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| fixed length control field |
a |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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211203b xxu||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9780691218700 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
332.632220285631 |
| Item number |
NAG |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Nagel, Stefan |
| 245 ## - TITLE STATEMENT |
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| Title |
Machine learning in asset pricing |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Name of publisher, distributor, etc |
Princeton University Press, |
| Date of publication, distribution, etc |
2021 |
| Place of publication, distribution, etc |
Princeton : |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
x, 144 p. ; |
| Other physical details |
ill., |
| Dimensions |
25 cm |
| 365 ## - TRADE PRICE |
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| Price amount |
50.00 |
| Price type code |
USD |
| Unit of pricing |
78.70 |
| 490 ## - SERIES STATEMENT |
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| Series statement |
Princeton lectures in finance |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc |
Includes bibliographical references and index. |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc |
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. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Capital assets pricing model |
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Machine learning |
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Prices |
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Finance |
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Investments |
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Mathematical models |
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Artificial intelligence |
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Financial applications |
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Asset demand system |
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Bayesian regression |
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Ridge regression |
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Cross-validation |
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Hyperparameter |
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Market efficiency |
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Neural network |
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Interaction effects |
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Investment opportunities |
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Stock return prediction |
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Regression trees |
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Sharpe ratio |
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Signal-to-noise ratio |
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Sparsity |
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Structural change |
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Financial markets |
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Stochastic discount factor |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
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| Item type |
Books |