Introduction to Financial Forecasting in Investment Analysis PDF
Preview Introduction to Financial Forecasting in Investment Analysis
Introduction to Financial Forecasting in Investment Analysis John B. Guerard, Jr. Introduction to Financial Forecasting in Investment Analysis JohnB.Guerard,Jr. McKinleyCapitalManagement,LLC Anchorage,AK,USA ISBN978-1-4614-5238-6 ISBN978-1-4614-5239-3(eBook) DOI10.1007/978-1-4614-5239-3 SpringerNewYorkHeidelbergDordrechtLondon LibraryofCongressControlNumber:2012952930 #SpringerScience+BusinessMediaNewYork2013 Thisworkissubjecttocopyright.AllrightsarereservedbythePublisher,whetherthewholeorpart of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation,broadcasting,reproductiononmicrofilmsorinanyotherphysicalway,andtransmissionor informationstorageandretrieval,electronicadaptation,computersoftware,orbysimilarordissimilar methodologynowknownorhereafterdeveloped.Exemptedfromthislegalreservationarebriefexcerpts inconnectionwithreviewsorscholarlyanalysisormaterialsuppliedspecificallyforthepurposeofbeing enteredandexecutedonacomputersystem,forexclusiveusebythepurchaserofthework.Duplication ofthispublicationorpartsthereofispermittedonlyundertheprovisionsoftheCopyrightLawofthe Publisher’s location, in its current version, and permission for use must always be obtained from Springer.PermissionsforusemaybeobtainedthroughRightsLinkattheCopyrightClearanceCenter. 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Printedonacid-freepaper SpringerispartofSpringerScience+BusinessMedia(www.springer.com) Preface An Introduction to Financial Forecasting in Investment Analysis Theobjectiveofthisproposedtextisa250pageintroductoryfinancialforecasting text that exposes the reader to applications of financial forecasting and the use of financialforecastsinmakingbusinessdecisions.Theprimaryforecastsexaminedin thistextareearningspershares(eps).ThistextwillmakeextensiveuseofI/B/E/S data,bothhistoricincomestatementandbalancesheetdataandanalysts’forecasts ofeps.Wecalculatefinancialratiosthatareusefulincreatingportfoliosthathave generated statistically significant excess returns in the world of business. The intended audience is investment students in universities and investment professionalswhoarenotfamiliarwithmanyapplicationsoffinancialforecasting. This text is a data-oriented text on financial forecasting, understanding financial data, and creating efficient portfolios. Many regression and time series examples use E-Views, OxMetrics, Scientific Computing Associates (SCA), and SAS software. The first chapter is an introduction to financial forecasting. We tell the reader why one needs to forecast. We introduce the reader to the moving average and exponentialsmoothingmodelstoserveasforecastingbenchmarks. Thesecondchapterintroducesthereadertotheregressionanalysisandforecasting. Inthethirdchapter,weuseregressionanalysistoexaminetheforecastingeffective- nessofthe composite index ofleading economic indicators, LEI.Economists have constructedleadingeconomicindicatorseriestoserveasabusinessbarometerofthe changingUSeconomysincethetimeofWesleyC.Mitchell(1913).Thepurposeof this study is to examine the time series forecasts of composite economic indexes, produced by The Conference Board (TCB) and test the hypothesis that the leading indicatorsareusefulasaninputtoatimeseriesmodeltoforecastrealoutputinthe USA.Economicindicatorsaredescriptiveandanticipatorytime-seriesdataareusedto analyze and forecast changing businessconditions. Cyclical indicatorsare compre- hensiveseriesthataresystemicallyrelatedtothebusinesscycle. v vi Preface Thethirdchapterintroducesthereadertotheforecastingprocessandillustrates exponential smoothing and (Box–Jenkins) time series model estimations and forecasts using the US Real Gross Domestic Product (GDP). The chapter is a “hands-on” exercise in model estimating and forecasting. In this chapter, we examinetheforecastingeffectivenessofthecompositeindexofleadingeconomic indicators,LEI.Theleadingindicatorscanbeaninputtoatransferfunctionmodel of real Gross Domestic Product, GDP. The transfer function model forecasts are compared to several na¨ıve models in terms of testing which model produces the mostaccurateforecastofrealGDP.No-changeforecastsofrealGDPandrandom walk with drift models may be useful as a forecasting benchmark (Mincer and Zarnowitz1969;GrangerandNewbold1977). The fourth chapter addresses the issue of composite forecasting using equally weighted and regression-weighted models. We discuss the use of GDP forecasts. We analyzeamodel of United States equity returns,the USER Model, toaddress issues of outliers and multicollinearity. The USER Model combines Graham & Dodd variables, such as earnings, book value, cash flow, and sales with analysts’ revisions,breadth,andyieldsandpricemomentumtorankUSequitiesandidentify undervalued securities. Expected returns modeling has been analyzed with a regression model in which security returns are functions of fundamental stock data, such as earnings, book value, cash flow, and sales, relative to stock prices, and forecast earnings per share (Fama and French 1992, 1995; Bloch et al 1993; HaugenandBaker2010;StoneandGuerard2010). InChapter5,weexpanduponthetimeseriesmodelsofChap.2andintroduce the reader to multiple time series model and Granger causality testing as in the Ashley, Granger, and Schmalensee (1980) and Chen and Lee (1990) tests. We illustrate causality testing with mergers, stock prices, and LEI data in the USA in thepostwarperiod. In Chapter 6, we examine analysts’ forecasts in portfolio construction and management. We use the Barra risk optimization analysis system, the standard portfolio risk model in industry, to create efficient portfolios. The Barra Aegis systemproducesstatisticallysignificantassetselectionusingtheUSERModelfor the1980–2009period. In Chapter 7, we show how US, Non-US, and Global portfolio returns can be enhanced by use of eps forecasts and revisions. We use the Sungard APT and Axioma systems to create efficient portfolios using principal components-based riskmodels.McKinleyCapitalManagementhostedaresearchseminarinAnchor- age in July 2011. The APT and Axioma results presented in Chapter 7 extend portfolio construction applications presented at the McKinley conference and published in the Spring 2012 Journal of Investing special edition on Quantitative RiskModels. WeillustrateglobalmarkettimingandtacticalassetmanagementinChapter8. Theabilitytoforecastmarketshiftsallowsthemanagertoincreasehisorherrisk acceptanceandenhancetherisk-returntradeoff. We summarize our processes, tests, and results in Chapter 9. We produce conclusionsthatarerelevanttotheindividualinvestorandportfoliomanager. Preface vii Theauthoracknowledgesthesupportofhiswifeof30-plusyears,Julie,andour three children, Richard, Katherine, and Stephanie. The author gratefully acknowledgesthecommentsandsuggestionsofseveralgentlemenwhoeach read several chapters of this monograph. Professors Derek Bunn, of the London Busi- nessSchool,MartinGruber,ofNewYorkUniversity,DimitriosThomakos,ofthe University of Peloponnese (Greece), and Jose Menchero of MSCI Barra. Special thanks to Anureef Saxena, Robert Stubbs, and Dewitt Miller for suggestions and edits of Chapters 7 and 8. Robert (Rob) Gillam, the Chief Investment Officer of McKinleyCapitalManagement,isacknowledgedforhissupportofAPT,whichhe brought into our firm seven years ago when he hired me, and Axioma, which we purchasedlastyear.Anyerrorsremainingaretheresponsibilityoftheauthor. Anchorage,AK,USA JohnB.Guerard,Jr. Contents 1 Forecasting:ItsPurposeandAccuracy. . . . .. . . . . .. . . . .. . . . . .. 1 ForecastRationality. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 AbsoluteandRelativeForecastAccuracy. . . . . . . . . . . . . . . . . . . . . . 8 Appendix. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 ExponentialSmoothing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2 RegressionAnalysisandForecastingModels. . . . . . . . . . . . . . . . . . 19 ExamplesofFinancialEconomicData. . . . . . . . .. . . . . . . . . . . . . .. . 25 Autocorrelation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 MultipleRegression. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 TheConferenceBoardCompositeIndexofLeading EconomicIndicatorsandRealUSGDPGrowth: ARegressionExample. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 Appendix. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 LeadingIndexComponents. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 3 AnIntroductiontoTimeSeriesModeling andForecasting. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 BasicStatisticalPropertiesofEconomicSeries. . . . . . . . . . . . . . . . . . 48 TheAutoregressiveandMoving AverageProcesses. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 ARMAModelIdentificationinPractice. . . . . . . . . . . . . . . . . . . . . . . . 57 ModelingRealGDP:AnExample. . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 LeadingEconomicIndicatorsandRealGDPAnalysis: TheStatisticalEvidence,1970–2002. . . . . . . . . . . . . . . . . . . . . . . . . . 63 USandG7Post-sampleRealGDPForecastingAnalysis. . . . . . . . . . . 69 Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 ix x Contents 4 RegressionAnalysisandMulticollinearity:TwoCaseStudies. . . . . 73 TheFirstExample:CombiningGNPForecasts. . . .. . . . . . .. . . . . . .. 78 TheSecondExample:ModelingtheReturns oftheUSEquities. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 SummaryandConclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 5 TransferFunctionModelingandGranger CausalityTesting. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 TestingforCausality:TheAshleyetal.(1980)Test. . . . . . . . . . . . . . . 97 QuarterlyMergers,1992–2011:AutomaticTimeSeries ModelingandanApplicationoftheAshley etal.(1980)Test. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 CausalityTesting:AnAlternativeApproach byChenandLee. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 CausalityAnalysisofQuarterlyMergers,1992–2011: AnApplicationoftheChenandLeeTest. . . . . . . . . . . . . . . . . . . . . . 117 MoneySupplyandStockPrices,1967–2011. . . . . . . . . . . . . . . . . . . . 139 References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142 6 ACaseStudyofPortfolioConstructionUsingtheUSER DataandtheBarraAegisSystem. . . . . . . . . . . . . . . . . . . . . . . . . . . 145 TheBARRAModel:ThePrimaryInstitutional RiskModel. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148 StockSelectionModeling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162 EfficientPortfolioConstructionUsingtheBarra AegisSystem. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163 DMCModelIIICalculation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170 Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171 References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171 7 MoreMarkowitzEfficientPortfoliosFeaturing theUSERDataandanExtensiontoGlobalData andInvestmentUniverses. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175 ConstructingEfficientPortfolios. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177 ExtensionstotheTraditionalMean–VarianceModel. . . . . . . . . . . . . . 179 PortfolioConstruction,Management,andAnalysis: AnIntroductiontoTrackingErroratRisk. . . . . . . . . . . . . . . . . . . . . . 179 PortfolioConstruction,Management,andAnalysis: AnIntroductiontoSystematicTracking ErrorOptimization. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 186 MarkowitzRestored:TheAlphaAlignment FactorApproach. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191
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