Natural Computing in Computational Finance PDF
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AnthonyBrabazonandMichaelO’Neill(Eds.) NaturalComputinginComputationalFinance StudiesinComputationalIntelligence,Volume100 Editor-in-chief Prof.JanuszKacprzyk SystemsResearchInstitute PolishAcademyofSciences ul.Newelska6 01-447Warsaw Poland E-mail:[email protected] Furthervolumesofthisseriescanbefoundonour Vol.90.SimoneMarinaiandHiromichiFujisawa(Eds.) homepage:springer.com MachineLearninginDocumentAnalysis andRecognition,2008 Vol.78.CostinBadicaandMarcinPaprzycki(Eds.) ISBN978-3-540-76279-9 IntelligentandDistributedComputing,2008 Vol.91.HorstBunke,KandelAbrahamandLastMark(Eds.) ISBN978-3-540-74929-5 AppliedPatternRecognition,2008 Vol.79.XingCaiandT.-C.JimYeh(Eds.) ISBN978-3-540-76830-2 QuantitativeInformationFusionforHydrological Vol.92.AngYang,YinShanandLamThuBui(Eds.) 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Natural Computing in Computational Finance With79Figuresand61Tables 123 Dr.AnthonyBrabazon Dr.MichaelO’Neill HeadofResearch-SchoolofBusiness Director-NaturalComputingResearch QuinnSchool andApplications UniversityCollegeDublin SchoolofComputerScienceandInformatics Belfield,Dublin4 UniversityCollegeDublin Ireland Belfield,Dublin4 [email protected] Ireland [email protected] ISBN978-3-540-77476-1 e-ISBN978-3-540-77477-8 StudiesinComputationalIntelligenceISSN1860-949X LibraryofCongressControlNumber:2008922057 (cid:1)c 2008Springer-VerlagBerlinHeidelberg Thisworkissubjecttocopyright.Allrightsarereserved,whetherthewholeorpartofthematerial isconcerned,specificallytherightsoftranslation,reprinting,reuseofillustrations,recitation,broad- casting,reproductiononmicrofilmorinanyotherway,andstorageindatabanks.Duplicationof thispublicationorpartsthereofispermittedonlyundertheprovisionsoftheGermanCopyrightLaw ofSeptember9,1965,initscurrentversion,andpermissionforusemustalwaysbeobtainedfrom Springer-Verlag.ViolationsareliabletoprosecutionundertheGermanCopyrightLaw. Theuseofgeneraldescriptivenames,registerednames,trademarks,etc.inthispublicationdoesnot imply, even in the absence of a specific statement, that such names are exempt from the relevant protectivelawsandregulationsandthereforefreeforgeneraluse. Coverdesign:Deblik,Berlin,Germany Printedonacid-freepaper 9 8 7 6 5 4 3 2 1 springer.com ToMaria Tony ToGra´inneand Aoife Michael Preface The inspiration for this book stemmed from the success of EvoFin 2007, the first EuropeanWorkshoponEvolutionaryComputationinFinanceandEconomics,which washeldaspartoftheEvoWorkshopsatEvo*inValencia,SpaininApril2007.The rangeandqualityofpaperssubmittedfortheworkshopunderscoredthesignificant levelofresearchactivitywhichistakingplaceattheinterfaceofnaturalcomputing and finance. After the workshop, a call for papers was issued for this volume and following a rigorous, peer-reviewed, selection process a total of fourteen chapters werefinallyselected.Thechapterswereselectedonthebasisoftechnicalexcellence andasexamplesoftheapplicationofarangeofnaturalcomputingandagent-based methodologies to a broad array of financial domains. The book is intended to be accessible to a wide audience and should be of interest to academics, students and practitionersinthefieldsofbothnaturalcomputingandfinance. We would like to thank all the authors for their high-quality contributions and we would also like to thank the reviewers who generously gave of their time to peer-review all submissions. We also extend our thanks to Dr. Thomas Ditzinger ofSpringer-VerlagandtoProfessorJanuszKacprzyk,editorofthisbookseries,for theirencouragementofandtheirsupportduringthepreparationofthisbook. Dublin AnthonyBrabazon December2007 MichaelO’Neill Contents 1 NaturalComputinginComputationalFinance:AnIntroduction AnthonyBrabazonandMichaelO’Neill................................ 1 PartI Optimisation 2 ConstrainedIndexTrackingunderLossAversionUsingDifferential Evolution DietmarMaringer................................................. 7 3 AnEvolutionaryApproachtoAssetAllocationinDefinedContribution PensionSchemes KeremSenel,A.BulentPamukcu,SerhatYanik .......................... 25 4 EvolutionaryStrategiesforBuildingRisk-OptimalPortfolios PiotrLipinski .................................................... 53 5 EvolutionaryStochasticPortfolioOptimization RonaldHochreiter................................................. 67 6 Non-linearPrincipalComponentAnalysisoftheImpliedVolatility SmileusingaQuantum-inspiredEvolutionaryAlgorithm KaiFan,ConallO’Sullivan,AnthonyBrabazon,MichaelO’Neill ........... 89 7 Estimation of an EGARCH Volatility Option Pricing Model usingaBacteriaForagingOptimisationAlgorithm JingDang,AnthonyBrabazon,MichaelO’Neill,DavidEdelman............109 PartII ModelInduction 8 Fuzzy-EvolutionaryModelingforSingle-PositionDayTrading Ce´liadaCostaPereira,AndreaG.B.Tettamanzi ........................131 X Contents 9 StrongTyping, VariableReductionandBloatControlforSolving theBankruptcyPredictionProblemUsingGeneticProgramming EvaAlfaro-Cid,AlbertoCuesta-Can˜ada,KenSharman, AnnaI.Esparcia-Alca´zar ...........................................161 10 UsingKalman-filteredRadialBasisFunctionNetworksforIndex ArbitrageintheFinancialMarkets DavidEdelman ...................................................187 11 On Predictability and Profitability: Would GP Induced Trading RulesbeSensitivetotheObservedEntropyofTimeSeries? NicolasNavet,Shu-HengChen ......................................197 12 HybridNeuralSystemsinExchangeRatePrediction AndrzejBielecki,PawelHajto,RobertSchaefer..........................211 PartIII Agent-basedModelling 13 EvolutionaryLearningoftheOptimalPricingStrategyinanArtificial PaymentCardMarket BilianaAlexandrova-Kabadjova,EdwardTsang,AndreasKrause ...........233 14 Can Trend Followers Survive in the Long-Run? Insights fromAgent-BasedModeling Xue-ZhongHe,PhilipHamill,YouweiLi ...............................253 15 Co-EvolutionaryMulti-AgentSystemforPortfolioOptimization RafałDrez˙ewski,LeszekSiwik .......................................271 Index ............................................................. 301 1 Natural Computing in Computational Finance: An Introduction AnthonyBrabazonandMichaelO’Neill NaturalComputingResearchandApplicationsGroup,UCDCASL,UniversityCollege Dublin,Dublin,[email protected], [email protected] 1.1 Introduction Natural computing can be broadly defined as the development of computer pro- grams and computational algorithms using metaphorical inspiration from systems andphenomenathatoccurinthenaturalworld.Theinspirationfornaturalcomput- ing methodologies typically stem from real-world phenomena which exist in high- dimensional, noisy and uncertain, dynamic environments. These are characteristics which fit well with the nature of financial markets. Prima facie, this makes natural computingmethodsinterestingforfinancialmodellingapplications.Anotherfeature ofnaturalenvironmentsisthephenomenonofemergence,ortheactivitiesofmulti- pleindividualagentscombiningtocreatetheirownenvironment. Thisbookcontainsfourteenchapterswhichillustratethecutting-edgeofnatural computingandagent-basedmodellinginmoderncomputationalfinance.Arangeof methodsareemployedincluding,DifferentialEvolution,GeneticAlgorithms,Evo- lution Strategies, Quantum-Inspired Evolutionary Algorithms, Bacterial Foraging Algorithms, Genetic Programming, Agent-based Modelling and hybrid approaches including Fuzzy-Evolutionary Algorithms, Radial-Basis Function Networks with Kalman Filters, and a Multi-Layer Perceptron-Wavelet hybrid. A complementary rangeofapplicationsareaddressedincludingFundAllocation,AssetPricing,Market Prediction, Market Trading, Bankruptcy Prediction, and the agent based modelling ofpaymentcardandfinancialmarkets. Thebookisdividedintothreesectionseachcorrespondingtoadistinctgrouping of chapters. The first section deals with optimisation applications of natural com- putinginfinance,thesecondsectionexplorestheuseofnaturalcomputingmethod- ologies for model induction and the final section illustrates a range of agent-based applicationsinfinance. A.BrabazonandM.O’Neill:NaturalComputinginComputationalFinance:AnIntroduction,StudiesinComputational Intelligence(SCI)100,1–4(2008) www.springerlink.com (cid:1)c Springer-VerlagBerlinHeidelberg2008 2 A.BrabazonandM.O’Neill 1.2 Optimisation A wide variety of natural computing methodologies including genetic algorithms, evolutionary strategies, differential evolution and particle swarm optimisation have been applied for optimisation purposes in finance. A particular advantage of these methodologies is that, applied properly, they can cope with difficult, multi-modal, error surfaces. In the first six chapters, a series of these algorithms are introduced andappliedtoavarietyoffinancialoptimisationproblems. Passive portfolio management strategies have become very common in recent decades. In spite of the apparent simplicity of constructing an asset portfolio in order to track an index of interest, it is difficult to do this in practice due to the dynamic nature of the market and due to transactions constraints. As discussed in chpt.2(ConstrainedIndexTrackingunderLossAversionUsingDifferentialEvolu- tionbyDietmarMaringer),thesolutionspaceisnon-convexsuggestingausefulrole forpopulation-based,globaloptimisation,heuristicslikedifferentialevolution.This chapterappliesdifferentialevolutionforassetselectioninapassiveportfolio. The issue of optimal asset allocation for defined contribution pension funds is addressedinchpt.3(AnEvolutionaryApproachtoAssetAllocationinDefinedCon- tributionPensionSchemesbyKeremSenel,BulentPamukcuandSerhatYanik).To date,therehavebeenfewexamplesofapplicationsofnaturalcomputinginthepen- sionsdomain.Chpt.3showstheapplicationofthegeneticalgorithmforassetallo- cationinapensionfund. Theclassicalportfoliooptimisationproblemistackledusingevolutionarystrate- giesinchpt.4(EvolutionaryStrategiesforBuildingRisk-OptimalPortfoliosbyPiotr Lipinski). A particular advantage when using evolutionary algorithms for this task is that a modeller can easily employ differing risk measures and real-world invest- mentconstraintswhendeterminingoptimalportfolios.Thischapterprovidesaclear illustrationofhowthiscanbedone. Avariantonclassicalportfoliooptimisationisprovidedinchpt.5(Evolutionary Stochastic Portfolio Optimization by Ronald Hochreiter). This chapter focusses on stochastic portfolio optimisation and combines theory from the fields of stochastic programming,evolutionarycomputation,portfoliooptimisation,aswellasfinancial riskmanagementinordertoproduceageneralisedframeworkforcomputingoptimal portfoliosunderuncertaintyforvariousprobabilisticriskmeasures. Aconsiderableamountofresearchhasbeenundertakeninrecentyearsinorderto improvethescalabilityofevolutionaryalgorithms.Thishasledtothedevelopment of several new algorithms and methodological approaches including the compact genetic algorithm and estimation of distribution algorithms (see chpt. 13 for an in- troductiontoEDAs).Oneinterestingavenueofthisworkhasseenthemetaphorical combination of concepts from evolution and quantum mechanics to form the sub- fieldofquantum-inspiredevolutionaryalgorithms(QIEA).Chpt.6(Non-linearPrin- cipalComponentAnalysisoftheImpliedVolatilitySmileusingaQuantum-inspired EvolutionaryAlgorithm byKaiFanetal)providesanintroductiontothisareaand illustrates the application of a QIEA for the purposes of undertaking a non-linear principalcomponentanalysisoftheimpliedvolatilitysmileofstockoptions.
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