Financial Analytics with R - Building a Laptop Laboratory for Data Science

393 Pages·2016·18.2 MB·English

by  Mark J. Bennett

FinancialAnalyticswithR
BuildingaLaptopLaboratoryforDataScience
Areyouinnatelycuriousaboutdynamicallyinter-operatingfinancialmarkets?Sincethe
crisisof2008,thereisaneedforprofessionalswithmoreunderstandingaboutstatistics
anddataanalysis,whocandiscussthevariousriskmetrics,particularlythoseinvolving
extremeevents.
Byproviding aresource for trainingstudents and professionals inbasic and sophis-
ticated analytics, this book meets that need. It offers both the intuition and basic
vocabularyasasteptowardthefinancial,statistical,andalgorithmicknowledgerequired
toresolvetheindustryproblems,anditdepictsasystematicwayofdevelopinganalytical
programsforfinanceinthestatisticallanguageR.Buildahands-onlaboratoryandrun
manysimulations.Exploretheanalyticalfringesofinvestmentsandriskmanagement.
Bennett and Hugen help profit-seeking investors and data science students sharpen
theirskillsinmanyareas,includingtime-series,forecasting,portfolioselection,covari-
anceclustering,prediction,andderivativesecurities.
MarkJ.Bennettisaseniordatascientistwithamajorinvestmentbankandalecturerin
theUniversityofChicago’sMaster’sprograminAnalytics.Hehasheldsoftwareposi-
tions at Argonne National Laboratory, Unisys Corporation, AT&T Bell Laboratories,
NorthropGrumman,andXRTradingSecurities.
DirkL.HugenisagraduatestudentintheDepartmentofStatisticsandActuarialScience
attheUniversityofIowa.Hepreviouslyworkedasasignalprocessingengineer.
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Financial Analytics with R
Building a Laptop Laboratory for Data Science
MARK J. BENNETT
UniversityofChicago
DIRK L. HUGEN
UniversityofIowa
06:21:40, subject to the Cambridge Core terms of use, available at
UniversityPrintingHouse,CambridgeCB28BS,UnitedKingdom
CambridgeUniversityPressispartoftheUniversityofCambridge.
ItfurtherstheUniversity’smissionbydisseminatingknowledgeinthepursuitof
education,learning,andresearchatthehighestinternationallevelsofexcellence.
www.cambridge.org
Informationonthistitle:www.cambridge.org/9781107150751
©MarkJ.BennettandDirkL.Hugen2016
Thispublicationisincopyright.Subjecttostatutoryexception
andtotheprovisionsofrelevantcollectivelicensingagreements,
noreproductionofanypartmaytakeplacewithoutthewritten
permissionofCambridgeUniversityPress.
Firstpublished2016
PrintedintheUnitedKingdombyClays,StIvesplc
AcataloguerecordforthispublicationisavailablefromtheBritishLibrary.
LibraryofCongressCataloging-in-PublicationData
Names:Bennett,MarkJ.(MarkJoseph),1959–author.|Hugen,DirkL.,author.
Title:FinancialanalyticswithR:buildingalaptoplaboratoryfordata
science/MarkJ.Bennett,UniversityofChicago,DirkL.Hugen,
UniversityofIowa.
Description:Cambridge,UK:CambridgeUniversityPress,2016.
Identifiers:LCCN2016026635|ISBN9781107150751
Subjects:LCSH:Finance–Mathematicalmodels–Dataprocessing.|
Finance–Databases.|R(Computerprogramlanguage)
Classification:LCCHG104.B462016|DDC332.0285/513--dc23
LCrecordavailableathttps://lccn.loc.gov/2016026635
ISBN978-1-107-15075-1Hardback
CambridgeUniversityPresshasnoresponsibilityforthepersistenceoraccuracyof
URLsforexternalorthird-partyInternetWebsitesreferredtointhispublication
anddoesnotguaranteethatanycontentonsuchWebsitesis,orwillremain,
accurateorappropriate.
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Toourparents:
MaryandHerbandPatriciaandBernard
andfamily:
Rachel,Austin,andCheryl
foralltheirkindness,love,andsupport
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Contents
Preface pagexiii
Acknowledgments xvii
1 AnalyticalThinking 1
1.1 WhatIsFinancialAnalytics? 2
1.2 WhatIstheLaptopLaboratoryforDataScience? 3
1.3 WhatIsRandHowCanItBeUsedintheProfessionalAnalyticsWorld? 5
1.4 Exercises 6
2 TheRLanguageforStatisticalComputing 7
2.1 GettingStartedwithR 7
2.2 LanguageFeatures:Functions,Assignment,Arguments,andTypes 10
2.3 LanguageFeatures:BindingandArrays 13
2.4 ErrorHandling 17
2.5 Numeric,Statistical,andCharacterFunctions 18
2.6 DataFramesandInput–Output 19
2.7 Lists 20
2.8 Exercises 22
3 FinancialStatistics 23
3.1 Probability 23
3.2 Combinatorics 24
3.3 MathematicalExpectation 31
3.4 SampleMean,StandardDeviation,andVariance 35
3.5 SampleSkewnessandKurtosis 36
3.6 SampleCovarianceandCorrelation 36
3.7 FinancialReturns 39
3.8 CapitalAssetPricingModel 40
3.9 Exercises 42
4 FinancialSecurities 44
4.1 BondInvestments 45
4.2 StockInvestments 48
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viii Contents
4.3 TheHousingCrisis 49
4.4 TheEuroCrisis 50
4.5 SecuritiesDatasetsandVisualization 52
4.6 AdjustingforStockSplits 55
4.7 AdjustingforMergers 61
4.8 PlottingMultipleSeries 62
4.9 SecuritiesDataImporting 64
4.10 SecuritiesDataCleansing 71
4.11 SecuritiesQuoting 74
4.12 Exercises 75
5 DatasetAnalyticsandRiskMeasurement 77
5.1 GeneratingPricesfromLogReturns 77
5.2 NormalMixtureModelsofPriceMovements 80
5.3 SuddenCurrencyPriceMovementin2015 86
5.4 Exercises 90
6 TimeSeriesAnalysis 92
6.1 ExaminingTimeSeries 92
6.2 StationaryTimeSeries 97
6.3 Auto-RegressiveMovingAverageProcesses 98
6.4 PowerTransformations 98
6.5 TheTSAPackage 99
6.6 Auto-RegressiveIntegratedMovingAverageProcesses 109
6.7 CaseStudy:EarningsofJohnson&Johnson 110
6.8 CaseStudy:MonthlyAirlinePassengers 114
6.9 CaseStudy:ElectricityProduction 117
6.10 GeneralizedAuto-RegressiveConditionalHeteroskedasticity 120
6.11 CaseStudy:VolatilityofGoogleStockReturns 121
6.12 Exercises 128
7 TheSharpeRatio 130
7.1 SharpeRatioFormula 131
7.2 TimePeriodsandAnnualizing 131
7.3 RankingInvestmentCandidates 132
7.4 TheQuantmodPackage 136
7.5 MeasuringIncomeStatementGrowth 141
7.6 SharpeRatiosforIncomeStatementGrowth 144
7.7 Exercises 155
8 MarkowitzMean-VarianceOptimization 157
8.1 OptimalPortfolioofTwoRiskyAssets 157
8.2 QuadraticProgramming 160
8.3 DataMiningwithPortfolioOptimization 162
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Contents ix
8.4 Constraints,Penalization,andtheLasso 165
8.5 ExtendingtoHighDimensions 171
8.6 CaseStudy:SurvivingStocksoftheS&P500Indexfrom2003to2008 179
8.7 CaseStudy:ThousandsofCandidateStocksfrom2008to2014 182
8.8 CaseStudy:Exchange-TradedFunds 186
8.9 Exercises 195
9 ClusterAnalysis 197
9.1 K-MeansClustering 197
9.2 DissectingtheK-MeansAlgorithm 204
9.3 SparsityandConnectednessofUndirectedGraphs 208
9.4 CovarianceandPrecisionMatrices 211
9.5 VisualizingCovariance 215
9.6 TheWishartDistribution 221
9.7 Glasso:PenalizationforUndirectedGraphs 225
9.8 RunningtheGlassoAlgorithm 225
9.9 TrackingaValueStockthroughtheYears 226
9.10 RegressiononYearlySparsity 231
9.11 RegressiononQuarterlySparsity 235
9.12 RegressiononMonthlySparsity 236
9.13 ArchitectureandExtension 238
9.14 Exercises 239
10 GaugingtheMarketSentiment 240
10.1 MarkovRegimeSwitchingModel 241
10.2 ReadingtheMarketData 244
10.3 BayesianReasoning 247
10.4 TheBetaDistribution 250
10.5 PriorandPosteriorDistributions 250
10.6 ExaminingLogReturnsforCorrelation 253
10.7 MomentumGraphs 255
10.8 Exercises 259
11 SimulatingTradingStrategies 261
11.1 ForeignExchangeMarkets 261
11.2 ChartAnalytics 263
11.3 InitializationandFinalization 264
11.4 MomentumIndicators 265
11.5 BayesianReasoningwithinPositions 266
11.6 Entries 268
11.7 Exits 269
11.8 Profitability 270
11.9 Short-TermVolatility 270
11.10 TheStateMachine 271
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x Contents
11.11 SimulationSummary 278
11.12 Exercises 280
12 DataExplorationUsingFundamentals 281
12.1 TheRSQLitePackage 281
12.2 FindingMarket-to-BookRatios 283
12.3 TheReshape2Package 285
12.4 CaseStudy:Google 288
12.5 CaseStudy:Walmart 289
12.6 ValueInvesting 290
12.7 Lab:TryingtoBeattheMarket 294
12.8 Lab:FinancialStrength 295
12.9 Exercises 296
13 PredictionUsingFundamentals 297
13.1 BestIncomeStatementPortfolio 298
13.2 ReformattingIncomeStatementGrowthFigures 298
13.3 ObtainingPriceStatistics 300
13.4 CombiningtheIncomeStatementwithPriceStatistics 306
13.5 PredictionUsingClassificationTreesandRecursivePartitioning 308
13.6 ComparingPredictionRatesamongClassifiers 314
13.7 Exercises 316
14 BinomialModelforOptions 318
14.1 ApplyingComputationalFinance 318
14.2 Risk-NeutralPricingandNoArbitrage 322
14.3 HighRisk-FreeRateEnvironment 322
14.4 ConvergenceofBinomialModelforOptionData 324
14.5 Put–CallParity 327
14.6 FromBinomialtoLog-Normal 328
14.7 Exercises 330
15 Black–ScholesModelandOption-ImpliedVolatility 331
15.1 GeometricBrownianMotion 332
15.2 MonteCarloSimulationofGeometricBrownianMotion 333
15.3 Black–ScholesDerivation 335
15.4 AlgorithmforImpliedVolatility 338
15.5 ImplementationofImpliedVolatility 339
15.6 TheRcppPackage 345
15.7 Exercises 348
Appendix ProbabilityDistributionsandStatisticalAnalysis 350
A.1 Distributions 350
A.2 BernoulliDistribution 350
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