TY - BOOK AU - Nagel,Stefan TI - Machine Learning in Asset Pricing T2 - Princeton Lectures in Finance SN - 9780691218717 U1 - 332.63/2220285631 23 PY - 2021///] CY - Princeton, NJ : PB - Princeton University Press, KW - Capital assets pricing model KW - Finance KW - Mathematical models KW - Investments KW - Machine learning KW - Economic aspects KW - Prices KW - BUSINESS & ECONOMICS / Finance / Financial Engineering KW - bisacsh KW - Advances in Financial Learning KW - Bayesian estimation KW - Bayesian regression KW - Igor Halperin KW - Machine Learning in Finance KW - Marcos Lopez de Prado KW - Matthew Dixon KW - Paul Bilokon KW - Supervised learning KW - asset prices KW - cross-section of stock returns KW - data-driven methods of tuning KW - elastic-net estimator KW - factor models KW - firm fundamentals KW - high-dimensional prediction KW - market efficiency KW - mean-variance optimization framework KW - neural networks KW - out-of-sample performance KW - regularization KW - return predictability KW - ridge regression KW - risk premia estimation KW - trees and random forests N1 - Frontmatter --; CONTENTS --; Preface --; Machine Learning in Asset Pricing --; Chapter 1 Introduction --; Chapter 2 Supervised Learning --; Chapter 3 Supervised Learning in Asset Pricing --; Chapter 4 ML in Cross-Sectional Asset Pricing --; Chapter 5 ML as Model of Investor Belief Formation --; Chapter 6 A Research Agenda --; Bibliography --; Index; restricted access N2 - A groundbreaking, authoritative introduction to how machine learning can be applied to asset pricingInvestors 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 UR - https://doi.org/10.1515/9780691218717?locatt=mode:legacy UR - https://www.degruyter.com/isbn/9780691218717 UR - https://www.degruyter.com/document/cover/isbn/9780691218717/original ER -