Applications of Artificial Intelligence in Finance and Economics, Volume 19 (Advances in Econometrics)

291 Pages·2005·2.9 MB·English

by  BINNER

APPLICATIONS OF ARTIFICIAL
INTELLIGENCE IN FINANCE AND
ECONOMICS
ADVANCES IN ECONOMETRICS
Series Editors: T. Fomby and R. Carter Hill
ADVANCESINECONOMETRICS VOLUME19
APPLICATIONS OF
ARTIFICIAL
INTELLIGENCE IN
FINANCE AND
ECONOMICS
EDITEDBY
JANE M. BINNER
DepartmentofStrategicManagement,AstonBusinessSchool,UK
GRAHAM KENDALL
SchoolofComputerScienceandInformationTechnology,
UniversityofNottingham,UK
SHU-HENG CHEN
DepartmentofEconomics,NationalChengchiUniversity,Taiwan
2004
Amsterdam–Boston–Heidelberg–London–NewYork–Oxford
Paris–SanDiego–SanFrancisco–Singapore–Sydney–Tokyo
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CONTENTS
LISTOFCONTRIBUTORS vii
STATISTICALANALYSISOFGENETICALGORITHMSIN
DISCOVERINGTECHNICALTRADINGSTRATEGIES
Chueh-YungTsaoandShu-HengChen 1
AGENETICPROGRAMMINGAPPROACHTOMODEL
INTERNATIONALSHORT-TERMCAPITALFLOW
TinaYu,Shu-HengChenandTzu-WenKuo 45
TOOLSFORNON-LINEARTIMESERIESFORECASTINGIN
ECONOMICS–ANEMPIRICALCOMPARISONOFREGIME
SWITCHINGVECTORAUTOREGRESSIVEMODELSAND
RECURRENTNEURALNETWORKS
JaneM.Binner,ThomasElger,BirgerNilsson
andJonathanA.Tepper 71
USINGNON-PARAMETRICSEARCHALGORITHMSTO
FORECASTDAILYEXCESSSTOCKRETURNS
NathanLaelJoseph,DavidS.BréeandEfstathiosKalyvas 93
CO-EVOLVINGNEURALNETWORKSWITH
EVOLUTIONARYSTRATEGIES:ANEWAPPLICATIONTO
DIVISIAMONEY
JaneM.Binner,GrahamKendallandAliciaGazely 127
FORECASTINGTHEEMUINFLATIONRATE:LINEAR
ECONOMETRICVS.NON-LINEARCOMPUTATIONAL
MODELSUSINGGENETICNEURALFUZZYSYSTEMS
StefanKooths,TimoMitzeandEricRinghut 145
v
vi
FINDINGORNOTFINDINGRULESINTIMESERIES
JessicaLinandEamonnKeogh 175
ACOMPARISONOFVARANDNEURALNETWORKSWITH
GENETICALGORITHMINFORECASTINGPRICEOFOIL
SamMirmiraniandHsiChengLi 203
SEARCHINGFORDIVISIA/INFLATIONRELATIONSHIPS
WITHTHEAGGREGATEFEEDFORWARDNEURAL
NETWORK
VincentA.SchmidtandJaneM.Binner 225
PREDICTINGHOUSINGVALUE:GENETICALGORITHM
ATTRIBUTESELECTIONANDDEPENDENCEMODELLING
UTILISINGTHEGAMMATEST
IanD.Wilson,AntoniaJ.Jones,DavidH.Jenkins
andJ.A.Ware 243
LIST OF CONTRIBUTORS
JaneM.Binner DepartmentofStrategicManagement,Aston
BusinessSchool,AstonTriangle,Aston
University,Birmingham,UK
DavidS.Bre´e ComputerScienceDepartment,Universityof
Manchester,UK
Shu-HengChen AI-ECONResearchCenter,Departmentof
Economics,NationalChengchiUniversity,
Taiwan
ThomasElger DepartmentofEconomics,LundUniversity,
Sweden
AliciaGazely DepartmentofInformationSystemsand
Management,NottinghamTrentUniversity,UK
David.H.Jenkins SchoolofTechnology,Universityof
Glamorgan,UK
AntoniaJ.Jones DepartmentofComputerScience,Cardiff
University,UK
NathanLaelJoseph AstonBusinessSchool,AstonUniversity,
AstonTriangle,Birmingham,UK
EfstathiosKalyvas ComputerScienceDepartment,Universityof
Manchester,UK
GrahamKendall SchoolofComputerScienceandInformation
Technology,UniversityofNottingham,UK
EamonnKeogh ComputerScience&EngineeringDepartment,
UniversityofCalifornia–Riverside,USA
StefanKooths MuensterInstituteforComputational
Economics,Germany
vii
viii
Tzu-WenKuo AI-ECONResearchCenter,Departmentof
Economics,NationalChengchiUniversity,
Taiwan
HsiChengLi BryantUniversity,Smithfield,USA
JessicaLin ComputerScience&EngineeringDepartment,
UniversityofCalifornia–Riverside,USA
SamMirmirani BryantUniversity,Smithfield,USA
TimoMitze GesellschaftfuerFinanz-undRegionalanalysen
(GEFRA),Germany
BirgerNilsson DepartmentofEconomics,LundUniversity,
Sweden
EricRinghut MuensterInstituteforComputational
Economics,Germany
VincentA.Schmidt WrightStateUniversity,Dayton,OH,USA
JonathanA.Tepper SchoolofComputingandTechnology,Faculty
ofConstruction,TheNottinghamTrent
University,Nottingham,UK
Chueh-YungTsao DepartmentofBusinessAdministration,
Chang-GungUniversity,Taiwan
AndrewWare SchoolofComputing,Universityof
Glamorgan,UK
IanD.Wilson SchoolofComputing,Universityof
Glamorgan,UK
TinaYu ChevronTexacoInformationTechnology
Company,SanRamon,CA,USA
INTRODUCTION
Artificialintelligenceisaconsortiumofdata-drivenmethodologieswhichincludes
artificialneuralnetworks,geneticalgorithms,fuzzylogic,probabilisticbeliefnet-
worksandmachinelearningasitscomponents.Wehavewitnessedaphenomenal
impactofthisdata-drivenconsortiumofmethodologiesinmanyareasofstudies,
theeconomicandfinancialfieldsbeingofnoexception.Inparticular,thisvolume
of collected works will give examples of its impact on the field of economics
and finance. This volume is the result of the selection of high-quality papers
presented at a special session entitled “Applications of Artificial Intelligence
in Economics and Finance” at the “2003 International Conference on Artificial
Intelligence”(IC-AI’03)heldattheMonteCarloResort,LasVegas,NV,USA,
June23–262003.Thespecialsession,organisedbyJaneBinner,GrahamKendall
andShu-HengChen,waspresentedinordertodrawattentiontothetremendous
diversityandrichnessoftheapplicationsofartificialintelligencetoproblemsin
Economics and Finance. This volume should appeal to economists interested in
adoptinganinterdisciplinaryapproachtothestudyofeconomicproblems,com-
puterscientistswhoarelookingforpotentialapplicationsofartificialintelligence
andpractitionerswhoarelookingfornewperspectivesonhowtobuildmodelsfor
everydayoperations.
The structure of this volume is as follows; in the first chapter by Shu-Heng
Chen and Chueh-Yung Tsao a statistical approach to testing the performance of
GA-based trading strategies is proposed. The paper asks the question, what are
the statistical properties which distinguish a successful application of GA from
anunsuccessfulone?TheperformanceofordinalGA-basedtradingstrategiesis
evaluatedundersixclassesoftimeseriesmodel,namely,thelinearARMAmodel,
thebilinearmodel,theARCHmodel,theGARCHmodel,thethresholdmodeland
thechaoticmodel.Theperformancecriteriaemployedarethewinningprobability,
accumulatedreturns,Sharperatioandluckcoefficient.Asymptoticteststatistics
for these criteria are derived. The hypothesis as to the superiority of GA over
abenchmark,say,buy-and-hold,isthenbetestedusingMonteCarlosimulation.
Fromthisrigorously-establishedevaluationprocess,simplegeneticalgorithmsare
foundtoworkverywellinlinearstochasticenvironments,andtheyarealsofound
toworkverywellinnonlineardeterministic(chaotic)environments.However,they
may perform much worse in pure nonlinear stochastic cases. These results shed
ix