High Frequency Trading Models

338 Pages·2011·2.2 MB·English

by  Gewei Ye

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High-Frequency
Trading Models
GEWEI YE, Ph.D.
JohnWiley&Sons,Inc.
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Copyright(cid:1)C 2011byGeweiYe.Allrightsreserved.
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LibraryofCongressCataloging-in-PublicationData:
Ye,Gewei,1971–
High-frequencytradingmodels/GeweiYe.
p.cm.–(Wileytradingseries)
Includesbibliographicalreferencesandindex.
ISBN978-0-470-63373-1(cloth)
1.Investmentanalysis. 2.Speculation–Mathematicalmodels. 3.Portfolio
management–Mathematicalmodels. 4.Financialengineering. I.Title.
HG4529.Y422011
332.64(cid:2)501–dc22
2010024731
PrintedintheUnitedStatesofAmerica
10 9 8 7 6 5 4 3 2 1
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Tomyparents,Lei,Jessica,andfriends
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Contents
Preface xi
Acknowledgments xiv
PART I Revenue Models of High-Frequency
Trading 1
CHAPTER 1 High-Frequency Trading and
Existing Revenue Models 3
WhatIsHigh-FrequencyTrading? 3
WhyHigh-FrequencyTradingIsImportant 5
MajorHigh-FrequencyTradingFirmsintheUnitedStates 6
ExistingRevenueModelsofHigh-Frequency
TradingOperations 8
CategorizingHigh-FrequencyTradingOperations 9
Conclusion 10
CHAPTER 2 Roots of High-Frequency Trading in
Revenue Models of Investment
Management 13
RevenueModel1:Investing 14
RevenueModel2:InvestmentBanking 17
RevenueModel3:MarketMaking 18
RevenueModel4:Trading 18
RevenueModel5:CashManagement 19
RevenueModel6:MergersandAcquisitions 20
RevenueModel7:Back-OfficeActivities 20
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RevenueModel8:VentureCapital 20
CreatingYourOwnRevenueModel 21
HowtoAchieveSuccess:FourPersonalDrivers 22
Conclusion 27
CHAPTER 3 History and Future of
High-Frequency Trading
with Investment Management 29
RevenueModelsintheFuture 30
InvestmentManagementandFinancialInstitutions 31
High-FrequencyTradingandInvestmentManagement 32
TechnologyInventionstoDriveFinancialInventions 34
TheUltimateGoalforModelsandFinancialInventions 34
Conclusion 37
PART II Theoretical Models as Foundation of
Computer Algos for High-Frequency
Trading 39
CHAPTER 4 Behavioral Economics Models on
Loss Aversion 41
WhatIsLossAversion? 41
TheLocusEffect 41
TheoryandHypotheses 45
Study1:TheLocusEffectonInertiaEquity 49
Study2:Assumption A and A 51
1 2
GeneralDiscussion 53
Conclusion 55
CHAPTER 5 Loss Aversion in Option Pricing:
Integrating Two Nobel Models 57
DemonstratingLossAversionwithComputerAlgos 57
VisualizingtheFindings 59
ComputerAlgosfortheFinding 61
ExplainingtheFindingwiththeBlack-ScholesFormula 63
Conclusion 64
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Contents vii
CHAPTER 6 Expanding the Size of Options in
Option Pricing 65
TheNBAEvent 66
WebData 67
TheoreticalAnalysis 69
TheNBAEventandtheUncertaintyAccount 72
ControlledOfflineData 77
GeneralDiscussion 80
Conclusion 82
CHAPTER 7 Multinomial Models for
Equity Returns 85
LiteratureReview 87
AComputationalFramework:TheMDPModel 89
ImplicitConsumerDecisionTheory 94
EmpiricalApproaches 102
Analysis1:ExaminationofCorrelationsanda
RegressionModel 102
Analysis2:StructuralEquationModel 106
ContributionsoftheICDTheory 111
Conclusion 115
CHAPTER 8 More Multinomial Models
and Signal Detection Models for
Risk Propensity 117
MultinomialModelsforRetailInvestorGrowth 117
DerivingImplicitUtilityFunctions 131
TransformingLikeabilityRatingDataintoObserved
Frequencies 140
SignalDetectionTheory 143
AssessingaFund’sPerformancewithSDT 146
AssessingValueatRiskwithRiskPropensityofSDTfor
PortfolioManagers 147
DefiningRiskPropensitySurface 148
Conclusion 149
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CHAPTER 9 Behavioral Economics Models
on Fund Switching and Reference
Prices 151
WhatIsVisualFundsforFundSwitching? 151
BehavioralFactorsThatAffectFundSwitching 152
TheoryandPredictions 157
Study1:ArbitraryAnchoringonInertiaEquity 164
Study2:AnchorCompetition 166
Study3:DoubleLogLaw 169
Conclusion 179
PART III A Unique Model of Sentiment Asset
Pricing Engine for Portfolio
Management 181
CHAPTER 10 A Sentiment Asset Pricing Model 185
WhatIstheSentimentAssetPricingEngine? 185
ContributionsofSAPE 187
TestingtheEffectivenessofSAPEAlgos 190
PrimaryUsersofSAPE 191
ThreeImplementationsofSAPE 191
SAPEExtensions:TopTickEngine,FundEngine,
PortfolioEngine,andTestEngine 193
SummaryonSAPE 194
AlternativeAssessmentToolsofMacroInvestor
Sentiment 194
Conclusion 200
CHAPTER 11 SAPE for Portfolio Management—
Effectiveness and Strategies 201
ContributionsofSAPEtoPortfolioManagement 202
IntradayEvidenceofSAPEEffectiveness 203
TradingStrategiesBasedontheSAPEFunds 206
CaseStudy1:ExecutionofSAPEInvestmentStrategies 206
CaseStudy2:TheTradingProcesswithSAPE 214
CaseStudy3:AdvancedTradingStrategieswithSAPE 217