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Machine learning in asset pricing / Stefan Nagel.

By: Nagel, Stefan, 1973- [author.].
Material type: materialTypeLabelBookSeries: Princeton lectures in finance: Publisher: Princeton : Princeton University Press, [2021]Description: 1 online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 0691218714; 9780691218717.Subject(s): Capital assets pricing model | Machine learning -- Economic aspects | Finance -- Mathematical models | Investments -- Mathematical models | Prices -- Mathematical models | Mod�ele d'�evaluation des actifs financiers | Apprentissage automatique -- Aspect �economique | Finances -- Mod�eles math�ematiques | Investissements -- Mod�eles math�ematiques | Prix -- Mod�eles math�ematiques | BUSINESS & ECONOMICS -- Finance -- Financial Engineering | Prices -- Mathematical models | Investments -- Mathematical models | Finance -- Mathematical models | Capital assets pricing model | Machine learningGenre/Form: Electronic books. | Electronic books.Additional physical formats: Print version:: Machine Learning in Asset Pricing.DDC classification: 332.63/2220285631 Online resources: Click here to access online
Contents:
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
Summary: 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.
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Online resource; title from digital title page (viewed on May 11, 2021).

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

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.

Includes bibliographical references and index.

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