Machine Learning in Asset Pricing / / Stefan Nagel.
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, techniq...
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Superior document: | Title is part of eBook package: De Gruyter EBOOK PACKAGE Business and Economics 2021 English |
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Place / Publishing House: | Princeton, NJ : : Princeton University Press, , [2021] ©2021 |
Year of Publication: | 2021 |
Language: | English |
Series: | Princeton Lectures in Finance ;
1 |
Online Access: | |
Physical Description: | 1 online resource (160 p.) :; 17 b/w illus. 4 tables. |
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100 | 1 | |a Nagel, Stefan, |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
245 | 1 | 0 | |a Machine Learning in Asset Pricing / |c Stefan Nagel. |
264 | 1 | |a Princeton, NJ : |b Princeton University Press, |c [2021] | |
264 | 4 | |c ©2021 | |
300 | |a 1 online resource (160 p.) : |b 17 b/w illus. 4 tables. | ||
336 | |a text |b txt |2 rdacontent | ||
337 | |a computer |b c |2 rdamedia | ||
338 | |a online resource |b cr |2 rdacarrier | ||
347 | |a text file |b PDF |2 rda | ||
490 | 0 | |a Princeton Lectures in Finance ; |v 1 | |
505 | 0 | 0 | |t Frontmatter -- |t CONTENTS -- |t Preface -- |t Machine Learning in Asset Pricing -- |t Chapter 1 Introduction -- |t Chapter 2 Supervised Learning -- |t Chapter 3 Supervised Learning in Asset Pricing -- |t Chapter 4 ML in Cross-Sectional Asset Pricing -- |t Chapter 5 ML as Model of Investor Belief Formation -- |t Chapter 6 A Research Agenda -- |t Bibliography -- |t Index |
506 | 0 | |a restricted access |u http://purl.org/coar/access_right/c_16ec |f online access with authorization |2 star | |
520 | |a 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. | ||
538 | |a Mode of access: Internet via World Wide Web. | ||
546 | |a In English. | ||
588 | 0 | |a Description based on online resource; title from PDF title page (publisher's Web site, viewed 01. Dez 2022) | |
650 | 0 | |a Capital assets pricing model. | |
650 | 0 | |a Finance |x Mathematical models. | |
650 | 0 | |a Investments |x Mathematical models. | |
650 | 0 | |a Machine learning |x Economic aspects. | |
650 | 0 | |a Prices |x Mathematical models. | |
650 | 7 | |a BUSINESS & ECONOMICS / Finance / Financial Engineering. |2 bisacsh | |
653 | |a Advances in Financial Learning. | ||
653 | |a Bayesian estimation. | ||
653 | |a Bayesian regression. | ||
653 | |a Igor Halperin. | ||
653 | |a Machine Learning in Finance. | ||
653 | |a Marcos Lopez de Prado. | ||
653 | |a Matthew Dixon. | ||
653 | |a Paul Bilokon. | ||
653 | |a Supervised learning. | ||
653 | |a asset prices. | ||
653 | |a cross-section of stock returns. | ||
653 | |a data-driven methods of tuning. | ||
653 | |a elastic-net estimator. | ||
653 | |a factor models. | ||
653 | |a firm fundamentals. | ||
653 | |a high-dimensional prediction. | ||
653 | |a market efficiency. | ||
653 | |a mean-variance optimization framework. | ||
653 | |a neural networks. | ||
653 | |a out-of-sample performance. | ||
653 | |a regularization. | ||
653 | |a return predictability. | ||
653 | |a ridge regression. | ||
653 | |a risk premia estimation. | ||
653 | |a trees and random forests. | ||
773 | 0 | 8 | |i Title is part of eBook package: |d De Gruyter |t EBOOK PACKAGE Business and Economics 2021 English |z 9783110754049 |
773 | 0 | 8 | |i Title is part of eBook package: |d De Gruyter |t EBOOK PACKAGE COMPLETE 2021 English |z 9783110754001 |
773 | 0 | 8 | |i Title is part of eBook package: |d De Gruyter |t EBOOK PACKAGE COMPLETE 2021 |z 9783110753776 |o ZDB-23-DGG |
773 | 0 | 8 | |i Title is part of eBook package: |d De Gruyter |t EBOOK PACKAGE Economics 2021 |z 9783110753820 |o ZDB-23-DBV |
773 | 0 | 8 | |i Title is part of eBook package: |d De Gruyter |t Princeton University Press Complete eBook-Package 2021 |z 9783110739121 |
856 | 4 | 0 | |u https://doi.org/10.1515/9780691218717?locatt=mode:legacy |
856 | 4 | 0 | |u https://www.degruyter.com/isbn/9780691218717 |
856 | 4 | 2 | |3 Cover |u https://www.degruyter.com/document/cover/isbn/9780691218717/original |
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912 | |a 978-3-11-075400-1 EBOOK PACKAGE COMPLETE 2021 English |b 2021 | ||
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