Machine Learning in Asset Pricing PDF
Preview Machine Learning in Asset Pricing
Machine Learning in Asset Pricing Princeton Lectures in Finance MarkusK.Brunnermeier,SeriesEditor The Princeton Lectures in Finance, published by arrangement with the BendheimCenterforFinanceofPrincetonUniversity,arebasedonannual lectures offered at Princeton University.Each year,the Bendheim Center invitesaleadingfigureinthefieldoffinancetodeliverasetoflectureson atopicofmajorsignificancetoresearchersandprofessionalsaroundthe world. StephenA.Ross,NeoclassicalFinance WilliamF.Sharpe,InvestorsandMarkets:PortfolioChoices, AssetPrices,andInvestmentAdvice DarrellDuffie,DarkMarkets:AssetPricingandInformation TransmissioninOver-the-CounterMarkets StefanNagel,MachineLearninginAssetPricing Machine Learning in Asset Pricing Stefan Nagel princeton university press princeton and oxford Copyright(cid:2)c 2021byPrincetonUniversityPress PrincetonUniversityPressiscommittedtotheprotectionofcopyrightandtheintellectual propertyourauthorsentrusttous.Copyrightpromotestheprogressandintegrityof knowledge.Thankyouforsupportingfreespeechandtheglobalexchangeofideasby purchasinganauthorizededitionofthisbook.Ifyouwishtoreproduceordistribute anypartofitinanyform,pleaseobtainpermission. Requestsforpermissiontoreproducematerialfromthiswork [email protected] PublishedbyPrincetonUniversityPress 41WilliamStreet,Princeton,NewJersey08540 6OxfordStreet,Woodstock,OxfordshireOX201TR press.princeton.edu AllRightsReserved ISBN978-0-691-218700 ISBN(e-book)978-0-691-218717 BritishLibraryCataloging-in-PublicationDataisavailable Editorial:JoeJacksonandJacquelineDelaney ProductionEditorial:BrigittePelner JacketDesign:KarlSpurzem Production:ErinSuydam Publicity:KateHensley(US)andKathrynStevens(UK) ThisbookhasbeencomposedinSabon Printedonacid-freepaper∞ PrintedintheUnitedStatesofAmerica 1 3 5 7 9 10 8 6 4 2 To Ksenija and Marko CONTENTS Preface ix Chapter1 Introduction 1 Chapter2 SupervisedLearning 11 Chapter3 SupervisedLearninginAssetPricing 31 Chapter4 MLinCross-SectionalAssetPricing 64 Chapter5 MLasModelofInvestorBeliefFormation 93 Chapter6 AResearchAgenda 119 Bibliography 135 Index 141 PREFACE This book is an expanded version of the Princeton Lectures in Finance thatIgaveatPrincetonUniversityinMay2019.IamgratefultoMarkus BrunnermeierandtheBendheimCenterforFinanceatPrincetonUniver- sity for their hospitality. I also thank Princeton University Press and its economics editor, Joe Jackson, for supporting this book project. It gave meagreatopportunitytoreflectontherecentprogressofmachinelearn- ing in asset pricing and the open questions that research could tackle in thefuture. Myinterestinmachinelearningapplicationsinassetpricinggrewout of joint projects with my co-authors Serhiy Kozak and Shrihari Santosh that we started when Serhiy and I were colleagues at the University of Michigan. Looking at the academic literature on stock returns, one of ourareasofinterest,wesawachallengethatcalledforanewapproach. At the time, research on the determinants of stock returns was strug- gling to make sense of the fact that a huge number of different firm characteristics seemed to have a role in predicting differences in future returns between stocks. Yet, published research studies that proposed a new predictor evaluated its predictive performance relative to a sparse selection of only a small number of already-known predictors. This begged the question whether many of the predictors documented in the literature would actually be redundant if evaluated jointly. It also left open the question whether these predictors could have important inter- actioneffects.Apropercharacterizationoftheinvestmentopportunities inequitymarketswouldhavetoconsideralargesetofpredictorsjointly. Machinelearningmethodswereappealingtousasanaturalsolutionfor thesechallenges.Oneofthepapersthatresultedfromthiscollaboration (Kozak,Nagel,andSantosh2020)formsthecoreofChapter4. Morerecently,IanMartinandIstartedthinkingaboutmachinelearn- ing methods as a model of belief formation for sophisticated economic agents. For investors, for example, forecasts are crucial decision inputs. Likedatascientistsapplyingmachinelearningtechniquestobigdatasets, investorsfaceanenormousnumberofpotentiallyrelevantpredictorvari- ables.Toexplainthepropertiesofassetprices,itthereforeseemsimpor- tant that theoretical models account for the high-dimensional nature of investors’ learning problem. Modeling economic agents as machine learners gives them sophisticated tools to deal with this problem in
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