Stock Market Modeling and Forecasting: A System Adaptation Approach

166 Pages·2013·4.69 MB·English

by  Xiaolian Zheng

LLeecctture Notes in Control

and Information Sciences
442
Xiaolian Zheng
Ben M. Chen
Stock Market Modeling
and Forecasting
A System Adaptation Approach
123
Lecture Notes
in Control and Information Sciences 442
Editors
ProfessorDr.-Ing.ManfredThoma
InstitutfuerRegelungstechnik,UniversitätHannover,Appelstr.11,30167Hannover,
Germany
E-mail:[email protected]
ProfessorDr.FrankAllgöwer
InstituteforSystemsTheoryandAutomaticControl,UniversityofStuttgart,
Pfaffenwaldring9,70550Stuttgart,Germany
E-mail:[email protected]
ProfessorDr.ManfredMorari
ETH/ETLI29,Physikstr.3,8092Zürich,Switzerland
E-mail:[email protected]
SeriesAdvisoryBoard
P.Fleming
UniversityofSheffield,UK
P.Kokotovic
UniversityofCalifornia,SantaBarbara,CA,USA
A.B.Kurzhanski
MoscowStateUniversity,Russia
H.Kwakernaak
UniversityofTwente,Enschede,TheNetherlands
A.Rantzer
LundInstituteofTechnology,Sweden
J.N.Tsitsiklis
MIT,Cambridge,MA,USA
Forfurthervolumes:
http://www.springer.com/series/642
Xiaolian Zheng and Ben M. Chen
Stock Market Modeling
and Forecasting
A System Adaptation Approach
ABC
XiaolianZheng BenM.Chen
DepartmentofElectricaland DepartmentofElectricaland
ComputerEngineering ComputerEngineering
NationalUniversityofSingapore NationalUniversityofSingapore
Singapore Singapore
ISSN0170-8643 ISSN1610-7411 (electronic)
ISBN978-1-4471-5154-8 ISBN978-1-4471-5155-5 (eBook)
DOI10.1007/978-1-4471-5155-5
SpringerLondonHeidelbergNewYorkDordrecht
LibraryofCongressControlNumber:2013936055
(cid:2)c Springer-VerlagLondon2013
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To MyParents
X. Zheng
To MyGranduncle,Paul,andMyDad, Joseph
B. M.Chen
Preface
Inrecentdecades,modelingoffinancialmarketshasarousedgreatinterestsinthe
academiaandinthefinancialindustryworldwide.Financialmarketisahighlycom-
plex system involvinga large numberof interacting factors ranging from psycho-
logical, social and politicalaspects to generaleconomicperformance.There is no
conclusiveknowledgeoftheinfluenceofthesefactorsandthecorrespondingmarket
responses,eventhepublicopinionsondecisivefactorstoaparticularfinancialevent
are usuallydiversified.The financialmarketis also knownforits noisydata, non-
linearityanddynamicbehavior,allofwhichmakeitsmodelingextremelydifficult.
These challengescall for a comprehensivestudy of the market behavior. Systems
theory provides a totally different angle to investigate financial market modeling
problems by analyzing the market as a complex system. It is a powerful tool in
studying the interconnections and interactions inside the market and between the
marketanditsenvironment.Aframeworkthatcombinesvariousfoundationsofboth
systemsconceptsandfinancialmarketscanthusprovidemoremeaningfulinsights
intomarketbehaviors.
Thismonographaimstodevelopageneralframeworkormodelstructurebased
onsystemstheorytodepictandanalyzethefinancialmarkets,inparticularthestock
markets,andtoprovideaccuratepredictionsofmarketpricesandmajormarketre-
visions,i.e.,turningperiods.Theframeworkiscarefullydesignedtoconsiderboth
internalcharactersofthemarketandexternalinfluentialfactors,andthusiscapable
ofcapturingdifferenttypesofmarketdynamicsandbehaviors.Thestructureofthe
frameworkand the processingof informationinside and outsidethe marketare to
beaddressedindetail.Theapplicationsofourframeworkinfindingfeaturesinthe
stockmarketsfromtheU.S.,China,HongKong,andSingapore,examplesofboth
developedandemergingmarkets,aregivenandanalyzed.Someinterestingobser-
vationsrelatedtoourframeworkandresults,andpossiblefutureresearchdirections
thatwouldfurtherextendourstudyandunderstandingofthestockmarket,arealso
tobehighlighted.
Theintendedaudienceforthismonographincludesgraduatestudents,researchers
inareasrelatedtofinancialmodelingandanalystsinfinancialsectors.Itisassumed
VIII Preface
thatthereaderhassomepreviousknowledgeinfinancialmodelingandstatisticsas
wellasinbasicsystemstheory.
Theauthorshavebenefitedagreatdealfrommanyfruitfuldiscussionswithour
colleagues and research teammates at the National University of Singapore. Par-
ticularly, we would like to thank Dr. Nan Jiang, Dr. Feng Lin, Mr. Limiao Bai,
Professor Delin Chu, Professor Qing-Guo Wang and Professor Cheng Xiang for
their valuable commentsand help duringthe course of studies and preparation of
this monograph. We are also indebted to Dr. Sen Yan of Xiamen University, Ms.
JieZhengofRiceUniversity,Dr.ShanleWuofUBSAGandDr.LichaoChengof
China Life Asset ManagementCompanyfortheir insightfulsuggestionsand their
generousassistance.
This monographwas typeset by the authors using LATEX. All simulations, case
studiesandnumericalcomputationswerecarriedoutin MATLAB.Diagramswere
generatedusingXFIGandMATLABwithSIMULINK.
KentRidge,Singapore XiaolianZheng
BenM.Chen
Contents
1 Introduction................................................... 1
1.1 Introduction ............................................... 1
1.2 StockMarketAnalysis ...................................... 4
1.3 ContributionofThisWork ................................... 7
1.4 PreviewofEachChapter .................................... 9
2 ASystemAdaptationFramework................................ 13
2.1 Introduction ............................................... 13
2.2 StructureofSystemAdaptationFramework..................... 14
2.3 InternalModelDesign ...................................... 17
2.4 AdaptiveFilterDesign ...................................... 20
2.5 CaseStudy:DowJonesIndustrialAverage ..................... 24
2.5.1 DataDescription..................................... 25
2.5.2 InternalModelEstimation............................. 25
3 MarketInputAnalysis.......................................... 29
3.1 Introduction ............................................... 29
3.2 InfluentialFactorSelection .................................. 30
3.2.1 CausalityTests ...................................... 31
3.2.2 RedundantVariableTest .............................. 35
3.3 InfluentialFactorsofDowJonesIndustrialAverage.............. 35
3.3.1 EmpiricalSelection .................................. 35
3.3.2 Time-VaryingCausalityTestResults .................... 37
3.3.3 NonlinearCausalityTestResults ....................... 42
3.3.4 RedundantVariableTestResults ....................... 42
4 AnalysisofDowJonesIndustrialAverage......................... 43
4.1 Introduction ............................................... 43
4.2 MeasureofPredictingPerformance ........................... 44
4.3 AdaptiveFilterandPredictingPerformance..................... 45
4.3.1 PreliminaryAnalysis ................................. 45
X Contents
4.3.2 AdaptiveFilterHyperparameters ....................... 47
4.3.3 PredictingPerformance ............................... 48
5 SelectedAsianMarkets ......................................... 53
5.1 Introduction ............................................... 53
5.2 ShanghaiStockExchangeCompositeIndex .................... 53
5.2.1 InputSelection ...................................... 54
5.2.2 CausalityTests ...................................... 57
5.2.3 PredictingPerformanceandAnalysis ................... 63
5.3 HongKongHangSengIndex ................................ 65
5.3.1 InputSelection ...................................... 66
5.3.2 CausalityTests ...................................... 68
5.3.3 PredictingPerformanceandAnalysis ................... 74
5.4 SingaporeStraitsTimesIndex................................ 78
5.4.1 InputSelection ...................................... 78
5.4.2 CausalityTests ...................................... 80
5.4.3 PredictingPerformanceandAnalysis ................... 86
6 MarketTurningPeriodForecasting .............................. 91
6.1 Introduction ............................................... 91
6.2 AFrequencyDomainForecastingTechnique ................... 92
6.2.1 DowJonesIndustrialAverage.......................... 95
6.2.2 China,HongKongandSingaporeMarkets...............102
6.3 ASystemStability-BasedConfirmationMethod.................111
6.3.1 DowJonesIndustrialAverage..........................116
6.3.2 HongKongHangSengIndex ..........................116
6.3.3 SingaporeStraitsTimesIndex .........................118
6.3.4 ShanghaiStockExchangeCompositeIndex..............118
7 TechnicalAnalysisToolkit.......................................123
7.1 Introduction ...............................................123
7.2 T-TASFunctions ...........................................125
7.2.1 UserandDataManagement ...........................125
7.2.2 OnlineDataLoadingSystem ..........................127
7.2.3 TechnicalAnalysis...................................129
7.2.4 SystemAdaptationFramework.........................143
8 FutureResearch ...............................................149
References.........................................................151
Index .............................................................159
Abbreviations
AIC AkaikeInformationCriterion
ANN ArtificialNeuralNetwork
AMH AdaptiveMarketHypothesis
AR Autoregressive
ARCH AutoregressiveConditionalHeteroscedasticity
ARIMA AutoregressiveIntegratedMovingAverage
ARMA AutoregressiveMovingAverage
ARMAX AutoregressiveMovingAverageModelwithExogenousInput
BDI BalticDryIndex
CAPM CapitalAssetPricingModel
CBOE ChicagoBoardOptionsExchange
CBR CaseBasedReasoning
CNY ChineseYuan
CPI ConsumerPriceIndex
DJIA DowJonesIndustrialAverage
DEFFR DailyEffectiveFederalFundsRate
EIU EconomistIntelligenceUnit
EMA ExponentialMovingAverage
EMH EfficientMarketHypothesis
EUR/JPY EuroversusJapaneseYen
FFFR FederalFundsFutureRate
FFR FederalFundsRate
FFRT FederalFundsRateTarget
FFT FastFourierTransform
FMH FractalMarketHypothesis
FTSE FinancialTimesandStockExchange
GARCH GeneralizedAutoregressiveConditionalHeteroskedasticity
GDP GrossDomesticProduct
GUI GraphicalUserInterfaces
GUIDE GUIDevelopmentEnvironment
HKAB HongKongAssociationofBanks