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Machine learning in asset pricing

Nagel, Stefan, (1973-)

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Machine learning in asset pricing

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine learning in asset pricing/ Stefan Nagel.
Author:	Nagel, Stefan,
Published:	Princeton, NJ :Princeton University Press, : c2021.,
Description:	1 online resource (157 p.)
[NT 15003449]:	Machine learning in asset pricing -- Contents -- Preface -- 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.
Subject:	Capital assets pricing model. -
Online resource:	https://portal.igpublish.com/iglibrary/search/PUPB0008254.html
ISBN:	9780691218717

Machine learning in asset pricing
Nagel, Stefan,1973-

Machine learning in asset pricing[electronic resource] /Stefan Nagel. - Princeton, NJ :Princeton University Press,c2021. - 1 online resource (157 p.) - Princeton lectures in finance. - Princeton lectures in finance..

Includes bibliographical references and index.

Machine learning in asset pricing -- Contents -- Preface -- 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.

Access restricted to authorized users and institutions.

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.

Mode of access: World Wide Web.

ISBN: 9780691218717Subjects--Topical Terms:

646740
Capital assets pricing model.

LC Class. No.: HG4636 / .N34 2021

Dewey Class. No.: 332.63/2220285631

Machine learning in asset pricing
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300 $a 1 online resource (157 p.)
490 1 $a Princeton lectures in finance
504 $a Includes bibliographical references and index.
505 0 $a Machine learning in asset pricing -- Contents -- Preface -- 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.
506 $a Access restricted to authorized users and institutions.
520 3 $a 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.
538 $a Mode of access: World Wide Web.
588 $a Description based on print version record.
650 0 $a Capital assets pricing model. $3 646740
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830 0 $a Princeton lectures in finance. $3 1558063
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