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Machine learning in asset pricing

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Machine learning in asset pricing/ Stefan Nagel.
作者:	Nagel, Stefan,
出版者:	Princeton, NJ :Princeton University Press, : c2021.,
面頁冊數:	1 online resource (157 p.)
標題:	Prices - Mathematical models. -
電子資源:	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:

1346103
Prices
--Mathematical models.

LC Class. No.: HG4636 / .N34 2021

Dewey Class. No.: 332.63/2220285631

Machine learning in asset pricing
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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.
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