Machine Learning in Asset Pricing
Seiten
2021
Princeton University Press (Verlag)
978-0-691-21870-0 (ISBN)
Princeton University Press (Verlag)
978-0-691-21870-0 (ISBN)
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 machin
A groundbreaking, authoritative introduction to how machine learning can be applied to asset pricing
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.
A groundbreaking, authoritative introduction to how machine learning can be applied to asset pricing
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.
Stefan Nagel is the Fama Family Professor of Finance at the University of Chicago, Booth School of Business. He is the executive editor of the Journal of Finance, a research associate at the National Bureau of Economic Research, and a research fellow at both the Centre for Economic Policy Research in London and the CESIfo in Munich. Twitter @ProfStefanNagel
| Erscheinungsdatum | 02.06.2021 |
|---|---|
| Reihe/Serie | Princeton Lectures in Finance |
| Zusatzinfo | 17 b/w illus. 4 tables. |
| Verlagsort | New Jersey |
| Sprache | englisch |
| Maße | 156 x 235 mm |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| Wirtschaft ► Betriebswirtschaft / Management ► Finanzierung | |
| ISBN-10 | 0-691-21870-6 / 0691218706 |
| ISBN-13 | 978-0-691-21870-0 / 9780691218700 |
| Zustand | Neuware |
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