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Machine Learning in Asset Pricing

English

By (author): Stefan Nagel

A groundbreaking, authoritative introduction to how machine learning can be applied to asset pricing

Investors in financial markets are faced with an abundance of potentially value-relevant information from a wide variety of different sources. In such data-rich, high-dimensional environments, techniques from the rapidly advancing field of machine learning (ML) are well-suited for solving prediction problems. Accordingly, ML methods are quickly becoming part of the toolkit in asset pricing research and quantitative investing. In this book, Stefan Nagel examines the promises and challenges of ML applications in asset pricing.

Asset pricing problems are substantially different from the settings for which ML tools were developed originally. To realize the potential of ML methods, they must be adapted for the specific conditions in asset pricing applications. Economic considerations, such as portfolio optimization, absence of near arbitrage, and investor learning can guide the selection and modification of ML tools. Beginning with a brief survey of basic supervised ML methods, Nagel then discusses the application of these techniques in empirical research in asset pricing and shows how they promise to advance the theoretical modeling of financial markets.

Machine Learning in Asset Pricing presents the exciting possibilities of using cutting-edge methods in research on financial asset valuation.

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A01=Stefan NagelAge Group_UncategorizedArbitrageAuthor_Stefan Nagelautomatic-updateBayesianBayesian inferenceBayesian linear regressionBayesian statisticsBehavioral economicsBias–variance tradeoffBootstrapping (statistics)CalculationCapital asset pricing modelCash flowCash flow forecastingCategory1=Non-FictionCategory=KFFCategory=UYQMCoefficientComputational complexity theoryCOP=United StatesCost curveCovariance matrixCovariateCredit riskCross-sectional dataCross-sectional regressionCross-validation (statistics)Data scienceDecision tree learningDelivery_Delivery within 10-20 working daysDemand curveEconometricsEnsemble learningeq_business-finance-laweq_computingeq_isMigrated=2eq_non-fictionErrors and residualsEstimationEstimation theoryEstimatorFactor analysisFinancial economicsForecast errorForecastingGreedy algorithmHyperparameterHyperparameter optimizationInteraction (statistics)Investment AdviceInvestment strategyKernel regressionLanguage_EnglishLinear regressionMachine learningMarket clearingMarket liquidityMathematical optimizationMoment (mathematics)Optimization problemPA=AvailableParameter (computer programming)PredictabilityPredictionPrice elasticity of demandPrice_€50 to €100PricingPrincipal component analysisPrior probabilityProfit (economics)PS=ActiveQuasi-Newton methodRational expectationsRegularization (mathematics)Risk premiumSharpe ratiosoftlaunchSparse matrixStochastic discount factorSupervised learningSupply (economics)Test statisticTikhonov regularizationTrading strategyVariable (mathematics)Weighted arithmetic mean
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Product Details
  • Dimensions: 156 x 235mm
  • Publication Date: 11 May 2021
  • Publisher: Princeton University Press
  • Publication City/Country: US
  • Language: English
  • ISBN13: 9780691218700

About Stefan Nagel

Stefan Nagel is the Fama Family Professor of Finance at the University of Chicago, Booth School of Business. He is the executive editor of the Journal of Finance, a research associate at the National Bureau of Economic Research, and a research fellow at both the Centre for Economic Policy Research in London and the CESIfo in Munich. Twitter @ProfStefanNagel

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