Nagel, Stefan, 1973-
Machine learning in asset pricing / Stefan Nagel. - 1 online resource - Princeton lectures in finance . - Princeton lectures in finance. .
Includes bibliographical references and index.
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.
0691218714 9780691218717
22573/ctv19f7x75 JSTOR 9519805 IEEE
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 learning.
Electronic books.
Electronic books.
HG4636 / .N34 2021
332.63/2220285631
Machine learning in asset pricing / Stefan Nagel. - 1 online resource - Princeton lectures in finance . - Princeton lectures in finance. .
Includes bibliographical references and index.
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.
0691218714 9780691218717
22573/ctv19f7x75 JSTOR 9519805 IEEE
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 learning.
Electronic books.
Electronic books.
HG4636 / .N34 2021
332.63/2220285631