Zero-Revelaon RegTech: Detec+ng Risk through Corporate...

S. Kim / SCU 2017 Data Concluding Remarks Motivation Analyses

Zero-Revela+onRegTech:

Detec+ngRiskthroughCorporateEmails

SeoyoungKimSantaClaraUniversity

@R/FinanceConference

Chicago,ILMay19,2017

Jointworkwith:

SanjivDas(SCU)andBhushanKothari(GoogleInc.)

BigPicture

§  FinancialsareoIendelayedindicatorsofcorporatequality

§  Internaldiscussion(e.g.,emails)maybeusedasanearlywarningsystem

§  An automated plaRorm that parses emails and produces summarysta+s+cswouldbehighlyvaluable,since…

–  It can analyze vast quan++es of textual not amenable to humanprocessing

–  Itdoesnotrequirerevela+onof individualemailcontentexplicitlytomonitors/regulators


OurPurpose

§  Ourpurpose is toexplore thepredic+vepowerof informa+onconveyedbyemployeeemails

§  Specifically,weareinterestedin:

§  Thesen+mentconveyedbyemailcontent

§  Theinforma+onconveyedbystructuralcharacteris+cs,suchasemailvolumeorlength

§  Othernon-verbalindicatorsofpoten+altrouble(e.g.,shiIingemailnetworkpaYerns)


PreviewofResults

§  We find that the net sen+ment conveyed by Enron employee emailcontentisasignificantpredictorofstock-returnperformance

§  Interes+ngly, email lengthwas a stronger predictor of subsequent pricedeclinesthanthenetsen+mentconveyedbythemessagebodyitself.

§  Wealsoiden+fyotherpoten+alindicators/predictorsofescala+ngriskormalfeasance.


Data


TheEnronEmailCorpus


TheEnronEmailCorpus

§  Ini+alSample:–  Approximately500,000emails


TheEnronEmailCorpus


–  January2000throughDecember2001


TheEnronEmailCorpus



–  First made publicly available by the Federal Energy Regulatory Commission (FERC)duringitsinves+ga+onofEnron


TheEnronEmailCorpus




–  SubsequentlyculledanddistributedbytheCarnegieMellonCALOproject


TheEnronEmailCorpus





§  Caveats/Redac+ons–  TheEnroncorpushasbeenscrubbedover+meforlegalreasonsandtohonorrequests

fromaffectedemployees.


TheEnronEmailCorpus







–  Ex(1):user“fastow-a”isnotablymissing


TheEnronEmailCorpus








–  Ex(2): Email chaYer surrounding Mr. Skilling’s sudden resigna+on on 8/14/2001 hasbeenexpunged.


TheEnronEmailCorpus








–  Ex(2): Email chaYer surrounding Mr. Skilling’s sudden resigna+on on 8/14/2001 hasbeenexpunged.

–  Overall,detailsregardingexclusioncriteriahavenotbeenmadepublic,andouranalysesshouldbeviewedasexploratoryandprescrip+ve


CuringtheData

§  Wefocuson“sent”emails(ratherthanallemails)inorderto…–  AnalyzecontentspecificallywriYenbyEnronemployees

–  Avoidprocessingthesamecontentmorethanonce

–  i.e.,ifuser“lay-k”sendsanemailto“skilling-j”

§  Otherfiltersappliedtoremovenoisy(junk)mail:–  Emailsgreaterthan3,000charactersinlength

–  Emailssenttomorethan20recipients


OurFinalSample

§  Overall,weobtain…–  TheEnronemailcorpusfromtheCarnegieMellonCSsite

–  Stockpriceandstockreturninforma+onfromCRSP

–  Newsar+clesfromFac+vaPRNewswire

–  Sen+mentworddic+onariesfromtheHarvardInquirerandtheLoughranandMcDonaldsen+mentwordlists

§  FinalSample:–  144dis+nctemployees

–  113,266sentemails



EnronCodePipeline


S1.Rmd(readinallemails,preprocess)

S2.Rmd(processandparseallemails)

Mails2.RDataDataframewithheader,messagebody,date,from,to,cc,nchar,year,month,week.

Mails1.RData(1.3GB)>½millionemails

S3.RmdResolvemul+pleuseraccounts;summarystats;de-duplica+on;plotsover+me;moodscoring(weekxuser);calculatereturnsfromstockdata;regressionsofsen+mentandreturns;wordclouds;emailnetworks.

ENE_daily.csv(stockdata)

MoodScoredDF.RData(weeklysen+mentscoreandreturns)

Mails3.RData(allemailsdataandreturns)

S3_2.Rmd(networkanalysis)

Networkoutput(adjacencymatrices,stats,degreedistribu+on)

WeeklyReturn.csv(returns)


S4.Rmd(listof1000mostusedwordsover+me)

Mails3.RData(allemailsdataandreturns)

TDM.RData(termdocumentmatrix:76518wordsxweeks;1353negwords,288poswords,206uncwords,105weeks.)

WeeklyReturn.csv(returns)

MoodScoredDF.RData(weeklysen+mentscoreandreturns)

ui.R,server.R(Shinyapptoshowwordplayover+me)

S5.Rmd(topicanalysis)

FacEva_Extract.Rmd(Extractnewsar+clesfromFac+va) FacEva

FacEva_Analysis.Rmd(Processdatatodataframeanddoanalysis)

MoodScoredNews.RData(weeklysen+mentscore;POStagging)

TDMNews.RData(Weeklynews)


Analyses


Table1.SummarySta+s+csofSentMail

Theaverageemailis362charactersinlength,withamedianof163characters…



…withanaverageof1.77recipientspersentmail.



Manyemails(closeto11%)aresimplyforwardedwithoutaddedtext.


Figure1.AverageEmailLengthoverTime



Year2000:

Year2001:



Year2000:

Year2001:

Ini+ally,averageemaillengthisfairlystable,straddlingroughly400charactersperemail.



Year2000:

Year2001:

MarkeddeclineinaveragelengthasweapproachEnron’sdemise(approx.50%).


Figure2.EmailSen+mentandDisagreementoverTime

NetSenEment:

Disagreement:

𝑃𝑜𝑠−𝑁𝑒𝑔/𝑃𝑜𝑠+𝑁𝑒𝑔 

1− |𝑃𝑜𝑠−𝑁𝑒𝑔|/𝑃𝑜𝑠+𝑁𝑒𝑔 


Figure2.EmailSen+mentandDisagreementoverTime

NetSenEment:

Disagreement:

𝑃𝑜𝑠−𝑁𝑒𝑔/𝑃𝑜𝑠+𝑁𝑒𝑔 

1− |𝑃𝑜𝑠−𝑁𝑒𝑔|/𝑃𝑜𝑠+𝑁𝑒𝑔 

Basedoncontext-dependentsen+mentdic+onariesforwordclassifica+on


Figure3.Fac+vaNewsCoverageoverTime


Figure3.Fac+vaNewsCoverageoverTime

Spikeinnumberofar+clesasweapproachEnron’sdemise.


Figure4.Fac+vaNewsSen+mentoverTime


Figure4.Fac+vaNewsSen+mentoverTime

Netsen+mentfrombody

Netsen+mentfromheader


Figure5.StockReturnsandNetSen+mentoverTime

Netsen+mentacrossEnronemployeeemailsover+me

MovingaveragestockreturnsforEnronover+me


Figure6.StockPricesandNetSen+mentoverTime

Netsen+mentacrossEnronemployeeemailsover+me

MovingaveragestockpriceforEnronover+me


Figure7.StockReturnsandEmailLengthoverTime

MovingaveragestockreturnsforEnronover+me

MovingaverageemaillengthacrossEnronemployeeemailsover+me


Figure8.StockPricesandEmailLengthoverTime




Figure8.StockPricesandEmailLengthoverTime



Perhaps,asrisk/malfeasanceescalates,emailsbecomeshorter,asemployeesarelesslikelytoincludedetailsinmessagesentviathecorporateserver


Table2.EmailContentandStockReturns

DependentVariable=StockReturnst




Onestdev(i.e.,0.019)decreaseinNetSen+mentisassociatedwitha4.5%declineinstockreturns…



DependentVariable=StockReturnst …butnolongersignificantwhenwecontrolforemaillength.


Table2.EmailContentandStockReturnsoverTime


Overall,20-characterdeclineinmovingaverageemaillengthisassociatedwitha1.17%declineinstockreturns.


Table3.EmailContentversusFac+vaNewsContent





Emailcontentcontainsmoreinforma+onthannews-headercontent….




….Butneitherissignificantwhenaccoun+ngforemaillength.




Ontheotherhand,emailcontentcontainslessinforma+onthancontentfromthenewsbody…(couldthisbeduetoredac+onsontheEnronemailcorpus?)




….But,again,neitherissignificantwhenaccoun+ngforemaillength.


SummaryandImplica+ons

§  Thus far,wehave shown that thenet sen+ment conveyedbyemployeesentmailsisasignificantpredictorofstock-returnperformance

§  Interes+ngly, email lengthwas a stronger predictor of subsequent pricedeclinesthanthenetsen+mentconveyedbythemessagebodyitself.

§  Overall,emailcontentmaybecontrolledormanipulated

–  Thus, we are also (and perhaps evenmore!) interested in the non-verbal,interac+on-ornetwork-basedindicatorsofpoten+altrouble.


Addi+onalExplora+ons

Otherdimensionsripeforinves+ga+on….


Figure11.EmailNetworks

Year2000,Q4:


Figure11.EmailNetworks

Year2001,Q4:


Figure13.VocabularyTrends


Figure14.TopicAnalysisoverTime


Figure14.TopicAnalysisoverTime


TopicsassignedvialatentDirichletalloca+on(LDA)

ConcludingRemarks

§  We introduce an automatedplaRorm to parse corporate email content,andwefindthatthenetsen+mentconveyedbyemployeesentmailsisa+melyindicatorofstock-returnperformance.

§  Non-verbal indicators, such as email length and network structure, arepar+cularlypromisingavenuestoexplore.

§  Overall, we suggest the promise of a regulatory technology (RegTech)approach by which to systema+cally parse email content and networkstructure to detect indicators of risk ormalfeasance on an ongoing andmore+melybasis.

Thankyou.


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