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NeuralNetworks
inFinance:
Gaining
PredictiveEdge
intheMarket
PaulD.McNelis
Contents
Prefacexi
1Introduction1
1.1Forecasting,Classi?cation,andDimensionality
Reduction ..........................1
1.2Synergies ...........................4
1.3TheInterfaceProblems ...................6
1.4PlanoftheBook ......................8
IEconometricFoundations11
2WhatAreNeuralNetworks?13
2.1LinearRegressionModel ..................13
2.2GARCHNonlinearModels .................15
2.2.1PolynomialApproximation .............17
2.2.2OrthogonalPolynomials ...............18
2.3ModelTypology .......................20
2.4WhatIsANeuralNetwork? ................21
2.4.1FeedforwardNetworks ................21
2.4.2SquasherFunctions .................24
2.4.3RadialBasisFunctions ...............28
2.4.4RidgeletNetworks ..................29
2.4.5JumpConnections ..................30
2.4.6MultilayeredFeedforwardNetworks ........32
2.4.7RecurrentNetworks .................34
2.4.8NetworkswithMultipleOutputs ..........36
2.5NeuralNetworkSmooth-TransitionRegimeSwitching
Models ............................38
2.5.1Smooth-TransitionRegimeSwitchingModels...38
2.5.2NeuralNetworkExtensions .............39
2.6NonlinearPrincipalComponents:Intrinsic
Dimensionality ........................41
2.6.1LinearPrincipalComponents ............42
2.6.2NonlinearPrincipalComponents ..........44
2.6.3ApplicationtoAssetPricing ............46
2.7NeuralNetworksandDiscreteChoice ...........49
2.7.1DiscriminantAnalysis ................49
2.7.2LogitRegression ...................50
2.7.3ProbitRegression ..................51
2.7.4WeibullRegression .................52
2.7.5NeuralNetworkModelsforDiscreteChoice ....52
2.7.6ModelswithMultinomialOrderedChoice .....53
2.8TheBlackBoxCriticismandDataMining ........55
2.9Conclusion ..........................57
2.9.1MATLABProgramNotes ..............58
2.9.2SuggestedExercises .................58
3EstimationofaNetworkwithEvolutionaryComputation59
3.1DataPreprocessing .....................59
3.1.1Stationarity:Dickey-FullerTest ...........59
3.1.2SeasonalAdjustment:CorrectionforCalendar
E?ects ........................61
3.1.3DataScaling .....................64
3.2TheNonlinearEstimationProblem ............65
3.2.1LocalGradient-BasedSearch:TheQuasi-Newton
MethodandBackpropagation ...........67
3.2.2StochasticSearch:SimulatedAnnealing ......70
3.2.3EvolutionaryStochasticSearch:TheGenetic
Algorithm ......................72
3.2.4EvolutionaryGeneticAlgorithms ..........75
3.2.5Hybridization:CouplingGradient-Descent,
Stochastic,andGeneticSearchMethods ......75
3.3RepeatedEstimationandThickModels ..........77
3.4MATLABExamples:NumericalOptimizationand
NetworkPerformance ....................78
3.4.1NumericalOptimization ...............78
3.4.2ApproximationwithPolynomialsand
NeuralNetworks ...................80
Contentsvii
3.5Conclusion ..........................83
3.5.1MATLABProgramNotes ..............83
3.5.2SuggestedExercises .................84
4EvaluationofNetworkEstimation85
4.1In-SampleCriteria ......................85
4.1.1GoodnessofFitMeasure ..............86
4.1.2Hannan-QuinnInformationCriterion .......86
4.1.3SerialIndependence:Ljung-BoxandMcLeod-Li
Tests .........................86
4.1.4Symmetry ......................89
4.1.5Normality ......................89
4.1.6NeuralNetworkTestforNeglectedNonlinearity:
Lee-White-GrangerTest ..............90
4.1.7Brock-Deckert-ScheinkmanTestforNonlinear
Patterns .......................91
4.1.8SummaryofIn-SampleCriteria ...........93
4.1.9MATLABExample .................93
4.2Out-of-SampleCriteria ...................94
4.2.1RecursiveMethodology ...............95
4.2.2RootMeanSquaredErrorStatistic .........96
4.2.3Diebold-MarianoTestforOut-of-SampleErrors..96
4.2.4Harvey,Leybourne,andNewboldSizeCorrection
ofDiebold-MarianoTest ..............97
4.2.5Out-of-SampleComparisonwithNestedModels..98
4.2.6SuccessRatioforSignPredictions:Directional
Accuracy .......................99
4.2.7PredictiveStochasticComplexity ..........100
4.2.8Cross-Validationandthe.632Bootstrapping
Method ........................101
4.2.9DataRequirements:HowLargeforPredictive
Accuracy? ......................102
4.3InterpretiveCriteriaandSigni?canceofResults ......104
4.3.1AnalyticDerivatives .................105
4.3.2FiniteDi?erences ..................106
4.3.3DoesItMatter? ...................107
4.3.4MATLABExample:AnalyticandFinite
Di?erences ......................107
4.3.5BootstrappingforAssessingSigni?cance ......108
4.4ImplementationStrategy ..................109
4.5Conclusion ..........................110
4.5.1MATLABProgramNotes ..............110
4.5.2SuggestedExercises .................111
viiiContents
IIApplicationsandExamples113
5EstimatingandForecastingwithArti?cialData115
5.1Introduction .........................115
5.2StochasticChaosModel ...................117
5.2.1In-SamplePerformance ...............118
5.2.2Out-of-SamplePerformance .............120
5.3StochasticVolatility/JumpDi?usionModel ........122
5.3.1In-SamplePerformance ...............123
5.3.2Out-of-SamplePerformance .............125
5.4TheMarkovRegimeSwitchingModel ...........125
5.4.1In-SamplePerformance ...............128
5.4.2Out-of-SamplePerformance .............130
5.5VolatalityRegimeSwitchingModel ............130
5.5.1In-SamplePerformance ...............132
5.5.2Out-of-SamplePerformance .............132
5.6DistortedLong-MemoryModel ...............135
5.6.1In-SamplePerformance ...............136
5.6.2Out-of-SamplePerformance .............137
5.7Black-SholesOptionPricingModel:ImpliedVolatility
Forecasting ..........................137
5.7.1In-SamplePerformance ...............140
5.7.2Out-of-SamplePerformance .............142
5.8Conclusion ..........................142
5.8.1MATLABProgramNotes ..............142
5.8.2SuggestedExercises .................143
6TimesSeries:ExamplesfromIndustryandFinance145
6.1ForecastingProductionintheAutomotiveIndustry...145
6.1.1TheData .......................146
6.1.2ModelsofQuantityAdjustment ..........148
6.1.3In-SamplePerformance ...............150
6.1.4Out-of-SamplePerformance .............151
6.1.5InterpretationofResults ..............152
6.2CorporateBonds:WhichFactorsDeterminethe
Spreads? ...........................156
6.2.1TheData .......................157
6.2.2AModelfortheAdjustmentofSpreads ......157
6.2.3In-SamplePerformance ...............160
6.2.4Out-of-SamplePerformance .............160
6.2.5InterpretationofResults ..............161
Contentsix
6.3Conclusion ..........................165
6.3.1MATLABProgramNotes ..............166
6.3.2SuggestedExercises .................166
7In?ationandDe?ation:HongKongandJapan167
7.1HongKong ..........................168
7.1.1TheData .......................169
7.1.2ModelSpeci?cation .................174
7.1.3In-SamplePerformance ...............177
7.1.4Out-of-SamplePerformance .............177
7.1.5InterpretationofResults ..............178
7.2Japan ............................182
7.2.1TheData .......................184
7.2.2ModelSpeci?cation .................189
7.2.3In-SamplePerformance ...............189
7.2.4Out-of-SamplePerformance .............190
7.2.5InterpretationofResults ..............191
7.3Conclusion ..........................196
7.3.1MATLABProgramNotes ..............196
7.3.2SuggestedExercises .................196
8Classi?cation:CreditCardDefaultandBankFailures199
8.1CreditCardRisk ......................200
8.1.1TheData .......................200
8.1.2In-SamplePerformance ...............200
8.1.3Out-of-SamplePerformance .............202
8.1.4InterpretationofResults ..............203
8.2BankingIntervention ....................204
8.2.1TheData .......................204
8.2.2In-SamplePerformance ...............205
8.2.3Out-of-SamplePerformance .............207
8.2.4InterpretationofResults ..............208
8.3Conclusion ..........................209
8.3.1MATLABProgramNotes ..............210
8.3.2SuggestedExercises .................210
9DimensionalityReductionandImpliedVolatility
Forecasting211
9.1HongKong ..........................212
9.1.1TheData .......................212
9.1.2In-SamplePerformance ...............213
9.1.3Out-of-SamplePerformance .............214
xContents
9.2UnitedStates ........................216
9.2.1TheData .......................216
9.2.2In-SamplePerformance ...............216
9.2.3Out-of-SamplePerformance .............218
9.3Conclusion ..........................219
9.3.1MATLABProgramNotes ..............220
9.3.2SuggestedExercises .................220
Bibliography221
Index233
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