStates,maybelessimpacted

by a surprise election result because of other operations around the world, their ability to

repatriateprofitstotheirhomecountry,andtherealitythattheyaren’tsubjecttothesametax

systems.444546

2.CleanStocks Selectingcompaniesbasedontheirenvironmentalstewardshipistougherthanitseems

onthesurface.Althoughsinglingoutnotoriouslyenvironmentally-destructivesectorsliketheoil

industryissimpler,thereisn’tanyoneindustryclassificationthatsinglesoutcleancompaniesin

the United States. In contrast, the Toronto Stock Exchange lists “clean technologies” as an

industrysector,butthereisn’tanycomparabledistinctionmadeontheNYSEortheNASDAQ.

U.S.INDUSTRYCLASSIFICATIONS(NYSE&NASDAQ)-BasicIndustries-CapitalGoods-ConsumerDurables-ConsumerNon-Durables

-ConsumerServices-Energy-Finance-Healthcare

-Miscellaneous-PublicUtilities-Technology-Transportation

When talking about clean companies, they are present in almost all the industries

mentionedabove.Teslaisagoodexampleofthis.Thecompanypredominantlymakeselectric

vehicles,whichclassifiesitasacapitalgoodscompanyontheNASDAQ.Ontheotherhand,the

44Cogman,D.,&Poon,M.(2012).Choosingwheretolistyourcompany.45Beattie,A.(n.d.).AlibabaIPO:WhyListIntheU.S.?46NYSE.(2018).10ReasonsCompaniesListontheNYSE.

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companyalsodesignsbatterytechnologythatcangreatlyimprovethewayweconserveenergy

inourhomes.Furthermore,thecompanydesigns,producesandinstallssolarpanels,inaddition

tothesolarelectricityitsells.Evidently,companiesthatstrivetoimproveeveryaspectofour

energy consumption can have significant impacts in many industry sectors, whether it be

transportation,energy,technologyorcapitalgoods.4748

Tocircumventthisissue,andtohelpparsethroughthemorethan6000companieslisted

onthetwomajorU.S.exchanges,adefinitionofwhatisconsidereda“cleancompany”hadtobe

developed.Theutilizedapproachstartswithalonglistofveryspecificbusinesslinesandactivities

that arebeneficial to theenvironmentor that have significant implications for sustainability.

Examples include renewable energy production, renewable energy distribution, renewable

energy technologies, energy efficiency,wastemanagement,water quality, andmany others.

Afterenvisioningthefullrangeofpossiblecleanbusinessesoutthere,asetofbroadercategories

was formulated to group companies with similar environmentally-focused purposes. This

resultedinalistofsixcategoriesusedtoscreenwhetheracandidatestockisconsideredcleanin

thecontextofthisstudy.49

CLEANCOMPANYDEFINITIONSFORTHISEVENTSTUDY

-RenewableEnergyGeneration,Distribution-RenewableEnergyTechnology,Manufacturing-EnergyIntelligence,Storage,Conversion

-ElectricTransportation-EnvironmentalManagement

-AdvancedMaterials

3.CompanyValue

The final screening concerned the size of companies. Firms were ranked by market

capitalizationandcategorizedusingthestandardmarketcapannotations(small-cap,mid-cap,

large-cap, etc.). As previously mentioned, the idea here is to remove companies that are

extremely big (mega-cap stocks) ormuch too small (micro-cap and nano-cap stocks). Bigger

companieshirethousandsofworkersandareusuallymuchmoreinsulatedfromunanticipated

47TMXMoney.(2018).ResearchSectors-CleanTechnology.48NASDAQ.(2018).CompanyList(NASDAQ,NYSE,&AMEX).49Ibid.

14

risksorexternalities.Theyareoftenindustryleaderswhichsometimesforcesgovernmentsto

makedrastic actions to guarantee that these companies remain in good financial health and

competitiveinthemarketplacetohelpkeeporcreateevermorejobs.Thiscanbeattestedby

the automotive industry bailouts of 2007 or the banking sector bailouts of 2008 during the

housingcrisis.Incontrast,verysmallcompaniesareusuallyhighlydependentonafewkeyfactors

suchastheirindustry’sperformanceandbusinesscycle,regulatoryenvironments,orresearch

anddevelopmentsrequirements.Theyarealsomore likelytodefault ineconomicdownturns

andoftentradeatpenny-stocklevelswhichismuchriskierthanatbiggervaluations.Duetothese

reasonsandmanyothers,verysmallcompanies’sharesaremuchmorevolatilethanbiggerones

whenunanticipatedeventsrockmarkets.Inthecontextofthiseventstudy,neithervaluations

aredesiredandarethereforeremoved.Thevolatilereactionsofmicro-capandnano-capstocks

couldskewtheperceivedimpactseenintheentireeventstudy,whilemega-capcorporations

coulddiluteitduetotheirsolidstatusasleadersintheirrespectivefieldsandeconomies.After

screeningforthis,theremaining74cleancompanies,listedinAppendixA,areusedtocarryout

theeventstudy.5051525354

MARKETCAPITALIZATIONANNOTATIONSANDTHEIRCORRESPONDINGDOLLARVALUE

Mega-Cap: Over$200Billion RejectedLarge-Cap: $10Billionto$200Billion Selected(14)Mid-Cap: $2Billionto$10Billion Selected(27)Small-Cap: $300Millionto$2Billion Selected(33)Micro-Cap: $50Millionto$300Million RejectedNano-Cap: Under$50Million Rejected

50Amadeo,K.(2018).MarketCapandWhyIsItImportant.51DTSSystemsInc.(2017).Understandingmarketcapitalization.52Investopedia.(2017).MarketCapitalizationDefined.53Amadeo,K.(2018).AutoIndustryBailout(GM,Chrysler,Ford).54BerkeleyUniversityofCalifornia.(2011).2008EmergencyEconomicStabilizationAct.

15

4.3.DATASOURCES Thefinancialassetsofinterestusedinthiseventstudyarethecommonsharesofthe74

selected companies and the S&P 1500 Composite Index. To calculate daily real returns, and

subsequentlydailynormalreturns,closingpricesareusedastherawdatainputstothemodel.

AllclosingpricequotesandmarketcapitalizationvaluesusedwereobtainedfromtheNASDAQ

andS&PDowJonesIndiceswebsites.5556

4.4.NORMALRETURNMODEL Asakeypieceofeventstudies,thechoiceofarelevantnormalreturnmodelneedstobe

made.Inthepresentcase,theMarketModelisemployedtocarryoutnormalreturncalculations.

Thisstatisticalmodelpaintsalinearrelationshipbetweenasecurity’sreturnandthereturnof

themarket,i.e.itsrelationtoamarketindexorbenchmark.Assumingthejointnormalityofasset

returns,itcanapproximatetheirlinearspecificationwithamarketproxy.Consideringthatthe

cleanstocksinthisstudyrangefromsmall-captolarge-capvaluations,ameasureofthebroad

U.S.stockmarketwasdesired.TheS&P1500CompositeIndexisthereforeusedasthemarket

proxy,asitcoversapproximately90%ofthetotalU.S.stockmarketcapitalizationbycombining

theS&P500,theS&PMid-Cap400,andtheS&PSmall-Cap600.5758

Themarketmodelforanysecurity“i”attime“t”isasfollows:

(2) NR#$ = 𝛼#$ + 𝛽#R/$ + 𝜀#$

(3) E(𝜀#$) = 0

(4) var 𝜀#$ = 𝜎𝜀78

55NASDAQ.(2018).U.S.StockQuotes,Charts,andResearch.56S&PDowJones.(2018).S&PComposite1500.57MacKinlay,A.C.(1997).EventStudiesinEconomicsandFinance.58S&PDowJones.(2018).S&PComposite1500.

16

Where: NR#$ =Normalreturnofsecurity“i”forperiod"t" R/$ =Realreturnofmarketindex(S&P1500)forperiod"t" 𝛼# =Interceptmodelparameterofsecurity“i”relativetomarketindex 𝛽# =Slopeparameterofsecurity“i”relativetomarketindex 𝜀#$ =Zeromeandisturbanceterm(withexpectedvalueof0) 𝜎𝜀7 =Standarderrormodelparameterofsecurity“i”relativetomarketindex4.5.ESTIMATIONWINDOW In this section, the framework for estimating normal returns is discussed. First, daily

returnsareannexedineventtimewiththenotation“t”.Settingeventdayatt=0,wecandelimit

thelengthoftheestimationwindowfromt=T0+1tot=T1,andthelengthoftheeventwindow

fromt=T1+1tot=T2.Forsimplification,letnotationsL1=T1–T0andL2=T2–T1betherespective

lengthsoftheestimationwindowandtheeventwindow,asdepictedinFigure1a.59

Intermsofestimationwindows,thechoiceoflengthislefttotheresearcher’sdiscretion.

AnexamplestudyincludedinMacKinlay’s1997paperusesa250-dayswindowtoestimatelinear

regression parameters. For the present research, a 200-trading-days estimationwindowwas

employed,withdailystockreturnsgoingbacktoJanuary12th,2016.Asforthelengthoftheevent

window,itis21tradingdayslongwith10pre-eventdaysand10post-eventdays.Inlinewiththe

previous notation, this yields L1 = 200 days and L2 = 21 days. The complete study timeline

employedinthisresearchisillustratedinFigure1b.60

59MacKinlay,A.C.(1997).EventStudiesinEconomicsandFinance.60Ibid.

T0 T1 0 T2

EstimationWindow(L1)

EventWindow(L2)

EventDay(t=0)

Figure1a.Eventstudytimelinewithbasicnotation.

17

4.6.MARKETMODELPARAMETERESTIMATION Ordinaryleastsquares(OLS)regressionisusedtoestimatethemarketmodelparameters

thatdescribethelinearrelationshipbetweencleanstocksandtheS&P1500index.Inlinewith

the procedure depicted inMacKinlay’s 1997 paper, this estimation procedure is accepted as

beingconsistentandefficient.Thismethodestimatesanintercept(a)andaslope(b)parameter

foreachcleanstockfromwhichnormalreturnsareapproximated.61

Security“i”modelparameters,estimatedusingOLSregressionduringestimationwindowL1,are:

(5) 𝛽# =(𝑅𝑖𝑡−𝜇𝑖)(𝑅𝑚𝑡−𝜇𝑚)𝑇1

𝑡=𝑇0+1

(𝑅𝑚𝑡−𝜇𝑚)2𝑇1𝑡=𝑇0+1

(6) 𝛼# = 𝜇# − 𝛽#𝜇/

(7) 𝜎𝜀72 = 1

𝐿B−2 (𝑅𝑖𝑡 − 𝛼𝑖 − 𝛽𝑖𝑅𝑚𝑡)2

𝑇1𝑡=𝑇0+1

Where: 𝛽# =Slope(beta)modelparameterestimatedusingOLSregressionfor security“i”duringestimationwindowL1

𝛼# =Intercept(alpha)modelparameterestimatedusingOLSregressionfor security“i”duringestimationwindowL1

𝜇# =Meandailyrealreturnofsecurity“i”duringestimationwindowL1

61MacKinlay,A.C.(1997).EventStudiesinEconomicsandFinance.

T0 T1 0 T2

EventDay(t=0)

L1=200daysL2=21days

Jan.12th Oct.26th Nov.23rdNov.9th

Figure1b.Eventstudytimelineemployedinthisresearch.

18

𝜇/ =Meandailyrealreturnofmarketindex(S&P1500)duringL1

𝜎C7 =StandarderrormodelparameterestimatedusingOLSregressionfor security“i”duringestimationwindowL1

Usingtheestimatedmodelparameters fromabove,theeventstudycanproceedwith

calculatingthenormalreturnsforeachcleanstockforanygivendayoftheeventwindow(L2).

Bysubtractingthesevaluesfromtherealreturnsobservedforeachday inL2,dailyabnormal

returnscanbeisolatedandanalysed.Thisistheresultingdatathatisscrutinizedtogaininsights

relatingtotheeventstudy’sstatedhypothesis.62

4.7.STATISTICALTESTINGFRAMEWORK Beforeabnormalreturns’statisticalrelevancecanbeassessed,theiraggregationneeds

totakeplacetoallowforanalysisofcleanstocksbymarketcapitalizationgroupings.Thethree

resultingsamplescontain33small-capcleanstocks,27mid-capstocksand14large-capstocks

respectivelyforatotalof74companies.Onceabnormalreturnsareobtainedforeachdayofthe

eventwindow, theyareaggregatedtoobtain thecumulativeabnormal returns (CAR)ofeach

security,thekeymetricthatdisplayshoweachreactedthroughoutthe21-dayperiod,notjust

oneventday.Forsimplification,cumulativeabnormalreturnsfollowthenotationCAR(t1,t2)for

theperiodt1 tot2whereT1<t1£t2£ T2. For thepurposesof this research, CAR is always

observedforthefulllengthoftheeventwindow,i.e.21days.63

Letthecumulativeabnormalreturn(CAR)ofsecurity“i”forperiodL2(t2-t1=21days)be:

(8) CAR𝑖(𝜏1, 𝜏2) = 𝐴𝑅𝑖𝑡

H2$IH1

Asymptotically(asL1grows),thedailyvarianceestimatorofCARiiscomputedthrough:

(9) 𝜎𝑖8(𝜏1, 𝜏2) = (𝜏8 − 𝜏J + 1) ∙ 𝜎𝜀7

8

62MacKinlay,A.C.(1997).EventStudiesinEconomicsandFinance.63Ibid.

19

As stated inMacKinlay’s 1997 paper, this estimator of the daily variance of CAR for

security“i”canbeusedwhenbasedonreasonablevaluesofL1, i.e. theestimationwindow’s

length.Inthiseventstudy,L1lasts200tradingdays,whichisquitesufficient.Nowarmedwith

eachsecurity’scumulativeabnormal returnanddailyvarianceestimators,wecanproceedto

aggregatingthembymarketcapitalizationgroupings.Thisisthefinalstepthatyieldsanaverage

CARandanaveragedailyvarianceestimatorforallthreegroupsofcleanstockvaluations.64

Definetheaveragecumulativeabnormalreturnof“N”securitiesforperiodL2(21days)as:

(10) CAR 𝜏1, 𝜏2 = 1N ∙ CAR𝑖 𝜏1, 𝜏2L

#IJ

AndletthedailyvarianceestimatoroftheaverageCARof“N”securitiesforperiodL2be:

(11) var(CAR 𝜏J, 𝜏8 ) =1N2

∙ 𝜎#2 𝜏J, 𝜏8𝑁𝑖=1

HavingcalculatedtheaverageCARandtheaveragedailyvarianceestimatorofeachof

sampleofsecurities,thenullhypothesiscanbetested.Todothis,thestandardT-test isused

withasignificancethresholdof+/-1.96standarderrorsfromthemean(95%confidence).65

Letthet-statisticusedtoassessstatisticalsignificanceforasampleof“N”securitiesbe:

(12) 𝜃J =CAR 𝜏1,𝜏2

var(CAR 𝜏1,𝜏2 )(1/2) ~𝑁(0,1)

64MacKinlay,A.C.(1997).EventStudiesinEconomicsandFinance.65Ibid.

20

5.RESULTS Withthestudy’sdesignfullymappedout,computationofitsresultscantakeplace.Inthis

research’s case, results indicate a stock reaction that is somewhat in line with the stated

hypothesisforsmall-capcompanies,stronglyagainstthehypothesisformid-capcompanies,and

moderatelyinlinewiththehypothesisforlarge-capstocks.

Inaggregate,thestudy’sfinalresultsdon’tsupportthehypothesisthatcleancompanies

we’redestinedtosufferstatisticallysignificantnegativereturnsfollowingthesurpriseoutcome

ofthe2016U.S.election.AlthoughFigure2clearlyexhibitshowallthreevaluationsamplesdid

suffer negative average abnormal returns on event day, small-cap andmid-cap stocks either

rebounded or trended higher towards the end of the event window, hinting at a probable

recovery frompost-event lows. In the contrasting caseof large-cap stocks, this drawdown is

sustainedthroughoutthesecondhalfoftheeventwindow,finishingwiththelowestcumulative

abnormalreturnofallthreesamples.Allthreesamples’resultsarelistedinTable1.

Figure2.Plotofcumulativeabnormalreturns(CAR)forallthreecleanstocksamples.

21

Date EventTime(t) AR CAR AR CAR AR CAR2016-10-26 -10 -0.32% -0.32% 0.20% 0.20% 0.53% 0.53%2016-10-27 -9 -0.92% -1.24% -0.25% -0.05% -0.22% 0.31%2016-10-28 -8 0.10% -1.14% 0.27% 0.21% 0.83% 1.15%2016-10-31 -7 -0.24% -1.38% 0.94% 1.16% 0.84% 1.99%2016-11-01 -6 -0.27% -1.65% 0.21% 1.36% -1.09% 0.91%2016-11-02 -5 0.40% -1.25% -0.74% 0.63% -0.05% 0.85%2016-11-03 -4 -1.41% -2.66% 1.03% 1.66% -0.62% 0.24%2016-11-04 -3 0.65% -2.01% 0.95% 2.61% 0.49% 0.73%2016-11-07 -2 -1.46% -3.47% -1.01% 1.60% 0.06% 0.79%2016-11-08 -1 1.07% -2.40% -0.49% 1.11% 0.26% 1.05%2016-11-09 0 -2.26% -4.66% -1.11% -0.0023% -1.99% -0.94%2016-11-10 1 0.58% -4.08% -0.18% -0.19% -0.15% -1.09%2016-11-11 2 1.54% -2.53% 1.73% 1.54% -0.65% -1.73%2016-11-14 3 0.30% -2.24% 1.68% 3.22% -0.82% -2.56%2016-11-15 4 -0.64% -2.88% -0.05% 3.16% 0.47% -2.09%2016-11-16 5 0.62% -2.26% -0.44% 2.72% 0.20% -1.89%2016-11-17 6 -1.07% -3.33% -0.64% 2.08% 0.17% -1.73%2016-11-18 7 0.71% -2.62% 0.13% 2.22% -0.22% -1.94%2016-11-21 8 -1.65% -4.26% -0.27% 1.95% 0.05% -1.90%2016-11-22 9 1.16% -3.10% 0.65% 2.60% -0.03% -1.93%2016-11-23 10 1.30% -1.79% 0.85% 3.45% -0.10% -2.04%

SMALL-CAP MID-CAP LARGE-CAPTable1.Results

Ateventday (t=0), the33small-capstocks sufferedanaverageabnormal returnof

-2.26%whichbroughtthesmall-capcumulativeabnormalreturntoitslowestoftheentireevent

windowat -4.66%. This result implies that small-caps trended lower in the days prior to the

election.Followingtheelection,theyreboundeddrasticallyforthreedays(fromt=1tot=3)

whichcompletelyerasedthelossessufferedoneventday.Withthatinmind,thesampleof33

smallercleancompaniesdidfinishtheeventwindowwithanegativeCARof-1.79%,althoughat

thatpoint,theimmediatelossesfollowingtheelectionhadalreadybeenrecouped.

Mid-capstockshadamuchdifferentreactionthansmallerindustrycounterparts.Before

eventday,mid-capshadaccumulatedapositiveCARthatreachedahighof+2.61%bytheeighth

dayoftheeventwindow(t=-3).Thesampleof27companiesthensustainednegativereturns

forthefollowingfourdays,withanaverageabnormalreturnof-1.11%oneventday(t=0).At

thispoint,theinitialpositiveCARthatwasaccumulateduntilthenwascompletelyerasedtoyield

acumulativeabnormalreturnof-0.0023%foreventday(t=0).Atessentiallyzero,thisvalueof

CARoneventdayindicatesthatmid-capstockshavehadnorealdiscernibleabnormalreturns,

Table1.Abnormalreturnsforaneventstudyofcleancompaniesduringthe2016U.S.election.Thetotalsamplecontains74small-to-large-capcompaniesbothlistedandheadquarteredintheU.S.

22

relativetothebroadermarket,inthetenpre-eventdaysleadinguptotheelection.Afterelection

day,mid-capsbenefitedfromastrong2-dayrally(fromt=2tot=3)thatbroughtcumulative

abnormalreturnsinsustainedpositiveterritoryfortheremainderoftheeventwindow.Bythe

endof it(t=10),mid-capCARwasmeasuredat+3.45%,indicatingthatthesecompanieshad

reactedverypositivelytotheelectionresultsoverthefullspanoftheeventwindow.

As forthesampleof large-capcompanies, theobservedreactionseemstobetheone

mostin-linewiththestatedhypothesisoutofallthreevaluationgroups.Thesampleof14clean

stocksstartedoutbyaccumulatingasmallpositiveCARthatpeakedat+1.99%bythefourthday

oftheeventwindow(t=-7)andthatgyratedslightlyaroundthe+1%CARrangeuntileventday.

Onthatday(t=0),thelarge-capabnormalreturnwasof-1.99%onaverage,whichbrought

downthesample’scumulativeabnormalreturnto-0.94%.Afterthisstrongnegativeeventday

reaction, large-capscontinued theirdownward trend for threedays (fromt=1 to t=3)and

settledinthe-2%CARterritoryfortheremainderoftheeventwindow.Theyfinishedwithafinal

CAR(t=10)of-2.04%.Thisresultisinlinewiththepositedhypothesisandtheory.Expectinga

Clinton win, market participants seem to have pushed this group of stocks upwards in

expectationsofafavorablepresidentfortheirrespectiveindustries.UponrealisingthatTrump

would win, i.e. only at the verymoment that election results were known, large-cap stocks

sufferedastrongnegativereactionthatwassustainedfortheremainderoftheeventwindow.

5.1.STATISTICALSIGNIFICANCE To test whether the above results are statistically significant, the standard T-test (as

previouslydescribed) is employedusingeach sample’s cumulativeabnormal returnandeach

sample’saveragedailyvarianceestimators.T-testingresultsarelistedinTable2.

AR CAR AR CAR AR CARDailySampleStandardError:

t=0 -0.96 -1.98 -0.68 0.00 -1.18 -0.55t=1 0.25 -1.73 -0.11 -0.11 -0.09 -0.64t=5 0.26 -0.96 -0.27 1.66 0.12 -1.12t=10 0.55 -0.76 0.52 2.10 -0.06 -1.20

T-StatisticsatEventTime:

Table2.StatisticalTesting

1.69%1.64%2.35%

LARGE-CAPMID-CAPSMALL-CAP

Table2.SampleT-statisticsderivedfromabnormalreturnsanddailystandarderrorestimates.

23

Concentratingonlyoneventday(t=0),thesampleof33small-capstockssufferedan

averageabnormalreturnof-2.26%.Giventhestandarderrorofdailyaverageabnormalreturns

forthissampleis2.35%,thevalueoftheteststatistic(T-statisticorq1)isof-0.96standarderrors

from the mean, and the hypothesis that a surprise Trump victory would cause significantly

negativestockreactionscan’tbesupportedhere.Althoughtheseresultsdoindicateanegative

abnormaleffectforeventday,thiseffectwasn’tstrongenoughtoconcludewith95%confidence

(+/-1.96standarderrorsfromthemean)thatitwassolelycausedbytheelectionresultitselfand

notbychance.Asfortheaveragecumulativeabnormalreturn,thingschangeabit.SinceCAR

capturesthesumofalltheabnormalreturnsuptoacertaindayinsidetheeventwindow,itcan

beusedtoassesswhetherthecumulativereturnoveraperiodofdaysisstatisticallysignificant.

Inthecaseofsmall-capstocks,andonlylookingattheCARuptoeventday(t=0)of-4.66%,

usingastandarderrorof2.35%weobtainaT-statisticof-1.98standarderrorsfromthemean.

Thisresultbarelycrossesthe95%confidencethresholdtoconcludethattheelection’soutcome

didhaveastatisticallysignificantnegativeimpactonsmall-capstockswhenaccountingforthe

11-dayperiodoftimeendingoneventday.AswecarrythistestwithCARpasteventday,theT-

statistic immediately recedes under the 95% confidence threshold, since small-cap stocks

reboundedquicklyinthepost-eventdays.Whentestingforthefull21-dayCAR(t=10)valueof

-1.79%,small-capshaveT-statisticof-0.76standarderrorsfromthemean.Thisresult implies

that,overthespanofthefulleventwindow,thesurpriseelectionoutcomehadnostatistically

significantimpactonsmall-capstocks.Althoughitdidhaveasomewhatstrongnegativeeffect

oneventdayitself,thiseffectwasn’tstatisticallystronganditbarelykeptholdduetotheensuing

rallythatthissampleexperienced.

Thestoryisquitedifferentforthesampleof27mid-capcompanies.Oneventday(t=0),

theysustainedanaverageabnormal returnof -1.11%.Withastandarderrorofdailyaverage

abnormalreturnsof1.64%,thisyieldsaT-statisticof-0.68standarderrorsfromthemean.Similar

tosmall-capstocks,thissampledidsuffernegativereturnsrelativetothemarketoneventday,

but theywere far from being even close to statistically significant. On this basis, the stated

hypothesiscan’tbesupportedforthissampleofmid-capsecurities.Thisconclusionisamplified

whenlookingatvaluesofcumulativeabnormalreturns.Asstatedpreviously,mid-capstockshad

24

apositiveCARinthedaysrunninguptotheelection.Thesesecuritiesdidsuffersomenegative

abnormalreturnsaroundtheelection,butquicklyrecoveredtofinishwithaCARof+3.45%at

theendoftheeventwindow.Bytestingforthestatisticalsignificanceofthisresultoverthen

entire21-daylengthoftheeventwindow(t=10),theT-statisticobtainedis2.10standarderrors

fromthemean.Thisresultisnotonlystatisticallysignificant(morethan1.96standarderrors),

thedescribedeffectisintheoppositedirectionthantheonepostulatedbythenullhypothesis.

Notonlydidmid-capstocksexperienceastatisticallysignificanteffectovertheeventwindow

period,thiseffectwasverypositiveinsteadofnegative.Uponconsiderationoftheseresults,the

nullhypothesisisstronglyrejectedforthissampleofmid-capsecurities.

Finally,asexpressedearlieron,large-capcompaniesconstitutethesamplethatreacted

most similarly to the stated hypothesis and to event study theory. It terms of statistical

significancethough,theresultssimplyweren’tstrongenough.Onlylookingateventday(t=0),

thissample’saverageabnormalreturn isof-1.99%.Giventhestandarderrorofdailyaverage

abnormalreturnsforthisgroupisof1.69%,theresultingT-statisticisof-1.18standarderrors

fromthemean.Althoughthisresultisthestrongestofallthreecleanstocksamplesintermsof

averageabnormalreturnoneventday,itisstillfarfromthestatisticalsignificancethresholdof

1.96 standard errors. In this light, the stated hypothesis that a Trump victory would entail

significantnegativestockreactionscan’tbesupported for thissample.Thesizeof theeffect,

relativetothebroadermarket,simplyisn’tlargeenoughtoconfidentlydiscountthepossibility

thatitcouldhaveoccurredbychance.Intermsofcumulativeabnormalreturns,theresultsare

alongthesametheme.UsingtheCARateventday(t=0)of-0.94%,theresultingT-statisticisof

only-0.55standarderrorsfromthemean.Bytheendoftheeventwindow(t=10),thevalueof

CARreaches-1.21standarddeviationsfromthemean.AlthoughtheseprogressivelynegativeT-

statistics seem to indicate that, as the eventwindowgrows through time, the impact of the

electionoutcomebecomesmoreandmoresignificant,theirvalueremainsmuchlowerthanthe

1.96 thresholdneeded.Again, due to the reasons stated above, thenull hypothesis can’t be

supportedforlarge-capsecurities.

25

6.DISCUSSION&CONCLUSION Overall, this study indicates that the negative implications directly attributable to the

2016U.S. election surprisewere negligible for clean companies in termsof short-term stock

performance.Thehypothesisthatthesecompanies’shareswouldsufferstatisticallysignificant

negative abnormal returns following a surprise Trump victory is not supported in any strong

fashionbytheevidenceuncoveredinthisresearch.Eventhoughallthreecleancompanysamples

did experience somenegative abnormal returns on event day, these stock reactionsweren’t

strongenoughtoconfidentlydiscardthepossibilitythattheyweretheresultofpurechance.

Nevertheless,someinsightswereretainedfromthisexercise.

For starters, the more volatile nature of small-cap stocks was quite apparent in the

graphicalrepresentationofsmall-capCARandbythefactthatthissampleofcleancompanies

hasahigherstandarderrorthanthetwobiggervaluationgroupings(2.35%forsmall-capsversus

1.64%and1.69%formid-capsandlarge-capsrespectively).Thisalleviatestheweightgivento

theseresultsintheoverallstudyconsideringthenegativereactionwitnessedoneventdayfor

thesestockswasmostlikelyamplifiedduetothishighervolatility.Inlinewiththis,thewayin

whichthissamplerecoveredandwhipsawedpositivelyandnegativelyinpost-eventdaysshows

howitneedstobetakenmorelightlythanthetwobiggervaluationsamples.66

Intermsofthemid-capssample,theresultsaremuchmoreintriguingandsignificant.This

groupofcleancompaniesreactedcontrarilyfromwhatwasinitiallyhypothesised,anddidsoin

a statistically significant manner. The market sentiment regarding this range of company

valuationsseemstobethattheyaremuchmoresolidinthelightofapotentiallydetrimental

presidency to their industry. This reaction could stem from the fact that, beingmuchmore

mature companies relative to their smaller counterparts, they boast a stronger financial

situation,geographicallymorediverseoperations,andaresimplymoreinsulatedfromnegative

externalities.Otherpotentialexplanationsarethat,althoughnotasupporterofthefightagainst

climatechange,DonaldTrumpisasupporterofAmericanbusinessesandthecreationofjobs.

66Amadeo,K.(2018).MarketCapandWhyIsItImportant.

26

Considering this, and even though the short-term implications could be negative, market

participantsmightconsiderthatinthelongrun,aDonaldTrumppresidencywillequatetoamuch

more pro-business environment for U.S. businesses, including clean companies. Supporting

eventssincetheelectionseemtobe inaccordancewiththis lineof thinking.Sincebecoming

President,DonaldTrumphasenactedasweepingtaxcodeoverallinthecountry.Whetherthis

taxsystemreformwillbenefitcleancompaniesmovingforwardornot,inthedaysleadingtoand

surroundingtheelection,theprospectaloneofsuchareformcouldhaveenticedinvestorsto

viewaTrumppresidencyasapositiveforallbusinesses,regardlessoftheirindustry.Havingmany

marketplayerssharethisopinioncouldjustifywhytheselargercompaniesdidn’treactstrongly

tothedownsideoncetheelectionresultsbecamepublicknowledge.6768697071

If the above logic holds, we would expect to see large-cap stocks react even more

favorablythandidmiddlevaluations,whichwasnotthecasehere.Onereasonforwhylarge-cap

companies didn’t experience better-than-mid-cap returns could be that they aremuchmore

globalised.InthewakeofisolationismcrossingoverfromtheU.K.’sBrexitsurpriseanditbeing

embodied by Trump during his campaign, investorsmight have perceived a potential Trump

presidency as a similarly negative situation, if notworse, considering the U.S. is theworld’s

economic leader. Isolating the U.S. from the rest of themodern globalised economy would

certainlyentailnegativeimplicationsforAmericanbusinessesdependantonpreservinghealthy

trade relations with other countries. Also, major market participants such as institutional

investors usually hold much larger positions in large-corporations relative to smaller ones.

ConsideringaTrumppresidencywouldbringaheavy loadofuncertainty for the foreseeable

future,theseinvestorsmighthavedecidedtoadoptawait-and-seeapproachinthewakeofthe

electionoutcomeeventhoughtheydidsuffersomenegativereturnsoneventday.72737475

67Amadeo,K.(2018).Mid-CapStocksandFundswithTheirEffectontheEconomy.68Ghosh,I.(2016).Trumpvs.Clinton:Wheretheystandontheissues69CBSNews.(2016).WheredoesDonaldTrumpstand?70BBCNews.(2018).TrumptaxreformgivesBuffett$29bnboost.71TheGlobeandMail.(2017).Globeeditorial:Trump’staxreformisacreaturefromtheswamp.72Amadeo,K.(2018).LargeCapStocksandFundswithTheirEffectontheEconomy.73Kaletsky,A.(2016).Trump’sriseandBrexitvotearemoreanoutcomeofculturethaneconomics.74Zurcher,A.(2016).USelection:HillaryClintonandDonaldTrumpcomparedtoworldleaders.75Investopedia.(n.d.).InstitutionalInvestor.

27

Althoughthe2016U.S.electionhascomeandwent,itsunprecedentednatureallowsus

to derive interesting insights in hindsight aboutwhat happened surrounding this event. This

research attempted to do just that by employing an event study approach to analyse stock

marketreturnsofcleancompanies.Resultsindicatethat,althoughsmall-to-large-capitalization

cleanstocksallsufferednegativereturnsonNovember9th2016,noneofthosedrawdownswere

strong enough to completely discount the possibility that they occurred by chance, i.e. they

weren’tstatisticallynoteworthy.Inaddition,whenlookingatcumulativestockreturnsduringthe

entire21-dayeventwindowsurroundingelectionresultsdisclosure,small-capsecuritiesquickly

reboundedfrompost-eventlowstofinishslightlyinnegativeterritory,mid-capsecuritiesbriskly

experienced strongpositive returns to finish in strongpositive territory, and large-cap stocks

sufferedsustainednegativereturnsinweaknegativeterritory.All inall,statisticalsignificance

formostoftheseresultswasquiteweak,pointingtothefactthatthestatedhypothesis isn’t

supported here. The evidence seems to indicate that, although clean companies did suffer

negative returns upon learning the election’s outcome as expected, these drawdowns either

quicklyrecoveredoronlyfinishedinweaknegativeterritory.Thisallpointstocleancompanies

havingreactedquitewellfollowingtheRepublicanupset,contrarytoexpectations.

Asthe2016U.S.electionisnowpartofthepast,theworldwitnessedthatthefirstyear

andahalfoftheTrumppresidencyentailedstrongpositivestockmarketreturnsacrosstheboard

ofindustries.AlthoughcleancompanyinvestorsmighthavebeenspookedonNovember9thof

thatyear,thegeneralmarketsentimentsincethenseemstohaveshiftedtooptimisminthelight

ofTrump’spro-American-businessagenda.Nowthatthisinitialsentimentseemstobefadingin

thewakeofhisrecentoffensivetraderhetoricdirectedatmajorglobaltradeplayers,corrections

andvolatilityarereturningtomarkets.Onecanonlywait-and-seewhatwillensueinthewakeof

thisglobaleconomicuncertainty.7677

76Grant,M.(2018).ATradeWarWillLeaveMarketsWithFewWinners.77Mayeda,A.(2018).Trump’s“ArtoftheDeal”TacticsFaceUltimateTestWithChina.

28

7.REFERENCES

Agiesta,J.(2016).Poll:MostseeaHillaryClintonvictoryandafaircountahead.CNNPolitics.Retrievedfrom:https://www.cnn.com/2016/10/25/politics/hillary-clinton-2016-election-poll/index.html

Amadeo,K.(2018).AutoIndustryBailout(GM,Chrysler,Ford).TheBalance.Retrievedfrom:https://www.thebalance.com/auto-industry-bailout-gm-ford-chrysler-3305670

Amadeo,K.(2018).LargeCapStocksandFundsWithTheirEffectontheEconomy.TheBalance.Retrievedfrom:https://www.thebalance.com/why-buy-large-cap-stocks-3305861

Amadeo,K.(2018).MarketCapandWhyIsItImportant.TheBalance. Retrievedfrom:https://www.thebalance.com/market-capitalization-3305826

Amadeo,K.(2018).Mid-CapStocksandFundsWithTheirEffectontheEconomy.TheBalance.Retrievedfrom:https://www.thebalance.com/what-are-mid-cap-stocks-4-reasons-to-buy-3305865

Baker,D.R.(2016).ClintonandTrumppolaroppositesonglobalwarmingandenergy.SanFranciscoChronicle. Retrievedfrom:https://www.sfchronicle.com/politics/article/Clinton-and-Trump-polar-opposites-on-global-9214954.php

BBCNews.(2010).Timeline:BPoilspill.BBCNews–US&Canada. Retrievedfrom:http://www.bbc.com/news/world-us-canada-10656239

BBCNews.(2018).TrumptaxreformgivesBuffett$29bnboost.BBCNews–US&Canada. Retrievedfrom:http://www.bbc.com/news/world-us-canada-43183737

Beattie,A.(n.d.).AlibabaIPO:WhyListIntheU.S.?Investopedia. Retrievedfrom:https://www.investopedia.com/articles/investing/112614/alibaba-ipo-why-list-us.asp

Berenson,T.(2016).DonaldTrumpWinsthe2016Election.TimeMagazine. Retrievedfrom:http://time.com/4563685/donald-trump-wins/

BerkeleyUniversityofCalifornia.(2011).2008EmergencyEconomicStabilizationAct.SlayingtheDragonofDebt. Retrievedfrom:http://bancroft.berkeley.edu/debteconomicstabilizationact.html

29

Blau,B.M.,&Pinegar,J.M.(2013).Areshortsellersincrementallyinformedpriortoearningsannouncements?JournalofEmpiricalFinance,Vol.21,pp.142–155.Retrievedfrom:https://www.sciencedirect.com/science/article/abs/pii/S09275

Boudreaux,D.,Rao,S.,Das,P.,&Rumore,N.(2013,May24).HowMuchDidTheGulfOilSpillActuallyCostBritishPetroleumShareholders?JournalofInternationalEnergyPolicy(JIEP),Vol.2,Issue1,pp.15-30. Retrievedfrom:https://www.researchgate.net/publication/292951910

Brown,S.J.,&Warner,J.B.(1980).Measuringsecuritypriceperformance.JournalofFinancialEconomics,Vol.8,Issue3,pp.205–258. Retrievedfrom:https://www.sciencedirect.com/science/article/pii/0304405X80900021

CBSNews.(2016).WheredoesDonaldTrumpstand? Retrievedfrom:https://www.cbsnews.com/media/where-does-donald-trump-stand/2/

Christophe,S.E.,Ferri,M.G.,&Angel,J.J.(2004).Short-SellingPriortoEarningsAnnouncements.TheJournalofFinance,Vol.59,No.4,pp.1845–1875.[AmericanFinanceAssociation,Wiley]. Retrievedfromhttp://www.jstor.org/stable/3694880

CNNNews.(2016).Whowonthetownhalldebate?CNN. Retrievedfrom:https://www.cnn.com/2016/10/10/opinions/clinton-trump-second-debate-roundup/index.html

Cogman,D.,&Poon,M.(2012).Choosingwheretolistyourcompany.McKinsey&Company.Retrievedfrom:https://www.mckinsey.com/business-functions/strategy-and-corporate-finance/our-insights/choosing-where-to-list-your-company

ColumbusDispatch.(2016).Editorial:Forpresident|Trumpunfit,Clintonisqualified.Retrievedfrom:http://www.dispatch.com/content/stories/editorials/2016/10/09/1-editorial-for-president-trump-unfit-clinton-is-qualified.html

DTSSystemsInc.(2017).Understandingmarketcapitalization.FidelityInvestments.Retrievedfrom:https://www.fidelity.com/learning-center/trading-investing/fundamental-analysis/understanding-market-capitalization

EventStudyTools.(2018).EventStudyAssumptions. Retrievedfrom:https://www.eventstudytools.com/assumptions-event-study

Fama,Fisher,Jensen,&R.(1969).TheAdjustmentofStockPricestoNewInformation.InternationalEconomicReview,Vol.10,Issue1,pp.1-21. Retrievedfrom:https://www.jstor.org/stable/2525569?seq=1

30

FiveThirtyEight.(2016).Whowillwinthepresidency?-2016ElectionForecast. Retrievedfrom:https://projects.fivethirtyeight.com/2016-election-forecast/

Geiling,N.(2016).Inatiradeagainstrenewables,Trumpclaimswindpower“killsallthebirds.”ThinkProgress. Retrievedfrom:https://thinkprogress.org/donald-trump-wind-kills-all-the-eagles

Ghosh,I.(2016).Trumpvs.Clinton:Wheretheystandontheissues.TheGlobeandMail.Retrievedfrom:https://www.theglobeandmail.com/news/world/trump-vs-clinton-where-they-stand-on-contentious-issues/article31693615/

Grant,M.(2018).ATradeWarWillLeaveMarketsWithFewWinners.Bloomberg.Retrievedfrom:https://www.bloomberg.com/view/articles/2018-04-04/a-trade-war-will-leave-markets-with-few-winners

Grenier,E.(2016).HillaryClintonhastheleadforthefinalweekend,butwillitholdthroughTuesday?CBCNews. Retrievedfrom:http://www.cbc.ca/news/politics/grenier-uselection-final-weekend

Griffin,D.,Black,N.,&Devine,C.(2015).5yearsaftertheGulfoilspill:Whatwedo(anddon’t)know.CNNNews. Retrievedfrom:https://www.cnn.com/2015/04/14/us/gulf-oil-spill-unknowns/index.html

Grim,R.,Terkel,A.,&Date,S.V.(2016).DonaldTrumpMadeItClear-Again-ThatHe’sUnfitForThePresidency.HuffingtonPost. Retrievedfrom:https://www.huffingtonpost.ca/entry/third-presidential-debate

Investopedia.(n.d.).InstitutionalInvestor. Retrievedfrom:https://www.investopedia.com/terms/i/institutionalinvestor.asp

Investopedia.(2017).MarketCapitalizationDefined. Retrievedfrom:https://www.investopedia.com/articles/basics/03/031703.asp

Investopedia.(n.d.).ShortSelling. Retrievedfrom:https://www.investopedia.com/terms/s/shortselling.asp

Jackson,N.,&Hooper,A.(2016).Election2016Forecast-President&Senate.HuffingtonPost.Retrievedfrom:http://elections.huffingtonpost.com/2016/forecast/president

Kaletsky,A.(2016).Trump’sriseandBrexitvotearemoreanoutcomeofculturethaneconomics.TheGuardian. Retrievedfrom:https://www.theguardian.com/business/2016/oct/28/trumps-rise-and-brexit-vote-are-more-an-outcome-of-culture-than-economics

31

MacKinlay,A.C.(1997).EventStudiesinEconomicsandFinance.JournalofEconomicLiterature,Vol.35,No.1,pp.13–39. Retrievedfrom:http://www.bu.edu/econ/files/2011/01/MacKinlay-1996-Event-Studies-in-Economics-and-Finance.pdf

Mayeda,A.(2018).Trump’s“ArtoftheDeal”TacticsFaceUltimateTestWithChina.Bloomberg.Retrievedfrom:https://www.bloomberg.com/news/articles/2018-04-08/trump-s-art-of-the-deal-bombast-faces-ultimate-test-with-china

Miller,J.(2016).BobGates:DonaldTrump“unfittobecommander-in-chief.”CBSNews.Retrievedfrom:https://www.cbsnews.com/news/bob-gates-donald-trump-unfit-to-be-commander-in-chief-election-2016/

NASDAQ.(2018).CompanyList(NASDAQ,NYSE,&AMEX). Retrievedfrom:https://www.nasdaq.com/screening/company-list.aspx

NASDAQ.(2018).U.S.StockQuotes,Charts,andResearch. Retrievedfrom:https://www.nasdaq.com/quotes/

NYSE.(2018).10ReasonsCompaniesListontheNYSE. Retrievedfrom:https://www.nyse.com/network/article/10-reasons-companies-list

Ong,H.(2017).Howdoquarterlyearningsreportsaffectstockprices?BusinessInquirer.Retrievedfrom:http://business.inquirer.net/229677/quarterly-earnings-reports-affect-stock-prices

S&PDowJones.(2018).S&PComposite1500. Retrievedfrom:https://ca.spindices.com/indices/equity/sp-composite-1500

TheGlobeandMail.(2017).Globeeditorial:Trump’staxreformisacreaturefromtheswamp. Retrievedfromhttps://www.theglobeandmail.com/opinion/editorials/globe-editorial-trumps-tax-reform-is-a-creature-from-the-swamp/article37391022/

TMXMoney.(2018).ResearchSectors-CleanTechnology. Retrievedfrom:https://www.tmxmoney.com/en/research/cleantech.html

TrefisTeam.(2016).Clinton,TrumpandTheFutureOfTheU.S.SolarIndustry.Forbes.Retrievedfrom:https://www.forbes.com/sites/greatspeculations/2016/09/16/clinton-trump-and-the-future-of-the-u-s-solar-industry/#1d3f66801f25

USAToday.(2016).USAToday’sEditorialBoard:Trumpis“unfitforthepresidency.”Retrievedfrom:https://www.usatoday.com/story/opinion/2016/09/29/dont-vote-for-donald-trump-editorial-board-editorials-debates/91295020/

32

Wolfers,J.,&Zitzewitz,E.(2016).Whatdofinancialmarketsthinkofthe2016election?Retrievedfrom:https://www.brookings.edu/wp-content/uploads/2016/10/what-do-financial-markets-think-of-the-2016-election_102016_wolferszitzewitz.pdf

Zitzewitz,E.(2016).Column:Thestockmarketdoesn’tliketheideaofaTrumppresidency.PBSNews. Retrievedfrom:https://www.pbs.org/newshour/economy/column-stock-market-doesnt-like-idea-trump-presidency

Zurcher,A.(2016).USelection:HillaryClintonandDonaldTrumpcomparedtoworldleaders.BBCNews-US&Canada. Retrievedfrom:http://www.bbc.com/news/election-us-2016-37423550

Zurcher,A.(2016).USelection:IsTrumporClintongoingtowin?BBCNews–US&Canada.Retrievedfrom:http://www.bbc.com/news/election-us-2016-37884603

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AppendixA.Listofthecleancompaniesselectedforthisstudy.

CompanyNameSecurityTicker Exchange

CleanIndex/ETFMembership

MarketValuation

AmerescoInc AMRC NYSE PBW Small-CapAZZInc AZZ NYSE QGRD Small-CapGeneralCableCorporation BGC NYSE GWE Small-CapBallardPowerSystemsInc.(US) BLDP NASDAQ CELS Small-CapBadgerMeter,Inc. BMI NYSE QGRD Small-Cap8Point3EnergyPartners,LP CAFD NASDAQ CELS Small-CapCalgonCarbonCorp CCC NYSE EVX Small-CapCanadianSolar CSIQ NASDAQ CELS Small-CapCasellaWasteSystemsInc CWST NASDAQ EVX Small-CapDaqoNewEnergyCorpADR DQ NYSE PBW Small-CapUsEcologyInc ECOL NASDAQ EVX Small-CapESCOTechnologiesInc. ESE NYSE QGRD Small-CapGreenPlains,Inc. GPRE NASDAQ CELS Small-CapHannonArmstrongSustainableInfrastructure HASI NYSE CELS Small-CapHeritage-CrystalCleanInc HCCI NASDAQ EVX Small-CapJASolarHoldings JASO NASDAQ CELS Small-CapJinkoSolar JKS NYSE CELS Small-CapKadantInc KAI NYSE PZD Small-CapLindsayCorp LNN NYSE PZD Small-CapPatternEnergyGroup,Inc. PEGI NASDAQ CELS Small-CapPlugPower,Inc. PLUG NASDAQ CELS Small-CapPowerIntegrations POWI NASDAQ CELS Small-CapRavenIndustriesInc RAVN NASDAQ PZD Small-CapRenewableEnergyGroup REGI NASDAQ CELS Small-CapSunrun,Inc. RUN NASDAQ CELS Small-CapSchnitzerSteelIndustriesInc SCHN NASDAQ EVX Small-CapSolarEdgeTechnologies,Inc. SEDG NASDAQ CELS Small-CapSunPowerCorporation SPWR NASDAQ CELS Small-CapTerraFormPower,Inc TERP NASDAQ CELS Small-CapGenthermInc THRM NASDAQ PBW Small-CapTPIComposites,Inc. TPIC NASDAQ CELS Small-CapVeecoInstruments VECO NASDAQ CELS Small-CapVivintSolar,Inc. VSLR NYSE CELS Small-CapAdvancedEnergy AEIS NASDAQ CELS Mid-CapAVXCorporation AVX NYSE CELS Mid-CapAtlanticaYield AY NASDAQ CELS Mid-CapAcuityBrands,Inc. AYI NYSE CELS Mid-CapCleanHarborsInc CLH NYSE EVX Mid-CapCree,Inc. CREE NASDAQ CELS Mid-CapCovantaHoldingCorp CVA NYSE EVX Mid-CapDarlingIngredientsInc DAR NYSE EVX Mid-CapEnerSys ENS NYSE CELS Mid-CapFirstSolar,Inc. FSLR NASDAQ CELS Mid-CapHexcelCorporation HXL NYSE CELS Mid-CapIntegratedDeviceTechnology,Inc. IDTI NASDAQ CELS Mid-CapItron,Inc. ITRI NASDAQ CELS Mid-CapAlliantEnergyCorporation LNT NYSE GWE Mid-CapMicrosemiCorp MSCC NASDAQ CELS Mid-CapMasTec,Inc. MTZ NYSE QGRD Mid-CapNextEraEnergyPartners,LP NEP NYSE CELS Mid-CapNRGEnergyInc NRG NYSE CELS Mid-Cap

8.APPENDIXA–CLEANCOMPANYLIST

34

AppendixA.Listofthecleancompaniesselectedforthisstudy.

CompanyNameSecurityTicker Exchange

CleanIndex/ETFMembership

MarketValuation

NRGYield NYLD NYSE CELS Mid-CapUniversalDisplay OLED NASDAQ CELS Mid-CapONSemiconductor ON NASDAQ CELS Mid-CapOrmatTechnologies,Inc. ORA NYSE CELS Mid-CapStericycleInc SRCL NASDAQ EVX Mid-CapSensataTechnologiesHoldingNV ST NYSE PZD Mid-CapTennecoInc TEN NYSE EVX Mid-CapTetraTechInc TTEK NASDAQ EVX Mid-CapWoodwardInc WWD NASDAQ GWE Mid-CapAutodeskInc ADSK NASDAQ PZD Large-CapAlbemarleCorp ALB NYSE PBW Large-CapANSYSInc ANSS NASDAQ PZD Large-CapAirProducts&ChemicalsInc APD NYSE PBW Large-CapBorgWarnerInc BWA NYSE PZD Large-CapDukeEnergyCorp DUK NYSE GWE Large-CapEatonCorporation,PLC ETN NYSE QGRD Large-CapFortisInc FTS NYSE QGRD Large-CapJohnsonControlsInternationalplc JCI NYSE QGRD Large-CapNextEraEnergyInc NEE NYSE CELS Large-CapRepublicServicesInc RSG NYSE EVX Large-CapTesla,Inc. TSLA NASDAQ CELS Large-CapWasteConnectionsInc WCN NYSE EVX Large-CapWasteManagementInc WM NYSE EVX Large-Cap

8.APPENDIXA–CLEANCOMPANYLIST(CONTINUED)