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Machine Learning in Asset Pricing / Stefan Nagel.

By: Material type: TextTextSeries: Princeton Lectures in Finance ; 1Publisher: Princeton, NJ : Princeton University Press, [2021]Copyright date: ©2021Description: 1 online resource (160 p.) : 17 b/w illus. 4 tablesContent type:
Media type:
Carrier type:
ISBN:
  • 9780691218717
Subject(s): DDC classification:
  • 332.63/2220285631 23
Other classification:
  • online - DeGruyter
Online resources:
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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Item type Current library Call number URL Status Notes Barcode
eBook eBook Biblioteca "Angelicum" Pont. Univ. S.Tommaso d'Aquino Nuvola online online - DeGruyter (Browse shelf(Opens below)) Online access Not for loan (Accesso limitato) Accesso per gli utenti autorizzati / Access for authorized users (dgr)9780691218717

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

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http://purl.org/coar/access_right/c_16ec

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.

Mode of access: Internet via World Wide Web.

In English.

Description based on online resource; title from PDF title page (publisher's Web site, viewed 01. Dez 2022)