Essays on Empirical Asset Pricing Via Machine Learning
Title | Essays on Empirical Asset Pricing Via Machine Learning PDF eBook |
Author | Gerrit Liedtke |
Publisher | |
Pages | 0 |
Release | 2023 |
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Empirical Asset Pricing Via Machine Learning
Title | Empirical Asset Pricing Via Machine Learning PDF eBook |
Author | Shihao Gu |
Publisher | |
Pages | 0 |
Release | 2018 |
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We perform a comparative analysis of machine learning methods for the canonical problem of empirical asset pricing: measuring asset risk premia. We demonstrate large economic gains to investors using machine learning forecasts, in some cases doubling the performance of leading regression-based strategies from the literature. We identify the best performing methods (trees and neural networks) and trace their predictive gains to allowance of nonlinear predictor interactions that are missed by other methods. All methods agree on the same set of dominant predictive signals which includes variations on momentum, liquidity, and volatility. Improved risk premium measurement through machine learning simplifies the investigation into economic mechanisms of asset pricing and highlights the value of machine learning in financial innovation.
Machine Learning in Asset Pricing
Title | Machine Learning in Asset Pricing PDF eBook |
Author | Stefan Nagel |
Publisher | Princeton University Press |
Pages | 156 |
Release | 2021-05-11 |
Genre | Business & Economics |
ISBN | 0691218706 |
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.
Empirical Asset Pricing
Title | Empirical Asset Pricing PDF eBook |
Author | Wayne Ferson |
Publisher | MIT Press |
Pages | 497 |
Release | 2019-03-12 |
Genre | Business & Economics |
ISBN | 0262039370 |
An introduction to the theory and methods of empirical asset pricing, integrating classical foundations with recent developments. This book offers a comprehensive advanced introduction to asset pricing, the study of models for the prices and returns of various securities. The focus is empirical, emphasizing how the models relate to the data. The book offers a uniquely integrated treatment, combining classical foundations with more recent developments in the literature and relating some of the material to applications in investment management. It covers the theory of empirical asset pricing, the main empirical methods, and a range of applied topics. The book introduces the theory of empirical asset pricing through three main paradigms: mean variance analysis, stochastic discount factors, and beta pricing models. It describes empirical methods, beginning with the generalized method of moments (GMM) and viewing other methods as special cases of GMM; offers a comprehensive review of fund performance evaluation; and presents selected applied topics, including a substantial chapter on predictability in asset markets that covers predicting the level of returns, volatility and higher moments, and predicting cross-sectional differences in returns. Other chapters cover production-based asset pricing, long-run risk models, the Campbell-Shiller approximation, the debate on covariance versus characteristics, and the relation of volatility to the cross-section of stock returns. An extensive reference section captures the current state of the field. The book is intended for use by graduate students in finance and economics; it can also serve as a reference for professionals.
Machine Learning in Empirical Asset Pricing
Title | Machine Learning in Empirical Asset Pricing PDF eBook |
Author | Colm Kelly |
Publisher | |
Pages | 0 |
Release | 2023 |
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Essays in Empirical Asset Pricing with Machine Learning
Title | Essays in Empirical Asset Pricing with Machine Learning PDF eBook |
Author | Matthias Bûchner |
Publisher | |
Pages | |
Release | 2020 |
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Essays in Empirical Asset Pricing with Machine Learning
Title | Essays in Empirical Asset Pricing with Machine Learning PDF eBook |
Author | Matthias Büchner |
Publisher | |
Pages | 0 |
Release | 2021 |
Genre | Capital assets pricing model |
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