Automated knowledge acquisition using inductive learning : application to Mutual Fund classification

228 Pages·1997·4.7 MB·English

by  Norris, Robert Clayton

AUTOMATEDKNOWLEDGEACQUISITION
USINGINDUCTIVELEARNING:
APPLICATIONTOMUTUALFUNDCLASSIFICATION
By
ROBERTCLAYTONNORRIS,JR.
ADISSERTATIONPRESENTEDTOTHEGRADUATESCHOOL
OFTHEUNIVERSITYOFFLORIDAINPARTIALFULFILLMENT
OFTHEREQUIREMENTSFORTHEDEGREEOF
DOCTOROFPHILOSOPHY
UNIVERSITYOFFLORIDA
1997
Copyright1997
by
RobertClaytonNorris,Jr.
Tomywife,Suzanne
andmydaughter,Christina
ACKNOWLEDGEMENTS
IwishtothankDr.GaryJ.Koehler,thechairmanofmycommittee,andDr.
RobertC.Radcliffe,theexternalmember,whogavemetheirtime,support,guidance,
andpatiencethroughoutthisresearch. DrKoehlerprovidedtheinitialconceptofthe
researchtopicintheareaofartificialintelligenceandfinance.Dr.Radcliffeprovidedthe
ideaofstudyingtheMorningstarratingsystem.IwishtothankDr.RichardElnickiand
Dr.PatrickThompsonforservingonmycommitteeandfortheirhelpandadviceover
theseyears.
IwishtothankDr.H.RussellFoglerwhohasalwaysshownaninterestinmy
research.IalsowouldliketothankDeanEarleC.TraynhamandDr.RobertC.Pickhardt
oftheCollegeofBusinessAdministration,UniversityofNorthFlorida,fortheirsupport.
Iwishtothankmywife,Suzanne,forherlove,assistance,andunderstandingasI
workedonthismostimportantundertaking. Iwouldalsoliketothankmyauntand
uncle,LillianandJackNorris,forencouragingmetocontinuemyeducation. Finally,I
wouldliketothankmylatefatherforhisadviceandguidanceovertheyearsandmylate
motherforherlove.
5
TABLEOFCONTENTS
ACKNOWLEDGEMENTS iv
ABSTRACT viii
CHAPTERS
1 INTRODUCTION 1
1.1 Background 1
1.2 ResearchProblem 3
1.3 Purpose 5
1.4 Motivation 6
1.5 ChapterOrganization 6
2 LITERATUREREVIEW 8
2.1 HistoricalOverviewofMachineLearning 8
2.1.1 BriefHistoryofAIResearchonLearning 8
2.1.2 FourPerspectivesonLearning 10
2.2 AIandFinancialApplications 15
2.2.1 ExpertSystems 15
2.2.2 NeuralNetworksandFinancialApplications 17
2.2.3 GeneticAlgorithmsandFinancialApplications 27
2.3 TheC4.5LearningSystem 32
2.3.1 BriefHistoryofC45 33
2.3.2 C4.5Algorithms 37
2.3.3 LimitationsofC4. 44
2.3.4 C4.5FinancialApplications 45
2.4 LinearDiscriminantAnalysis 47
2.4.1 Overview 47
2.4.2 LimitationsofLDA 48
2.5 LogisticRegression(Logit) 49
2.6 Summary 50
DOMAINPROBLEM 51
3.1 OverviewofMutualFundRatingsSystems 51
3.11 Morningstar,Inc.Overview 54
3.1.2 MorningstarRatingSystem 55
33.11..34 RInevveisetwmeanntdMCarniatigceirssmUosfethoefMRoartniinnggsstarRatingSystem.5598
3.1.5 PerformancePersistenceinMutualFunds 61
3.1.6 ReviewofYearlyVariationofMorningstarRatings 64
3.2 ProblemSpecification 68
CLASSIFICATIONOFMUTUALFUNDSBYRATINGS 71
4.1 ResearchGoals 71
4.2 ResearchCaveats 71
4.3 ExampleDatabases 72
4.4 BriefOverviewoftheResearchPhases 72
4.5 Phase1-Classifying1993Funds 73
4.5.1 Methodology 73
4.5.2 Results 77
4.5.3 Conclusions 81
4.6 Phase2-1993DatawithDerivedFeatures 81
4.6.1 Methodology 81
4.6.2 ResultsfortheRegularDataset 84
4.6.3 ResultsfortheDerivedFeaturesDataset 87
4.6.4 Conclusions 89
4.7 Phase3-Comparing5-Starand3-StarClassifications 91
4.7.1 Methodology 91
4.7.2 Results 93
4.7.3 Conclusions 100
4.8 Phase4-CrossvalidationwithC4.5 101
4.8.1 Methodology 101
4.8.2 Results 103
4.8.3 Conclusions 104
4.9 OverallSummary 105
PREDICTIONOFMUTUALFUNDRATINGSAND
RATINGSCHANGES 107
5.1 Phase5V-ePcrteodricOtvienrgTRawtoinYgesawristhaCommonFeature 109
5.1.1 Methodology 109
5.1.2 Results 110
5.1.3 Conclusions 112
5.2 Phase6-PredictingMatchedMutualFundRatingChanges 113
5.2.1 Methodology 113
5.2.2 Resultsfor1994DataPredicting1995Ratings 116
5.2.3 Resultsfor1995DataPredicting1996Ratings 125
5.2.4 Conclusions 133
5.3 Phase7-PredictingUnmatchedMutualFundRatings 134
5.3.1 Methodology 134
5.3.2 Resultsfor1994DataPredicting1995Ratings 135
5.3.3 Resultsfor1995DataPredicting1996Ratings 141
5.3.4 Conclusions 148
5.4 OverallSummary 148
6 SUMMARYANDFUTURERESEARCH 150
APPENDICES
A DESCRIPTIONOFMUTUALFUNDFEATURES 155
B PHASE1CLASSIFICATIONFEATURES 161
C PHASE2CLASSIFICATIONFEATURES 165
D PHASE3CLASSIFICATIONFEATURES 174
E BESTCLASSIFICATIONTREESFROMPHASES1-4 185
REFERENCES 208
BIOGRAPHICALSKETCH 217
AbstractofDissertationPresentedtotheGraduateSchool
oftheUniversityofFloridainPartialFulfillmentofthe
RequirementsfortheDegreeofDoctorofPhilosophy
AUTOMATEDKNOWLEDGEACQUISITION
USINGINDUCTIVELEARNING:
APPLICATIONTOMUTUALFUNDCLASSIFICATION
By
RobertClaytonNorris,Jr.
December1997
Chairman:Dr.GaryJ.Koehler
MajorDepartment:DecisionandInformationSciences
Thisresearchusesaninductivelearningmethodologythatbuildsdecisiontrees,
Quinlan'sC4.5,toclassifymutualfundsaccordingtotheMorningstarMutualFundfive
classorstarratingsystemandpredictthemutualfundratingsoneyearinthefuture.In
thefirstpartoftheresearch,IcomparetheperformanceofC4.5,LogisticRegressionand
LinearDiscriminantAnalysisinclassifyingmutualfundsaccordingtotheMorningstar
ratingsystem. Asthesizeofthetrainingsetincreases,sodoesC4.5'sperformance
versusthetwostatisticalmethods. Overall,C4.5performedaswellasLogistic
RegressioninclassifyingthemutualfundsandoutperformedLinearDiscriminant
Analysis. ThispartoftheresearchalsoexploredtheabilityofC4.5toclassifyequity
mutualfundsthatwereunratedbyMorningstar. Theresultssuggestedthat,withthe
properfeaturesandamodificationtotheMorningstarfiveclassratingsystemtothree
classes,unratedmutualfundscouldbeclassifiedwitha30%error.
Anecdotalevidencesuggestedthatinvestorspurchasemutualfundsbyratingsand
haveanexpectationthattheratingwillstaythesameorimprove Thesecondpartofthe
researchusedatrainingsetofoneyeartoconstructadecisiontreewithC4.5topredict
theratingsofmutualfundsoneyearinthefuture.Thetestingsetconsistedofexamples
fromthepredictionyearinquestionandthepredictionswerecomparedtotheactual
ratingsforthatyear. Theresultswerethat,withthenecessaryfeaturevector,five-star
fundratingscouldbepredictedwith65%accuracy. Withamodificationtotherating
systemchangingittothreestars,predictedmutualfundratingswere75%accurate.
Thisresearchalsoidentifiesfeaturesthatareusefulfortheclassifyingmutual
fundsbytheMorningstarratingsystemandforthepredictionoffundratings
INCTHRAOPDTUECRTI1ON
1.1Background
GlancingthroughacopyofTechnicalAnalysisofStocks&Commodities
magazine,youfindagreatdealofinformationaboutartificialintelligence(AI)andthe
selectionofstocksandcommoditiesforportfolios. IntheFebruary1997issueofthe
magazine,theTraders'Glossaryevendefinesthetermneuralnetwork. However,itis
difficulttofindscientificresearchaboutAIsystemsusedonWallStreetsinceusually
theyareproprietaryandcouldprovideacompetitiveadvantagetotheinvestmentfirm.
FiveyearsagoAIuseinfinancialapplicationswasjustbeginningtobenoticed.
Forexample,thisstoryintheWallStreetJournalofOctober27,1992abouttheuseof
artificialintelligencetoselectstocksforamutualfundportfolio:
"Bradford Lewis, manager of Fidelity Investment Inc.'s Fidelity
DisciplinedEquityFund,hasfoundawaytoout-performstandardindices
using neural networksoftware. Aneural networkis anartificial
intelligenceprogramthatcopiestheworkingsofthehumanbrain. The
mPpoeuortrcu'easnlt5f0fuo0nrdst,thorwcehkeiiycnehdaerixsnv(reSusnt&nsiPning5ta0h0ne)d,sihasammseawibnoutnsaiionnveiesnsrgetsihtesaspientrdhefexorSbmtyaann2cd.ea3rtidnoaF5n.Yd6
1993...LewischeckswithanalystsatFidelitytodouble-checkhisresults,
butsometimeswhenhebuysstockcontrarytothecomputer'sadvice,he
losesmoney."(McGough,1992,p.CI)
Academic research concerning mutual funds and artificial intelligence is
relativelynewsinceonlythreestudieswerecitedintheliteraturefrom1986tothe
present. Chiangetal.(1996)describedaneuralnetworkthatusedhistoricaleconomic